diff --git a/.circleci/config.yml b/.circleci/config.yml
index 84b15572a66..0b369477c28 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -112,7 +112,7 @@ jobs:
             python -m mypy .
             cd ..
           no_output_timeout: 10m
-  local_testing_part1:
+  local_testing:
     docker:
       - image: cimg/python:3.12
         auth:
@@ -205,143 +205,13 @@ jobs:
 
       # Run pytest and generate JUnit XML report
       - run:
-          name: Run tests (Part 1 - A-M)
-          command: |
-            mkdir test-results
-
-            # Discover test files (A-M)
-            TEST_FILES=$(circleci tests glob "tests/local_testing/**/test_[a-mA-M]*.py")
-
-            echo "$TEST_FILES" | circleci tests run \
-              --split-by=timings \
-              --verbose \
-              --command="xargs python -m pytest \
-                -vv \
-                --cov=litellm \
-                --cov-report=xml \
-                --junitxml=test-results/junit.xml \
-                --durations=20 \
-                -k \"not test_python_38.py and not test_basic_python_version.py and not router and not assistants and not langfuse and not caching and not cache\" \
-                -n 4 \
-                --timeout=300 \
-                --timeout_method=thread"
-          no_output_timeout: 120m
-      - run:
-          name: Rename the coverage files
-          command: |
-            mv coverage.xml local_testing_part1_coverage.xml
-            mv .coverage local_testing_part1_coverage
-
-      # Store test results
-      - store_test_results:
-          path: test-results
-      - persist_to_workspace:
-          root: .
-          paths:
-            - local_testing_part1_coverage.xml
-            - local_testing_part1_coverage
-  local_testing_part2:
-    docker:
-      - image: cimg/python:3.12
-        auth:
-          username: ${DOCKERHUB_USERNAME}
-          password: ${DOCKERHUB_PASSWORD}
-    working_directory: ~/project
-    parallelism: 4
-    steps:
-      - checkout
-      - setup_google_dns
-      - run:
-          name: Show git commit hash
-          command: |
-            echo "Git commit hash: $CIRCLE_SHA1"
-
-      - restore_cache:
-          keys:
-            - v1-dependencies-{{ checksum ".circleci/requirements.txt" }}
-      - run:
-          name: Install Dependencies
-          command: |
-            python -m pip install --upgrade pip
-            python -m pip install -r .circleci/requirements.txt
-            pip install "pytest==7.3.1"
-            pip install "pytest-retry==1.6.3"
-            pip install "pytest-asyncio==0.21.1"
-            pip install "pytest-cov==5.0.0"
-            pip install "mypy==1.18.2"
-            pip install "google-generativeai==0.3.2"
-            pip install "google-cloud-aiplatform==1.43.0"
-            pip install pyarrow
-            pip install "boto3==1.36.0"
-            pip install "aioboto3==13.4.0"
-            pip install langchain
-            pip install lunary==0.2.5
-            pip install "azure-identity==1.16.1"
-            pip install "langfuse==2.59.7"
-            pip install "logfire==0.29.0"
-            pip install numpydoc
-            pip install traceloop-sdk==0.21.1
-            pip install opentelemetry-api==1.25.0
-            pip install opentelemetry-sdk==1.25.0
-            pip install opentelemetry-exporter-otlp==1.25.0
-            pip install openai==1.100.1
-            pip install prisma==0.11.0
-            pip install "detect_secrets==1.5.0"
-            pip install "httpx==0.24.1"
-            pip install "respx==0.22.0"
-            pip install fastapi
-            pip install "gunicorn==21.2.0"
-            pip install "anyio==4.2.0"
-            pip install "aiodynamo==23.10.1"
-            pip install "asyncio==3.4.3"
-            pip install "apscheduler==3.10.4"
-            pip install "PyGithub==1.59.1"
-            pip install argon2-cffi
-            pip install "pytest-mock==3.12.0"
-            pip install python-multipart
-            pip install google-cloud-aiplatform
-            pip install prometheus-client==0.20.0
-            pip install "pydantic==2.10.2"
-            pip install "diskcache==5.6.1"
-            pip install "Pillow==10.3.0"
-            pip install "jsonschema==4.22.0"
-            pip install "pytest-xdist==3.6.1"
-            pip install "pytest-timeout==2.2.0"
-            pip install "websockets==13.1.0"
-            pip install semantic_router --no-deps
-            pip install aurelio_sdk --no-deps
-            pip uninstall posthog -y
-      - setup_litellm_enterprise_pip
-      - save_cache:
-          paths:
-            - ./venv
-          key: v1-dependencies-{{ checksum ".circleci/requirements.txt" }}
-      - run:
-          name: Run prisma ./docker/entrypoint.sh
-          command: |
-            set +e
-            chmod +x docker/entrypoint.sh
-            ./docker/entrypoint.sh
-            set -e
-      - run:
-          name: Black Formatting
-          command: |
-            cd litellm
-            python -m pip install black
-            python -m black .
-            cd ..
-
-      # Run pytest and generate JUnit XML report
-      - run:
-          name: Run tests (Part 2 - N-Z)
+          name: Run tests
           command: |
             mkdir test-results
-
-            # Discover test files (N-Z)
-            TEST_FILES=$(circleci tests glob "tests/local_testing/**/test_[n-zN-Z]*.py")
-
+            # Discover test files
+            TEST_FILES=$(circleci tests glob "tests/local_testing/**/test_*.py")
             echo "$TEST_FILES" | circleci tests run \
-              --split-by=timings \
+              --split-by=filesize \
               --verbose \
               --command="xargs python -m pytest \
                 -vv \
@@ -357,8 +227,8 @@ jobs:
       - run:
           name: Rename the coverage files
           command: |
-            mv coverage.xml local_testing_part2_coverage.xml
-            mv .coverage local_testing_part2_coverage
+            mv coverage.xml local_testing_coverage.xml
+            mv .coverage local_testing_coverage
 
       # Store test results
       - store_test_results:
@@ -366,8 +236,8 @@ jobs:
       - persist_to_workspace:
           root: .
           paths:
-            - local_testing_part2_coverage.xml
-            - local_testing_part2_coverage
+            - local_testing_coverage.xml
+            - local_testing_coverage
   langfuse_logging_unit_tests:
     docker:
       - image: cimg/python:3.11
@@ -639,6 +509,7 @@ jobs:
           username: ${DOCKERHUB_USERNAME}
           password: ${DOCKERHUB_PASSWORD}
     working_directory: ~/project
+    parallelism: 4
     steps:
       - checkout
       - setup_google_dns
@@ -660,9 +531,21 @@ jobs:
       - run:
           name: Run tests
           command: |
-            pwd
-            ls
-            python -m pytest tests/local_testing --cov=litellm --cov-report=xml -vv -k "router" -v --junitxml=test-results/junit.xml --durations=5
+            mkdir test-results
+            # Find test files only in local_testing
+            TEST_FILES=$(circleci tests glob "tests/local_testing/**/test_*.py")
+            echo "$TEST_FILES" | circleci tests run \
+              --split-by=filesize \
+              --verbose \
+              --command="xargs python -m pytest -o junit_family=legacy \
+                -k 'router' \
+                --cov=litellm \
+                --cov-report=xml \
+                -n 4 \
+                --dist=loadscope \
+                --junitxml=test-results/junit.xml \
+                --durations=5 \
+                -vv"
           no_output_timeout: 120m
       - run:
           name: Rename the coverage files
@@ -715,8 +598,8 @@ jobs:
       - run:
           name: Rename the coverage files
           command: |
-            mv coverage.xml litellm_router_unit_coverage.xml
-            mv .coverage litellm_router_unit_coverage
+            mv coverage.xml litellm_router_coverage.xml
+            mv .coverage litellm_router_coverage
       # Store test results
       - store_test_results:
           path: test-results
@@ -724,8 +607,8 @@ jobs:
       - persist_to_workspace:
           root: .
           paths:
-            - litellm_router_unit_coverage.xml
-            - litellm_router_unit_coverage
+            - litellm_router_coverage.xml
+            - litellm_router_coverage
   litellm_security_tests:
     machine:
       image: ubuntu-2204:2023.10.1
@@ -1933,7 +1816,6 @@ jobs:
             pip install "mlflow==2.17.2"
             pip install "anthropic==0.52.0"
             pip install "blockbuster==1.5.24"
-            pip install "pytest-xdist==3.6.1"
       # Run pytest and generate JUnit XML report
       - setup_litellm_enterprise_pip
       - run:
@@ -1941,7 +1823,7 @@ jobs:
           command: |
             pwd
             ls
-            python -m pytest -vv tests/logging_callback_tests --cov=litellm -n 4 --cov-report=xml -s -v --junitxml=test-results/junit.xml --durations=5
+            python -m pytest -vv tests/logging_callback_tests --cov=litellm --cov-report=xml -s -v --junitxml=test-results/junit.xml --durations=5
           no_output_timeout: 120m
       - run:
           name: Rename the coverage files
@@ -3428,7 +3310,7 @@ jobs:
             python -m venv venv
             . venv/bin/activate
             pip install coverage
-            coverage combine llm_translation_coverage llm_responses_api_coverage ocr_coverage search_coverage mcp_coverage logging_coverage audio_coverage litellm_router_coverage litellm_router_unit_coverage local_testing_part1_coverage local_testing_part2_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_part1_coverage litellm_proxy_unit_tests_part2_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage litellm_mapped_tests_coverage
+            coverage combine llm_translation_coverage llm_responses_api_coverage ocr_coverage search_coverage mcp_coverage logging_coverage audio_coverage litellm_router_coverage local_testing_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_part1_coverage litellm_proxy_unit_tests_part2_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage litellm_mapped_tests_coverage
             coverage xml
       - codecov/upload:
           file: ./coverage.xml
@@ -3478,22 +3360,8 @@ jobs:
                 ls dist/
                 twine upload --verbose dist/*
             else
-                echo "Version ${VERSION} of package is already published on PyPI."
-                
-                # Check if corresponding Docker nightly image exists
-                NIGHTLY_TAG="v${VERSION}-nightly"
-                echo "Checking for Docker nightly image: litellm/litellm:${NIGHTLY_TAG}"
-                
-                # Check Docker Hub for the nightly image
-                if curl -s "https://hub.docker.com/v2/repositories/litellm/litellm/tags/${NIGHTLY_TAG}" | grep -q "name"; then
-                    echo "Docker nightly image ${NIGHTLY_TAG} exists. This release was already completed successfully."
-                    echo "Skipping PyPI publish and continuing to ensure Docker images are up to date."
-                    circleci step halt
-                else
-                    echo "ERROR: PyPI package ${VERSION} exists but Docker nightly image ${NIGHTLY_TAG} does not exist!"
-                    echo "This indicates an incomplete release. Please investigate."
-                    exit 1
-                fi
+                echo "Version ${VERSION} of package is already published on PyPI. Skipping PyPI publish."
+                circleci step halt
             fi
       - run:
           name: Trigger Github Action for new Docker Container + Trigger Load Testing
@@ -3502,21 +3370,11 @@ jobs:
             python3 -m pip install toml
             VERSION=$(python3 -c "import toml; print(toml.load('pyproject.toml')['tool']['poetry']['version'])")
             echo "LiteLLM Version ${VERSION}"
-
-            # Determine which branch to use for Docker build
-            if [[ "$CIRCLE_BRANCH" =~ ^litellm_release_day_.* ]]; then
-              BUILD_BRANCH="$CIRCLE_BRANCH"
-              echo "Using release branch: $BUILD_BRANCH"
-            else
-              BUILD_BRANCH="main"
-              echo "Using default branch: $BUILD_BRANCH"
-            fi
-
             curl -X POST \
               -H "Accept: application/vnd.github.v3+json" \
               -H "Authorization: Bearer $GITHUB_TOKEN" \
               "https://api.github.com/repos/BerriAI/litellm/actions/workflows/ghcr_deploy.yml/dispatches" \
-              -d "{\"ref\":\"${BUILD_BRANCH}\", \"inputs\":{\"tag\":\"v${VERSION}-nightly\", \"commit_hash\":\"$CIRCLE_SHA1\"}}"
+              -d "{\"ref\":\"main\", \"inputs\":{\"tag\":\"v${VERSION}-nightly\", \"commit_hash\":\"$CIRCLE_SHA1\"}}"
             echo "triggering load testing server for version ${VERSION} and commit ${CIRCLE_SHA1}"
             curl -X POST "https://proxyloadtester-production.up.railway.app/start/load/test?version=${VERSION}&commit_hash=${CIRCLE_SHA1}&release_type=nightly"
 
@@ -3907,13 +3765,7 @@ workflows:
               only:
                 - main
                 - /litellm_.*/
-      - local_testing_part1:
-          filters:
-            branches:
-              only:
-                - main
-                - /litellm_.*/
-      - local_testing_part2:
+      - local_testing:
           filters:
             branches:
               only:
@@ -4218,8 +4070,7 @@ workflows:
             - litellm_proxy_unit_testing_part2
             - litellm_security_tests
             - langfuse_logging_unit_tests
-            - local_testing_part1
-            - local_testing_part2
+            - local_testing
             - litellm_assistants_api_testing
             - auth_ui_unit_tests
       - db_migration_disable_update_check:
@@ -4259,12 +4110,10 @@ workflows:
             branches:
               only:
                 - main
-                - /litellm_release_day_.*/
       - publish_to_pypi:
           requires:
             - mypy_linting
-            - local_testing_part1
-            - local_testing_part2
+            - local_testing
             - build_and_test
             - e2e_openai_endpoints
             - test_bad_database_url
diff --git a/.circleci/requirements.txt b/.circleci/requirements.txt
index a5ec74424fe..8c44dc18305 100644
--- a/.circleci/requirements.txt
+++ b/.circleci/requirements.txt
@@ -16,5 +16,4 @@ uvloop==0.21.0
 mcp==1.25.0    # for MCP server  
 semantic_router==0.1.10 # for auto-routing with litellm
 fastuuid==0.12.0
-responses==0.25.7  # for proxy client tests
-pytest-retry==1.6.3  # for automatic test retries
\ No newline at end of file
+responses==0.25.7  # for proxy client tests
\ No newline at end of file
diff --git a/.github/workflows/test-linting.yml b/.github/workflows/test-linting.yml
index 7c5c269f899..35ebffeada3 100644
--- a/.github/workflows/test-linting.yml
+++ b/.github/workflows/test-linting.yml
@@ -73,4 +73,4 @@ jobs:
 
     - name: Check import safety
       run: |
-        poetry run python -c "from litellm import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1)
+        poetry run python -c "from litellm import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1)
\ No newline at end of file
diff --git a/.github/workflows/test-litellm.yml b/.github/workflows/test-litellm.yml
index d9cf2e74a11..ba32dc1bf54 100644
--- a/.github/workflows/test-litellm.yml
+++ b/.github/workflows/test-litellm.yml
@@ -34,7 +34,7 @@ jobs:
         poetry run pip install "google-genai==1.22.0"
         poetry run pip install "google-cloud-aiplatform>=1.38"
         poetry run pip install "fastapi-offline==1.7.3"
-        poetry run pip install "python-multipart==0.0.22"
+        poetry run pip install "python-multipart==0.0.18"
         poetry run pip install "openapi-core"
     - name: Setup litellm-enterprise as local package
       run: |
diff --git a/.github/workflows/test-model-map.yaml b/.github/workflows/test-model-map.yaml
deleted file mode 100644
index ae5ac402e23..00000000000
--- a/.github/workflows/test-model-map.yaml
+++ /dev/null
@@ -1,15 +0,0 @@
-name: Validate model_prices_and_context_window.json
-
-on:
-  pull_request:
-    branches: [ main ]
-
-jobs:
-  validate-model-prices-json:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Validate model_prices_and_context_window.json
-        run: |
-          jq empty model_prices_and_context_window.json
diff --git a/.gitignore b/.gitignore
index 32f1b6f8e1f..0248d68c1e1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -60,6 +60,10 @@ litellm/proxy/_super_secret_config.yaml
 litellm/proxy/myenv/bin/activate
 litellm/proxy/myenv/bin/Activate.ps1
 myenv/*
+litellm/proxy/_experimental/out/_next/
+litellm/proxy/_experimental/out/404/index.html
+litellm/proxy/_experimental/out/model_hub/index.html
+litellm/proxy/_experimental/out/onboarding/index.html
 litellm/tests/log.txt
 litellm/tests/langfuse.log
 litellm/tests/langfuse.log
@@ -72,6 +76,9 @@ tests/local_testing/log.txt
 litellm/proxy/_new_new_secret_config.yaml
 litellm/proxy/custom_guardrail.py
 .mypy_cache/*
+litellm/proxy/_experimental/out/404.html
+litellm/proxy/_experimental/out/404.html
+litellm/proxy/_experimental/out/model_hub.html
 .mypy_cache/*
 litellm/proxy/application.log
 tests/llm_translation/vertex_test_account.json
@@ -93,6 +100,7 @@ litellm_config.yaml
 litellm/proxy/to_delete_loadtest_work/*
 update_model_cost_map.py
 tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py
+litellm/proxy/_experimental/out/guardrails/index.html
 scripts/test_vertex_ai_search.py
 LAZY_LOADING_IMPROVEMENTS.md
 **/test-results
diff --git a/AGENTS.md b/AGENTS.md
index 5a48049ef45..61afbd035fe 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -51,14 +51,12 @@ LiteLLM is a unified interface for 100+ LLMs that:
 
 ### MAKING CODE CHANGES FOR THE UI (IGNORE FOR BACKEND)
 
-1. **Tremor is DEPRECATED, do not use Tremor components in new features/changes**
-   - The only exception is the Tremor Table component and its required Tremor Table sub components.
-
-2. **Use Common Components as much as possible**:
+1. **Use Common Components as much as possible**:
    - These are usually defined in the `common_components` directory
    - Use these components as much as possible and avoid building new components unless needed
+   - Tremor components are deprecated; prefer using Ant Design (AntD) as much as possible
 
-3. **Testing**:
+2. **Testing**:
    - The codebase uses **Vitest** and **React Testing Library**
    - **Query Priority Order**: Use query methods in this order: `getByRole`, `getByLabelText`, `getByPlaceholderText`, `getByText`, `getByTestId`
    - **Always use `screen`** instead of destructuring from `render()` (e.g., use `screen.getByText()` not `getByText`)
diff --git a/README.md b/README.md
index 77adddf8978..dee5c7167ff 100644
--- a/README.md
+++ b/README.md
@@ -259,17 +259,12 @@ LiteLLM Performance: **8ms P95 latency** at 1k RPS (See benchmarks [here](https:
 Support for more providers. Missing a provider or LLM Platform, raise a [feature request](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.yml&title=%5BFeature%5D%3A+).
 
 ## OSS Adopters 
+<img width="250" height="104" alt="Stripe wordmark - Blurple - Small" src="https://github.com/user-attachments/assets/f7296d4f-9fbd-460d-9d05-e4df31697c4b" />
+
+<img width="250" height="69" alt="download__1_-removebg-preview" src="https://github.com/user-attachments/assets/0be4bd8a-7cfa-48d3-9090-f415fe948280" />
+
+
 
-<table>
-  <tr>
-    <td><img height="60" alt="Stripe" src="https://github.com/user-attachments/assets/f7296d4f-9fbd-460d-9d05-e4df31697c4b" /></td>
-    <td><img height="60" alt="Google ADK" src="https://github.com/user-attachments/assets/caf270a2-5aee-45c4-8222-41a2070c4f19" /></td>
-    <td><img height="60" alt="Greptile" src="https://github.com/user-attachments/assets/0be4bd8a-7cfa-48d3-9090-f415fe948280" /></td>
-    <td><img height="60" alt="OpenHands" src="https://github.com/user-attachments/assets/a6150c4c-149e-4cae-888b-8b92be6e003f" /></td>
-    <td><h2>Netflix</h2></td>
-    <td><img height="60" alt="OpenAI Agents SDK" src="https://github.com/user-attachments/assets/c02f7be0-8c2e-4d27-aea7-7c024bfaebc0" /></td>
-  </tr>
-</table>
 
 ## Supported Providers ([Website Supported Models](https://models.litellm.ai/) | [Docs](https://docs.litellm.ai/docs/providers))
 
diff --git a/deploy/charts/litellm-helm/templates/deployment.yaml b/deploy/charts/litellm-helm/templates/deployment.yaml
index 4ac5582d060..c3e0055e380 100644
--- a/deploy/charts/litellm-helm/templates/deployment.yaml
+++ b/deploy/charts/litellm-helm/templates/deployment.yaml
@@ -38,10 +38,6 @@ spec:
       serviceAccountName: {{ include "litellm.serviceAccountName" . }}
       securityContext:
         {{- toYaml .Values.podSecurityContext | nindent 8 }}
-      {{- with .Values.extraInitContainers }}
-      initContainers:
-        {{- toYaml . | nindent 8 }}
-      {{- end }}
       containers:
         - name: {{ include "litellm.name" . }}
           securityContext:
diff --git a/deploy/charts/litellm-helm/templates/migrations-job.yaml b/deploy/charts/litellm-helm/templates/migrations-job.yaml
index 3459fa12d1c..f8893a47afe 100644
--- a/deploy/charts/litellm-helm/templates/migrations-job.yaml
+++ b/deploy/charts/litellm-helm/templates/migrations-job.yaml
@@ -35,10 +35,6 @@ spec:
         {{- toYaml . | nindent 8 }}
       {{- end }}
       serviceAccountName: {{ include "litellm.serviceAccountName" . }}
-      {{- with .Values.migrationJob.extraInitContainers }}
-      initContainers:
-        {{- toYaml . | nindent 8 }}
-      {{- end }} 
       containers:
         - name: prisma-migrations
           image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default (printf "main-%s" .Chart.AppVersion) }}"
diff --git a/deploy/charts/litellm-helm/values.yaml b/deploy/charts/litellm-helm/values.yaml
index cea25974bb0..b75ce640370 100644
--- a/deploy/charts/litellm-helm/values.yaml
+++ b/deploy/charts/litellm-helm/values.yaml
@@ -281,7 +281,6 @@ migrationJob:
   #    cpu: 100m
   #    memory: 100Mi
   extraContainers: []
-  extraInitContainers: []
 
   # Hook configuration
   hooks:
diff --git a/docs/my-website/docs/a2a.md b/docs/my-website/docs/a2a.md
index a7e8b52d99a..7b863f185d0 100644
--- a/docs/my-website/docs/a2a.md
+++ b/docs/my-website/docs/a2a.md
@@ -68,7 +68,7 @@ Follow [this guide, to add your pydantic ai agent to LiteLLM Agent Gateway](./pr
 
 ## Invoking your Agents
 
-Use the [A2A Python SDK](https://pypi.org/project/a2a-sdk) to invoke agents through LiteLLM.
+Use the [A2A Python SDK](https://pypi.org/project/a2a/) to invoke agents through LiteLLM.
 
 This example shows how to:
 1. **List available agents** - Query `/v1/agents` to see which agents your key can access
diff --git a/docs/my-website/docs/observability/datadog.md b/docs/my-website/docs/observability/datadog.md
index 6f785be1013..7cf91ced34c 100644
--- a/docs/my-website/docs/observability/datadog.md
+++ b/docs/my-website/docs/observability/datadog.md
@@ -7,7 +7,6 @@ import TabItem from '@theme/TabItem';
 LiteLLM Supports logging to the following Datdog Integrations:
 - `datadog` [Datadog Logs](https://docs.datadoghq.com/logs/)
 - `datadog_llm_observability` [Datadog LLM Observability](https://www.datadoghq.com/product/llm-observability/)
-- `datadog_cost_management` [Datadog Cloud Cost Management](#datadog-cloud-cost-management)
 - `ddtrace-run` [Datadog Tracing](#datadog-tracing)
 
 ## Datadog Logs
@@ -74,7 +73,7 @@ Send logs through a local DataDog agent (useful for containerized environments):
 ```shell
 LITELLM_DD_AGENT_HOST="localhost"         # hostname or IP of DataDog agent
 LITELLM_DD_AGENT_PORT="10518"             # [OPTIONAL] port of DataDog agent (default: 10518)
-DD_API_KEY="5f2d0f310***********"         # [OPTIONAL] your datadog API Key (Agent handles auth for Logs. REQUIRED for LLM Observability)
+DD_API_KEY="5f2d0f310***********"         # [OPTIONAL] your datadog API Key (agent handles auth)
 DD_SOURCE="litellm_dev"                   # [OPTIONAL] your datadog source
 ```
 
@@ -85,9 +84,6 @@ When `LITELLM_DD_AGENT_HOST` is set, logs are sent to the agent instead of direc
 
 **Note:** We use `LITELLM_DD_AGENT_HOST` instead of `DD_AGENT_HOST` to avoid conflicts with `ddtrace` which automatically sets `DD_AGENT_HOST` for APM tracing.
 
-> [!IMPORTANT]
-> **Datadog LLM Observability**: `DD_API_KEY` is **REQUIRED** even when using the Datadog Agent (`LITELLM_DD_AGENT_HOST`). The agent acts as a proxy but the API key header is mandatory for the LLM Observability endpoint.
-
 **Step 3**: Start the proxy, make a test request
 
 Start proxy
@@ -165,50 +161,6 @@ On the Datadog LLM Observability page, you should see that both input messages a
 
 
 
-<Image img={require('../../img/dd_llm_obs.png')} />
-
-
-## Datadog Cloud Cost Management
-
-| Feature | Details |
-|---------|---------|
-| **What is logged** | Aggregated LLM Costs (FOCUS format) |
-| **Events** | Periodic Uploads of Aggregated Cost Data |
-| **Product Link** | [Datadog Cloud Cost Management](https://docs.datadoghq.com/cost_management/) |
-
-We will use the `--config` to set `litellm.callbacks = ["datadog_cost_management"]`. This will periodically upload aggregated LLM cost data to Datadog.
-
-**Step 1**: Create a `config.yaml` file and set `litellm_settings`: `success_callback`
-
-```yaml
-model_list:
- - model_name: gpt-3.5-turbo
-    litellm_params:
-      model: gpt-3.5-turbo
-litellm_settings:
-  callbacks: ["datadog_cost_management"]
-```
-
-**Step 2**: Set Required env variables
-
-```shell
-DD_API_KEY="your-api-key"
-DD_APP_KEY="your-app-key" # REQUIRED for Cost Management
-DD_SITE="us5.datadoghq.com"
-```
-
-**Step 3**: Start the proxy
-
-```shell
-litellm --config config.yaml
-```
-
-**How it works**
-* LiteLLM aggregates costs in-memory by Provider, Model, Date, and Tags.
-* Requires `DD_APP_KEY` for the Custom Costs API.
-* Costs are uploaded periodically (flushed).
-
-
 ### Datadog Tracing
 
 Use `ddtrace-run` to enable [Datadog Tracing](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html) on litellm proxy
@@ -251,5 +203,5 @@ LiteLLM supports customizing the following Datadog environment variables
 | `POD_NAME` | Pod name tag (useful for Kubernetes deployments) | "unknown" | ❌ No |
 
 \* **Required when using Direct API** (default): `DD_API_KEY` and `DD_SITE` are required  
-\* **Optional when using DataDog Agent**: Set `LITELLM_DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required for **Datadog Logs**. (**Note: `DD_API_KEY` IS REQUIRED for Datadog LLM Observability**)
+\* **Optional when using DataDog Agent**: Set `LITELLM_DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required
 
diff --git a/docs/my-website/docs/providers/anthropic_tool_search.md b/docs/my-website/docs/providers/anthropic_tool_search.md
index 203a2947ebc..28ce5688eeb 100644
--- a/docs/my-website/docs/providers/anthropic_tool_search.md
+++ b/docs/my-website/docs/providers/anthropic_tool_search.md
@@ -1,46 +1,43 @@
-# Tool Search
+# Anthropic Tool Search
 
 Tool search enables Claude to dynamically discover and load tools on-demand from large tool catalogs (10,000+ tools). Instead of loading all tool definitions into the context window upfront, Claude searches your tool catalog and loads only the tools it needs.
 
-## Supported Providers
-
-| Provider | Chat Completions API | Messages API |
-|----------|---------------------|--------------|
-| **Anthropic API** | ✅ | ✅ |
-| **Azure Anthropic** (Microsoft Foundry) | ✅ | ✅ |
-| **Google Cloud Vertex AI** | ✅ | ✅ |
-| **Amazon Bedrock** | ✅ (Invoke API only, Opus 4.5 only) | ✅ (Invoke API only, Opus 4.5 only) |
-
-
 ## Benefits
 
 - **Context efficiency**: Avoid consuming massive portions of your context window with tool definitions
 - **Better tool selection**: Claude's tool selection accuracy degrades with more than 30-50 tools. Tool search maintains accuracy even with thousands of tools
 - **On-demand loading**: Tools are only loaded when Claude needs them
 
+## Supported Models
+
+Tool search is available on:
+- Claude Opus 4.5
+- Claude Sonnet 4.5
+
+## Supported Platforms
+
+- Anthropic API (direct)
+- Azure Anthropic (Microsoft Foundry)
+- Google Cloud Vertex AI
+- Amazon Bedrock (invoke API only, not converse API)
+
 ## Tool Search Variants
 
 LiteLLM supports both tool search variants:
 
 ### 1. Regex Tool Search (`tool_search_tool_regex_20251119`)
 
-Claude constructs regex patterns to search for tools. Best for exact pattern matching (faster).
+Claude constructs regex patterns to search for tools.
 
 ### 2. BM25 Tool Search (`tool_search_tool_bm25_20251119`)
 
-Claude uses natural language queries to search for tools using the BM25 algorithm. Best for natural language semantic search.
-
-**Note**: BM25 variant is not supported on Bedrock.
-
----
-
-## Chat Completions API
+Claude uses natural language queries to search for tools using the BM25 algorithm.
 
-### SDK Usage
+## Quick Start
 
-#### Basic Example with Regex Tool Search
+### Basic Example with Regex Tool Search
 
-```python showLineNumbers title="Basic Tool Search Example"
+```python
 import litellm
 
 response = litellm.completion(
@@ -73,6 +70,26 @@ response = litellm.completion(
                 }
             },
             "defer_loading": True  # Mark for deferred loading
+        },
+        # Another deferred tool
+        {
+            "type": "function",
+            "function": {
+                "name": "search_files",
+                "description": "Search through files in the workspace",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {"type": "string"},
+                        "file_types": {
+                            "type": "array",
+                            "items": {"type": "string"}
+                        }
+                    },
+                    "required": ["query"]
+                }
+            },
+            "defer_loading": True
         }
     ]
 )
@@ -80,9 +97,9 @@ response = litellm.completion(
 print(response.choices[0].message.content)
 ```
 
-#### BM25 Tool Search Example
+### BM25 Tool Search Example
 
-```python showLineNumbers title="BM25 Tool Search"
+```python
 import litellm
 
 response = litellm.completion(
@@ -117,9 +134,9 @@ response = litellm.completion(
 )
 ```
 
-#### Azure Anthropic Example
+## Using with Azure Anthropic
 
-```python showLineNumbers title="Azure Anthropic Tool Search"
+```python
 import litellm
 
 response = litellm.completion(
@@ -153,9 +170,9 @@ response = litellm.completion(
 )
 ```
 
-#### Vertex AI Example
+## Using with Vertex AI
 
-```python showLineNumbers title="Vertex AI Tool Search"
+```python
 import litellm
 
 response = litellm.completion(
@@ -175,9 +192,11 @@ response = litellm.completion(
 )
 ```
 
-#### Streaming Support
+## Streaming Support
 
-```python showLineNumbers title="Streaming with Tool Search"
+Tool search works with streaming:
+
+```python
 import litellm
 
 response = litellm.completion(
@@ -214,13 +233,13 @@ for chunk in response:
         print(chunk.choices[0].delta.content, end="")
 ```
 
-### AI Gateway Usage
+## LiteLLM Proxy
 
-Tool search works automatically through the LiteLLM proxy.
+Tool search works automatically through the LiteLLM proxy:
 
-#### Proxy Configuration
+### Proxy Config
 
-```yaml showLineNumbers title="config.yaml"
+```yaml
 model_list:
   - model_name: claude-sonnet
     litellm_params:
@@ -228,19 +247,18 @@ model_list:
       api_key: os.environ/ANTHROPIC_API_KEY
 ```
 
-#### Client Request
+### Client Request
 
-```python showLineNumbers title="Client Request via Proxy"
-from anthropic import Anthropic
+```python
+import openai
 
-client = Anthropic(
+client = openai.OpenAI(
     api_key="your-litellm-proxy-key",
     base_url="http://0.0.0.0:4000"
 )
 
-response = client.messages.create(
+response = client.chat.completions.create(
     model="claude-sonnet",
-    max_tokens=1024,
     messages=[
         {"role": "user", "content": "What's the weather?"}
     ],
@@ -250,14 +268,17 @@ response = client.messages.create(
             "name": "tool_search_tool_regex"
         },
         {
-            "name": "get_weather",
-            "description": "Get weather information",
-            "input_schema": {
-                "type": "object",
-                "properties": {
-                    "location": {"type": "string"}
-                },
-                "required": ["location"]
+            "type": "function",
+            "function": {
+                "name": "get_weather",
+                "description": "Get weather information",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "location": {"type": "string"}
+                    },
+                    "required": ["location"]
+                }
             },
             "defer_loading": True
         }
@@ -265,278 +286,127 @@ response = client.messages.create(
 )
 ```
 
----
+## Important Notes
 
-## Messages API
+### Beta Header
 
-The Messages API provides native Anthropic-style tool search support via the `litellm.anthropic.messages` interface.
+LiteLLM automatically detects tool search tools and adds the appropriate beta header based on your provider:
 
-### SDK Usage
+- **Anthropic API & Microsoft Foundry**: `advanced-tool-use-2025-11-20`
+- **Google Cloud Vertex AI**: `tool-search-tool-2025-10-19`
+- **Amazon Bedrock** (Invoke API, Opus 4.5 only): `tool-search-tool-2025-10-19`
 
-#### Basic Example
+You don't need to manually specify beta headers—LiteLLM handles this automatically.
 
-```python showLineNumbers title="Messages API - Basic Tool Search"
-import litellm
+### Deferred Loading
 
-response = await litellm.anthropic.messages.acreate(
-    model="anthropic/claude-sonnet-4-20250514",
-    messages=[
-        {
-            "role": "user",
-            "content": "What's the weather in San Francisco?"
-        }
-    ],
-    tools=[
-        {
-            "type": "tool_search_tool_regex_20251119",
-            "name": "tool_search_tool_regex"
-        },
-        {
-            "name": "get_weather",
-            "description": "Get the current weather for a location",
-            "input_schema": {
-                "type": "object",
-                "properties": {
-                    "location": {
-                        "type": "string",
-                        "description": "The city and state, e.g. San Francisco, CA"
-                    }
-                },
-                "required": ["location"]
-            },
-            "defer_loading": True
-        }
-    ],
-    max_tokens=1024,
-    extra_headers={"anthropic-beta": "advanced-tool-use-2025-11-20"}
-)
+- Tools with `defer_loading: true` are only loaded when Claude discovers them via search
+- At least one tool must be non-deferred (the tool search tool itself)
+- Keep your 3-5 most frequently used tools as non-deferred for optimal performance
 
-print(response)
-```
+### Tool Descriptions
 
-#### Azure Anthropic Messages Example
+Write clear, descriptive tool names and descriptions that match how users describe tasks. The search algorithm uses:
+- Tool names
+- Tool descriptions
+- Argument names
+- Argument descriptions
 
-```python showLineNumbers title="Azure Anthropic Messages API"
-import litellm
+### Usage Tracking
 
-response = await litellm.anthropic.messages.acreate(
-    model="azure_anthropic/claude-sonnet-4-20250514",
-    messages=[
-        {
-            "role": "user",
-            "content": "What's the stock price of Apple?"
-        }
-    ],
-    tools=[
-        {
-            "type": "tool_search_tool_regex_20251119",
-            "name": "tool_search_tool_regex"
-        },
-        {
-            "name": "get_stock_price",
-            "description": "Get the current stock price for a ticker symbol",
-            "input_schema": {
-                "type": "object",
-                "properties": {
-                    "ticker": {
-                        "type": "string",
-                        "description": "The stock ticker symbol, e.g. AAPL"
-                    }
-                },
-                "required": ["ticker"]
-            },
-            "defer_loading": True
-        }
-    ],
-    max_tokens=1024,
-    extra_headers={"anthropic-beta": "advanced-tool-use-2025-11-20"}
-)
-```
-
-#### Vertex AI Messages Example
+Tool search requests are tracked in the usage object:
 
-```python showLineNumbers title="Vertex AI Messages API"
-import litellm
-
-response = await litellm.anthropic.messages.acreate(
-    model="vertex_ai/claude-sonnet-4@20250514",
-    messages=[
-        {
-            "role": "user",
-            "content": "Search the web for information about AI"
-        }
-    ],
-    tools=[
-        {
-            "type": "tool_search_tool_bm25_20251119",
-            "name": "tool_search_tool_bm25"
-        },
-        {
-            "name": "search_web",
-            "description": "Search the web for information",
-            "input_schema": {
-                "type": "object",
-                "properties": {
-                    "query": {
-                        "type": "string",
-                        "description": "The search query"
-                    }
-                },
-                "required": ["query"]
-            },
-            "defer_loading": True
-        }
-    ],
-    max_tokens=1024,
-    extra_headers={"anthropic-beta": "tool-search-tool-2025-10-19"}
+```python
+response = litellm.completion(
+    model="anthropic/claude-sonnet-4-5-20250929",
+    messages=[{"role": "user", "content": "Search for tools"}],
+    tools=[...]
 )
-```
 
-#### Bedrock Messages Example
-
-```python showLineNumbers title="Bedrock Messages API (Invoke)"
-import litellm
+# Check tool search usage
+if response.usage.server_tool_use:
+    print(f"Tool search requests: {response.usage.server_tool_use.tool_search_requests}")
+```
 
-response = await litellm.anthropic.messages.acreate(
-    model="bedrock/invoke/anthropic.claude-opus-4-20250514-v1:0",
-    messages=[
-        {
-            "role": "user",
-            "content": "What's the weather?"
-        }
-    ],
-    tools=[
-        {
-            "type": "tool_search_tool_regex_20251119",
-            "name": "tool_search_tool_regex"
-        },
-        {
-            "name": "get_weather",
-            "description": "Get weather information",
-            "input_schema": {
-                "type": "object",
-                "properties": {
-                    "location": {"type": "string"}
-                },
-                "required": ["location"]
-            },
-            "defer_loading": True
-        }
-    ],
-    max_tokens=1024,
-    extra_headers={"anthropic-beta": "tool-search-tool-2025-10-19"}
-)
+## Error Handling
+
+### All Tools Deferred
+
+```python
+# ❌ This will fail - at least one tool must be non-deferred
+tools = [
+    {
+        "type": "function",
+        "function": {...},
+        "defer_loading": True
+    }
+]
+
+# ✅ Correct - tool search tool is non-deferred
+tools = [
+    {
+        "type": "tool_search_tool_regex_20251119",
+        "name": "tool_search_tool_regex"
+    },
+    {
+        "type": "function",
+        "function": {...},
+        "defer_loading": True
+    }
+]
 ```
 
-#### Streaming Support
+### Missing Tool Definition
 
-```python showLineNumbers title="Messages API - Streaming"
-import litellm
-import json
+If Claude references a tool that isn't in your deferred tools list, you'll get an error. Make sure all tools that might be discovered are included in the tools parameter with `defer_loading: true`.
 
-response = await litellm.anthropic.messages.acreate(
-    model="anthropic/claude-sonnet-4-20250514",
-    messages=[
-        {
-            "role": "user",
-            "content": "What's the weather in Tokyo?"
-        }
-    ],
-    tools=[
-        {
-            "type": "tool_search_tool_regex_20251119",
-            "name": "tool_search_tool_regex"
-        },
-        {
-            "name": "get_weather",
-            "description": "Get weather information",
-            "input_schema": {
-                "type": "object",
-                "properties": {
-                    "location": {"type": "string"}
-                },
-                "required": ["location"]
-            },
-            "defer_loading": True
-        }
-    ],
-    max_tokens=1024,
-    stream=True,
-    extra_headers={"anthropic-beta": "advanced-tool-use-2025-11-20"}
-)
+## Best Practices
 
-async for chunk in response:
-    if isinstance(chunk, bytes):
-        chunk_str = chunk.decode("utf-8")
-        for line in chunk_str.split("\n"):
-            if line.startswith("data: "):
-                try:
-                    json_data = json.loads(line[6:])
-                    print(json_data)
-                except json.JSONDecodeError:
-                    pass
-```
+1. **Keep frequently used tools non-deferred**: Your 3-5 most common tools should not have `defer_loading: true`
 
-### AI Gateway Usage
+2. **Use semantic descriptions**: Tool descriptions should use natural language that matches user queries
 
-Configure the proxy to use Messages API endpoints.
+3. **Choose the right variant**:
+   - Use **regex** for exact pattern matching (faster)
+   - Use **BM25** for natural language semantic search
 
-#### Proxy Configuration
+4. **Monitor usage**: Track `tool_search_requests` in the usage object to understand search patterns
 
-```yaml showLineNumbers title="config.yaml"
-model_list:
-  - model_name: claude-sonnet-messages
-    litellm_params:
-      model: anthropic/claude-sonnet-4-20250514
-      api_key: os.environ/ANTHROPIC_API_KEY
-```
+5. **Optimize tool catalog**: Remove unused tools and consolidate similar functionality
 
-#### Client Request
+## When to Use Tool Search
 
-```python showLineNumbers title="Client Request via Proxy (Messages API)"
-from anthropic import Anthropic
+**Good use cases:**
+- 10+ tools available in your system
+- Tool definitions consuming >10K tokens
+- Experiencing tool selection accuracy issues
+- Building systems with multiple tool categories
+- Tool library growing over time
 
-client = Anthropic(
-    api_key="your-litellm-proxy-key",
-    base_url="http://0.0.0.0:4000"
-)
+**When traditional tool calling is better:**
+- Less than 10 tools total
+- All tools are frequently used
+- Very small tool definitions (\<100 tokens total)
 
-response = client.messages.create(
-    model="claude-sonnet-messages",
-    max_tokens=1024,
-    messages=[
-        {
-            "role": "user",
-            "content": "What's the weather?"
-        }
-    ],
-    tools=[
-        {
-            "type": "tool_search_tool_regex_20251119",
-            "name": "tool_search_tool_regex"
-        },
-        {
-            "name": "get_weather",
-            "description": "Get weather information",
-            "input_schema": {
-                "type": "object",
-                "properties": {
-                    "location": {"type": "string"}
-                },
-                "required": ["location"]
-            },
-            "defer_loading": True
-        }
-    ],
-    extra_headers={"anthropic-beta": "advanced-tool-use-2025-11-20"}
-)
+## Limitations
 
-print(response)
-```
+- Not compatible with tool use examples
+- Requires Claude Opus 4.5 or Sonnet 4.5
+- On Bedrock, only available via invoke API (not converse API)
+- On Bedrock, only supported for Claude Opus 4.5 (not Sonnet 4.5)
+- BM25 variant (`tool_search_tool_bm25_20251119`) is not supported on Bedrock
+- Maximum 10,000 tools in catalog
+- Returns 3-5 most relevant tools per search
 
----
+### Bedrock-Specific Notes
+
+When using Bedrock's Invoke API:
+- The regex variant (`tool_search_tool_regex_20251119`) is automatically normalized to `tool_search_tool_regex`
+- The BM25 variant (`tool_search_tool_bm25_20251119`) is automatically filtered out as it's not supported
+- Tool search is only available for Claude Opus 4.5 models
 
 ## Additional Resources
 
 - [Anthropic Tool Search Documentation](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/tool-search)
 - [LiteLLM Tool Calling Guide](https://docs.litellm.ai/docs/completion/function_call)
+
diff --git a/docs/my-website/docs/providers/gemini.md b/docs/my-website/docs/providers/gemini.md
index b9ad7820dd4..23a02f7365c 100644
--- a/docs/my-website/docs/providers/gemini.md
+++ b/docs/my-website/docs/providers/gemini.md
@@ -1840,57 +1840,6 @@ content = response.get('choices', [{}])[0].get('message', {}).get('content')
 print(content)
 ```
 
-## gemini-robotics-er-1.5-preview Usage
-
-```python
-from litellm import api_base
-from openai import OpenAI
-import os
-import base64
-
-client = OpenAI(base_url="http://0.0.0.0:4000", api_key="sk-12345")
-base64_image = base64.b64encode(open("closeup-object-on-table-many-260nw-1216144471.webp", "rb").read()).decode()
-
-import json
-import re
-tools = [{"codeExecution": {}}] 
-response = client.chat.completions.create(
-    model="gemini/gemini-robotics-er-1.5-preview",
-    messages=[
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "Point to no more than 10 items in the image. The label returned should be an identifying name for the object detected. The answer should follow the json format: [{\"point\": [y, x], \"label\": <label1>}, ...]. The points are in [y, x] format normalized to 0-1000."
-                },
-                {
-                    "type": "image_url",
-                    "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}
-                }
-            ]
-        }
-    ],
-    tools=tools
-)
-
-# Extract JSON from markdown code block if present
-content = response.choices[0].message.content
-# Look for triple-backtick JSON block
-match = re.search(r'```json\s*(.*?)\s*```', content, re.DOTALL)
-if match:
-    json_str = match.group(1)
-else:
-    json_str = content
-
-try:
-    data = json.loads(json_str)
-    print(json.dumps(data, indent=2))
-except Exception as e:
-    print("Error parsing response as JSON:", e)
-    print("Response content:", content)
-```
-
 ## Usage - PDF / Videos / etc. Files
 
 ### Inline Data (e.g. audio stream)
diff --git a/docs/my-website/docs/providers/sarvam.md b/docs/my-website/docs/providers/sarvam.md
deleted file mode 100644
index d77e9c0c75f..00000000000
--- a/docs/my-website/docs/providers/sarvam.md
+++ /dev/null
@@ -1,89 +0,0 @@
-# Sarvam.ai
-
-LiteLLM supports all the text models from [Sarvam ai](https://docs.sarvam.ai/api-reference-docs/chat/chat-completions)
-
-## Usage
-
-```python
-import os
-from litellm import completion
-
-# Set your Sarvam API key
-os.environ["SARVAM_API_KEY"] = ""
-
-messages = [{"role": "user", "content": "Hello"}]
-
-response = completion(
-    model="sarvam/sarvam-m",
-    messages=messages,
-)
-print(response)
-```
-
-## Usage with LiteLLM Proxy Server
-
-Here's how to call a Sarvam.ai model with the LiteLLM Proxy Server
-
-1. **Modify the `config.yaml`:**
-
-    ```yaml
-    model_list:
-      - model_name: my-model
-        litellm_params:
-          model: sarvam/<your-model-name>  # add sarvam/ prefix to route as Sarvam provider
-          api_key: api-key                 # api key to send your model
-    ```
-
-2. **Start the proxy:**
-
-    ```bash
-    $ litellm --config /path/to/config.yaml
-    ```
-
-3. **Send a request to LiteLLM Proxy Server:**
-
-    <Tabs>
-
-    <TabItem value="openai" label="OpenAI Python v1.0.0+">
-
-    ```python
-    import openai
-
-    client = openai.OpenAI(
-        api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
-        base_url="http://0.0.0.0:4000" # litellm-proxy-base url
-    )
-
-    response = client.chat.completions.create(
-        model="my-model",
-        messages=[
-            {
-                "role": "user",
-                "content": "what llm are you"
-            }
-        ],
-    )
-
-    print(response)
-    ```
-    </TabItem>
-
-    <TabItem value="curl" label="curl">
-
-    ```shell
-    curl --location 'http://0.0.0.0:4000/chat/completions' \
-        --header 'Authorization: Bearer sk-1234' \
-        --header 'Content-Type: application/json' \
-        --data '{
-        "model": "my-model",
-        "messages": [
-            {
-            "role": "user",
-            "content": "what llm are you"
-            }
-        ]
-    }'
-    ```
-    </TabItem>
-
-    </Tabs>
diff --git a/docs/my-website/docs/providers/vercel_ai_gateway.md b/docs/my-website/docs/providers/vercel_ai_gateway.md
index 3ff007171ed..91f0a18ea1c 100644
--- a/docs/my-website/docs/providers/vercel_ai_gateway.md
+++ b/docs/my-website/docs/providers/vercel_ai_gateway.md
@@ -11,7 +11,7 @@ import TabItem from '@theme/TabItem';
 | Provider Route on LiteLLM | `vercel_ai_gateway/` |
 | Link to Provider Doc | [Vercel AI Gateway Documentation ↗](https://vercel.com/docs/ai-gateway) |
 | Base URL | `https://ai-gateway.vercel.sh/v1` |
-| Supported Operations | `/chat/completions`, `/embeddings`, `/models` |
+| Supported Operations | `/chat/completions`, `/models` |
 
 <br />
 <br />
@@ -73,7 +73,7 @@ messages = [{"content": "Hello, how are you?", "role": "user"}]
 
 # Vercel AI Gateway call with streaming
 response = completion(
-    model="vercel_ai_gateway/openai/gpt-4o",
+    model="vercel_ai_gateway/openai/gpt-4o", 
     messages=messages,
     stream=True
 )
@@ -82,33 +82,6 @@ for chunk in response:
     print(chunk)
 ```
 
-### Embeddings
-
-```python showLineNumbers title="Vercel AI Gateway Embeddings"
-import os
-from litellm import embedding
-
-os.environ["VERCEL_AI_GATEWAY_API_KEY"] = "your-api-key"
-
-# Vercel AI Gateway embedding call
-response = embedding(
-    model="vercel_ai_gateway/openai/text-embedding-3-small",
-    input="Hello world"
-)
-
-print(response.data[0]["embedding"][:5])  # Print first 5 dimensions
-```
-
-You can also specify the `dimensions` parameter:
-
-```python showLineNumbers title="Vercel AI Gateway Embeddings with Dimensions"
-response = embedding(
-    model="vercel_ai_gateway/openai/text-embedding-3-small",
-    input=["Hello world", "Goodbye world"],
-    dimensions=768
-)
-```
-
 ## Usage - LiteLLM Proxy
 
 Add the following to your LiteLLM Proxy configuration file:
@@ -124,11 +97,6 @@ model_list:
     litellm_params:
       model: vercel_ai_gateway/anthropic/claude-4-sonnet
       api_key: os.environ/VERCEL_AI_GATEWAY_API_KEY
-
-  - model_name: text-embedding-3-small-gateway
-    litellm_params:
-      model: vercel_ai_gateway/openai/text-embedding-3-small
-      api_key: os.environ/VERCEL_AI_GATEWAY_API_KEY
 ```
 
 Start your LiteLLM Proxy server:
diff --git a/docs/my-website/docs/proxy/cli_sso.md b/docs/my-website/docs/proxy/cli_sso.md
index ad0f033f802..cde6bf266d4 100644
--- a/docs/my-website/docs/proxy/cli_sso.md
+++ b/docs/my-website/docs/proxy/cli_sso.md
@@ -28,37 +28,6 @@ EXPERIMENTAL_UI_LOGIN="True" litellm --config config.yaml
 
 :::
 
-### Configuration
-
-#### JWT Token Expiration
-
-By default, CLI authentication tokens expire after **24 hours**. You can customize this expiration time by setting the `LITELLM_CLI_JWT_EXPIRATION_HOURS` environment variable when starting your LiteLLM Proxy:
-
-```bash
-# Set CLI JWT tokens to expire after 48 hours
-export LITELLM_CLI_JWT_EXPIRATION_HOURS=48
-export EXPERIMENTAL_UI_LOGIN="True"
-litellm --config config.yaml
-```
-
-Or in a single command:
-
-```bash
-LITELLM_CLI_JWT_EXPIRATION_HOURS=48 EXPERIMENTAL_UI_LOGIN="True" litellm --config config.yaml
-```
-
-**Examples:**
-- `LITELLM_CLI_JWT_EXPIRATION_HOURS=12` - Tokens expire after 12 hours
-- `LITELLM_CLI_JWT_EXPIRATION_HOURS=168` - Tokens expire after 7 days (168 hours)
-- `LITELLM_CLI_JWT_EXPIRATION_HOURS=720` - Tokens expire after 30 days (720 hours)
-
-:::tip
-You can check your current token's age and expiration status using:
-```bash
-litellm-proxy whoami
-```
-:::
-
 ### Steps
 
 1. **Install the CLI**
diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md
index 37e58d55407..6d3fa206113 100644
--- a/docs/my-website/docs/proxy/config_settings.md
+++ b/docs/my-website/docs/proxy/config_settings.md
@@ -462,7 +462,6 @@ router_settings:
 | CHATGPT_USER_AGENT_SUFFIX | Suffix to append to the ChatGPT user agent string
 | CIRCLE_OIDC_TOKEN | OpenID Connect token for CircleCI
 | CIRCLE_OIDC_TOKEN_V2 | Version 2 of the OpenID Connect token for CircleCI
-| CLI_JWT_EXPIRATION_HOURS | Expiration time in hours for CLI-generated JWT tokens. Default is 24 hours. Can also be set via LITELLM_CLI_JWT_EXPIRATION_HOURS
 | CLOUDZERO_API_KEY | CloudZero API key for authentication
 | CLOUDZERO_CONNECTION_ID | CloudZero connection ID for data submission
 | CLOUDZERO_EXPORT_INTERVAL_MINUTES | Interval in minutes for CloudZero data export operations
@@ -724,7 +723,6 @@ router_settings:
 | LITERAL_API_URL | API URL for Literal service
 | LITERAL_BATCH_SIZE | Batch size for Literal operations
 | LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX | Disable automatic URL suffix appending for Anthropic API base URLs. When set to `true`, prevents LiteLLM from automatically adding `/v1/messages` or `/v1/complete` to custom Anthropic API endpoints
-| LITELLM_CLI_JWT_EXPIRATION_HOURS | Expiration time in hours for CLI-generated JWT tokens. Default is 24 hours
 | LITELLM_DD_AGENT_HOST | Hostname or IP of DataDog agent for LiteLLM-specific logging. When set, logs are sent to agent instead of direct API
 | LITELLM_DD_AGENT_PORT | Port of DataDog agent for LiteLLM-specific log intake. Default is 10518
 | LITELLM_DONT_SHOW_FEEDBACK_BOX | Flag to hide feedback box in LiteLLM UI
diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md
index 0761e0e9fa8..7393e73ba87 100644
--- a/docs/my-website/docs/proxy/deploy.md
+++ b/docs/my-website/docs/proxy/deploy.md
@@ -200,7 +200,6 @@ Example `requirements.txt`
 
 ```shell
 litellm[proxy]==1.57.3 # Specify the litellm version you want to use
-litellm-enterprise
 prometheus_client
 langfuse
 prisma
diff --git a/docs/my-website/docs/proxy/guardrails/onyx_security.md b/docs/my-website/docs/proxy/guardrails/onyx_security.md
index d240902eb52..85b0ba9f830 100644
--- a/docs/my-website/docs/proxy/guardrails/onyx_security.md
+++ b/docs/my-website/docs/proxy/guardrails/onyx_security.md
@@ -128,7 +128,6 @@ guardrails:
       mode: ["pre_call", "post_call", "during_call"] # Run at multiple stages
       api_key: os.environ/ONYX_API_KEY
       api_base: os.environ/ONYX_API_BASE
-      timeout: 10.0 # Optional, defaults to 10 seconds
 ```
 
 ### Required Parameters
@@ -138,7 +137,6 @@ guardrails:
 ### Optional Parameters
 
 - **`api_base`**: Onyx API base URL (defaults to `https://ai-guard.onyx.security`)
-- **`timeout`**: Request timeout in seconds (defaults to `10.0`)
 
 ## Environment Variables
 
@@ -147,5 +145,4 @@ You can set these environment variables instead of hardcoding values in your con
 ```shell
 export ONYX_API_KEY="your-api-key-here"
 export ONYX_API_BASE="https://ai-guard.onyx.security"   # Optional
-export ONYX_TIMEOUT=10                                  # Optional, timeout in seconds
 ```
diff --git a/docs/my-website/docs/proxy/guardrails/quick_start.md b/docs/my-website/docs/proxy/guardrails/quick_start.md
index ddb215fcb66..cb6379d49f4 100644
--- a/docs/my-website/docs/proxy/guardrails/quick_start.md
+++ b/docs/my-website/docs/proxy/guardrails/quick_start.md
@@ -405,10 +405,14 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
 
 ## **Proxy Admin Controls**
 
-### Monitoring Guardrails
+### ✨ Monitoring Guardrails
 
 Monitor which guardrails were executed and whether they passed or failed. e.g. guardrail going rogue and failing requests we don't intend to fail
 
+:::info
+
+✨ This is an Enterprise only feature [Get a free trial](https://www.litellm.ai/enterprise#trial)
+
 :::
 
 #### Setup
diff --git a/docs/my-website/docs/proxy/keys_teams_router_settings.md b/docs/my-website/docs/proxy/keys_teams_router_settings.md
deleted file mode 100644
index ec59e8f271b..00000000000
--- a/docs/my-website/docs/proxy/keys_teams_router_settings.md
+++ /dev/null
@@ -1,150 +0,0 @@
-import Image from '@theme/IdealImage';
-
-# UI - Router Settings for Keys and Teams
-
-Configure router settings at the key and team level to achieve granular control over routing behavior, fallbacks, retries, and other router configurations. This enables you to customize routing behavior for specific keys or teams without affecting global settings.
-
-## Overview
-
-Router Settings for Keys and Teams allows you to configure router behavior at different levels of granularity. Previously, router settings could only be configured globally, applying the same routing strategy, fallbacks, timeouts, and retry policies to all requests across your entire proxy instance.
-
-With key-level and team-level router settings, you can now:
-
-- **Customize routing strategies** per key or team (e.g., use `least-busy` for high-priority keys, `latency-based-routing` for others)
-- **Configure different fallback chains** for different keys or teams
-- **Set key-specific or team-specific timeouts** and retry policies
-- **Apply different reliability settings** (cooldowns, allowed failures) per key or team
-- **Override global settings** when needed for specific use cases
-
-<Image img={require('../../img/ui_granular_router_settings.png')} />
-
-## Summary
-
-Router settings follow a **hierarchical resolution order**: **Keys > Teams > Global**. When a request is made:
-
-1. **Key-level settings** are checked first. If router settings are configured for the API key being used, those settings are applied.
-2. **Team-level settings** are checked next. If the key belongs to a team and that team has router settings configured, those settings are used (unless key-level settings exist).
-3. **Global settings** are used as the final fallback. If neither key nor team settings are found, the global router settings from your proxy configuration are applied.
-
-This hierarchical approach ensures that the most specific settings take precedence, allowing you to fine-tune routing behavior for individual keys or teams while maintaining sensible defaults at the global level.
-
-## How Router Settings Resolution Works
-
-Router settings are resolved in the following priority order:
-
-### Resolution Order: Key > Team > Global
-
-1. **Key-level router settings** (highest priority)
-   - Applied when router settings are configured directly on an API key
-   - Takes precedence over all other settings
-   - Useful for individual key customization
-
-2. **Team-level router settings** (medium priority)
-   - Applied when the API key belongs to a team with router settings configured
-   - Only used if no key-level settings exist
-   - Useful for applying consistent settings across multiple keys in a team
-
-3. **Global router settings** (lowest priority)
-   - Applied from your proxy configuration file or database
-   - Used as the default when no key or team settings are found
-   - Previously, this was the only option available
-
-## How to Configure Router Settings
-
-### Configuring Router Settings for Keys
-
-Follow these steps to configure router settings for an API key:
-
-1. Navigate to [http://localhost:4000/ui/?login=success](http://localhost:4000/ui/?login=success)
-
-![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/61889da3-32de-4ebf-9cf3-7dc1db2fc993/ascreenshot_2492cf6d916a4ab98197cc8336e3a371_text_export.jpeg)
-
-2. Click "+ Create New Key" (or edit an existing key)
-
-![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/61889da3-32de-4ebf-9cf3-7dc1db2fc993/ascreenshot_5a25380cf5044b4f93c146139d84403a_text_export.jpeg)
-
-3. Click "Optional Settings"
-
-![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/e5eb5858-1cc1-4273-90bd-19ad139feebd/ascreenshot_33888989cfb9445bb83660f702ba32e0_text_export.jpeg)
-
-4. Click "Router Settings"
-
-![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/d9eeca83-1f76-4fcf-bf61-d89edf3454d3/ascreenshot_825c7993f4b24949aee9b31d4a788d8a_text_export.jpeg)
-
-5. Configure your desired router settings. For example, click "Fallbacks" to configure fallback models:
-
-![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/30ff647f-0254-4410-8311-660eef7ec0c4/ascreenshot_16966c8a0160473eb03e0f2c3b5c3afa_text_export.jpeg)
-
-6. Click "Select a model to begin configuring fallbacks" and configure your fallback chain:
-
-![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/918f1b5b-c656-4864-98bd-d8c58924b6d9/ascreenshot_79ca6cd93be04033929f080e0c8d040a_text_export.jpeg)
-
-### Configuring Router Settings for Teams
-
-Follow these steps to configure router settings for a team:
-
-1. Navigate to [http://localhost:4000/ui/?login=success](http://localhost:4000/ui/?login=success)
-
-![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/60a33a8c-2e48-4788-a1a2-e5bcffa98cca/ascreenshot_9e255ba48f914c72ae57db7d3c1c7cd5_text_export.jpeg)
-
-2. Click "Teams"
-
-![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/60a33a8c-2e48-4788-a1a2-e5bcffa98cca/ascreenshot_070934fa9c17453987f21f58117e673b_text_export.jpeg)
-
-3. Click "+ Create New Team" (or edit an existing team)
-
-![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/6f964ce2-f458-4719-a070-1af444ad92f5/ascreenshot_10f427f3106a4032a65d1046668880bd_text_export.jpeg)
-
-4. Click "Router Settings"
-
-![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/a923c4ae-29f2-42b5-93ae-12f62d442691/ascreenshot_144520f2dd2f419dad79dffb1579ec04_text_export.jpeg)
-
-5. Configure your desired router settings. For example, click "Fallbacks" to configure fallback models:
-
-![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/b062ecfa-bf5b-4c99-93a1-84b8b56fdb4c/ascreenshot_ea9acbc4e75448709b64a22addfb4157_text_export.jpeg)
-
-6. Click "Select a model to begin configuring fallbacks" and configure your fallback chain:
-
-![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/67ca2655-4e82-4f93-be9a-7244ad22640f/ascreenshot_4fdbed826cd546d784e8738626be835d_text_export.jpeg)
-
-## Use Cases
-
-### Different Routing Strategies per Key
-
-Configure different routing strategies for different use cases:
-
-- **High-priority production keys**: Use `latency-based-routing` for optimal performance
-- **Development keys**: Use `simple-shuffle` for simplicity
-- **Cost-sensitive keys**: Use `cost-based-routing` to minimize expenses
-
-### Team-Level Consistency
-
-Apply consistent router settings across all keys in a team:
-
-- Set team-wide fallback chains for reliability
-- Configure team-specific timeout policies
-- Apply uniform retry policies across team members
-
-### Override Global Settings
-
-Override global settings for specific scenarios:
-
-- Production keys may need stricter timeout policies than development
-- Certain teams may require different fallback models
-- Individual keys may need custom retry policies for specific use cases
-
-### Gradual Rollout
-
-Test new router settings on specific keys or teams before applying globally:
-
-- Configure new routing strategies on a test key first
-- Validate fallback chains on a small team before global rollout
-- A/B test different timeout values across different keys
-
-## Related Features
-
-- [Router Settings Reference](./config_settings.md#router_settings---reference) - Complete reference of all router settings
-- [Load Balancing](./load_balancing.md) - Learn about routing strategies and load balancing
-- [Reliability](./reliability.md) - Configure fallbacks, retries, and error handling
-- [Keys](./keys.md) - Manage API keys and their settings
-- [Teams](./teams.md) - Organize keys into teams
diff --git a/docs/my-website/docs/proxy/litellm_managed_files.md b/docs/my-website/docs/proxy/litellm_managed_files.md
index 6272180bd40..7aba173f35b 100644
--- a/docs/my-website/docs/proxy/litellm_managed_files.md
+++ b/docs/my-website/docs/proxy/litellm_managed_files.md
@@ -11,7 +11,7 @@ import Image from '@theme/IdealImage';
 
 This is a free LiteLLM Enterprise feature.
 
-Available via the `litellm` docker image. If you are using the pip package, you must install [`litellm-enterprise`](https://pypi.org/project/litellm-enterprise/).
+Available via the `litellm[proxy]` package or any `litellm` docker image.
 
 :::
 
diff --git a/docs/my-website/docs/proxy/prometheus.md b/docs/my-website/docs/proxy/prometheus.md
index 93a0675f097..cd2b3b68f37 100644
--- a/docs/my-website/docs/proxy/prometheus.md
+++ b/docs/my-website/docs/proxy/prometheus.md
@@ -121,8 +121,8 @@ Use this to track overall LiteLLM Proxy usage.
 
 | Metric Name          | Description                          |
 |----------------------|--------------------------------------|
-| `litellm_proxy_failed_requests_metric`             | Total number of failed responses from proxy - the client did not get a success response from litellm proxy. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "user_email", "exception_status", "exception_class", "route", "model_id"`          |
-| `litellm_proxy_total_requests_metric`             | Total number of requests made to the proxy server - track number of client side requests. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "status_code", "user_email", "route", "model_id"`          |
+| `litellm_proxy_failed_requests_metric`             | Total number of failed responses from proxy - the client did not get a success response from litellm proxy. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "exception_status", "exception_class", "route"`          |
+| `litellm_proxy_total_requests_metric`             | Total number of requests made to the proxy server - track number of client side requests. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "status_code", "user_email", "route"`          |
 
 ### Callback Logging Metrics
 
@@ -130,12 +130,7 @@ Monitor failures while shipping logs to downstream callbacks like `s3_v3` cold s
 
 | Metric Name          | Description                          |
 |----------------------|--------------------------------------|
-| `litellm_callback_logging_failures_metric` | Total number of failed attempts to emit logs to a configured callback. Labels: `"callback_name"`. Use this to alert on callback delivery issues such as repeated failures when writing to `s3_v3`, `langfuse`, or `langfuse_otel` and other otel providers |
-
-**Supported Callbacks:**
-- `S3Logger` - S3 v2 cold storage failures
-- `langfuse` - Langfuse logging failures
-- `otel` -  OpenTelemetry logging failures
+| `litellm_callback_logging_failures_metric` | Total number of failed attempts to emit logs to a configured callback. Labels: `"callback_name"`. Use this to alert on callback delivery issues such as repeated failures when writing to `s3_v3`. |
 
 ## LLM Provider Metrics
 
@@ -196,10 +191,10 @@ Use this for LLM API Error monitoring and tracking remaining rate limits and tok
 
 | Metric Name          | Description                          |
 |----------------------|--------------------------------------|
-| `litellm_request_total_latency_metric`             | Total latency (seconds) for a request to LiteLLM Proxy Server - tracked for labels "end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model", "model_id" |
+| `litellm_request_total_latency_metric`             | Total latency (seconds) for a request to LiteLLM Proxy Server - tracked for labels "end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model" |
 | `litellm_overhead_latency_metric`             | Latency overhead (seconds) added by LiteLLM processing - tracked for labels "model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias" |
 | `litellm_llm_api_latency_metric`  | Latency (seconds) for just the LLM API call - tracked for labels "model", "hashed_api_key", "api_key_alias", "team", "team_alias", "requested_model", "end_user", "user" |
-| `litellm_llm_api_time_to_first_token_metric`             | Time to first token for LLM API call - tracked for labels `model`, `hashed_api_key`, `api_key_alias`, `team`, `team_alias`, `requested_model`, `end_user`, `user`, `model_id` [Note: only emitted for streaming requests] |
+| `litellm_llm_api_time_to_first_token_metric`             | Time to first token for LLM API call - tracked for labels `model`, `hashed_api_key`, `api_key_alias`, `team`, `team_alias` [Note: only emitted for streaming requests] |
 
 ## Tracking `end_user` on Prometheus
 
diff --git a/docs/my-website/docs/proxy/ui/page_visibility.md b/docs/my-website/docs/proxy/ui/page_visibility.md
deleted file mode 100644
index 06b06f33219..00000000000
--- a/docs/my-website/docs/proxy/ui/page_visibility.md
+++ /dev/null
@@ -1,121 +0,0 @@
-import Image from '@theme/IdealImage';
-
-# Control Page Visibility for Internal Users
-
-Configure which navigation tabs and pages are visible to internal users (non-admin developers) in the LiteLLM UI.
-
-Use this feature to simplify the UI and control which pages your internal users/developers can see when signing in.
-
-## Overview
-
-By default, all pages accessible to internal users are visible in the navigation sidebar. The page visibility control allows admins to restrict which pages internal users can see, creating a more focused and streamlined experience.
-
-
-## Configure Page Visibility
-
-### 1. Navigate to Settings
-
-Click the **Settings** icon in the sidebar.
-
-![Navigate to Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/cbb6f272-ab18-4996-b57d-7ed4aad721ea/ascreenshot_ab80f3175b1a41b0bdabdd2cd3980573_text_export.jpeg)
-
-### 2. Go to Admin Settings
-
-Click **Admin Settings** from the settings menu.
-
-![Go to Admin Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/e2b327bf-1cfd-4519-a9ce-8a6ecb2de53a/ascreenshot_23bb1577b3f84d22be78e0faa58dee3d_text_export.jpeg)
-
-### 3. Select UI Settings
-
-Click **UI Settings** to access the page visibility controls.
-
-![Select UI Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/fff0366a-4944-457a-8f6a-e22018dde108/ascreenshot_0e268e8651654e75bb9fb40d2ed366a9_text_export.jpeg)
-
-### 4. Open Page Visibility Configuration
-
-Click **Configure Page Visibility** to expand the configuration panel.
-
-![Open Configuration](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/3a4761d6-145a-4afd-8abf-d92744b9ac9f/ascreenshot_23c16eb79c32481887b879d961f1f00a_text_export.jpeg)
-
-### 5. Select Pages to Make Visible
-
-Check the boxes for the pages you want internal users to see. Pages are organized by category for easy navigation.
-
-![Select Pages](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/b9c96b54-6c20-484f-8b0b-3a86decb5717/ascreenshot_3347ade01ebe4ea390bc7b57e53db43f_text_export.jpeg)
-
-**Available pages include:**
-- Virtual Keys
-- Playground
-- Models + Endpoints
-- Agents
-- MCP Servers
-- Search Tools
-- Vector Stores
-- Logs
-- Teams
-- Organizations
-- Usage
-- Budgets
-- And more...
-
-### 6. Save Your Configuration
-
-Click **Save Page Visibility Settings** to apply the changes.
-
-![Save Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/8a215378-44f5-4bb8-b984-06fa2aa03903/ascreenshot_44e7aeebe25a477ba92f73a3ed3df644_text_export.jpeg)
-
-### 7. Verify Changes
-
-Internal users will now only see the selected pages in their navigation sidebar.
-
-![Verify Changes](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/493a7718-b276-40b9-970f-5814054932d9/ascreenshot_ad23b8691f824095ba60256f91ad24f8_text_export.jpeg)
-
-## Reset to Default
-
-To restore all pages to internal users:
-
-1. Open the Page Visibility configuration
-2. Click **Reset to Default (All Pages)**
-3. Click **Save Page Visibility Settings**
-
-This will clear the restriction and show all accessible pages to internal users.
-
-## API Configuration
-
-You can also configure page visibility programmatically using the API:
-
-### Get Current Settings
-
-```bash
-curl -X GET 'http://localhost:4000/ui_settings/get' \
-  -H 'Authorization: Bearer <your-admin-key>'
-```
-
-### Update Page Visibility
-
-```bash
-curl -X PATCH 'http://localhost:4000/ui_settings/update' \
-  -H 'Authorization: Bearer <your-admin-key>' \
-  -H 'Content-Type: application/json' \
-  -d '{
-    "enabled_ui_pages_internal_users": [
-      "api-keys",
-      "agents",
-      "mcp-servers",
-      "logs",
-      "teams"
-    ]
-  }'
-```
-
-### Clear Page Visibility Restrictions
-
-```bash
-curl -X PATCH 'http://localhost:4000/ui_settings/update' \
-  -H 'Authorization: Bearer <your-admin-key>' \
-  -H 'Content-Type: application/json' \
-  -d '{
-    "enabled_ui_pages_internal_users": null
-  }'
-```
-
diff --git a/docs/my-website/docs/rag_ingest.md b/docs/my-website/docs/rag_ingest.md
index 7adc2d70b5b..1133b85f206 100644
--- a/docs/my-website/docs/rag_ingest.md
+++ b/docs/my-website/docs/rag_ingest.md
@@ -5,7 +5,7 @@ All-in-one document ingestion pipeline: **Upload → Chunk → Embed → Vector
 | Feature | Supported |
 |---------|-----------|
 | Logging | Yes |
-| Supported Providers | `openai`, `bedrock`, `vertex_ai`, `gemini`, `s3_vectors` |
+| Supported Providers | `openai`, `bedrock`, `vertex_ai`, `gemini` |
 
 :::tip
 After ingesting documents, use [/rag/query](./rag_query.md) to search and generate responses with your ingested content.
@@ -75,31 +75,6 @@ curl -X POST "http://localhost:4000/v1/rag/ingest" \
     }"
 ```
 
-### AWS S3 Vectors
-
-```bash showLineNumbers title="Ingest to S3 Vectors"
-curl -X POST "http://localhost:4000/v1/rag/ingest" \
-    -H "Authorization: Bearer sk-1234" \
-    -H "Content-Type: application/json" \
-    -d "{
-        \"file\": {
-            \"filename\": \"document.txt\",
-            \"content\": \"$(base64 -i document.txt)\",
-            \"content_type\": \"text/plain\"
-        },
-        \"ingest_options\": {
-            \"embedding\": {
-                \"model\": \"text-embedding-3-small\"
-            },
-            \"vector_store\": {
-                \"custom_llm_provider\": \"s3_vectors\",
-                \"vector_bucket_name\": \"my-embeddings\",
-                \"aws_region_name\": \"us-west-2\"
-            }
-        }
-    }"
-```
-
 ## Response
 
 ```json
@@ -290,57 +265,6 @@ When `vector_store_id` is omitted, LiteLLM automatically creates:
 4. Install: `pip install 'google-cloud-aiplatform>=1.60.0'`
 :::
 
-### vector_store (AWS S3 Vectors)
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `custom_llm_provider` | string | - | `"s3_vectors"` |
-| `vector_bucket_name` | string | **required** | S3 vector bucket name |
-| `index_name` | string | auto-create | Vector index name |
-| `dimension` | integer | auto-detect | Vector dimension (auto-detected from embedding model) |
-| `distance_metric` | string | `cosine` | Distance metric: `cosine` or `euclidean` |
-| `non_filterable_metadata_keys` | array | `["source_text"]` | Metadata keys excluded from filtering |
-| `aws_region_name` | string | `us-west-2` | AWS region |
-| `aws_access_key_id` | string | env | AWS access key |
-| `aws_secret_access_key` | string | env | AWS secret key |
-
-:::info S3 Vectors Auto-Creation
-When `index_name` is omitted, LiteLLM automatically creates:
-- S3 vector bucket (if it doesn't exist)
-- Vector index with auto-detected dimensions from your embedding model
-
-**Dimension Auto-Detection**: The vector dimension is automatically detected by making a test embedding request to your specified model. No need to manually specify dimensions!
-
-**Supported Embedding Models**: Works with any LiteLLM-supported embedding model (OpenAI, Cohere, Bedrock, Azure, etc.)
-:::
-
-**Example with auto-detection:**
-```json
-{
-  "embedding": {
-    "model": "text-embedding-3-small"  // Dimension auto-detected as 1536
-  },
-  "vector_store": {
-    "custom_llm_provider": "s3_vectors",
-    "vector_bucket_name": "my-embeddings"
-  }
-}
-```
-
-**Example with custom embedding provider:**
-```json
-{
-  "embedding": {
-    "model": "cohere/embed-english-v3.0"  // Dimension auto-detected as 1024
-  },
-  "vector_store": {
-    "custom_llm_provider": "s3_vectors",
-    "vector_bucket_name": "my-embeddings",
-    "distance_metric": "cosine"
-  }
-}
-```
-
 ## Input Examples
 
 ### File (Base64)
diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md
index 2b3a28edf75..47967775e1e 100644
--- a/docs/my-website/docs/routing.md
+++ b/docs/my-website/docs/routing.md
@@ -830,12 +830,6 @@ asyncio.run(router_acompletion())
 </TabItem>
 </Tabs>
 
-## Traffic Mirroring / Silent Experiments
-
-Traffic mirroring allows you to "mimic" production traffic to a secondary (silent) model for evaluation purposes. The silent model's response is gathered in the background and does not affect the latency or result of the primary request.
-
-[**See detailed guide on A/B Testing - Traffic Mirroring here**](./traffic_mirroring.md)
-
 ## Basic Reliability
 
 ### Deployment Ordering (Priority)
diff --git a/docs/my-website/docs/traffic_mirroring.md b/docs/my-website/docs/traffic_mirroring.md
deleted file mode 100644
index 3bdcb0f1614..00000000000
--- a/docs/my-website/docs/traffic_mirroring.md
+++ /dev/null
@@ -1,83 +0,0 @@
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-
-# A/B Testing - Traffic Mirroring
-
-Traffic mirroring allows you to "mimic" production traffic to a secondary (silent) model for evaluation purposes. The silent model's response is gathered in the background and does not affect the latency or result of the primary request.
-
-This is useful for:
-- Testing a new model's performance on production prompts before switching.
-- Comparing costs and latency between different providers.
-- Debugging issues by mirroring traffic to a more verbose model.
-
-## Quick Start
-
-To enable traffic mirroring, add `silent_model` to the `litellm_params` of a deployment.
-
-<Tabs>
-<TabItem value="sdk" label="SDK">
-
-```python
-from litellm import Router
-
-model_list = [
-    {
-        "model_name": "gpt-3.5-turbo",
-        "litellm_params": {
-            "model": "azure/chatgpt-v-2",
-            "api_key": "...",
-            "silent_model": "gpt-4" # 👈 Mirror traffic to gpt-4
-        },
-    },
-    {
-        "model_name": "gpt-4",
-        "litellm_params": {
-            "model": "openai/gpt-4",
-            "api_key": "..."
-        },
-    }
-]
-
-router = Router(model_list=model_list)
-
-# The request to "gpt-3.5-turbo" will trigger a background call to "gpt-4"
-response = await router.acompletion(
-    model="gpt-3.5-turbo",
-    messages=[{"role": "user", "content": "How does traffic mirroring work?"}]
-)
-```
-
-</TabItem>
-<TabItem value="proxy" label="Proxy">
-
-Add `silent_model` to your `config.yaml`:
-
-```yaml
-model_list:
-  - model_name: primary-model
-    litellm_params:
-      model: azure/gpt-35-turbo
-      api_key: os.environ/AZURE_API_KEY
-      silent_model: evaluation-model # 👈 Mirror traffic here
-  - model_name: evaluation-model
-    litellm_params:
-      model: openai/gpt-4o
-      api_key: os.environ/OPENAI_API_KEY
-```
-
-</TabItem>
-</Tabs>
-
-## How it works
-1. **Request Received**: A request is made to a model group (e.g. `primary-model`).
-2. **Deployment Picked**: LiteLLM picks a deployment from the group.
-3. **Primary Call**: LiteLLM makes the call to the primary deployment.
-4. **Mirroring**: If `silent_model` is present, LiteLLM triggers a background call to that model. 
-   - For **Sync** calls: Uses a shared thread pool.
-   - For **Async** calls: Uses `asyncio.create_task`.
-5. **Isolation**: The background call uses a `deepcopy` of the original request parameters and sets `metadata["is_silent_experiment"] = True`. It also strips out logging IDs to prevent collisions in usage tracking.
-
-## Key Features
-- **Latency Isolation**: The primary request returns as soon as it's ready. The background (silent) call does not block.
-- **Unified Logging**: Background calls are processed via the Router, meaning they are automatically logged to your configured observability tools (Langfuse, S3, etc.).
-- **Evaluation**: Use the `is_silent_experiment: True` flag in your logs to filter and compare results between the primary and mirrored calls.
diff --git a/docs/my-website/img/ui_granular_router_settings.png b/docs/my-website/img/ui_granular_router_settings.png
deleted file mode 100644
index 6242679956c..00000000000
Binary files a/docs/my-website/img/ui_granular_router_settings.png and /dev/null differ
diff --git a/docs/my-website/release_notes/v1.81.0/index.md b/docs/my-website/release_notes/v1.81.0/index.md
index e61d7d2d593..d69a62b6941 100644
--- a/docs/my-website/release_notes/v1.81.0/index.md
+++ b/docs/my-website/release_notes/v1.81.0/index.md
@@ -1,5 +1,5 @@
 ---
-title: "v1.81.0-stable - Claude Code - Web Search Across All Providers"
+title: "v1.81.0 - Claude Code - Web Search Across All Providers"
 slug: "v1-81-0"
 date: 2026-01-18T10:00:00
 authors:
@@ -27,7 +27,7 @@ import TabItem from '@theme/TabItem';
 docker run \
 -e STORE_MODEL_IN_DB=True \
 -p 4000:4000 \
-docker.litellm.ai/berriai/litellm:v1.81.0-stable
+docker.litellm.ai/berriai/litellm:v1.81.0.rc.1
 ```
 
 </TabItem>
diff --git a/docs/my-website/release_notes/v1.81.3-stable/index.md b/docs/my-website/release_notes/v1.81.3-stable/index.md
deleted file mode 100644
index 22b6f43deef..00000000000
--- a/docs/my-website/release_notes/v1.81.3-stable/index.md
+++ /dev/null
@@ -1,423 +0,0 @@
----
-title: "v1.81.3-stable - Performance - 25% CPU Usage Reduction"
-slug: "v1-81-3"
-date: 2026-01-26T10:00:00
-authors:
-  - name: Krrish Dholakia
-    title: CEO, LiteLLM
-    url: https://www.linkedin.com/in/krish-d/
-    image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
-  - name: Ishaan Jaff
-    title: CTO, LiteLLM
-    url: https://www.linkedin.com/in/reffajnaahsi/
-    image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
-hide_table_of_contents: false
----
-
-import Image from '@theme/IdealImage';
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-
-## Deploy this version
-
-<Tabs>
-<TabItem value="docker" label="Docker">
-
-``` showLineNumbers title="docker run litellm"
-docker run \
--e STORE_MODEL_IN_DB=True \
--p 4000:4000 \
-docker.litellm.ai/berriai/litellm:v1.81.3.rc.2
-```
-
-</TabItem>
-
-<TabItem value="pip" label="Pip">
-
-``` showLineNumbers title="pip install litellm"
-pip install litellm==1.81.3.rc.2
-```
-
-</TabItem>
-</Tabs>
-
----
-
-## New Models / Updated Models
-
-### New Model Support
-
-| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Deprecation Date |
-| -------- | ----- | -------------- | ------------------- | -------------------- | ---------------- |
-| OpenAI | `gpt-audio`, `gpt-audio-2025-08-28` | 128K | $32/1M audio tokens, $2.5/1M text tokens | $64/1M audio tokens, $10/1M text tokens | - | 
-| OpenAI | `gpt-audio-mini`, `gpt-audio-mini-2025-08-28` | 128K | $10/1M audio tokens, $0.6/1M text tokens | $20/1M audio tokens, $2.4/1M text tokens | - |
-| Deepinfra, Vertex AI, Google AI Studio, OpenRouter, Vercel AI Gateway | `gemini-2.0-flash-001`, `gemini-2.0-flash` |  - | - | - | 2026-03-31 |
-| Groq | `openai/gpt-oss-120b` | 131K | 0.075/1M cache read | 0.6/1M output tokens | - |
-| Groq | `groq/openai/gpt-oss-20b` | 131K | 0.0375/1M cache read, $0.075/1M text tokens | 0.3/1M output tokens | - |
-| Vertex AI | `gemini-2.5-computer-use-preview-10-2025` | 128K |  $1.25 | $10 | - |
-| Azure AI | `claude-haiku-4-5` | $1.25/1M cache read, $2/1M cache read above 1 hr, $0.1/1M text tokens | $5/1M output tokens | - |
-| Azure AI | `claude-sonnet-4-5` | $3.75/1M cache read, $6/1M cache read above 1 hr, $3/1M text tokens | $15/1M output tokens | - |
-| Azure AI | `claude-opus-4-5` | $6.25/1M cache read, $10/1M cache read above 1 hr, $0.5/1M text tokens | $25/1M output tokens | - |
-| Azure AI | `claude-opus-4-1` | $18.75/1M cache read, $30/1M cache read above 1 hr, $1.5/1M text tokens | $75/1M output tokens | - |
-
-### Features
-
-- **[OpenAI](../../docs/providers/openai)**
-    - Add gpt-audio and gpt-audio-mini models to pricing - [PR #19509](https://github.com/BerriAI/litellm/pull/19509)
-    - correct audio token costs for gpt-4o-audio-preview models - [PR #19500](https://github.com/BerriAI/litellm/pull/19500)
-    - Limit stop sequence as per openai spec (ensures JetBrains IDE compatibility) - [PR #19562](https://github.com/BerriAI/litellm/pull/19562)
-
-- **[VertexAI](../../docs/providers/vertex)**
-    - Docs - Google Workload Identity Federation (WIF) support - [PR #19320](https://github.com/BerriAI/litellm/pull/19320)
-
-- **[Agentcore](../../docs/providers/bedrock_agentcore)**
-    - Fixes streaming issues with AWS Bedrock AgentCore where responses would stop after the first chunk, particularly affecting OAuth-enabled agents - [PR #17141](https://github.com/BerriAI/litellm/pull/17141)
-
-- **[Chatgpt](../../docs/providers/chatgpt)**
-    - Adds support for calling chatgpt subscription via LiteLLM - [PR #19030](https://github.com/BerriAI/litellm/pull/19030)
-    - Adds responses API bridge support for chatgpt subscription provider - [PR #19030](https://github.com/BerriAI/litellm/pull/19030)
-
-- **[Bedrock](../../docs/providers/bedrock)**
-    - support for output format for bedrock invoke via v1/messages - [PR #19560](https://github.com/BerriAI/litellm/pull/19560)
-    
-- **[Azure](../../docs/providers/azure/azure)**
-    - Add support for Azure OpenAI v1 API - [PR #19313](https://github.com/BerriAI/litellm/pull/19313)
-    - preserve content_policy_violation details for images (#19328) - [PR #19372](https://github.com/BerriAI/litellm/pull/19372)
-    - Support OpenAI-format nested tool definitions for Responses API - [PR #19526](https://github.com/BerriAI/litellm/pull/19526)
-
-- **Gemini([Vertex AI](../../docs/providers/vertex), [Google AI Studio](../../docs/providers/gemini))**
-    - use responseJsonSchema for Gemini 2.0+ models - [PR #19314](https://github.com/BerriAI/litellm/pull/19314)
-
-- **[Volcengine](../../docs/providers/volcano)**
-    - Support Volcengine responses api - [PR #18508](https://github.com/BerriAI/litellm/pull/18508)
-
-- **[Anthropic](../../docs/providers/anthropic)**
-    - Add Support for calling Claude Code Max subscriptions via LiteLLM - [PR #19453](https://github.com/BerriAI/litellm/pull/19453)
-    - Add Structured output for /v1/messages with Anthropic API, Azure Anthropic API, Bedrock Converse - [PR #19545](https://github.com/BerriAI/litellm/pull/19545)
-
-- **[Brave Search](../../docs/search/brave)**
-    - New Search provider - [PR #19433](https://github.com/BerriAI/litellm/pull/19433)
-
-- **Sarvam ai**
-    - Add support for new sarvam models  - [PR #19479](https://github.com/BerriAI/litellm/pull/19479)
-
-- **[GMI](../../docs/providers/gmi)**
-    - add GMI Cloud provider support - [PR #19376](https://github.com/BerriAI/litellm/pull/19376)
-
-
-### Bug Fixes
-
-- **[Anthropic](../../docs/providers/anthropic)**
-    - Fix anthropic-beta sent client side being overridden instead of appended to - [PR #19343](https://github.com/BerriAI/litellm/pull/19343)
-    - Filter out unsupported fields from JSON schema for Anthropic's output_format API - [PR #19482](https://github.com/BerriAI/litellm/pull/19482)
-
-- **[Bedrock](../../docs/providers/bedrock)**
-    - Expose stability models via /image_edits endpoint and ensure proper request transformation - [PR #19323](https://github.com/BerriAI/litellm/pull/19323)
-    - Claude Code x Bedrock Invoke fails with advanced-tool-use-2025-11-20 - [PR #19373](https://github.com/BerriAI/litellm/pull/19373)
-    - deduplicate tool calls in assistant history - [PR #19324](https://github.com/BerriAI/litellm/pull/19324)
-    - fix: correct us.anthropic.claude-opus-4-5 In-region pricing - [PR #19310](https://github.com/BerriAI/litellm/pull/19310)
-    - Fix request validation errors when using Claude 4 via bedrock invoke - [PR #19381](https://github.com/BerriAI/litellm/pull/19381)
-    - Handle thinking with tool calls for Claude 4 models - [PR #19506](https://github.com/BerriAI/litellm/pull/19506)
-    - correct streaming choice index for tool calls - [PR #19506](https://github.com/BerriAI/litellm/pull/19506)
-
-- **[Ollama](../../docs/providers/ollama)**
-    - Fix tool call errors due with improved message extraction - [PR #19369](https://github.com/BerriAI/litellm/pull/19369)
-
-- **[VertexAI](../../docs/providers/vertex)**
-    - Removed optional vertex_count_tokens_location param before request is sent to vertex - [PR #19359](https://github.com/BerriAI/litellm/pull/19359)
-
-- **Gemini([Vertex AI](../../docs/providers/vertex), [Google AI Studio](../../docs/providers/gemini))**
-    - Supports setting media_resolution and fps parameters on each video file, when using Gemini video understanding - [PR #19273](https://github.com/BerriAI/litellm/pull/19273)
-    - handle reasoning_effort as dict from OpenAI Agents SDK - [PR #19419](https://github.com/BerriAI/litellm/pull/19419)
-    - add file content support in tool results - [PR #19416](https://github.com/BerriAI/litellm/pull/19416)
-
-- **[Azure](../../docs/providers/azure_ai)**
-    - Fix Azure AI costs for Anthropic models - [PR #19530](https://github.com/BerriAI/litellm/pull/19530)
-
-- **[Giga Chat](../../docs/providers/gigachat)**
-    - Add tool choice mapping - [PR #19645](https://github.com/BerriAI/litellm/pull/19645)
----
-
-## AI API Endpoints (LLMs, MCP, Agents)
-
-### Features
-
-- **[Files API](../../docs/files_endpoints)**
-    - Add managed files support when load_balancing is True - [PR #19338](https://github.com/BerriAI/litellm/pull/19338)
-
-- **[Claude Plugin Marketplace](../../docs/tutorials/claude_code_plugin_marketplace)**
-    - Add self hosted Claude Code Plugin Marketplace - [PR #19378](https://github.com/BerriAI/litellm/pull/19378)
-
-- **[MCP](../../docs/mcp)**
-    - Add MCP Protocol version 2025-11-25 support - [PR #19379](https://github.com/BerriAI/litellm/pull/19379)
-    - Log MCP tool calls and list tools in the LiteLLM Spend Logs table for easier debugging - [PR #19469](https://github.com/BerriAI/litellm/pull/19469)
-
-- **[Vertex AI](../../docs/providers/vertex)**
-    - Ensure only anthropic betas are forwarded down to LLM API (by default) - [PR #19542](https://github.com/BerriAI/litellm/pull/19542)
-    - Allow overriding to support forwarding incoming headers are forwarded down to target - [PR #19524](https://github.com/BerriAI/litellm/pull/19524)
-
-- **[Chat/Completions](../../docs/completion/input)**
-    - Add MCP tools response to chat completions - [PR #19552](https://github.com/BerriAI/litellm/pull/19552)
-    - Add custom vertex ai finish reasons to the output - [PR #19558](https://github.com/BerriAI/litellm/pull/19558)
-    - Return MCP execution in /chat/completions before model output during streaming - [PR #19623](https://github.com/BerriAI/litellm/pull/19623)
-
-### Bugs
-
-- **[Responses API](../../docs/response_api)**
-    - Fix duplicate messages during MCP streaming tool execution - [PR #19317](https://github.com/BerriAI/litellm/pull/19317)
-    - Fix pickle error when using OpenAI's Responses API with stream=True and tool_choice of type allowed_tools (an OpenAI-native parameter) - [PR #17205](https://github.com/BerriAI/litellm/pull/17205)
-    - stream tool call events for non-openai models - [PR #19368](https://github.com/BerriAI/litellm/pull/19368)
-    - preserve tool output ordering for gemini in responses bridge - [PR #19360](https://github.com/BerriAI/litellm/pull/19360)
-    - Add ID caching to prevent ID mismatch text-start and text-delta - [PR #19390](https://github.com/BerriAI/litellm/pull/19390)
-    - Include output_item, reasoning_summary_Text_done and reasoning_summary_part_done events for non-openai models - [PR #19472](https://github.com/BerriAI/litellm/pull/19472) 
-
-- **[Chat/Completions](../../docs/completion/input)**
-    - fix: drop_params not dropping prompt_cache_key for non-OpenAI providers - [PR #19346](https://github.com/BerriAI/litellm/pull/19346)
-
-- **[Realtime API](../../docs/realtime)**
-    - disable SSL for ws:// WebSocket connections - [PR #19345](https://github.com/BerriAI/litellm/pull/19345)
-
-- **[Generate Content](../../docs/generateContent)**
-    - Log actual user input when google genai/vertex endpoints are called client-side - [PR #19156](https://github.com/BerriAI/litellm/pull/19156)
-
-- **[/messages/count_tokens Anthropic Token Counting](../../docs/anthropic_count_tokens)**
-    - ensure it works for Anthropic, Azure AI Anthropic on AI Gateway - [PR #19432](https://github.com/BerriAI/litellm/pull/19432)
-
-- **[MCP](../../docs/mcp)**
-    - forward static_headers to MCP servers - [PR #19366](https://github.com/BerriAI/litellm/pull/19366)
-
-- **[Batch API](../../docs/batches)**
-    - Fix: generation config empty for batch - [PR #19556](https://github.com/BerriAI/litellm/pull/19556)
-
-- **[Pass Through Endpoints](../../docs/proxy/pass_through)**
-    - Always reupdate registry - [PR #19420](https://github.com/BerriAI/litellm/pull/19420)
----
-
-## Management Endpoints / UI
-
-### Features
-
-- **Cost Estimator**
-    - Fix model dropdown - [PR #19529](https://github.com/BerriAI/litellm/pull/19529)
-
-- **Claude Code Plugins**
-    - Allow Adding Claude Code Plugins via UI - [PR #19387](https://github.com/BerriAI/litellm/pull/19387)
-
-- **Guardrails**
-    - New Policy management UI - [PR #19668](https://github.com/BerriAI/litellm/pull/19668)
-    - Allow adding policies on Keys/Teams + Viewing on Info panels - [PR #19688](https://github.com/BerriAI/litellm/pull/19688)
-
-- **General**
-    - respects custom authentication header override - [PR #19276](https://github.com/BerriAI/litellm/pull/19276)
-
-- **Playground**
-    - Button to Fill Custom API Base - [PR #19440](https://github.com/BerriAI/litellm/pull/19440)
-    - display mcp output on the play ground - [PR #19553](https://github.com/BerriAI/litellm/pull/19553)
-
-- **Models**
-    - Paginate /v2/models/info - [PR #19521](https://github.com/BerriAI/litellm/pull/19521)
-    - All Model Tab Pagination - [PR #19525](https://github.com/BerriAI/litellm/pull/19525)
-    - Adding Optional scope Param to /models - [PR #19539](https://github.com/BerriAI/litellm/pull/19539)
-    - Model Search - [PR #19622](https://github.com/BerriAI/litellm/pull/19622)
-    - Filter by Model ID and Team ID - [PR #19713](https://github.com/BerriAI/litellm/pull/19713)
-
-- **MCP Servers**
-    - MCP Tools Tab Resetting to Overview - [PR #19468](https://github.com/BerriAI/litellm/pull/19468)
-
-- **Organizations**
-    - Prevent org admin from creating a new user with proxy_admin permissions - [PR #19296](https://github.com/BerriAI/litellm/pull/19296)
-    - Edit Page: Reusable Model Select - [PR #19601](https://github.com/BerriAI/litellm/pull/19601)
-
-- **Teams**
-    - Reusable Model Select - [PR #19543](https://github.com/BerriAI/litellm/pull/19543)
-    - [Fix] Team Update with Organization having All Proxy Models - [PR #19604](https://github.com/BerriAI/litellm/pull/19604)
-
-- **Logs**
-    - Include tool arguments in spend logs table - [PR #19640](https://github.com/BerriAI/litellm/pull/19640)
-
-- **Fallbacks / Loadbalancing**
-    - New fallbacks modal - [PR #19673](https://github.com/BerriAI/litellm/pull/19673)
-    - Set fallbacks/loadbalancing by team/key - [PR #19686](https://github.com/BerriAI/litellm/pull/19686)
-
-### Bugs
-
-- **Playground**
-    - increase model selector width in playground Compare view - [PR #19423](https://github.com/BerriAI/litellm/pull/19423)
-
-- **Virtual Keys**
-    - Sorting Shows Incorrect Entries - [PR #19534](https://github.com/BerriAI/litellm/pull/19534)
-
-- **General**
-    - UI 404 error when SERVER_ROOT_PATH is set - [PR #19467](https://github.com/BerriAI/litellm/pull/19467)
-    - Redirect to ui/login on expired JWT - [PR #19687](https://github.com/BerriAI/litellm/pull/19687)
-
-- **SSO**
-    - Fix SSO user roles not updating for existing users - [PR #19621](https://github.com/BerriAI/litellm/pull/19621)
-
-- **Guardrails**
-    - ensure guardrail patterns persist on edit and mode toggle - [PR #19265](https://github.com/BerriAI/litellm/pull/19265)
----
-
-## AI Integrations
-
-### Logging
-
-- **General Logging**
-    - prevent printing duplicate StandardLoggingPayload logs - [PR #19325](https://github.com/BerriAI/litellm/pull/19325)
-    - Fix: log duplication when json_logs is enabled - [PR #19705](https://github.com/BerriAI/litellm/pull/19705)
-- **Langfuse OTEL**
-    - ignore service logs and fix callback shadowing - [PR #19298](https://github.com/BerriAI/litellm/pull/19298)
-- **Langfuse**
-    - Send litellm_trace_id - [PR #19528](https://github.com/BerriAI/litellm/pull/19528)
-    - Add Langfuse mock mode for testing without API calls - [PR #19676](https://github.com/BerriAI/litellm/pull/19676)
-- **GCS Bucket**
-    - prevent unbounded queue growth due to slow API calls - [PR #19297](https://github.com/BerriAI/litellm/pull/19297)
-    - Add GCS mock mode for testing without API calls - [PR #19683](https://github.com/BerriAI/litellm/pull/19683)
-- **Responses API Logging**
-    - Fix pydantic serialization error - [PR #19486](https://github.com/BerriAI/litellm/pull/19486)
-- **Arize Phoenix**
-    - add openinference span kinds to arize phoenix - [PR #19267](https://github.com/BerriAI/litellm/pull/19267)
-- **Prometheus**
-    - Added new prometheus metrics for user count and team count - [PR #19520](https://github.com/BerriAI/litellm/pull/19520)
-
-### Guardrails
-
-- **Bedrock Guardrails**
-    - Ensure post_call guardrail checks input+output - [PR #19151](https://github.com/BerriAI/litellm/pull/19151)
-- **Prompt Security**
-    - fixing prompt-security's guardrail implementation - [PR #19374](https://github.com/BerriAI/litellm/pull/19374)
-- **Presidio**
-    - Fixes crash in Presidio Guardrail when running in background threads (logging_hook) - [PR #19714](https://github.com/BerriAI/litellm/pull/19714)
-- **Pillar Security**
-    - Migrate Pillar Security to Generic Guardrail API - [PR #19364](https://github.com/BerriAI/litellm/pull/19364)
-- **Policy Engine**
-    - New LiteLLM Policy engine - create policies to manage guardrails, conditions - permissions per Key, Team - [PR #19612](https://github.com/BerriAI/litellm/pull/19612)
-- **General**
-    - add case-insensitive support for guardrail mode and actions - [PR #19480](https://github.com/BerriAI/litellm/pull/19480)
-
-### Prompt Management
-
-- **General**
-    - fix prompt info lookup and delete using correct IDs - [PR #19358](https://github.com/BerriAI/litellm/pull/19358)
-
-### Secret Manager
-
-- **AWS Secret Manager**
-    - ensure auto-rotation updates existing AWS secret instead of creating new one - [PR #19455](https://github.com/BerriAI/litellm/pull/19455)
-- **Hashicorp Vault**
-    - Ensure key rotations work with Vault - [PR #19634](https://github.com/BerriAI/litellm/pull/19634)
-
----
-
-## Spend Tracking, Budgets and Rate Limiting
-
-- **Pricing Updates**
-    - Add openai/dall-e base pricing entries - [PR #19133](https://github.com/BerriAI/litellm/pull/19133)
-    - Add `input_cost_per_video_per_second` in ModelInfoBase - [PR #19398](https://github.com/BerriAI/litellm/pull/19398)
-
----
-
-## Performance / Loadbalancing / Reliability improvements
-
-
-- **General**
-    - Fix date overflow/division by zero in proxy utils - [PR #19527](https://github.com/BerriAI/litellm/pull/19527)
-    - Fix in-flight request termination on SIGTERM when health-check runs in a separate process - [PR #19427](https://github.com/BerriAI/litellm/pull/19427)
-    - Fix Pass through routes to work with server root path - [PR #19383](https://github.com/BerriAI/litellm/pull/19383)
-    - Fix logging error for stop iteration - [PR #19649](https://github.com/BerriAI/litellm/pull/19649)
-    - prevent retrying 4xx client errors - [PR #19275](https://github.com/BerriAI/litellm/pull/19275)
-    - add better error handling for misconfig on health check - [PR #19441](https://github.com/BerriAI/litellm/pull/19441)
-
-- **Router**
-    - Fix Azure RPM calculation formula - [PR #19513](https://github.com/BerriAI/litellm/pull/19513)
-    - Persist scheduler request queue to redis - [PR #19304](https://github.com/BerriAI/litellm/pull/19304)
-    - pass search_tools to Router during DB-triggered initialization - [PR #19388](https://github.com/BerriAI/litellm/pull/19388)
-    - Fixed PromptCachingCache to correctly handle messages where cache_control is a sibling key of string content - [PR #19266](https://github.com/BerriAI/litellm/pull/19266)
-
-- **Memory Leaks/OOM**
-    - prevent OOM with nested $defs in tool schemas - [PR #19112](https://github.com/BerriAI/litellm/pull/19112)
-    - fix: HTTP client memory leaks in Presidio, OpenAI, and Gemini - [PR #19190](https://github.com/BerriAI/litellm/pull/19190)
-
-- **Non root**
-    - fix logfile and pidfile of supervisor for non root environment - [PR #17267](https://github.com/BerriAI/litellm/pull/17267)
-    - resolve Read-only file system error in non-root images - [PR #19449](https://github.com/BerriAI/litellm/pull/19449)
-
-- **Dockerfile**
-    - Redis Semantic Caching - add missing redisvl dependency to requirements.txt - [PR #19417](https://github.com/BerriAI/litellm/pull/19417)
-    - Bump OTEL versions to support a2a dependency - resolves modulenotfounderror for Microsoft Agents by @Harshit28j in #18991
-
-- **DB**
-    - Handle PostgreSQL cached plan errors during rolling deployments - [PR #19424](https://github.com/BerriAI/litellm/pull/19424)
-
-- **Timeouts**
-    - Fix: total timeout is not respected - [PR #19389](https://github.com/BerriAI/litellm/pull/19389)
-
-- **SDK**
-    - Field-Existence Checks to Type Classes to Prevent Attribute Errors - [PR #18321](https://github.com/BerriAI/litellm/pull/18321)
-    - add google-cloud-aiplatform as optional dependency with clear error message - [PR #19437](https://github.com/BerriAI/litellm/pull/19437)
-    - Make grpc dependency optional - [PR #19447](https://github.com/BerriAI/litellm/pull/19447)
-    - Add support for retry policies - [PR #19645](https://github.com/BerriAI/litellm/pull/19645)
-
-- **Performance**
-    - Cut chat_completion latency by ~21% by reducing pre-call processing time - [PR #19535](https://github.com/BerriAI/litellm/pull/19535)
-    - Optimize strip_trailing_slash with O(1) index check - [PR #19679](https://github.com/BerriAI/litellm/pull/19679)
-    - Optimize use_custom_pricing_for_model with set intersection - [PR #19677](https://github.com/BerriAI/litellm/pull/19677)
-    - perf: skip pattern_router.route() for non-wildcard models - [PR #19664](https://github.com/BerriAI/litellm/pull/19664)
-    - perf: Add LRU caching to get_model_info for faster cost lookups - [PR #19606](https://github.com/BerriAI/litellm/pull/19606)
-
----
-
-## General Proxy Improvements
-
-### Doc Improvements
-    - new tutorial for adding MCPs to Cursor via LiteLLM - [PR #19317](https://github.com/BerriAI/litellm/pull/19317)
-    - fix vertex_region to vertex_location in Vertex AI pass-through docs - [PR #19380](https://github.com/BerriAI/litellm/pull/19380)
-    - clarify Gemini and Vertex AI model prefix in json file - [PR #19443](https://github.com/BerriAI/litellm/pull/19443)
-    - update Claude Code integration guides - [PR #19415](https://github.com/BerriAI/litellm/pull/19415)
-    - adjust opencode tutorial - [PR #19605](https://github.com/BerriAI/litellm/pull/19605)
-    - add spend-queue-troubleshooting docs - [PR #19659](https://github.com/BerriAI/litellm/pull/19659)
-    - docs: add litellm-enterprise requirement for managed files - [PR #19689](https://github.com/BerriAI/litellm/pull/19689)
-
-### Helm
-    - Add support for keda in helm chart - [PR #19337](https://github.com/BerriAI/litellm/pull/19337)
-    - sync Helm chart version with LiteLLM release version - [PR #19438](https://github.com/BerriAI/litellm/pull/19438)
-    - Enable PreStop hook configuration in values.yaml - [PR #19613](https://github.com/BerriAI/litellm/pull/19613)
-
-### General
-    - Add health check scripts and parallel execution support - [PR #19295](https://github.com/BerriAI/litellm/pull/19295)
-
-
----
-
-## New Contributors
-
-
-* @dushyantzz made their first contribution in [PR #19158](https://github.com/BerriAI/litellm/pull/19158)
-* @obod-mpw made their first contribution in [PR #19133](https://github.com/BerriAI/litellm/pull/19133)
-* @msexxeta made their first contribution in [PR #19030](https://github.com/BerriAI/litellm/pull/19030)
-* @rsicart made their first contribution in [PR #19337](https://github.com/BerriAI/litellm/pull/19337)
-* @cluebbehusen made their first contribution in [PR #19311](https://github.com/BerriAI/litellm/pull/19311)
-* @Lucky-Lodhi2004 made their first contribution in [PR #19315](https://github.com/BerriAI/litellm/pull/19315)
-* @binbandit made their first contribution in [PR #19324](https://github.com/BerriAI/litellm/pull/19324)
-* @flex-myeonghyeon made their first contribution in [PR #19381](https://github.com/BerriAI/litellm/pull/19381)
-* @Lrakotoson made their first contribution in [PR #18321](https://github.com/BerriAI/litellm/pull/18321)
-* @bensi94 made their first contribution in [PR #18787](https://github.com/BerriAI/litellm/pull/18787)
-* @victorigualada made their first contribution in [PR #19368](https://github.com/BerriAI/litellm/pull/19368)
-* @VedantMadane made their first contribution in #19266
-* @stiyyagura0901 made their first contribution in #19276
-* @kamilio made their first contribution in [PR #19447](https://github.com/BerriAI/litellm/pull/19447)
-* @jonathansampson made their first contribution in [PR #19433](https://github.com/BerriAI/litellm/pull/19433)
-* @rynecarbone made their first contribution in [PR #19416](https://github.com/BerriAI/litellm/pull/19416)
-* @jayy-77 made their first contribution in #19366
-* @davida-ps made their first contribution in [PR #19374](https://github.com/BerriAI/litellm/pull/19374)
-* @joaodinissf made their first contribution in [PR #19506](https://github.com/BerriAI/litellm/pull/19506)
-* @ecao310 made their first contribution in [PR #19520](https://github.com/BerriAI/litellm/pull/19520)
-* @mpcusack-altos made their first contribution in [PR #19577](https://github.com/BerriAI/litellm/pull/19577)
-* @milan-berri made their first contribution in [PR #19602](https://github.com/BerriAI/litellm/pull/19602)
-* @xqe2011 made their first contribution in #19621
-
----
-
-## Full Changelog
-
-**[View complete changelog on GitHub](https://github.com/BerriAI/litellm/releases/tag/v1.81.3.rc)**
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 4cc46f97727..a72f295c597 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -274,19 +274,11 @@ const sidebars = {
             "proxy/custom_sso",
             "proxy/ai_hub",
             "proxy/model_compare_ui",
+            "proxy/public_teams",
+            "proxy/self_serve",
+            "proxy/ui/bulk_edit_users",
             "proxy/ui_credentials",
             "tutorials/scim_litellm",
-            {
-              type: "category",
-              label: "UI User/Team Management",
-              items: [
-            "proxy/access_control",
-                "proxy/public_teams",
-                "proxy/self_serve",
-                "proxy/ui/bulk_edit_users",
-                "proxy/ui/page_visibility",
-              ]
-            },
             {
               type: "category",
               label: "UI Usage Tracking",
@@ -372,7 +364,6 @@ const sidebars = {
           label: "Load Balancing, Routing, Fallbacks",
           href: "https://docs.litellm.ai/docs/routing-load-balancing",
         },
-        "traffic_mirroring",
         {
           type: "category",
           label: "Logging, Alerting, Metrics",
@@ -784,7 +775,6 @@ const sidebars = {
         "providers/oci",
         "providers/ollama",
         "providers/openrouter",
-        "providers/sarvam",
         "providers/ovhcloud",
         "providers/perplexity",
         "providers/petals",
@@ -853,7 +843,6 @@ const sidebars = {
         "completion/image_generation_chat",
         "completion/json_mode",
         "completion/knowledgebase",
-        "providers/anthropic_tool_search",
         "guides/code_interpreter",
         "completion/message_trimming",
         "completion/model_alias",
@@ -890,7 +879,6 @@ const sidebars = {
         "scheduler",
         "proxy/auto_routing",
         "proxy/load_balancing",
-        "proxy/keys_teams_router_settings",
         "proxy/provider_budget_routing",
         "proxy/reliability",
         "proxy/fallback_management",
diff --git a/enterprise/litellm_enterprise/proxy/auth/route_checks.py b/enterprise/litellm_enterprise/proxy/auth/route_checks.py
index 6f7cf9143f4..6cce781faf3 100644
--- a/enterprise/litellm_enterprise/proxy/auth/route_checks.py
+++ b/enterprise/litellm_enterprise/proxy/auth/route_checks.py
@@ -36,7 +36,7 @@ def is_management_routes_disabled() -> bool:
             if not premium_user:
                 raise HTTPException(
                     status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                    detail=f"🚨🚨🚨 DISABLING ADMIN ENDPOINTS is an Enterprise feature\n🚨 {CommonProxyErrors.not_premium_user.value}",
+                    detail=f"🚨🚨🚨 DISABLING LLM API ENDPOINTS is an Enterprise feature\n🚨 {CommonProxyErrors.not_premium_user.value}",
                 )
 
         return get_secret_bool("DISABLE_ADMIN_ENDPOINTS") is True
diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
index dd0613f87ff..445d2b242b4 100644
--- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
+++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
@@ -244,78 +244,6 @@ async def can_user_call_unified_object_id(
             return managed_object.created_by == user_id
         return True  # don't raise error if managed object is not found
 
-    async def list_user_batches(
-        self,
-        user_api_key_dict: UserAPIKeyAuth,
-        limit: Optional[int] = None,
-        after: Optional[str] = None,
-        provider: Optional[str] = None,
-        target_model_names: Optional[str] = None,
-        llm_router: Optional[Router] = None,
-    ) -> Dict[str, Any]:
-        # Provider filtering is not supported for managed batches
-        # This is because the encoded object ids stored in the managed objects table do not contain the provider information
-        # To support provider filtering, we would need to store the provider information in the encoded object ids
-        if provider:
-            raise Exception(
-                "Filtering by 'provider' is not supported when using managed batches."
-            )
-
-        # Model name filtering is not supported for managed batches
-        # This is because the encoded object ids stored in the managed objects table do not contain the model name
-        # A hash of the model name + litellm_params for the model name is encoded as the model id. This is not sufficient to reliably map the target model names to the model ids.
-        if target_model_names:
-            raise Exception(
-                "Filtering by 'target_model_names' is not supported when using managed batches."
-            )
-        
-        where_clause: Dict[str, Any] = {"file_purpose": "batch"}
-        
-        # Filter by user who created the batch
-        if user_api_key_dict.user_id:
-            where_clause["created_by"] = user_api_key_dict.user_id
-        
-        if after:
-            where_clause["id"] = {"gt": after}
-        
-        # Fetch more than needed to allow for post-fetch filtering
-        fetch_limit = limit or 20
-        if target_model_names:
-            # Fetch extra to account for filtering
-            fetch_limit = max(fetch_limit * 3, 100)
-        
-        batches = await self.prisma_client.db.litellm_managedobjecttable.find_many(
-            where=where_clause,
-            take=fetch_limit,
-            order={"created_at": "desc"},
-        )
-                
-        batch_objects: List[LiteLLMBatch] = []
-        for batch in batches:
-            try:
-                # Stop once we have enough after filtering
-                if len(batch_objects) >= (limit or 20):
-                    break
-
-                batch_data = json.loads(batch.file_object) if isinstance(batch.file_object, str) else batch.file_object
-                batch_obj = LiteLLMBatch(**batch_data)
-                batch_obj.id = batch.unified_object_id
-                batch_objects.append(batch_obj)
-
-            except Exception as e:
-                verbose_logger.warning(
-                    f"Failed to parse batch object {batch.unified_object_id}: {e}"
-                )
-                continue
-        
-        return {
-            "object": "list",
-            "data": batch_objects,
-            "first_id": batch_objects[0].id if batch_objects else None,
-            "last_id": batch_objects[-1].id if batch_objects else None,
-            "has_more": len(batch_objects) == (limit or 20),
-        }
-
     async def get_user_created_file_ids(
         self, user_api_key_dict: UserAPIKeyAuth, model_object_ids: List[str]
     ) -> List[OpenAIFileObject]:
@@ -745,7 +673,6 @@ async def return_unified_file_id(
             bytes=file_objects[0].bytes,
             filename=file_objects[0].filename,
             status="uploaded",
-            expires_at=file_objects[0].expires_at,
         )
 
         return response
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.27-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.27-py3-none-any.whl
deleted file mode 100644
index f1dc450a0fc..00000000000
Binary files a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.27-py3-none-any.whl and /dev/null differ
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.27.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.27.tar.gz
deleted file mode 100644
index 742b129eaa8..00000000000
Binary files a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.27.tar.gz and /dev/null differ
diff --git a/litellm-proxy-extras/litellm_proxy_extras/_logging.py b/litellm-proxy-extras/litellm_proxy_extras/_logging.py
index 15173005ce8..118caecf488 100644
--- a/litellm-proxy-extras/litellm_proxy_extras/_logging.py
+++ b/litellm-proxy-extras/litellm_proxy_extras/_logging.py
@@ -1,40 +1,12 @@
-import json
 import logging
-import os
-from datetime import datetime
-
-
-class JsonFormatter(logging.Formatter):
-    def formatTime(self, record, datefmt=None):
-        dt = datetime.fromtimestamp(record.created)
-        return dt.isoformat()
-
-    def format(self, record):
-        json_record = {
-            "message": record.getMessage(),
-            "level": record.levelname,
-            "timestamp": self.formatTime(record),
-        }
-        if record.exc_info:
-            json_record["stacktrace"] = self.formatException(record.exc_info)
-        return json.dumps(json_record)
-
-
-def _is_json_enabled():
-    try:
-        import litellm
-        return getattr(litellm, 'json_logs', False)
-    except (ImportError, AttributeError):
-        return os.getenv("JSON_LOGS", "false").lower() == "true"
-
 
+# Set up package logger
 logger = logging.getLogger("litellm_proxy_extras")
-
-if not logger.handlers:
+if not logger.handlers:  # Only add handler if none exists
     handler = logging.StreamHandler()
-    if _is_json_enabled():
-        handler.setFormatter(JsonFormatter())
-    else:
-        handler.setFormatter(logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s"))
+    formatter = logging.Formatter(
+        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    )
+    handler.setFormatter(formatter)
     logger.addHandler(handler)
     logger.setLevel(logging.INFO)
diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251119131227_add_prompt_versioning/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251119131227_add_prompt_versioning/migration.sql
index 43eb2401422..a9d9528bd24 100644
--- a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251119131227_add_prompt_versioning/migration.sql
+++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251119131227_add_prompt_versioning/migration.sql
@@ -1,12 +1,12 @@
 -- DropIndex
-DROP INDEX IF EXISTS "LiteLLM_PromptTable_prompt_id_key";
+DROP INDEX "LiteLLM_PromptTable_prompt_id_key";
 
 -- AlterTable
-ALTER TABLE "LiteLLM_PromptTable"
-ADD COLUMN "version" INTEGER NOT NULL DEFAULT 1;
+ALTER TABLE "LiteLLM_PromptTable" ADD COLUMN     "version" INTEGER NOT NULL DEFAULT 1;
 
 -- CreateIndex
-CREATE INDEX "LiteLLM_PromptTable_prompt_id_idx" ON "LiteLLM_PromptTable" ("prompt_id");
+CREATE INDEX "LiteLLM_PromptTable_prompt_id_idx" ON "LiteLLM_PromptTable"("prompt_id");
 
 -- CreateIndex
-CREATE UNIQUE INDEX "LiteLLM_PromptTable_prompt_id_version_key" ON "LiteLLM_PromptTable" ("prompt_id", "version");
\ No newline at end of file
+CREATE UNIQUE INDEX "LiteLLM_PromptTable_prompt_id_version_key" ON "LiteLLM_PromptTable"("prompt_id", "version");
+
diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
index ca60b9e1bec..d7aa6e9f0d0 100644
--- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
+++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
@@ -760,11 +760,6 @@ model LiteLLM_ManagedVectorStoresTable {
   updated_at DateTime @updatedAt
   litellm_credential_name String?
   litellm_params Json?
-  team_id String?
-  user_id String?
-  
-  @@index([team_id])
-  @@index([user_id])
 }
 
 // Guardrails table for storing guardrail configurations
diff --git a/litellm-proxy-extras/litellm_proxy_extras/utils.py b/litellm-proxy-extras/litellm_proxy_extras/utils.py
index f3155722187..7ffbe95be13 100644
--- a/litellm-proxy-extras/litellm_proxy_extras/utils.py
+++ b/litellm-proxy-extras/litellm_proxy_extras/utils.py
@@ -18,15 +18,14 @@ def str_to_bool(value: Optional[str]) -> bool:
     return value.lower() in ("true", "1", "t", "y", "yes")
 
 
+
 def _get_prisma_env() -> dict:
     """Get environment variables for Prisma, handling offline mode if configured."""
     prisma_env = os.environ.copy()
     if str_to_bool(os.getenv("PRISMA_OFFLINE_MODE")):
         # These env vars prevent Prisma from attempting downloads
         prisma_env["NPM_CONFIG_PREFER_OFFLINE"] = "true"
-        prisma_env["NPM_CONFIG_CACHE"] = os.getenv(
-            "NPM_CONFIG_CACHE", "/app/.cache/npm"
-        )
+        prisma_env["NPM_CONFIG_CACHE"] = os.getenv("NPM_CONFIG_CACHE", "/app/.cache/npm")
     return prisma_env
 
 
@@ -35,28 +34,29 @@ def _get_prisma_command() -> str:
     if str_to_bool(os.getenv("PRISMA_OFFLINE_MODE")):
         # Primary location where Prisma Python package installs the CLI
         default_cli_path = "/app/.cache/prisma-python/binaries/node_modules/.bin/prisma"
-
+        
         # Check if custom path is provided (for flexibility)
         custom_cli_path = os.getenv("PRISMA_CLI_PATH")
         if custom_cli_path and os.path.exists(custom_cli_path):
             logger.info(f"Using custom Prisma CLI at {custom_cli_path}")
             return custom_cli_path
-
+        
         # Check the default location
         if os.path.exists(default_cli_path):
             logger.info(f"Using cached Prisma CLI at {default_cli_path}")
             return default_cli_path
-
+        
         # If not found, log warning and fall back
         logger.warning(
             f"Prisma CLI not found at {default_cli_path}. "
             "Falling back to Python wrapper (may attempt downloads)"
         )
-
+    
     # Fall back to the Python wrapper (will work in online mode)
     return "prisma"
 
 
+
 class ProxyExtrasDBManager:
     @staticmethod
     def _get_prisma_dir() -> str:
@@ -119,7 +119,7 @@ def _create_baseline_migration(schema_path: str) -> bool:
                 stdout=open(migration_file, "w"),
                 check=True,
                 timeout=30,
-                env=prisma_env,
+                env=prisma_env
             )
 
             # 3. Mark the migration as applied since it represents current state
@@ -134,7 +134,7 @@ def _create_baseline_migration(schema_path: str) -> bool:
                 ],
                 check=True,
                 timeout=30,
-                env=prisma_env,
+                env=prisma_env
             )
 
             return True
@@ -159,20 +159,14 @@ def _get_migration_names(migrations_dir: str) -> list:
     @staticmethod
     def _roll_back_migration(migration_name: str):
         """Mark a specific migration as rolled back"""
-        # Set up environment for offline mode if configured
+         # Set up environment for offline mode if configured
         prisma_env = _get_prisma_env()
         subprocess.run(
-            [
-                _get_prisma_command(),
-                "migrate",
-                "resolve",
-                "--rolled-back",
-                migration_name,
-            ],
+            [_get_prisma_command(), "migrate", "resolve", "--rolled-back", migration_name],
             timeout=60,
             check=True,
             capture_output=True,
-            env=prisma_env,
+            env=prisma_env
         )
 
     @staticmethod
@@ -184,7 +178,7 @@ def _resolve_specific_migration(migration_name: str):
             timeout=60,
             check=True,
             capture_output=True,
-            env=prisma_env,
+            env=prisma_env
         )
 
     @staticmethod
@@ -234,8 +228,6 @@ def _is_idempotent_error(error_message: str) -> bool:
             r"duplicate key value violates",
             r"relation .* already exists",
             r"constraint .* already exists",
-            r"does not exist",
-            r"Can't drop database.* because it doesn't exist",
         ]
 
         for pattern in idempotent_patterns:
@@ -256,7 +248,7 @@ def _resolve_all_migrations(
         if not database_url:
             logger.error("DATABASE_URL not set")
             return
-
+        
         diff_dir = (
             Path(migrations_dir)
             / "migrations"
@@ -291,7 +283,7 @@ def _resolve_all_migrations(
                     check=True,
                     timeout=60,
                     stdout=f,
-                    env=_get_prisma_env(),
+                    env=_get_prisma_env()
                 )
         except subprocess.CalledProcessError as e:
             logger.warning(f"Failed to generate migration diff: {e.stderr}")
@@ -321,7 +313,7 @@ def _resolve_all_migrations(
                 check=True,
                 capture_output=True,
                 text=True,
-                env=_get_prisma_env(),
+                env=_get_prisma_env()
             )
             logger.info(f"prisma db execute stdout: {result.stdout}")
             logger.info("✅ Migration diff applied successfully")
@@ -339,18 +331,12 @@ def _resolve_all_migrations(
             try:
                 logger.info(f"Resolving migration: {migration_name}")
                 subprocess.run(
-                    [
-                        _get_prisma_command(),
-                        "migrate",
-                        "resolve",
-                        "--applied",
-                        migration_name,
-                    ],
+                    [_get_prisma_command(), "migrate", "resolve", "--applied", migration_name],
                     timeout=60,
                     check=True,
                     capture_output=True,
                     text=True,
-                    env=_get_prisma_env(),
+                    env=_get_prisma_env()
                 )
                 logger.debug(f"Resolved migration: {migration_name}")
             except subprocess.CalledProcessError as e:
@@ -389,7 +375,7 @@ def setup_database(use_migrate: bool = False) -> bool:
                             check=True,
                             capture_output=True,
                             text=True,
-                            env=_get_prisma_env(),
+                            env=_get_prisma_env()
                         )
                         logger.info(f"prisma migrate deploy stdout: {result.stdout}")
 
@@ -411,42 +397,27 @@ def setup_database(use_migrate: bool = False) -> bool:
                             )
                             if migration_match:
                                 failed_migration = migration_match.group(1)
-                                if ProxyExtrasDBManager._is_idempotent_error(e.stderr):
-                                    logger.info(
-                                        f"Migration {failed_migration} failed due to idempotent error (e.g., column already exists), resolving as applied"
-                                    )
-                                    ProxyExtrasDBManager._roll_back_migration(
-                                        failed_migration
-                                    )
-                                    ProxyExtrasDBManager._resolve_specific_migration(
-                                        failed_migration
-                                    )
-                                    logger.info(
-                                        f"✅ Migration {failed_migration} resolved."
-                                    )
-                                    return True
-                                else:
-                                    logger.info(
-                                        f"Found failed migration: {failed_migration}, marking as rolled back"
-                                    )
-                                    # Mark the failed migration as rolled back
-                                    subprocess.run(
-                                        [
-                                            _get_prisma_command(),
-                                            "migrate",
-                                            "resolve",
-                                            "--rolled-back",
-                                            failed_migration,
-                                        ],
-                                        timeout=60,
-                                        check=True,
-                                        capture_output=True,
-                                        text=True,
-                                        env=_get_prisma_env(),
-                                    )
-                                    logger.info(
-                                        f"✅ Migration {failed_migration} marked as rolled back... retrying"
-                                    )
+                                logger.info(
+                                    f"Found failed migration: {failed_migration}, marking as rolled back"
+                                )
+                                # Mark the failed migration as rolled back
+                                subprocess.run(
+                                    [
+                                        _get_prisma_command(),
+                                        "migrate",
+                                        "resolve",
+                                        "--rolled-back",
+                                        failed_migration,
+                                    ],
+                                    timeout=60,
+                                    check=True,
+                                    capture_output=True,
+                                    text=True,
+                                    env=_get_prisma_env()
+                                )
+                                logger.info(
+                                    f"✅ Migration {failed_migration} marked as rolled back... retrying"
+                                )
                         elif (
                             "P3005" in e.stderr
                             and "database schema is not empty" in e.stderr
diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml
index 5a0aa364e7d..862ebaffe2a 100644
--- a/litellm-proxy-extras/pyproject.toml
+++ b/litellm-proxy-extras/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm-proxy-extras"
-version = "0.4.27"
+version = "0.4.26"
 description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
 authors = ["BerriAI"]
 readme = "README.md"
@@ -22,7 +22,7 @@ requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "0.4.27"
+version = "0.4.26"
 version_files = [
     "pyproject.toml:version",
     "../requirements.txt:litellm-proxy-extras==",
diff --git a/litellm/__init__.py b/litellm/__init__.py
index f5db57f76fd..e5c09702b9b 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -80,8 +80,6 @@
 litellm_mode = os.getenv("LITELLM_MODE", "DEV")  # "PRODUCTION", "DEV"
 if litellm_mode == "DEV":
     dotenv.load_dotenv()
-
-
 ####################################################
 if set_verbose:
     _turn_on_debug()
diff --git a/litellm/_logging.py b/litellm/_logging.py
index e222627e76c..b3156b15ba7 100644
--- a/litellm/_logging.py
+++ b/litellm/_logging.py
@@ -166,66 +166,6 @@ def _initialize_loggers_with_handler(handler: logging.Handler):
         lg.propagate = False  # prevent bubbling to parent/root
 
 
-def _get_uvicorn_json_log_config():
-    """
-    Generate a uvicorn log_config dictionary that applies JSON formatting to all loggers.
-    
-    This ensures that uvicorn's access logs, error logs, and all application logs
-    are formatted as JSON when json_logs is enabled.
-    """
-    json_formatter_class = "litellm._logging.JsonFormatter"
-    
-    # Use the module-level log_level variable for consistency
-    uvicorn_log_level = log_level.upper()
-    
-    log_config = {
-        "version": 1,
-        "disable_existing_loggers": False,
-        "formatters": {
-            "json": {
-                "()": json_formatter_class,
-            },
-            "default": {
-                "()": json_formatter_class,
-            },
-            "access": {
-                "()": json_formatter_class,
-            },
-        },
-        "handlers": {
-            "default": {
-                "formatter": "json",
-                "class": "logging.StreamHandler",
-                "stream": "ext://sys.stdout",
-            },
-            "access": {
-                "formatter": "access",
-                "class": "logging.StreamHandler",
-                "stream": "ext://sys.stdout",
-            },
-        },
-        "loggers": {
-            "uvicorn": {
-                "handlers": ["default"],
-                "level": uvicorn_log_level,
-                "propagate": False,
-            },
-            "uvicorn.error": {
-                "handlers": ["default"],
-                "level": uvicorn_log_level,
-                "propagate": False,
-            },
-            "uvicorn.access": {
-                "handlers": ["access"],
-                "level": uvicorn_log_level,
-                "propagate": False,
-            },
-        },
-    }
-    
-    return log_config
-
-
 def _turn_on_json():
     """
     Turn on JSON logging
diff --git a/litellm/a2a_protocol/card_resolver.py b/litellm/a2a_protocol/card_resolver.py
deleted file mode 100644
index 7c4c5af149d..00000000000
--- a/litellm/a2a_protocol/card_resolver.py
+++ /dev/null
@@ -1,97 +0,0 @@
-"""
-Custom A2A Card Resolver for LiteLLM.
-
-Extends the A2A SDK's card resolver to support multiple well-known paths.
-"""
-
-from typing import TYPE_CHECKING, Any, Dict, Optional
-
-from litellm._logging import verbose_logger
-
-if TYPE_CHECKING:
-    from a2a.types import AgentCard
-
-# Runtime imports with availability check
-_A2ACardResolver: Any = None
-AGENT_CARD_WELL_KNOWN_PATH: str = "/.well-known/agent-card.json"
-PREV_AGENT_CARD_WELL_KNOWN_PATH: str = "/.well-known/agent.json"
-
-try:
-    from a2a.client import A2ACardResolver as _A2ACardResolver  # type: ignore[no-redef]
-    from a2a.utils.constants import (  # type: ignore[no-redef]
-        AGENT_CARD_WELL_KNOWN_PATH,
-        PREV_AGENT_CARD_WELL_KNOWN_PATH,
-    )
-except ImportError:
-    pass
-
-
-class LiteLLMA2ACardResolver(_A2ACardResolver):  # type: ignore[misc]
-    """
-    Custom A2A card resolver that supports multiple well-known paths.
-    
-    Extends the base A2ACardResolver to try both:
-    - /.well-known/agent-card.json (standard)
-    - /.well-known/agent.json (previous/alternative)
-    """
-    
-    async def get_agent_card(
-        self,
-        relative_card_path: Optional[str] = None,
-        http_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> "AgentCard":
-        """
-        Fetch the agent card, trying multiple well-known paths.
-        
-        First tries the standard path, then falls back to the previous path.
-        
-        Args:
-            relative_card_path: Optional path to the agent card endpoint.
-                If None, tries both well-known paths.
-            http_kwargs: Optional dictionary of keyword arguments to pass to httpx.get
-            
-        Returns:
-            AgentCard from the A2A agent
-            
-        Raises:
-            A2AClientHTTPError or A2AClientJSONError if both paths fail
-        """
-        # If a specific path is provided, use the parent implementation
-        if relative_card_path is not None:
-            return await super().get_agent_card(
-                relative_card_path=relative_card_path,
-                http_kwargs=http_kwargs,
-            )
-        
-        # Try both well-known paths
-        paths = [
-            AGENT_CARD_WELL_KNOWN_PATH,
-            PREV_AGENT_CARD_WELL_KNOWN_PATH,
-        ]
-        
-        last_error = None
-        for path in paths:
-            try:
-                verbose_logger.debug(
-                    f"Attempting to fetch agent card from {self.base_url}{path}"
-                )
-                return await super().get_agent_card(
-                    relative_card_path=path,
-                    http_kwargs=http_kwargs,
-                )
-            except Exception as e:
-                verbose_logger.debug(
-                    f"Failed to fetch agent card from {self.base_url}{path}: {e}"
-                )
-                last_error = e
-                continue
-        
-        # If we get here, all paths failed - re-raise the last error
-        if last_error is not None:
-            raise last_error
-        
-        # This shouldn't happen, but just in case
-        raise Exception(
-            f"Failed to fetch agent card from {self.base_url}. "
-            f"Tried paths: {', '.join(paths)}"
-        )
diff --git a/litellm/a2a_protocol/main.py b/litellm/a2a_protocol/main.py
index b326f9e7ed5..ae95faede22 100644
--- a/litellm/a2a_protocol/main.py
+++ b/litellm/a2a_protocol/main.py
@@ -6,7 +6,6 @@
 
 import asyncio
 import datetime
-import uuid
 from typing import TYPE_CHECKING, Any, AsyncIterator, Coroutine, Dict, Optional, Union
 
 import litellm
@@ -21,6 +20,7 @@
 )
 from litellm.types.agents import LiteLLMSendMessageResponse
 from litellm.utils import client
+import uuid
 
 if TYPE_CHECKING:
     from a2a.client import A2AClient as A2AClientType
@@ -36,18 +36,13 @@
 _A2AClient: Any = None
 
 try:
+    from a2a.client import A2ACardResolver  # type: ignore[no-redef]
     from a2a.client import A2AClient as _A2AClient  # type: ignore[no-redef]
 
     A2A_SDK_AVAILABLE = True
 except ImportError:
     pass
 
-# Import our custom card resolver that supports multiple well-known paths
-from litellm.a2a_protocol.card_resolver import LiteLLMA2ACardResolver
-
-# Use our custom resolver instead of the default A2A SDK resolver
-A2ACardResolver = LiteLLMA2ACardResolver
-
 
 def _set_usage_on_logging_obj(
     kwargs: Dict[str, Any],
diff --git a/litellm/constants.py b/litellm/constants.py
index 91cdc0ebdfc..49ca3a509b1 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -1165,12 +1165,6 @@
 LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token"
 CLI_SSO_SESSION_CACHE_KEY_PREFIX = "cli_sso_session"
 CLI_JWT_TOKEN_NAME = "cli-jwt-token"
-# Support both CLI_JWT_EXPIRATION_HOURS and LITELLM_CLI_JWT_EXPIRATION_HOURS for backwards compatibility
-CLI_JWT_EXPIRATION_HOURS = int(
-    os.getenv("CLI_JWT_EXPIRATION_HOURS") 
-    or os.getenv("LITELLM_CLI_JWT_EXPIRATION_HOURS") 
-    or 24
-)
 
 ########################### DB CRON JOB NAMES ###########################
 DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job"
@@ -1332,13 +1326,6 @@
 DEFAULT_CHUNK_SIZE = int(os.getenv("DEFAULT_CHUNK_SIZE", 1000))
 DEFAULT_CHUNK_OVERLAP = int(os.getenv("DEFAULT_CHUNK_OVERLAP", 200))
 
-########################### S3 Vectors RAG Constants ###########################
-S3_VECTORS_DEFAULT_DIMENSION = int(os.getenv("S3_VECTORS_DEFAULT_DIMENSION", 1024))
-S3_VECTORS_DEFAULT_DISTANCE_METRIC = str(
-    os.getenv("S3_VECTORS_DEFAULT_DISTANCE_METRIC", "cosine")
-)
-S3_VECTORS_DEFAULT_NON_FILTERABLE_METADATA_KEYS = ["source_text"]
-
 ########################### Microsoft SSO Constants ###########################
 MICROSOFT_USER_EMAIL_ATTRIBUTE = str(
     os.getenv("MICROSOFT_USER_EMAIL_ATTRIBUTE", "userPrincipalName")
diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index 490f0288b00..1ab7e260a83 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -23,11 +23,7 @@
 from litellm.litellm_core_utils.llm_cost_calc.utils import (
     CostCalculatorUtils,
     _generic_cost_per_character,
-    _get_service_tier_cost_key,
-    _parse_prompt_tokens_details,
-    calculate_cost_component,
     generic_cost_per_token,
-    get_billable_input_tokens,
     select_cost_metric_for_model,
 )
 from litellm.llms.anthropic.cost_calculation import (
@@ -435,18 +431,12 @@ def cost_per_token(  # noqa: PLR0915
             model=model, custom_llm_provider=custom_llm_provider
         )
 
-        if (
-            model_info.get("input_cost_per_token", 0) > 0
-            or model_info.get("output_cost_per_token", 0) > 0
-        ):
-            return generic_cost_per_token(
-                model=model,
-                usage=usage_block,
-                custom_llm_provider=custom_llm_provider,
-                service_tier=service_tier,
+        if model_info["input_cost_per_token"] > 0:
+            ## COST PER TOKEN ##
+            prompt_tokens_cost_usd_dollar = (
+                model_info["input_cost_per_token"] * prompt_tokens
             )
-
-        if (
+        elif (
             model_info.get("input_cost_per_second", None) is not None
             and response_time_ms is not None
         ):
@@ -461,7 +451,11 @@ def cost_per_token(  # noqa: PLR0915
                 model_info["input_cost_per_second"] * response_time_ms / 1000  # type: ignore
             )
 
-        if (
+        if model_info["output_cost_per_token"] > 0:
+            completion_tokens_cost_usd_dollar = (
+                model_info["output_cost_per_token"] * completion_tokens
+            )
+        elif (
             model_info.get("output_cost_per_second", None) is not None
             and response_time_ms is not None
         ):
@@ -961,10 +955,7 @@ def completion_cost(  # noqa: PLR0915
             router_model_id=router_model_id,
         )
 
-        potential_model_names = [
-            selected_model,
-            _get_response_model(completion_response),
-        ]
+        potential_model_names = [selected_model, _get_response_model(completion_response)]
         if model is not None:
             potential_model_names.append(model)
 
@@ -1719,16 +1710,10 @@ def default_image_cost_calculator(
         )
 
     # Priority 1: Use per-image pricing if available (for gpt-image-1 and similar models)
-    if (
-        "input_cost_per_image" in cost_info
-        and cost_info["input_cost_per_image"] is not None
-    ):
+    if "input_cost_per_image" in cost_info and cost_info["input_cost_per_image"] is not None:
         return cost_info["input_cost_per_image"] * n
     # Priority 2: Fall back to per-pixel pricing for backward compatibility
-    elif (
-        "input_cost_per_pixel" in cost_info
-        and cost_info["input_cost_per_pixel"] is not None
-    ):
+    elif "input_cost_per_pixel" in cost_info and cost_info["input_cost_per_pixel"] is not None:
         return cost_info["input_cost_per_pixel"] * height * width * n
     else:
         raise Exception(
@@ -1848,22 +1833,9 @@ def batch_cost_calculator(
     if input_cost_per_token_batches:
         total_prompt_cost = usage.prompt_tokens * input_cost_per_token_batches
     elif input_cost_per_token:
-        # Subtract cached tokens from prompt_tokens before calculating cost
-        # Fixes issue where cached tokens are being charged again
         total_prompt_cost = (
-            get_billable_input_tokens(usage) * (input_cost_per_token) / 2
+            usage.prompt_tokens * (input_cost_per_token) / 2
         )  # batch cost is usually half of the regular token cost
-
-        # Add cache read cost if applicable
-        details = _parse_prompt_tokens_details(usage)
-        cache_read_tokens = details["cache_hit_tokens"]
-        cache_read_cost_key = _get_service_tier_cost_key(
-            "cache_read_input_token_cost", None
-        )
-        total_prompt_cost += (
-            calculate_cost_component(model_info, cache_read_cost_key, cache_read_tokens)
-            / 2
-        )
     if output_cost_per_token_batches:
         total_completion_cost = usage.completion_tokens * output_cost_per_token_batches
     elif output_cost_per_token:
diff --git a/litellm/experimental_mcp_client/client.py b/litellm/experimental_mcp_client/client.py
index e2de3cd5021..582d044023c 100644
--- a/litellm/experimental_mcp_client/client.py
+++ b/litellm/experimental_mcp_client/client.py
@@ -4,26 +4,22 @@
 
 import asyncio
 import base64
-from typing import Any, Awaitable, Callable, Dict, List, Optional, Tuple, TypeVar, Union
+from typing import Awaitable, Callable, Dict, List, Optional, TypeVar, Union
 
 import httpx
 from mcp import ClientSession, ReadResourceResult, Resource, StdioServerParameters
 from mcp.client.sse import sse_client
 from mcp.client.stdio import stdio_client
-
-try:
-    from mcp.client.streamable_http import streamable_http_client  # type: ignore
-except ImportError:
-    streamable_http_client = None
-from mcp.types import CallToolRequestParams as MCPCallToolRequestParams
-from mcp.types import CallToolResult as MCPCallToolResult
+from mcp.client.streamable_http import streamable_http_client
 from mcp.types import (
+    CallToolRequestParams as MCPCallToolRequestParams,
     GetPromptRequestParams,
     GetPromptResult,
     Prompt,
     ResourceTemplate,
-    TextContent,
 )
+from mcp.types import CallToolResult as MCPCallToolResult
+from mcp.types import TextContent
 from mcp.types import Tool as MCPTool
 from pydantic import AnyUrl
 
@@ -78,91 +74,57 @@ def __init__(
         if auth_value:
             self.update_auth_value(auth_value)
 
-    def _create_transport_context(
-        self,
-    ) -> Tuple[Any, Optional[httpx.AsyncClient]]:
-        """
-        Create the appropriate transport context based on transport type.
-
-        Returns:
-            Tuple of (transport_context, http_client).
-            http_client is only set for HTTP transport and needs cleanup.
-        """
-        http_client: Optional[httpx.AsyncClient] = None
-
-        if self.transport_type == MCPTransport.stdio:
-            if not self.stdio_config:
-                raise ValueError("stdio_config is required for stdio transport")
-            server_params = StdioServerParameters(
-                command=self.stdio_config.get("command", ""),
-                args=self.stdio_config.get("args", []),
-                env=self.stdio_config.get("env", {}),
-            )
-            return stdio_client(server_params), None
-
-        if self.transport_type == MCPTransport.sse:
-            headers = self._get_auth_headers()
-            httpx_client_factory = self._create_httpx_client_factory()
-            return sse_client(
-                url=self.server_url,
-                timeout=self.timeout,
-                headers=headers,
-                httpx_client_factory=httpx_client_factory,
-            ), None
-
-        # HTTP transport (default)
-        headers = self._get_auth_headers()
-        httpx_client_factory = self._create_httpx_client_factory()
-        verbose_logger.debug(
-            "litellm headers for streamable_http_client: %s", headers
-        )
-        http_client = httpx_client_factory(
-            headers=headers,
-            timeout=httpx.Timeout(self.timeout),
-        )
-        transport_ctx = streamable_http_client(
-            url=self.server_url,
-            http_client=http_client,
-        )
-        return transport_ctx, http_client
-
-    async def _execute_session_operation(
-        self,
-        transport_ctx: Any,
-        operation: Callable[[ClientSession], Awaitable[TSessionResult]],
-    ) -> TSessionResult:
-        """
-        Execute an operation within a transport and session context.
-
-        Handles entering/exiting contexts and running the operation.
-        """
-        transport = await transport_ctx.__aenter__()
-        try:
-            read_stream, write_stream = transport[0], transport[1]
-            session_ctx = ClientSession(read_stream, write_stream)
-            session = await session_ctx.__aenter__()
-            try:
-                await session.initialize()
-                return await operation(session)
-            finally:
-                try:
-                    await session_ctx.__aexit__(None, None, None)
-                except BaseException as e:
-                    verbose_logger.debug(f"Error during session context exit: {e}")
-        finally:
-            try:
-                await transport_ctx.__aexit__(None, None, None)
-            except BaseException as e:
-                verbose_logger.debug(f"Error during transport context exit: {e}")
-
     async def run_with_session(
         self, operation: Callable[[ClientSession], Awaitable[TSessionResult]]
     ) -> TSessionResult:
         """Open a session, run the provided coroutine, and clean up."""
+        transport_ctx = None
         http_client: Optional[httpx.AsyncClient] = None
+
         try:
-            transport_ctx, http_client = self._create_transport_context()
-            return await self._execute_session_operation(transport_ctx, operation)
+            if self.transport_type == MCPTransport.stdio:
+                if not self.stdio_config:
+                    raise ValueError("stdio_config is required for stdio transport")
+
+                server_params = StdioServerParameters(
+                    command=self.stdio_config.get("command", ""),
+                    args=self.stdio_config.get("args", []),
+                    env=self.stdio_config.get("env", {}),
+                )
+                transport_ctx = stdio_client(server_params)
+            elif self.transport_type == MCPTransport.sse:
+                headers = self._get_auth_headers()
+                httpx_client_factory = self._create_httpx_client_factory()
+                transport_ctx = sse_client(
+                    url=self.server_url,
+                    timeout=self.timeout,
+                    headers=headers,
+                    httpx_client_factory=httpx_client_factory,
+                )
+            else:
+                headers = self._get_auth_headers()
+                httpx_client_factory = self._create_httpx_client_factory()
+                verbose_logger.debug(
+                    "litellm headers for streamable_http_client: %s", headers
+                )
+                http_client = httpx_client_factory(
+                    headers=headers,
+                    timeout=httpx.Timeout(self.timeout),
+                )
+                transport_ctx = streamable_http_client(
+                    url=self.server_url,
+                    http_client=http_client,
+                )
+
+            if transport_ctx is None:
+                raise RuntimeError("Failed to create transport context")
+
+            async with transport_ctx as transport:
+                read_stream, write_stream = transport[0], transport[1]
+                session_ctx = ClientSession(read_stream, write_stream)
+                async with session_ctx as session:
+                    await session.initialize()
+                    return await operation(session)
         except Exception:
             verbose_logger.warning(
                 "MCP client run_with_session failed for %s", self.server_url or "stdio"
@@ -170,10 +132,7 @@ async def run_with_session(
             raise
         finally:
             if http_client is not None:
-                try:
-                    await http_client.aclose()
-                except BaseException as e:
-                    verbose_logger.debug(f"Error during http_client cleanup: {e}")
+                await http_client.aclose()
 
     def update_auth_value(self, mcp_auth_value: Union[str, Dict[str, str]]):
         """
@@ -286,9 +245,7 @@ async def _list_tools_operation(session: ClientSession):
             return []
 
     async def call_tool(
-        self,
-        call_tool_request_params: MCPCallToolRequestParams,
-        host_progress_callback: Optional[Callable] = None
+        self, call_tool_request_params: MCPCallToolRequestParams
     ) -> MCPCallToolResult:
         """
         Call an MCP Tool.
@@ -297,28 +254,13 @@ async def call_tool(
             f"MCP client calling tool '{call_tool_request_params.name}' with arguments: {call_tool_request_params.arguments}"
         )
 
-        async def on_progress(progress: float, total: float | None, message: str | None):
-            percentage = (progress / total * 100) if total else 0
-            verbose_logger.info(
-                f"MCP Tool '{call_tool_request_params.name}' progress: "
-                f"{progress}/{total} ({percentage:.0f}%) - {message or ''}"
-            )
-            
-            # Forward to Host if callback provided
-            if host_progress_callback:
-                try:
-                    await host_progress_callback(progress, total)
-                except Exception as e:
-                    verbose_logger.warning(f"Failed to forward to Host: {e}")
-
         async def _call_tool_operation(session: ClientSession):
             verbose_logger.debug("MCP client sending tool call to session")
             return await session.call_tool(
                 name=call_tool_request_params.name,
                 arguments=call_tool_request_params.arguments,
-                progress_callback=on_progress,
-
             )
+
         try:
             tool_result = await self.run_with_session(_call_tool_operation)
             verbose_logger.info(
diff --git a/litellm/integrations/callback_configs.json b/litellm/integrations/callback_configs.json
index 6a003b8c499..6b30b6b736e 100644
--- a/litellm/integrations/callback_configs.json
+++ b/litellm/integrations/callback_configs.json
@@ -83,33 +83,6 @@
     },
     "description": "Datadog Logging Integration"
   },
-  {
-    "id": "datadog_cost_management",
-    "displayName": "Datadog Cost Management",
-    "logo": "datadog.png",
-    "supports_key_team_logging": false,
-    "dynamic_params": {
-      "dd_api_key": {
-        "type": "password",
-        "ui_name": "API Key",
-        "description": "Datadog API key for authentication",
-        "required": true
-      },
-      "dd_app_key": {
-        "type": "password",
-        "ui_name": "App Key",
-        "description": "Datadog Application Key for Cloud Cost Management",
-        "required": true
-      },
-      "dd_site": {
-        "type": "text",
-        "ui_name": "Site",
-        "description": "Datadog site URL (e.g., us5.datadoghq.com)",
-        "required": true
-      }
-    },
-    "description": "Datadog Cloud Cost Management Integration"
-  },
   {
     "id": "lago",
     "displayName": "Lago",
@@ -434,4 +407,4 @@
     },
     "description": "SQS Queue (AWS) Logging Integration"
   }
-]
\ No newline at end of file
+]
diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py
index a5bb530fc56..6a76b57e7f7 100644
--- a/litellm/integrations/custom_guardrail.py
+++ b/litellm/integrations/custom_guardrail.py
@@ -516,9 +516,7 @@ def add_standard_logging_guardrail_information_to_request_data(
         from litellm.types.utils import GuardrailMode
 
         # Use event_type if provided, otherwise fall back to self.event_hook
-        guardrail_mode: Union[
-            GuardrailEventHooks, GuardrailMode, List[GuardrailEventHooks]
-        ]
+        guardrail_mode: Union[GuardrailEventHooks, GuardrailMode, List[GuardrailEventHooks]]
         if event_type is not None:
             guardrail_mode = event_type
         elif isinstance(self.event_hook, Mode):
@@ -526,21 +524,11 @@ def add_standard_logging_guardrail_information_to_request_data(
         else:
             guardrail_mode = self.event_hook  # type: ignore[assignment]
 
-        from litellm.litellm_core_utils.core_helpers import (
-            filter_exceptions_from_params,
-        )
-
-        # Sanitize the response to ensure it's JSON serializable and free of circular refs
-        # This prevents RecursionErrors in downstream loggers (Langfuse, Datadog, etc.)
-        clean_guardrail_response = filter_exceptions_from_params(
-            guardrail_json_response
-        )
-
         slg = StandardLoggingGuardrailInformation(
             guardrail_name=self.guardrail_name,
             guardrail_provider=guardrail_provider,
             guardrail_mode=guardrail_mode,
-            guardrail_response=clean_guardrail_response,
+            guardrail_response=guardrail_json_response,
             guardrail_status=guardrail_status,
             start_time=start_time,
             end_time=end_time,
diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py
index 735d1005d2c..503e8d8c87a 100644
--- a/litellm/integrations/datadog/datadog.py
+++ b/litellm/integrations/datadog/datadog.py
@@ -32,7 +32,6 @@
     get_datadog_service,
     get_datadog_source,
     get_datadog_tags,
-    get_datadog_base_url_from_env,
 )
 from litellm.litellm_core_utils.dd_tracing import tracer
 from litellm.llms.custom_httpx.http_handler import (
@@ -101,9 +100,7 @@ def __init__(
                 self._configure_dd_direct_api()
 
             # Optional override for testing
-            dd_base_url = get_datadog_base_url_from_env()
-            if dd_base_url:
-                self.intake_url = f"{dd_base_url}/api/v2/logs"
+            self._apply_dd_base_url_override()
             self.sync_client = _get_httpx_client()
             asyncio.create_task(self.periodic_flush())
             self.flush_lock = asyncio.Lock()
@@ -162,6 +159,18 @@ def _configure_dd_direct_api(self) -> None:
         self.DD_API_KEY = os.getenv("DD_API_KEY")
         self.intake_url = f"https://http-intake.logs.{os.getenv('DD_SITE')}/api/v2/logs"
 
+    def _apply_dd_base_url_override(self) -> None:
+        """
+        Apply base URL override for testing purposes
+        """
+        dd_base_url: Optional[str] = (
+            os.getenv("_DATADOG_BASE_URL")
+            or os.getenv("DATADOG_BASE_URL")
+            or os.getenv("DD_BASE_URL")
+        )
+        if dd_base_url is not None:
+            self.intake_url = f"{dd_base_url}/api/v2/logs"
+
     async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
         """
         Async Log success events to Datadog
diff --git a/litellm/integrations/datadog/datadog_cost_management.py b/litellm/integrations/datadog/datadog_cost_management.py
deleted file mode 100644
index 2eb94b59dd8..00000000000
--- a/litellm/integrations/datadog/datadog_cost_management.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import asyncio
-import os
-import time
-from datetime import datetime
-from typing import Dict, List, Optional, Tuple
-
-from litellm._logging import verbose_logger
-from litellm.integrations.custom_batch_logger import CustomBatchLogger
-from litellm.llms.custom_httpx.http_handler import (
-    get_async_httpx_client,
-    httpxSpecialProvider,
-)
-from litellm.types.integrations.datadog_cost_management import (
-    DatadogFOCUSCostEntry,
-)
-from litellm.types.utils import StandardLoggingPayload
-
-
-class DatadogCostManagementLogger(CustomBatchLogger):
-    def __init__(self, **kwargs):
-        self.dd_api_key = os.getenv("DD_API_KEY")
-        self.dd_app_key = os.getenv("DD_APP_KEY")
-        self.dd_site = os.getenv("DD_SITE", "datadoghq.com")
-
-        if not self.dd_api_key or not self.dd_app_key:
-            verbose_logger.warning(
-                "Datadog Cost Management: DD_API_KEY and DD_APP_KEY are required. Integration will not work."
-            )
-
-        self.upload_url = f"https://api.{self.dd_site}/api/v2/cost/custom_costs"
-
-        self.async_client = get_async_httpx_client(
-            llm_provider=httpxSpecialProvider.LoggingCallback
-        )
-
-        # Initialize lock and start periodic flush task
-        self.flush_lock = asyncio.Lock()
-        asyncio.create_task(self.periodic_flush())
-
-        # Check if flush_lock is already in kwargs to avoid double passing (unlikely but safe)
-        if "flush_lock" not in kwargs:
-            kwargs["flush_lock"] = self.flush_lock
-
-        super().__init__(**kwargs)
-
-    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
-        try:
-            standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get(
-                "standard_logging_object", None
-            )
-
-            if standard_logging_object is None:
-                return
-
-            # Only log if there is a cost associated
-            if standard_logging_object.get("response_cost", 0) > 0:
-                self.log_queue.append(standard_logging_object)
-
-                if len(self.log_queue) >= self.batch_size:
-                    await self.async_send_batch()
-
-        except Exception as e:
-            verbose_logger.exception(
-                f"Datadog Cost Management: Error in async_log_success_event: {str(e)}"
-            )
-
-    async def async_send_batch(self):
-        if not self.log_queue:
-            return
-
-        try:
-            # Aggregate costs from the batch
-            aggregated_entries = self._aggregate_costs(self.log_queue)
-
-            if not aggregated_entries:
-                return
-
-            # Send to Datadog
-            await self._upload_to_datadog(aggregated_entries)
-
-            # Clear queue only on success (or if we decide to drop on failure)
-            # CustomBatchLogger clears queue in flush_queue, so we just process here
-
-        except Exception as e:
-            verbose_logger.exception(
-                f"Datadog Cost Management: Error in async_send_batch: {str(e)}"
-            )
-
-    def _aggregate_costs(
-        self, logs: List[StandardLoggingPayload]
-    ) -> List[DatadogFOCUSCostEntry]:
-        """
-        Aggregates costs by Provider, Model, and Date.
-        Returns a list of DatadogFOCUSCostEntry.
-        """
-        aggregator: Dict[Tuple[str, str, str, Tuple[Tuple[str, str], ...]], DatadogFOCUSCostEntry] = {}
-
-        for log in logs:
-            try:
-                # Extract keys for aggregation
-                provider = log.get("custom_llm_provider") or "unknown"
-                model = log.get("model") or "unknown"
-                cost = log.get("response_cost", 0)
-
-                if cost == 0:
-                    continue
-
-                # Get date strings (FOCUS format requires specific keys, but for aggregation we group by Day)
-                # UTC date
-                # We interpret "ChargePeriod" as the day of the request.
-                ts = log.get("startTime") or time.time()
-                dt = datetime.fromtimestamp(ts)
-                date_str = dt.strftime("%Y-%m-%d")
-
-                # ChargePeriodStart and End
-                # If we want daily granularity, end date is usually same day or next day?
-                # Datadog Custom Costs usually expects periods.
-                # "ChargePeriodStart": "2023-01-01", "ChargePeriodEnd": "2023-12-31" in example.
-                # If we send daily, we can say Start=Date, End=Date.
-
-                # Grouping Key: Provider + Model + Date + Tags?
-                # For simplicity, let's aggregate by Provider + Model + Date first.
-                # If we handle tags, we need to include them in the key.
-
-                tags = self._extract_tags(log)
-                tags_key = tuple(sorted(tags.items())) if tags else ()
-
-                key = (provider, model, date_str, tags_key)
-
-                if key not in aggregator:
-                    aggregator[key] = {
-                        "ProviderName": provider,
-                        "ChargeDescription": f"LLM Usage for {model}",
-                        "ChargePeriodStart": date_str,
-                        "ChargePeriodEnd": date_str,
-                        "BilledCost": 0.0,
-                        "BillingCurrency": "USD",
-                        "Tags": tags if tags else None,
-                    }
-
-                aggregator[key]["BilledCost"] += cost
-
-            except Exception as e:
-                verbose_logger.warning(
-                    f"Error processing log for cost aggregation: {e}"
-                )
-                continue
-
-        return list(aggregator.values())
-
-    def _extract_tags(self, log: StandardLoggingPayload) -> Dict[str, str]:
-        from litellm.integrations.datadog.datadog_handler import (
-            get_datadog_env,
-            get_datadog_hostname,
-            get_datadog_pod_name,
-            get_datadog_service,
-        )
-
-        tags = {
-            "env": get_datadog_env(),
-            "service": get_datadog_service(),
-            "host": get_datadog_hostname(),
-            "pod_name": get_datadog_pod_name(),
-        }
-
-        # Add metadata as tags
-        metadata = log.get("metadata", {})
-        if metadata:
-            # Add user info
-            if "user_api_key_alias" in metadata:
-                tags["user"] = str(metadata["user_api_key_alias"])
-            if "user_api_key_team_alias" in metadata:
-                tags["team"] = str(metadata["user_api_key_team_alias"])
-            # model_group is not in StandardLoggingMetadata TypedDict, so we need to access it via dict.get()
-            model_group = metadata.get("model_group")  # type: ignore[misc]
-            if model_group:
-                tags["model_group"] = str(model_group)
-
-        return tags
-
-    async def _upload_to_datadog(self, payload: List[Dict]):
-        if not self.dd_api_key or not self.dd_app_key:
-            return
-
-        headers = {
-            "Content-Type": "application/json",
-            "DD-API-KEY": self.dd_api_key,
-            "DD-APPLICATION-KEY": self.dd_app_key,
-        }
-
-        # The API endpoint expects a list of objects directly in the body (file content behavior)
-        from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
-
-        data_json = safe_dumps(payload)
-
-        response = await self.async_client.put(
-            self.upload_url, content=data_json, headers=headers
-        )
-
-        response.raise_for_status()
-
-        verbose_logger.debug(
-            f"Datadog Cost Management: Uploaded {len(payload)} cost entries. Status: {response.status_code}"
-        )
diff --git a/litellm/integrations/datadog/datadog_handler.py b/litellm/integrations/datadog/datadog_handler.py
index e2f30f2f614..26fab77759e 100644
--- a/litellm/integrations/datadog/datadog_handler.py
+++ b/litellm/integrations/datadog/datadog_handler.py
@@ -20,14 +20,6 @@ def get_datadog_hostname() -> str:
     return os.getenv("HOSTNAME", "")
 
 
-def get_datadog_base_url_from_env() -> Optional[str]:
-    """
-    Get base URL override from common DD_BASE_URL env var.
-    This is useful for testing or custom endpoints.
-    """
-    return os.getenv("DD_BASE_URL")
-
-
 def get_datadog_env() -> str:
     return os.getenv("DD_ENV", "unknown")
 
diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py
index 4f6a5b339a7..6ffdbc0a005 100644
--- a/litellm/integrations/datadog/datadog_llm_obs.py
+++ b/litellm/integrations/datadog/datadog_llm_obs.py
@@ -21,7 +21,6 @@
 from litellm.integrations.datadog.datadog_handler import (
     get_datadog_service,
     get_datadog_tags,
-    get_datadog_base_url_from_env,
 )
 from litellm.litellm_core_utils.dd_tracing import tracer
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
@@ -44,22 +43,24 @@ class DataDogLLMObsLogger(CustomBatchLogger):
     def __init__(self, **kwargs):
         try:
             verbose_logger.debug("DataDogLLMObs: Initializing logger")
-            # Configure DataDog endpoint (Agent or Direct API)
-            # Use LITELLM_DD_AGENT_HOST to avoid conflicts with ddtrace's DD_AGENT_HOST
-            dd_agent_host = os.getenv("LITELLM_DD_AGENT_HOST")
+            if os.getenv("DD_API_KEY", None) is None:
+                raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>'")
+            if os.getenv("DD_SITE", None) is None:
+                raise Exception(
+                    "DD_SITE is not set, set 'DD_SITE=<>', example sit = `us5.datadoghq.com`"
+                )
 
             self.async_client = get_async_httpx_client(
                 llm_provider=httpxSpecialProvider.LoggingCallback
             )
             self.DD_API_KEY = os.getenv("DD_API_KEY")
+            self.DD_SITE = os.getenv("DD_SITE")
+            self.intake_url = (
+                f"https://api.{self.DD_SITE}/api/intake/llm-obs/v1/trace/spans"
+            )
 
-            if dd_agent_host:
-                self._configure_dd_agent(dd_agent_host=dd_agent_host)
-            else:
-                self._configure_dd_direct_api()
-
-            # Optional override for testing
-            dd_base_url = get_datadog_base_url_from_env()
+            # testing base url
+            dd_base_url = os.getenv("DD_BASE_URL")
             if dd_base_url:
                 self.intake_url = f"{dd_base_url}/api/intake/llm-obs/v1/trace/spans"
 
@@ -77,38 +78,6 @@ def __init__(self, **kwargs):
             verbose_logger.exception(f"DataDogLLMObs: Error initializing - {str(e)}")
             raise e
 
-    def _configure_dd_agent(self, dd_agent_host: str):
-        """
-        Configure the Datadog logger to send traces to the Agent.
-        """
-        # When using the Agent, LLM Observability Intake does NOT require the API Key
-        # Reference: https://docs.datadoghq.com/llm_observability/setup/sdk/#agent-setup
-
-        # Use specific port for LLM Obs (Trace Agent) to avoid conflict with Logs Agent (10518)
-        agent_port = os.getenv("LITELLM_DD_LLM_OBS_PORT", "8126")
-        self.DD_SITE = "localhost"  # Not used for URL construction in agent mode
-        self.intake_url = (
-            f"http://{dd_agent_host}:{agent_port}/api/intake/llm-obs/v1/trace/spans"
-        )
-        verbose_logger.debug(f"DataDogLLMObs: Using DD Agent at {self.intake_url}")
-
-    def _configure_dd_direct_api(self):
-        """
-        Configure the Datadog logger to send traces directly to the Datadog API.
-        """
-        if not self.DD_API_KEY:
-            raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>'")
-
-        self.DD_SITE = os.getenv("DD_SITE")
-        if not self.DD_SITE:
-            raise Exception(
-                "DD_SITE is not set, set 'DD_SITE=<>', example site = `us5.datadoghq.com`"
-            )
-
-        self.intake_url = (
-            f"https://api.{self.DD_SITE}/api/intake/llm-obs/v1/trace/spans"
-        )
-
     def _get_datadog_llm_obs_params(self) -> Dict:
         """
         Get the datadog_llm_observability_params from litellm.datadog_llm_observability_params
@@ -195,14 +164,13 @@ async def async_send_batch(self):
 
             json_payload = safe_dumps(payload)
 
-            headers = {"Content-Type": "application/json"}
-            if self.DD_API_KEY:
-                headers["DD-API-KEY"] = self.DD_API_KEY
-
             response = await self.async_client.post(
                 url=self.intake_url,
                 content=json_payload,
-                headers=headers,
+                headers={
+                    "DD-API-KEY": self.DD_API_KEY,
+                    "Content-Type": "application/json",
+                },
             )
 
             if response.status_code != 202:
diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py
index 7bf97665fd2..46ada3c3930 100644
--- a/litellm/integrations/langfuse/langfuse.py
+++ b/litellm/integrations/langfuse/langfuse.py
@@ -23,7 +23,6 @@
 from litellm.litellm_core_utils.core_helpers import (
     safe_deep_copy,
     reconstruct_model_name,
-    filter_exceptions_from_params,
 )
 from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info
 from litellm.integrations.langfuse.langfuse_mock_client import (
@@ -76,8 +75,9 @@ def _extract_cache_read_input_tokens(usage_obj) -> int:
     # Check prompt_tokens_details.cached_tokens (used by Gemini and other providers)
     if hasattr(usage_obj, "prompt_tokens_details"):
         prompt_tokens_details = getattr(usage_obj, "prompt_tokens_details", None)
-        if prompt_tokens_details is not None and hasattr(
-            prompt_tokens_details, "cached_tokens"
+        if (
+            prompt_tokens_details is not None
+            and hasattr(prompt_tokens_details, "cached_tokens")
         ):
             cached_tokens = getattr(prompt_tokens_details, "cached_tokens", None)
             if (
@@ -540,6 +540,7 @@ def _log_langfuse_v2(  # noqa: PLR0915
         verbose_logger.debug("Langfuse Layer Logging - logging to langfuse v2")
 
         try:
+            metadata = metadata or {}
             standard_logging_object: Optional[StandardLoggingPayload] = cast(
                 Optional[StandardLoggingPayload],
                 kwargs.get("standard_logging_object", None),
@@ -705,10 +706,9 @@ def _log_langfuse_v2(  # noqa: PLR0915
 
             clean_metadata["litellm_response_cost"] = cost
             if standard_logging_object is not None:
-                hidden_params = standard_logging_object.get("hidden_params", {})
-                clean_metadata["hidden_params"] = filter_exceptions_from_params(
-                    hidden_params
-                )
+                clean_metadata["hidden_params"] = standard_logging_object[
+                    "hidden_params"
+                ]
 
             if (
                 litellm.langfuse_default_tags is not None
diff --git a/litellm/integrations/langfuse/langfuse_prompt_management.py b/litellm/integrations/langfuse/langfuse_prompt_management.py
index 3986fc6a6ef..8f73eabad44 100644
--- a/litellm/integrations/langfuse/langfuse_prompt_management.py
+++ b/litellm/integrations/langfuse/langfuse_prompt_management.py
@@ -300,59 +300,43 @@ def log_failure_event(self, kwargs, response_obj, start_time, end_time):
         )
 
     async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
-        try:
-            standard_callback_dynamic_params = kwargs.get(
-                "standard_callback_dynamic_params"
-            )
-            langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request(
-                globalLangfuseLogger=self,
-                standard_callback_dynamic_params=standard_callback_dynamic_params,
-                in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache,
-            )
-            langfuse_logger_to_use.log_event_on_langfuse(
-                kwargs=kwargs,
-                response_obj=response_obj,
-                start_time=start_time,
-                end_time=end_time,
-                user_id=kwargs.get("user", None),
-            )
-        except Exception as e:
-            from litellm._logging import verbose_logger
-            
-            verbose_logger.exception(
-                f"Langfuse Layer Error - Exception occurred while logging success event: {str(e)}"
-            )
-            self.handle_callback_failure(callback_name="langfuse")
+        standard_callback_dynamic_params = kwargs.get(
+            "standard_callback_dynamic_params"
+        )
+        langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request(
+            globalLangfuseLogger=self,
+            standard_callback_dynamic_params=standard_callback_dynamic_params,
+            in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache,
+        )
+        langfuse_logger_to_use.log_event_on_langfuse(
+            kwargs=kwargs,
+            response_obj=response_obj,
+            start_time=start_time,
+            end_time=end_time,
+            user_id=kwargs.get("user", None),
+        )
 
     async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
-        try:
-            standard_callback_dynamic_params = kwargs.get(
-                "standard_callback_dynamic_params"
-            )
-            langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request(
-                globalLangfuseLogger=self,
-                standard_callback_dynamic_params=standard_callback_dynamic_params,
-                in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache,
-            )
-            standard_logging_object = cast(
-                Optional[StandardLoggingPayload],
-                kwargs.get("standard_logging_object", None),
-            )
-            if standard_logging_object is None:
-                return
-            langfuse_logger_to_use.log_event_on_langfuse(
-                start_time=start_time,
-                end_time=end_time,
-                response_obj=None,
-                user_id=kwargs.get("user", None),
-                status_message=standard_logging_object["error_str"],
-                level="ERROR",
-                kwargs=kwargs,
-            )
-        except Exception as e:
-            from litellm._logging import verbose_logger
-            
-            verbose_logger.exception(
-                f"Langfuse Layer Error - Exception occurred while logging failure event: {str(e)}"
-            )
-            self.handle_callback_failure(callback_name="langfuse")
+        standard_callback_dynamic_params = kwargs.get(
+            "standard_callback_dynamic_params"
+        )
+        langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request(
+            globalLangfuseLogger=self,
+            standard_callback_dynamic_params=standard_callback_dynamic_params,
+            in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache,
+        )
+        standard_logging_object = cast(
+            Optional[StandardLoggingPayload],
+            kwargs.get("standard_logging_object", None),
+        )
+        if standard_logging_object is None:
+            return
+        langfuse_logger_to_use.log_event_on_langfuse(
+            start_time=start_time,
+            end_time=end_time,
+            response_obj=None,
+            user_id=kwargs.get("user", None),
+            status_message=standard_logging_object["error_str"],
+            level="ERROR",
+            kwargs=kwargs,
+        )
diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py
index 2da747f1100..a8a7fa77b3d 100644
--- a/litellm/integrations/opentelemetry.py
+++ b/litellm/integrations/opentelemetry.py
@@ -144,7 +144,6 @@ def __init__(
         self.OTEL_EXPORTER = self.config.exporter
         self.OTEL_ENDPOINT = self.config.endpoint
         self.OTEL_HEADERS = self.config.headers
-        self._tracer_provider_cache: Dict[str, Any] = {}
         self._init_tracing(tracer_provider)
 
         _debug_otel = str(os.getenv("DEBUG_OTEL", "False")).lower()
@@ -616,20 +615,12 @@ def _get_tracer_with_dynamic_headers(self, dynamic_headers: dict):
         """Create a temporary tracer with dynamic headers for this request only."""
         from opentelemetry.sdk.trace import TracerProvider
 
-        # Prevents thread exhaustion by reusing providers for the same credential sets (e.g. per-team keys)
-        cache_key = str(sorted(dynamic_headers.items()))
-        if cache_key in self._tracer_provider_cache:
-            return self._tracer_provider_cache[cache_key].get_tracer(LITELLM_TRACER_NAME)
-
         # Create a temporary tracer provider with dynamic headers
         temp_provider = TracerProvider(resource=self._get_litellm_resource(self.config))
         temp_provider.add_span_processor(
             self._get_span_processor(dynamic_headers=dynamic_headers)
         )
 
-        # Store in cache for reuse
-        self._tracer_provider_cache[cache_key] = temp_provider
-
         return temp_provider.get_tracer(LITELLM_TRACER_NAME)
 
     def construct_dynamic_otel_headers(
@@ -1006,7 +997,7 @@ def _emit_semantic_logs(self, kwargs, response_obj, span: Span):
         try:
             from opentelemetry.sdk._logs import LogRecord as SdkLogRecord  # type: ignore[attr-defined]  # OTEL < 1.39.0
         except ImportError:
-            from opentelemetry.sdk._logs._internal import LogRecord as SdkLogRecord  # type: ignore[attr-defined, no-redef]  # OTEL >= 1.39.0
+            from opentelemetry.sdk._logs._internal import LogRecord as SdkLogRecord  # OTEL >= 1.39.0
 
         otel_logger = get_logger(LITELLM_LOGGER_NAME)
 
@@ -1627,7 +1618,6 @@ def set_attributes(  # noqa: PLR0915
                                     )
 
         except Exception as e:
-            self.handle_callback_failure(callback_name= self.callback_name)  
             verbose_logger.exception(
                 "OpenTelemetry logging error in set_attributes %s", str(e)
             )
diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py
index 2c897cb0692..bafb0d88c82 100644
--- a/litellm/integrations/prometheus.py
+++ b/litellm/integrations/prometheus.py
@@ -229,18 +229,14 @@ def __init__(  # noqa: PLR0915
             self.litellm_remaining_api_key_requests_for_model = self._gauge_factory(
                 "litellm_remaining_api_key_requests_for_model",
                 "Remaining Requests API Key can make for model (model based rpm limit on key)",
-                labelnames=self.get_labels_for_metric(
-                    "litellm_remaining_api_key_requests_for_model"
-                ),
+                labelnames=["hashed_api_key", "api_key_alias", "model"],
             )
 
             # Remaining MODEL TPM limit for API Key
             self.litellm_remaining_api_key_tokens_for_model = self._gauge_factory(
                 "litellm_remaining_api_key_tokens_for_model",
                 "Remaining Tokens API Key can make for model (model based tpm limit on key)",
-                labelnames=self.get_labels_for_metric(
-                    "litellm_remaining_api_key_tokens_for_model"
-                ),
+                labelnames=["hashed_api_key", "api_key_alias", "model"],
             )
 
             ########################################
@@ -316,18 +312,6 @@ def __init__(  # noqa: PLR0915
                 labelnames=self.get_labels_for_metric("litellm_deployment_state"),
             )
 
-            self.litellm_deployment_tpm_limit = self._gauge_factory(
-                "litellm_deployment_tpm_limit",
-                "Deployment TPM limit found in config",
-                labelnames=self.get_labels_for_metric("litellm_deployment_tpm_limit"),
-            )
-
-            self.litellm_deployment_rpm_limit = self._gauge_factory(
-                "litellm_deployment_rpm_limit",
-                "Deployment RPM limit found in config",
-                labelnames=self.get_labels_for_metric("litellm_deployment_rpm_limit"),
-            )
-
             self.litellm_deployment_cooled_down = self._counter_factory(
                 "litellm_deployment_cooled_down",
                 "LLM Deployment Analytics - Number of times a deployment has been cooled down by LiteLLM load balancing logic. exception_status is the status of the exception that caused the deployment to be cooled down",
@@ -389,9 +373,15 @@ def __init__(  # noqa: PLR0915
             self.litellm_llm_api_failed_requests_metric = self._counter_factory(
                 name="litellm_llm_api_failed_requests_metric",
                 documentation="deprecated - use litellm_proxy_failed_requests_metric",
-                labelnames=self.get_labels_for_metric(
-                    "litellm_llm_api_failed_requests_metric"
-                ),
+                labelnames=[
+                    "end_user",
+                    "hashed_api_key",
+                    "api_key_alias",
+                    "model",
+                    "team",
+                    "team_alias",
+                    "user",
+                ],
             )
 
             self.litellm_requests_metric = self._counter_factory(
@@ -901,7 +891,7 @@ async def async_log_success_event(self, kwargs, response_obj, start_time, end_ti
 
         model = kwargs.get("model", "")
         litellm_params = kwargs.get("litellm_params", {}) or {}
-        _metadata = litellm_params.get("metadata") or {}
+        _metadata = litellm_params.get("metadata", {})
         get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking()
 
         end_user_id = get_end_user_id_for_cost_tracking(
@@ -964,8 +954,6 @@ async def async_log_success_event(self, kwargs, response_obj, start_time, end_ti
             route=standard_logging_payload["metadata"].get(
                 "user_api_key_request_route"
             ),
-            client_ip=standard_logging_payload["metadata"].get("requester_ip_address"),
-            user_agent=standard_logging_payload["metadata"].get("user_agent"),
         )
 
         if (
@@ -1023,7 +1011,6 @@ async def async_log_success_event(self, kwargs, response_obj, start_time, end_ti
             user_api_key_alias=user_api_key_alias,
             kwargs=kwargs,
             metadata=_metadata,
-            model_id=enum_values.model_id,
         )
 
         # set latency metrics
@@ -1178,15 +1165,26 @@ async def _increment_remaining_budget_metrics(
         response_cost: float,
         user_id: Optional[str] = None,
     ):
-        _metadata = litellm_params.get("metadata") or {}
-        _team_spend = _metadata.get("user_api_key_team_spend", None)
-        _team_max_budget = _metadata.get("user_api_key_team_max_budget", None)
+        _team_spend = litellm_params.get("metadata", {}).get(
+            "user_api_key_team_spend", None
+        )
+        _team_max_budget = litellm_params.get("metadata", {}).get(
+            "user_api_key_team_max_budget", None
+        )
 
-        _api_key_spend = _metadata.get("user_api_key_spend", None)
-        _api_key_max_budget = _metadata.get("user_api_key_max_budget", None)
+        _api_key_spend = litellm_params.get("metadata", {}).get(
+            "user_api_key_spend", None
+        )
+        _api_key_max_budget = litellm_params.get("metadata", {}).get(
+            "user_api_key_max_budget", None
+        )
 
-        _user_spend = _metadata.get("user_api_key_user_spend", None)
-        _user_max_budget = _metadata.get("user_api_key_user_max_budget", None)
+        _user_spend = litellm_params.get("metadata", {}).get(
+            "user_api_key_user_spend", None
+        )
+        _user_max_budget = litellm_params.get("metadata", {}).get(
+            "user_api_key_user_max_budget", None
+        )
 
         await self._set_api_key_budget_metrics_after_api_request(
             user_api_key=user_api_key,
@@ -1247,7 +1245,6 @@ def _set_virtual_key_rate_limit_metrics(
         user_api_key_alias: Optional[str],
         kwargs: dict,
         metadata: dict,
-        model_id: Optional[str] = None,
     ):
         from litellm.proxy.common_utils.callback_utils import (
             get_model_group_from_litellm_kwargs,
@@ -1269,11 +1266,11 @@ def _set_virtual_key_rate_limit_metrics(
         )
 
         self.litellm_remaining_api_key_requests_for_model.labels(
-            user_api_key, user_api_key_alias, model_group, model_id
+            user_api_key, user_api_key_alias, model_group
         ).set(remaining_requests)
 
         self.litellm_remaining_api_key_tokens_for_model.labels(
-            user_api_key, user_api_key_alias, model_group, model_id
+            user_api_key, user_api_key_alias, model_group
         ).set(remaining_tokens)
 
     def _set_latency_metrics(
@@ -1299,14 +1296,12 @@ def _set_latency_metrics(
             time_to_first_token_seconds is not None
             and kwargs.get("stream", False) is True  # only emit for streaming requests
         ):
-            _ttft_labels = prometheus_label_factory(
-                supported_enum_labels=self.get_labels_for_metric(
-                    metric_name="litellm_llm_api_time_to_first_token_metric"
-                ),
-                enum_values=enum_values,
-            )
             self.litellm_llm_api_time_to_first_token_metric.labels(
-                **_ttft_labels
+                model,
+                user_api_key,
+                user_api_key_alias,
+                user_api_team,
+                user_api_team_alias,
             ).observe(time_to_first_token_seconds)
         else:
             verbose_logger.debug(
@@ -1346,7 +1341,7 @@ def _set_latency_metrics(
 
         # request queue time (time from arrival to processing start)
         _litellm_params = kwargs.get("litellm_params", {}) or {}
-        queue_time_seconds = (_litellm_params.get("metadata") or {}).get(
+        queue_time_seconds = _litellm_params.get("metadata", {}).get(
             "queue_time_seconds"
         )
         if queue_time_seconds is not None and queue_time_seconds >= 0:
@@ -1370,14 +1365,14 @@ async def async_log_failure_event(self, kwargs, response_obj, start_time, end_ti
         standard_logging_payload: StandardLoggingPayload = kwargs.get(
             "standard_logging_object", {}
         )
-
+        
         if self._should_skip_metrics_for_invalid_key(
             kwargs=kwargs, standard_logging_payload=standard_logging_payload
         ):
             return
-
+        
         model = kwargs.get("model", "")
-
+        
         litellm_params = kwargs.get("litellm_params", {}) or {}
         get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking()
 
@@ -1401,7 +1396,6 @@ async def async_log_failure_event(self, kwargs, response_obj, start_time, end_ti
                 user_api_team,
                 user_api_team_alias,
                 user_id,
-                standard_logging_payload.get("model_id", ""),
             ).inc()
             self.set_llm_deployment_failure_metrics(kwargs)
         except Exception as e:
@@ -1419,57 +1413,49 @@ def _extract_status_code(
     ) -> Optional[int]:
         """
         Extract HTTP status code from various input formats for validation.
-
+        
         This is a centralized helper to extract status code from different
         callback function signatures. Handles both ProxyException (uses 'code')
         and standard exceptions (uses 'status_code').
-
+        
         Args:
             kwargs: Dictionary potentially containing 'exception' key
             enum_values: Object with 'status_code' attribute
             exception: Exception object to extract status code from directly
-
+            
         Returns:
             Status code as integer if found, None otherwise
         """
         status_code = None
-
+        
         # Try from enum_values first (most common in our callbacks)
-        if (
-            enum_values
-            and hasattr(enum_values, "status_code")
-            and enum_values.status_code
-        ):
+        if enum_values and hasattr(enum_values, "status_code") and enum_values.status_code:
             try:
                 status_code = int(enum_values.status_code)
             except (ValueError, TypeError):
                 pass
-
+        
         if not status_code and exception:
             # ProxyException uses 'code' attribute, other exceptions may use 'status_code'
-            status_code = getattr(exception, "status_code", None) or getattr(
-                exception, "code", None
-            )
+            status_code = getattr(exception, "status_code", None) or getattr(exception, "code", None)
             if status_code is not None:
                 try:
                     status_code = int(status_code)
                 except (ValueError, TypeError):
                     status_code = None
-
+        
         if not status_code and kwargs:
             exception_in_kwargs = kwargs.get("exception")
             if exception_in_kwargs:
-                status_code = getattr(
-                    exception_in_kwargs, "status_code", None
-                ) or getattr(exception_in_kwargs, "code", None)
+                status_code = getattr(exception_in_kwargs, "status_code", None) or getattr(exception_in_kwargs, "code", None)
                 if status_code is not None:
                     try:
                         status_code = int(status_code)
                     except (ValueError, TypeError):
                         status_code = None
-
+        
         return status_code
-
+    
     def _is_invalid_api_key_request(
         self,
         status_code: Optional[int],
@@ -1477,23 +1463,23 @@ def _is_invalid_api_key_request(
     ) -> bool:
         """
         Determine if a request has an invalid API key based on status code and exception.
-
+        
         This method prevents invalid authentication attempts from being recorded in
         Prometheus metrics. A 401 status code is the definitive indicator of authentication
         failure. Additionally, we check exception messages for authentication error patterns
         to catch cases where the exception hasn't been converted to a ProxyException yet.
-
+        
         Args:
             status_code: HTTP status code (401 indicates authentication error)
             exception: Exception object to check for auth-related error messages
-
+            
         Returns:
             True if the request has an invalid API key and metrics should be skipped,
             False otherwise
         """
         if status_code == 401:
             return True
-
+        
         # Handle cases where AssertionError is raised before conversion to ProxyException
         if exception is not None:
             exception_str = str(exception).lower()
@@ -1506,9 +1492,9 @@ def _is_invalid_api_key_request(
             ]
             if any(pattern in exception_str for pattern in auth_error_patterns):
                 return True
-
+        
         return False
-
+    
     def _should_skip_metrics_for_invalid_key(
         self,
         kwargs: Optional[dict] = None,
@@ -1519,18 +1505,18 @@ def _should_skip_metrics_for_invalid_key(
     ) -> bool:
         """
         Determine if Prometheus metrics should be skipped for invalid API key requests.
-
+        
         This is a centralized validation method that extracts status code and exception
         information from various callback function signatures and determines if the request
         represents an invalid API key attempt that should be filtered from metrics.
-
+        
         Args:
             kwargs: Dictionary potentially containing exception and other data
             user_api_key_dict: User API key authentication object (currently unused)
             enum_values: Object with status_code attribute
             standard_logging_payload: Standard logging payload dictionary
             exception: Exception object to check directly
-
+            
         Returns:
             True if metrics should be skipped (invalid key detected), False otherwise
         """
@@ -1539,17 +1525,17 @@ def _should_skip_metrics_for_invalid_key(
             enum_values=enum_values,
             exception=exception,
         )
-
+        
         if exception is None and kwargs:
             exception = kwargs.get("exception")
-
+        
         if self._is_invalid_api_key_request(status_code, exception=exception):
             verbose_logger.debug(
                 "Skipping Prometheus metrics for invalid API key request: "
                 f"status_code={status_code}, exception={type(exception).__name__ if exception else None}"
             )
             return True
-
+        
         return False
 
     async def async_post_call_failure_hook(
@@ -1590,10 +1576,6 @@ async def async_post_call_failure_hook(
                 litellm_params=request_data,
                 proxy_server_request=request_data.get("proxy_server_request", {}),
             )
-            _metadata = request_data.get("metadata", {}) or {}
-            model_id = _metadata.get("model_info", {}).get("id") or request_data.get(
-                "model_info", {}
-            ).get("id")
             enum_values = UserAPIKeyLabelValues(
                 end_user=user_api_key_dict.end_user_id,
                 user=user_api_key_dict.user_id,
@@ -1608,9 +1590,6 @@ async def async_post_call_failure_hook(
                 exception_class=self._get_exception_class_name(original_exception),
                 tags=_tags,
                 route=user_api_key_dict.request_route,
-                client_ip=_metadata.get("requester_ip_address"),
-                user_agent=_metadata.get("user_agent"),
-                model_id=model_id,
             )
             _labels = prometheus_label_factory(
                 supported_enum_labels=self.get_labels_for_metric(
@@ -1650,7 +1629,6 @@ async def async_post_call_success_hook(
             ):
                 return
 
-            _metadata = data.get("metadata", {}) or {}
             enum_values = UserAPIKeyLabelValues(
                 end_user=user_api_key_dict.end_user_id,
                 hashed_api_key=user_api_key_dict.api_key,
@@ -1666,8 +1644,6 @@ async def async_post_call_success_hook(
                     litellm_params=data,
                     proxy_server_request=data.get("proxy_server_request", {}),
                 ),
-                client_ip=_metadata.get("requester_ip_address"),
-                user_agent=_metadata.get("user_agent"),
             )
             _labels = prometheus_label_factory(
                 supported_enum_labels=self.get_labels_for_metric(
@@ -1708,7 +1684,7 @@ def set_llm_deployment_failure_metrics(self, request_kwargs: dict):
             exception = request_kwargs.get("exception", None)
 
             llm_provider = _litellm_params.get("custom_llm_provider", None)
-
+            
             if self._should_skip_metrics_for_invalid_key(
                 kwargs=request_kwargs,
                 standard_logging_payload=standard_logging_payload,
@@ -1740,10 +1716,6 @@ def set_llm_deployment_failure_metrics(self, request_kwargs: dict):
                     "user_api_key_team_alias"
                 ],
                 tags=standard_logging_payload.get("request_tags", []),
-                client_ip=standard_logging_payload["metadata"].get(
-                    "requester_ip_address"
-                ),
-                user_agent=standard_logging_payload["metadata"].get("user_agent"),
             )
 
             """
@@ -1781,49 +1753,6 @@ def set_llm_deployment_failure_metrics(self, request_kwargs: dict):
                 )
             )
 
-    def _set_deployment_tpm_rpm_limit_metrics(
-        self,
-        model_info: dict,
-        litellm_params: dict,
-        litellm_model_name: Optional[str],
-        model_id: Optional[str],
-        api_base: Optional[str],
-        llm_provider: Optional[str],
-    ):
-        """
-        Set the deployment TPM and RPM limits metrics
-        """
-        tpm = model_info.get("tpm") or litellm_params.get("tpm")
-        rpm = model_info.get("rpm") or litellm_params.get("rpm")
-
-        if tpm is not None:
-            _labels = prometheus_label_factory(
-                supported_enum_labels=self.get_labels_for_metric(
-                    metric_name="litellm_deployment_tpm_limit"
-                ),
-                enum_values=UserAPIKeyLabelValues(
-                    litellm_model_name=litellm_model_name,
-                    model_id=model_id,
-                    api_base=api_base,
-                    api_provider=llm_provider,
-                ),
-            )
-            self.litellm_deployment_tpm_limit.labels(**_labels).set(tpm)
-
-        if rpm is not None:
-            _labels = prometheus_label_factory(
-                supported_enum_labels=self.get_labels_for_metric(
-                    metric_name="litellm_deployment_rpm_limit"
-                ),
-                enum_values=UserAPIKeyLabelValues(
-                    litellm_model_name=litellm_model_name,
-                    model_id=model_id,
-                    api_base=api_base,
-                    api_provider=llm_provider,
-                ),
-            )
-            self.litellm_deployment_rpm_limit.labels(**_labels).set(rpm)
-
     def set_llm_deployment_success_metrics(
         self,
         request_kwargs: dict,
@@ -1857,16 +1786,6 @@ def set_llm_deployment_success_metrics(
             _model_info = _metadata.get("model_info") or {}
             model_id = _model_info.get("id", None)
 
-            if _model_info or _litellm_params:
-                self._set_deployment_tpm_rpm_limit_metrics(
-                    model_info=_model_info,
-                    litellm_params=_litellm_params,
-                    litellm_model_name=litellm_model_name,
-                    model_id=model_id,
-                    api_base=api_base,
-                    llm_provider=llm_provider,
-                )
-
             remaining_requests: Optional[int] = None
             remaining_tokens: Optional[int] = None
             if additional_headers := standard_logging_payload["hidden_params"][
@@ -2344,10 +2263,7 @@ async def _initialize_api_key_budget_metrics(self):
 
         async def fetch_keys(
             page_size: int, page: int
-        ) -> Tuple[
-            List[Union[str, UserAPIKeyAuth, LiteLLM_DeletedVerificationToken]],
-            Optional[int],
-        ]:
+        ) -> Tuple[List[Union[str, UserAPIKeyAuth, LiteLLM_DeletedVerificationToken]], Optional[int]]:
             key_list_response = await _list_key_helper(
                 prisma_client=prisma_client,
                 page=page,
@@ -2463,16 +2379,12 @@ async def _initialize_user_and_team_count_metrics(self):
             # Get total user count
             total_users = await prisma_client.db.litellm_usertable.count()
             self.litellm_total_users_metric.set(total_users)
-            verbose_logger.debug(
-                f"Prometheus: set litellm_total_users to {total_users}"
-            )
+            verbose_logger.debug(f"Prometheus: set litellm_total_users to {total_users}")
 
             # Get total team count
             total_teams = await prisma_client.db.litellm_teamtable.count()
             self.litellm_teams_count_metric.set(total_teams)
-            verbose_logger.debug(
-                f"Prometheus: set litellm_teams_count to {total_teams}"
-            )
+            verbose_logger.debug(f"Prometheus: set litellm_teams_count to {total_teams}")
         except Exception as e:
             verbose_logger.exception(
                 f"Error initializing user/team count metrics: {str(e)}"
@@ -2500,8 +2412,8 @@ async def _set_team_budget_metrics_after_api_request(
         self,
         user_api_team: Optional[str],
         user_api_team_alias: Optional[str],
-        team_spend: Optional[float],
-        team_max_budget: Optional[float],
+        team_spend: float,
+        team_max_budget: float,
         response_cost: float,
     ):
         """
@@ -2663,7 +2575,7 @@ async def _set_api_key_budget_metrics_after_api_request(
         user_api_key: Optional[str],
         user_api_key_alias: Optional[str],
         response_cost: float,
-        key_max_budget: Optional[float],
+        key_max_budget: float,
         key_spend: Optional[float],
     ):
         if user_api_key:
@@ -2680,7 +2592,7 @@ async def _assemble_key_object(
         self,
         user_api_key: str,
         user_api_key_alias: str,
-        key_max_budget: Optional[float],
+        key_max_budget: float,
         key_spend: Optional[float],
         response_cost: float,
     ) -> UserAPIKeyAuth:
diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py
index 060e98fd49f..e290101f8bf 100644
--- a/litellm/litellm_core_utils/get_litellm_params.py
+++ b/litellm/litellm_core_utils/get_litellm_params.py
@@ -93,11 +93,8 @@ def get_litellm_params(
         "text_completion": text_completion,
         "azure_ad_token_provider": azure_ad_token_provider,
         "user_continue_message": user_continue_message,
-        "base_model": base_model
-        or (
-            _get_base_model_from_litellm_call_metadata(metadata=metadata)
-            if metadata
-            else None
+        "base_model": base_model or (
+            _get_base_model_from_litellm_call_metadata(metadata=metadata) if metadata else None
         ),
         "litellm_trace_id": litellm_trace_id,
         "litellm_session_id": litellm_session_id,
@@ -142,7 +139,5 @@ def get_litellm_params(
         "aws_sts_endpoint": kwargs.get("aws_sts_endpoint"),
         "aws_external_id": kwargs.get("aws_external_id"),
         "aws_bedrock_runtime_endpoint": kwargs.get("aws_bedrock_runtime_endpoint"),
-        "tpm": kwargs.get("tpm"),
-        "rpm": kwargs.get("rpm"),
     }
     return litellm_params
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index 0d9245a686a..d3bcfe8200e 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -205,11 +205,6 @@
 
 ### GLOBAL VARIABLES ###
 
-# Cache custom pricing keys as frozenset for O(1) lookups instead of looping through 49 keys
-_CUSTOM_PRICING_KEYS: frozenset = frozenset(
-    CustomPricingLiteLLMParams.model_fields.keys()
-)
-
 sentry_sdk_instance = None
 capture_exception = None
 add_breadcrumb = None
@@ -335,9 +330,7 @@ def __init__(
         self.start_time = start_time  # log the call start time
         self.call_type = call_type
         self.litellm_call_id = litellm_call_id
-        self.litellm_trace_id: str = (
-            litellm_trace_id if litellm_trace_id else str(uuid.uuid4())
-        )
+        self.litellm_trace_id: str = litellm_trace_id if litellm_trace_id else str(uuid.uuid4())
         self.function_id = function_id
         self.streaming_chunks: List[Any] = []  # for generating complete stream response
         self.sync_streaming_chunks: List[
@@ -546,11 +539,10 @@ def update_environment_variables(
         if "stream_options" in additional_params:
             self.stream_options = additional_params["stream_options"]
         ## check if custom pricing set ##
-        if any(
-            litellm_params.get(key) is not None
-            for key in _CUSTOM_PRICING_KEYS & litellm_params.keys()
-        ):
-            self.custom_pricing = True
+        custom_pricing_keys = CustomPricingLiteLLMParams.model_fields.keys()
+        for key in custom_pricing_keys:
+            if litellm_params.get(key) is not None:
+                self.custom_pricing = True
 
         if "custom_llm_provider" in self.model_call_details:
             self.custom_llm_provider = self.model_call_details["custom_llm_provider"]
@@ -1638,19 +1630,11 @@ def _transform_usage_objects(self, result):
                     "standard_logging_object"
                 )
             ) is not None:
-                response_dict = (
+                standard_logging_payload["response"] = (
                     result.model_dump()
                     if hasattr(result, "model_dump")
                     else dict(result)
                 )
-                # Ensure usage is properly included with transformed chat format
-                if transformed_usage is not None:
-                    response_dict["usage"] = (
-                        transformed_usage.model_dump()
-                        if hasattr(transformed_usage, "model_dump")
-                        else dict(transformed_usage)
-                    )
-                standard_logging_payload["response"] = response_dict
         elif isinstance(result, TranscriptionResponse):
             from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import (
                 TranscriptionUsageObjectTransformation,
@@ -3312,7 +3296,6 @@ def _get_masked_values(
         "token",
         "key",
         "secret",
-        "vertex_credentials",
     ]
     return {
         k: (
@@ -4265,21 +4248,15 @@ def use_custom_pricing_for_model(litellm_params: Optional[dict]) -> bool:
     if litellm_params is None:
         return False
 
-    # Check litellm_params using set intersection (only check keys that exist in both)
-    matching_keys = _CUSTOM_PRICING_KEYS & litellm_params.keys()
-    for key in matching_keys:
-        if litellm_params.get(key) is not None:
-            return True
-
-    # Check model_info
     metadata: dict = litellm_params.get("metadata", {}) or {}
     model_info: dict = metadata.get("model_info", {}) or {}
 
-    if model_info:
-        matching_keys = _CUSTOM_PRICING_KEYS & model_info.keys()
-        for key in matching_keys:
-            if model_info.get(key) is not None:
-                return True
+    custom_pricing_keys = CustomPricingLiteLLMParams.model_fields.keys()
+    for key in custom_pricing_keys:
+        if litellm_params.get(key, None) is not None:
+            return True
+        elif model_info.get(key, None) is not None:
+            return True
 
     return False
 
@@ -4467,7 +4444,6 @@ def get_standard_logging_metadata(
             user_api_key_request_route=None,
             spend_logs_metadata=None,
             requester_ip_address=None,
-            user_agent=None,
             requester_metadata=None,
             prompt_management_metadata=prompt_management_metadata,
             applied_guardrails=applied_guardrails,
@@ -4548,10 +4524,6 @@ def get_usage_from_response_obj(
             )
         elif isinstance(usage, Usage):
             return usage
-        elif isinstance(usage, ResponseAPIUsage):
-            return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
-                usage
-            )
         elif isinstance(usage, dict):
             if ResponseAPILoggingUtils._is_response_api_usage(usage):
                 return (
@@ -4680,10 +4652,7 @@ def get_hidden_params(
     @staticmethod
     def strip_trailing_slash(api_base: Optional[str]) -> Optional[str]:
         if api_base:
-            if api_base.endswith("//"):
-                return api_base.rstrip("/")
-            if api_base[-1] == "/":
-                return api_base[:-1]
+            return api_base.rstrip("/")
         return api_base
 
     @staticmethod
@@ -5157,7 +5126,6 @@ def get_standard_logging_object_payload(
             model_group=_model_group,
             model_id=_model_id,
             requester_ip_address=clean_metadata.get("requester_ip_address", None),
-            user_agent=clean_metadata.get("user_agent", None),
             messages=StandardLoggingPayloadSetup.append_system_prompt_messages(
                 kwargs=kwargs, messages=kwargs.get("messages")
             ),
@@ -5223,7 +5191,6 @@ def get_standard_logging_metadata(
         user_api_key_team_alias=None,
         spend_logs_metadata=None,
         requester_ip_address=None,
-        user_agent=None,
         requester_metadata=None,
         user_api_key_end_user_id=None,
         prompt_management_metadata=None,
diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py
index fe06641a389..785976ed319 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/utils.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py
@@ -23,15 +23,6 @@ def _is_above_128k(tokens: float) -> bool:
     return False
 
 
-def get_billable_input_tokens(usage: Usage) -> int:
-    """
-    Returns the number of billable input tokens.
-    Subtracts cached tokens from prompt tokens if applicable.
-    """
-    details = _parse_prompt_tokens_details(usage)
-    return usage.prompt_tokens - details["cache_hit_tokens"]
-
-
 def select_cost_metric_for_model(
     model_info: ModelInfo,
 ) -> Literal["cost_per_character", "cost_per_token"]:
@@ -199,6 +190,7 @@ def _get_token_base_cost(
                     1000 if "k" in threshold_str else 1
                 )
                 if usage.prompt_tokens > threshold:
+
                     prompt_base_cost = cast(
                         float, _get_cost_per_unit(model_info, key, prompt_base_cost)
                     )
@@ -574,28 +566,14 @@ def generic_cost_per_token(  # noqa: PLR0915
     if usage.prompt_tokens_details:
         prompt_tokens_details = _parse_prompt_tokens_details(usage)
 
-    ## EDGE CASE - text tokens not set or includes cached tokens (double-counting)
-    ## Some providers (like xAI) report text_tokens = prompt_tokens (including cached)
-    ## We detect this when: text_tokens + cached_tokens + other > prompt_tokens
-    ## Ref: https://github.com/BerriAI/litellm/issues/19680, #14874, #14875
-
-    cache_hit = prompt_tokens_details["cache_hit_tokens"]
-    text_tokens = prompt_tokens_details["text_tokens"]
-    audio_tokens = prompt_tokens_details["audio_tokens"]
-    cache_creation = prompt_tokens_details["cache_creation_tokens"]
-    image_tokens = prompt_tokens_details["image_tokens"]
-
-    # Check for double-counting: sum of details > prompt_tokens means overlap
-    total_details = text_tokens + cache_hit + audio_tokens + cache_creation + image_tokens
-    has_double_counting = cache_hit > 0 and total_details > usage.prompt_tokens
+    ## EDGE CASE - text tokens not set inside PromptTokensDetails
 
-    if text_tokens == 0 or has_double_counting:
+    if prompt_tokens_details["text_tokens"] == 0:
         text_tokens = (
             usage.prompt_tokens
-            - cache_hit
-            - audio_tokens
-            - cache_creation
-            - image_tokens
+            - prompt_tokens_details["cache_hit_tokens"]
+            - prompt_tokens_details["audio_tokens"]
+            - prompt_tokens_details["cache_creation_tokens"]
         )
         prompt_tokens_details["text_tokens"] = text_tokens
 
@@ -641,11 +619,7 @@ def generic_cost_per_token(  # noqa: PLR0915
             # Calculate text tokens as remainder when we have a breakdown
             # This handles cases like OpenAI's reasoning models where text_tokens isn't provided
             text_tokens = max(
-                0,
-                usage.completion_tokens
-                - reasoning_tokens
-                - audio_tokens
-                - image_tokens,
+                0, usage.completion_tokens - reasoning_tokens - audio_tokens - image_tokens
             )
         else:
             # No breakdown at all, all tokens are text tokens
diff --git a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
index 25ad0a570cb..bbe28e3ec2c 100644
--- a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
+++ b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
@@ -21,13 +21,11 @@
     ChatCompletionMessageToolCall,
     ChatCompletionRedactedThinkingBlock,
     Choices,
-    CompletionTokensDetailsWrapper,
     Delta,
     EmbeddingResponse,
     Function,
     HiddenParams,
     ImageResponse,
-    PromptTokensDetailsWrapper,
 )
 from litellm.types.utils import Logprobs as TextCompletionLogprobs
 from litellm.types.utils import (
@@ -306,22 +304,6 @@ def convert_to_image_response(
                     "text_tokens": 0,
                 }
 
-            # Map Responses API naming to Chat Completions API naming for cost calculator
-            if usage.get("prompt_tokens") is None:
-                usage["prompt_tokens"] = usage.get("input_tokens", 0)
-            if usage.get("completion_tokens") is None:
-                usage["completion_tokens"] = usage.get("output_tokens", 0)
-
-            # Convert dicts to wrapper objects so getattr() works in cost calculation
-            if isinstance(usage.get("input_tokens_details"), dict):
-                usage["prompt_tokens_details"] = PromptTokensDetailsWrapper(
-                    **usage["input_tokens_details"]
-                )
-            if isinstance(usage.get("output_tokens_details"), dict):
-                usage["completion_tokens_details"] = CompletionTokensDetailsWrapper(
-                    **usage["output_tokens_details"]
-                )
-
         if model_response_object is None:
             model_response_object = ImageResponse(**response_object)
             return model_response_object
diff --git a/litellm/litellm_core_utils/logging_worker.py b/litellm/litellm_core_utils/logging_worker.py
index d5eca9eeb55..13a83956edd 100644
--- a/litellm/litellm_core_utils/logging_worker.py
+++ b/litellm/litellm_core_utils/logging_worker.py
@@ -415,28 +415,6 @@ def _safe_log(self, level: str, message: str) -> None:
         """
         Safely log a message during shutdown, suppressing errors if logging is closed.
         """
-        # Check if logger has valid handlers before attempting to log
-        # During shutdown, handlers may be closed, causing ValueError when writing
-        if not hasattr(verbose_logger, 'handlers') or not verbose_logger.handlers:
-            return
-        
-        # Check if any handler has a valid stream
-        has_valid_handler = False
-        for handler in verbose_logger.handlers:
-            try:
-                if hasattr(handler, 'stream') and handler.stream and not handler.stream.closed:
-                    has_valid_handler = True
-                    break
-                elif not hasattr(handler, 'stream'):
-                    # Non-stream handlers (like NullHandler) are always valid
-                    has_valid_handler = True
-                    break
-            except (AttributeError, ValueError):
-                continue
-        
-        if not has_valid_handler:
-            return
-        
         try:
             if level == "debug":
                 verbose_logger.debug(message)
diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py
index 03488ad0183..30263543fc6 100644
--- a/litellm/litellm_core_utils/prompt_templates/factory.py
+++ b/litellm/litellm_core_utils/prompt_templates/factory.py
@@ -1677,16 +1677,13 @@ def convert_to_anthropic_tool_result(
         ] = []
         for content in content_list:
             if content["type"] == "text":
-                # Only include cache_control if explicitly set and not None
-                # to avoid sending "cache_control": null which breaks some API channels
-                text_content: AnthropicMessagesToolResultContent = {
-                    "type": "text",
-                    "text": content["text"],
-                }
-                cache_control_value = content.get("cache_control")
-                if cache_control_value is not None:
-                    text_content["cache_control"] = cache_control_value
-                anthropic_content_list.append(text_content)
+                anthropic_content_list.append(
+                    AnthropicMessagesToolResultContent(
+                        type="text",
+                        text=content["text"],
+                        cache_control=content.get("cache_control", None),
+                    )
+                )
             elif content["type"] == "image_url":
                 format = (
                     content["image_url"].get("format")
@@ -4411,7 +4408,7 @@ def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]:
     ]
     """
     """
-    Bedrock toolConfig looks like:
+    Bedrock toolConfig looks like: 
     "tools": [
         {
             "toolSpec": {
@@ -4439,7 +4436,6 @@ def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]:
 
     tool_block_list: List[BedrockToolBlock] = []
     for tool in tools:
-        # Handle regular function tools
         parameters = tool.get("function", {}).get(
             "parameters", {"type": "object", "properties": {}}
         )
diff --git a/litellm/litellm_core_utils/prompt_templates/image_handling.py b/litellm/litellm_core_utils/prompt_templates/image_handling.py
index 7137a4e4222..5d0bedb776d 100644
--- a/litellm/litellm_core_utils/prompt_templates/image_handling.py
+++ b/litellm/litellm_core_utils/prompt_templates/image_handling.py
@@ -31,19 +31,15 @@ def _process_image_response(response: Response, url: str) -> str:
                 f"Error: Image size ({size_mb:.2f}MB) exceeds maximum allowed size ({MAX_IMAGE_URL_DOWNLOAD_SIZE_MB}MB). url={url}"
             )
 
-    # Stream download with size checking to prevent downloading huge files
-    max_bytes = int(MAX_IMAGE_URL_DOWNLOAD_SIZE_MB * 1024 * 1024)
-    image_bytes = bytearray()
-    bytes_downloaded = 0
+    image_bytes = response.content
     
-    for chunk in response.iter_bytes(chunk_size=8192):
-        bytes_downloaded += len(chunk)
-        if bytes_downloaded > max_bytes:
-            size_mb = bytes_downloaded / (1024 * 1024)
+    # Check actual size after download if Content-Length was not available
+    if content_length is None:
+        size_mb = len(image_bytes) / (1024 * 1024)
+        if size_mb > MAX_IMAGE_URL_DOWNLOAD_SIZE_MB:
             raise litellm.ImageFetchError(
                 f"Error: Image size ({size_mb:.2f}MB) exceeds maximum allowed size ({MAX_IMAGE_URL_DOWNLOAD_SIZE_MB}MB). url={url}"
             )
-        image_bytes.extend(chunk)
     
     base64_image = base64.b64encode(image_bytes).decode("utf-8")
 
diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py
index 1b61b533275..82eccee596d 100644
--- a/litellm/llms/anthropic/chat/transformation.py
+++ b/litellm/llms/anthropic/chat/transformation.py
@@ -290,19 +290,10 @@ def _map_tool_choice(
         elif tool_choice == "none":
             _tool_choice = AnthropicMessagesToolChoice(type="none")
         elif isinstance(tool_choice, dict):
-            if "type" in tool_choice and "function" not in tool_choice:
-                tool_type = tool_choice.get("type")
-                if tool_type == "auto":
-                    _tool_choice = AnthropicMessagesToolChoice(type="auto")
-                elif tool_type == "required" or tool_type == "any":
-                    _tool_choice = AnthropicMessagesToolChoice(type="any")
-                elif tool_type == "none":
-                    _tool_choice = AnthropicMessagesToolChoice(type="none")
-            else:
-                _tool_name = tool_choice.get("function", {}).get("name")
-                if _tool_name is not None:
-                    _tool_choice = AnthropicMessagesToolChoice(type="tool")
-                    _tool_choice["name"] = _tool_name
+            _tool_name = tool_choice.get("function", {}).get("name")
+            _tool_choice = AnthropicMessagesToolChoice(type="tool")
+            if _tool_name is not None:
+                _tool_choice["name"] = _tool_name
 
         if parallel_tool_use is not None:
             # Anthropic uses 'disable_parallel_tool_use' flag to determine if parallel tool use is allowed
@@ -1378,7 +1369,7 @@ def calculate_usage(
             else 0
         )
         completion_token_details = CompletionTokensDetailsWrapper(
-            reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else 0,
+            reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else None,
             text_tokens=completion_tokens - reasoning_tokens if reasoning_tokens > 0 else completion_tokens,
         )
         total_tokens = prompt_tokens + completion_tokens
diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
index 5ba0754b744..1706f045f14 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
@@ -168,36 +168,6 @@ def _extract_signature_from_tool_use_content(
             return provider_specific_fields.get("signature")
         return None
 
-    def _add_cache_control_if_applicable(
-        self,
-        source: Any,
-        target: Any,
-        model: Optional[str],
-    ) -> None:
-        """
-        Extract cache_control from source and add to target if it should be preserved.
-
-        This method accepts Any type to support both regular dicts and TypedDict objects.
-        TypedDict objects (like ChatCompletionTextObject, ChatCompletionImageObject, etc.)
-        are dicts at runtime but have specific types at type-check time. Using Any allows
-        this method to work with both while maintaining runtime correctness.
-
-        Args:
-            source: Dict or TypedDict containing potential cache_control field
-            target: Dict or TypedDict to add cache_control to
-            model: Model name to check if cache_control should be preserved
-        """
-        # TypedDict objects are dicts at runtime, so .get() works
-        cache_control = source.get("cache_control") if isinstance(source, dict) else getattr(source, "cache_control", None)
-        if cache_control and model and self.is_anthropic_claude_model(model):
-            # TypedDict objects support dict operations at runtime
-            # Use type ignore consistent with codebase pattern (see anthropic/chat/transformation.py:432)
-            if isinstance(target, dict):
-                target["cache_control"] = cache_control  # type: ignore[typeddict-item]
-            else:
-                # Fallback for non-dict objects (shouldn't happen in practice)
-                cast(Dict[str, Any], target)["cache_control"] = cache_control
-
     def translatable_anthropic_params(self) -> List:
         """
         Which anthropic params, we need to translate to the openai format.
@@ -235,8 +205,12 @@ def translate_anthropic_messages_to_openai(  # noqa: PLR0915
                             text_obj = ChatCompletionTextObject(
                                 type="text", text=content.get("text", "")
                             )
-                            self._add_cache_control_if_applicable(content, text_obj, model)
-                            new_user_content_list.append(text_obj)  # type: ignore
+                            # Preserve cache_control if present (for prompt caching)
+                            # Only for Anthropic models that support prompt caching
+                            cache_control = content.get("cache_control")
+                            if cache_control and model and self.is_anthropic_claude_model(model):
+                                text_obj["cache_control"] = cache_control  # type: ignore
+                            new_user_content_list.append(text_obj)
                         elif content.get("type") == "image":
                             # Convert Anthropic image format to OpenAI format
                             source = content.get("source", {})
@@ -251,24 +225,7 @@ def translate_anthropic_messages_to_openai(  # noqa: PLR0915
                                 image_obj = ChatCompletionImageObject(
                                     type="image_url", image_url=image_url_obj
                                 )
-                                self._add_cache_control_if_applicable(content, image_obj, model)
-                                new_user_content_list.append(image_obj)  # type: ignore
-                        elif content.get("type") == "document":
-                            # Convert Anthropic document format (PDF, etc.) to OpenAI format
-                            source = content.get("source", {})
-                            openai_image_url = (
-                                self._translate_anthropic_image_to_openai(cast(dict, source))
-                            )
-
-                            if openai_image_url:
-                                image_url_obj = ChatCompletionImageUrlObject(
-                                    url=openai_image_url
-                                )
-                                doc_obj = ChatCompletionImageObject(
-                                    type="image_url", image_url=image_url_obj
-                                )
-                                self._add_cache_control_if_applicable(content, doc_obj, model)
-                                new_user_content_list.append(doc_obj)  # type: ignore
+                                new_user_content_list.append(image_obj)
                         elif content.get("type") == "tool_result":
                             if "content" not in content:
                                 tool_result = ChatCompletionToolMessage(
@@ -276,16 +233,14 @@ def translate_anthropic_messages_to_openai(  # noqa: PLR0915
                                     tool_call_id=content.get("tool_use_id", ""),
                                     content="",
                                 )
-                                self._add_cache_control_if_applicable(content, tool_result, model)
-                                tool_message_list.append(tool_result)  # type: ignore[arg-type]
+                                tool_message_list.append(tool_result)
                             elif isinstance(content.get("content"), str):
                                 tool_result = ChatCompletionToolMessage(
                                     role="tool",
                                     tool_call_id=content.get("tool_use_id", ""),
                                     content=str(content.get("content", "")),
                                 )
-                                self._add_cache_control_if_applicable(content, tool_result, model)
-                                tool_message_list.append(tool_result)  # type: ignore[arg-type]
+                                tool_message_list.append(tool_result)
                             elif isinstance(content.get("content"), list):
                                 # Combine all content items into a single tool message
                                 # to avoid creating multiple tool_result blocks with the same ID
@@ -301,8 +256,7 @@ def translate_anthropic_messages_to_openai(  # noqa: PLR0915
                                             tool_call_id=content.get("tool_use_id", ""),
                                             content=c,
                                         )
-                                        self._add_cache_control_if_applicable(content, tool_result, model)
-                                        tool_message_list.append(tool_result)  # type: ignore[arg-type]
+                                        tool_message_list.append(tool_result)
                                     elif isinstance(c, dict):
                                         if c.get("type") == "text":
                                             tool_result = ChatCompletionToolMessage(
@@ -312,8 +266,7 @@ def translate_anthropic_messages_to_openai(  # noqa: PLR0915
                                                 ),
                                                 content=c.get("text", ""),
                                             )
-                                            self._add_cache_control_if_applicable(content, tool_result, model)
-                                            tool_message_list.append(tool_result)  # type: ignore[arg-type]
+                                            tool_message_list.append(tool_result)
                                         elif c.get("type") == "image":
                                             source = c.get("source", {})
                                             openai_image_url = (
@@ -329,8 +282,7 @@ def translate_anthropic_messages_to_openai(  # noqa: PLR0915
                                                 ),
                                                 content=openai_image_url,
                                             )
-                                            self._add_cache_control_if_applicable(content, tool_result, model)
-                                            tool_message_list.append(tool_result)  # type: ignore[arg-type]
+                                            tool_message_list.append(tool_result)
                                 else:
                                     # For multiple content items, combine into a single tool message
                                     # with list content to preserve all items while having one tool_use_id
@@ -379,8 +331,7 @@ def translate_anthropic_messages_to_openai(  # noqa: PLR0915
                                             tool_call_id=content.get("tool_use_id", ""),
                                             content=combined_content_parts,  # type: ignore
                                         )
-                                        self._add_cache_control_if_applicable(content, tool_result, model)
-                                        tool_message_list.append(tool_result)  # type: ignore[arg-type]
+                                        tool_message_list.append(tool_result)
 
             if len(tool_message_list) > 0:
                 new_messages.extend(tool_message_list)
@@ -393,8 +344,6 @@ def translate_anthropic_messages_to_openai(  # noqa: PLR0915
 
             ## ASSISTANT MESSAGE ##
             assistant_message_str: Optional[str] = None
-            assistant_content_list: List[Dict[str, Any]] = []  # For content blocks with cache_control
-            has_cache_control_in_text = False
             tool_calls: List[ChatCompletionAssistantToolCall] = []
             thinking_blocks: List[
                 Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
@@ -408,14 +357,10 @@ def translate_anthropic_messages_to_openai(  # noqa: PLR0915
                             assistant_message_str = str(content)
                         elif isinstance(content, dict):
                             if content.get("type") == "text":
-                                text_block: Dict[str, Any] = {
-                                    "type": "text",
-                                    "text": content.get("text", ""),
-                                }
-                                self._add_cache_control_if_applicable(content, text_block, model)
-                                if "cache_control" in text_block:
-                                    has_cache_control_in_text = True
-                                assistant_content_list.append(text_block)
+                                if assistant_message_str is None:
+                                    assistant_message_str = content.get("text", "")
+                                else:
+                                    assistant_message_str += content.get("text", "")
                             elif content.get("type") == "tool_use":
                                 function_chunk: ChatCompletionToolCallFunctionChunk = {
                                     "name": content.get("name", ""),
@@ -439,13 +384,13 @@ def translate_anthropic_messages_to_openai(  # noqa: PLR0915
                                         provider_specific_fields
                                     )
 
-                                tool_call = ChatCompletionAssistantToolCall(
-                                    id=content.get("id", ""),
-                                    type="function",
-                                    function=function_chunk,
+                                tool_calls.append(
+                                    ChatCompletionAssistantToolCall(
+                                        id=content.get("id", ""),
+                                        type="function",
+                                        function=function_chunk,
+                                    )
                                 )
-                                self._add_cache_control_if_applicable(content, tool_call, model)
-                                tool_calls.append(tool_call)
                             elif content.get("type") == "thinking":
                                 thinking_block = ChatCompletionThinkingBlock(
                                     type="thinking",
@@ -466,30 +411,18 @@ def translate_anthropic_messages_to_openai(  # noqa: PLR0915
 
             if (
                 assistant_message_str is not None
-                or len(assistant_content_list) > 0
                 or len(tool_calls) > 0
                 or len(thinking_blocks) > 0
             ):
-                # Use list format if any text block has cache_control, otherwise use string
-                if has_cache_control_in_text and len(assistant_content_list) > 0:
-                    assistant_content: Any = assistant_content_list
-                elif len(assistant_content_list) > 0 and not has_cache_control_in_text:
-                    # Concatenate text blocks into string when no cache_control
-                    assistant_content = "".join(
-                        block.get("text", "") for block in assistant_content_list
-                    )
-                else:
-                    assistant_content = assistant_message_str
-
                 assistant_message = ChatCompletionAssistantMessage(
                     role="assistant",
-                    content=assistant_content,
+                    content=assistant_message_str,
                     thinking_blocks=(
                         thinking_blocks if len(thinking_blocks) > 0 else None
                     ),
                 )
                 if len(tool_calls) > 0:
-                    assistant_message["tool_calls"] = tool_calls  # type: ignore
+                    assistant_message["tool_calls"] = tool_calls
                 if len(thinking_blocks) > 0:
                     assistant_message["thinking_blocks"] = thinking_blocks  # type: ignore
                 new_messages.append(assistant_message)
@@ -599,10 +532,10 @@ def translate_anthropic_tool_choice_to_openai(
             )
 
     def translate_anthropic_tools_to_openai(
-        self, tools: List[AllAnthropicToolsValues], model: Optional[str] = None
+        self, tools: List[AllAnthropicToolsValues]
     ) -> List[ChatCompletionToolParam]:
         new_tools: List[ChatCompletionToolParam] = []
-        mapped_tool_params = ["name", "input_schema", "description", "cache_control"]
+        mapped_tool_params = ["name", "input_schema", "description"]
         for tool in tools:
             function_chunk = ChatCompletionToolParamFunctionChunk(
                 name=tool["name"],
@@ -615,11 +548,11 @@ def translate_anthropic_tools_to_openai(
             for k, v in tool.items():
                 if k not in mapped_tool_params:  # pass additional computer kwargs
                     function_chunk.setdefault("parameters", {}).update({k: v})
-            tool_param = ChatCompletionToolParam(type="function", function=function_chunk)
-            self._add_cache_control_if_applicable(tool, tool_param, model)
-            new_tools.append(tool_param)  # type: ignore[arg-type]
+            new_tools.append(
+                ChatCompletionToolParam(type="function", function=function_chunk)
+            )
 
-        return new_tools  # type: ignore[return-value]
+        return new_tools
 
     def translate_anthropic_output_format_to_openai(
         self, output_format: Any
@@ -657,41 +590,6 @@ def translate_anthropic_output_format_to_openai(
             },
         }
 
-    def _add_system_message_to_messages(
-        self,
-        new_messages: List[AllMessageValues],
-        anthropic_message_request: AnthropicMessagesRequest,
-    ) -> None:
-        """Add system message to messages list if present in request."""
-        if "system" not in anthropic_message_request:
-            return
-        system_content = anthropic_message_request["system"]
-        if not system_content:
-            return
-        # Handle system as string or array of content blocks
-        if isinstance(system_content, str):
-            new_messages.insert(
-                0,
-                ChatCompletionSystemMessage(role="system", content=system_content),
-            )
-        elif isinstance(system_content, list):
-            # Convert Anthropic system content blocks to OpenAI format
-            openai_system_content: List[Dict[str, Any]] = []
-            model_name = anthropic_message_request.get("model", "")
-            for block in system_content:
-                if isinstance(block, dict) and block.get("type") == "text":
-                    text_block: Dict[str, Any] = {
-                        "type": "text",
-                        "text": block.get("text", ""),
-                    }
-                    self._add_cache_control_if_applicable(block, text_block, model_name)
-                    openai_system_content.append(text_block)
-            if openai_system_content:
-                new_messages.insert(
-                    0,
-                    ChatCompletionSystemMessage(role="system", content=openai_system_content),  # type: ignore
-                )
-
     def translate_anthropic_to_openai(
         self, anthropic_message_request: AnthropicMessagesRequest
     ) -> ChatCompletionRequest:
@@ -720,7 +618,13 @@ def translate_anthropic_to_openai(
             model=anthropic_message_request.get("model"),
         )
         ## ADD SYSTEM MESSAGE TO MESSAGES
-        self._add_system_message_to_messages(new_messages, anthropic_message_request)
+        if "system" in anthropic_message_request:
+            system_content = anthropic_message_request["system"]
+            if system_content:
+                new_messages.insert(
+                    0,
+                    ChatCompletionSystemMessage(role="system", content=system_content),
+                )
 
         new_kwargs: ChatCompletionRequest = {
             "model": anthropic_message_request["model"],
@@ -751,8 +655,7 @@ def translate_anthropic_to_openai(
             tools = anthropic_message_request["tools"]
             if tools:
                 new_kwargs["tools"] = self.translate_anthropic_tools_to_openai(
-                    tools=cast(List[AllAnthropicToolsValues], tools),
-                    model=new_kwargs.get("model"),
+                    tools=cast(List[AllAnthropicToolsValues], tools)
                 )
 
         ## CONVERT THINKING
@@ -940,7 +843,7 @@ def translate_openai_response_to_anthropic(
             role="assistant",
             model=response.model or "unknown-model",
             stop_sequence=None,
-            usage=anthropic_usage,  # type: ignore
+            usage=anthropic_usage,
             content=anthropic_content,  # type: ignore
             stop_reason=anthropic_finish_reason,
         )
@@ -1089,7 +992,7 @@ def translate_streaming_openai_response_to_anthropic(
             else:
                 usage_delta = UsageDelta(input_tokens=0, output_tokens=0)
             return MessageBlockDelta(
-                type="message_delta", delta=delta, usage=usage_delta  # type: ignore
+                type="message_delta", delta=delta, usage=usage_delta
             )
         (
             type_of_content,
diff --git a/litellm/llms/azure/chat/gpt_5_transformation.py b/litellm/llms/azure/chat/gpt_5_transformation.py
index eeb55911ecf..506b7fdfe5e 100644
--- a/litellm/llms/azure/chat/gpt_5_transformation.py
+++ b/litellm/llms/azure/chat/gpt_5_transformation.py
@@ -22,8 +22,7 @@ def is_model_gpt_5_model(cls, model: str) -> bool:
         Accepts both explicit gpt-5 model names and the ``gpt5_series/`` prefix
         used for manual routing.
         """
-        # gpt-5-chat* is a chat model and shouldn't go through GPT-5 reasoning restrictions.
-        return ("gpt-5" in model and "gpt-5-chat" not in model) or "gpt5_series" in model
+        return "gpt-5" in model or "gpt5_series" in model
 
     def get_supported_openai_params(self, model: str) -> List[str]:
         """Get supported parameters for Azure OpenAI GPT-5 models.
@@ -38,11 +37,6 @@ def get_supported_openai_params(self, model: str) -> List[str]:
         """
         params = OpenAIGPT5Config.get_supported_openai_params(self, model=model)
 
-        # Azure supports tool_choice for GPT-5 deployments, but the base GPT-5 config
-        # can drop it when the deployment name isn't in the OpenAI model registry.
-        if "tool_choice" not in params:
-            params.append("tool_choice")
-
         # Only gpt-5.2 has been verified to support logprobs on Azure
         if self.is_model_gpt_5_2_model(model):
             azure_supported_params = ["logprobs", "top_logprobs"]
diff --git a/litellm/llms/azure/cost_calculation.py b/litellm/llms/azure/cost_calculation.py
index 5b411095ea1..96c58d95ff2 100644
--- a/litellm/llms/azure/cost_calculation.py
+++ b/litellm/llms/azure/cost_calculation.py
@@ -1,12 +1,11 @@
 """
 Helper util for handling azure openai-specific cost calculation
-- e.g.: prompt caching, audio tokens
+- e.g.: prompt caching
 """
 
 from typing import Optional, Tuple
 
 from litellm._logging import verbose_logger
-from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
 from litellm.types.utils import Usage
 from litellm.utils import get_model_info
 
@@ -19,15 +18,34 @@ def cost_per_token(
 
     Input:
         - model: str, the model name without provider prefix
-        - usage: LiteLLM Usage block, containing caching and audio token information
+        - usage: LiteLLM Usage block, containing anthropic caching information
 
     Returns:
         Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
     """
     ## GET MODEL INFO
     model_info = get_model_info(model=model, custom_llm_provider="azure")
+    cached_tokens: Optional[int] = None
+    ## CALCULATE INPUT COST
+    non_cached_text_tokens = usage.prompt_tokens
+    if usage.prompt_tokens_details and usage.prompt_tokens_details.cached_tokens:
+        cached_tokens = usage.prompt_tokens_details.cached_tokens
+        non_cached_text_tokens = non_cached_text_tokens - cached_tokens
+    prompt_cost: float = non_cached_text_tokens * model_info["input_cost_per_token"]
+
+    ## CALCULATE OUTPUT COST
+    completion_cost: float = (
+        usage["completion_tokens"] * model_info["output_cost_per_token"]
+    )
+
+    ## Prompt Caching cost calculation
+    if model_info.get("cache_read_input_token_cost") is not None and cached_tokens:
+        # Note: We read ._cache_read_input_tokens from the Usage - since cost_calculator.py standardizes the cache read tokens on usage._cache_read_input_tokens
+        prompt_cost += cached_tokens * (
+            model_info.get("cache_read_input_token_cost", 0) or 0
+        )
 
-    ## Speech / Audio cost calculation (cost per second for TTS models)
+    ## Speech / Audio cost calculation
     if (
         "output_cost_per_second" in model_info
         and model_info["output_cost_per_second"] is not None
@@ -37,14 +55,7 @@ def cost_per_token(
             f"For model={model} - output_cost_per_second: {model_info.get('output_cost_per_second')}; response time: {response_time_ms}"
         )
         ## COST PER SECOND ##
-        prompt_cost = 0.0
+        prompt_cost = 0
         completion_cost = model_info["output_cost_per_second"] * response_time_ms / 1000
-        return prompt_cost, completion_cost
-
-    ## Use generic cost calculator for all other cases
-    ## This properly handles: text tokens, audio tokens, cached tokens, reasoning tokens, etc.
-    return generic_cost_per_token(
-        model=model,
-        usage=usage,
-        custom_llm_provider="azure",
-    )
+
+    return prompt_cost, completion_cost
diff --git a/litellm/llms/base_llm/vector_store/transformation.py b/litellm/llms/base_llm/vector_store/transformation.py
index 935fd53c199..89f2094d5df 100644
--- a/litellm/llms/base_llm/vector_store/transformation.py
+++ b/litellm/llms/base_llm/vector_store/transformation.py
@@ -5,8 +5,8 @@
 
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
-    VECTOR_STORE_OPENAI_PARAMS,
     BaseVectorStoreAuthCredentials,
+    VECTOR_STORE_OPENAI_PARAMS,
     VectorStoreCreateOptionalRequestParams,
     VectorStoreCreateResponse,
     VectorStoreIndexEndpoints,
@@ -64,30 +64,6 @@ def transform_search_vector_store_request(
 
         pass
 
-    async def atransform_search_vector_store_request(
-        self,
-        vector_store_id: str,
-        query: Union[str, List[str]],
-        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
-        api_base: str,
-        litellm_logging_obj: LiteLLMLoggingObj,
-        litellm_params: dict,
-    ) -> Tuple[str, Dict]:
-        """
-        Optional async version of transform_search_vector_store_request.
-        If not implemented, the handler will fall back to the sync version.
-        Providers that need to make async calls (e.g., generating embeddings) should override this.
-        """
-        # Default implementation: call the sync version
-        return self.transform_search_vector_store_request(
-            vector_store_id=vector_store_id,
-            query=query,
-            vector_store_search_optional_params=vector_store_search_optional_params,
-            api_base=api_base,
-            litellm_logging_obj=litellm_logging_obj,
-            litellm_params=litellm_params,
-        )
-
     @abstractmethod
     def transform_search_vector_store_response(
         self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj
diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py
index 1de1c40c438..642d15fe3ed 100644
--- a/litellm/llms/bedrock/base_aws_llm.py
+++ b/litellm/llms/bedrock/base_aws_llm.py
@@ -1163,7 +1163,7 @@ def _filter_headers_for_aws_signature(self, headers: dict) -> dict:
 
     def _sign_request(
         self,
-        service_name: Literal["bedrock", "sagemaker", "bedrock-agentcore", "s3vectors"],
+        service_name: Literal["bedrock", "sagemaker", "bedrock-agentcore"],
         headers: dict,
         optional_params: dict,
         request_data: dict,
diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py
index ec665142073..cb26a22edfa 100644
--- a/litellm/llms/bedrock/chat/converse_transformation.py
+++ b/litellm/llms/bedrock/chat/converse_transformation.py
@@ -298,39 +298,6 @@ def _is_nova_lite_2_model(self, model: str) -> bool:
         # Check if the model is specifically Nova Lite 2
         return "nova-2-lite" in model_without_region
 
-    def _map_web_search_options(
-        self,
-        web_search_options: dict,
-        model: str
-    ) -> Optional[BedrockToolBlock]:
-        """
-        Map web_search_options to Nova grounding systemTool.
-
-        Nova grounding (web search) is only supported on Amazon Nova models.
-        Returns None for non-Nova models.
-
-        Args:
-            web_search_options: The web_search_options dict from the request
-            model: The model identifier string
-
-        Returns:
-            BedrockToolBlock with systemTool for Nova models, None otherwise
-
-        Reference: https://docs.aws.amazon.com/nova/latest/userguide/grounding.html
-        """
-        # Only Nova models support nova_grounding
-        # Model strings can be like: "amazon.nova-pro-v1:0", "us.amazon.nova-pro-v1:0", etc.
-        if "nova" not in model.lower():
-            verbose_logger.debug(
-                f"web_search_options passed but model {model} is not a Nova model. "
-                "Nova grounding is only supported on Amazon Nova models."
-            )
-            return None
-
-        # Nova doesn't support search_context_size or user_location params
-        # (unlike Anthropic), so we just enable grounding with no options
-        return BedrockToolBlock(systemTool={"name": "nova_grounding"})
-
     def _transform_reasoning_effort_to_reasoning_config(
         self, reasoning_effort: str
     ) -> dict:
@@ -471,10 +438,6 @@ def get_supported_openai_params(self, model: str) -> List[str]:
         ):
             supported_params.append("tools")
 
-        # Nova models support web_search_options (mapped to nova_grounding systemTool)
-        if base_model.startswith("amazon.nova"):
-            supported_params.append("web_search_options")
-
         if litellm.utils.supports_tool_choice(
             model=model, custom_llm_provider=self.custom_llm_provider
         ) or litellm.utils.supports_tool_choice(
@@ -767,13 +730,6 @@ def map_openai_params(
                 if bedrock_tier in ("default", "flex", "priority"):
                     optional_params["serviceTier"] = {"type": bedrock_tier}
 
-            if param == "web_search_options" and value and isinstance(value, dict):                                                                                 
-                grounding_tool = self._map_web_search_options(value, model)                                                                                         
-                if grounding_tool is not None:                                                                                                                      
-                    optional_params = self._add_tools_to_optional_params(                                                                                           
-                        optional_params=optional_params, tools=[grounding_tool]                                                                                     
-                    )  
-
         # Only update thinking tokens for non-GPT-OSS models and non-Nova-Lite-2 models
         # Nova Lite 2 handles token budgeting differently through reasoningConfig
         if "gpt-oss" not in model and not self._is_nova_lite_2_model(model):
@@ -1432,23 +1388,20 @@ def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tupl
         str,
         List[ChatCompletionToolCallChunk],
         Optional[List[BedrockConverseReasoningContentBlock]],
-        Optional[List[CitationsContentBlock]],
     ]:
         """
-        Translate the message content to a string and a list of tool calls, reasoning content blocks, and citations.
+        Translate the message content to a string and a list of tool calls and reasoning content blocks
 
         Returns:
             content_str: str
             tools: List[ChatCompletionToolCallChunk]
             reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]]
-            citationsContentBlocks: Optional[List[CitationsContentBlock]] - Citations from Nova grounding
         """
         content_str = ""
         tools: List[ChatCompletionToolCallChunk] = []
         reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = (
             None
         )
-        citationsContentBlocks: Optional[List[CitationsContentBlock]] = None
         for idx, content in enumerate(content_blocks):
             """
             - Content is either a tool response or text
@@ -1493,15 +1446,10 @@ def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tupl
                 if reasoningContentBlocks is None:
                     reasoningContentBlocks = []
                 reasoningContentBlocks.append(content["reasoningContent"])
-            # Handle Nova grounding citations content
-            if "citationsContent" in content:
-                if citationsContentBlocks is None:
-                    citationsContentBlocks = []
-                citationsContentBlocks.append(content["citationsContent"])
 
-        return content_str, tools, reasoningContentBlocks, citationsContentBlocks
+        return content_str, tools, reasoningContentBlocks
 
-    def _transform_response( # noqa: PLR0915
+    def _transform_response(
         self,
         model: str,
         response: httpx.Response,
@@ -1577,27 +1525,18 @@ def _transform_response( # noqa: PLR0915
         reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = (
             None
         )
-        citationsContentBlocks: Optional[List[CitationsContentBlock]] = None
 
         if message is not None:
             (
                 content_str,
                 tools,
                 reasoningContentBlocks,
-                citationsContentBlocks,
             ) = self._translate_message_content(message["content"])
 
-        # Initialize provider_specific_fields if we have any special content blocks
-        provider_specific_fields: dict = {}
-        if reasoningContentBlocks is not None:
-            provider_specific_fields["reasoningContentBlocks"] = reasoningContentBlocks
-        if citationsContentBlocks is not None:
-            provider_specific_fields["citationsContent"] = citationsContentBlocks
-
-        if provider_specific_fields:
-            chat_completion_message["provider_specific_fields"] = provider_specific_fields
-
         if reasoningContentBlocks is not None:
+            chat_completion_message["provider_specific_fields"] = {
+                "reasoningContentBlocks": reasoningContentBlocks,
+            }
             chat_completion_message["reasoning_content"] = (
                 self._transform_reasoning_content(reasoningContentBlocks)
             )
diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py
index 1c58a11eebe..17474fa022b 100644
--- a/litellm/llms/bedrock/chat/invoke_handler.py
+++ b/litellm/llms/bedrock/chat/invoke_handler.py
@@ -1476,11 +1476,6 @@ def _handle_converse_delta_event(
                 reasoning_content = (
                     ""  # set to non-empty string to ensure consistency with Anthropic
                 )
-        elif "citationsContent" in delta_obj:
-            # Handle Nova grounding citations in streaming responses
-            provider_specific_fields = {
-                "citationsContent": delta_obj["citationsContent"],
-            }
         return (
             text,
             tool_use,
diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
index c936b2cd23c..53e08229799 100644
--- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
+++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
@@ -53,26 +53,13 @@ def map_openai_params(
         model: str,
         drop_params: bool,
     ) -> dict:
-        # Force tool-based structured outputs for Bedrock Invoke
-        # (similar to VertexAI fix in #19201)
-        # Bedrock Invoke doesn't support output_format parameter
-        original_model = model
-        if "response_format" in non_default_params:
-            # Use a model name that forces tool-based approach
-            model = "claude-3-sonnet-20240229"
-        
-        optional_params = AnthropicConfig.map_openai_params(
+        return AnthropicConfig.map_openai_params(
             self,
             non_default_params,
             optional_params,
             model,
             drop_params,
         )
-        
-        # Restore original model name
-        model = original_model
-        
-        return optional_params
 
 
     def transform_request(
@@ -103,8 +90,6 @@ def transform_request(
 
         _anthropic_request.pop("model", None)
         _anthropic_request.pop("stream", None)
-        # Bedrock Invoke doesn't support output_format parameter
-        _anthropic_request.pop("output_format", None)
         if "anthropic_version" not in _anthropic_request:
             _anthropic_request["anthropic_version"] = self.anthropic_version
 
@@ -132,26 +117,6 @@ def transform_request(
             if "opus-4" in model.lower() or "opus_4" in model.lower():
                 beta_set.add("tool-search-tool-2025-10-19")
 
-        # Filter out beta headers that Bedrock Invoke doesn't support
-        # AWS Bedrock only supports a specific whitelist of beta flags
-        # Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html
-        BEDROCK_SUPPORTED_BETAS = {
-            "computer-use-2024-10-22",  # Legacy computer use
-            "computer-use-2025-01-24",  # Current computer use (Claude 3.7 Sonnet)
-            "token-efficient-tools-2025-02-19",  # Tool use (Claude 3.7+ and Claude 4+)
-            "interleaved-thinking-2025-05-14",  # Interleaved thinking (Claude 4+)
-            "output-128k-2025-02-19",  # 128K output tokens (Claude 3.7 Sonnet)
-            "dev-full-thinking-2025-05-14",  # Developer mode for raw thinking (Claude 4+)
-            "context-1m-2025-08-07",  # 1 million tokens (Claude Sonnet 4)
-            "context-management-2025-06-27",  # Context management (Claude Sonnet/Haiku 4.5)
-            "effort-2025-11-24",  # Effort parameter (Claude Opus 4.5)
-            "tool-search-tool-2025-10-19",  # Tool search (Claude Opus 4.5)
-            "tool-examples-2025-10-29",  # Tool use examples (Claude Opus 4.5)
-        }
-        
-        # Only keep beta headers that Bedrock supports
-        beta_set = {beta for beta in beta_set if beta in BEDROCK_SUPPORTED_BETAS}
-
         if beta_set:
             _anthropic_request["anthropic_beta"] = list(beta_set)
 
diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py
index 65d237bdbdf..89b42f5e947 100644
--- a/litellm/llms/bedrock/common_utils.py
+++ b/litellm/llms/bedrock/common_utils.py
@@ -797,7 +797,7 @@ def _parse_message_from_event(self, event) -> Optional[str]:
 def get_anthropic_beta_from_headers(headers: dict) -> List[str]:
     """
     Extract anthropic-beta header values and convert them to a list.
-    Supports both JSON array format and comma-separated values from user headers.
+    Supports comma-separated values from user headers.
 
     Used by both converse and invoke transformations for consistent handling
     of anthropic-beta headers that should be passed to AWS Bedrock.
@@ -812,25 +812,8 @@ def get_anthropic_beta_from_headers(headers: dict) -> List[str]:
     if not anthropic_beta_header:
         return []
 
-    # If it's already a list, return it
-    if isinstance(anthropic_beta_header, list):
-        return anthropic_beta_header
-    
-    # Try to parse as JSON array first (e.g., '["interleaved-thinking-2025-05-14", "claude-code-20250219"]')
-    if isinstance(anthropic_beta_header, str):
-        anthropic_beta_header = anthropic_beta_header.strip()
-        if anthropic_beta_header.startswith("[") and anthropic_beta_header.endswith("]"):
-            try:
-                parsed = json.loads(anthropic_beta_header)
-                if isinstance(parsed, list):
-                    return [str(beta).strip() for beta in parsed]
-            except json.JSONDecodeError:
-                pass  # Fall through to comma-separated parsing
-        
-        # Fall back to comma-separated values
-        return [beta.strip() for beta in anthropic_beta_header.split(",")]
-    
-    return []
+    # Split comma-separated values and strip whitespace
+    return [beta.strip() for beta in anthropic_beta_header.split(",")]
 
 
 class CommonBatchFilesUtils:
diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
index fc4220c054e..a7065caece2 100644
--- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
+++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
@@ -162,49 +162,6 @@ def _supports_extended_thinking_on_bedrock(self, model: str) -> bool:
 
         return any(pattern in model_lower for pattern in supported_patterns)
 
-    def _is_claude_opus_4_5(self, model: str) -> bool:
-        """
-        Check if the model is Claude Opus 4.5.
-
-        Args:
-            model: The model name
-
-        Returns:
-            True if the model is Claude Opus 4.5
-        """
-        model_lower = model.lower()
-        opus_4_5_patterns = [
-            "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5",
-        ]
-        return any(pattern in model_lower for pattern in opus_4_5_patterns)
-
-    def _supports_tool_search_on_bedrock(self, model: str) -> bool:
-        """
-        Check if the model supports tool search on Bedrock.
-
-        On Amazon Bedrock, server-side tool search is supported on Claude Opus 4.5
-        and Claude Sonnet 4.5 with the tool-search-tool-2025-10-19 beta header.
-
-        Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool
-
-        Args:
-            model: The model name
-
-        Returns:
-            True if the model supports tool search on Bedrock
-        """
-        model_lower = model.lower()
-
-        # Supported models for tool search on Bedrock
-        supported_patterns = [
-            # Opus 4.5
-            "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5",
-            # Sonnet 4.5
-            "sonnet-4.5", "sonnet_4.5", "sonnet-4-5", "sonnet_4_5",
-        ]
-
-        return any(pattern in model_lower for pattern in supported_patterns)
-
     def _filter_unsupported_beta_headers_for_bedrock(
         self, model: str, beta_set: set
     ) -> None:
@@ -212,33 +169,25 @@ def _filter_unsupported_beta_headers_for_bedrock(
         Remove beta headers that are not supported on Bedrock for the given model.
 
         Extended thinking beta headers are only supported on specific Claude 4+ models.
-        Advanced tool use headers are not supported on Bedrock Invoke API, but need to be
-        translated to Bedrock-specific headers for models that support tool search
-        (Claude Opus 4.5, Sonnet 4.5).
+        Advanced tool use headers are not supported on Bedrock Invoke API.
         This prevents 400 "invalid beta flag" errors on Bedrock.
 
         Note: Bedrock Invoke API fails with a 400 error when unsupported beta headers
         are sent, returning: {"message":"invalid beta flag"}
 
-        Translation for models supporting tool search (Opus 4.5, Sonnet 4.5):
-        - advanced-tool-use-2025-11-20 -> tool-search-tool-2025-10-19 + tool-examples-2025-10-29
-
         Args:
             model: The model name
             beta_set: The set of beta headers to filter in-place
         """
         beta_headers_to_remove = set()
-        has_advanced_tool_use = False
 
-        # 1. Filter out beta headers that are universally unsupported on Bedrock Invoke and track if advanced-tool-use header is present
+        # 1. Filter out beta headers that are universally unsupported on Bedrock Invoke
         for beta in beta_set:
             for unsupported_pattern in self.UNSUPPORTED_BEDROCK_INVOKE_BETA_PATTERNS:
                 if unsupported_pattern in beta.lower():
                     beta_headers_to_remove.add(beta)
-                    has_advanced_tool_use = True
                     break
-    
-        
+
         # 2. Filter out extended thinking headers for models that don't support them
         extended_thinking_patterns = [
             "extended-thinking",
@@ -255,14 +204,6 @@ def _filter_unsupported_beta_headers_for_bedrock(
         for beta in beta_headers_to_remove:
             beta_set.discard(beta)
 
-        # 3. Translate advanced-tool-use to Bedrock-specific headers for models that support tool search
-        # Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html
-        # Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool
-        if has_advanced_tool_use and self._supports_tool_search_on_bedrock(model):
-            beta_set.add("tool-search-tool-2025-10-19")
-            beta_set.add("tool-examples-2025-10-29")
-
-
     def _get_tool_search_beta_header_for_bedrock(
         self,
         model: str,
@@ -315,7 +256,7 @@ def _convert_output_format_to_inline_schema(
         Ref: https://aws.amazon.com/blogs/machine-learning/structured-data-response-with-amazon-bedrock-prompt-engineering-and-tool-use/
         """
         import json
-
+        
         # Extract schema from output_format
         schema = output_format.get("schema")
         if not schema:
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index d2ea7e872a2..6a87967c3aa 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -7033,31 +7033,17 @@ async def async_vector_store_search_handler(
             litellm_params=dict(litellm_params),
         )
 
-        # Check if provider has async transform method
-        if hasattr(vector_store_provider_config, "atransform_search_vector_store_request"):
-            (
-                url,
-                request_body,
-            ) = await vector_store_provider_config.atransform_search_vector_store_request(
-                vector_store_id=vector_store_id,
-                query=query,
-                vector_store_search_optional_params=vector_store_search_optional_params,
-                api_base=api_base,
-                litellm_logging_obj=logging_obj,
-                litellm_params=dict(litellm_params),
-            )
-        else:
-            (
-                url,
-                request_body,
-            ) = vector_store_provider_config.transform_search_vector_store_request(
-                vector_store_id=vector_store_id,
-                query=query,
-                vector_store_search_optional_params=vector_store_search_optional_params,
-                api_base=api_base,
-                litellm_logging_obj=logging_obj,
-                litellm_params=dict(litellm_params),
-            )
+        (
+            url,
+            request_body,
+        ) = vector_store_provider_config.transform_search_vector_store_request(
+            vector_store_id=vector_store_id,
+            query=query,
+            vector_store_search_optional_params=vector_store_search_optional_params,
+            api_base=api_base,
+            litellm_logging_obj=logging_obj,
+            litellm_params=dict(litellm_params),
+        )
         all_optional_params: Dict[str, Any] = dict(litellm_params)
         all_optional_params.update(vector_store_search_optional_params or {})
         headers, signed_json_body = vector_store_provider_config.sign_request(
diff --git a/litellm/llms/gemini/chat/transformation.py b/litellm/llms/gemini/chat/transformation.py
index d5a5ab667a6..f6d075392b2 100644
--- a/litellm/llms/gemini/chat/transformation.py
+++ b/litellm/llms/gemini/chat/transformation.py
@@ -92,7 +92,7 @@ def get_supported_openai_params(self, model: str) -> List[str]:
             "parallel_tool_calls",
             "web_search_options",
         ]
-        if supports_reasoning(model, custom_llm_provider="gemini"):
+        if supports_reasoning(model):
             supported_params.append("reasoning_effort")
             supported_params.append("thinking")
         if self.is_model_gemini_audio_model(model):
diff --git a/litellm/llms/gigachat/chat/transformation.py b/litellm/llms/gigachat/chat/transformation.py
index ba14de1f65d..90cf67da6b2 100644
--- a/litellm/llms/gigachat/chat/transformation.py
+++ b/litellm/llms/gigachat/chat/transformation.py
@@ -31,16 +31,6 @@
 GIGACHAT_BASE_URL = "https://gigachat.devices.sberbank.ru/api/v1"
 
 
-def is_valid_json(value: str) -> bool:
-    """Checks whether the value passed is a valid serialized JSON string"""
-    try:
-        json.loads(value)
-    except json.JSONDecodeError:
-        return False
-    else:
-        return True
-
-
 class GigaChatError(BaseLLMException):
     """GigaChat API error."""
 
@@ -111,11 +101,7 @@ def validate_environment(
         Set up headers with OAuth token.
         """
         # Get access token
-        credentials = (
-            api_key
-            or get_secret_str("GIGACHAT_CREDENTIALS")
-            or get_secret_str("GIGACHAT_API_KEY")
-        )
+        credentials = api_key or get_secret_str("GIGACHAT_CREDENTIALS") or get_secret_str("GIGACHAT_API_KEY")
         access_token = get_access_token(credentials=credentials)
 
         # Store credentials for image uploads
@@ -207,13 +193,11 @@ def _convert_tools_to_functions(self, tools: List[dict]) -> List[dict]:
         for tool in tools:
             if tool.get("type") == "function":
                 func = tool.get("function", {})
-                functions.append(
-                    {
-                        "name": func.get("name", ""),
-                        "description": func.get("description", ""),
-                        "parameters": func.get("parameters", {}),
-                    }
-                )
+                functions.append({
+                    "name": func.get("name", ""),
+                    "description": func.get("description", ""),
+                    "parameters": func.get("parameters", {}),
+                })
         return functions
 
     def _map_tool_choice(
@@ -297,14 +281,8 @@ def transform_request(
         }
 
         # Add optional params
-        for key in [
-            "temperature",
-            "top_p",
-            "max_tokens",
-            "stream",
-            "repetition_penalty",
-            "profanity_check",
-        ]:
+        for key in ["temperature", "top_p", "max_tokens", "stream",
+                    "repetition_penalty", "profanity_check"]:
             if key in optional_params:
                 request_data[key] = optional_params[key]
 
@@ -336,7 +314,7 @@ def _transform_messages(self, messages: List[AllMessageValues]) -> List[dict]:
             elif role == "tool":
                 message["role"] = "function"
                 content = message.get("content", "")
-                if not isinstance(content, str) or not is_valid_json(content):
+                if not isinstance(content, str):
                     message["content"] = json.dumps(content, ensure_ascii=False)
 
             # Handle None content
@@ -463,16 +441,14 @@ def transform_response(
                     # Convert to tool_calls format
                     if isinstance(args, dict):
                         args = json.dumps(args, ensure_ascii=False)
-                    message_data["tool_calls"] = [
-                        {
-                            "id": f"call_{uuid.uuid4().hex[:24]}",
-                            "type": "function",
-                            "function": {
-                                "name": func_call.get("name", ""),
-                                "arguments": args,
-                            },
+                    message_data["tool_calls"] = [{
+                        "id": f"call_{uuid.uuid4().hex[:24]}",
+                        "type": "function",
+                        "function": {
+                            "name": func_call.get("name", ""),
+                            "arguments": args,
                         }
-                    ]
+                    }]
                     message_data.pop("function_call", None)
                     finish_reason = "tool_calls"
 
diff --git a/litellm/llms/groq/chat/transformation.py b/litellm/llms/groq/chat/transformation.py
index 34ea7b03dd9..a75ecd8cc7b 100644
--- a/litellm/llms/groq/chat/transformation.py
+++ b/litellm/llms/groq/chat/transformation.py
@@ -323,12 +323,4 @@ def chunk_parser(self, chunk: dict) -> ModelResponseStream:
                 status_code=error.get("code"), message=error.get("message"), body=error
             )
 
-        # Map Groq's 'reasoning' field to LiteLLM's 'reasoning_content' field
-        # Groq returns delta.reasoning, but LiteLLM expects delta.reasoning_content
-        choices = chunk.get("choices", [])
-        for choice in choices:
-            delta = choice.get("delta", {})
-            if "reasoning" in delta:
-                delta["reasoning_content"] = delta.pop("reasoning")
-
         return super().chunk_parser(chunk)
diff --git a/litellm/llms/hosted_vllm/chat/transformation.py b/litellm/llms/hosted_vllm/chat/transformation.py
index e955800b947..1d21490ea31 100644
--- a/litellm/llms/hosted_vllm/chat/transformation.py
+++ b/litellm/llms/hosted_vllm/chat/transformation.py
@@ -23,7 +23,7 @@
 class HostedVLLMChatConfig(OpenAIGPTConfig):
     def get_supported_openai_params(self, model: str) -> List[str]:
         params = super().get_supported_openai_params(model)
-        params.extend(["reasoning_effort", "thinking"])
+        params.append("reasoning_effort")
         return params
 
     def map_openai_params(
@@ -41,27 +41,6 @@ def map_openai_params(
             _tools = _remove_strict_from_schema(_tools)
         if _tools is not None:
             non_default_params["tools"] = _tools
-
-        # Handle thinking parameter - convert Anthropic-style to OpenAI-style reasoning_effort
-        # vLLM is OpenAI-compatible, so it understands reasoning_effort, not thinking
-        # Reference: https://github.com/BerriAI/litellm/issues/19761
-        thinking = non_default_params.pop("thinking", None)
-        if thinking is not None and isinstance(thinking, dict):
-            if thinking.get("type") == "enabled":
-                # Only convert if reasoning_effort not already set
-                if "reasoning_effort" not in non_default_params:
-                    budget_tokens = thinking.get("budget_tokens", 0)
-                    # Map budget_tokens to reasoning_effort level
-                    # Same logic as Anthropic adapter (translate_anthropic_thinking_to_reasoning_effort)
-                    if budget_tokens >= 10000:
-                        non_default_params["reasoning_effort"] = "high"
-                    elif budget_tokens >= 5000:
-                        non_default_params["reasoning_effort"] = "medium"
-                    elif budget_tokens >= 2000:
-                        non_default_params["reasoning_effort"] = "low"
-                    else:
-                        non_default_params["reasoning_effort"] = "minimal"
-
         return super().map_openai_params(
             non_default_params, optional_params, model, drop_params
         )
diff --git a/litellm/llms/minimax/chat/transformation.py b/litellm/llms/minimax/chat/transformation.py
index 3e9dc0209f2..ed80ff8aed1 100644
--- a/litellm/llms/minimax/chat/transformation.py
+++ b/litellm/llms/minimax/chat/transformation.py
@@ -1,12 +1,11 @@
 """
 MiniMax OpenAI transformation config - extends OpenAI chat config for MiniMax's OpenAI-compatible API
 """
-from typing import List, Optional, Tuple
+from typing import Optional
 
 import litellm
 from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
 from litellm.secret_managers.main import get_secret_str
-from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam
 
 
 class MinimaxChatConfig(OpenAIGPTConfig):
@@ -74,33 +73,11 @@ def get_complete_url(
         else:
             return f"{base_url}/v1/chat/completions"
 
-    def remove_cache_control_flag_from_messages_and_tools(
-        self,
-        model: str,
-        messages: List[AllMessageValues],
-        tools: Optional[List[ChatCompletionToolParam]] = None,
-    ) -> Tuple[List[AllMessageValues], Optional[List[ChatCompletionToolParam]]]:
-        """
-        Override to preserve cache_control for MiniMax.
-        MiniMax supports cache_control - don't strip it.
-        """
-        # MiniMax supports cache_control, so return messages and tools unchanged
-        return messages, tools
-
     def get_supported_openai_params(self, model: str) -> list:
         """
         Get supported OpenAI parameters for MiniMax.
-        Adds reasoning_split and thinking to the list of supported params.
+        Adds reasoning_split to the list of supported params.
         """
         base_params = super().get_supported_openai_params(model=model)
-        additional_params = ["reasoning_split"]
-        
-        # Add thinking parameter if model supports reasoning
-        try:
-            if litellm.supports_reasoning(model=model, custom_llm_provider="minimax"):
-                additional_params.append("thinking")
-        except Exception:
-            pass
-        
-        return base_params + additional_params
+        return base_params + ["reasoning_split"]
 
diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py
index 84f39ef2525..7af7be2094a 100644
--- a/litellm/llms/oci/chat/transformation.py
+++ b/litellm/llms/oci/chat/transformation.py
@@ -32,7 +32,6 @@
     OCICompletionResponse,
     OCIContentPartUnion,
     OCIImageContentPart,
-    OCIImageUrl,
     OCIMessage,
     OCIRoles,
     OCIServingMode,
@@ -1130,7 +1129,7 @@ def adapt_messages_to_generic_oci_standard_content_message(
                 image_url = image_url.get("url")
             if not isinstance(image_url, str):
                 raise Exception("Prop `image_url` must be a string or an object with a `url` property")
-            new_content.append(OCIImageContentPart(imageUrl=OCIImageUrl(url=image_url)))
+            new_content.append(OCIImageContentPart(imageUrl=image_url))
 
     return OCIMessage(
         role=open_ai_to_generic_oci_role_map[role],
diff --git a/litellm/llms/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py
index 05c003c8b7a..3fffa335fdc 100644
--- a/litellm/llms/openai/chat/gpt_5_transformation.py
+++ b/litellm/llms/openai/chat/gpt_5_transformation.py
@@ -19,9 +19,7 @@ class OpenAIGPT5Config(OpenAIGPTConfig):
 
     @classmethod
     def is_model_gpt_5_model(cls, model: str) -> bool:
-        # gpt-5-chat* behaves like a regular chat model (supports temperature, etc.)
-        # Don't route it through GPT-5 reasoning-specific parameter restrictions.
-        return "gpt-5" in model and "gpt-5-chat" not in model
+        return "gpt-5" in model
 
     @classmethod
     def is_model_gpt_5_codex_model(cls, model: str) -> bool:
diff --git a/litellm/llms/openai/image_generation/cost_calculator.py b/litellm/llms/openai/image_generation/cost_calculator.py
index 988d5626134..35caaf6e9b1 100644
--- a/litellm/llms/openai/image_generation/cost_calculator.py
+++ b/litellm/llms/openai/image_generation/cost_calculator.py
@@ -8,7 +8,8 @@
 
 from litellm import verbose_logger
 from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
-from litellm.types.utils import ImageResponse, Usage
+from litellm.responses.utils import ResponseAPILoggingUtils
+from litellm.types.utils import ImageResponse
 
 
 def cost_calculator(
@@ -38,18 +39,11 @@ def cost_calculator(
         )
         return 0.0
 
-    # If usage is already a Usage object with completion_tokens_details set,
-    # use it directly (it was already transformed in convert_to_image_response)
-    if isinstance(usage, Usage) and usage.completion_tokens_details is not None:
-        chat_usage = usage
-    else:
-        # Transform ImageUsage to Usage using the existing helper
-        # ImageUsage has the same format as ResponseAPIUsage
-        from litellm.responses.utils import ResponseAPILoggingUtils
-
-        chat_usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
-            usage
-        )
+    # Transform ImageUsage to Usage using the existing helper
+    # ImageUsage has the same format as ResponseAPIUsage
+    chat_usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
+        usage
+    )
 
     # Use generic_cost_per_token for cost calculation
     prompt_cost, completion_cost = generic_cost_per_token(
diff --git a/litellm/llms/openrouter/chat/transformation.py b/litellm/llms/openrouter/chat/transformation.py
index e3770dbbf49..b5610852fd2 100644
--- a/litellm/llms/openrouter/chat/transformation.py
+++ b/litellm/llms/openrouter/chat/transformation.py
@@ -26,9 +26,6 @@ class CacheControlSupportedModels(str, Enum):
     """Models that support cache_control in content blocks."""
     CLAUDE = "claude"
     GEMINI = "gemini"
-    MINIMAX = "minimax"
-    GLM = "glm"
-    ZAI = "z-ai"
 
 
 class OpenrouterConfig(OpenAIGPTConfig):
@@ -42,7 +39,6 @@ def get_supported_openai_params(self, model: str) -> list:
                 model=model, custom_llm_provider="openrouter"
             ) or litellm.supports_reasoning(model=model):
                 supported_params.append("reasoning_effort")
-                supported_params.append("thinking")
         except Exception:
             pass
         return list(dict.fromkeys(supported_params))
diff --git a/litellm/llms/s3_vectors/__init__.py b/litellm/llms/s3_vectors/__init__.py
deleted file mode 100644
index e8367949c3e..00000000000
--- a/litellm/llms/s3_vectors/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# S3 Vectors LLM integration
diff --git a/litellm/llms/s3_vectors/vector_stores/__init__.py b/litellm/llms/s3_vectors/vector_stores/__init__.py
deleted file mode 100644
index ac24b4a38da..00000000000
--- a/litellm/llms/s3_vectors/vector_stores/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# S3 Vectors vector store integration
diff --git a/litellm/llms/s3_vectors/vector_stores/transformation.py b/litellm/llms/s3_vectors/vector_stores/transformation.py
deleted file mode 100644
index df81a78289a..00000000000
--- a/litellm/llms/s3_vectors/vector_stores/transformation.py
+++ /dev/null
@@ -1,254 +0,0 @@
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
-
-import httpx
-
-from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
-from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
-from litellm.types.router import GenericLiteLLMParams
-from litellm.types.vector_stores import (
-    VECTOR_STORE_OPENAI_PARAMS,
-    BaseVectorStoreAuthCredentials,
-    VectorStoreIndexEndpoints,
-    VectorStoreResultContent,
-    VectorStoreSearchOptionalRequestParams,
-    VectorStoreSearchResponse,
-    VectorStoreSearchResult,
-)
-
-if TYPE_CHECKING:
-    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
-else:
-    LiteLLMLoggingObj = Any
-
-
-class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM):
-    """Vector store configuration for AWS S3 Vectors."""
-
-    def __init__(self) -> None:
-        BaseVectorStoreConfig.__init__(self)
-        BaseAWSLLM.__init__(self)
-
-    def get_auth_credentials(
-        self, litellm_params: dict
-    ) -> BaseVectorStoreAuthCredentials:
-        return {}
-
-    def get_vector_store_endpoints_by_type(self) -> VectorStoreIndexEndpoints:
-        return {
-            "read": [("POST", "/QueryVectors")],
-            "write": [],
-        }
-
-    def get_supported_openai_params(
-        self, model: str
-    ) -> List[VECTOR_STORE_OPENAI_PARAMS]:
-        return ["max_num_results"]
-
-    def map_openai_params(
-        self,
-        non_default_params: dict,
-        optional_params: dict,
-        drop_params: bool,
-    ) -> dict:
-        for param, value in non_default_params.items():
-            if param == "max_num_results":
-                optional_params["maxResults"] = value
-        return optional_params
-
-    def validate_environment(
-        self, headers: dict, litellm_params: Optional[GenericLiteLLMParams]
-    ) -> dict:
-        headers = headers or {}
-        headers.setdefault("Content-Type", "application/json")
-        return headers
-
-    def get_complete_url(self, api_base: Optional[str], litellm_params: dict) -> str:
-        aws_region_name = litellm_params.get("aws_region_name")
-        if not aws_region_name:
-            raise ValueError("aws_region_name is required for S3 Vectors")
-        return f"https://s3vectors.{aws_region_name}.api.aws"
-
-    def transform_search_vector_store_request(
-        self,
-        vector_store_id: str,
-        query: Union[str, List[str]],
-        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
-        api_base: str,
-        litellm_logging_obj: LiteLLMLoggingObj,
-        litellm_params: dict,
-    ) -> Tuple[str, Dict]:
-        """Sync version - generates embedding synchronously."""
-        # For S3 Vectors, vector_store_id should be in format: bucket_name:index_name
-        # If not in that format, try to construct it from litellm_params
-        bucket_name: str
-        index_name: str
-        
-        if ":" in vector_store_id:
-            bucket_name, index_name = vector_store_id.split(":", 1)
-        else:
-            # Try to get bucket_name from litellm_params
-            bucket_name_from_params = litellm_params.get("vector_bucket_name")
-            if not bucket_name_from_params or not isinstance(bucket_name_from_params, str):
-                raise ValueError(
-                    "vector_store_id must be in format 'bucket_name:index_name' for S3 Vectors, "
-                    "or vector_bucket_name must be provided in litellm_params"
-                )
-            bucket_name = bucket_name_from_params
-            index_name = vector_store_id
-
-        if isinstance(query, list):
-            query = " ".join(query)
-
-        # Generate embedding for the query
-        embedding_model = litellm_params.get("embedding_model", "text-embedding-3-small")
-        
-        import litellm as litellm_module
-        embedding_response = litellm_module.embedding(model=embedding_model, input=[query])
-        query_embedding = embedding_response.data[0]["embedding"]
-
-        url = f"{api_base}/QueryVectors"
-
-        request_body: Dict[str, Any] = {
-            "vectorBucketName": bucket_name,
-            "indexName": index_name,
-            "queryVector": {"float32": query_embedding},
-            "topK": vector_store_search_optional_params.get("max_num_results", 5),  # Default to 5
-            "returnDistance": True,
-            "returnMetadata": True,
-        }
-
-        litellm_logging_obj.model_call_details["query"] = query
-        return url, request_body
-
-    async def atransform_search_vector_store_request(
-        self,
-        vector_store_id: str,
-        query: Union[str, List[str]],
-        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
-        api_base: str,
-        litellm_logging_obj: LiteLLMLoggingObj,
-        litellm_params: dict,
-    ) -> Tuple[str, Dict]:
-        """Async version - generates embedding asynchronously."""
-        # For S3 Vectors, vector_store_id should be in format: bucket_name:index_name
-        # If not in that format, try to construct it from litellm_params
-        bucket_name: str
-        index_name: str
-        
-        if ":" in vector_store_id:
-            bucket_name, index_name = vector_store_id.split(":", 1)
-        else:
-            # Try to get bucket_name from litellm_params
-            bucket_name_from_params = litellm_params.get("vector_bucket_name")
-            if not bucket_name_from_params or not isinstance(bucket_name_from_params, str):
-                raise ValueError(
-                    "vector_store_id must be in format 'bucket_name:index_name' for S3 Vectors, "
-                    "or vector_bucket_name must be provided in litellm_params"
-                )
-            bucket_name = bucket_name_from_params
-            index_name = vector_store_id
-
-        if isinstance(query, list):
-            query = " ".join(query)
-
-        # Generate embedding for the query asynchronously
-        embedding_model = litellm_params.get("embedding_model", "text-embedding-3-small")
-        
-        import litellm as litellm_module
-        embedding_response = await litellm_module.aembedding(model=embedding_model, input=[query])
-        query_embedding = embedding_response.data[0]["embedding"]
-
-        url = f"{api_base}/QueryVectors"
-
-        request_body: Dict[str, Any] = {
-            "vectorBucketName": bucket_name,
-            "indexName": index_name,
-            "queryVector": {"float32": query_embedding},
-            "topK": vector_store_search_optional_params.get("max_num_results", 5),  # Default to 5
-            "returnDistance": True,
-            "returnMetadata": True,
-        }
-
-        litellm_logging_obj.model_call_details["query"] = query
-        return url, request_body
-
-    def sign_request(
-        self,
-        headers: dict,
-        optional_params: Dict,
-        request_data: Dict,
-        api_base: str,
-        api_key: Optional[str] = None,
-    ) -> Tuple[dict, Optional[bytes]]:
-        return self._sign_request(
-            service_name="s3vectors",
-            headers=headers,
-            optional_params=optional_params,
-            request_data=request_data,
-            api_base=api_base,
-            api_key=api_key,
-        )
-
-    def transform_search_vector_store_response(
-        self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj
-    ) -> VectorStoreSearchResponse:
-        try:
-            response_data = response.json()
-            results: List[VectorStoreSearchResult] = []
-
-            for item in response_data.get("vectors", []) or []:
-                metadata = item.get("metadata", {}) or {}
-                source_text = metadata.get("source_text", "")
-
-                if not source_text:
-                    continue
-
-                # Extract file information from metadata
-                chunk_index = metadata.get("chunk_index", "0")
-                file_id = f"s3-vectors-chunk-{chunk_index}"
-                filename = metadata.get("filename", f"document-{chunk_index}")
-
-                # S3 Vectors returns distance, convert to similarity score (0-1)
-                # Lower distance = higher similarity
-                # We'll normalize using 1 / (1 + distance) to get a 0-1 score
-                distance = item.get("distance")
-                score = None
-                if distance is not None:
-                    # Convert distance to similarity score between 0 and 1
-                    # For cosine distance: similarity = 1 - distance
-                    # For euclidean: use 1 / (1 + distance)
-                    # Assuming cosine distance here
-                    score = max(0.0, min(1.0, 1.0 - float(distance)))
-
-                results.append(
-                    VectorStoreSearchResult(
-                        score=score,
-                        content=[VectorStoreResultContent(text=source_text, type="text")],
-                        file_id=file_id,
-                        filename=filename,
-                        attributes=metadata,
-                    )
-                )
-
-            return VectorStoreSearchResponse(
-                object="vector_store.search_results.page",
-                search_query=litellm_logging_obj.model_call_details.get("query", ""),
-                data=results,
-            )
-        except Exception as e:
-            raise self.get_error_class(
-                error_message=str(e),
-                status_code=response.status_code,
-                headers=response.headers,
-            )
-
-    # Vector store creation is not yet implemented
-    def transform_create_vector_store_request(
-        self,
-        vector_store_create_optional_params,
-        api_base: str,
-    ) -> Tuple[str, Dict]:
-        raise NotImplementedError
-
-    def transform_create_vector_store_response(self, response: httpx.Response):
-        raise NotImplementedError
diff --git a/litellm/llms/vercel_ai_gateway/embedding/__init__.py b/litellm/llms/vercel_ai_gateway/embedding/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/litellm/llms/vercel_ai_gateway/embedding/transformation.py b/litellm/llms/vercel_ai_gateway/embedding/transformation.py
deleted file mode 100644
index 7238b05f10d..00000000000
--- a/litellm/llms/vercel_ai_gateway/embedding/transformation.py
+++ /dev/null
@@ -1,176 +0,0 @@
-"""
-Vercel AI Gateway Embedding API Configuration.
-
-This module provides the configuration for Vercel AI Gateway's Embedding API.
-Vercel AI Gateway is OpenAI-compatible and supports embeddings via the /v1/embeddings endpoint.
-
-Docs: https://vercel.com/docs/ai-gateway/openai-compat/embeddings
-"""
-
-from typing import TYPE_CHECKING, Any, Optional
-
-import httpx
-
-from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
-from litellm.secret_managers.main import get_secret_str
-from litellm.types.llms.openai import AllEmbeddingInputValues
-from litellm.types.utils import EmbeddingResponse
-from litellm.utils import convert_to_model_response_object
-
-from ..common_utils import VercelAIGatewayException
-
-if TYPE_CHECKING:
-    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
-
-    LiteLLMLoggingObj = _LiteLLMLoggingObj
-else:
-    LiteLLMLoggingObj = Any
-
-
-class VercelAIGatewayEmbeddingConfig(BaseEmbeddingConfig):
-    """
-    Configuration for Vercel AI Gateway's Embedding API.
-
-    Reference: https://vercel.com/docs/ai-gateway/openai-compat/embeddings
-    """
-
-    def validate_environment(
-        self,
-        headers: dict,
-        model: str,
-        messages: list,
-        optional_params: dict,
-        litellm_params: dict,
-        api_key: Optional[str] = None,
-        api_base: Optional[str] = None,
-    ) -> dict:
-        """
-        Validate environment and set up headers for Vercel AI Gateway API.
-
-        Vercel AI Gateway requires:
-        - Authorization header with Bearer token (API key or OIDC token)
-        """
-        vercel_headers = {
-            "Content-Type": "application/json",
-        }
-
-        # Add Authorization header if api_key is provided
-        if api_key:
-            vercel_headers["Authorization"] = f"Bearer {api_key}"
-
-        # Merge with existing headers (user's extra_headers take priority)
-        merged_headers = {**vercel_headers, **headers}
-
-        return merged_headers
-
-    def get_complete_url(
-        self,
-        api_base: Optional[str],
-        api_key: Optional[str],
-        model: str,
-        optional_params: dict,
-        litellm_params: dict,
-        stream: Optional[bool] = None,
-    ) -> str:
-        """
-        Get the complete URL for Vercel AI Gateway Embedding API endpoint.
-        """
-        if api_base:
-            api_base = api_base.rstrip("/")
-        else:
-            api_base = (
-                get_secret_str("VERCEL_AI_GATEWAY_API_BASE")
-                or "https://ai-gateway.vercel.sh/v1"
-            )
-
-        return f"{api_base}/embeddings"
-
-    def transform_embedding_request(
-        self,
-        model: str,
-        input: AllEmbeddingInputValues,
-        optional_params: dict,
-        headers: dict,
-    ) -> dict:
-        """
-        Transform embedding request to Vercel AI Gateway format (OpenAI-compatible).
-        """
-        # Ensure input is a list
-        if isinstance(input, str):
-            input = [input]
-
-        # Strip 'vercel_ai_gateway/' prefix if present
-        if model.startswith("vercel_ai_gateway/"):
-            model = model.replace("vercel_ai_gateway/", "", 1)
-
-        return {
-            "model": model,
-            "input": input,
-            **optional_params,
-        }
-
-    def transform_embedding_response(
-        self,
-        model: str,
-        raw_response: httpx.Response,
-        model_response: EmbeddingResponse,
-        logging_obj: LiteLLMLoggingObj,
-        api_key: Optional[str],
-        request_data: dict,
-        optional_params: dict,
-        litellm_params: dict,
-    ) -> EmbeddingResponse:
-        """
-        Transform embedding response from Vercel AI Gateway format (OpenAI-compatible).
-        """
-        logging_obj.post_call(original_response=raw_response.text)
-
-        # Vercel AI Gateway returns standard OpenAI-compatible embedding response
-        response_json = raw_response.json()
-
-        return convert_to_model_response_object(
-            response_object=response_json,
-            model_response_object=model_response,
-            response_type="embedding",
-        )
-
-    def get_supported_openai_params(self, model: str) -> list:
-        """
-        Get list of supported OpenAI parameters for Vercel AI Gateway embeddings.
-
-        Vercel AI Gateway supports the standard OpenAI embeddings parameters
-        and auto-maps 'dimensions' to each provider's expected field.
-        """
-        return [
-            "timeout",
-            "dimensions",
-            "encoding_format",
-            "user",
-        ]
-
-    def map_openai_params(
-        self,
-        non_default_params: dict,
-        optional_params: dict,
-        model: str,
-        drop_params: bool,
-    ) -> dict:
-        """
-        Map OpenAI parameters to Vercel AI Gateway format.
-        """
-        for param, value in non_default_params.items():
-            if param in self.get_supported_openai_params(model):
-                optional_params[param] = value
-        return optional_params
-
-    def get_error_class(
-        self, error_message: str, status_code: int, headers: Any
-    ) -> Any:
-        """
-        Get the error class for Vercel AI Gateway errors.
-        """
-        return VercelAIGatewayException(
-            message=error_message,
-            status_code=status_code,
-            headers=headers,
-        )
diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py
index a0e2ddf5e98..152b99ca4db 100644
--- a/litellm/llms/vertex_ai/common_utils.py
+++ b/litellm/llms/vertex_ai/common_utils.py
@@ -849,7 +849,7 @@ def get_vertex_model_id_from_url(url: str) -> Optional[str]:
 
     `https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/publishers/google/models/${MODEL_ID}:streamGenerateContent`
     """
-    match = re.search(r"/models/([^:]+)", url)
+    match = re.search(r"/models/([^/:]+)", url)
     return match.group(1) if match else None
 
 
diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
index a9ac21bb56f..b78ac8f9e98 100644
--- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -1657,17 +1657,7 @@ def _calculate_usage(  # noqa: PLR0915
         ## This is necessary because promptTokensDetails includes both cached and non-cached tokens
         ## See: https://github.com/BerriAI/litellm/issues/18750
         if cached_text_tokens is not None and prompt_text_tokens is not None:
-            # Explicit caching: subtract cached tokens per modality from cacheTokensDetails
             prompt_text_tokens = prompt_text_tokens - cached_text_tokens
-        elif (
-            cached_tokens is not None
-            and prompt_text_tokens is not None
-            and cached_text_tokens is None
-        ):
-            # Implicit caching: only cachedContentTokenCount is provided (no cacheTokensDetails)
-            # Subtract from text tokens since implicit caching is primarily for text content
-            # See: https://github.com/BerriAI/litellm/issues/16341
-            prompt_text_tokens = prompt_text_tokens - cached_tokens
         if cached_audio_tokens is not None and prompt_audio_tokens is not None:
             prompt_audio_tokens = prompt_audio_tokens - cached_audio_tokens
         if cached_image_tokens is not None and prompt_image_tokens is not None:
diff --git a/litellm/llms/xai/responses/transformation.py b/litellm/llms/xai/responses/transformation.py
index 90a7faf5a7b..bd422c8d81e 100644
--- a/litellm/llms/xai/responses/transformation.py
+++ b/litellm/llms/xai/responses/transformation.py
@@ -1,11 +1,10 @@
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
 
 import litellm
 from litellm._logging import verbose_logger
 from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams
-from litellm.types.llms.xai import XAIWebSearchTool, XAIXSearchTool
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.utils import LlmProviders
 
@@ -50,85 +49,6 @@ def get_supported_openai_params(self, model: str) -> list:
         
         return supported_params
 
-    def _transform_web_search_tool(self, tool: Dict[str, Any]) -> Union[XAIWebSearchTool, Dict[str, Any]]:
-        """
-        Transform web_search tool to XAI format.
-        
-        XAI supports web_search with specific filters:
-        - allowed_domains (max 5)
-        - excluded_domains (max 5)
-        - enable_image_understanding
-        
-        XAI does NOT support search_context_size (OpenAI-specific).
-        """
-        xai_tool: Dict[str, Any] = {"type": "web_search"}
-        
-        # Remove search_context_size if present (not supported by XAI)
-        if "search_context_size" in tool:
-            verbose_logger.info(
-                "XAI does not support 'search_context_size' parameter. Removing it from web_search tool."
-            )
-        
-        # Handle filters (XAI-specific structure)
-        filters = {}
-        if "allowed_domains" in tool:
-            allowed_domains = tool["allowed_domains"]
-            filters["allowed_domains"] = allowed_domains
-        
-        if "excluded_domains" in tool:
-            excluded_domains = tool["excluded_domains"]
-            filters["excluded_domains"] = excluded_domains
-        
-        # Add filters if any were specified
-        if filters:
-            xai_tool["filters"] = filters
-        
-        # Handle enable_image_understanding (top-level in XAI format)
-        if "enable_image_understanding" in tool:
-            xai_tool["enable_image_understanding"] = tool["enable_image_understanding"]
-        
-        return xai_tool
-    
-    def _transform_x_search_tool(self, tool: Dict[str, Any]) -> Union[XAIXSearchTool, Dict[str, Any]]:
-        """
-        Transform x_search tool to XAI format.
-        
-        XAI supports x_search with specific parameters:
-        - allowed_x_handles (max 10)
-        - excluded_x_handles (max 10)
-        - from_date (ISO8601: YYYY-MM-DD)
-        - to_date (ISO8601: YYYY-MM-DD)
-        - enable_image_understanding
-        - enable_video_understanding
-        """
-        xai_tool: Dict[str, Any] = {"type": "x_search"}
-        
-        # Handle allowed_x_handles
-        if "allowed_x_handles" in tool:
-            allowed_handles = tool["allowed_x_handles"]
-            xai_tool["allowed_x_handles"] = allowed_handles
-        
-        # Handle excluded_x_handles
-        if "excluded_x_handles" in tool:
-            excluded_handles = tool["excluded_x_handles"]
-            xai_tool["excluded_x_handles"] = excluded_handles
-        
-        # Handle date range
-        if "from_date" in tool:
-            xai_tool["from_date"] = tool["from_date"]
-        
-        if "to_date" in tool:
-            xai_tool["to_date"] = tool["to_date"]
-        
-        # Handle media understanding flags
-        if "enable_image_understanding" in tool:
-            xai_tool["enable_image_understanding"] = tool["enable_image_understanding"]
-        
-        if "enable_video_understanding" in tool:
-            xai_tool["enable_video_understanding"] = tool["enable_video_understanding"]
-        
-        return xai_tool
-
     def map_openai_params(
         self,
         response_api_optional_params: ResponsesAPIOptionalRequestParams,
@@ -141,9 +61,7 @@ def map_openai_params(
         Handles XAI-specific transformations:
         1. Drops 'instructions' parameter (not supported)
         2. Transforms code_interpreter tools to remove 'container' field
-        3. Transforms web_search tools to XAI format (removes search_context_size, adds filters)
-        4. Transforms x_search tools to XAI format
-        5. Sets store=false when images are detected (recommended by XAI)
+        3. Sets store=false when images are detected (recommended by XAI)
         """
         params = dict(response_api_optional_params)
         
@@ -154,7 +72,7 @@ def map_openai_params(
             )
             params.pop("instructions")
         
-        # Transform tools
+        # Transform code_interpreter tools - remove container field
         if "tools" in params and params["tools"]:
             tools_list = params["tools"]
             # Ensure tools is a list for iteration
@@ -163,36 +81,15 @@ def map_openai_params(
             
             transformed_tools: List[Any] = []
             for tool in tools_list:
-                if isinstance(tool, dict):
-                    tool_type = tool.get("type")
-                    
-                    if tool_type == "code_interpreter":
-                        # XAI supports code_interpreter but doesn't use the container field
-                        verbose_logger.debug(
-                            "XAI: Transforming code_interpreter tool, removing container field"
-                        )
-                        transformed_tools.append({"type": "code_interpreter"})
-                    
-                    elif tool_type == "web_search":
-                        # Transform web_search to XAI format
-                        verbose_logger.debug(
-                            "XAI: Transforming web_search tool to XAI format"
-                        )
-                        transformed_tools.append(self._transform_web_search_tool(tool))
-                    
-                    elif tool_type == "x_search":
-                        # Transform x_search to XAI format
-                        verbose_logger.debug(
-                            "XAI: Transforming x_search tool to XAI format"
-                        )
-                        transformed_tools.append(self._transform_x_search_tool(tool))
-                    
-                    else:
-                        # Keep other tools as-is
-                        transformed_tools.append(tool)
+                if isinstance(tool, dict) and tool.get("type") == "code_interpreter":
+                    # XAI supports code_interpreter but doesn't use the container field
+                    # Keep only the type field
+                    verbose_logger.debug(
+                        "XAI: Transforming code_interpreter tool, removing container field"
+                    )
+                    transformed_tools.append({"type": "code_interpreter"})
                 else:
                     transformed_tools.append(tool)
-            
             params["tools"] = transformed_tools
         
         return params
diff --git a/litellm/llms/zai/chat/transformation.py b/litellm/llms/zai/chat/transformation.py
index fb1d67df357..4380256f0a4 100644
--- a/litellm/llms/zai/chat/transformation.py
+++ b/litellm/llms/zai/chat/transformation.py
@@ -1,7 +1,6 @@
-from typing import List, Optional, Tuple
+from typing import Optional, Tuple
 
 from litellm.secret_managers.main import get_secret_str
-from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam
 
 from ...openai.chat.gpt_transformation import OpenAIGPTConfig
 
@@ -20,19 +19,6 @@ def _get_openai_compatible_provider_info(
         dynamic_api_key = api_key or get_secret_str("ZAI_API_KEY")
         return api_base, dynamic_api_key
 
-    def remove_cache_control_flag_from_messages_and_tools(
-        self,
-        model: str,
-        messages: List[AllMessageValues],
-        tools: Optional[List[ChatCompletionToolParam]] = None,
-    ) -> Tuple[List[AllMessageValues], Optional[List[ChatCompletionToolParam]]]:
-        """
-        Override to preserve cache_control for GLM/ZAI.
-        GLM supports cache_control - don't strip it.
-        """
-        # GLM/ZAI supports cache_control, so return messages and tools unchanged
-        return messages, tools
-
     def get_supported_openai_params(self, model: str) -> list:
         base_params = [
             "max_tokens",
diff --git a/litellm/main.py b/litellm/main.py
index 99bf224c5b7..ce84c8988e0 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -148,7 +148,7 @@
     validate_and_fix_openai_messages,
     validate_and_fix_openai_tools,
     validate_chat_completion_tool_choice,
-    validate_openai_optional_params,
+    validate_openai_optional_params
 )
 
 from ._logging import verbose_logger
@@ -368,7 +368,7 @@ async def create(self, messages, model=None, **kwargs):
 
 @tracer.wrap()
 @client
-async def acompletion(  # noqa: PLR0915
+async def acompletion( # noqa: PLR0915
     model: str,
     # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
     messages: List = [],
@@ -599,8 +599,16 @@ async def acompletion(  # noqa: PLR0915
         # Add the context to the function
         ctx = contextvars.copy_context()
         func_with_context = partial(ctx.run, func)
+
+        if timeout is not None and isinstance(timeout, (int, float)):
+            timeout_value = float(timeout)
+            init_response = await asyncio.wait_for(
+                loop.run_in_executor(None, func_with_context),
+                timeout=timeout_value
+            )
+        else:
+            init_response = await loop.run_in_executor(None, func_with_context)
             
-        init_response = await loop.run_in_executor(None, func_with_context)
         if isinstance(init_response, dict) or isinstance(
             init_response, ModelResponse
         ):  ## CACHING SCENARIO
@@ -608,7 +616,11 @@ async def acompletion(  # noqa: PLR0915
                 response = ModelResponse(**init_response)
             response = init_response
         elif asyncio.iscoroutine(init_response):
-            response = await init_response
+            if timeout is not None and isinstance(timeout, (int, float)):
+                timeout_value = float(timeout)
+                response = await asyncio.wait_for(init_response, timeout=timeout_value)
+            else:
+                response = await init_response
         else:
             response = init_response  # type: ignore
 
@@ -625,6 +637,14 @@ async def acompletion(  # noqa: PLR0915
                 loop=loop
             )  # sets the logging event loop if the user does sync streaming (e.g. on proxy for sagemaker calls)
         return response
+    except asyncio.TimeoutError:
+        custom_llm_provider = custom_llm_provider or "openai"
+        from litellm.exceptions import Timeout
+        raise Timeout(
+            message=f"Request timed out after {timeout} seconds",
+            model=model,
+            llm_provider=custom_llm_provider,
+        )
     except Exception as e:
         custom_llm_provider = custom_llm_provider or "openai"
         raise exception_type(
@@ -1098,6 +1118,7 @@ def completion(  # type: ignore # noqa: PLR0915
     # validate optional params
     stop = validate_openai_optional_params(stop=stop)
 
+
     ######### unpacking kwargs #####################
     args = locals()
 
@@ -1114,9 +1135,7 @@ def completion(  # type: ignore # noqa: PLR0915
         # Check if MCP tools are present (following responses pattern)
         # Cast tools to Optional[Iterable[ToolParam]] for type checking
         tools_for_mcp = cast(Optional[Iterable[ToolParam]], tools)
-        if LiteLLM_Proxy_MCP_Handler._should_use_litellm_mcp_gateway(
-            tools=tools_for_mcp
-        ):
+        if LiteLLM_Proxy_MCP_Handler._should_use_litellm_mcp_gateway(tools=tools_for_mcp):
             # Return coroutine - acompletion will await it
             # completion() can return a coroutine when MCP tools are present, which acompletion() awaits
             return acompletion_with_mcp(  # type: ignore[return-value]
@@ -1517,8 +1536,6 @@ def completion(  # type: ignore # noqa: PLR0915
             max_retries=max_retries,
             timeout=timeout,
             litellm_request_debug=kwargs.get("litellm_request_debug", False),
-            tpm=kwargs.get("tpm"),
-            rpm=kwargs.get("rpm"),
         )
         cast(LiteLLMLoggingObj, logging).update_environment_variables(
             model=model,
@@ -2344,7 +2361,11 @@ def completion(  # type: ignore # noqa: PLR0915
                 input=messages, api_key=api_key, original_response=response
             )
         elif custom_llm_provider == "minimax":
-            api_key = api_key or get_secret_str("MINIMAX_API_KEY") or litellm.api_key
+            api_key = (
+                api_key
+                or get_secret_str("MINIMAX_API_KEY")
+                or litellm.api_key
+            )
 
             api_base = (
                 api_base
@@ -2392,9 +2413,7 @@ def completion(  # type: ignore # noqa: PLR0915
             or custom_llm_provider == "wandb"
             or custom_llm_provider == "clarifai"
             or custom_llm_provider in litellm.openai_compatible_providers
-            or JSONProviderRegistry.exists(
-                custom_llm_provider
-            )  # JSON-configured providers
+            or JSONProviderRegistry.exists(custom_llm_provider)  # JSON-configured providers
             or "ft:gpt-3.5-turbo" in model  # finetune gpt-3.5-turbo
         ):  # allow user to make an openai call with a custom base
             # note: if a user sets a custom base - we should ensure this works
@@ -4705,7 +4724,7 @@ def embedding(  # noqa: PLR0915
 
             if headers is not None and headers != {}:
                 optional_params["extra_headers"] = headers
-
+            
             if encoding_format is not None:
                 optional_params["encoding_format"] = encoding_format
             else:
@@ -4847,36 +4866,6 @@ def embedding(  # noqa: PLR0915
 
             headers = openrouter_headers
 
-            response = base_llm_http_handler.embedding(
-                model=model,
-                input=input,
-                custom_llm_provider=custom_llm_provider,
-                api_base=api_base,
-                api_key=api_key,
-                logging_obj=logging,
-                timeout=timeout,
-                model_response=EmbeddingResponse(),
-                optional_params=optional_params,
-                client=client,
-                aembedding=aembedding,
-                litellm_params=litellm_params_dict,
-                headers=headers,
-            )
-        elif custom_llm_provider == "vercel_ai_gateway":
-            api_base = (
-                api_base
-                or litellm.api_base
-                or get_secret_str("VERCEL_AI_GATEWAY_API_BASE")
-                or "https://ai-gateway.vercel.sh/v1"
-            )
-
-            api_key = (
-                api_key
-                or litellm.api_key
-                or get_secret_str("VERCEL_AI_GATEWAY_API_KEY")
-                or get_secret_str("VERCEL_OIDC_TOKEN")
-            )
-
             response = base_llm_http_handler.embedding(
                 model=model,
                 input=input,
@@ -6770,7 +6759,9 @@ def speech(  # noqa: PLR0915
         if text_to_speech_provider_config is None:
             text_to_speech_provider_config = MinimaxTextToSpeechConfig()
 
-        minimax_config = cast(MinimaxTextToSpeechConfig, text_to_speech_provider_config)
+        minimax_config = cast(
+            MinimaxTextToSpeechConfig, text_to_speech_provider_config
+        )
 
         if api_base is not None:
             litellm_params_dict["api_base"] = api_base
@@ -6910,7 +6901,7 @@ async def ahealth_check(
         custom_llm_provider_from_params = model_params.get("custom_llm_provider", None)
         api_base_from_params = model_params.get("api_base", None)
         api_key_from_params = model_params.get("api_key", None)
-
+        
         model, custom_llm_provider, _, _ = get_llm_provider(
             model=model,
             custom_llm_provider=custom_llm_provider_from_params,
@@ -7280,16 +7271,12 @@ def _get_encoding():
 def __getattr__(name: str) -> Any:
     """Lazy import handler for main module"""
     if name == "encoding":
-        # Use _get_default_encoding which properly sets TIKTOKEN_CACHE_DIR
-        # before loading tiktoken, ensuring the local cache is used
-        # instead of downloading from the internet
-        from litellm._lazy_imports import _get_default_encoding
-        _encoding = _get_default_encoding()
+        # Lazy load encoding to avoid heavy tiktoken import at module load time
+        _encoding = tiktoken.get_encoding("cl100k_base")
         # Cache it in the module's __dict__ for subsequent accesses
         import sys
-
         sys.modules[__name__].__dict__["encoding"] = _encoding
         global _encoding_cache
         _encoding_cache = _encoding
         return _encoding
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
\ No newline at end of file
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index fad5f243fff..209c0794e50 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -3130,7 +3130,7 @@
         "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_system_messages": true,
-        "supports_tool_choice": true,
+        "supports_tool_choice": false,
         "supports_vision": true
     },
     "azure/gpt-5-chat-latest": {
@@ -3162,7 +3162,7 @@
         "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_system_messages": true,
-        "supports_tool_choice": true,
+        "supports_tool_choice": false,
         "supports_vision": true
     },
     "azure/gpt-5-codex": {
@@ -3653,9 +3653,10 @@
         "max_input_tokens": 128000,
         "max_output_tokens": 16384,
         "max_tokens": 16384,
-        "mode": "responses",
+        "mode": "chat",
         "output_cost_per_token": 1.4e-05,
         "supported_endpoints": [
+            "/v1/chat/completions",
             "/v1/responses"
         ],
         "supported_modalities": [
@@ -9787,7 +9788,6 @@
         "supports_tool_choice": true
     },
     "deepinfra/google/gemini-2.0-flash-001": {
-        "deprecation_date": "2026-03-31",
         "max_tokens": 1000000,
         "max_input_tokens": 1000000,
         "max_output_tokens": 1000000,
@@ -10231,48 +10231,6 @@
         "mode": "completion",
         "output_cost_per_token": 5e-07
     },
-    "deepseek-v3-2-251201": {
-        "input_cost_per_token": 0.0,
-        "litellm_provider": "volcengine",
-        "max_input_tokens": 98304,
-        "max_output_tokens": 32768,
-        "max_tokens": 32768,
-        "mode": "chat",
-        "output_cost_per_token": 0.0,
-        "supports_assistant_prefill": true,
-        "supports_function_calling": true,
-        "supports_prompt_caching": true,
-        "supports_reasoning": true,
-        "supports_tool_choice": true
-    },
-    "glm-4-7-251222": {
-        "input_cost_per_token": 0.0,
-        "litellm_provider": "volcengine",
-        "max_input_tokens": 204800,
-        "max_output_tokens": 131072,
-        "max_tokens": 131072,
-        "mode": "chat",
-        "output_cost_per_token": 0.0,
-        "supports_assistant_prefill": true,
-        "supports_function_calling": true,
-        "supports_prompt_caching": true,
-        "supports_reasoning": true,
-        "supports_tool_choice": true
-    },
-    "kimi-k2-thinking-251104": {
-        "input_cost_per_token": 0.0,
-        "litellm_provider": "volcengine",
-        "max_input_tokens": 229376,
-        "max_output_tokens": 32768,
-        "max_tokens": 32768,
-        "mode": "chat",
-        "output_cost_per_token": 0.0,
-        "supports_assistant_prefill": true,
-        "supports_function_calling": true,
-        "supports_prompt_caching": true,
-        "supports_reasoning": true,
-        "supports_tool_choice": true
-    },
     "doubao-embedding": {
         "input_cost_per_token": 0.0,
         "litellm_provider": "volcengine",
@@ -12147,7 +12105,6 @@
     },
     "gemini-2.0-flash": {
         "cache_read_input_token_cost": 2.5e-08,
-        "deprecation_date": "2026-03-31",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -12187,7 +12144,7 @@
     },
     "gemini-2.0-flash-001": {
         "cache_read_input_token_cost": 3.75e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-02-05",
         "input_cost_per_audio_token": 1e-06,
         "input_cost_per_token": 1.5e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -12273,7 +12230,6 @@
     },
     "gemini-2.0-flash-lite": {
         "cache_read_input_token_cost": 1.875e-08,
-        "deprecation_date": "2026-03-31",
         "input_cost_per_audio_token": 7.5e-08,
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "vertex_ai-language-models",
@@ -12309,7 +12265,7 @@
     },
     "gemini-2.0-flash-lite-001": {
         "cache_read_input_token_cost": 1.875e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-02-25",
         "input_cost_per_audio_token": 7.5e-08,
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "vertex_ai-language-models",
@@ -13521,79 +13477,6 @@
         "supports_vision": true,
         "supports_web_search": true
     },
-    "gemini-robotics-er-1.5-preview": {
-        "cache_read_input_token_cost": 0,
-        "input_cost_per_token": 3e-07,
-        "input_cost_per_audio_token": 1e-06,
-        "litellm_provider": "vertex_ai-language-models",
-        "max_input_tokens": 1048576,
-        "max_output_tokens": 65535,
-        "max_tokens": 65535,
-        "mode": "chat",
-        "output_cost_per_token": 2.5e-06,
-        "output_cost_per_reasoning_token": 2.5e-06,
-        "source": "https://ai.google.dev/gemini-api/docs/models#gemini-robotics-er-1-5-preview",
-        "supported_endpoints": [
-            "/v1/chat/completions",
-            "/v1/completions"
-        ],
-        "supported_modalities": [
-            "text",
-            "image",
-            "video",
-            "audio"
-        ],
-        "supported_output_modalities": [
-            "text"
-        ],
-        "supports_audio_output": false,
-        "supports_function_calling": true,
-        "supports_parallel_function_calling": true,
-        "supports_prompt_caching": false,
-        "supports_reasoning": true,
-        "supports_response_schema": true,
-        "supports_system_messages": true,
-        "supports_tool_choice": true,
-        "supports_url_context": true,
-        "supports_vision": true
-    },
-    "gemini/gemini-robotics-er-1.5-preview": {
-        "cache_read_input_token_cost": 0,
-        "input_cost_per_token": 3e-07,
-        "input_cost_per_audio_token": 1e-06,
-        "litellm_provider": "gemini",
-        "max_input_tokens": 1048576,
-        "max_output_tokens": 65535,
-        "max_tokens": 65535,
-        "mode": "chat",
-        "output_cost_per_token": 2.5e-06,
-        "output_cost_per_reasoning_token": 2.5e-06,
-        "source": "https://ai.google.dev/gemini-api/docs/models#gemini-robotics-er-1-5-preview",
-        "supported_endpoints": [
-            "/v1/chat/completions",
-            "/v1/completions"
-        ],
-        "supported_modalities": [
-            "text",
-            "image",
-            "video",
-            "audio"
-        ],
-        "supported_output_modalities": [
-            "text"
-        ],
-        "supports_audio_output": false,
-        "supports_function_calling": true,
-        "supports_parallel_function_calling": true,
-        "supports_prompt_caching": false,
-        "supports_reasoning": true,
-        "supports_response_schema": true,
-        "supports_system_messages": true,
-        "supports_tool_choice": true,
-        "supports_url_context": true,
-        "supports_vision": true,
-        "supports_web_search": true
-    },
     "gemini-2.5-computer-use-preview-10-2025": {
         "input_cost_per_token": 1.25e-06,
         "input_cost_per_token_above_200k_tokens": 2.5e-06,
@@ -14047,7 +13930,6 @@
     },
     "gemini/gemini-2.0-flash": {
         "cache_read_input_token_cost": 2.5e-08,
-        "deprecation_date": "2026-03-31",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "gemini",
@@ -14088,7 +13970,6 @@
     },
     "gemini/gemini-2.0-flash-001": {
         "cache_read_input_token_cost": 2.5e-08,
-        "deprecation_date": "2026-03-31",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "gemini",
@@ -14176,7 +14057,6 @@
     },
     "gemini/gemini-2.0-flash-lite": {
         "cache_read_input_token_cost": 1.875e-08,
-        "deprecation_date": "2026-03-31",
         "input_cost_per_audio_token": 7.5e-08,
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "gemini",
@@ -20663,7 +20543,6 @@
         "supports_function_calling": true,
         "supports_tool_choice": true,
         "supports_prompt_caching": true,
-        "supports_reasoning": true,
         "supports_system_messages": true,
         "max_input_tokens": 1000000,
         "max_output_tokens": 8192
@@ -20678,7 +20557,6 @@
         "supports_function_calling": true,
         "supports_tool_choice": true,
         "supports_prompt_caching": true,
-        "supports_reasoning": true,
         "supports_system_messages": true,
         "max_input_tokens": 1000000,
         "max_output_tokens": 8192
@@ -20693,7 +20571,6 @@
         "supports_function_calling": true,
         "supports_tool_choice": true,
         "supports_prompt_caching": true,
-        "supports_reasoning": true,
         "supports_system_messages": true,
         "max_input_tokens": 200000,
         "max_output_tokens": 8192
@@ -23281,7 +23158,6 @@
         "supports_tool_choice": true
     },
     "openrouter/google/gemini-2.0-flash-001": {
-        "deprecation_date": "2026-03-31",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "openrouter",
@@ -23578,7 +23454,7 @@
         "mode": "chat",
         "output_cost_per_token": 1.02e-06,
         "supports_function_calling": true,
-        "supports_prompt_caching": true,
+        "supports_prompt_caching": false,
         "supports_reasoning": true,
         "supports_tool_choice": true
     },
@@ -23930,11 +23806,8 @@
         "max_input_tokens": 400000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
-        "mode": "responses",
+        "mode": "chat",
         "output_cost_per_token": 1.4e-05,
-        "supported_endpoints": [
-            "/v1/responses"
-        ],
         "supported_modalities": [
             "text",
             "image"
@@ -24273,7 +24146,6 @@
         "output_cost_per_token": 1.75e-06,
         "source": "https://openrouter.ai/z-ai/glm-4.6",
         "supports_function_calling": true,
-        "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
     },
@@ -24287,76 +24159,9 @@
         "output_cost_per_token": 1.9e-06,
         "source": "https://openrouter.ai/z-ai/glm-4.6:exacto",
         "supports_function_calling": true,
-        "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
     },
-    "openrouter/xiaomi/mimo-v2-flash": {
-            "input_cost_per_token": 9e-08,
-            "output_cost_per_token": 2.9e-07,
-            "cache_creation_input_token_cost": 0.0,
-            "cache_read_input_token_cost": 0.0,
-            "litellm_provider": "openrouter",
-            "max_input_tokens": 262144,
-            "max_output_tokens": 16384,
-            "max_tokens": 16384,
-            "mode": "chat",
-            "supports_function_calling": true,
-            "supports_tool_choice": true,
-            "supports_reasoning": true,
-            "supports_vision": false,
-            "supports_prompt_caching": false
-        },
-    "openrouter/z-ai/glm-4.7": {
-        "input_cost_per_token": 4e-07,
-        "output_cost_per_token": 1.5e-06,
-        "cache_creation_input_token_cost": 0.0,
-        "cache_read_input_token_cost": 0.0,
-        "litellm_provider": "openrouter",
-        "max_input_tokens": 202752,
-        "max_output_tokens": 64000,
-        "max_tokens": 64000,
-        "mode": "chat",
-        "supports_function_calling": true,
-        "supports_tool_choice": true,
-        "supports_reasoning": true,
-        "supports_vision": true,
-        "supports_prompt_caching": false,
-        "supports_assistant_prefill": true
-    },
-    "openrouter/z-ai/glm-4.7-flash": {
-        "input_cost_per_token": 7e-08,
-        "output_cost_per_token": 4e-07,
-        "cache_creation_input_token_cost": 0.0,
-        "cache_read_input_token_cost": 0.0,
-        "litellm_provider": "openrouter",
-        "max_input_tokens": 200000,
-        "max_output_tokens": 32000,
-        "max_tokens": 32000,
-        "mode": "chat",
-        "supports_function_calling": true,
-        "supports_tool_choice": true,
-        "supports_reasoning": true,
-        "supports_vision": true,
-        "supports_prompt_caching": false
-    },
-    "openrouter/minimax/minimax-m2.1": {
-        "input_cost_per_token": 2.7e-07,
-        "output_cost_per_token": 1.2e-06,
-        "cache_creation_input_token_cost": 0.0,
-        "cache_read_input_token_cost": 0.0,
-        "litellm_provider": "openrouter",
-        "max_input_tokens": 204000,
-        "max_output_tokens": 64000,
-        "max_tokens": 64000,
-        "mode": "chat",
-        "supports_function_calling": true,
-        "supports_tool_choice": true,
-        "supports_reasoning": true,
-        "supports_vision": true,
-        "supports_prompt_caching": false,
-        "supports_computer_use": false
-    },
     "ovhcloud/DeepSeek-R1-Distill-Llama-70B": {
         "input_cost_per_token": 6.7e-07,
         "litellm_provider": "ovhcloud",
@@ -27890,7 +27695,6 @@
         "output_cost_per_token": 9e-07
     },
     "vercel_ai_gateway/google/gemini-2.0-flash": {
-        "deprecation_date": "2026-03-31",
         "input_cost_per_token": 1.5e-07,
         "litellm_provider": "vercel_ai_gateway",
         "max_input_tokens": 1048576,
@@ -27900,7 +27704,6 @@
         "output_cost_per_token": 6e-07
     },
     "vercel_ai_gateway/google/gemini-2.0-flash-lite": {
-        "deprecation_date": "2026-03-31",
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "vercel_ai_gateway",
         "max_input_tokens": 1048576,
@@ -30570,7 +30373,6 @@
         "supports_web_search": true
     },
     "xai/grok-3": {
-        "cache_read_input_token_cost": 7.5e-07,
         "input_cost_per_token": 3e-06,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30585,7 +30387,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-beta": {
-        "cache_read_input_token_cost": 7.5e-07,
         "input_cost_per_token": 3e-06,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30600,7 +30401,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-fast-beta": {
-        "cache_read_input_token_cost": 1.25e-06,
         "input_cost_per_token": 5e-06,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30615,7 +30415,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-fast-latest": {
-        "cache_read_input_token_cost": 1.25e-06,
         "input_cost_per_token": 5e-06,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30630,7 +30429,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-latest": {
-        "cache_read_input_token_cost": 7.5e-07,
         "input_cost_per_token": 3e-06,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30645,7 +30443,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-mini": {
-        "cache_read_input_token_cost": 7.5e-08,
         "input_cost_per_token": 3e-07,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30661,7 +30458,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-mini-beta": {
-        "cache_read_input_token_cost": 7.5e-08,
         "input_cost_per_token": 3e-07,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30677,7 +30473,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-mini-fast": {
-        "cache_read_input_token_cost": 1.5e-07,
         "input_cost_per_token": 6e-07,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30693,7 +30488,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-mini-fast-beta": {
-        "cache_read_input_token_cost": 1.5e-07,
         "input_cost_per_token": 6e-07,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30709,7 +30503,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-mini-fast-latest": {
-        "cache_read_input_token_cost": 1.5e-07,
         "input_cost_per_token": 6e-07,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30725,7 +30518,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-mini-latest": {
-        "cache_read_input_token_cost": 7.5e-08,
         "input_cost_per_token": 3e-07,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30992,14 +30784,11 @@
         "max_output_tokens": 128000,
         "mode": "chat",
         "supports_function_calling": true,
-        "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
         "source": "https://docs.z.ai/guides/overview/pricing"
     },
     "zai/glm-4.6": {
-        "cache_creation_input_token_cost": 0,
-        "cache_read_input_token_cost": 1.1e-07,
         "input_cost_per_token": 6e-07,
         "output_cost_per_token": 2.2e-06,
         "litellm_provider": "zai",
@@ -31007,8 +30796,6 @@
         "max_output_tokens": 128000,
         "mode": "chat",
         "supports_function_calling": true,
-        "supports_prompt_caching": true,
-        "supports_reasoning": true,
         "supports_tool_choice": true,
         "source": "https://docs.z.ai/guides/overview/pricing"
     },
@@ -34656,4 +34443,4 @@
         "output_cost_per_token": 0,
         "supports_reasoning": true
     }
-}
\ No newline at end of file
+}
diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py
index 56feff548ad..ded591a8f53 100644
--- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py
+++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py
@@ -387,57 +387,25 @@ async def callback(code: str, state: str):
     1. Try resource_metadata from WWW-Authenticate header (if present)
     2. Fall back to path-based well-known URI: /.well-known/oauth-protected-resource/{path}
     (
-    If the resource identifier value contains a path or query component, any terminating slash (/)
-    following the host component MUST be removed before inserting /.well-known/ and the well-known
-    URI path suffix between the host component and the path(include root path) and/or query components.
+    If the resource identifier value contains a path or query component, any terminating slash (/) 
+    following the host component MUST be removed before inserting /.well-known/ and the well-known 
+    URI path suffix between the host component and the path(include root path) and/or query components. 
     https://datatracker.ietf.org/doc/html/rfc9728#section-3.1)
     3. Fall back to root-based well-known URI: /.well-known/oauth-protected-resource
-
-    Dual Pattern Support:
-    - Standard MCP pattern: /mcp/{server_name} (recommended, used by mcp-inspector, VSCode Copilot)
-    - LiteLLM legacy pattern: /{server_name}/mcp (backward compatibility)
-
-    The resource URL returned matches the pattern used in the discovery request.
 """
-
-
-def _build_oauth_protected_resource_response(
-    request: Request,
-    mcp_server_name: Optional[str],
-    use_standard_pattern: bool,
-) -> dict:
-    """
-    Build OAuth protected resource response with the appropriate URL pattern.
-
-    Args:
-        request: FastAPI Request object
-        mcp_server_name: Name of the MCP server
-        use_standard_pattern: If True, use /mcp/{server_name} pattern;
-                             if False, use /{server_name}/mcp pattern
-
-    Returns:
-        OAuth protected resource metadata dict
-    """
+@router.get(f"/.well-known/oauth-protected-resource{'' if get_server_root_path() == '/' else get_server_root_path()}/{{mcp_server_name}}/mcp")
+@router.get("/.well-known/oauth-protected-resource")
+async def oauth_protected_resource_mcp(
+    request: Request, mcp_server_name: Optional[str] = None
+):
     from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
         global_mcp_server_manager,
     )
-
+    # Get the correct base URL considering X-Forwarded-* headers
     request_base_url = get_request_base_url(request)
     mcp_server: Optional[MCPServer] = None
     if mcp_server_name:
         mcp_server = global_mcp_server_manager.get_mcp_server_by_name(mcp_server_name)
-
-    # Build resource URL based on the pattern
-    if mcp_server_name:
-        if use_standard_pattern:
-            # Standard MCP pattern: /mcp/{server_name}
-            resource_url = f"{request_base_url}/mcp/{mcp_server_name}"
-        else:
-            # LiteLLM legacy pattern: /{server_name}/mcp
-            resource_url = f"{request_base_url}/{mcp_server_name}/mcp"
-    else:
-        resource_url = f"{request_base_url}/mcp"
-
     return {
         "authorization_servers": [
             (
@@ -446,55 +414,14 @@ def _build_oauth_protected_resource_response(
                 else f"{request_base_url}"
             )
         ],
-        "resource": resource_url,
+        "resource": (
+            f"{request_base_url}/{mcp_server_name}/mcp"
+            if mcp_server_name
+            else f"{request_base_url}/mcp"
+        ),  # this is what Claude will call
         "scopes_supported": mcp_server.scopes if mcp_server else [],
     }
 
-
-# Standard MCP pattern: /.well-known/oauth-protected-resource/mcp/{server_name}
-# This is the pattern expected by standard MCP clients (mcp-inspector, VSCode Copilot)
-@router.get(f"/.well-known/oauth-protected-resource{'' if get_server_root_path() == '/' else get_server_root_path()}/mcp/{{mcp_server_name}}")
-async def oauth_protected_resource_mcp_standard(
-    request: Request, mcp_server_name: str
-):
-    """
-    OAuth protected resource discovery endpoint using standard MCP URL pattern.
-
-    Standard pattern: /mcp/{server_name}
-    Discovery path: /.well-known/oauth-protected-resource/mcp/{server_name}
-
-    This endpoint is compliant with MCP specification and works with standard
-    MCP clients like mcp-inspector and VSCode Copilot.
-    """
-    return _build_oauth_protected_resource_response(
-        request=request,
-        mcp_server_name=mcp_server_name,
-        use_standard_pattern=True,
-    )
-
-
-# LiteLLM legacy pattern: /.well-known/oauth-protected-resource/{server_name}/mcp
-# Kept for backward compatibility with existing deployments
-@router.get(f"/.well-known/oauth-protected-resource{'' if get_server_root_path() == '/' else get_server_root_path()}/{{mcp_server_name}}/mcp")
-@router.get("/.well-known/oauth-protected-resource")
-async def oauth_protected_resource_mcp(
-    request: Request, mcp_server_name: Optional[str] = None
-):
-    """
-    OAuth protected resource discovery endpoint using LiteLLM legacy URL pattern.
-
-    Legacy pattern: /{server_name}/mcp
-    Discovery path: /.well-known/oauth-protected-resource/{server_name}/mcp
-
-    This endpoint is kept for backward compatibility. New integrations should
-    use the standard MCP pattern (/mcp/{server_name}) instead.
-    """
-    return _build_oauth_protected_resource_response(
-        request=request,
-        mcp_server_name=mcp_server_name,
-        use_standard_pattern=False,
-    )
-
 """
     https://datatracker.ietf.org/doc/html/rfc8414#section-3.1
     RFC 8414: Path-aware OAuth discovery
@@ -503,26 +430,15 @@ async def oauth_protected_resource_mcp(
     the well-known URI suffix between the host component and the path(include root path)
     component.
 """
-
-
-def _build_oauth_authorization_server_response(
-    request: Request,
-    mcp_server_name: Optional[str],
-) -> dict:
-    """
-    Build OAuth authorization server metadata response.
-
-    Args:
-        request: FastAPI Request object
-        mcp_server_name: Name of the MCP server
-
-    Returns:
-        OAuth authorization server metadata dict
-    """
+@router.get(f"/.well-known/oauth-authorization-server{'' if get_server_root_path() == '/' else get_server_root_path()}/{{mcp_server_name}}")
+@router.get("/.well-known/oauth-authorization-server")
+async def oauth_authorization_server_mcp(
+    request: Request, mcp_server_name: Optional[str] = None
+):
     from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
         global_mcp_server_manager,
     )
-
+    # Get the correct base URL considering X-Forwarded-* headers
     request_base_url = get_request_base_url(request)
 
     authorization_endpoint = (
@@ -554,58 +470,18 @@ def _build_oauth_authorization_server_response(
     }
 
 
-# Standard MCP pattern: /.well-known/oauth-authorization-server/mcp/{server_name}
-@router.get(f"/.well-known/oauth-authorization-server{'' if get_server_root_path() == '/' else get_server_root_path()}/mcp/{{mcp_server_name}}")
-async def oauth_authorization_server_mcp_standard(
-    request: Request, mcp_server_name: str
-):
-    """
-    OAuth authorization server discovery endpoint using standard MCP URL pattern.
-
-    Standard pattern: /mcp/{server_name}
-    Discovery path: /.well-known/oauth-authorization-server/mcp/{server_name}
-    """
-    return _build_oauth_authorization_server_response(
-        request=request,
-        mcp_server_name=mcp_server_name,
-    )
-
-
-# LiteLLM legacy pattern and root endpoint
-@router.get(f"/.well-known/oauth-authorization-server{'' if get_server_root_path() == '/' else get_server_root_path()}/{{mcp_server_name}}")
-@router.get("/.well-known/oauth-authorization-server")
-async def oauth_authorization_server_mcp(
-    request: Request, mcp_server_name: Optional[str] = None
-):
-    """
-    OAuth authorization server discovery endpoint.
-
-    Supports both legacy pattern (/{server_name}) and root endpoint.
-    """
-    return _build_oauth_authorization_server_response(
-        request=request,
-        mcp_server_name=mcp_server_name,
-    )
-
-
 # Alias for standard OpenID discovery
 @router.get("/.well-known/openid-configuration")
 async def openid_configuration(request: Request):
     return await oauth_authorization_server_mcp(request)
 
 
-# Additional legacy pattern support
 @router.get("/.well-known/oauth-authorization-server/{mcp_server_name}/mcp")
-async def oauth_authorization_server_legacy(
-    request: Request, mcp_server_name: str
+@router.get("/.well-known/oauth-authorization-server")
+async def oauth_authorization_server_root(
+    request: Request, mcp_server_name: Optional[str] = None
 ):
-    """
-    OAuth authorization server discovery for legacy /{server_name}/mcp pattern.
-    """
-    return _build_oauth_authorization_server_response(
-        request=request,
-        mcp_server_name=mcp_server_name,
-    )
+    return await oauth_authorization_server_mcp(request, mcp_server_name)
 
 
 @router.post("/{mcp_server_name}/register")
diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
index 92fd54e8775..e0217cd9e00 100644
--- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
+++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
@@ -11,7 +11,7 @@
 import hashlib
 import json
 import re
-from typing import Any, Dict, List, Literal, Optional, Set, Tuple, Union, cast, Callable
+from typing import Any, Dict, List, Literal, Optional, Set, Tuple, Union, cast
 from urllib.parse import urlparse
 
 from fastapi import HTTPException
@@ -63,20 +63,7 @@
     MCPOAuthMetadata,
     MCPServer,
 )
-
-try:
-    from mcp.shared.tool_name_validation import SEP_986_URL, validate_tool_name  # type: ignore
-except ImportError:
-    SEP_986_URL = "https://github.com/modelcontextprotocol/protocol/blob/main/proposals/0001-tool-name-validation.md"
-
-    def validate_tool_name(name: str):
-        from pydantic import BaseModel
-
-        class MockResult(BaseModel):
-            is_valid: bool = True
-            warnings: list = []
-
-        return MockResult()
+from mcp.shared.tool_name_validation import SEP_986_URL, validate_tool_name
 
 
 # Probe includes characters on both sides of the separator to mimic real prefixed tool names.
@@ -103,9 +90,7 @@ def _warn(field_name: str, value: Optional[str]) -> None:
         if result.is_valid:
             return
 
-        warning_text = (
-            "; ".join(result.warnings) if result.warnings else "Validation failed"
-        )
+        warning_text = "; ".join(result.warnings) if result.warnings else "Validation failed"
         verbose_logger.warning(
             "MCP server '%s' has invalid %s '%s': %s",
             server_id,
@@ -118,6 +103,7 @@ def _warn(field_name: str, value: Optional[str]) -> None:
     _warn("server_name", server_name)
 
 
+
 def _deserialize_json_dict(data: Any) -> Optional[Dict[str, str]]:
     """
     Deserialize optional JSON mappings stored in the database.
@@ -405,13 +391,10 @@ def _register_openapi_tools(self, spec_path: str, server: MCPServer, base_url: s
             # Note: `extra_headers` on MCPServer is a List[str] of header names to forward
             # from the client request (not available in this OpenAPI tool generation step).
             # `static_headers` is a dict of concrete headers to always send.
-            headers = (
-                merge_mcp_headers(
-                    extra_headers=headers,
-                    static_headers=server.static_headers,
-                )
-                or {}
-            )
+            headers = merge_mcp_headers(
+                extra_headers=headers,
+                static_headers=server.static_headers,
+            ) or {}
 
             verbose_logger.debug(
                 f"Using headers for OpenAPI tools (excluding sensitive values): "
@@ -1842,7 +1825,6 @@ async def _call_regular_mcp_tool(
         oauth2_headers: Optional[Dict[str, str]],
         raw_headers: Optional[Dict[str, str]],
         proxy_logging_obj: Optional[ProxyLogging],
-        host_progress_callback: Optional[Callable] = None,
     ) -> CallToolResult:
         """
         Call a regular MCP tool using the MCP client.
@@ -1927,7 +1909,7 @@ async def _call_regular_mcp_tool(
         )
 
         async def _call_tool_via_client(client, params):
-            return await client.call_tool(params, host_progress_callback=host_progress_callback)
+            return await client.call_tool(params)
 
         tasks.append(
             asyncio.create_task(_call_tool_via_client(client, call_tool_params))
@@ -1964,8 +1946,6 @@ async def call_tool(
         proxy_logging_obj: Optional[ProxyLogging] = None,
         oauth2_headers: Optional[Dict[str, str]] = None,
         raw_headers: Optional[Dict[str, str]] = None,
-        host_progress_callback: Optional[Callable] = None,
-
     ) -> CallToolResult:
         """
         Call a tool with the given name and arguments
@@ -2041,7 +2021,6 @@ async def call_tool(
                 oauth2_headers=oauth2_headers,
                 raw_headers=raw_headers,
                 proxy_logging_obj=proxy_logging_obj,
-                host_progress_callback=host_progress_callback,
             )
 
         # For OpenAPI tools, await outside the client context
diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py
index 6d54c3871e5..03652ae155e 100644
--- a/litellm/proxy/_experimental/mcp_server/server.py
+++ b/litellm/proxy/_experimental/mcp_server/server.py
@@ -8,7 +8,8 @@
 from datetime import datetime
 import traceback
 import uuid
-from typing import Any, AsyncIterator, Dict, List, Optional, Tuple, Union, cast, Callable
+from typing import Any, AsyncIterator, Dict, List, Optional, Tuple, Union, cast
+
 from fastapi import FastAPI, HTTPException
 from pydantic import AnyUrl, ConfigDict
 from starlette.types import Receive, Scope, Send
@@ -73,11 +74,7 @@
         AuthContextMiddleware,
         auth_context_var,
     )
-
-    try:
-        from mcp.server.streamable_http_manager import StreamableHTTPSessionManager
-    except ImportError:
-        StreamableHTTPSessionManager = None  # type: ignore
+    from mcp.server.streamable_http_manager import StreamableHTTPSessionManager
     from mcp.types import (
         CallToolResult,
         EmbeddedResource,
@@ -127,8 +124,8 @@ class ListMCPToolsRestAPIResponseObject(MCPTool):
     session_manager = StreamableHTTPSessionManager(
         app=server,
         event_store=None,
-        json_response=False, # enables SSE streaming
-        stateless=False, # enables session state
+        json_response=True,  # Use JSON responses instead of SSE by default
+        stateless=True,
     )
 
     # Create SSE session manager
@@ -281,30 +278,6 @@ async def mcp_server_tool_call(
         verbose_logger.debug(
             f"MCP mcp_server_tool_call - User API Key Auth from context: {user_api_key_auth}"
         )
-        host_progress_callback = None
-        try:
-            host_ctx = server.request_context
-            if host_ctx and hasattr(host_ctx, 'meta') and host_ctx.meta:
-                host_token = getattr(host_ctx.meta, 'progressToken', None)
-                if host_token and hasattr(host_ctx, 'session') and host_ctx.session:
-                    host_session = host_ctx.session
-                    
-                    async def forward_progress(progress: float, total: float | None):
-                        """Forward progress notifications from external MCP to Host"""
-                        try:
-                            await host_session.send_progress_notification(
-                                progress_token=host_token,
-                                progress=progress,
-                                total=total
-                            )
-                            verbose_logger.debug(f"Forwarded progress {progress}/{total} to Host")
-                        except Exception as e:
-                            verbose_logger.error(f"Failed to forward progress to Host: {e}")
-                    
-                    host_progress_callback = forward_progress
-                    verbose_logger.debug(f"Host progressToken captured: {host_token[:8]}...")
-        except Exception as e:
-            verbose_logger.warning(f"Could not capture host progress context: {e}")
         try:
             # Create a body date for logging
             body_data = {"name": name, "arguments": arguments}
@@ -334,7 +307,6 @@ async def forward_progress(progress: float, total: float | None):
                 mcp_server_auth_headers=mcp_server_auth_headers,
                 oauth2_headers=oauth2_headers,
                 raw_headers=raw_headers,
-                host_progress_callback=host_progress_callback,
                 **data,  # for logging
             )
         except BlockedPiiEntityError as e:
@@ -1369,7 +1341,6 @@ async def execute_mcp_tool(
         mcp_server_auth_headers: Optional[Dict[str, Dict[str, str]]] = None,
         oauth2_headers: Optional[Dict[str, str]] = None,
         raw_headers: Optional[Dict[str, str]] = None,
-        host_progress_callback: Optional[Callable] = None,
         **kwargs: Any,
     ) -> CallToolResult:
         """
@@ -1467,7 +1438,6 @@ async def execute_mcp_tool(
                     oauth2_headers=oauth2_headers,
                     raw_headers=raw_headers,
                     litellm_logging_obj=litellm_logging_obj,
-                    host_progress_callback=host_progress_callback,
                 )
 
             # Fall back to local tool registry with original name (legacy support)
@@ -1715,7 +1685,6 @@ async def _handle_managed_mcp_tool(
         oauth2_headers: Optional[Dict[str, str]] = None,
         raw_headers: Optional[Dict[str, str]] = None,
         litellm_logging_obj: Optional[Any] = None,
-        host_progress_callback: Optional[Callable] = None, 
     ) -> CallToolResult:
         """Handle tool execution for managed server tools"""
         # Import here to avoid circular import
@@ -1731,7 +1700,6 @@ async def _handle_managed_mcp_tool(
             oauth2_headers=oauth2_headers,
             raw_headers=raw_headers,
             proxy_logging_obj=proxy_logging_obj,
-            host_progress_callback=host_progress_callback,
         )
         verbose_logger.debug("CALL TOOL RESULT: %s", call_tool_result)
         return call_tool_result
diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html
deleted file mode 100644
index a8ef84a338e..00000000000
--- a/litellm/proxy/_experimental/out/404.html
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1098-a1702da59647cf14.js b/litellm/proxy/_experimental/out/_next/static/chunks/1098-a1702da59647cf14.js
deleted file mode 100644
index 8bdb738f169..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/1098-a1702da59647cf14.js
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new file mode 100644
index 00000000000..fe98281d76f
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/137-c6f74fedf576a11b.js
@@ -0,0 +1 @@
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deleted file mode 100644
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+++ /dev/null
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deleted file mode 100644
index b252b89cf2f..00000000000
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+++ /dev/null
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new file mode 100644
index 00000000000..f19de7216fa
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/6894-8c74216e23aa271e.js b/litellm/proxy/_experimental/out/_next/static/chunks/2068-2c78bfc32dc0de5f.js
similarity index 89%
rename from litellm/proxy/_experimental/out/_next/static/chunks/6894-8c74216e23aa271e.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/2068-2c78bfc32dc0de5f.js
index 7b7fe0e8953..7a344f59032 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/6894-8c74216e23aa271e.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/2068-2c78bfc32dc0de5f.js
@@ -1,4 +1,4 @@
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new file mode 100644
index 00000000000..70adfcf7461
--- /dev/null
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deleted file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/292-7bd148a17bc0a05b.js b/litellm/proxy/_experimental/out/_next/static/chunks/292-7bd148a17bc0a05b.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/2926-ac542d9fa707b8a4.js b/litellm/proxy/_experimental/out/_next/static/chunks/2926-ac542d9fa707b8a4.js
deleted file mode 100644
index 77a0fac9b1d..00000000000
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/3367-33bb84b3d3d247b2.js b/litellm/proxy/_experimental/out/_next/static/chunks/3367-58830187e9e5b9fa.js
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n},tw=e=>{if(0===e.length)return e;let t=e[e.length-1];switch(t.type){case"separator":return tw(e=e.slice(0,e.length-1));case"number":let n=t.value[t.value.length-1];if("."===n||"-"===n)return tw(e=e.slice(0,e.length-1));case"string":let r=e[e.length-2];if(r?.type==="delimiter"||r?.type==="brace"&&"{"===r.value)return tw(e=e.slice(0,e.length-1));break;case"delimiter":return tw(e=e.slice(0,e.length-1))}return e},t_=e=>{let t=[];return e.map(e=>{"brace"===e.type&&("{"===e.value?t.push("}"):t.splice(t.lastIndexOf("}"),1)),"paren"===e.type&&("["===e.value?t.push("]"):t.splice(t.lastIndexOf("]"),1))}),t.length>0&&t.reverse().map(t=>{"}"===t?e.push({type:"brace",value:"}"}):"]"===t&&e.push({type:"paren",value:"]"})}),e},tk=e=>{let t="";return e.map(e=>{"string"===e.type?t+='"'+e.value+'"':t+=e.value}),t},tS=e=>JSON.parse(tk(t_(tw(ty(e))))),tx="__json_buf";function tM(e){return"tool_use"===e.type||"server_tool_use"===e.type||"mcp_tool_use"===e.type}class tE{constructor(){o.add(this),this.messages=[],this.receivedMessages=[],l.set(this,void 0),this.controller=new AbortController,c.set(this,void 0),u.set(this,()=>{}),d.set(this,()=>{}),h.set(this,void 0),f.set(this,()=>{}),p.set(this,()=>{}),g.set(this,{}),m.set(this,!1),b.set(this,!1),v.set(this,!1),y.set(this,!1),w.set(this,void 0),_.set(this,void 0),x.set(this,e=>{if(et(this,b,!0,"f"),ea(e)&&(e=new el),e instanceof el)return et(this,v,!0,"f"),this._emit("abort",e);if(e instanceof ei)return this._emit("error",e);if(e instanceof Error){let t=new ei(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new ei(String(e)))}),et(this,c,new Promise((e,t)=>{et(this,u,e,"f"),et(this,d,t,"f")}),"f"),et(this,h,new Promise((e,t)=>{et(this,f,e,"f"),et(this,p,t,"f")}),"f"),en(this,c,"f").catch(()=>{}),en(this,h,"f").catch(()=>{})}get response(){return en(this,w,"f")}get request_id(){return en(this,_,"f")}async withResponse(){let e=await en(this,c,"f");if(!e)throw Error("Could 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el;en(this,o,"m",C).call(this)}_connected(e){this.ended||(et(this,w,e,"f"),et(this,_,e?.headers.get("request-id"),"f"),en(this,u,"f").call(this,e),this._emit("connect"))}get ended(){return en(this,m,"f")}get errored(){return en(this,b,"f")}get aborted(){return en(this,v,"f")}abort(){this.controller.abort()}on(e,t){return(en(this,g,"f")[e]||(en(this,g,"f")[e]=[])).push({listener:t}),this}off(e,t){let n=en(this,g,"f")[e];if(!n)return this;let r=n.findIndex(e=>e.listener===t);return r>=0&&n.splice(r,1),this}once(e,t){return(en(this,g,"f")[e]||(en(this,g,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,n)=>{et(this,y,!0,"f"),"error"!==e&&this.once("error",n),this.once(e,t)})}async done(){et(this,y,!0,"f"),await en(this,h,"f")}get currentMessage(){return en(this,l,"f")}async finalMessage(){return await this.done(),en(this,o,"m",k).call(this)}async finalText(){return await this.done(),en(this,o,"m",S).call(this)}_emit(e,...t){if(en(this,m,"f"))return;"end"===e&&(et(this,m,!0,"f"),en(this,f,"f").call(this));let n=en(this,g,"f")[e];if(n&&(en(this,g,"f")[e]=n.filter(e=>!e.once),n.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];en(this,y,"f")||n?.length||Promise.reject(e),en(this,d,"f").call(this,e),en(this,p,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];en(this,y,"f")||n?.length||Promise.reject(e),en(this,d,"f").call(this,e),en(this,p,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",en(this,o,"m",k).call(this))}async _fromReadableStream(e,t){let n=t?.signal;n&&(n.aborted&&this.controller.abort(),n.addEventListener("abort",()=>this.controller.abort())),en(this,o,"m",M).call(this),this._connected(null);let r=eV.fromReadableStream(e,this.controller);for await(let e of r)en(this,o,"m",E).call(this,e);if(r.controller.signal?.aborted)throw new el;en(this,o,"m",C).call(this)}[(l=new WeakMap,c=new WeakMap,u=new WeakMap,d=new WeakMap,h=new WeakMap,f=new WeakMap,p=new WeakMap,g=new WeakMap,m=new WeakMap,b=new WeakMap,v=new WeakMap,y=new WeakMap,w=new WeakMap,_=new WeakMap,x=new WeakMap,o=new WeakSet,k=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},S=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new ei("stream ended without producing a content block with type=text");return e.join(" ")},M=function(){this.ended||et(this,l,void 0,"f")},E=function(e){if(this.ended)return;let t=en(this,o,"m",R).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tM(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,l,t,"f")}},C=function(){if(this.ended)throw new ei("stream has ended, this shouldn't happen");let e=en(this,l,"f");if(!e)throw new ei("request ended without sending any chunks");return et(this,l,void 0,"f"),e},R=function(e){let t=en(this,l,"f");if("message_start"===e.type){if(t)throw new ei(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new ei(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tM(n)){let t=n[tx]||"";if(Object.defineProperty(n,tx,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{n.input=tS(t)}catch(n){let e=new ei(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${n}. JSON: ${t}`);en(this,x,"f").call(this,e)}}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eV(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}let tC={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tR={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tP extends tc{constructor(){super(...arguments),this.batches=new tv(this._client)}create(e,t){let{betas:n,...r}=e;r.model in tR&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tR[r.model]}
+Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=tC[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:th([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return tE.createMessage(this,e,t)}countTokens(e,t){let{betas:n,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:th([{"anthropic-beta":[...n??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tP.Batches=tv;class tO extends tc{constructor(){super(...arguments),this.models=new tm(this._client),this.messages=new tP(this._client),this.files=new tg(this._client)}}tO.Models=tm,tO.Messages=tP,tO.Files=tg;class tj extends tc{create(e,t){let{betas:n,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:th([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tI="__json_buf";function tA(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tL{constructor(){P.add(this),this.messages=[],this.receivedMessages=[],O.set(this,void 0),this.controller=new AbortController,j.set(this,void 0),I.set(this,()=>{}),A.set(this,()=>{}),L.set(this,void 0),Z.set(this,()=>{}),T.set(this,()=>{}),N.set(this,{}),z.set(this,!1),$.set(this,!1),q.set(this,!1),D.set(this,!1),B.set(this,void 0),H.set(this,void 0),F.set(this,e=>{if(et(this,$,!0,"f"),ea(e)&&(e=new el),e instanceof el)return et(this,q,!0,"f"),this._emit("abort",e);if(e instanceof ei)return this._emit("error",e);if(e instanceof Error){let t=new ei(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new ei(String(e)))}),et(this,j,new Promise((e,t)=>{et(this,I,e,"f"),et(this,A,t,"f")}),"f"),et(this,L,new Promise((e,t)=>{et(this,Z,e,"f"),et(this,T,t,"f")}),"f"),en(this,j,"f").catch(()=>{}),en(this,L,"f").catch(()=>{})}get response(){return en(this,B,"f")}get request_id(){return en(this,H,"f")}async withResponse(){let e=await en(this,j,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tL;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,n){let r=new tL;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...n,headers:{...n?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},en(this,F,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,n){let r=n?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),en(this,P,"m",V).call(this);let{response:a,data:s}=await e.create({...t,stream:!0},{...n,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),s))en(this,P,"m",X).call(this,e);if(s.controller.signal?.aborted)throw new el;en(this,P,"m",J).call(this)}_connected(e){this.ended||(et(this,B,e,"f"),et(this,H,e?.headers.get("request-id"),"f"),en(this,I,"f").call(this,e),this._emit("connect"))}get ended(){return en(this,z,"f")}get errored(){return en(this,$,"f")}get aborted(){return en(this,q,"f")}abort(){this.controller.abort()}on(e,t){return(en(this,N,"f")[e]||(en(this,N,"f")[e]=[])).push({listener:t}),this}off(e,t){let n=en(this,N,"f")[e];if(!n)return this;let r=n.findIndex(e=>e.listener===t);return r>=0&&n.splice(r,1),this}once(e,t){return(en(this,N,"f")[e]||(en(this,N,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,n)=>{et(this,D,!0,"f"),"error"!==e&&this.once("error",n),this.once(e,t)})}async done(){et(this,D,!0,"f"),await en(this,L,"f")}get currentMessage(){return en(this,O,"f")}async finalMessage(){return await this.done(),en(this,P,"m",U).call(this)}async finalText(){return await this.done(),en(this,P,"m",W).call(this)}_emit(e,...t){if(en(this,z,"f"))return;"end"===e&&(et(this,z,!0,"f"),en(this,Z,"f").call(this));let n=en(this,N,"f")[e];if(n&&(en(this,N,"f")[e]=n.filter(e=>!e.once),n.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];en(this,D,"f")||n?.length||Promise.reject(e),en(this,A,"f").call(this,e),en(this,T,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];en(this,D,"f")||n?.length||Promise.reject(e),en(this,A,"f").call(this,e),en(this,T,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",en(this,P,"m",U).call(this))}async _fromReadableStream(e,t){let n=t?.signal;n&&(n.aborted&&this.controller.abort(),n.addEventListener("abort",()=>this.controller.abort())),en(this,P,"m",V).call(this),this._connected(null);let r=eV.fromReadableStream(e,this.controller);for await(let e of r)en(this,P,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new el;en(this,P,"m",J).call(this)}[(O=new WeakMap,j=new WeakMap,I=new WeakMap,A=new WeakMap,L=new WeakMap,Z=new WeakMap,T=new WeakMap,N=new WeakMap,z=new WeakMap,$=new WeakMap,q=new WeakMap,D=new WeakMap,B=new WeakMap,H=new WeakMap,F=new WeakMap,P=new WeakSet,U=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},W=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new ei("stream ended without producing a content block with type=text");return e.join(" ")},V=function(){this.ended||et(this,O,void 0,"f")},X=function(e){if(this.ended)return;let t=en(this,P,"m",G).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tA(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,O,t,"f")}},J=function(){if(this.ended)throw new ei("stream has ended, this shouldn't happen");let e=en(this,O,"f");if(!e)throw new ei("request ended without sending any chunks");return et(this,O,void 0,"f"),e},G=function(e){let t=en(this,O,"f");if("message_start"===e.type){if(t)throw new ei(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new ei(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tA(n)){let t=n[tI]||"";Object.defineProperty(n,tI,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(n.input=tS(t))}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eV(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}class tZ extends tc{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tp`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",e2,{query:e,...t})}delete(e,t){return this._client.delete(tp`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tp`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let n=await this.retrieve(e);if(!n.results_url)throw new ei(`No batch \`results_url\`; Has it finished processing? ${n.processing_status} - ${n.id}`);return this._client.get(n.results_url,{...t,headers:th([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tb.fromResponse(t.response,t.controller))}}class tT extends tc{constructor(){super(...arguments),this.batches=new tZ(this._client)}create(e,t){e.model in tN&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tN[e.model]}
+Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let n=this._client._options.timeout;if(!e.stream&&null==n){let t=tC[e.model]??void 0;n=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}return this._client.post("/v1/messages",{body:e,timeout:n??6e5,...t,stream:e.stream??!1})}stream(e,t){return tL.createMessage(this,e,t)}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let tN={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};tT.Batches=tZ;class tz extends tc{retrieve(e,t={},n){let{betas:r}=t??{};return this._client.get(tp`/v1/models/${e}`,{...n,headers:th([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},n?.headers])})}list(e={},t){let{betas:n,...r}=e??{};return this._client.getAPIList("/v1/models",e2,{query:r,...t,headers:th([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers])})}}let t$=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()??void 0:void 0!==globalThis.Deno?globalThis.Deno.env?.get?.(e)?.trim():void 0;class tq{constructor({baseURL:e=t$("ANTHROPIC_BASE_URL"),apiKey:t=t$("ANTHROPIC_API_KEY")??null,authToken:n=t$("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){K.set(this,void 0);let a={apiKey:t,authToken:n,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&eL())throw new ei("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??tD.DEFAULT_TIMEOUT,this.logger=a.logger??console;let s="warn";this.logLevel=s,this.logLevel=eE(a.logLevel,"ClientOptions.logLevel",this)??eE(t$("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??s,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("undefined"!=typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),et(this,K,eH,"f"),this._options=a,this.apiKey=t,this.authToken=n}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization")||t.has("authorization")))throw Error('Could not resolve authentication method. 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/3634-5083d080185955ff.js b/litellm/proxy/_experimental/out/_next/static/chunks/3634-5083d080185955ff.js
deleted file mode 100644
index c9beeb9626a..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/3634-5083d080185955ff.js
+++ /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/4891-a6a8811399a4a3df.js b/litellm/proxy/_experimental/out/_next/static/chunks/4891-a6a8811399a4a3df.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/5105-2998cbe1c9fc8ee4.js b/litellm/proxy/_experimental/out/_next/static/chunks/5105-2998cbe1c9fc8ee4.js
new file mode 100644
index 00000000000..021cd8e246d
--- /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/5869-aa0b3213b1b23ec5.js b/litellm/proxy/_experimental/out/_next/static/chunks/5869-426268ba6ad0ce0c.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/5869-aa0b3213b1b23ec5.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/5869-426268ba6ad0ce0c.js
index 7224a73aee3..63b2a7aa742 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/5869-aa0b3213b1b23ec5.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/5869-426268ba6ad0ce0c.js
@@ -1 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/5992-243bba762148af9b.js b/litellm/proxy/_experimental/out/_next/static/chunks/5992-243bba762148af9b.js
deleted file mode 100644
index a464e6bc3db..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/5992-243bba762148af9b.js
+++ /dev/null
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new file mode 100644
index 00000000000..484989d68a1
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deleted file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/6399-9e22a1275286c0df.js b/litellm/proxy/_experimental/out/_next/static/chunks/6399-ccf9cdbdcd5f7abb.js
similarity index 65%
rename from litellm/proxy/_experimental/out/_next/static/chunks/6399-9e22a1275286c0df.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/6399-ccf9cdbdcd5f7abb.js
index 81308d73901..cc51572bdc6 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/6399-9e22a1275286c0df.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/6399-ccf9cdbdcd5f7abb.js
@@ -1 +1 @@
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similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/6600-077d81439e75d3a3.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/6600-0ec5e2dc66d8b41a.js
index d3032abe15a..cfbd2b1a58a 100644
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/6600-0ec5e2dc66d8b41a.js
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deleted file mode 100644
index 5ef3d33ed21..00000000000
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+++ /dev/null
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e,t=this.state,n=t.focused,r=t.flattenNodes,a=t.keyEntities,l=t.draggingNodeKey,c=t.activeKey,i=t.dropLevelOffset,d=t.dropContainerKey,s=t.dropTargetKey,u=t.dropPosition,f=t.dragOverNodeKey,m=t.indent,h=this.props,v=h.prefixCls,g=h.className,y=h.style,b=h.showLine,x=h.focusable,w=h.tabIndex,k=h.selectable,S=h.showIcon,N=h.icon,K=h.switcherIcon,O=h.draggable,I=h.checkable,R=h.checkStrictly,P=h.disabled,T=h.motion,M=h.loadData,D=h.filterTreeNode,L=h.height,j=h.itemHeight,B=h.scrollWidth,H=h.virtual,z=h.titleRender,A=h.dropIndicatorRender,W=h.onContextMenu,_=h.onScroll,q=h.direction,V=h.rootClassName,X=h.rootStyle,U=(0,F.Z)(this.props,{aria:!0,data:!0});O&&(e="object"===(0,E.Z)(O)?O:"function"==typeof O?{nodeDraggable:O}:{});var 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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/3705-dde102fd596f74e8.js b/litellm/proxy/_experimental/out/_next/static/chunks/7138-5b134dc8ad670770.js
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rename from litellm/proxy/_experimental/out/_next/static/chunks/3705-dde102fd596f74e8.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/7138-5b134dc8ad670770.js
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new file mode 100644
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base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(S,'",\n    messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(S,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(N,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(A).length>0,n="";if(e){let e=JSON.stringify({metadata:A},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=w.length>0?w:[{role:"user",content:y}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(S,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(S,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(N,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===f?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(S,'",\n	prompt="').concat(i,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(N,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(S,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===f?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(N,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(S,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(N,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(S,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.EMBEDDINGS:t='\nresponse = client.embeddings.create(\n	input="'.concat(i||"Your string here",'",\n	model="').concat(S,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case a.KP.TRANSCRIPTION:t='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(S,'",\n	file=audio_file').concat(i?',\n	prompt="'.concat(i.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case a.KP.SPEECH:t='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(S,'",\n	input="').concat(i||"Your text to convert to speech here",'",\n	voice="').concat(g,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer\n)\n\n# Save the audio to a file\noutput_filename = "output_speech.mp3"\nresponse.stream_to_file(output_filename)\nprint(f"Audio saved to {output_filename}")\n\n# Optional: Customize response format and speed\n# response = client.audio.speech.create(\n#     model="').concat(S,'",\n#     input="').concat(i||"Your text to convert to speech here",'",\n#     voice="alloy",\n#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm\n#     speed=1.0  # Range: 0.25 to 4.0\n# )\n# response.stream_to_file("output_speech.mp3")\n');break;default:t="\n# Code generation for this endpoint is not implemented yet."}return"".concat(C,"\n").concat(t)}},8443:function(e,t,n){var a,s,r,i;n.d(t,{KP:function(){return s},vf:function(){return o}}),(r=a||(a={})).AUDIO_SPEECH="audio_speech",r.AUDIO_TRANSCRIPTION="audio_transcription",r.IMAGE_GENERATION="image_generation",r.VIDEO_GENERATION="video_generation",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDING="embedding",(i=s||(s={})).IMAGE="image",i.VIDEO="video",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages",i.EMBEDDINGS="embeddings",i.SPEECH="speech",i.TRANSCRIPTION="transcription",i.A2A_AGENTS="a2a_agents";let l={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"},o=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=l[e];return console.log("endpointType:",t),t}return"chat"}},42673:function(e,t,n){var a,s;n.d(t,{Cl:function(){return a},bK:function(){return d},cd:function(){return l},dr:function(){return o},fK:function(){return r},ph:function(){return c}}),(s=a||(a={})).A2A_Agent="A2A Agent",s.AIML="AI/ML API",s.Bedrock="Amazon Bedrock",s.Anthropic="Anthropic",s.AssemblyAI="AssemblyAI",s.SageMaker="AWS SageMaker",s.Azure="Azure",s.Azure_AI_Studio="Azure AI Foundry (Studio)",s.Cerebras="Cerebras",s.Cohere="Cohere",s.Dashscope="Dashscope",s.Databricks="Databricks (Qwen API)",s.DeepInfra="DeepInfra",s.Deepgram="Deepgram",s.Deepseek="Deepseek",s.ElevenLabs="ElevenLabs",s.FalAI="Fal AI",s.FireworksAI="Fireworks AI",s.Google_AI_Studio="Google AI Studio",s.GradientAI="GradientAI",s.Groq="Groq",s.Hosted_Vllm="vllm",s.Infinity="Infinity",s.JinaAI="Jina AI",s.MiniMax="MiniMax",s.MistralAI="Mistral AI",s.Ollama="Ollama",s.OpenAI="OpenAI",s.OpenAI_Compatible="OpenAI-Compatible Endpoints (Together AI, etc.)",s.OpenAI_Text="OpenAI Text Completion",s.OpenAI_Text_Compatible="OpenAI-Compatible Text Completion Models (Together AI, 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Agent":"".concat(i,"a2a_agent.png"),"AI/ML API":"".concat(i,"aiml_api.svg"),Anthropic:"".concat(i,"anthropic.svg"),AssemblyAI:"".concat(i,"assemblyai_small.png"),Azure:"".concat(i,"microsoft_azure.svg"),"Azure AI Foundry (Studio)":"".concat(i,"microsoft_azure.svg"),"Amazon Bedrock":"".concat(i,"bedrock.svg"),"AWS SageMaker":"".concat(i,"bedrock.svg"),Cerebras:"".concat(i,"cerebras.svg"),Cohere:"".concat(i,"cohere.svg"),"Databricks (Qwen API)":"".concat(i,"databricks.svg"),Dashscope:"".concat(i,"dashscope.svg"),Deepseek:"".concat(i,"deepseek.svg"),"Fireworks AI":"".concat(i,"fireworks.svg"),Groq:"".concat(i,"groq.svg"),"Google AI Studio":"".concat(i,"google.svg"),vllm:"".concat(i,"vllm.png"),Infinity:"".concat(i,"infinity.png"),MiniMax:"".concat(i,"minimax.svg"),"Mistral AI":"".concat(i,"mistral.svg"),Ollama:"".concat(i,"ollama.svg"),OpenAI:"".concat(i,"openai_small.svg"),"OpenAI Text Completion":"".concat(i,"openai_small.svg"),"OpenAI-Compatible Text Completion Models (Together AI, etc.)":"".concat(i,"openai_small.svg"),"OpenAI-Compatible Endpoints (Together AI, etc.)":"".concat(i,"openai_small.svg"),Openrouter:"".concat(i,"openrouter.svg"),"Oracle Cloud Infrastructure (OCI)":"".concat(i,"oracle.svg"),Perplexity:"".concat(i,"perplexity-ai.svg"),RunwayML:"".concat(i,"runwayml.png"),Sambanova:"".concat(i,"sambanova.svg"),Snowflake:"".concat(i,"snowflake.svg"),TogetherAI:"".concat(i,"togetherai.svg"),"Vertex AI (Anthropic, Gemini, etc.)":"".concat(i,"google.svg"),xAI:"".concat(i,"xai.svg"),GradientAI:"".concat(i,"gradientai.svg"),Triton:"".concat(i,"nvidia_triton.png"),Deepgram:"".concat(i,"deepgram.png"),ElevenLabs:"".concat(i,"elevenlabs.png"),"Fal AI":"".concat(i,"fal_ai.jpg"),"Voyage AI":"".concat(i,"voyage.webp"),"Jina AI":"".concat(i,"jina.png"),VolcEngine:"".concat(i,"volcengine.png"),DeepInfra:"".concat(i,"deepinfra.png"),"SAP Generative AI Hub":"".concat(i,"sap.png")},o=e=>{if(!e)return{logo:"",displayName:"-"};if("gemini"===e.toLowerCase()){let e="Google 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/7526-e76a2c2b549bf2d2.js b/litellm/proxy/_experimental/out/_next/static/chunks/7526-e76a2c2b549bf2d2.js
deleted file mode 100644
index 11f4d19cb27..00000000000
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GitHub"}),!1,(0,a.jsx)("a",{href:"https://docs.litellm.ai/docs/",target:"_blank",rel:"noopener noreferrer",className:"text-sm text-gray-600 hover:text-gray-900 transition-colors",children:"Docs"}),!E&&(0,a.jsx)(A.Z,{menu:{items:V,className:"min-w-[200px]",style:{padding:"8px",marginTop:"8px",borderRadius:"12px",boxShadow:"0 4px 24px rgba(0, 0, 0, 0.08)"}},overlayStyle:{minWidth:"200px"},children:(0,a.jsxs)("button",{className:"inline-flex items-center text-sm text-gray-600 hover:text-gray-900 transition-colors",children:["User",(0,a.jsx)("svg",{className:"ml-1 w-5 h-5 text-gray-500",fill:"none",stroke:"currentColor",viewBox:"0 0 24 24",children:(0,a.jsx)("path",{strokeLinecap:"round",strokeLinejoin:"round",strokeWidth:1.5,d:"M19 9l-7 7-7-7"})})]})})]})]})})})}},82971:function(e,t,n){n.d(t,{L:function(){return s}});var a=n(8443);let s=e=>{let t;let{apiKeySource:n,accessToken:s,apiKey:r,inputMessage:i,chatHistory:l,selectedTags:o,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:m,selectedMCPServers:p,mcpServers:u,mcpServerToolRestrictions:g,selectedVoice:h,endpointType:x,selectedModel:f,selectedSdk:_,proxySettings:b}=e,v="session"===n?s:r,j=window.location.origin,y=null==b?void 0:b.LITELLM_UI_API_DOC_BASE_URL;y&&y.trim()?j=y:(null==b?void 0:b.PROXY_BASE_URL)&&(j=b.PROXY_BASE_URL);let N=i||"Your prompt here",w=N.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),S=l.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),A={};o.length>0&&(A.tags=o),c.length>0&&(A.vector_stores=c),d.length>0&&(A.guardrails=d),m.length>0&&(A.policies=m);let C=f||"your-model-name",I="azure"===_?'import openai\n\nclient = openai.AzureOpenAI(\n	api_key="'.concat(v||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(j,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(v||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(j,'"\n)');switch(x){case a.KP.CHAT:{let e=Object.keys(A).length>0,n="";if(e){let e=JSON.stringify({metadata:A},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=S.length>0?S:[{role:"user",content:N}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(C,'",\n    messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(C,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(w,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(A).length>0,n="";if(e){let e=JSON.stringify({metadata:A},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=S.length>0?S:[{role:"user",content:N}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(C,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(C,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(w,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===_?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(C,'",\n	prompt="').concat(i,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(w,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(C,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===_?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(w,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(C,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(w,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(C,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.EMBEDDINGS:t='\nresponse = client.embeddings.create(\n	input="'.concat(i||"Your string here",'",\n	model="').concat(C,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case a.KP.TRANSCRIPTION:t='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(C,'",\n	file=audio_file').concat(i?',\n	prompt="'.concat(i.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case a.KP.SPEECH:t='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(C,'",\n	input="').concat(i||"Your text to convert to speech here",'",\n	voice="').concat(h,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer\n)\n\n# Save the audio to a file\noutput_filename = "output_speech.mp3"\nresponse.stream_to_file(output_filename)\nprint(f"Audio saved to {output_filename}")\n\n# Optional: Customize response format and speed\n# response = client.audio.speech.create(\n#     model="').concat(C,'",\n#     input="').concat(i||"Your text to convert to speech here",'",\n#     voice="alloy",\n#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm\n#     speed=1.0  # Range: 0.25 to 4.0\n# )\n# response.stream_to_file("output_speech.mp3")\n');break;default:t="\n# Code generation for this endpoint is not implemented yet."}return"".concat(I,"\n").concat(t)}},8443:function(e,t,n){var a,s,r,i;n.d(t,{KP:function(){return s},vf:function(){return o}}),(r=a||(a={})).AUDIO_SPEECH="audio_speech",r.AUDIO_TRANSCRIPTION="audio_transcription",r.IMAGE_GENERATION="image_generation",r.VIDEO_GENERATION="video_generation",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDING="embedding",(i=s||(s={})).IMAGE="image",i.VIDEO="video",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages",i.EMBEDDINGS="embeddings",i.SPEECH="speech",i.TRANSCRIPTION="transcription",i.A2A_AGENTS="a2a_agents";let l={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"},o=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=l[e];return console.log("endpointType:",t),t}return"chat"}},42673:function(e,t,n){var a,s;n.d(t,{Cl:function(){return a},bK:function(){return d},cd:function(){return l},dr:function(){return o},fK:function(){return r},ph:function(){return c}}),(s=a||(a={})).A2A_Agent="A2A Agent",s.AIML="AI/ML API",s.Bedrock="Amazon Bedrock",s.Anthropic="Anthropic",s.AssemblyAI="AssemblyAI",s.SageMaker="AWS SageMaker",s.Azure="Azure",s.Azure_AI_Studio="Azure AI Foundry (Studio)",s.Cerebras="Cerebras",s.Cohere="Cohere",s.Dashscope="Dashscope",s.Databricks="Databricks (Qwen API)",s.DeepInfra="DeepInfra",s.Deepgram="Deepgram",s.Deepseek="Deepseek",s.ElevenLabs="ElevenLabs",s.FalAI="Fal AI",s.FireworksAI="Fireworks AI",s.Google_AI_Studio="Google AI Studio",s.GradientAI="GradientAI",s.Groq="Groq",s.Hosted_Vllm="vllm",s.Infinity="Infinity",s.JinaAI="Jina AI",s.MiniMax="MiniMax",s.MistralAI="Mistral AI",s.Ollama="Ollama",s.OpenAI="OpenAI",s.OpenAI_Compatible="OpenAI-Compatible Endpoints (Together AI, etc.)",s.OpenAI_Text="OpenAI Text Completion",s.OpenAI_Text_Compatible="OpenAI-Compatible Text Completion Models (Together AI, etc.)",s.Openrouter="Openrouter",s.Oracle="Oracle Cloud Infrastructure (OCI)",s.Perplexity="Perplexity",s.RunwayML="RunwayML",s.Sambanova="Sambanova",s.Snowflake="Snowflake",s.TogetherAI="TogetherAI",s.Triton="Triton",s.Vertex_AI="Vertex AI (Anthropic, Gemini, etc.)",s.VolcEngine="VolcEngine",s.Voyage="Voyage AI",s.xAI="xAI",s.SAP="SAP Generative AI Hub",s.Watsonx="Watsonx";let r={A2A_Agent:"a2a_agent",AIML:"aiml",OpenAI:"openai",OpenAI_Text:"text-completion-openai",Azure:"azure",Azure_AI_Studio:"azure_ai",Anthropic:"anthropic",Google_AI_Studio:"gemini",Bedrock:"bedrock",Groq:"groq",MiniMax:"minimax",MistralAI:"mistral",Cohere:"cohere",OpenAI_Compatible:"openai",OpenAI_Text_Compatible:"text-completion-openai",Vertex_AI:"vertex_ai",Databricks:"databricks",Dashscope:"dashscope",xAI:"xai",Deepseek:"deepseek",Ollama:"ollama",AssemblyAI:"assemblyai",Cerebras:"cerebras",Sambanova:"sambanova",Perplexity:"perplexity",RunwayML:"runwayml",TogetherAI:"together_ai",Openrouter:"openrouter",Oracle:"oci",Snowflake:"snowflake",FireworksAI:"fireworks_ai",GradientAI:"gradient_ai",Triton:"triton",Deepgram:"deepgram",ElevenLabs:"elevenlabs",FalAI:"fal_ai",SageMaker:"sagemaker_chat",Voyage:"voyage",JinaAI:"jina_ai",VolcEngine:"volcengine",DeepInfra:"deepinfra",Hosted_Vllm:"hosted_vllm",Infinity:"infinity",SAP:"sap",Watsonx:"watsonx"},i="../ui/assets/logos/",l={"A2A 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index 7b5d0fdca13..75ef1828cf0 100644
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t(e){e.languages.sqf=e.languages.extend("clike",{string:{pattern:/"(?:(?:"")?[^"])*"(?!")|'(?:[^'])*'/,greedy:!0},keyword:/\b(?:breakOut|breakTo|call|case|catch|default|do|echo|else|execFSM|execVM|exitWith|for|forEach|forEachMember|forEachMemberAgent|forEachMemberTeam|from|goto|if|nil|preprocessFile|preprocessFileLineNumbers|private|scopeName|spawn|step|switch|then|throw|to|try|while|with)\b/i,boolean:/\b(?:false|true)\b/i,function:/\b(?:abs|accTime|acos|action|actionIDs|actionKeys|actionKeysImages|actionKeysNames|actionKeysNamesArray|actionName|actionParams|activateAddons|activatedAddons|activateKey|add3DENConnection|add3DENEventHandler|add3DENLayer|addAction|addBackpack|addBackpackCargo|addBackpackCargoGlobal|addBackpackGlobal|addCamShake|addCuratorAddons|addCuratorCameraArea|addCuratorEditableObjects|addCuratorEditingArea|addCuratorPoints|addEditorObject|addEventHandler|addForce|addForceGeneratorRTD|addGoggles|addGroupIcon|addHandgunItem|addHeadgear|addItem|addItemCargo|addItemCargoGlobal|addItemPool|addItemToBackpack|addItemToUniform|addItemToVest|addLiveStats|addMagazine|addMagazineAmmoCargo|addMagazineCargo|addMagazineCargoGlobal|addMagazineGlobal|addMagazinePool|addMagazines|addMagazineTurret|addMenu|addMenuItem|addMissionEventHandler|addMPEventHandler|addMusicEventHandler|addOwnedMine|addPlayerScores|addPrimaryWeaponItem|addPublicVariableEventHandler|addRating|addResources|addScore|addScoreSide|addSecondaryWeaponItem|addSwitchableUnit|addTeamMember|addToRemainsCollector|addTorque|addUniform|addVehicle|addVest|addWaypoint|addWeapon|addWeaponCargo|addWeaponCargoGlobal|addWeaponGlobal|addWeaponItem|addWeaponPool|addWeaponTurret|admin|agent|agents|AGLToASL|aimedAtTarget|aimPos|airDensityCurveRTD|airDensityRTD|airplaneThrottle|airportSide|AISFinishHeal|alive|all3DENEntities|allAirports|allControls|allCurators|allCutLayers|allDead|allDeadMen|allDisplays|allGroups|allMapMarkers|allMines|allMissionObjects|allow3DMode|allowCrewInImmobile|allowCuratorLogicIgnoreAreas|allowDamage|allowDammage|allowFileOperations|allowFleeing|allowGetIn|allowSprint|allPlayers|allSimpleObjects|allSites|allTurrets|allUnits|allUnitsUAV|allVariables|ammo|ammoOnPylon|animate|animateBay|animateDoor|animatePylon|animateSource|animationNames|animationPhase|animationSourcePhase|animationState|append|apply|armoryPoints|arrayIntersect|asin|ASLToAGL|ASLToATL|assert|assignAsCargo|assignAsCargoIndex|assignAsCommander|assignAsDriver|assignAsGunner|assignAsTurret|assignCurator|assignedCargo|assignedCommander|assignedDriver|assignedGunner|assignedItems|assignedTarget|assignedTeam|assignedVehicle|assignedVehicleRole|assignItem|assignTeam|assignToAirport|atan|atan2|atg|ATLToASL|attachedObject|attachedObjects|attachedTo|attachObject|attachTo|attackEnabled|backpack|backpackCargo|backpackContainer|backpackItems|backpackMagazines|backpackSpaceFor|behaviour|benchmark|binocular|blufor|boundingBox|boundingBoxReal|boundingCenter|briefingName|buildingExit|buildingPos|buldozer_EnableRoadDiag|buldozer_IsEnabledRoadDiag|buldozer_LoadNewRoads|buldozer_reloadOperMap|buttonAction|buttonSetAction|cadetMode|callExtension|camCommand|camCommit|camCommitPrepared|camCommitted|camConstuctionSetParams|camCreate|camDestroy|cameraEffect|cameraEffectEnableHUD|cameraInterest|cameraOn|cameraView|campaignConfigFile|camPreload|camPreloaded|camPrepareBank|camPrepareDir|camPrepareDive|camPrepareFocus|camPrepareFov|camPrepareFovRange|camPreparePos|camPrepareRelPos|camPrepareTarget|camSetBank|camSetDir|camSetDive|camSetFocus|camSetFov|camSetFovRange|camSetPos|camSetRelPos|camSetTarget|camTarget|camUseNVG|canAdd|canAddItemToBackpack|canAddItemToUniform|canAddItemToVest|cancelSimpleTaskDestination|canFire|canMove|canSlingLoad|canStand|canSuspend|canTriggerDynamicSimulation|canUnloadInCombat|canVehicleCargo|captive|captiveNum|cbChecked|cbSetChecked|ceil|channelEnabled|cheatsEnabled|checkAIFeature|checkVisibility|civilian|className|clear3DENAttribute|clear3DENInventory|clearAllItemsFromBackpack|clearBackpackCargo|clearBackpackCargoGlobal|clearForcesRTD|clearGroupIcons|clearItemCargo|clearItemCargoGlobal|clearItemPool|clearMagazineCargo|clearMagazineCargoGlobal|clearMagazinePool|clearOverlay|clearRadio|clearVehicleInit|clearWeaponCargo|clearWeaponCargoGlobal|clearWeaponPool|clientOwner|closeDialog|closeDisplay|closeOverlay|collapseObjectTree|collect3DENHistory|collectiveRTD|combatMode|commandArtilleryFire|commandChat|commander|commandFire|commandFollow|commandFSM|commandGetOut|commandingMenu|commandMove|commandRadio|commandStop|commandSuppressiveFire|commandTarget|commandWatch|comment|commitOverlay|compile|compileFinal|completedFSM|composeText|configClasses|configFile|configHierarchy|configName|configNull|configProperties|configSourceAddonList|configSourceMod|configSourceModList|confirmSensorTarget|connectTerminalToUAV|controlNull|controlsGroupCtrl|copyFromClipboard|copyToClipboard|copyWaypoints|cos|count|countEnemy|countFriendly|countSide|countType|countUnknown|create3DENComposition|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t(e){e.languages.sqf=e.languages.extend("clike",{string:{pattern:/"(?:(?:"")?[^"])*"(?!")|'(?:[^'])*'/,greedy:!0},keyword:/\b(?:breakOut|breakTo|call|case|catch|default|do|echo|else|execFSM|execVM|exitWith|for|forEach|forEachMember|forEachMemberAgent|forEachMemberTeam|from|goto|if|nil|preprocessFile|preprocessFileLineNumbers|private|scopeName|spawn|step|switch|then|throw|to|try|while|with)\b/i,boolean:/\b(?:false|true)\b/i,function:/\b(?:abs|accTime|acos|action|actionIDs|actionKeys|actionKeysImages|actionKeysNames|actionKeysNamesArray|actionName|actionParams|activateAddons|activatedAddons|activateKey|add3DENConnection|add3DENEventHandler|add3DENLayer|addAction|addBackpack|addBackpackCargo|addBackpackCargoGlobal|addBackpackGlobal|addCamShake|addCuratorAddons|addCuratorCameraArea|addCuratorEditableObjects|addCuratorEditingArea|addCuratorPoints|addEditorObject|addEventHandler|addForce|addForceGeneratorRTD|addGoggles|addGroupIcon|addHandgunItem|addHeadgear|addItem|addItemCargo|addItemCargoGlobal|addItemPool|addItemToBackpack|addItemToUniform|addItemToVest|addLiveStats|addMagazine|addMagazineAmmoCargo|addMagazineCargo|addMagazineCargoGlobal|addMagazineGlobal|addMagazinePool|addMagazines|addMagazineTurret|addMenu|addMenuItem|addMissionEventHandler|addMPEventHandler|addMusicEventHandler|addOwnedMine|addPlayerScores|addPrimaryWeaponItem|addPublicVariableEventHandler|addRating|addResources|addScore|addScoreSide|addSecondaryWeaponItem|addSwitchableUnit|addTeamMember|addToRemainsCollector|addTorque|addUniform|addVehicle|addVest|addWaypoint|addWeapon|addWeaponCargo|addWeaponCargoGlobal|addWeaponGlobal|addWeaponItem|addWeaponPool|addWeaponTurret|admin|agent|agents|AGLToASL|aimedAtTarget|aimPos|airDensityCurveRTD|airDensityRTD|airplaneThrottle|airportSide|AISFinishHeal|alive|all3DENEntities|allAirports|allControls|allCurators|allCutLayers|allDead|allDeadMen|allDisplays|allGroups|allMapMarkers|allMines|allMissionObjects|allow3DMode|allowCrewInImmobile|allowCuratorLogicIgnoreAreas|allowDamage|allowDammage|allowFileOperations|allowFleeing|allowGetIn|allowSprint|allPlayers|allSimpleObjects|allSites|allTurrets|allUnits|allUnitsUAV|allVariables|ammo|ammoOnPylon|animate|animateBay|animateDoor|animatePylon|animateSource|animationNames|animationPhase|animationSourcePhase|animationState|append|apply|armoryPoints|arrayIntersect|asin|ASLToAGL|ASLToATL|assert|assignAsCargo|assignAsCargoIndex|assignAsCommander|assignAsDriver|assignAsGunner|assignAsTurret|assignCurator|assignedCargo|assignedCommander|assignedDriver|assignedGunner|assignedItems|assignedTarget|assignedTeam|assignedVehicle|assignedVehicleRole|assignItem|assignTeam|assignToAirport|atan|atan2|atg|ATLToASL|attachedObject|attachedObjects|attachedTo|attachObject|attachTo|attackEnabled|backpack|backpackCargo|backpackContainer|backpackItems|backpackMagazines|backpackSpaceFor|behaviour|benchmark|binocular|blufor|boundingBox|boundingBoxReal|boundingCenter|briefingName|buildingExit|buildingPos|buldozer_EnableRoadDiag|buldozer_IsEnabledRoadDiag|buldozer_LoadNewRoads|buldozer_reloadOperMap|buttonAction|buttonSetAction|cadetMode|callExtension|camCommand|camCommit|camCommitPrepared|camCommitted|camConstuctionSetParams|camCreate|camDestroy|cameraEffect|cameraEffectEnableHUD|cameraInterest|cameraOn|cameraView|campaignConfigFile|camPreload|camPreloaded|camPrepareBank|camPrepareDir|camPrepareDive|camPrepareFocus|camPrepareFov|camPrepareFovRange|camPreparePos|camPrepareRelPos|camPrepareTarget|camSetBank|camSetDir|camSetDive|camSetFocus|camSetFov|camSetFovRange|camSetPos|camSetRelPos|camSetTarget|camTarget|camUseNVG|canAdd|canAddItemToBackpack|canAddItemToUniform|canAddItemToVest|cancelSimpleTaskDestination|canFire|canMove|canSlingLoad|canStand|canSuspend|canTriggerDynamicSimulation|canUnloadInCombat|canVehicleCargo|captive|captiveNum|cbChecked|cbSetChecked|ceil|channelEnabled|cheatsEnabled|checkAIFeature|checkVisibility|civilian|className|clear3DENAttribute|clear3DENInventory|clearAllItemsFromBackpack|clearBackpackCargo|clearBackpackCargoGlobal|clearForcesRTD|clearGroupIcons|clearItemCargo|clearItemCargoGlobal|clearItemPool|clearMagazineCargo|clearMagazineCargoGlobal|clearMagazinePool|clearOverlay|clearRadio|clearVehicleInit|clearWeaponCargo|clearWeaponCargoGlobal|clearWeaponPool|clientOwner|closeDialog|closeDisplay|closeOverlay|collapseObjectTree|collect3DENHistory|collectiveRTD|combatMode|commandArtilleryFire|commandChat|commander|commandFire|commandFollow|commandFSM|commandGetOut|commandingMenu|commandMove|commandRadio|commandStop|commandSuppressiveFire|commandTarget|commandWatch|comment|commitOverlay|compile|compileFinal|completedFSM|composeText|configClasses|configFile|configHierarchy|configName|configNull|configProperties|configSourceAddonList|configSourceMod|configSourceModList|confirmSensorTarget|connectTerminalToUAV|controlNull|controlsGroupCtrl|copyFromClipboard|copyToClipboard|copyWaypoints|cos|count|countEnemy|countFriendly|countSide|countType|countUnknown|create3DENComposition|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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/8049-cb52b16664f13e28.js b/litellm/proxy/_experimental/out/_next/static/chunks/8049-cb52b16664f13e28.js
new file mode 100644
index 00000000000..6882416fdd4
--- /dev/null
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index 68260ac6464..00000000000
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n={method:"GET",headers:{[v]:"Bearer ".concat(e)}},c=await fetch(r,n);if(!c.ok){let e=await c.json(),t=am(e);throw j(t),Error(t)}let l=await c.json();return console.log(l),l}catch(e){throw console.error("Failed to fetch spend data:",e),e}},eX=async(e,t,o,a)=>{try{let r=h?"".concat(h,"/global/activity/exceptions/deployment"):"/global/activity/exceptions/deployment";t&&o&&(r+="?start_date=".concat(t,"&end_date=").concat(o)),a&&(r+="&model_group=".concat(a));let n={method:"GET",headers:{[v]:"Bearer ".concat(e)}},c=await fetch(r,n);if(!c.ok){let e=await c.json(),t=am(e);throw j(t),Error(t)}let l=await c.json();return console.log(l),l}catch(e){throw console.error("Failed to fetch spend data:",e),e}},e0=async e=>{try{let t=h?"".concat(h,"/global/spend/models?limit=5"):"/global/spend/models?limit=5",o=await fetch(t,{method:"GET",headers:{[v]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!o.ok){let e=await o.json(),t=am(e);throw j(t),Error(t)}let a=await o.json();return 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console.error("Received non-JSON response:",e),Error("Received non-JSON response (".concat(c.status,": ").concat(c.statusText,"). 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/8143-774574f553d5fa4b.js b/litellm/proxy/_experimental/out/_next/static/chunks/8143-9e4312f059e9ed27.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/8143-774574f553d5fa4b.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/8143-9e4312f059e9ed27.js
index 6a81ef05f35..35ea04a5f1c 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/8143-774574f553d5fa4b.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/8143-9e4312f059e9ed27.js
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deleted file mode 100644
index 3d696cc13ec..00000000000
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+++ /dev/null
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Please use expo/fetch: https://docs.expo.dev/versions/latest/sdk/expo/#expofetch-api");throw new ei("Attempted to iterate over a response with no body")}return new tv(eB(e.body),t)}}class tb extends tc{create(e,t){let{betas:n,...r}=e;return this._client.post("/v1/messages/batches?beta=true",{body:r,...t,headers:th([{"anthropic-beta":[...n??[],"message-batches-2024-09-24"].toString()},t?.headers])})}retrieve(e,t={},n){let{betas:r}=t??{};return this._client.get(tp`/v1/messages/batches/${e}?beta=true`,{...n,headers:th([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},n?.headers])})}list(e={},t){let{betas:n,...r}=e??{};return this._client.getAPIList("/v1/messages/batches?beta=true",e2,{query:r,...t,headers:th([{"anthropic-beta":[...n??[],"message-batches-2024-09-24"].toString()},t?.headers])})}delete(e,t={},n){let{betas:r}=t??{};return this._client.delete(tp`/v1/messages/batches/${e}?beta=true`,{...n,headers:th([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},n?.headers])})}cancel(e,t={},n){let{betas:r}=t??{};return this._client.post(tp`/v1/messages/batches/${e}/cancel?beta=true`,{...n,headers:th([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},n?.headers])})}async results(e,t={},n){let r=await this.retrieve(e);if(!r.results_url)throw new ei(`No batch \`results_url\`; Has it finished processing? ${r.processing_status} - ${r.id}`);let{betas:s}=t??{};return this._client.get(r.results_url,{...n,headers:th([{"anthropic-beta":[...s??[],"message-batches-2024-09-24"].toString(),Accept:"application/binary"},n?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tv.fromResponse(t.response,t.controller))}}let ty=e=>{let t=0,n=[];for(;t<e.length;){let r=e[t];if("\\"===r){t++;continue}if("{"===r){n.push({type:"brace",value:"{"}),t++;continue}if("}"===r){n.push({type:"brace",value:"}"}),t++;continue}if("["===r){n.push({type:"paren",value:"["}),t++;continue}if("]"===r){n.push({type:"paren",value:"]"}),t++;continue}if(":"===r){n.push({type:"separator",value:":"}),t++;continue}if(","===r){n.push({type:"delimiter",value:","}),t++;continue}if('"'===r){let s="",a=!1;for(r=e[++t];'"'!==r;){if(t===e.length){a=!0;break}if("\\"===r){if(++t===e.length){a=!0;break}s+=r+e[t],r=e[++t]}else s+=r,r=e[++t]}r=e[++t],a||n.push({type:"string",value:s});continue}let s=/\s/;if(r&&s.test(r)){t++;continue}let a=/[0-9]/;if(r&&a.test(r)||"-"===r||"."===r){let s="";for("-"===r&&(s+=r,r=e[++t]);r&&a.test(r)||"."===r;)s+=r,r=e[++t];n.push({type:"number",value:s});continue}let i=/[a-z]/i;if(r&&i.test(r)){let s="";for(;r&&i.test(r)&&t!==e.length;)s+=r,r=e[++t];"true"==s||"false"==s||"null"===s?n.push({type:"name",value:s}):t++;continue}t++}return n},tw=e=>{if(0===e.length)return e;let t=e[e.length-1];switch(t.type){case"separator":return tw(e=e.slice(0,e.length-1));case"number":let n=t.value[t.value.length-1];if("."===n||"-"===n)return tw(e=e.slice(0,e.length-1));case"string":let r=e[e.length-2];if(r?.type==="delimiter"||r?.type==="brace"&&"{"===r.value)return tw(e=e.slice(0,e.length-1));break;case"delimiter":return tw(e=e.slice(0,e.length-1))}return e},t_=e=>{let t=[];return e.map(e=>{"brace"===e.type&&("{"===e.value?t.push("}"):t.splice(t.lastIndexOf("}"),1)),"paren"===e.type&&("["===e.value?t.push("]"):t.splice(t.lastIndexOf("]"),1))}),t.length>0&&t.reverse().map(t=>{"}"===t?e.push({type:"brace",value:"}"}):"]"===t&&e.push({type:"paren",value:"]"})}),e},tS=e=>{let t="";return e.map(e=>{"string"===e.type?t+='"'+e.value+'"':t+=e.value}),t},tk=e=>JSON.parse(tS(t_(tw(ty(e))))),tx="__json_buf";function tR(e){return"tool_use"===e.type||"server_tool_use"===e.type||"mcp_tool_use"===e.type}class tC{constructor(){o.add(this),this.messages=[],this.receivedMessages=[],l.set(this,void 0),this.controller=new AbortController,c.set(this,void 0),u.set(this,()=>{}),d.set(this,()=>{}),h.set(this,void 0),f.set(this,()=>{}),p.set(this,()=>{}),g.set(this,{}),m.set(this,!1),v.set(this,!1),b.set(this,!1),y.set(this,!1),w.set(this,void 0),_.set(this,void 0),x.set(this,e=>{if(et(this,v,!0,"f"),es(e)&&(e=new el),e instanceof el)return et(this,b,!0,"f"),this._emit("abort",e);if(e instanceof ei)return this._emit("error",e);if(e instanceof Error){let t=new ei(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new ei(String(e)))}),et(this,c,new Promise((e,t)=>{et(this,u,e,"f"),et(this,d,t,"f")}),"f"),et(this,h,new Promise((e,t)=>{et(this,f,e,"f"),et(this,p,t,"f")}),"f"),en(this,c,"f").catch(()=>{}),en(this,h,"f").catch(()=>{})}get response(){return en(this,w,"f")}get request_id(){return en(this,_,"f")}async withResponse(){let e=await en(this,c,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tC;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,n){let r=new tC;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...n,headers:{...n?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},en(this,x,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,n){let r=n?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),en(this,o,"m",R).call(this);let{response:s,data:a}=await e.create({...t,stream:!0},{...n,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(s),a))en(this,o,"m",C).call(this,e);if(a.controller.signal?.aborted)throw new el;en(this,o,"m",M).call(this)}_connected(e){this.ended||(et(this,w,e,"f"),et(this,_,e?.headers.get("request-id"),"f"),en(this,u,"f").call(this,e),this._emit("connect"))}get ended(){return en(this,m,"f")}get errored(){return en(this,v,"f")}get aborted(){return en(this,b,"f")}abort(){this.controller.abort()}on(e,t){return(en(this,g,"f")[e]||(en(this,g,"f")[e]=[])).push({listener:t}),this}off(e,t){let n=en(this,g,"f")[e];if(!n)return this;let r=n.findIndex(e=>e.listener===t);return r>=0&&n.splice(r,1),this}once(e,t){return(en(this,g,"f")[e]||(en(this,g,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,n)=>{et(this,y,!0,"f"),"error"!==e&&this.once("error",n),this.once(e,t)})}async done(){et(this,y,!0,"f"),await en(this,h,"f")}get currentMessage(){return en(this,l,"f")}async finalMessage(){return await this.done(),en(this,o,"m",S).call(this)}async finalText(){return await this.done(),en(this,o,"m",k).call(this)}_emit(e,...t){if(en(this,m,"f"))return;"end"===e&&(et(this,m,!0,"f"),en(this,f,"f").call(this));let n=en(this,g,"f")[e];if(n&&(en(this,g,"f")[e]=n.filter(e=>!e.once),n.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];en(this,y,"f")||n?.length||Promise.reject(e),en(this,d,"f").call(this,e),en(this,p,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];en(this,y,"f")||n?.length||Promise.reject(e),en(this,d,"f").call(this,e),en(this,p,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",en(this,o,"m",S).call(this))}async _fromReadableStream(e,t){let n=t?.signal;n&&(n.aborted&&this.controller.abort(),n.addEventListener("abort",()=>this.controller.abort())),en(this,o,"m",R).call(this),this._connected(null);let r=eV.fromReadableStream(e,this.controller);for await(let e of r)en(this,o,"m",C).call(this,e);if(r.controller.signal?.aborted)throw new el;en(this,o,"m",M).call(this)}[(l=new WeakMap,c=new WeakMap,u=new WeakMap,d=new WeakMap,h=new WeakMap,f=new WeakMap,p=new WeakMap,g=new WeakMap,m=new WeakMap,v=new WeakMap,b=new WeakMap,y=new WeakMap,w=new WeakMap,_=new WeakMap,x=new WeakMap,o=new WeakSet,S=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},k=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new ei("stream ended without producing a content block with type=text");return e.join(" ")},R=function(){this.ended||et(this,l,void 0,"f")},C=function(e){if(this.ended)return;let t=en(this,o,"m",E).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tR(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,l,t,"f")}},M=function(){if(this.ended)throw new ei("stream has ended, this shouldn't happen");let e=en(this,l,"f");if(!e)throw new ei("request ended without sending any chunks");return et(this,l,void 0,"f"),e},E=function(e){let t=en(this,l,"f");if("message_start"===e.type){if(t)throw new ei(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new ei(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tR(n)){let t=n[tx]||"";if(Object.defineProperty(n,tx,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{n.input=tk(t)}catch(n){let e=new ei(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${n}. JSON: ${t}`);en(this,x,"f").call(this,e)}}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eV(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}let tM={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tE={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tj extends tc{constructor(){super(...arguments),this.batches=new tb(this._client)}create(e,t){let{betas:n,...r}=e;r.model in tE&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tE[r.model]}
-Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let s=this._client._options.timeout;if(!r.stream&&null==s){let e=tM[r.model]??void 0;s=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:s??6e5,...t,headers:th([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return tC.createMessage(this,e,t)}countTokens(e,t){let{betas:n,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:th([{"anthropic-beta":[...n??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tj.Batches=tb;class tO extends tc{constructor(){super(...arguments),this.models=new tm(this._client),this.messages=new tj(this._client),this.files=new tg(this._client)}}tO.Models=tm,tO.Messages=tj,tO.Files=tg;class tP extends tc{create(e,t){let{betas:n,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:th([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tA="__json_buf";function tI(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tL{constructor(){j.add(this),this.messages=[],this.receivedMessages=[],O.set(this,void 0),this.controller=new AbortController,P.set(this,void 0),A.set(this,()=>{}),I.set(this,()=>{}),L.set(this,void 0),z.set(this,()=>{}),T.set(this,()=>{}),Z.set(this,{}),N.set(this,!1),F.set(this,!1),q.set(this,!1),B.set(this,!1),$.set(this,void 0),D.set(this,void 0),W.set(this,e=>{if(et(this,F,!0,"f"),es(e)&&(e=new el),e instanceof el)return et(this,q,!0,"f"),this._emit("abort",e);if(e instanceof ei)return this._emit("error",e);if(e instanceof Error){let t=new ei(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new ei(String(e)))}),et(this,P,new Promise((e,t)=>{et(this,A,e,"f"),et(this,I,t,"f")}),"f"),et(this,L,new Promise((e,t)=>{et(this,z,e,"f"),et(this,T,t,"f")}),"f"),en(this,P,"f").catch(()=>{}),en(this,L,"f").catch(()=>{})}get response(){return en(this,$,"f")}get request_id(){return en(this,D,"f")}async withResponse(){let e=await en(this,P,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tL;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,n){let r=new tL;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...n,headers:{...n?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},en(this,W,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,n){let r=n?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),en(this,j,"m",V).call(this);let{response:s,data:a}=await e.create({...t,stream:!0},{...n,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(s),a))en(this,j,"m",X).call(this,e);if(a.controller.signal?.aborted)throw new el;en(this,j,"m",J).call(this)}_connected(e){this.ended||(et(this,$,e,"f"),et(this,D,e?.headers.get("request-id"),"f"),en(this,A,"f").call(this,e),this._emit("connect"))}get ended(){return en(this,N,"f")}get errored(){return en(this,F,"f")}get aborted(){return en(this,q,"f")}abort(){this.controller.abort()}on(e,t){return(en(this,Z,"f")[e]||(en(this,Z,"f")[e]=[])).push({listener:t}),this}off(e,t){let n=en(this,Z,"f")[e];if(!n)return this;let r=n.findIndex(e=>e.listener===t);return r>=0&&n.splice(r,1),this}once(e,t){return(en(this,Z,"f")[e]||(en(this,Z,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,n)=>{et(this,B,!0,"f"),"error"!==e&&this.once("error",n),this.once(e,t)})}async done(){et(this,B,!0,"f"),await en(this,L,"f")}get currentMessage(){return en(this,O,"f")}async finalMessage(){return await this.done(),en(this,j,"m",H).call(this)}async finalText(){return await this.done(),en(this,j,"m",U).call(this)}_emit(e,...t){if(en(this,N,"f"))return;"end"===e&&(et(this,N,!0,"f"),en(this,z,"f").call(this));let n=en(this,Z,"f")[e];if(n&&(en(this,Z,"f")[e]=n.filter(e=>!e.once),n.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];en(this,B,"f")||n?.length||Promise.reject(e),en(this,I,"f").call(this,e),en(this,T,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];en(this,B,"f")||n?.length||Promise.reject(e),en(this,I,"f").call(this,e),en(this,T,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",en(this,j,"m",H).call(this))}async _fromReadableStream(e,t){let n=t?.signal;n&&(n.aborted&&this.controller.abort(),n.addEventListener("abort",()=>this.controller.abort())),en(this,j,"m",V).call(this),this._connected(null);let r=eV.fromReadableStream(e,this.controller);for await(let e of r)en(this,j,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new el;en(this,j,"m",J).call(this)}[(O=new WeakMap,P=new WeakMap,A=new WeakMap,I=new WeakMap,L=new WeakMap,z=new WeakMap,T=new WeakMap,Z=new WeakMap,N=new WeakMap,F=new WeakMap,q=new WeakMap,B=new WeakMap,$=new WeakMap,D=new WeakMap,W=new WeakMap,j=new WeakSet,H=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},U=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new ei("stream ended without producing a content block with type=text");return e.join(" ")},V=function(){this.ended||et(this,O,void 0,"f")},X=function(e){if(this.ended)return;let t=en(this,j,"m",G).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tI(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,O,t,"f")}},J=function(){if(this.ended)throw new ei("stream has ended, this shouldn't happen");let e=en(this,O,"f");if(!e)throw new ei("request ended without sending any chunks");return et(this,O,void 0,"f"),e},G=function(e){let t=en(this,O,"f");if("message_start"===e.type){if(t)throw new ei(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new ei(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tI(n)){let t=n[tA]||"";Object.defineProperty(n,tA,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(n.input=tk(t))}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eV(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}class tz extends tc{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tp`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",e2,{query:e,...t})}delete(e,t){return this._client.delete(tp`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tp`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let n=await this.retrieve(e);if(!n.results_url)throw new ei(`No batch \`results_url\`; Has it finished processing? ${n.processing_status} - ${n.id}`);return this._client.get(n.results_url,{...t,headers:th([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tv.fromResponse(t.response,t.controller))}}class tT extends tc{constructor(){super(...arguments),this.batches=new tz(this._client)}create(e,t){e.model in tZ&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tZ[e.model]}
-Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let n=this._client._options.timeout;if(!e.stream&&null==n){let t=tM[e.model]??void 0;n=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}return this._client.post("/v1/messages",{body:e,timeout:n??6e5,...t,stream:e.stream??!1})}stream(e,t){return tL.createMessage(this,e,t)}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let tZ={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};tT.Batches=tz;class tN extends tc{retrieve(e,t={},n){let{betas:r}=t??{};return this._client.get(tp`/v1/models/${e}`,{...n,headers:th([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},n?.headers])})}list(e={},t){let{betas:n,...r}=e??{};return this._client.getAPIList("/v1/models",e2,{query:r,...t,headers:th([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers])})}}let tF=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()??void 0:void 0!==globalThis.Deno?globalThis.Deno.env?.get?.(e)?.trim():void 0;class tq{constructor({baseURL:e=tF("ANTHROPIC_BASE_URL"),apiKey:t=tF("ANTHROPIC_API_KEY")??null,authToken:n=tF("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){K.set(this,void 0);let s={apiKey:t,authToken:n,...r,baseURL:e||"https://api.anthropic.com"};if(!s.dangerouslyAllowBrowser&&eL())throw new ei("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=s.baseURL,this.timeout=s.timeout??tB.DEFAULT_TIMEOUT,this.logger=s.logger??console;let a="warn";this.logLevel=a,this.logLevel=eC(s.logLevel,"ClientOptions.logLevel",this)??eC(tF("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??a,this.fetchOptions=s.fetchOptions,this.maxRetries=s.maxRetries??2,this.fetch=s.fetch??function(){if("undefined"!=typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),et(this,K,eD,"f"),this._options=s,this.apiKey=t,this.authToken=n}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization")||t.has("authorization")))throw Error('Could not resolve authentication method. 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See https://github.com/anthropics/anthropic-sdk-typescript#streaming-responses for more details");return 6e5}async prepareOptions(e){}async prepareRequest(e,{url:t,options:n}){}get(e,t){return this.methodRequest("get",e,t)}post(e,t){return this.methodRequest("post",e,t)}patch(e,t){return this.methodRequest("patch",e,t)}put(e,t){return this.methodRequest("put",e,t)}delete(e,t){return this.methodRequest("delete",e,t)}methodRequest(e,t,n){return this.request(Promise.resolve(n).then(n=>({method:e,path:t,...n})))}request(e,t=null){return new eQ(this,this.makeRequest(e,t,void 0))}async makeRequest(e,t,n){let r=await e,s=r.maxRetries??this.maxRetries;null==t&&(t=s),await this.prepareOptions(r);let{req:a,url:i,timeout:o}=this.buildRequest(r,{retryCount:s-t});await this.prepareRequest(a,{url:i,options:r});let l="log_"+(16777216*Math.random()|0).toString(16).padStart(6,"0"),c=void 0===n?"":`, retryOf: ${n}`,u=Date.now();if(eP(this).debug(`[${l}] sending request`,eA({retryOfRequestLogID:n,method:r.method,url:i,options:r,headers:a.headers})),r.signal?.aborted)throw new el;let d=new AbortController,h=await this.fetchWithTimeout(i,a,o,d).catch(ea),f=Date.now();if(h instanceof Error){let e=`retrying, ${t} attempts remaining`;if(r.signal?.aborted)throw new el;let s=es(h)||/timed? ?out/i.test(String(h)+("cause"in h?String(h.cause):""));if(t)return eP(this).info(`[${l}] connection ${s?"timed out":"failed"} - ${e}`),eP(this).debug(`[${l}] connection ${s?"timed out":"failed"} (${e})`,eA({retryOfRequestLogID:n,url:i,durationMs:f-u,message:h.message})),this.retryRequest(r,t,n??l);if(eP(this).info(`[${l}] connection ${s?"timed out":"failed"} - error; no more retries left`),eP(this).debug(`[${l}] connection ${s?"timed out":"failed"} (error; no more retries left)`,eA({retryOfRequestLogID:n,url:i,durationMs:f-u,message:h.message})),s)throw new eu;throw new ec({cause:h})}let p=[...h.headers.entries()].filter(([e])=>"request-id"===e).map(([e,t])=>", "+e+": "+JSON.stringify(t)).join(""),g=`[${l}${c}${p}] ${a.method} ${i} ${h.ok?"succeeded":"failed"} with status ${h.status} in ${f-u}ms`;if(!h.ok){let e=this.shouldRetry(h);if(t&&e){let e=`retrying, ${t} attempts remaining`;return await e$(h.body),eP(this).info(`${g} - ${e}`),eP(this).debug(`[${l}] response error (${e})`,eA({retryOfRequestLogID:n,url:h.url,status:h.status,headers:h.headers,durationMs:f-u})),this.retryRequest(r,t,n??l,h.headers)}let s=e?"error; no more retries left":"error; not retryable";eP(this).info(`${g} - ${s}`);let a=await h.text().catch(e=>ea(e).message),i=ek(a),o=i?void 0:a;throw eP(this).debug(`[${l}] response error (${s})`,eA({retryOfRequestLogID:n,url:h.url,status:h.status,headers:h.headers,message:o,durationMs:Date.now()-u})),this.makeStatusError(h.status,i,o,h.headers)}return eP(this).info(g),eP(this).debug(`[${l}] response start`,eA({retryOfRequestLogID:n,url:h.url,status:h.status,headers:h.headers,durationMs:f-u})),{response:h,options:r,controller:d,requestLogID:l,retryOfRequestLogID:n,startTime:u}}getAPIList(e,t,n){return this.requestAPIList(t,{method:"get",path:e,...n})}requestAPIList(e,t){return new e1(this,this.makeRequest(t,null,void 0),e)}async fetchWithTimeout(e,t,n,r){let{signal:s,method:a,...i}=t||{};s&&s.addEventListener("abort",()=>r.abort());let o=setTimeout(()=>r.abort(),n),l=globalThis.ReadableStream&&i.body instanceof globalThis.ReadableStream||"object"==typeof i.body&&null!==i.body&&Symbol.asyncIterator in i.body,c={signal:r.signal,...l?{duplex:"half"}:{},method:"GET",...i};a&&(c.method=a.toUpperCase());try{return await this.fetch.call(void 0,e,c)}finally{clearTimeout(o)}}shouldRetry(e){let t=e.headers.get("x-should-retry");return"true"===t||"false"!==t&&(408===e.status||409===e.status||429===e.status||e.status>=500)}async retryRequest(e,t,n,r){let s;let a=r?.get("retry-after-ms");if(a){let e=parseFloat(a);Number.isNaN(e)||(s=e)}let i=r?.get("retry-after");if(i&&!s){let e=parseFloat(i);s=Number.isNaN(e)?Date.parse(i)-Date.now():1e3*e}if(!(s&&0<=s&&s<6e4)){let n=e.maxRetries??this.maxRetries;s=this.calculateDefaultRetryTimeoutMillis(t,n)}return await ex(s),this.makeRequest(e,t-1,n)}calculateDefaultRetryTimeoutMillis(e,t){return Math.min(.5*Math.pow(2,t-e),8)*(1-.25*Math.random())*1e3}calculateNonstreamingTimeout(e,t){if(36e5*e/128e3>6e5||null!=t&&e>t)throw new ei("Streaming is strongly recommended for operations that may token longer than 10 minutes. 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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/8358-0821a1ee08903103.js b/litellm/proxy/_experimental/out/_next/static/chunks/8358-0821a1ee08903103.js
deleted file mode 100644
index 94c92e01e94..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/8358-0821a1ee08903103.js
+++ /dev/null
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similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/1954-82e3a4023f636492.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/9028-2bfc9f09930a0d61.js
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/9028-2bfc9f09930a0d61.js
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deleted file mode 100644
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new file mode 100644
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/9841-721a173be76941d1.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/api-reference/page-cc0fe29e352b9570.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/api-reference/page-a6a3e9e67b671303.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/api-reference/page-cc0fe29e352b9570.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/api-reference/page-a6a3e9e67b671303.js
index ff5664017dd..f163965ce2b 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/budgets/page-e471b9b73cfc894b.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/budgets/page-e471b9b73cfc894b.js
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/caching/page-f9ab4bd9b8938219.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/caching/page-f9ab4bd9b8938219.js
deleted file mode 100644
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new file mode 100644
index 00000000000..6dae65b1806
--- /dev/null
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openai.OpenAI(\n	api_key="'.concat(v||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(w,'"\n)');switch(f){case i.KP.CHAT:{let e=Object.keys(P).length>0,t="";if(e){let e=JSON.stringify({metadata:P},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let i=S.length>0?S:[{role:"user",content:E}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(A,'",\n    messages=').concat(JSON.stringify(i,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(A,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(I,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case i.KP.RESPONSES:{let e=Object.keys(P).length>0,t="";if(e){let e=JSON.stringify({metadata:P},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let i=S.length>0?S:[{role:"user",content:E}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(A,'",\n    input=').concat(JSON.stringify(i,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(A,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(I,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case i.KP.IMAGE:n="azure"===b?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(A,'",\n	prompt="').concat(r,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(I,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case i.KP.IMAGE_EDITS:n="azure"===b?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(I,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(I,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case i.KP.EMBEDDINGS:n='\nresponse = client.embeddings.create(\n	input="'.concat(r||"Your string here",'",\n	model="').concat(A,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case i.KP.TRANSCRIPTION:n='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(A,'",\n	file=audio_file').concat(r?',\n	prompt="'.concat(r.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case i.KP.SPEECH:n='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(A,'",\n	input="').concat(r||"Your text to convert to speech here",'",\n	voice="').concat(_,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer\n)\n\n# Save the audio to a file\noutput_filename = "output_speech.mp3"\nresponse.stream_to_file(output_filename)\nprint(f"Audio saved to {output_filename}")\n\n# Optional: Customize response format and speed\n# response = client.audio.speech.create(\n#     model="').concat(A,'",\n#     input="').concat(r||"Your text to convert to speech here",'",\n#     voice="alloy",\n#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm\n#     speed=1.0  # Range: 0.25 to 4.0\n# )\n# response.stream_to_file("output_speech.mp3")\n');break;default:n="\n# Code generation for this endpoint is not implemented yet."}return"".concat(O,"\n").concat(n)}},8443:function(e,n,t){"use strict";var i,a,o,r;t.d(n,{KP:function(){return a},vf:function(){return l}}),(o=i||(i={})).AUDIO_SPEECH="audio_speech",o.AUDIO_TRANSCRIPTION="audio_transcription",o.IMAGE_GENERATION="image_generation",o.VIDEO_GENERATION="video_generation",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",o.EMBEDDING="embedding",(r=a||(a={})).IMAGE="image",r.VIDEO="video",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDINGS="embeddings",r.SPEECH="speech",r.TRANSCRIPTION="transcription",r.A2A_AGENTS="a2a_agents";let s={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"},l=e=>{if(console.log("getEndpointType:",e),Object.values(i).includes(e)){let n=s[e];return console.log("endpointType:",n),n}return"chat"}},10703:function(e,n,t){"use strict";t.d(n,{p:function(){return a}});var i=t(19250);let a=async e=>{try{let n=await (0,i.modelHubCall)(e);if(console.log("model_info:",n),(null==n?void 0:n.data.length)>0){let e=n.data.map(e=>({model_group:e.model_group,mode:null==e?void 0:e.mode}));return e.sort((e,n)=>e.model_group.localeCompare(n.model_group)),e}return[]}catch(e){throw console.error("Error fetching model info:",e),e}}},87972:function(e,n,t){"use strict";var i=t(57437),a=t(2265),o=t(37592),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,disabled:p}=e,[c,u]=(0,a.useState)([]),[m,d]=(0,a.useState)(!1);return(0,a.useEffect)(()=>{(async()=>{if(l){d(!0);try{let e=await (0,r.getPoliciesList)(l);console.log("Policies response:",e),e.policies&&(console.log("Policies data:",e.policies),u(e.policies))}catch(e){console.error("Error fetching policies:",e)}finally{d(!1)}}})()},[l]),(0,i.jsx)("div",{children:(0,i.jsx)(o.default,{mode:"multiple",disabled:p,placeholder:p?"Setting policies is a premium feature.":"Select policies",onChange:e=>{console.log("Selected policies:",e),n(e)},value:t,loading:m,className:s,allowClear:!0,options:c.map(e=>(console.log("Mapping policy:",e),{label:"".concat(e.policy_name).concat(e.description?" - 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new file mode 100644
index 00000000000..5628e8c8a24
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/playground/page-fc3dff494dc4db08.js
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guardrails:",e)}finally{d(!1)}}})()},[l]),(0,i.jsx)("div",{children:(0,i.jsx)(o.default,{mode:"multiple",disabled:p,placeholder:p?"Setting guardrails is a premium feature.":"Select guardrails",onChange:e=>{console.log("Selected guardrails:",e),n(e)},value:t,loading:m,className:s,allowClear:!0,options:u.map(e=>(console.log("Mapping guardrail:",e),{label:"".concat(e.guardrail_name),value:e.guardrail_name})),optionFilterProp:"label",showSearch:!0,style:{width:"100%"}})})}},82971:function(e,n,t){"use strict";t.d(n,{L:function(){return a}});var i=t(8443);let a=e=>{let n;let{apiKeySource:t,accessToken:a,apiKey:o,inputMessage:r,chatHistory:s,selectedTags:l,selectedVectorStores:p,selectedGuardrails:u,selectedMCPServers:c,mcpServers:m,mcpServerToolRestrictions:d,selectedVoice:g,endpointType:_,selectedModel:f,selectedSdk:h,proxySettings:b}=e,y="session"===t?a:o,v=window.location.origin,w=null==b?void 0:b.LITELLM_UI_API_DOC_BASE_URL;w&&w.trim()?v=w:(null==b?void 0:b.PROXY_BASE_URL)&&(v=b.PROXY_BASE_URL);let E=r||"Your prompt here",I=E.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),x=s.filter(e=>!e.isImage).map(e=>{let{role:n,content:t}=e;return{role:n,content:t}}),S={};l.length>0&&(S.tags=l),p.length>0&&(S.vector_stores=p),u.length>0&&(S.guardrails=u);let A=f||"your-model-name",O="azure"===h?'import openai\n\nclient = openai.AzureOpenAI(\n	api_key="'.concat(y||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(v,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(y||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(v,'"\n)');switch(_){case i.KP.CHAT:{let e=Object.keys(S).length>0,t="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let i=x.length>0?x:[{role:"user",content:E}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(A,'",\n    messages=').concat(JSON.stringify(i,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(A,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(I,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case i.KP.RESPONSES:{let e=Object.keys(S).length>0,t="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let i=x.length>0?x:[{role:"user",content:E}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(A,'",\n    input=').concat(JSON.stringify(i,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(A,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(I,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case i.KP.IMAGE:n="azure"===h?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(A,'",\n	prompt="').concat(r,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(I,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case i.KP.IMAGE_EDITS:n="azure"===h?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(I,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(I,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case i.KP.EMBEDDINGS:n='\nresponse = client.embeddings.create(\n	input="'.concat(r||"Your string here",'",\n	model="').concat(A,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case i.KP.TRANSCRIPTION:n='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(A,'",\n	file=audio_file').concat(r?',\n	prompt="'.concat(r.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case i.KP.SPEECH:n='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(A,'",\n	input="').concat(r||"Your text to convert to speech here",'",\n	voice="').concat(g,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer\n)\n\n# Save the audio to a file\noutput_filename = "output_speech.mp3"\nresponse.stream_to_file(output_filename)\nprint(f"Audio saved to {output_filename}")\n\n# Optional: Customize response format and speed\n# response = client.audio.speech.create(\n#     model="').concat(A,'",\n#     input="').concat(r||"Your text to convert to speech here",'",\n#     voice="alloy",\n#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm\n#     speed=1.0  # Range: 0.25 to 4.0\n# )\n# response.stream_to_file("output_speech.mp3")\n');break;default:n="\n# Code generation for this endpoint is not implemented yet."}return"".concat(O,"\n").concat(n)}},8443:function(e,n,t){"use strict";var i,a,o,r;t.d(n,{KP:function(){return a},vf:function(){return l}}),(o=i||(i={})).AUDIO_SPEECH="audio_speech",o.AUDIO_TRANSCRIPTION="audio_transcription",o.IMAGE_GENERATION="image_generation",o.VIDEO_GENERATION="video_generation",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",o.EMBEDDING="embedding",(r=a||(a={})).IMAGE="image",r.VIDEO="video",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDINGS="embeddings",r.SPEECH="speech",r.TRANSCRIPTION="transcription",r.A2A_AGENTS="a2a_agents";let s={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"},l=e=>{if(console.log("getEndpointType:",e),Object.values(i).includes(e)){let n=s[e];return console.log("endpointType:",n),n}return"chat"}},10703:function(e,n,t){"use strict";t.d(n,{p:function(){return a}});var i=t(19250);let a=async e=>{try{let n=await (0,i.modelHubCall)(e);if(console.log("model_info:",n),(null==n?void 0:n.data.length)>0){let e=n.data.map(e=>({model_group:e.model_group,mode:null==e?void 0:e.mode}));return e.sort((e,n)=>e.model_group.localeCompare(n.model_group)),e}return[]}catch(e){throw console.error("Error fetching model info:",e),e}}},97415:function(e,n,t){"use strict";var i=t(57437),a=t(2265),o=t(37592),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,placeholder:p="Select vector stores",disabled:u=!1}=e,[c,m]=(0,a.useState)([]),[d,g]=(0,a.useState)(!1);return(0,a.useEffect)(()=>{(async()=>{if(l){g(!0);try{let e=await (0,r.vectorStoreListCall)(l);e.data&&m(e.data)}catch(e){console.error("Error fetching vector stores:",e)}finally{g(!1)}}})()},[l]),(0,i.jsx)("div",{children:(0,i.jsx)(o.default,{mode:"multiple",placeholder:p,onChange:n,value:t,loading:d,className:s,allowClear:!0,options:c.map(e=>({label:"".concat(e.vector_store_name||e.vector_store_id," (").concat(e.vector_store_id,")"),value:e.vector_store_id,title:e.vector_store_description||e.vector_store_id})),optionFilterProp:"label",showSearch:!0,style:{width:"100%"},disabled:u})})}},91624:function(e,n,t){"use strict";t.d(n,{C:function(){return a}});var i=t(19250);let a=async e=>{if(!e)return null;try{return await (0,i.getProxyUISettings)(e)}catch(e){return console.error("Error fetching proxy settings:",e),null}}}},function(e){e.O(0,[9028,9409,1713,4865,337,8135,1442,2926,2409,3709,353,1994,8565,5319,7906,816,7271,766,3567,8529,8049,5992,9039,2971,2117,1744],function(){return e(e.s=19235)}),_N_E=e.O()}]);
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/policies/page-33090e865d27d3fa.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/policies/page-33090e865d27d3fa.js
deleted file mode 100644
index 12fde1e1553..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/policies/page-33090e865d27d3fa.js
+++ /dev/null
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0:b.PROXY_BASE_URL)&&(y=b.PROXY_BASE_URL);let E=r||"Your prompt here",w=E.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),I=s.filter(e=>!e.isImage).map(e=>{let{role:n,content:t}=e;return{role:n,content:t}}),S={};l.length>0&&(S.tags=l),p.length>0&&(S.vector_stores=p),c.length>0&&(S.guardrails=c);let A=f||"your-model-name",O="azure"===h?'import openai\n\nclient = openai.AzureOpenAI(\n	api_key="'.concat(v||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(y,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(v||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(y,'"\n)');switch(_){case a.KP.CHAT:{let e=Object.keys(S).length>0,t="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let a=I.length>0?I:[{role:"user",content:E}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(A,'",\n    messages=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(A,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(w,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(S).length>0,t="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let a=I.length>0?I:[{role:"user",content:E}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(A,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(A,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(w,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:n="azure"===h?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(A,'",\n	prompt="').concat(r,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(w,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:n="azure"===h?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(w,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(w,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.EMBEDDINGS:n='\nresponse = client.embeddings.create(\n	input="'.concat(r||"Your string here",'",\n	model="').concat(A,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case a.KP.TRANSCRIPTION:n='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(A,'",\n	file=audio_file').concat(r?',\n	prompt="'.concat(r.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case a.KP.SPEECH:n='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(A,'",\n	input="').concat(r||"Your text to convert to speech here",'",\n	voice="').concat(g,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer\n)\n\n# Save the audio to a file\noutput_filename = "output_speech.mp3"\nresponse.stream_to_file(output_filename)\nprint(f"Audio saved to {output_filename}")\n\n# Optional: Customize response format and speed\n# response = client.audio.speech.create(\n#     model="').concat(A,'",\n#     input="').concat(r||"Your text to convert to speech here",'",\n#     voice="alloy",\n#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm\n#     speed=1.0  # Range: 0.25 to 4.0\n# )\n# response.stream_to_file("output_speech.mp3")\n');break;default:n="\n# Code generation for this endpoint is not implemented yet."}return"".concat(O,"\n").concat(n)}},8443:function(e,n,t){"use strict";var a,i,o,r;t.d(n,{KP:function(){return i},vf:function(){return l}}),(o=a||(a={})).AUDIO_SPEECH="audio_speech",o.AUDIO_TRANSCRIPTION="audio_transcription",o.IMAGE_GENERATION="image_generation",o.VIDEO_GENERATION="video_generation",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",o.EMBEDDING="embedding",(r=i||(i={})).IMAGE="image",r.VIDEO="video",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDINGS="embeddings",r.SPEECH="speech",r.TRANSCRIPTION="transcription",r.A2A_AGENTS="a2a_agents";let s={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"},l=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let n=s[e];return console.log("endpointType:",n),n}return"chat"}},10703:function(e,n,t){"use strict";t.d(n,{p:function(){return i}});var a=t(19250);let i=async e=>{try{let n=await (0,a.modelHubCall)(e);if(console.log("model_info:",n),(null==n?void 0:n.data.length)>0){let e=n.data.map(e=>({model_group:e.model_group,mode:null==e?void 0:e.mode}));return e.sort((e,n)=>e.model_group.localeCompare(n.model_group)),e}return[]}catch(e){throw console.error("Error fetching model info:",e),e}}},97415:function(e,n,t){"use strict";var a=t(57437),i=t(2265),o=t(37592),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,placeholder:p="Select vector stores",disabled:c=!1}=e,[u,m]=(0,i.useState)([]),[d,g]=(0,i.useState)(!1);return(0,i.useEffect)(()=>{(async()=>{if(l){g(!0);try{let e=await (0,r.vectorStoreListCall)(l);e.data&&m(e.data)}catch(e){console.error("Error fetching vector stores:",e)}finally{g(!1)}}})()},[l]),(0,a.jsx)("div",{children:(0,a.jsx)(o.default,{mode:"multiple",placeholder:p,onChange:n,value:t,loading:d,className:s,allowClear:!0,options:u.map(e=>({label:"".concat(e.vector_store_name||e.vector_store_id," (").concat(e.vector_store_id,")"),value:e.vector_store_id,title:e.vector_store_description||e.vector_store_id})),optionFilterProp:"label",showSearch:!0,style:{width:"100%"},disabled:c})})}},91624:function(e,n,t){"use strict";t.d(n,{C:function(){return i}});var a=t(19250);let i=async e=>{if(!e)return null;try{return await (0,a.getProxyUISettings)(e)}catch(e){return console.error("Error fetching proxy settings:",e),null}}}},function(e){e.O(0,[9028,9409,1713,4865,337,8135,2409,3709,353,1994,8565,5319,7906,816,7271,766,3567,8049,5992,2971,2117,1744],function(){return e(e.s=22859)}),_N_E=e.O()}]);
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P=h||"your-model-name",O="azure"===b?'import openai\n\nclient = openai.AzureOpenAI(\n	api_key="'.concat(y||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(w,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(y||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(w,'"\n)');switch(f){case a.KP.CHAT:{let e=Object.keys(A).length>0,t="";if(e){let e=JSON.stringify({metadata:A},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let a=S.length>0?S:[{role:"user",content:x}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(P,'",\n    messages=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(P,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(I,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(A).length>0,t="";if(e){let e=JSON.stringify({metadata:A},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let a=S.length>0?S:[{role:"user",content:x}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(P,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(P,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(I,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:n="azure"===b?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(P,'",\n	prompt="').concat(r,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(I,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(P,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:n="azure"===b?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(I,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(P,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(I,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(P,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.EMBEDDINGS:n='\nresponse = client.embeddings.create(\n	input="'.concat(r||"Your string here",'",\n	model="').concat(P,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case a.KP.TRANSCRIPTION:n='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(P,'",\n	file=audio_file').concat(r?',\n	prompt="'.concat(r.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case a.KP.SPEECH:n='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(P,'",\n	input="').concat(r||"Your text to convert to speech here",'",\n	voice="').concat(_,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer\n)\n\n# Save the audio to a file\noutput_filename = "output_speech.mp3"\nresponse.stream_to_file(output_filename)\nprint(f"Audio saved to {output_filename}")\n\n# Optional: Customize response format and speed\n# response = client.audio.speech.create(\n#     model="').concat(P,'",\n#     input="').concat(r||"Your text to convert to speech here",'",\n#     voice="alloy",\n#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm\n#     speed=1.0  # Range: 0.25 to 4.0\n# )\n# response.stream_to_file("output_speech.mp3")\n');break;default:n="\n# Code generation for this endpoint is not implemented yet."}return"".concat(O,"\n").concat(n)}},8443:function(e,n,t){"use strict";var a,i,o,r;t.d(n,{KP:function(){return i},vf:function(){return l}}),(o=a||(a={})).AUDIO_SPEECH="audio_speech",o.AUDIO_TRANSCRIPTION="audio_transcription",o.IMAGE_GENERATION="image_generation",o.VIDEO_GENERATION="video_generation",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",o.EMBEDDING="embedding",(r=i||(i={})).IMAGE="image",r.VIDEO="video",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDINGS="embeddings",r.SPEECH="speech",r.TRANSCRIPTION="transcription",r.A2A_AGENTS="a2a_agents";let s={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"},l=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let n=s[e];return console.log("endpointType:",n),n}return"chat"}},10703:function(e,n,t){"use strict";t.d(n,{p:function(){return i}});var a=t(19250);let i=async e=>{try{let n=await (0,a.modelHubCall)(e);if(console.log("model_info:",n),(null==n?void 0:n.data.length)>0){let e=n.data.map(e=>({model_group:e.model_group,mode:null==e?void 0:e.mode}));return e.sort((e,n)=>e.model_group.localeCompare(n.model_group)),e}return[]}catch(e){throw console.error("Error fetching model info:",e),e}}},87972:function(e,n,t){"use strict";var a=t(57437),i=t(2265),o=t(37592),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,disabled:p}=e,[c,u]=(0,i.useState)([]),[m,d]=(0,i.useState)(!1);return(0,i.useEffect)(()=>{(async()=>{if(l){d(!0);try{let e=await (0,r.getPoliciesList)(l);console.log("Policies response:",e),e.policies&&(console.log("Policies data:",e.policies),u(e.policies))}catch(e){console.error("Error fetching policies:",e)}finally{d(!1)}}})()},[l]),(0,a.jsx)("div",{children:(0,a.jsx)(o.default,{mode:"multiple",disabled:p,placeholder:p?"Setting policies is a premium feature.":"Select policies",onChange:e=>{console.log("Selected policies:",e),n(e)},value:t,loading:m,className:s,allowClear:!0,options:c.map(e=>(console.log("Mapping policy:",e),{label:"".concat(e.policy_name).concat(e.description?" - 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/virtual-keys/page-a6a4dc040440802a.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/virtual-keys/page-73444bcb0cfe86b7.js
similarity index 66%
rename from litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/virtual-keys/page-a6a4dc040440802a.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/virtual-keys/page-73444bcb0cfe86b7.js
index 628120bf8f4..415a65f46db 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/login/page-a5e4539372d51712.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/login/page-61bfa80619b62f6b.js
similarity index 99%
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/main-3f67160ac20e4399.js b/litellm/proxy/_experimental/out/_next/static/chunks/main-e4e168e4dfadea03.js
similarity index 67%
rename from litellm/proxy/_experimental/out/_next/static/chunks/main-3f67160ac20e4399.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/main-e4e168e4dfadea03.js
index a9fe270a05f..5d3580f0907 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/8YepvLrDdt6e_FwiLneCs/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/zHD7JXLXiWgn1NPp82VmF/_ssgManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/8YepvLrDdt6e_FwiLneCs/_ssgManifest.js
rename to litellm/proxy/_experimental/out/_next/static/zHD7JXLXiWgn1NPp82VmF/_ssgManifest.js
diff --git a/litellm/proxy/_experimental/out/api-reference.html b/litellm/proxy/_experimental/out/api-reference.html
deleted file mode 100644
index 21f33ef3ab3..00000000000
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diff --git a/litellm/proxy/_experimental/out/settings/router-settings.html b/litellm/proxy/_experimental/out/settings/router-settings/index.html
similarity index 82%
rename from litellm/proxy/_experimental/out/settings/router-settings.html
rename to litellm/proxy/_experimental/out/settings/router-settings/index.html
index a226e1270b7..83492c87c91 100644
--- a/litellm/proxy/_experimental/out/settings/router-settings.html
+++ b/litellm/proxy/_experimental/out/settings/router-settings/index.html
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diff --git a/litellm/proxy/_experimental/out/experimental/prompts.html b/litellm/proxy/_experimental/out/usage/index.html
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diff --git a/litellm/proxy/_experimental/out/users.html b/litellm/proxy/_experimental/out/users/index.html
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diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index d9fb809cc9a..c248082c263 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -227,7 +227,6 @@ class KeyManagementRoutes(str, enum.Enum):
     KEY_REGENERATE_WITH_PATH_PARAM = "/key/{key_id}/regenerate"
     KEY_BLOCK = "/key/block"
     KEY_UNBLOCK = "/key/unblock"
-    KEY_BULK_UPDATE = "/key/bulk_update"
 
     # info and health routes
     KEY_INFO = "/key/info"
@@ -354,9 +353,6 @@ class LiteLLMRoutes(enum.Enum):
         "/v1/vector_stores/{vector_store_id}/files/{file_id}",
         "/vector_stores/{vector_store_id}/files/{file_id}/content",
         "/v1/vector_stores/{vector_store_id}/files/{file_id}/content",
-        "/vector_store/list",
-        "/v1/vector_store/list",
-
         # search
         "/search",
         "/v1/search",
@@ -429,12 +425,12 @@ class LiteLLMRoutes(enum.Enum):
     ]
 
     google_routes = [
-        "/v1beta/models/{model_name:path}:countTokens",
-        "/v1beta/models/{model_name:path}:generateContent",
-        "/v1beta/models/{model_name:path}:streamGenerateContent",
-        "/models/{model_name:path}:countTokens",
-        "/models/{model_name:path}:generateContent",
-        "/models/{model_name:path}:streamGenerateContent",
+        "/v1beta/models/{model_name}:countTokens",
+        "/v1beta/models/{model_name}:generateContent",
+        "/v1beta/models/{model_name}:streamGenerateContent",
+        "/models/{model_name}:countTokens",
+        "/models/{model_name}:generateContent",
+        "/models/{model_name}:streamGenerateContent",
         # Google Interactions API
         "/interactions",
         "/v1beta/interactions",
@@ -498,7 +494,6 @@ class LiteLLMRoutes(enum.Enum):
         KeyManagementRoutes.KEY_LIST.value,
         KeyManagementRoutes.KEY_BLOCK.value,
         KeyManagementRoutes.KEY_UNBLOCK.value,
-        KeyManagementRoutes.KEY_BULK_UPDATE.value,
     ]
 
     management_routes = [
@@ -856,13 +851,6 @@ class GenerateRequestBase(LiteLLMPydanticObjectBase):
     aliases: Optional[dict] = {}
     object_permission: Optional[LiteLLM_ObjectPermissionBase] = None
 
-    @field_validator("max_budget", mode="before")
-    @classmethod
-    def check_max_budget(cls, v):
-        if v == "":
-            return None
-        return v
-
 
 class AllowedVectorStoreIndexItem(LiteLLMPydanticObjectBase):
     index_name: str
@@ -2078,14 +2066,6 @@ class ConfigGeneralSettings(LiteLLMPydanticObjectBase):
         None,
         description="Controls how non-admin users interact with MCP servers in the dashboard. 'restricted' shows only accessible servers, 'view_all' lists every server in read-only mode.",
     )
-    store_prompts_in_spend_logs: Optional[bool] = Field(
-        None,
-        description="If True, stores request messages and responses in spend logs. Default is False.",
-    )
-    maximum_spend_logs_retention_period: Optional[str] = Field(
-        None,
-        description="Maximum retention period for spend logs (e.g., '7d' for 7 days). Logs older than this will be deleted.",
-    )
 
 
 class ConfigYAML(LiteLLMPydanticObjectBase):
@@ -3922,8 +3902,6 @@ class LiteLLM_ManagedVectorStoresTable(LiteLLMPydanticObjectBase):
     updated_at: Optional[datetime]
     litellm_credential_name: Optional[str]
     litellm_params: Optional[Dict[str, Any]]
-    team_id: Optional[str]
-    user_id: Optional[str]
 
 
 class ResponseLiteLLM_ManagedVectorStore(TypedDict, total=False):
diff --git a/litellm/proxy/agent_endpoints/a2a_endpoints.py b/litellm/proxy/agent_endpoints/a2a_endpoints.py
index 24727aacd75..a21f3291d31 100644
--- a/litellm/proxy/agent_endpoints/a2a_endpoints.py
+++ b/litellm/proxy/agent_endpoints/a2a_endpoints.py
@@ -119,11 +119,6 @@ async def stream_response():
     tags=["[beta] A2A Agents"],
     dependencies=[Depends(user_api_key_auth)],
 )
-@router.get(
-    "/a2a/{agent_id}/.well-known/agent.json",
-    tags=["[beta] A2A Agents"],
-    dependencies=[Depends(user_api_key_auth)],
-)
 async def get_agent_card(
     agent_id: str,
     request: Request,
@@ -132,10 +127,6 @@ async def get_agent_card(
     """
     Get the agent card for an agent (A2A discovery endpoint).
 
-    Supports both standard paths:
-    - /.well-known/agent-card.json
-    - /.well-known/agent.json
-
     The URL in the agent card is rewritten to point to the LiteLLM proxy,
     so all subsequent A2A calls go through LiteLLM for logging and cost tracking.
     """
diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py
index e0b056d450f..5e0a211906e 100644
--- a/litellm/proxy/auth/auth_checks.py
+++ b/litellm/proxy/auth/auth_checks.py
@@ -21,8 +21,6 @@
 from litellm.caching.caching import DualCache
 from litellm.caching.dual_cache import LimitedSizeOrderedDict
 from litellm.constants import (
-    CLI_JWT_EXPIRATION_HOURS,
-    CLI_JWT_TOKEN_NAME,
     DEFAULT_IN_MEMORY_TTL,
     DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL,
     DEFAULT_MAX_RECURSE_DEPTH,
@@ -1016,10 +1014,8 @@ async def _get_fuzzy_user_object(
         )
 
     if response is None and user_email is not None:
-        # Use case-insensitive query to handle emails with different casing
-        # This matches the pattern used in _check_duplicate_user_email
         response = await prisma_client.db.litellm_usertable.find_first(
-            where={"user_email": {"equals": user_email, "mode": "insensitive"}},
+            where={"user_email": user_email},
             include={"organization_memberships": True},
         )
 
@@ -1606,10 +1602,7 @@ def get_cli_jwt_auth_token(
         user_info: LiteLLM_UserTable, team_id: Optional[str] = None
     ) -> str:
         """
-        Generate a JWT token for CLI authentication with configurable expiration.
-
-        The expiration time can be controlled via the LITELLM_CLI_JWT_EXPIRATION_HOURS
-        environment variable (defaults to 24 hours).
+        Generate a JWT token for CLI authentication with 24-hour expiration.
 
         Args:
             user_info: User information from the database
@@ -1620,6 +1613,7 @@ def get_cli_jwt_auth_token(
         """
         from datetime import timedelta
 
+        from litellm.constants import CLI_JWT_TOKEN_NAME
         from litellm.proxy.common_utils.encrypt_decrypt_utils import (
             encrypt_value_helper,
         )
@@ -1627,8 +1621,8 @@ def get_cli_jwt_auth_token(
         if user_info.user_role is None:
             raise Exception("User role is required for CLI JWT login")
 
-        # Calculate expiration time (configurable via LITELLM_CLI_JWT_EXPIRATION_HOURS env var)
-        expiration_time = get_utc_datetime() + timedelta(hours=CLI_JWT_EXPIRATION_HOURS)
+        # Calculate expiration time (24 hours from now - matching old CLI key behavior)
+        expiration_time = get_utc_datetime() + timedelta(hours=24)
 
         # Format the expiration time as ISO 8601 string
         expires = expiration_time.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3] + "+00:00"
diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py
index 2bd84a1d98d..9b9a988c07a 100644
--- a/litellm/proxy/auth/auth_utils.py
+++ b/litellm/proxy/auth/auth_utils.py
@@ -758,27 +758,11 @@ def get_model_from_request(
         if match:
             model = match.group(1)
 
-    # If still not found, extract model from Google generateContent-style routes.
-    # These routes put the model in the path and allow "/" inside the model id.
-    # Examples:
-    # - /v1beta/models/gemini-2.0-flash:generateContent
-    # - /v1beta/models/bedrock/claude-sonnet-3.7:generateContent
-    # - /models/custom/ns/model:streamGenerateContent
-    if model is None and not route.lower().startswith("/vertex"):
-        google_match = re.search(r"/(?:v1beta|beta)/models/([^:]+):", route)
-        if google_match:
-            model = google_match.group(1)
-
-    if model is None and not route.lower().startswith("/vertex"):
-        google_match = re.search(r"^/models/([^:]+):", route)
-        if google_match:
-            model = google_match.group(1)
-
     # If still not found, extract from Vertex AI passthrough route
     # Pattern: /vertex_ai/.../models/{model_id}:*
     # Example: /vertex_ai/v1/.../models/gemini-1.5-pro:generateContent
-    if model is None and route.lower().startswith("/vertex"):
-        vertex_match = re.search(r"/models/([^:]+)", route)
+    if model is None and "/vertex" in route.lower():
+        vertex_match = re.search(r"/models/([^/:]+)", route)
         if vertex_match:
             model = vertex_match.group(1)
 
diff --git a/litellm/proxy/auth/route_checks.py b/litellm/proxy/auth/route_checks.py
index fa63ff01b3a..96a70b8016f 100644
--- a/litellm/proxy/auth/route_checks.py
+++ b/litellm/proxy/auth/route_checks.py
@@ -392,15 +392,7 @@ def _route_matches_pattern(route: str, pattern: str) -> bool:
         # Ensure route is a string before attempting regex matching
         if not isinstance(route, str):
             return False
-
-        def _placeholder_to_regex(match: re.Match) -> str:
-            placeholder = match.group(0).strip("{}")
-            if placeholder.endswith(":path"):
-                # allow "/" in the placeholder value, but don't eat the route suffix after ":"
-                return r"[^:]+"
-            return r"[^/]+"
-
-        pattern = re.sub(r"\{[^}]+\}", _placeholder_to_regex, pattern)
+        pattern = re.sub(r"\{[^}]+\}", r"[^/]+", pattern)
         # Anchor the pattern to match the entire string
         pattern = f"^{pattern}$"
         if re.match(pattern, route):
diff --git a/litellm/proxy/batches_endpoints/endpoints.py b/litellm/proxy/batches_endpoints/endpoints.py
index 078e21f9bb4..086105042e8 100644
--- a/litellm/proxy/batches_endpoints/endpoints.py
+++ b/litellm/proxy/batches_endpoints/endpoints.py
@@ -542,26 +542,14 @@ async def list_batches(
             route_type="alist_batches",
         )
 
-        # Try to use managed objects table for listing batches (returns encoded IDs)
-        managed_files_obj = proxy_logging_obj.get_proxy_hook("managed_files")
-        if managed_files_obj is not None and hasattr(managed_files_obj, "list_user_batches"):
-            verbose_proxy_logger.debug(
-                "Using managed objects table for batch listing"
-            )
-            response = await managed_files_obj.list_user_batches(
-                user_api_key_dict=user_api_key_dict,
-                limit=limit,
-                after=after,
-                provider=provider,
-                target_model_names=target_model_names,
-                llm_router=llm_router,
-            )
-        elif (model_param := (
+        model_param = (
             data.get("model")
             or request.query_params.get("model")
             or request.headers.get("x-litellm-model")
-        )):
-            # SCENARIO 2: Use model-based routing from header/query/body
+        )
+        
+        # SCENARIO 2: Use model-based routing from header/query/body
+        if model_param:
             credentials = get_credentials_for_model(
                 llm_router=llm_router,
                 model_id=model_param,
diff --git a/litellm/proxy/client/cli/commands/auth.py b/litellm/proxy/client/cli/commands/auth.py
index 64b3233536f..bdc8d56d1c3 100644
--- a/litellm/proxy/client/cli/commands/auth.py
+++ b/litellm/proxy/client/cli/commands/auth.py
@@ -11,8 +11,6 @@
 from rich.console import Console
 from rich.table import Table
 
-from litellm.constants import CLI_JWT_EXPIRATION_HOURS
-
 
 # Token storage utilities
 def get_token_file_path() -> str:
@@ -276,71 +274,6 @@ def prompt_team_selection_fallback(
 
 
 # Polling-based authentication - no local server needed
-def _poll_for_ready_data(
-    url: str,
-    *,
-    total_timeout: int = 300,
-    poll_interval: int = 2,
-    request_timeout: int = 10,
-    pending_message: Optional[str] = None,
-    pending_log_every: int = 10,
-    other_status_message: Optional[str] = None,
-    other_status_log_every: int = 10,
-    http_error_log_every: int = 10,
-    connection_error_log_every: int = 10,
-) -> Optional[Dict[str, Any]]:
-    for attempt in range(total_timeout // poll_interval):
-        try:
-            response = requests.get(url, timeout=request_timeout)
-            if response.status_code == 200:
-                data = response.json()
-                status = data.get("status")
-                if status == "ready":
-                    return data
-                if status == "pending":
-                    if (
-                        pending_message
-                        and pending_log_every > 0
-                        and attempt % pending_log_every == 0
-                    ):
-                        click.echo(pending_message)
-                elif (
-                    other_status_message
-                    and other_status_log_every > 0
-                    and attempt % other_status_log_every == 0
-                ):
-                    click.echo(other_status_message)
-            elif http_error_log_every > 0 and attempt % http_error_log_every == 0:
-                click.echo(f"Polling error: HTTP {response.status_code}")
-        except requests.RequestException as e:
-            if (
-                connection_error_log_every > 0
-                and attempt % connection_error_log_every == 0
-            ):
-                click.echo(f"Connection error (will retry): {e}")
-        time.sleep(poll_interval)
-    return None
-
-
-def _normalize_teams(teams, team_details):
-    """If team_details are a
-
-    Args:
-        teams (_type_): _description_
-        team_details (_type_): _description_
-
-    Returns:
-        _type_: _description_
-    """
-    if isinstance(team_details, list) and team_details:
-        return [
-            {"team_id": i.get("team_id") or i.get("id"), "team_alias": i.get("team_alias")}
-            for i in team_details
-            if isinstance(i, dict) and (i.get("team_id") or i.get("id"))
-        ]
-    if isinstance(teams, list):
-        return [{"team_id": str(t), "team_alias": None} for t in teams]
-    return []
 
 
 def _poll_for_authentication(base_url: str, key_id: str) -> Optional[dict]:
@@ -351,58 +284,106 @@ def _poll_for_authentication(base_url: str, key_id: str) -> Optional[dict]:
         Dictionary with authentication data if successful, None otherwise
     """
     poll_url = f"{base_url}/sso/cli/poll/{key_id}"
-    data = _poll_for_ready_data(
-        poll_url,
-        pending_message="Still waiting for authentication...",
-    )
-    if not data:
-        return None
-    if data.get("requires_team_selection"):
-        teams = data.get("teams", [])
-        team_details = data.get("team_details")
-        user_id = data.get("user_id")
-        normalized_teams: List[Dict[str, Any]] = _normalize_teams(teams, team_details)
-        if not normalized_teams:
-            click.echo("⚠️ No teams available for selection.")
-            return None
-
-        # User has multiple teams - let them select
-        jwt_with_team = _handle_team_selection_during_polling(
-            base_url=base_url,
-            key_id=key_id,
-            teams=normalized_teams,
-        )
+    timeout = 300  # 5 minute timeout
+    poll_interval = 2  # Poll every 2 seconds
 
-        # Use the team-specific JWT if selection succeeded
-        if jwt_with_team:
-            return {
-                "api_key": jwt_with_team,
-                "user_id": user_id,
-                "teams": teams,
-                "team_id": None,  # Set by server in JWT
-            }
-
-        click.echo("❌ Team selection cancelled or JWT generation failed.")
-        return None
-
-    # JWT is ready (single team or team already selected)
-    api_key = data.get("key")
-    user_id = data.get("user_id")
-    teams = data.get("teams", [])
-    team_id = data.get("team_id")
+    for attempt in range(timeout // poll_interval):
+        try:
+            response = requests.get(poll_url, timeout=10)
+            if response.status_code == 200:
+                data = response.json()
+                if data.get("status") == "ready":
+                    # Check if we need team selection first
+                    if data.get("requires_team_selection"):
+                        # Server returned teams list without JWT - need to select team.
+                        # Newer servers may also return "team_details" containing
+                        # objects with both team_id and team_alias. We prefer those
+                        # for display, but continue to support the legacy list of
+                        # team IDs for backwards compatibility.
+                        teams = data.get("teams", [])
+                        team_details = data.get("team_details")
+                        user_id = data.get("user_id")
+
+                        # Build a normalized list of team objects that always have
+                        # "team_id" and optionally "team_alias".
+                        normalized_teams: List[Dict[str, Any]] = []
+                        if isinstance(team_details, list) and team_details:
+                            for item in team_details:
+                                if isinstance(item, dict):
+                                    team_id = item.get("team_id") or item.get("id")
+                                    if team_id is None:
+                                        continue
+                                    normalized_teams.append(
+                                        {
+                                            "team_id": team_id,
+                                            "team_alias": item.get("team_alias"),
+                                        }
+                                    )
+                        elif isinstance(teams, list):
+                            for t in teams:
+                                normalized_teams.append(
+                                    {
+                                        "team_id": str(t),
+                                        "team_alias": None,
+                                    }
+                                )
+
+                        if normalized_teams and len(normalized_teams) > 1:
+                            # User has multiple teams - let them select
+                            jwt_with_team = _handle_team_selection_during_polling(
+                                base_url=base_url,
+                                key_id=key_id,
+                                teams=normalized_teams,
+                            )
+
+                            # Use the team-specific JWT if selection succeeded
+                            if jwt_with_team:
+                                return {
+                                    "api_key": jwt_with_team,
+                                    "user_id": user_id,
+                                    "teams": teams,
+                                    "team_id": None,  # Set by server in JWT
+                                }
+                            else:
+                                # Selection failed or was skipped - poll again without team_id
+                                click.echo("⚠️ Team selection skipped, retrying...")
+                                continue
+                        else:
+                            # Shouldn't happen, but fallback
+                            click.echo("⚠️ No teams available, retrying...")
+                            continue
+                    else:
+                        # JWT is ready (single team or team already selected)
+                        api_key = data.get("key")
+                        user_id = data.get("user_id")
+                        teams = data.get("teams", [])
+                        team_id = data.get("team_id")
+
+                        # Show which team was assigned
+                        if team_id and len(teams) == 1:
+                            click.echo(f"\n✅ Automatically assigned to team: {team_id}")
+
+                        if api_key:
+                            return {
+                                "api_key": api_key,
+                                "user_id": user_id,
+                                "teams": teams,
+                                "team_id": team_id,
+                            }
+                elif data.get("status") == "pending":
+                    # Still pending
+                    if attempt % 10 == 0:  # Show progress every 20 seconds
+                        click.echo("Still waiting for authentication...")
+            else:
+                click.echo(f"Polling error: HTTP {response.status_code}")
 
-    # Show which team was assigned
-    if team_id and len(teams) == 1:
-        click.echo(f"\n✅ Automatically assigned to team: {team_id}")
+        except requests.RequestException as e:
+            if attempt % 10 == 0:
+                click.echo(f"Connection error (will retry): {e}")
 
-    if api_key:
-        return {
-            "api_key": api_key,
-            "user_id": user_id,
-            "teams": teams,
-            "team_id": team_id,
-        }
+        time.sleep(poll_interval)
 
+    # Timeout reached
     return None
 
 
@@ -435,21 +416,25 @@ def _handle_team_selection_during_polling(
 
     click.echo(f"\n🔄 Generating JWT for team: {team_id}")
 
-    poll_url = f"{base_url}/sso/cli/poll/{key_id}?team_id={team_id}"
-    data = _poll_for_ready_data(
-        poll_url,
-        pending_message="Still waiting for team authentication...",
-        other_status_message="Waiting for team authentication to complete...",
-        http_error_log_every=10,
-    )
-    if not data:
+    # Re-poll with team_id to get JWT with correct team
+    try:
+        poll_url = f"{base_url}/sso/cli/poll/{key_id}?team_id={team_id}"
+        response = requests.get(poll_url, timeout=10)
+
+        if response.status_code == 200:
+            data = response.json()
+            if data.get("status") == "ready":
+                jwt_token = data.get("key")
+                if jwt_token:
+                    click.echo(f"✅ Successfully generated JWT for team: {team_id}")
+                    return jwt_token
+
+        click.echo(f"❌ Failed to get JWT with team. Status: {response.status_code}")
         return None
-    jwt_token = data.get("key")
-    if jwt_token:
-        click.echo(f"✅ Successfully generated JWT for team: {team_id}")
-        return jwt_token
 
-    return None
+    except Exception as e:
+        click.echo(f"❌ Error getting JWT with team: {e}")
+        return None
 
 
 def _render_and_prompt_for_team_selection(teams: List[Dict[str, Any]]) -> Optional[str]:
@@ -607,8 +592,8 @@ def whoami():
     age_hours = (time.time() - timestamp) / 3600
     click.echo(f"Token age: {age_hours:.1f} hours")
 
-    if age_hours > CLI_JWT_EXPIRATION_HOURS:
-        click.echo(f"⚠️ Warning: Token is more than {CLI_JWT_EXPIRATION_HOURS} hours old and may have expired.")
+    if age_hours > 24:
+        click.echo("⚠️ Warning: Token is more than 24 hours old and may have expired.")
 
 
 # Export functions for use by other CLI commands
diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py
index 51f3e6482a4..0d3e61b75c7 100644
--- a/litellm/proxy/common_request_processing.py
+++ b/litellm/proxy/common_request_processing.py
@@ -650,15 +650,11 @@ async def base_process_llm_request(
         )
 
         tasks = []
-        # Start the moderation check (during_call_hook) as early as possible
-        # This gives it a head start to mask/validate input while the proxy handles routing
         tasks.append(
-            asyncio.create_task(
-                proxy_logging_obj.during_call_hook(
-                    data=self.data,
-                    user_api_key_dict=user_api_key_dict,
-                    call_type=route_type,  # type: ignore
-                )
+            proxy_logging_obj.during_call_hook(
+                data=self.data,
+                user_api_key_dict=user_api_key_dict,
+                call_type=route_type,  # type: ignore
             )
         )
 
diff --git a/litellm/proxy/common_utils/callback_utils.py b/litellm/proxy/common_utils/callback_utils.py
index faeca9b2aed..c0cff84bacd 100644
--- a/litellm/proxy/common_utils/callback_utils.py
+++ b/litellm/proxy/common_utils/callback_utils.py
@@ -274,20 +274,11 @@ def initialize_callbacks_on_proxy(  # noqa: PLR0915
                     WebSearchInterceptionLogger,
                 )
 
-                websearch_interception_obj = (
-                    WebSearchInterceptionLogger.initialize_from_proxy_config(
-                        litellm_settings=litellm_settings,
-                        callback_specific_params=callback_specific_params,
-                    )
+                websearch_interception_obj = WebSearchInterceptionLogger.initialize_from_proxy_config(
+                    litellm_settings=litellm_settings,
+                    callback_specific_params=callback_specific_params,
                 )
                 imported_list.append(websearch_interception_obj)
-            elif isinstance(callback, str) and callback == "datadog_cost_management":
-                from litellm.integrations.datadog.datadog_cost_management import (
-                    DatadogCostManagementLogger,
-                )
-
-                datadog_cost_management_obj = DatadogCostManagementLogger()
-                imported_list.append(datadog_cost_management_obj)
             elif isinstance(callback, CustomLogger):
                 imported_list.append(callback)
             else:
@@ -362,17 +353,17 @@ def get_remaining_tokens_and_requests_from_request_data(data: Dict) -> Dict[str,
     remaining_requests_variable_name = f"litellm-key-remaining-requests-{model_group}"
     remaining_requests = _metadata.get(remaining_requests_variable_name, None)
     if remaining_requests:
-        headers[
-            f"x-litellm-key-remaining-requests-{h11_model_group_name}"
-        ] = remaining_requests
+        headers[f"x-litellm-key-remaining-requests-{h11_model_group_name}"] = (
+            remaining_requests
+        )
 
     # Remaining Tokens
     remaining_tokens_variable_name = f"litellm-key-remaining-tokens-{model_group}"
     remaining_tokens = _metadata.get(remaining_tokens_variable_name, None)
     if remaining_tokens:
-        headers[
-            f"x-litellm-key-remaining-tokens-{h11_model_group_name}"
-        ] = remaining_tokens
+        headers[f"x-litellm-key-remaining-tokens-{h11_model_group_name}"] = (
+            remaining_tokens
+        )
 
     return headers
 
@@ -447,9 +438,9 @@ def add_guardrail_response_to_standard_logging_object(
 ):
     if litellm_logging_obj is None:
         return
-    standard_logging_object: Optional[
-        StandardLoggingPayload
-    ] = litellm_logging_obj.model_call_details.get("standard_logging_object")
+    standard_logging_object: Optional[StandardLoggingPayload] = (
+        litellm_logging_obj.model_call_details.get("standard_logging_object")
+    )
     if standard_logging_object is None:
         return
     guardrail_information = standard_logging_object.get("guardrail_information", [])
@@ -478,9 +469,7 @@ def get_metadata_variable_name_from_kwargs(
     return "litellm_metadata" if "litellm_metadata" in kwargs else "metadata"
 
 
-def process_callback(
-    _callback: str, callback_type: str, environment_variables: dict
-) -> dict:
+def process_callback(_callback: str, callback_type: str, environment_variables: dict) -> dict:
     """Process a single callback and return its data with environment variables"""
     env_vars = CustomLogger.get_callback_env_vars(_callback)
 
@@ -492,9 +481,11 @@ def process_callback(
         else:
             env_vars_dict[_var] = env_variable
 
-    return {"name": _callback, "variables": env_vars_dict, "type": callback_type}
-
-
+    return {
+        "name": _callback,
+        "variables": env_vars_dict,
+        "type": callback_type
+    }
 def normalize_callback_names(callbacks: Iterable[Any]) -> List[Any]:
     if callbacks is None:
         return []
diff --git a/litellm/proxy/google_endpoints/endpoints.py b/litellm/proxy/google_endpoints/endpoints.py
index 5c41b371ca4..bba5b87024b 100644
--- a/litellm/proxy/google_endpoints/endpoints.py
+++ b/litellm/proxy/google_endpoints/endpoints.py
@@ -264,7 +264,7 @@ async def create_interaction(
             general_settings=general_settings,
             proxy_config=proxy_config,
             select_data_generator=select_data_generator,
-            model=data.get("model") or data.get("agent"),
+            model=data.get("model"),
             user_model=user_model,
             user_temperature=user_temperature,
             user_request_timeout=user_request_timeout,
diff --git a/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py
index 083a407e9cf..c9bd0135a05 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py
@@ -198,15 +198,6 @@ def __init__(
         for pattern_config in normalized_patterns:
             self._add_pattern(pattern_config)
 
-        # Warn if using during_call with MASK action (unstable)
-        if self.event_hook == GuardrailEventHooks.during_call and any(
-            p["action"] == ContentFilterAction.MASK for p in self.compiled_patterns
-        ):
-            verbose_proxy_logger.warning(
-                f"ContentFilterGuardrail '{self.guardrail_name}': 'during_call' mode with 'MASK' action is unstable due to race conditions. "
-                "Use 'pre_call' mode for reliable request masking."
-            )
-
         # Load blocked words - always initialize as dict
         self.blocked_words: Dict[str, Tuple[ContentFilterAction, Optional[str]]] = {}
         for word in normalized_blocked_words:
@@ -914,15 +905,11 @@ async def _process_images(
                 elif isinstance(e.detail, str):
                     e.detail = e.detail + " (Image description): " + description
                 else:
-                    e.detail = (
-                        "Content blocked: Image description detected" + description
-                    )
+                    e.detail = "Content blocked: Image description detected" + description
                 raise e
 
     def _count_masked_entities(
-        self,
-        detections: List[ContentFilterDetection],
-        masked_entity_count: Dict[str, int],
+        self, detections: List[ContentFilterDetection], masked_entity_count: Dict[str, int]
     ) -> None:
         """
         Count masked entities by type from detections.
@@ -977,11 +964,9 @@ def _log_guardrail_information(
             dict(detection) for detection in detections
         ]
         if status != "success":
-            guardrail_json_response = (
-                exception_str
-                if exception_str
-                else [dict(detection) for detection in detections]
-            )
+            guardrail_json_response = exception_str if exception_str else [
+                dict(detection) for detection in detections
+            ]
 
         self.add_standard_logging_guardrail_information_to_request_data(
             guardrail_provider=self.guardrail_provider,
@@ -1081,84 +1066,99 @@ async def async_post_call_streaming_iterator_hook(
         Process streaming response chunks and check for blocked content.
 
         For BLOCK action: Raises HTTPException immediately when blocked content is detected.
-        For MASK action: Content is buffered to handle patterns split across chunks.
+        For MASK action: Content passes through (masking streaming responses is not supported).
         """
-        accumulated_full_text = ""
-        yielded_masked_text_len = 0
-        buffer_size = 50  # Increased buffer to catch patterns split across many chunks
 
-        verbose_proxy_logger.info(
-            f"ContentFilterGuardrail: Starting robust streaming masking for model {request_data.get('model')}"
-        )
+        # Accumulate content as we iterate through chunks
+        accumulated_content = ""
 
         async for item in response:
+            # Accumulate content from this chunk before checking
             if isinstance(item, ModelResponseStream) and item.choices:
-                delta_content = ""
-                is_final = False
                 for choice in item.choices:
                     if hasattr(choice, "delta") and choice.delta:
                         content = getattr(choice.delta, "content", None)
                         if content and isinstance(content, str):
-                            delta_content += content
-                    if getattr(choice, "finish_reason", None):
-                        is_final = True
-
-                accumulated_full_text += delta_content
-
-                # Check for blocking or apply masking
-                # Add a space at the end if it's the final chunk to trigger word boundaries (\b)
-                text_to_check = accumulated_full_text
-                if is_final:
-                    text_to_check += " "
-
-                try:
-                    masked_text = self._filter_single_text(text_to_check)
-                    if is_final and masked_text.endswith(" "):
-                        masked_text = masked_text[:-1]
-                except HTTPException:
-                    raise
-                except Exception as e:
-                    verbose_proxy_logger.error(
-                        f"ContentFilterGuardrail: Error in masking: {e}"
-                    )
-                    masked_text = text_to_check  # Fallback to current text
-
-                # Determine how much can be safely yielded
-                if is_final:
-                    safe_to_yield_len = len(masked_text)
-                else:
-                    safe_to_yield_len = max(0, len(masked_text) - buffer_size)
-
-                if safe_to_yield_len > yielded_masked_text_len:
-                    new_masked_content = masked_text[
-                        yielded_masked_text_len:safe_to_yield_len
-                    ]
-                    # Modify the chunk to contain only the new masked content
-                    if (
-                        item.choices
-                        and hasattr(item.choices[0], "delta")
-                        and item.choices[0].delta
-                    ):
-                        item.choices[0].delta.content = new_masked_content
-                        yielded_masked_text_len = safe_to_yield_len
-                        yield item
-                else:
-                    # Hold content by yielding empty content chunk (keeps metadata/structure)
-                    if (
-                        item.choices
-                        and hasattr(item.choices[0], "delta")
-                        and item.choices[0].delta
-                    ):
-                        item.choices[0].delta.content = ""
-                    yield item
-            else:
-                # Not a ModelResponseStream or no choices - yield as is
-                yield item
+                            accumulated_content += content
+
+                # Check accumulated content for blocked patterns/keywords after processing all choices
+                # Only check for BLOCK actions, not MASK (masking streaming is not supported)
+                if accumulated_content:
+                    try:
+                        # Check patterns
+                        pattern_match = self._check_patterns(accumulated_content)
+                        if pattern_match:
+                            matched_text, pattern_name, action = pattern_match
+                            if action == ContentFilterAction.BLOCK:
+                                error_msg = (
+                                    f"Content blocked: {pattern_name} pattern detected"
+                                )
+                                verbose_proxy_logger.warning(error_msg)
+                                raise HTTPException(
+                                    status_code=403,
+                                    detail={
+                                        "error": error_msg,
+                                        "pattern": pattern_name,
+                                    },
+                                )
+
+                        # Check blocked words
+                        blocked_word_match = self._check_blocked_words(
+                            accumulated_content
+                        )
+                        if blocked_word_match:
+                            keyword, action, description = blocked_word_match
+                            if action == ContentFilterAction.BLOCK:
+                                error_msg = (
+                                    f"Content blocked: keyword '{keyword}' detected"
+                                )
+                                if description:
+                                    error_msg += f" ({description})"
+                                verbose_proxy_logger.warning(error_msg)
+                                raise HTTPException(
+                                    status_code=403,
+                                    detail={
+                                        "error": error_msg,
+                                        "keyword": keyword,
+                                        "description": description,
+                                    },
+                                )
+
+                        # Check category keywords
+                        all_exceptions = []
+                        for category in self.loaded_categories.values():
+                            all_exceptions.extend(category.exceptions)
+                        category_match = self._check_category_keywords(
+                            accumulated_content, all_exceptions
+                        )
+                        if category_match:
+                            keyword, category_name, severity, action = category_match
+                            if action == ContentFilterAction.BLOCK:
+                                error_msg = (
+                                    f"Content blocked: {category_name} category keyword '{keyword}' detected "
+                                    f"(severity: {severity})"
+                                )
+                                verbose_proxy_logger.warning(error_msg)
+                                raise HTTPException(
+                                    status_code=403,
+                                    detail={
+                                        "error": error_msg,
+                                        "category": category_name,
+                                        "keyword": keyword,
+                                        "severity": severity,
+                                    },
+                                )
+                    except HTTPException:
+                        # Re-raise HTTPException (blocked content detected)
+                        raise
+                    except Exception as e:
+                        # Log other exceptions but don't block the stream
+                        verbose_proxy_logger.warning(
+                            f"Error checking content filter in streaming: {e}"
+                        )
 
-        # Any remaining content (should have been handled by is_final, but just in case)
-        if yielded_masked_text_len < len(accumulated_full_text):
-            # We already reached the end of the generator
-            pass
+            # Yield the chunk (only if no exception was raised above)
+            yield item
 
     @staticmethod
     def get_config_model():
diff --git a/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.json b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.json
index 1eff7804b42..f2427b5b920 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.json
+++ b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/patterns.json
@@ -108,7 +108,7 @@
     {
       "name": "ipv6",
       "display_name": "IP Address (IPv6)",
-      "pattern": "(?<![0-9a-fA-F:])(?:(?:[0-9a-fA-F]{1,4}:){7}[0-9a-fA-F]{1,4}|(?:[0-9a-fA-F]{1,4}:){1,7}:|:(?::[0-9a-fA-F]{1,4}){1,7}|::|(?:[0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-F]{1,4}|(?:[0-9a-fA-F]{1,4}:){1,5}(?::[0-9a-fA-F]{1,4}){1,2}|(?:[0-9a-fA-F]{1,4}:){1,4}(?::[0-9a-fA-F]{1,4}){1,3}|(?:[0-9a-fA-F]{1,4}:){1,3}(?::[0-9a-fA-F]{1,4}){1,4}|(?:[0-9a-fA-F]{1,4}:){1,2}(?::[0-9a-fA-F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:(?::[0-9a-fA-F]{1,4}){1,6})(?![0-9a-fA-F:])",
+      "pattern": "\\b(?:[0-9a-fA-F]{1,4}:){7}[0-9a-fA-F]{1,4}\\b",
       "category": "Network Patterns",
       "description": "Detects IPv6 addresses"
     },
@@ -122,9 +122,9 @@
     {
       "name": "passport_us",
       "display_name": "Passport (US)",
-      "pattern": "\\b([A-Z][0-9]{8}|[0-9]{9})\\b",
+      "pattern": "\\b[0-9]{9}\\b",
       "category": "PII Patterns",
-      "description": "US passport numbers (9 digits or alphanumeric letter + 8 digits)"
+      "description": "US passport numbers (9 digits)"
     },
     {
       "name": "passport_uk",
@@ -157,9 +157,9 @@
     {
       "name": "passport_canada",
       "display_name": "Passport (Canada)",
-      "pattern": "\\b([A-Z]{2}[0-9]{6}|[A-Z][0-9]{6}[A-Z]{2})\\b",
+      "pattern": "\\b[A-Z]{2}[0-9]{6}\\b",
       "category": "PII Patterns",
-      "description": "Canadian passport numbers (old: 2 letters + 6 digits; new: 1 letter + 6 digits + 2 letters)"
+      "description": "Canadian passport numbers (2 letters + 6 digits)"
     },
     {
       "name": "passport_india",
@@ -369,4 +369,4 @@
       "description": "Detects Brazilian RG identity card numbers (common pattern for SP, RJ, MG states)"
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/litellm/proxy/guardrails/guardrail_hooks/onyx/onyx.py b/litellm/proxy/guardrails/guardrail_hooks/onyx/onyx.py
index 3598dbe741e..5f57cab1db4 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/onyx/onyx.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/onyx/onyx.py
@@ -8,7 +8,6 @@
 import uuid
 from typing import TYPE_CHECKING, Any, Literal, Optional, Type
 
-import httpx
 from fastapi import HTTPException
 
 from litellm._logging import verbose_proxy_logger
@@ -26,12 +25,10 @@
 
 class OnyxGuardrail(CustomGuardrail):
     def __init__(
-        self, api_base: Optional[str] = None, api_key: Optional[str] = None, timeout: Optional[float] = 10.0, **kwargs
+        self, api_base: Optional[str] = None, api_key: Optional[str] = None, **kwargs
     ):
-        timeout = timeout or int(os.getenv("ONYX_TIMEOUT", 10.0))
         self.async_handler = get_async_httpx_client(
-            llm_provider=httpxSpecialProvider.GuardrailCallback,
-            params={"timeout": httpx.Timeout(timeout=timeout, connect=5.0)},
+            llm_provider=httpxSpecialProvider.GuardrailCallback
         )
         self.api_base = api_base or os.getenv(
             "ONYX_API_BASE",
diff --git a/litellm/proxy/guardrails/guardrail_hooks/presidio.py b/litellm/proxy/guardrails/guardrail_hooks/presidio.py
index 71ad9819146..20df54b62ce 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/presidio.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/presidio.py
@@ -9,10 +9,8 @@
 
 
 import asyncio
-import threading
 import json
 from datetime import datetime
-from contextlib import asynccontextmanager
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -109,14 +107,6 @@ def __init__(
         # Lock to prevent race conditions when creating session under concurrent load
         # Note: asyncio.Lock() can be created without an event loop; it only needs one when awaited
         self._session_lock: asyncio.Lock = asyncio.Lock()
-
-        # Track main thread ID to safely identity when we are running in main loop vs background thread
-
-        self._main_thread_id = threading.get_ident()
-
-        # Loop-bound session cache for background threads
-        self._loop_sessions: Dict[asyncio.AbstractEventLoop, aiohttp.ClientSession] = {}
-
         if mock_testing is True:  # for testing purposes only
             return
 
@@ -182,52 +172,46 @@ def validate_environment(
                 "http://" + self.presidio_anonymizer_api_base
             )
 
-    @asynccontextmanager
-    async def _get_session_iterator(
-        self,
-    ) -> AsyncGenerator[aiohttp.ClientSession, None]:
+    async def _get_http_session(self) -> aiohttp.ClientSession:
         """
-        Async context manager for yielding an HTTP session.
+        Get or create the shared HTTP session for Presidio API calls.
+
+        Fixes memory leak (issue #14540) where every guardrail check created
+        a new aiohttp.ClientSession that was never properly closed.
 
-        Logic:
-        1. If running in the main thread (where the object was initialized/destined to live normally),
-           use the shared `self._http_session` (protected by a lock).
-        2. If running in a background thread (e.g. logging hook), use a cached session for that loop.
+        Thread-safe: Uses asyncio.Lock to prevent race conditions when
+        multiple concurrent requests try to create the session simultaneously.
         """
-        current_loop = asyncio.get_running_loop()
-
-        # Check if we are in the stored main thread
-        if threading.get_ident() == self._main_thread_id:
-            # Main thread -> use shared session
-            async with self._session_lock:
-                if self._http_session is None or self._http_session.closed:
-                    self._http_session = aiohttp.ClientSession()
-                yield self._http_session
-        else:
-            # Background thread/loop -> use loop-bound session cache
-            # This avoids "attached to a different loop" or "no running event loop" errors
-            # when accessing the shared session created in the main loop
-            if (
-                current_loop not in self._loop_sessions
-                or self._loop_sessions[current_loop].closed
-            ):
-                self._loop_sessions[current_loop] = aiohttp.ClientSession()
-            yield self._loop_sessions[current_loop]
+        async with self._session_lock:
+            if self._http_session is None or self._http_session.closed:
+                self._http_session = aiohttp.ClientSession()
+            return self._http_session
 
     async def _close_http_session(self) -> None:
-        """Close all cached HTTP sessions."""
+        """Close the HTTP session if it exists."""
         if self._http_session is not None and not self._http_session.closed:
             await self._http_session.close()
             self._http_session = None
 
-        for session in self._loop_sessions.values():
-            if not session.closed:
-                await session.close()
-        self._loop_sessions.clear()
-
     def __del__(self):
-        """Cleanup: we try to close, but doing async cleanup in __del__ is risky."""
-        pass
+        """Cleanup: close HTTP session on instance destruction."""
+        if self._http_session is not None and not self._http_session.closed:
+            try:
+                # Try to close the session, but don't fail if event loop is gone
+                import asyncio
+                try:
+                    loop = asyncio.get_event_loop()
+                    if loop.is_running():
+                        # Schedule cleanup, don't block __del__
+                        asyncio.create_task(self._close_http_session())
+                    else:
+                        loop.run_until_complete(self._close_http_session())
+                except RuntimeError:
+                    # Event loop is closed, can't clean up - not ideal but better than crashing
+                    pass
+            except Exception:
+                # Suppress all exceptions in __del__ to avoid issues during shutdown
+                pass
 
     def _get_presidio_analyze_request_payload(
         self,
@@ -289,27 +273,28 @@ async def analyze_text(
                 return self.mock_redacted_text
 
             # Use shared session to prevent memory leak (issue #14540)
-            async with self._get_session_iterator() as session:
-                # Make the request to /analyze
-                analyze_url = f"{self.presidio_analyzer_api_base}analyze"
+            session = await self._get_http_session()
 
-                analyze_payload: PresidioAnalyzeRequest = (
-                    self._get_presidio_analyze_request_payload(
-                        text=text,
-                        presidio_config=presidio_config,
-                        request_data=request_data,
-                    )
-                )
+            # Make the request to /analyze
+            analyze_url = f"{self.presidio_analyzer_api_base}analyze"
 
-                verbose_proxy_logger.debug(
-                    "Making request to: %s with payload: %s",
-                    analyze_url,
-                    analyze_payload,
+            analyze_payload: PresidioAnalyzeRequest = (
+                self._get_presidio_analyze_request_payload(
+                    text=text,
+                    presidio_config=presidio_config,
+                    request_data=request_data,
                 )
+            )
 
-                async with session.post(analyze_url, json=analyze_payload) as response:
-                    analyze_results = await response.json()
-                    verbose_proxy_logger.debug("analyze_results: %s", analyze_results)
+            verbose_proxy_logger.debug(
+                "Making request to: %s with payload: %s",
+                analyze_url,
+                analyze_payload,
+            )
+
+            async with session.post(analyze_url, json=analyze_payload) as response:
+                analyze_results = await response.json()
+                verbose_proxy_logger.debug("analyze_results: %s", analyze_results)
 
                 # Handle error responses from Presidio (e.g., {'error': 'No text provided'})
                 # Presidio may return a dict instead of a list when errors occur
@@ -317,7 +302,7 @@ async def analyze_text(
                     if "error" in analyze_results:
                         verbose_proxy_logger.warning(
                             "Presidio analyzer returned error: %s, returning empty list",
-                            analyze_results.get("error"),
+                            analyze_results.get("error")
                         )
                         return []
                     # If it's a dict but not an error, try to process it as a single item
@@ -329,7 +314,7 @@ async def analyze_text(
                     except Exception as e:
                         verbose_proxy_logger.warning(
                             "Failed to parse Presidio dict response: %s, returning empty list",
-                            e,
+                            e
                         )
                         return []
 
@@ -366,19 +351,20 @@ async def anonymize_text(
                 return text
 
             # Use shared session to prevent memory leak (issue #14540)
-            async with self._get_session_iterator() as session:
-                # Make the request to /anonymize
-                anonymize_url = f"{self.presidio_anonymizer_api_base}anonymize"
-                verbose_proxy_logger.debug("Making request to: %s", anonymize_url)
-                anonymize_payload = {
-                    "text": text,
-                    "analyzer_results": analyze_results,
-                }
-
-                async with session.post(
-                    anonymize_url, json=anonymize_payload
-                ) as response:
-                    redacted_text = await response.json()
+            session = await self._get_http_session()
+
+            # Make the request to /anonymize
+            anonymize_url = f"{self.presidio_anonymizer_api_base}anonymize"
+            verbose_proxy_logger.debug("Making request to: %s", anonymize_url)
+            anonymize_payload = {
+                "text": text,
+                "analyzer_results": analyze_results,
+            }
+
+            async with session.post(
+                anonymize_url, json=anonymize_payload
+            ) as response:
+                redacted_text = await response.json()
 
             new_text = text
             if redacted_text is not None:
@@ -509,13 +495,12 @@ async def check_pii(
                 )
 
                 # Then anonymize the text using the analysis results
-                anonymized_text = await self.anonymize_text(
+                return await self.anonymize_text(
                     text=text,
                     analyze_results=analyze_results,
                     output_parse_pii=output_parse_pii,
                     masked_entity_count=masked_entity_count,
                 )
-                return anonymized_text
             return redacted_text["text"]
         except Exception as e:
             status = "guardrail_failed_to_respond"
diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py
index 9be78264e85..60472cd7790 100644
--- a/litellm/proxy/litellm_pre_call_utils.py
+++ b/litellm/proxy/litellm_pre_call_utils.py
@@ -856,9 +856,7 @@ async def add_litellm_data_to_request(  # noqa: PLR0915
 
     # Add headers to metadata for guardrails to access (fixes #17477)
     # Guardrails use metadata["headers"] to access request headers (e.g., User-Agent)
-    if _metadata_variable_name in data and isinstance(
-        data[_metadata_variable_name], dict
-    ):
+    if _metadata_variable_name in data and isinstance(data[_metadata_variable_name], dict):
         data[_metadata_variable_name]["headers"] = _headers
 
     # check for forwardable headers
@@ -1014,9 +1012,7 @@ async def add_litellm_data_to_request(  # noqa: PLR0915
 
     # User spend, budget - used by prometheus.py
     # Follow same pattern as team and API key budgets
-    data[_metadata_variable_name][
-        "user_api_key_user_spend"
-    ] = user_api_key_dict.user_spend
+    data[_metadata_variable_name]["user_api_key_user_spend"] = user_api_key_dict.user_spend
     data[_metadata_variable_name][
         "user_api_key_user_max_budget"
     ] = user_api_key_dict.user_max_budget
@@ -1043,8 +1039,8 @@ async def add_litellm_data_to_request(  # noqa: PLR0915
     ## [Enterprise Only]
     # Add User-IP Address
     requester_ip_address = ""
-    if True:  # Always set the IP Address if available
-        # logic for tracking IP Address
+    if premium_user is True:
+        # Only set the IP Address for Enterprise Users
 
         # logic for tracking IP Address
         if (
@@ -1064,16 +1060,6 @@ async def add_litellm_data_to_request(  # noqa: PLR0915
             requester_ip_address = request.client.host
     data[_metadata_variable_name]["requester_ip_address"] = requester_ip_address
 
-    # Add User-Agent
-    user_agent = ""
-    if (
-        request is not None
-        and hasattr(request, "headers")
-        and "user-agent" in request.headers
-    ):
-        user_agent = request.headers["user-agent"]
-    data[_metadata_variable_name]["user_agent"] = user_agent
-
     # Check if using tag based routing
     tags = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
         llm_router=llm_router,
@@ -1524,13 +1510,10 @@ def add_guardrails_from_policy_engine(
     Add guardrails from the policy engine based on request context.
 
     This function:
-    1. Extracts "policies" from request body (if present) for dynamic policy application
-    2. Gets matching policies based on team_alias, key_alias, and model (via attachments)
-    3. Combines dynamic policies with attachment-based policies
-    4. Resolves guardrails from all policies (including inheritance)
-    5. Adds guardrails to request metadata
-    6. Tracks applied policies in metadata for response headers
-    7. Removes "policies" from request body so it's not forwarded to LLM provider
+    1. Gets matching policies based on team_alias, key_alias, and model
+    2. Resolves guardrails from matching policies (including inheritance)
+    3. Adds guardrails to request metadata
+    4. Tracks applied policies in metadata for response headers
 
     Args:
         data: The request data to update
@@ -1546,19 +1529,13 @@ def add_guardrails_from_policy_engine(
     from litellm.proxy.policy_engine.policy_resolver import PolicyResolver
     from litellm.types.proxy.policy_engine import PolicyMatchContext
 
-    # Extract dynamic policies from request body (if present)
-    # These will be combined with attachment-based policies
-    request_body_policies = data.pop("policies", None)
-
     registry = get_policy_registry()
     verbose_proxy_logger.debug(
         f"Policy engine: registry initialized={registry.is_initialized()}, "
         f"policy_count={len(registry.get_all_policies())}"
     )
     if not registry.is_initialized():
-        verbose_proxy_logger.debug(
-            "Policy engine not initialized, skipping policy matching"
-        )
+        verbose_proxy_logger.debug("Policy engine not initialized, skipping policy matching")
         return
 
     # Build context from request
@@ -1576,30 +1553,18 @@ def add_guardrails_from_policy_engine(
     # Get matching policies via attachments
     matching_policy_names = PolicyMatcher.get_matching_policies(context=context)
 
-    verbose_proxy_logger.debug(
-        f"Policy engine: matched policies via attachments: {matching_policy_names}"
-    )
-
-    # Combine attachment-based policies with dynamic request body policies
-    all_policy_names = set(matching_policy_names)
-    if request_body_policies and isinstance(request_body_policies, list):
-        all_policy_names.update(request_body_policies)
-        verbose_proxy_logger.debug(
-            f"Policy engine: added dynamic policies from request body: {request_body_policies}"
-        )
+    verbose_proxy_logger.debug(f"Policy engine: matched policies via attachments: {matching_policy_names}")
 
-    if not all_policy_names:
+    if not matching_policy_names:
         return
 
     # Filter to only policies whose conditions match the context
     applied_policy_names = PolicyMatcher.get_policies_with_matching_conditions(
-        policy_names=list(all_policy_names),
+        policy_names=matching_policy_names,
         context=context,
     )
 
-    verbose_proxy_logger.debug(
-        f"Policy engine: applied policies (conditions matched): {applied_policy_names}"
-    )
+    verbose_proxy_logger.debug(f"Policy engine: applied policies (conditions matched): {applied_policy_names}")
 
     # Track applied policies in metadata for response headers
     for policy_name in applied_policy_names:
@@ -1610,9 +1575,7 @@ def add_guardrails_from_policy_engine(
     # Resolve guardrails from matching policies
     resolved_guardrails = PolicyResolver.resolve_guardrails_for_context(context=context)
 
-    verbose_proxy_logger.debug(
-        f"Policy engine: resolved guardrails: {resolved_guardrails}"
-    )
+    verbose_proxy_logger.debug(f"Policy engine: resolved guardrails: {resolved_guardrails}")
 
     if not resolved_guardrails:
         return
diff --git a/litellm/proxy/management_endpoints/internal_user_endpoints.py b/litellm/proxy/management_endpoints/internal_user_endpoints.py
index 38a867d031b..9a986326c03 100644
--- a/litellm/proxy/management_endpoints/internal_user_endpoints.py
+++ b/litellm/proxy/management_endpoints/internal_user_endpoints.py
@@ -814,9 +814,7 @@ def _update_internal_user_params(
 ) -> dict:
     non_default_values = {}
     for k, v in data_json.items():
-        if k == "max_budget":
-            non_default_values[k] = v
-        elif (
+        if (
             v is not None
             and v
             not in (
diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py
index 380e8bddc99..ab87e862ea6 100644
--- a/litellm/proxy/management_endpoints/key_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/key_management_endpoints.py
@@ -37,13 +37,6 @@
 )
 from litellm.proxy._types import *
 from litellm.proxy._types import LiteLLM_VerificationToken
-from litellm.types.proxy.management_endpoints.key_management_endpoints import (
-    BulkUpdateKeyRequest,
-    BulkUpdateKeyRequestItem,
-    BulkUpdateKeyResponse,
-    FailedKeyUpdate,
-    SuccessfulKeyUpdate,
-)
 from litellm.proxy.auth.auth_checks import (
     _cache_key_object,
     _delete_cache_key_object,
@@ -1445,211 +1438,6 @@ def is_different_team(
     return data.team_id != existing_key_row.team_id
 
 
-def _validate_max_budget(max_budget: Optional[float]) -> None:
-    """
-    Validate that max_budget is not negative.
-    
-    Args:
-        max_budget: The max_budget value to validate
-        
-    Raises:
-        HTTPException: If max_budget is negative
-    """
-    if max_budget is not None and max_budget < 0:
-        raise HTTPException(
-            status_code=400,
-            detail={
-                "error": f"max_budget cannot be negative. Received: {max_budget}"
-            },
-        )
-
-
-async def _get_and_validate_existing_key(
-    token: str, prisma_client: Optional[PrismaClient]
-) -> LiteLLM_VerificationToken:
-    """
-    Get existing key from database and validate it exists.
-    
-    Args:
-        token: The key token to look up
-        prisma_client: Prisma client instance
-        
-    Returns:
-        LiteLLM_VerificationToken: The existing key row
-        
-    Raises:
-        HTTPException: If key is not found
-    """
-    if prisma_client is None:
-        raise HTTPException(
-            status_code=500,
-            detail={"error": "Database not connected"},
-        )
-    
-    existing_key_row = await prisma_client.get_data(
-        token=token,
-        table_name="key",
-        query_type="find_unique",
-    )
-    
-    if existing_key_row is None:
-        raise HTTPException(
-            status_code=404,
-            detail={"error": f"Key not found: {token}"},
-        )
-    
-    return existing_key_row
-
-
-async def _process_single_key_update(
-    key_update_item: BulkUpdateKeyRequestItem,
-    user_api_key_dict: UserAPIKeyAuth,
-    litellm_changed_by: Optional[str],
-    prisma_client: Optional[PrismaClient],
-    user_api_key_cache: DualCache,
-    proxy_logging_obj: Any,
-    llm_router: Optional[Router],
-) -> Dict[str, Any]:
-    """
-    Process a single key update with all validations and checks.
-    
-    This function encapsulates all the logic for updating a single key,
-    including validation, permission checks, team checks, and database updates.
-    
-    Args:
-        key_update_item: The key update request item
-        user_api_key_dict: The authenticated user's API key info
-        litellm_changed_by: Optional header for tracking who made the change
-        prisma_client: Prisma client instance
-        user_api_key_cache: User API key cache
-        proxy_logging_obj: Proxy logging object
-        llm_router: LLM router instance
-        
-    Returns:
-        Dict containing the updated key information
-        
-    Raises:
-        HTTPException: For various validation and permission errors
-    """
-    # Validate max_budget
-    _validate_max_budget(key_update_item.max_budget)
-    
-    # Get and validate existing key
-    existing_key_row = await _get_and_validate_existing_key(
-        token=key_update_item.key,
-        prisma_client=prisma_client,
-    )
-    
-    # Check team member permissions
-    await TeamMemberPermissionChecks.can_team_member_execute_key_management_endpoint(
-        user_api_key_dict=user_api_key_dict,
-        route=KeyManagementRoutes.KEY_UPDATE,
-        prisma_client=prisma_client,
-        existing_key_row=existing_key_row,
-        user_api_key_cache=user_api_key_cache,
-    )
-    
-    # Create UpdateKeyRequest from BulkUpdateKeyRequestItem
-    update_key_request = UpdateKeyRequest(
-        key=key_update_item.key,
-        budget_id=key_update_item.budget_id,
-        max_budget=key_update_item.max_budget,
-        team_id=key_update_item.team_id,
-        tags=key_update_item.tags,
-    )
-    
-    # Get team object and check team limits if team_id is provided
-    team_obj: Optional[LiteLLM_TeamTableCachedObj] = None
-    if update_key_request.team_id is not None:
-        team_obj = await get_team_object(
-            team_id=update_key_request.team_id,
-            prisma_client=prisma_client,
-            user_api_key_cache=user_api_key_cache,
-            check_db_only=True,
-        )
-        
-        if team_obj is not None and prisma_client is not None:
-            await _check_team_key_limits(
-                team_table=team_obj,
-                data=update_key_request,
-                prisma_client=prisma_client,
-            )
-    
-    # Validate team change if team is being changed
-    if is_different_team(
-        data=update_key_request, existing_key_row=existing_key_row
-    ):
-        if llm_router is None:
-            raise HTTPException(
-                status_code=400,
-                detail={
-                    "error": "LLM router not found. Please set it up by passing in a valid config.yaml or adding models via the UI."
-                },
-            )
-        if team_obj is None:
-            raise HTTPException(
-                status_code=500,
-                detail={
-                    "error": "Team object not found for team change validation"
-                },
-            )
-        validate_key_team_change(
-            key=existing_key_row,
-            team=team_obj,
-            change_initiated_by=user_api_key_dict,
-            llm_router=llm_router,
-        )
-    
-    # Prepare update data
-    non_default_values = await prepare_key_update_data(
-        data=update_key_request, existing_key_row=existing_key_row
-    )
-    
-    # Update key in database
-    if prisma_client is None:
-        raise HTTPException(
-            status_code=500,
-            detail={"error": "Database not connected"},
-        )
-    
-    _data = {**non_default_values, "token": key_update_item.key}
-    response = await prisma_client.update_data(
-        token=key_update_item.key, data=_data
-    )
-    
-    # Delete cache
-    await _delete_cache_key_object(
-        hashed_token=hash_token(key_update_item.key),
-        user_api_key_cache=user_api_key_cache,
-        proxy_logging_obj=proxy_logging_obj,
-    )
-    
-    # Trigger async hook
-    asyncio.create_task(
-        KeyManagementEventHooks.async_key_updated_hook(
-            data=update_key_request,
-            existing_key_row=existing_key_row,
-            response=response,
-            user_api_key_dict=user_api_key_dict,
-            litellm_changed_by=litellm_changed_by,
-        )
-    )
-    
-    if response is None:
-        raise ValueError("Failed to update key got response = None")
-    
-    # Extract and format updated key info
-    updated_key_info = response.get("data", {})
-    if hasattr(updated_key_info, "model_dump"):
-        updated_key_info = updated_key_info.model_dump()
-    elif hasattr(updated_key_info, "dict"):
-        updated_key_info = updated_key_info.dict()
-    
-    updated_key_info.pop("token", None)
-    
-    return updated_key_info
-
-
 @router.post(
     "/key/update", tags=["key management"], dependencies=[Depends(user_api_key_auth)]
 )
@@ -1896,167 +1684,6 @@ async def update_key_fn(
         )
 
 
-@router.post(
-    "/key/bulk_update",
-    tags=["key management"],
-    dependencies=[Depends(user_api_key_auth)],
-    response_model=BulkUpdateKeyResponse,
-)
-@management_endpoint_wrapper
-async def bulk_update_keys(
-    data: BulkUpdateKeyRequest,
-    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
-    litellm_changed_by: Optional[str] = Header(
-        None,
-        description="The litellm-changed-by header enables tracking of actions performed by authorized users on behalf of other users, providing an audit trail for accountability",
-    ),
-):
-    """
-    Bulk update multiple keys at once.
-    
-    This endpoint allows updating multiple keys in a single request. Each key update
-    is processed independently - if some updates fail, others will still succeed.
-    
-    Parameters:
-    - keys: List[BulkUpdateKeyRequestItem] - List of key update requests, each containing:
-        - key: str - The key identifier (token) to update
-        - budget_id: Optional[str] - Budget ID associated with the key
-        - max_budget: Optional[float] - Max budget for key
-        - team_id: Optional[str] - Team ID associated with key
-        - tags: Optional[List[str]] - Tags for organizing keys
-    
-    Returns:
-    - total_requested: int - Total number of keys requested for update
-    - successful_updates: List[SuccessfulKeyUpdate] - List of successfully updated keys with their updated info
-    - failed_updates: List[FailedKeyUpdate] - List of failed updates with key_info and failed_reason
-    
-    Example request:
-    ```bash
-    curl --location 'http://0.0.0.0:4000/key/bulk_update' \
-    --header 'Authorization: Bearer sk-1234' \
-    --header 'Content-Type: application/json' \
-    --data '{
-        "keys": [
-            {
-                "key": "sk-1234",
-                "max_budget": 100.0,
-                "team_id": "team-123",
-                "tags": ["production", "api"]
-            },
-            {
-                "key": "sk-5678",
-                "budget_id": "budget-456",
-                "tags": ["staging"]
-            }
-        ]
-    }'
-    ```
-    """
-    from litellm.proxy.proxy_server import (
-        llm_router,
-        prisma_client,
-        proxy_logging_obj,
-        user_api_key_cache,
-    )
-    
-    if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN.value:
-        raise HTTPException(
-            status_code=403,
-            detail={
-                "error": "Only proxy admins can perform bulk key updates"
-            },
-        )
-
-    if prisma_client is None:
-        raise HTTPException(
-            status_code=500,
-            detail={"error": "Database not connected"},
-        )
-
-    if not data.keys:
-        raise HTTPException(
-            status_code=400,
-            detail={"error": "No keys provided for update"},
-        )
-
-    MAX_BATCH_SIZE = 500
-    if len(data.keys) > MAX_BATCH_SIZE:
-        raise HTTPException(
-            status_code=400,
-            detail={
-                "error": f"Maximum {MAX_BATCH_SIZE} keys can be updated at once. Found {len(data.keys)} keys."
-            },
-        )
-
-    successful_updates: List[SuccessfulKeyUpdate] = []
-    failed_updates: List[FailedKeyUpdate] = []
-
-    for key_update_item in data.keys:
-        try:
-            # Process single key update using reusable function
-            updated_key_info = await _process_single_key_update(
-                key_update_item=key_update_item,
-                user_api_key_dict=user_api_key_dict,
-                litellm_changed_by=litellm_changed_by,
-                prisma_client=prisma_client,
-                user_api_key_cache=user_api_key_cache,
-                proxy_logging_obj=proxy_logging_obj,
-                llm_router=llm_router,
-            )
-
-            successful_updates.append(
-                SuccessfulKeyUpdate(
-                    key=key_update_item.key,
-                    key_info=updated_key_info,
-                )
-            )
-
-        except Exception as e:
-            verbose_proxy_logger.exception(
-                f"Failed to update key {key_update_item.key}: {e}"
-            )
-
-            if isinstance(e, HTTPException):
-                error_detail = e.detail
-                if isinstance(error_detail, dict):
-                    error_message = error_detail.get("error", str(e))
-                else:
-                    error_message = str(error_detail)
-            else:
-                error_message = str(e)
-
-            key_info = None
-            try:
-                existing_key_row = await prisma_client.get_data(
-                    token=key_update_item.key,
-                    table_name="key",
-                    query_type="find_unique",
-                )
-                if existing_key_row is not None:
-                    if hasattr(existing_key_row, "model_dump"):
-                        key_info = existing_key_row.model_dump()
-                    elif hasattr(existing_key_row, "dict"):
-                        key_info = existing_key_row.dict()
-                    if key_info:
-                        key_info.pop("token", None)
-            except Exception:
-                pass
-
-            failed_updates.append(
-                FailedKeyUpdate(
-                    key=key_update_item.key,
-                    key_info=key_info,
-                    failed_reason=error_message,
-                )
-            )
-
-    return BulkUpdateKeyResponse(
-        total_requested=len(data.keys),
-        successful_updates=successful_updates,
-        failed_updates=failed_updates,
-    )
-
-
 def validate_key_team_change(
     key: LiteLLM_VerificationToken,
     team: LiteLLM_TeamTable,
diff --git a/litellm/proxy/management_endpoints/mcp_management_endpoints.py b/litellm/proxy/management_endpoints/mcp_management_endpoints.py
index 83d7f3fde4c..a15f47d13bd 100644
--- a/litellm/proxy/management_endpoints/mcp_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/mcp_management_endpoints.py
@@ -56,19 +56,7 @@
     MCP_AVAILABLE = False
 
 if MCP_AVAILABLE:
-    try:
-        from mcp.shared.tool_name_validation import validate_tool_name  # type: ignore
-    except ImportError:
-
-        def validate_tool_name(name: str):
-            from pydantic import BaseModel
-
-            class MockResult(BaseModel):
-                is_valid: bool = True
-                warnings: list = []
-
-            return MockResult()
-
+    from mcp.shared.tool_name_validation import validate_tool_name
     from litellm.proxy._experimental.mcp_server.db import (
         create_mcp_server,
         delete_mcp_server,
@@ -134,7 +122,9 @@ def _validate_mcp_server_name_fields(payload: Any) -> None:
             )
             if validation_result.warnings:
                 error_messages_text = (
-                    error_messages_text + "\n" + "\n".join(validation_result.warnings)
+                    error_messages_text
+                    + "\n"
+                    + "\n".join(validation_result.warnings)
                 )
             raise HTTPException(
                 status_code=status.HTTP_400_BAD_REQUEST,
diff --git a/litellm/proxy/management_endpoints/team_endpoints.py b/litellm/proxy/management_endpoints/team_endpoints.py
index 63db2d72fe4..c77b60649ab 100644
--- a/litellm/proxy/management_endpoints/team_endpoints.py
+++ b/litellm/proxy/management_endpoints/team_endpoints.py
@@ -497,18 +497,14 @@ async def _check_org_team_limits(
 
     # Validate team models against organization's allowed models
     if data.models is not None and len(org_table.models) > 0:
-        # If organization has 'all-proxy-models', skip validation as it allows all models
-        if SpecialModelNames.all_proxy_models.value in org_table.models:
-            pass
-        else:
-            for m in data.models:
-                if m not in org_table.models:
-                    raise HTTPException(
-                        status_code=400,
-                        detail={
-                            "error": f"Model '{m}' not in organization's allowed models. Organization allowed models={org_table.models}. Organization: {org_table.organization_id}"
-                        },
-                    )
+        for m in data.models:
+            if m not in org_table.models:
+                raise HTTPException(
+                    status_code=400,
+                    detail={
+                        "error": f"Model '{m}' not in organization's allowed models. Organization allowed models={org_table.models}. Organization: {org_table.organization_id}"
+                    },
+                )
 
     # Validate team TPM/RPM against organization's TPM/RPM limits (direct comparison)
     if (
@@ -1771,113 +1767,6 @@ async def _add_team_members_to_team(
     return updated_team, updated_users, updated_team_memberships
 
 
-async def _validate_and_populate_member_user_info(
-    member: Member,
-    prisma_client: PrismaClient,
-) -> Member:
-    """
-    Validate and populate user_email/user_id for a member.
-    
-    Logic:
-    1. If both user_email and user_id are provided, verify they belong to the same user (use user_email as source of truth)
-    2. If only user_email is provided, populate user_id from DB
-    3. If only user_id is provided, populate user_email from DB (if user exists)
-    4. If only user_id is provided and doesn't exist, allow it to pass with user_email as None (will be upserted later)
-    5. If user_email and user_id mismatch, throw error
-    
-    Returns a Member with user_email and user_id populated (user_email may be None if only user_id provided and user doesn't exist).
-    """
-    if member.user_email is None and member.user_id is None:
-        raise HTTPException(
-            status_code=400,
-            detail={"error": "Either user_id or user_email must be provided"},
-        )
-    
-    # Case 1: Both user_email and user_id provided - verify they match
-    if member.user_email is not None and member.user_id is not None:
-        # Use user_email as source of truth
-        # Check for multiple users with same email first
-        users_by_email = await prisma_client.get_data(
-            key_val={"user_email": member.user_email},
-            table_name="user",
-            query_type="find_all",
-        )
-        
-        if users_by_email is None or (
-            isinstance(users_by_email, list) and len(users_by_email) == 0
-        ):
-            # User doesn't exist yet - this is fine, will be created later
-            return member
-        
-        if isinstance(users_by_email, list) and len(users_by_email) > 1:
-            raise HTTPException(
-                status_code=400,
-                detail={
-                    "error": f"Multiple users found with email '{member.user_email}'. Please use 'user_id' instead."
-                },
-            )
-        
-        # Get the single user
-        user_by_email = users_by_email[0]
-        
-        # Verify the user_id matches
-        if user_by_email.user_id != member.user_id:
-            raise HTTPException(
-                status_code=400,
-                detail={
-                    "error": f"user_email '{member.user_email}' and user_id '{member.user_id}' do not belong to the same user."
-                },
-            )
-        
-        # Both match, return as is
-        return member
-    
-    # Case 2: Only user_email provided - populate user_id from DB
-    if member.user_email is not None and member.user_id is None:
-        user_by_email = await prisma_client.db.litellm_usertable.find_first(
-            where={"user_email": {"equals": member.user_email, "mode": "insensitive"}}
-        )
-        
-        if user_by_email is None:
-            # User doesn't exist yet - this is fine, will be created later
-            return member
-        
-        # Check for multiple users with same email
-        users_by_email = await prisma_client.get_data(
-            key_val={"user_email": member.user_email},
-            table_name="user",
-            query_type="find_all",
-        )
-        
-        if users_by_email and isinstance(users_by_email, list) and len(users_by_email) > 1:
-            raise HTTPException(
-                status_code=400,
-                detail={
-                    "error": f"Multiple users found with email '{member.user_email}'. Please use 'user_id' instead."
-                },
-            )
-        
-        # Populate user_id
-        member.user_id = user_by_email.user_id
-        return member
-    
-    # Case 3: Only user_id provided - populate user_email from DB if user exists
-    if member.user_id is not None and member.user_email is None:
-        user_by_id = await prisma_client.db.litellm_usertable.find_unique(
-            where={"user_id": member.user_id}
-        )
-        
-        if user_by_id is None:
-            # User doesn't exist yet - allow it to pass with user_email as None
-            # Will be upserted later with just user_id and null email
-            return member
-        
-        # Populate user_email
-        member.user_email = user_by_id.user_email
-        return member
-    
-    return member
-
 @router.post(
     "/team/member_add",
     tags=["team management"],
@@ -1953,19 +1842,6 @@ async def team_member_add(
         complete_team_data=complete_team_data,
     )
 
-    # Validate and populate user_email/user_id for members before processing
-    if isinstance(data.member, Member):
-        await _validate_and_populate_member_user_info(
-            member=data.member,
-            prisma_client=prisma_client,
-        )
-    elif isinstance(data.member, List):
-        for m in data.member:
-            await _validate_and_populate_member_user_info(
-                member=m,
-                prisma_client=prisma_client,
-            )
-
     updated_team, updated_users, updated_team_memberships = (
         await _add_team_members_to_team(
             data=data,
diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py
index 4048b3731c1..1756fe9c6f6 100644
--- a/litellm/proxy/management_endpoints/ui_sso.py
+++ b/litellm/proxy/management_endpoints/ui_sso.py
@@ -93,26 +93,6 @@
 router = APIRouter()
 
 
-def normalize_email(email: Optional[str]) -> Optional[str]:
-    """
-    Normalize email address to lowercase for consistent storage and comparison.
-    
-    Email addresses should be treated as case-insensitive for SSO purposes,
-    even though RFC 5321 technically allows case-sensitive local parts.
-    This prevents issues where SSO providers return emails with different casing
-    than what's stored in the database.
-    
-    Args:
-        email: Email address to normalize, can be None
-        
-    Returns:
-        Lowercased email address, or None if input is None
-    """
-    if email is None:
-        return None
-    return email.lower() if isinstance(email, str) else email
-
-
 def determine_role_from_groups(
     user_groups: List[str],
     role_mappings: "RoleMappings",
@@ -415,7 +395,7 @@ def generic_response_convertor(
         display_name=get_nested_value(
             response, generic_user_display_name_attribute_name
         ),
-        email=normalize_email(get_nested_value(response, generic_user_email_attribute_name)),
+        email=get_nested_value(response, generic_user_email_attribute_name),
         first_name=get_nested_value(response, generic_user_first_name_attribute_name),
         last_name=get_nested_value(response, generic_user_last_name_attribute_name),
         provider=get_nested_value(response, generic_provider_attribute_name),
@@ -751,7 +731,7 @@ async def get_user_info_from_db(
             if _id is not None and isinstance(_id, str):
                 potential_user_ids.append(_id)
 
-        user_email = normalize_email(
+        user_email = (
             getattr(result, "email", None)
             if not isinstance(result, dict)
             else result.get("email", None)
@@ -826,8 +806,8 @@ def _build_sso_user_update_data(
 
     Returns:
         dict: Update data containing user_email and optionally user_role if valid
-        """
-    update_data: dict = {"user_email": normalize_email(user_email)}
+    """
+    update_data: dict = {"user_email": user_email}
 
     # Get SSO role from result and include if valid
     sso_role = getattr(result, "user_role", None)
@@ -1219,7 +1199,7 @@ async def cli_poll_key(key_id: str, team_id: Optional[str] = None):
                 max_budget=litellm.max_ui_session_budget,
             )
 
-            # Generate CLI JWT on-demand (expiration configurable via LITELLM_CLI_JWT_EXPIRATION_HOURS)
+            # Generate CLI JWT on-demand (24hr expiration)
             # Pass selected team_id to ensure JWT has correct team
             jwt_token = ExperimentalUIJWTToken.get_cli_jwt_auth_token(
                 user_info=user_info, team_id=team_id
@@ -1336,7 +1316,7 @@ async def insert_sso_user(
 
     new_user_request = NewUserRequest(
         user_id=user_defined_values["user_id"],
-        user_email=normalize_email(user_defined_values["user_email"]),
+        user_email=user_defined_values["user_email"],
         user_role=user_defined_values["user_role"],  # type: ignore
         max_budget=user_defined_values["max_budget"],
         budget_duration=user_defined_values["budget_duration"],
@@ -2001,7 +1981,7 @@ def _get_user_email_and_id_from_result(
         """
         Gets the user email and id from the OpenID result after validating the email domain
         """
-        user_email: Optional[str] = normalize_email(getattr(result, "email", None))
+        user_email: Optional[str] = getattr(result, "email", None)
         user_id: Optional[str] = (
             getattr(result, "id", None) if result is not None else None
         )
@@ -2040,7 +2020,7 @@ def _get_user_email_and_id_from_result(
                 "GENERIC_USER_ROLE_ATTRIBUTE", "role"
             )
             user_id = getattr(result, "id", None)
-            user_email = normalize_email(getattr(result, "email", None))
+            user_email = getattr(result, "email", None)
             if user_role is None:
                 _role_from_attr = getattr(result, generic_user_role_attribute_name, None)  # type: ignore
                 if _role_from_attr is not None:
@@ -2433,7 +2413,7 @@ def openid_from_response(
         response = response or {}
         verbose_proxy_logger.debug(f"Microsoft SSO Callback Response: {response}")
         openid_response = CustomOpenID(
-            email=normalize_email(response.get(MICROSOFT_USER_EMAIL_ATTRIBUTE) or response.get("mail")),
+            email=response.get(MICROSOFT_USER_EMAIL_ATTRIBUTE) or response.get("mail"),
             display_name=response.get(MICROSOFT_USER_DISPLAY_NAME_ATTRIBUTE),
             provider="microsoft",
             id=response.get(MICROSOFT_USER_ID_ATTRIBUTE),
diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
index c11dcd79bc8..b079e161519 100644
--- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
+++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
@@ -16,7 +16,6 @@
 from starlette.websockets import WebSocketState
 
 import litellm
-from litellm import get_llm_provider
 from litellm._logging import verbose_proxy_logger
 from litellm.constants import (
     ALLOWED_VERTEX_AI_PASSTHROUGH_HEADERS,
@@ -1053,89 +1052,6 @@ async def bedrock_proxy_route(
     return received_value
 
 
-def _resolve_vertex_model_from_router(
-    model_id: str,
-    llm_router: Optional[litellm.Router],
-    encoded_endpoint: str,
-    endpoint: str,
-    vertex_project: Optional[str],
-    vertex_location: Optional[str],
-) -> Tuple[str, str, Optional[str], Optional[str]]:
-    """
-    Resolve Vertex AI model configuration from router.
-    
-    Args:
-        model_id: The model ID extracted from the URL (e.g., "gcp/google/gemini-2.5-flash")
-        llm_router: The LiteLLM router instance
-        encoded_endpoint: The encoded endpoint path
-        endpoint: The original endpoint path
-        vertex_project: Current vertex project (may be from URL)
-        vertex_location: Current vertex location (may be from URL)
-    
-    Returns:
-        Tuple of (encoded_endpoint, endpoint, vertex_project, vertex_location)
-        with resolved values from router config
-    """
-    if not llm_router:
-        return encoded_endpoint, endpoint, vertex_project, vertex_location
-    
-    try:
-        deployment = llm_router.get_available_deployment_for_pass_through(model=model_id)
-        if not deployment:
-            return encoded_endpoint, endpoint, vertex_project, vertex_location
-        
-        litellm_params = deployment.get("litellm_params", {})
-        
-        # Always override with router config values (they take precedence over URL values)
-        config_vertex_project = litellm_params.get("vertex_project")
-        config_vertex_location = litellm_params.get("vertex_location")
-        if config_vertex_project:
-            vertex_project = config_vertex_project
-        if config_vertex_location:
-            vertex_location = config_vertex_location
-        
-        # Get the actual Vertex AI model name by stripping the provider prefix
-        # e.g., "vertex_ai/gemini-2.0-flash-exp" -> "gemini-2.0-flash-exp"
-        model_from_config = litellm_params.get("model", "")
-        if model_from_config:
-
-            # get_llm_provider returns (model, custom_llm_provider, dynamic_api_key, api_base)
-            # For "vertex_ai/gemini-2.0-flash-exp" it returns:
-            # model="gemini-2.0-flash-exp", custom_llm_provider="vertex_ai"
-            actual_model, custom_llm_provider, _, _ = get_llm_provider(
-                model=model_from_config
-            )
-
-            # Log only non-sensitive information (model names and provider), never API keys or secrets.
-            safe_actual_model = actual_model
-            safe_custom_llm_provider = custom_llm_provider
-            verbose_proxy_logger.debug(
-                "get_llm_provider returned: actual_model=%s, custom_llm_provider=%s, model_id=%s",
-                safe_actual_model,
-                safe_custom_llm_provider,
-                model_id,
-            )
-
-            if actual_model and model_id != actual_model:
-                verbose_proxy_logger.debug(
-                    "Resolved router model '%s' to '%s' (provider=%s) with project=%s, location=%s",
-                    model_id,
-                    actual_model,
-                    custom_llm_provider,
-                    vertex_project,
-                    vertex_location,
-                )
-                encoded_endpoint = encoded_endpoint.replace(model_id, actual_model)
-                endpoint = endpoint.replace(model_id, actual_model)
-    
-    except Exception as e:
-        verbose_proxy_logger.debug(
-            f"Error resolving vertex model from router for model {model_id}: {e}"
-        )
-    
-    return encoded_endpoint, endpoint, vertex_project, vertex_location
-
-
 def _is_bedrock_agent_runtime_route(endpoint: str) -> bool:
     """
     Return True, if the endpoint should be routed to the `bedrock-agent-runtime` endpoint.
@@ -1596,11 +1512,8 @@ async def _prepare_vertex_auth_headers(
         if router_credentials is not None:
             vertex_credentials_str = None
         elif vertex_credentials is not None:
-            # Only override vertex_project and vertex_location if they're not already set from router config
-            if vertex_project is None:
-                vertex_project = vertex_credentials.vertex_project
-            if vertex_location is None:
-                vertex_location = vertex_credentials.vertex_location
+            vertex_project = vertex_credentials.vertex_project
+            vertex_location = vertex_credentials.vertex_location
             vertex_credentials_str = vertex_credentials.vertex_credentials
         else:
             raise ValueError("No vertex credentials found")
@@ -1670,7 +1583,6 @@ async def _base_vertex_proxy_route(
         get_vertex_model_id_from_url,
         get_vertex_project_id_from_url,
     )
-    from litellm.proxy.proxy_server import llm_router
 
     encoded_endpoint = httpx.URL(endpoint).path
     verbose_proxy_logger.debug("requested endpoint %s", endpoint)
@@ -1698,20 +1610,24 @@ async def _base_vertex_proxy_route(
         vertex_location=vertex_location,
     )
 
-    # Check if model is in router config - always do this to resolve custom model names
-    model_id = get_vertex_model_id_from_url(endpoint)
-    if model_id:
-
-        if llm_router:
-            # Resolve model configuration from router
-            encoded_endpoint, endpoint, vertex_project, vertex_location = _resolve_vertex_model_from_router(
-                model_id=model_id,
-                llm_router=llm_router,
-                encoded_endpoint=encoded_endpoint,
-                endpoint=endpoint,
-                vertex_project=vertex_project,
-                vertex_location=vertex_location,
-            )
+    if vertex_project is None or vertex_location is None:
+        # Check if model is in router config
+        model_id = get_vertex_model_id_from_url(endpoint)
+        if model_id:
+            from litellm.proxy.proxy_server import llm_router
+
+            if llm_router:
+                try:
+                    # Use the dedicated pass-through deployment selection method to automatically filter use_in_pass_through=True
+                    deployment = llm_router.get_available_deployment_for_pass_through(model=model_id)
+                    if deployment:
+                        litellm_params = deployment.get("litellm_params", {})
+                        vertex_project = litellm_params.get("vertex_project")
+                        vertex_location = litellm_params.get("vertex_location")
+                except Exception as e:
+                    verbose_proxy_logger.debug(
+                        f"Error getting available deployment for model {model_id}: {e}"
+                    )
 
     vertex_credentials = passthrough_endpoint_router.get_vertex_credentials(
         project_id=vertex_project,
diff --git a/litellm/proxy/proxy_cli.py b/litellm/proxy/proxy_cli.py
index 2bc1c8f8e98..2059246674b 100644
--- a/litellm/proxy/proxy_cli.py
+++ b/litellm/proxy/proxy_cli.py
@@ -127,7 +127,6 @@ def _get_default_unvicorn_init_args(
         Get the arguments for `uvicorn` worker
         """
         import litellm
-        from litellm._logging import _get_uvicorn_json_log_config
 
         uvicorn_args = {
             "app": "litellm.proxy.proxy_server:app",
@@ -138,8 +137,8 @@ def _get_default_unvicorn_init_args(
             print(f"Using log_config: {log_config}")  # noqa
             uvicorn_args["log_config"] = log_config
         elif litellm.json_logs:
-            # Use JSON log config for uvicorn to ensure all logs (including exceptions) are JSON
-            uvicorn_args["log_config"] = _get_uvicorn_json_log_config()
+            print("Using json logs. Setting log_config to None.")  # noqa
+            uvicorn_args["log_config"] = None
         if keepalive_timeout is not None:
             uvicorn_args["timeout_keep_alive"] = keepalive_timeout
         return uvicorn_args
diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index 53b10e03e13..8268e612bfb 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -11,7 +11,7 @@
 import time
 import traceback
 import warnings
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timedelta
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -550,9 +550,9 @@ def generate_feedback_box():
 server_root_path = get_server_root_path()
 _license_check = LicenseCheck()
 premium_user: bool = _license_check.is_premium()
-premium_user_data: Optional[
-    "EnterpriseLicenseData"
-] = _license_check.airgapped_license_data
+premium_user_data: Optional["EnterpriseLicenseData"] = (
+    _license_check.airgapped_license_data
+)
 global_max_parallel_request_retries_env: Optional[str] = os.getenv(
     "LITELLM_GLOBAL_MAX_PARALLEL_REQUEST_RETRIES"
 )
@@ -825,7 +825,6 @@ async def proxy_startup_event(app: FastAPI):  # noqa: PLR0915
     title=_title,
     description=_description,
     version=version,
-    root_path=server_root_path,
     lifespan=proxy_startup_event,  # type: ignore[reportGeneralTypeIssues]
 )
 
@@ -1210,9 +1209,9 @@ async def root_redirect():
 config_agents: Optional[List[AgentConfig]] = None
 otel_logging = False
 prisma_client: Optional[PrismaClient] = None
-shared_aiohttp_session: Optional[
-    "ClientSession"
-] = None  # Global shared session for connection reuse
+shared_aiohttp_session: Optional["ClientSession"] = (
+    None  # Global shared session for connection reuse
+)
 user_api_key_cache = DualCache(
     default_in_memory_ttl=UserAPIKeyCacheTTLEnum.in_memory_cache_ttl.value
 )
@@ -1220,11 +1219,10 @@ async def root_redirect():
     dual_cache=user_api_key_cache
 )
 litellm.logging_callback_manager.add_litellm_callback(model_max_budget_limiter)
-redis_usage_cache: Optional[
-    RedisCache
-] = None  # redis cache used for tracking spend, tpm/rpm limits
+redis_usage_cache: Optional[RedisCache] = (
+    None  # redis cache used for tracking spend, tpm/rpm limits
+)
 polling_via_cache_enabled: Union[Literal["all"], List[str], bool] = False
-native_background_mode: List[str] = []  # Models that should use native provider background mode instead of polling
 polling_cache_ttl: int = 3600  # Default 1 hour TTL for polling cache
 user_custom_auth = None
 user_custom_key_generate = None
@@ -1562,9 +1560,9 @@ async def _update_team_cache():
         _id = "team_id:{}".format(team_id)
         try:
             # Fetch the existing cost for the given user
-            existing_spend_obj: Optional[
-                LiteLLM_TeamTable
-            ] = await user_api_key_cache.async_get_cache(key=_id)
+            existing_spend_obj: Optional[LiteLLM_TeamTable] = (
+                await user_api_key_cache.async_get_cache(key=_id)
+            )
             if existing_spend_obj is None:
                 # do nothing if team not in api key cache
                 return
@@ -2457,17 +2455,14 @@ async def load_config(  # noqa: PLR0915
                     pass
                 elif key == "responses":
                     # Initialize global polling via cache settings
-                    global polling_via_cache_enabled, native_background_mode, polling_cache_ttl
+                    global polling_via_cache_enabled, polling_cache_ttl
                     background_mode = value.get("background_mode", {})
                     polling_via_cache_enabled = background_mode.get(
                         "polling_via_cache", False
                     )
-                    native_background_mode = background_mode.get(
-                        "native_background_mode", []
-                    )
                     polling_cache_ttl = background_mode.get("ttl", 3600)
                     verbose_proxy_logger.debug(
-                        f"{blue_color_code} Initialized polling via cache: enabled={polling_via_cache_enabled}, native_background_mode={native_background_mode}, ttl={polling_cache_ttl}{reset_color_code}"
+                        f"{blue_color_code} Initialized polling via cache: enabled={polling_via_cache_enabled}, ttl={polling_cache_ttl}{reset_color_code}"
                     )
                 elif key == "default_team_settings":
                     for idx, team_setting in enumerate(
@@ -2731,15 +2726,15 @@ async def load_config(  # noqa: PLR0915
         router_settings = config.get("router_settings", None)
 
         if router_settings and isinstance(router_settings, dict):
+            arg_spec = inspect.getfullargspec(litellm.Router)
             # model list and search_tools already set
             exclude_args = {
+                "self",
                 "model_list",
                 "search_tools",
             }
 
-            available_args = [
-                x for x in litellm.Router.get_valid_args() if x not in exclude_args
-            ]
+            available_args = [x for x in arg_spec.args if x not in exclude_args]
 
             for k, v in router_settings.items():
                 if k in available_args:
@@ -2861,7 +2856,6 @@ async def _init_policy_engine(
 
         from litellm.proxy.policy_engine.init_policies import init_policies
         from litellm.proxy.policy_engine.policy_validator import PolicyValidator
-
         if config is None:
             verbose_proxy_logger.debug("Policy engine: config is None, skipping")
             return
@@ -2873,9 +2867,7 @@ async def _init_policy_engine(
 
         policy_attachments_config = config.get("policy_attachments", None)
 
-        verbose_proxy_logger.info(
-            f"Policy engine: found {len(policies_config)} policies in config"
-        )
+        verbose_proxy_logger.info(f"Policy engine: found {len(policies_config)} policies in config")
 
         # Initialize policies
         await init_policies(
@@ -3544,79 +3536,6 @@ def _add_general_settings_from_db_config(
                 llm_router=llm_router,
             )
 
-    async def _reschedule_spend_log_cleanup_job(self):
-        """
-        Reschedule the spend log cleanup job based on current general_settings.
-        This is called when maximum_spend_logs_retention_period is updated dynamically.
-        If the retention period is None, the job will be removed.
-        """
-        global scheduler, general_settings, prisma_client
-        if scheduler is None:
-            return
-
-        # Remove existing job if it exists
-        try:
-            scheduler.remove_job("spend_log_cleanup_job")
-            verbose_proxy_logger.info("Removed existing spend log cleanup job")
-        except Exception:
-            pass  # Job might not exist, which is fine
-
-        # Schedule new job if retention period is set (not None)
-        retention_period = general_settings.get("maximum_spend_logs_retention_period")
-        if retention_period is not None:
-            from litellm.proxy.db.db_transaction_queue.spend_log_cleanup import (
-                SpendLogCleanup,
-            )
-
-            spend_log_cleanup = SpendLogCleanup()
-            cleanup_cron = general_settings.get("maximum_spend_logs_cleanup_cron")
-
-            if cleanup_cron:
-                from apscheduler.triggers.cron import CronTrigger
-
-                try:
-                    cron_trigger = CronTrigger.from_crontab(cleanup_cron)
-                    scheduler.add_job(
-                        spend_log_cleanup.cleanup_old_spend_logs,
-                        cron_trigger,
-                        args=[prisma_client],
-                        id="spend_log_cleanup_job",
-                        replace_existing=True,
-                        misfire_grace_time=APSCHEDULER_MISFIRE_GRACE_TIME,
-                    )
-                    verbose_proxy_logger.info(
-                        f"Spend log cleanup rescheduled with cron: {cleanup_cron}"
-                    )
-                except ValueError:
-                    verbose_proxy_logger.error(
-                        f"Invalid maximum_spend_logs_cleanup_cron value: {cleanup_cron}"
-                    )
-            else:
-                # Interval-based scheduling (existing behavior)
-                from litellm.litellm_core_utils.duration_parser import duration_in_seconds
-
-                retention_interval = general_settings.get(
-                    "maximum_spend_logs_retention_interval", "1d"
-                )
-                try:
-                    interval_seconds = duration_in_seconds(retention_interval)
-                    scheduler.add_job(
-                        spend_log_cleanup.cleanup_old_spend_logs,
-                        "interval",
-                        seconds=interval_seconds + random.randint(0, 60),
-                        args=[prisma_client],
-                        id="spend_log_cleanup_job",
-                        replace_existing=True,
-                        misfire_grace_time=APSCHEDULER_MISFIRE_GRACE_TIME,
-                    )
-                    verbose_proxy_logger.info(
-                        f"Spend log cleanup rescheduled with interval: {retention_interval}"
-                    )
-                except ValueError:
-                    verbose_proxy_logger.error(
-                        "Invalid maximum_spend_logs_retention_interval value"
-                    )
-
     async def _update_general_settings(self, db_general_settings: Optional[Json]):
         """
         Pull from DB, read general settings value
@@ -3656,32 +3575,6 @@ async def _update_general_settings(self, db_general_settings: Optional[Json]):
         if "ui_access_mode" in _general_settings:
             general_settings["ui_access_mode"] = _general_settings["ui_access_mode"]
 
-        ## STORE PROMPTS IN SPEND LOGS ##
-        if "store_prompts_in_spend_logs" in _general_settings:
-            value = _general_settings["store_prompts_in_spend_logs"]
-            # Normalize case: handle True/true/TRUE, False/false/FALSE, None/null
-            if value is None:
-                general_settings["store_prompts_in_spend_logs"] = None
-            elif isinstance(value, bool):
-                general_settings["store_prompts_in_spend_logs"] = value
-            elif isinstance(value, str):
-                # Case-insensitive string comparison
-                general_settings["store_prompts_in_spend_logs"] = (
-                    value.lower() == "true"
-                )
-            else:
-                # For other types, convert to bool
-                general_settings["store_prompts_in_spend_logs"] = bool(value)
-
-        ## MAXIMUM SPEND LOGS RETENTION PERIOD ##
-        if "maximum_spend_logs_retention_period" in _general_settings:
-            old_value = general_settings.get("maximum_spend_logs_retention_period")
-            new_value = _general_settings["maximum_spend_logs_retention_period"]
-            general_settings["maximum_spend_logs_retention_period"] = new_value
-            # Reschedule cleanup job if value changed (including when set to None)
-            if old_value != new_value:
-                await self._reschedule_spend_log_cleanup_job()
-
     def _update_config_fields(
         self,
         current_config: dict,
@@ -4116,10 +4009,10 @@ async def _init_guardrails_in_db(self, prisma_client: PrismaClient):
         )
 
         try:
-            guardrails_in_db: List[
-                Guardrail
-            ] = await GuardrailRegistry.get_all_guardrails_from_db(
-                prisma_client=prisma_client
+            guardrails_in_db: List[Guardrail] = (
+                await GuardrailRegistry.get_all_guardrails_from_db(
+                    prisma_client=prisma_client
+                )
             )
             verbose_proxy_logger.debug(
                 "guardrails from the DB %s", str(guardrails_in_db)
@@ -4153,9 +4046,7 @@ async def _init_policies_in_db(self, prisma_client: PrismaClient):
             await policy_registry.sync_policies_from_db(prisma_client=prisma_client)
 
             # Sync attachments from DB to in-memory registry
-            await attachment_registry.sync_attachments_from_db(
-                prisma_client=prisma_client
-            )
+            await attachment_registry.sync_attachments_from_db(prisma_client=prisma_client)
 
             verbose_proxy_logger.debug(
                 "Successfully synced policies and attachments from DB"
@@ -4478,9 +4369,9 @@ async def initialize(  # noqa: PLR0915
         user_api_base = api_base
         dynamic_config[user_model]["api_base"] = api_base
     if api_version:
-        os.environ[
-            "AZURE_API_VERSION"
-        ] = api_version  # set this for azure - litellm can read this from the env
+        os.environ["AZURE_API_VERSION"] = (
+            api_version  # set this for azure - litellm can read this from the env
+        )
     if max_tokens:  # model-specific param
         dynamic_config[user_model]["max_tokens"] = max_tokens
     if temperature:  # model-specific param
@@ -4791,7 +4682,7 @@ async def initialize_scheduled_background_jobs(  # noqa: PLR0915
         proxy_logging_obj: ProxyLogging,
     ):
         """Initializes scheduled background jobs"""
-        global store_model_in_db, scheduler
+        global store_model_in_db
 
         # MEMORY LEAK FIX: Configure scheduler with optimized settings
         # Memray analysis showed APScheduler's normalize() and _apply_jitter() causing
@@ -5201,20 +5092,7 @@ async def _setup_prisma_client(
                 except Exception as e:
                     raise e
 
-                try:
-                    await prisma_client.connect()
-                except Exception as e:
-                    if "P3018" in str(e) or "P3009" in str(e):
-                        verbose_proxy_logger.debug(
-                            "CRITICAL: DATABASE MIGRATION FAILED"
-                        )
-                        verbose_proxy_logger.debug(
-                            "Your database is in a 'dirty' state."
-                        )
-                        verbose_proxy_logger.debug(
-                            "FIX: Run 'prisma migrate resolve --applied <migration_name>'"
-                        )
-                    raise e
+                await prisma_client.connect()
 
                 ## Start RDS IAM token refresh background task if enabled ##
                 # This proactively refreshes IAM tokens before they expire,
@@ -5339,9 +5217,7 @@ async def model_list(
 
         # Include model access groups if requested
         if include_model_access_groups:
-            proxy_model_list = list(
-                set(proxy_model_list + list(model_access_groups.keys()))
-            )
+            proxy_model_list = list(set(proxy_model_list + list(model_access_groups.keys())))
 
         # Get complete model list including wildcard routes if requested
         from litellm.proxy.auth.model_checks import get_complete_model_list
@@ -7853,12 +7729,12 @@ def _enrich_model_info_with_litellm_data(
     """
     Enrich a model dictionary with litellm model info (pricing, context window, etc.)
     and remove sensitive information.
-
+    
     Args:
         model: Model dictionary to enrich
         debug: Whether to include debug information like openai_client
         llm_router: Optional router instance for debug info
-
+        
     Returns:
         Enriched model dictionary with sensitive info removed
     """
@@ -7868,7 +7744,9 @@ def _enrich_model_info_with_litellm_data(
         _openai_client = "None"
         if llm_router is not None:
             _openai_client = (
-                llm_router._get_client(deployment=model, kwargs={}, client_type="async")
+                llm_router._get_client(
+                    deployment=model, kwargs={}, client_type="async"
+                )
                 or "None"
             )
         else:
@@ -7916,463 +7794,6 @@ def _enrich_model_info_with_litellm_data(
     return model
 
 
-async def _apply_search_filter_to_models(
-    all_models: List[Dict[str, Any]],
-    search: str,
-    page: int,
-    size: int,
-    prisma_client: Optional[Any],
-    proxy_config: Any,
-    sort_by: Optional[str] = None,
-) -> Tuple[List[Dict[str, Any]], Optional[int]]:
-    """
-    Apply search filter to models, querying database for additional matching models.
-
-    Args:
-        all_models: List of models to filter
-        search: Search term (case-insensitive)
-        page: Current page number
-        size: Page size
-        prisma_client: Prisma client for database queries
-        proxy_config: Proxy config for decrypting models
-        sort_by: Optional sort field - if provided, fetch all matching models instead of paginating at DB level
-
-    Returns:
-        Tuple of (filtered_models, total_count). total_count is None if not searching.
-    """
-    if not search or not search.strip():
-        return all_models, None
-
-    search_lower = search.lower().strip()
-
-    # Filter models in router by search term
-    filtered_router_models = [
-        m for m in all_models if search_lower in m.get("model_name", "").lower()
-    ]
-
-    # Separate filtered models into config vs db models, and track db model IDs
-    filtered_config_models = []
-    db_model_ids_in_router = set()
-
-    for m in filtered_router_models:
-        model_info = m.get("model_info", {})
-        is_db_model = model_info.get("db_model", False)
-        model_id = model_info.get("id")
-
-        if is_db_model and model_id:
-            db_model_ids_in_router.add(model_id)
-        else:
-            filtered_config_models.append(m)
-
-    config_models_count = len(filtered_config_models)
-    db_models_in_router_count = len(db_model_ids_in_router)
-    router_models_count = config_models_count + db_models_in_router_count
-
-    # Query database for additional models with search term
-    db_models = []
-    db_models_total_count = 0
-    models_needed_for_page = size * page
-
-    # Only query database if prisma_client is available
-    if prisma_client is not None:
-        try:
-            # Build where condition for database query
-            db_where_condition: Dict[str, Any] = {
-                "model_name": {
-                    "contains": search_lower,
-                    "mode": "insensitive",
-                }
-            }
-            # Exclude models already in router if we have any
-            if db_model_ids_in_router:
-                db_where_condition["model_id"] = {
-                    "not": {"in": list(db_model_ids_in_router)}
-                }
-
-            # Get total count of matching database models
-            db_models_total_count = (
-                await prisma_client.db.litellm_proxymodeltable.count(
-                    where=db_where_condition
-                )
-            )
-
-            # Calculate total count for search results
-            search_total_count = router_models_count + db_models_total_count
-
-            # If sorting is requested, we need to fetch ALL matching models to sort correctly
-            # Otherwise, we can optimize by only fetching what's needed for the current page
-            if sort_by:
-                # Fetch all matching database models for sorting
-                if db_models_total_count > 0:
-                    db_models_raw = (
-                        await prisma_client.db.litellm_proxymodeltable.find_many(
-                            where=db_where_condition,
-                            take=db_models_total_count,  # Fetch all matching models
-                        )
-                    )
-
-                    # Convert database models to router format
-                    for db_model in db_models_raw:
-                        decrypted_models = proxy_config.decrypt_model_list_from_db(
-                            [db_model]
-                        )
-                        if decrypted_models:
-                            db_models.extend(decrypted_models)
-            else:
-                # Fetch database models if we need more for the current page
-                if router_models_count < models_needed_for_page:
-                    models_to_fetch = min(
-                        models_needed_for_page - router_models_count, db_models_total_count
-                    )
-
-                    if models_to_fetch > 0:
-                        db_models_raw = (
-                            await prisma_client.db.litellm_proxymodeltable.find_many(
-                                where=db_where_condition,
-                                take=models_to_fetch,
-                            )
-                        )
-
-                        # Convert database models to router format
-                        for db_model in db_models_raw:
-                            decrypted_models = proxy_config.decrypt_model_list_from_db(
-                                [db_model]
-                            )
-                            if decrypted_models:
-                                db_models.extend(decrypted_models)
-        except Exception as e:
-            verbose_proxy_logger.exception(
-                f"Error querying database models with search: {str(e)}"
-            )
-            # If error, use router models count as fallback
-            search_total_count = router_models_count
-    else:
-        # If no prisma_client, only use router models
-        search_total_count = router_models_count
-
-    # Combine all models
-    filtered_models = filtered_router_models + db_models
-    return filtered_models, search_total_count
-
-
-def _normalize_datetime_for_sorting(dt: Any) -> Optional[datetime]:
-    """
-    Normalize a datetime value to a timezone-aware UTC datetime for sorting.
-    
-    This function handles:
-    - None values: returns None
-    - String values: parses ISO format strings and converts to UTC-aware datetime
-    - Datetime objects: converts naive datetimes to UTC-aware, and aware datetimes to UTC
-    
-    Args:
-        dt: Datetime value (None, str, or datetime object)
-        
-    Returns:
-        UTC-aware datetime object, or None if input is None or cannot be parsed
-    """
-    if dt is None:
-        return None
-    
-    if isinstance(dt, str):
-        try:
-            # Handle ISO format strings, including 'Z' suffix
-            dt_str = dt.replace("Z", "+00:00") if dt.endswith("Z") else dt
-            parsed_dt = datetime.fromisoformat(dt_str)
-            # Ensure it's UTC-aware
-            if parsed_dt.tzinfo is None:
-                parsed_dt = parsed_dt.replace(tzinfo=timezone.utc)
-            else:
-                parsed_dt = parsed_dt.astimezone(timezone.utc)
-            return parsed_dt
-        except (ValueError, AttributeError):
-            return None
-    
-    if isinstance(dt, datetime):
-        # If naive, assume UTC and make it aware
-        if dt.tzinfo is None:
-            return dt.replace(tzinfo=timezone.utc)
-        # If aware, convert to UTC
-        return dt.astimezone(timezone.utc)
-    
-    return None
-
-
-def _sort_models(
-    all_models: List[Dict[str, Any]],
-    sort_by: Optional[str],
-    sort_order: str = "asc",
-) -> List[Dict[str, Any]]:
-    """
-    Sort models by the specified field and order.
-
-    Args:
-        all_models: List of models to sort
-        sort_by: Field to sort by (model_name, created_at, updated_at, costs, status)
-        sort_order: Sort order (asc or desc)
-
-    Returns:
-        Sorted list of models
-    """
-    if not sort_by or sort_by not in ["model_name", "created_at", "updated_at", "costs", "status"]:
-        return all_models
-
-    reverse = sort_order.lower() == "desc"
-
-    def get_sort_key(model: Dict[str, Any]) -> Any:
-        model_info = model.get("model_info", {})
-        
-        if sort_by == "model_name":
-            return model.get("model_name", "").lower()
-        
-        elif sort_by == "created_at":
-            created_at = model_info.get("created_at")
-            normalized_dt = _normalize_datetime_for_sorting(created_at)
-            if normalized_dt is None:
-                # Put None values at the end for asc, at the start for desc
-                return (datetime.max.replace(tzinfo=timezone.utc) if not reverse else datetime.min.replace(tzinfo=timezone.utc))
-            return normalized_dt
-        
-        elif sort_by == "updated_at":
-            updated_at = model_info.get("updated_at")
-            normalized_dt = _normalize_datetime_for_sorting(updated_at)
-            if normalized_dt is None:
-                return (datetime.max.replace(tzinfo=timezone.utc) if not reverse else datetime.min.replace(tzinfo=timezone.utc))
-            return normalized_dt
-        
-        elif sort_by == "costs":
-            input_cost = model_info.get("input_cost_per_token", 0) or 0
-            output_cost = model_info.get("output_cost_per_token", 0) or 0
-            total_cost = input_cost + output_cost
-            # Put 0 or None costs at the end for asc, at the start for desc
-            if total_cost == 0:
-                return (float("inf") if not reverse else float("-inf"))
-            return total_cost
-        
-        elif sort_by == "status":
-            # False (config) comes before True (db) for asc
-            db_model = model_info.get("db_model", False)
-            return db_model
-        
-        return None
-
-    try:
-        sorted_models = sorted(all_models, key=get_sort_key, reverse=reverse)
-        return sorted_models
-    except Exception as e:
-        verbose_proxy_logger.exception(f"Error sorting models by {sort_by}: {str(e)}")
-        return all_models
-
-
-def _paginate_models_response(
-    all_models: List[Dict[str, Any]],
-    page: int,
-    size: int,
-    total_count: Optional[int],
-    search: Optional[str],
-) -> Dict[str, Any]:
-    """
-    Paginate models and return response dictionary.
-
-    Args:
-        all_models: List of all models
-        page: Current page number
-        size: Page size
-        total_count: Total count (if None, uses len(all_models))
-        search: Search term (for logging)
-
-    Returns:
-        Paginated response dictionary
-    """
-    if total_count is None:
-        total_count = len(all_models)
-
-    skip = (page - 1) * size
-    total_pages = -(-total_count // size) if total_count > 0 else 0
-    paginated_models = all_models[skip : skip + size]
-
-    verbose_proxy_logger.debug(
-        f"Pagination: skip={skip}, take={size}, total_count={total_count}, total_pages={total_pages}, search={search}"
-    )
-
-    return {
-        "data": paginated_models,
-        "total_count": total_count,
-        "current_page": page,
-        "total_pages": total_pages,
-        "size": size,
-    }
-
-
-async def _filter_models_by_team_id(
-    all_models: List[Dict[str, Any]],
-    team_id: str,
-    prisma_client: PrismaClient,
-    llm_router: Router,
-) -> List[Dict[str, Any]]:
-    """
-    Filter models by team ID. Returns models where:
-    - direct_access is True, OR
-    - team_id is in access_via_team_ids
-
-    Also searches config and database for models accessible to the team.
-
-    Args:
-        all_models: List of models to filter
-        team_id: Team ID to filter by
-        prisma_client: Prisma client for database queries
-        llm_router: Router instance for config queries
-
-    Returns:
-        Filtered list of models
-    """
-    # Get team from database
-    try:
-        team_db_object = await prisma_client.db.litellm_teamtable.find_unique(
-            where={"team_id": team_id}
-        )
-        if team_db_object is None:
-            verbose_proxy_logger.warning(f"Team {team_id} not found in database")
-            # If team doesn't exist, return empty list
-            return []
-
-        team_object = LiteLLM_TeamTable(**team_db_object.model_dump())
-    except Exception as e:
-        verbose_proxy_logger.exception(f"Error fetching team {team_id}: {str(e)}")
-        return []
-
-    # Get models accessible to this team (similar to _add_team_models_to_all_models)
-    team_accessible_model_ids: Set[str] = set()
-
-    if (
-        len(team_object.models) == 0  # empty list = all model access
-        or SpecialModelNames.all_proxy_models.value in team_object.models
-    ):
-        # Team has access to all models
-        model_list = llm_router.get_model_list() if llm_router else []
-        if model_list is not None:
-            for model in model_list:
-                model_id = model.get("model_info", {}).get("id", None)
-                if model_id is None:
-                    continue
-                # if team model id set, check if team id matches
-                team_model_id = model.get("model_info", {}).get("team_id", None)
-                can_add_model = False
-                if team_model_id is None:
-                    can_add_model = True
-                elif team_model_id == team_id:
-                    can_add_model = True
-
-                if can_add_model:
-                    team_accessible_model_ids.add(model_id)
-    else:
-        # Team has access to specific models
-        for model_name in team_object.models:
-            _models = (
-                llm_router.get_model_list(model_name=model_name, team_id=team_id)
-                if llm_router
-                else []
-            )
-            if _models is not None:
-                for model in _models:
-                    model_id = model.get("model_info", {}).get("id", None)
-                    if model_id is not None:
-                        team_accessible_model_ids.add(model_id)
-
-    # Also search database for models accessible to this team
-    # This complements the config search done above
-    try:
-        if (
-            team_object.models
-            and SpecialModelNames.all_proxy_models.value not in team_object.models
-        ):
-            # Team has specific models - check database for those model names
-            db_models = await prisma_client.db.litellm_proxymodeltable.find_many(
-                where={"model_name": {"in": team_object.models}}
-            )
-            for db_model in db_models:
-                model_id = db_model.model_id
-                if model_id:
-                    team_accessible_model_ids.add(model_id)
-    except Exception as e:
-        verbose_proxy_logger.debug(
-            f"Error querying database models for team {team_id}: {str(e)}"
-        )
-
-    # Filter models based on direct_access or access_via_team_ids
-    # Models are already enriched with these fields before this function is called
-    filtered_models = []
-    for _model in all_models:
-        model_info = _model.get("model_info", {})
-        model_id = model_info.get("id", None)
-
-        # Include if direct_access is True
-        if model_info.get("direct_access", False):
-            filtered_models.append(_model)
-            continue
-
-        # Include if team_id is in access_via_team_ids
-        access_via_team_ids = model_info.get("access_via_team_ids", [])
-        if isinstance(access_via_team_ids, list) and team_id in access_via_team_ids:
-            filtered_models.append(_model)
-            continue
-
-        # Also include if model_id is in team_accessible_model_ids (from config/db search)
-        # This catches models that might not have been enriched with access_via_team_ids yet
-        if model_id and model_id in team_accessible_model_ids:
-            filtered_models.append(_model)
-
-    return filtered_models
-
-
-async def _find_model_by_id(
-    model_id: str,
-    search: Optional[str],
-    llm_router,
-    prisma_client,
-    proxy_config,
-) -> tuple[list, Optional[int]]:
-    """Find a model by its ID and optionally filter by search term."""
-    found_model = None
-
-    # First, search in config
-    if llm_router is not None:
-        found_model = llm_router.get_model_info(id=model_id)
-        if found_model:
-            found_model = copy.deepcopy(found_model)
-
-    # If not found in config, search in database
-    if found_model is None:
-        try:
-            db_model = await prisma_client.db.litellm_proxymodeltable.find_unique(
-                where={"model_id": model_id}
-            )
-            if db_model:
-                # Convert database model to router format
-                decrypted_models = proxy_config.decrypt_model_list_from_db(
-                    [db_model]
-                )
-                if decrypted_models:
-                    found_model = decrypted_models[0]
-        except Exception as e:
-            verbose_proxy_logger.exception(
-                f"Error querying database for modelId {model_id}: {str(e)}"
-            )
-
-    # If model found, verify search filter if provided
-    if found_model is not None:
-        if search is not None and search.strip():
-            search_lower = search.lower().strip()
-            model_name = found_model.get("model_name", "")
-            if search_lower not in model_name.lower():
-                # Model found but doesn't match search filter
-                found_model = None
-
-    # Set all_models to the found model or empty list
-    all_models = [found_model] if found_model is not None else []
-    search_total_count: Optional[int] = len(all_models)
-    return all_models, search_total_count
-
-
 @router.get(
     "/v2/model/info",
     description="v2 - returns models available to the user based on their API key permissions. Shows model info from config.yaml (except api key and api base). Filter to just user-added models with ?user_models_only=true",
@@ -8394,24 +7815,6 @@ async def model_info_v2(
     debug: Optional[bool] = False,
     page: int = Query(1, description="Page number", ge=1),
     size: int = Query(50, description="Page size", ge=1),
-    search: Optional[str] = fastapi.Query(
-        None, description="Search model names (case-insensitive partial match)"
-    ),
-    modelId: Optional[str] = fastapi.Query(
-        None, description="Search for a specific model by its unique ID"
-    ),
-    teamId: Optional[str] = fastapi.Query(
-        None,
-        description="Filter models by team ID. Returns models with direct_access=True or teamId in access_via_team_ids",
-    ),
-    sortBy: Optional[str] = fastapi.Query(
-        None,
-        description="Field to sort by. Options: model_name, created_at, updated_at, costs, status",
-    ),
-    sortOrder: Optional[str] = fastapi.Query(
-        "asc",
-        description="Sort order. Options: asc, desc",
-    ),
 ):
     """
     BETA ENDPOINT. Might change unexpectedly. Use `/v1/model/info` for now.
@@ -8436,37 +7839,14 @@ async def model_info_v2(
 
     # Load existing config
     await proxy_config.get_config()
+    all_models = copy.deepcopy(llm_router.model_list)
 
-    # If modelId is provided, search for the specific model
-    if modelId is not None:
-        all_models, search_total_count = await _find_model_by_id(
-            model_id=modelId,
-            search=search,
-            llm_router=llm_router,
-            prisma_client=prisma_client,
-            proxy_config=proxy_config,
-        )
-    else:
-        # Normal flow when modelId is not provided
-        all_models = copy.deepcopy(llm_router.model_list)
-
-        if user_model is not None:
-            # if user does not use a config.yaml, https://github.com/BerriAI/litellm/issues/2061
-            all_models += [user_model]
+    if user_model is not None:
+        # if user does not use a config.yaml, https://github.com/BerriAI/litellm/issues/2061
+        all_models += [user_model]
 
-        if model is not None:
-            all_models = [m for m in all_models if m["model_name"] == model]
-
-        # Apply search filter if provided
-        all_models, search_total_count = await _apply_search_filter_to_models(
-            all_models=all_models,
-            search=search or "",
-            page=page,
-            size=size,
-            prisma_client=prisma_client,
-            proxy_config=proxy_config,
-            sort_by=sortBy,
-        )
+    if model is not None:
+        all_models = [m for m in all_models if m["model_name"] == model]
 
     if user_models_only:
         all_models = await non_admin_all_models(
@@ -8483,55 +7863,33 @@ async def model_info_v2(
             llm_router=llm_router,
             all_models=all_models,
         )
-
-    # Fill in model info based on config.yaml and litellm model_prices_and_context_window.json
-    # This must happen before teamId filtering so that direct_access and access_via_team_ids are populated
+    # fill in model info based on config.yaml and litellm model_prices_and_context_window.json
     for i, _model in enumerate(all_models):
         all_models[i] = _enrich_model_info_with_litellm_data(
-            model=_model,
-            debug=debug if debug is not None else False,
-            llm_router=llm_router,
-        )
-
-    # Apply teamId filter if provided
-    if teamId is not None and teamId.strip():
-        all_models = await _filter_models_by_team_id(
-            all_models=all_models,
-            team_id=teamId.strip(),
-            prisma_client=prisma_client,
-            llm_router=llm_router,
-        )
-        # Update search_total_count after teamId filter is applied
-        search_total_count = len(all_models)
-
-    # If modelId was provided, update search_total_count after filters are applied
-    # to ensure pagination reflects the final filtered result (0 or 1)
-    if modelId is not None:
-        search_total_count = len(all_models)
-
-    # Apply sorting before pagination
-    if sortBy:
-        # Validate sortOrder
-        if sortOrder and sortOrder.lower() not in ["asc", "desc"]:
-            raise HTTPException(
-                status_code=400,
-                detail=f"Invalid sortOrder: {sortOrder}. Must be 'asc' or 'desc'",
-            )
-        all_models = _sort_models(
-            all_models=all_models,
-            sort_by=sortBy,
-            sort_order=sortOrder or "asc",
+            model=_model, debug=debug if debug is not None else False, llm_router=llm_router
         )
 
     verbose_proxy_logger.debug("all_models: %s", all_models)
-
-    return _paginate_models_response(
-        all_models=all_models,
-        page=page,
-        size=size,
-        total_count=search_total_count,
-        search=search,
+    
+    total_count = len(all_models)
+    
+    skip = (page - 1) * size
+    
+    total_pages = -(-total_count // size) if total_count > 0 else 0
+    
+    paginated_models = all_models[skip : skip + size]
+    
+    verbose_proxy_logger.debug(
+        f"Pagination: skip={skip}, take={size}, total_count={total_count}, total_pages={total_pages}"
     )
+    
+    return {
+        "data": paginated_models,
+        "total_count": total_count,
+        "current_page": page,
+        "total_pages": total_pages,
+        "size": size,
+    }
 
 
 @router.get(
@@ -9987,7 +9345,7 @@ def get_logo_url():
 
 
 @app.get("/get_image", include_in_schema=False)
-async def get_image():
+def get_image():
     """Get logo to show on admin UI"""
 
     # get current_dir
@@ -10006,37 +9364,25 @@ async def get_image():
     if is_non_root and not os.path.exists(default_logo):
         default_logo = default_site_logo
 
-    cache_dir = assets_dir if is_non_root else current_dir
-    cache_path = os.path.join(cache_dir, "cached_logo.jpg")
-
-    # [OPTIMIZATION] Check if the cached image exists first
-    if os.path.exists(cache_path):
-        return FileResponse(cache_path, media_type="image/jpeg")
-
     logo_path = os.getenv("UI_LOGO_PATH", default_logo)
     verbose_proxy_logger.debug("Reading logo from path: %s", logo_path)
 
     # Check if the logo path is an HTTP/HTTPS URL
     if logo_path.startswith(("http://", "https://")):
-        try:
-            # Download the image and cache it
-            from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
-
-            async_client = AsyncHTTPHandler(timeout=5.0)
-            response = await async_client.get(logo_path)
-            if response.status_code == 200:
-                # Save the image to a local file
-                with open(cache_path, "wb") as f:
-                    f.write(response.content)
-
-                # Return the cached image as a FileResponse
-                return FileResponse(cache_path, media_type="image/jpeg")
-            else:
-                # Handle the case when the image cannot be downloaded
-                return FileResponse(default_logo, media_type="image/jpeg")
-        except Exception as e:
-            # Handle any exceptions during the download (e.g., timeout, connection error)
-            verbose_proxy_logger.debug(f"Error downloading logo from {logo_path}: {e}")
+        # Download the image and cache it
+        client = HTTPHandler()
+        response = client.get(logo_path)
+        if response.status_code == 200:
+            # Save the image to a local file
+            cache_dir = assets_dir if is_non_root else current_dir
+            cache_path = os.path.join(cache_dir, "cached_logo.jpg")
+            with open(cache_path, "wb") as f:
+                f.write(response.content)
+
+            # Return the cached image as a FileResponse
+            return FileResponse(cache_path, media_type="image/jpeg")
+        else:
+            # Handle the case when the image cannot be downloaded
             return FileResponse(default_logo, media_type="image/jpeg")
     else:
         # Return the local image file if the logo path is not an HTTP/HTTPS URL
@@ -10654,9 +10000,9 @@ async def get_config_list(
                             hasattr(sub_field_info, "description")
                             and sub_field_info.description is not None
                         ):
-                            nested_fields[
-                                idx
-                            ].field_description = sub_field_info.description
+                            nested_fields[idx].field_description = (
+                                sub_field_info.description
+                            )
                         idx += 1
 
                     _stored_in_db = None
diff --git a/litellm/proxy/rag_endpoints/endpoints.py b/litellm/proxy/rag_endpoints/endpoints.py
index 39909df8e9f..79b4fd6873d 100644
--- a/litellm/proxy/rag_endpoints/endpoints.py
+++ b/litellm/proxy/rag_endpoints/endpoints.py
@@ -26,196 +26,6 @@
 router = APIRouter()
 
 
-def _build_file_metadata_entry(
-    response: Any,
-    file_data: Optional[Tuple[str, bytes, str]] = None,
-    file_url: Optional[str] = None,
-) -> Dict[str, Any]:
-    """
-    Build a file metadata entry for storing in vector_store_metadata.
-    
-    Args:
-        response: The response from litellm.aingest containing file_id
-        file_data: Optional tuple of (filename, content, content_type)
-        file_url: Optional URL if file was ingested from URL
-    
-    Returns:
-        Dictionary with file metadata (file_id, filename, file_url, ingested_at, etc.)
-    """
-    from datetime import datetime, timezone
-
-    # Extract file_id from response
-    file_id = None
-    if hasattr(response, "get"):
-        file_id = response.get("file_id")
-    elif hasattr(response, "file_id"):
-        file_id = response.file_id
-    
-    # Extract file information from file_data tuple
-    filename = None
-    file_size = None
-    content_type = None
-    
-    if file_data:
-        filename = file_data[0]
-        file_size = len(file_data[1]) if len(file_data) > 1 else None
-        content_type = file_data[2] if len(file_data) > 2 else None
-    
-    # Build file metadata entry
-    file_entry = {
-        "file_id": file_id,
-        "filename": filename,
-        "file_url": file_url,
-        "ingested_at": datetime.now(timezone.utc).isoformat(),
-    }
-    
-    # Add optional fields if available
-    if file_size is not None:
-        file_entry["file_size"] = file_size
-    if content_type is not None:
-        file_entry["content_type"] = content_type
-    
-    return file_entry
-
-
-async def _save_vector_store_to_db_from_rag_ingest(
-    response: Any,
-    ingest_options: Dict[str, Any],
-    prisma_client,
-    user_api_key_dict: UserAPIKeyAuth,
-    file_data: Optional[Tuple[str, bytes, str]] = None,
-    file_url: Optional[str] = None,
-) -> None:
-    """
-    Helper function to save a newly created vector store from RAG ingest to the database.
-    
-    This function:
-    - Extracts vector store ID and config from the ingest response
-    - Checks if the vector store already exists in the database
-    - Creates a new database entry if it doesn't exist
-    - Adds the vector store to the registry
-    - Tracks team_id and user_id for access control
-    
-    Args:
-        response: The response from litellm.aingest()
-        ingest_options: The ingest options containing vector store config
-        prisma_client: The Prisma database client
-        user_api_key_dict: User API key authentication info
-    """
-    from litellm.proxy.vector_store_endpoints.management_endpoints import (
-        create_vector_store_in_db,
-    )
-
-    # Handle both dict and object responses
-    if hasattr(response, "get"):
-        vector_store_id = response.get("vector_store_id")
-    elif hasattr(response, "vector_store_id"):
-        vector_store_id = response.vector_store_id
-    else:
-        verbose_proxy_logger.warning(
-            f"Unable to extract vector_store_id from response type: {type(response)}"
-        )
-        return
-
-    if vector_store_id is None or not isinstance(vector_store_id, str):
-        verbose_proxy_logger.warning(
-            "Vector store ID is None or not a string, skipping database save"
-        )
-        return
-
-    vector_store_config = ingest_options.get("vector_store", {})
-    custom_llm_provider = vector_store_config.get("custom_llm_provider")
-    
-    # Extract litellm_vector_store_params for custom name and description
-    litellm_vector_store_params = ingest_options.get("litellm_vector_store_params", {})
-    custom_vector_store_name = litellm_vector_store_params.get("vector_store_name")
-    custom_vector_store_description = litellm_vector_store_params.get("vector_store_description")
-    
-    # Extract provider-specific params from vector_store_config to save as litellm_params
-    # This ensures params like aws_region_name, embedding_model, etc. are available for search
-    provider_specific_params = {}
-    excluded_keys = {"custom_llm_provider", "vector_store_id"}
-    for key, value in vector_store_config.items():
-        if key not in excluded_keys and value is not None:
-            provider_specific_params[key] = value
-
-    # Build file metadata entry using helper
-    file_entry = _build_file_metadata_entry(
-        response=response,
-        file_data=file_data,
-        file_url=file_url,
-    )
-
-    try:
-        # Check if vector store already exists in database
-        existing_vector_store = (
-            await prisma_client.db.litellm_managedvectorstorestable.find_unique(
-                where={"vector_store_id": vector_store_id}
-            )
-        )
-
-        # Only create if it doesn't exist
-        if existing_vector_store is None:
-            verbose_proxy_logger.info(
-                f"Saving newly created vector store {vector_store_id} to database"
-            )
-
-            # Initialize metadata with first file
-            initial_metadata = {
-                "ingested_files": [file_entry]
-            }
-            
-            # Use custom name if provided, otherwise default
-            vector_store_name = custom_vector_store_name or f"RAG Vector Store - {vector_store_id[:8]}"
-            vector_store_description = custom_vector_store_description or "Created via RAG ingest endpoint"
-
-            await create_vector_store_in_db(
-                vector_store_id=vector_store_id,
-                custom_llm_provider=custom_llm_provider or "openai",
-                prisma_client=prisma_client,
-                vector_store_name=vector_store_name,
-                vector_store_description=vector_store_description,
-                vector_store_metadata=initial_metadata,
-                litellm_params=provider_specific_params if provider_specific_params else None,
-                team_id=user_api_key_dict.team_id,
-                user_id=user_api_key_dict.user_id,
-            )
-
-            verbose_proxy_logger.info(
-                f"Vector store {vector_store_id} saved to database successfully"
-            )
-        else:
-            verbose_proxy_logger.info(
-                f"Vector store {vector_store_id} already exists, appending file to metadata"
-            )
-            
-            # Update existing vector store with new file
-            existing_metadata = existing_vector_store.vector_store_metadata or {}
-            if isinstance(existing_metadata, str):
-                import json
-                existing_metadata = json.loads(existing_metadata)
-            
-            ingested_files = existing_metadata.get("ingested_files", [])
-            ingested_files.append(file_entry)
-            existing_metadata["ingested_files"] = ingested_files
-            
-            # Update the vector store
-            from litellm.proxy.utils import safe_dumps
-            await prisma_client.db.litellm_managedvectorstorestable.update(
-                where={"vector_store_id": vector_store_id},
-                data={"vector_store_metadata": safe_dumps(existing_metadata)}
-            )
-            
-            verbose_proxy_logger.info(
-                f"Added file {file_entry.get('filename') or file_entry.get('file_url', 'Unknown')} to vector store {vector_store_id} metadata"
-            )
-    except Exception as db_error:
-        # Log the error but don't fail the request since ingestion succeeded
-        verbose_proxy_logger.exception(
-            f"Failed to save vector store {vector_store_id} to database: {db_error}"
-        )
-
-
 async def parse_rag_ingest_request(
     request: Request,
 ) -> Tuple[Dict[str, Any], Optional[Tuple[str, bytes, str]], Optional[str], Optional[str]]:
@@ -348,7 +158,6 @@ async def rag_ingest(
         add_litellm_data_to_request,
         general_settings,
         llm_router,
-        prisma_client,
         proxy_config,
         version,
     )
@@ -380,25 +189,6 @@ async def rag_ingest(
             **request_data,
         )
 
-        # Save vector store to database if it was newly created and prisma_client is available
-        verbose_proxy_logger.debug(
-            f"RAG Ingest - Checking database save conditions: prisma_client={prisma_client is not None}, response={response is not None}, response_type={type(response)}"
-        )
-        
-        if prisma_client is not None and response is not None:
-            await _save_vector_store_to_db_from_rag_ingest(
-                response=response,
-                ingest_options=ingest_options,
-                prisma_client=prisma_client,
-                user_api_key_dict=user_api_key_dict,
-                file_data=file_data,
-                file_url=file_url,
-            )
-        else:
-            verbose_proxy_logger.warning(
-                f"Skipping database save: prisma_client={prisma_client is not None}, response={response is not None}"
-            )
-
         return response
 
     except HTTPException:
diff --git a/litellm/proxy/response_api_endpoints/endpoints.py b/litellm/proxy/response_api_endpoints/endpoints.py
index 44e8c42b2c1..ec1bc5497bd 100644
--- a/litellm/proxy/response_api_endpoints/endpoints.py
+++ b/litellm/proxy/response_api_endpoints/endpoints.py
@@ -68,7 +68,6 @@ async def responses_api(
         _read_request_body,
         general_settings,
         llm_router,
-        native_background_mode,
         polling_cache_ttl,
         polling_via_cache_enabled,
         proxy_config,
@@ -96,7 +95,6 @@ async def responses_api(
         redis_cache=redis_usage_cache,
         model=data.get("model", ""),
         llm_router=llm_router,
-        native_background_mode=native_background_mode,
     )
     
     # If polling is enabled, use polling mode
diff --git a/litellm/proxy/response_polling/polling_handler.py b/litellm/proxy/response_polling/polling_handler.py
index f0b850049bf..c47578c8d7b 100644
--- a/litellm/proxy/response_polling/polling_handler.py
+++ b/litellm/proxy/response_polling/polling_handler.py
@@ -3,7 +3,7 @@
 """
 import json
 from datetime import datetime, timezone
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, Optional
 
 from litellm._logging import verbose_proxy_logger
 from litellm._uuid import uuid4
@@ -257,7 +257,6 @@ def should_use_polling_for_request(
     redis_cache,  # RedisCache or None
     model: str,
     llm_router,  # Router instance or None
-    native_background_mode: Optional[List[str]] = None,  # List of models that should use native background mode
 ) -> bool:
     """
     Determine if polling via cache should be used for a request.
@@ -268,8 +267,6 @@ def should_use_polling_for_request(
         redis_cache: Redis cache instance (required for polling)
         model: Model name from the request (e.g., "gpt-5" or "openai/gpt-4o")
         llm_router: LiteLLM router instance for looking up model deployments
-        native_background_mode: List of model names that should use native provider 
-            background mode instead of polling via cache
     
     Returns:
         True if polling should be used, False otherwise
@@ -278,13 +275,6 @@ def should_use_polling_for_request(
     if not (background_mode and polling_via_cache_enabled and redis_cache):
         return False
     
-    # Check if model is in native_background_mode list - these use native provider background mode
-    if native_background_mode and model in native_background_mode:
-        verbose_proxy_logger.debug(
-            f"Model {model} is in native_background_mode list, skipping polling via cache"
-        )
-        return False
-    
     # "all" enables polling for all providers
     if polling_via_cache_enabled == "all":
         return True
diff --git a/litellm/proxy/route_llm_request.py b/litellm/proxy/route_llm_request.py
index af441ee43e4..0e061a839d9 100644
--- a/litellm/proxy/route_llm_request.py
+++ b/litellm/proxy/route_llm_request.py
@@ -197,13 +197,7 @@ async def route_request(
 
     elif "user_config" in data:
         router_config = data.pop("user_config")
-
-        # Filter router_config to only include valid Router.__init__ arguments
-        # This prevents TypeError when invalid parameters are stored in the database
-        valid_args = litellm.Router.get_valid_args()
-        filtered_config = {k: v for k, v in router_config.items() if k in valid_args}
-
-        user_router = litellm.Router(**filtered_config)
+        user_router = litellm.Router(**router_config)
         ret_val = getattr(user_router, f"{route_type}")(**data)
         user_router.discard()
         return ret_val
@@ -221,9 +215,8 @@ async def route_request(
             "aretrieve_container_file_content",
         ]:
             return getattr(llm_router, f"{route_type}")(**data)
-        # Interactions API: create with agent, get/delete/cancel don't need model routing
+        # Interactions API: get/delete/cancel don't need model routing
         if route_type in [
-            "acreate_interaction",
             "aget_interaction",
             "adelete_interaction",
             "acancel_interaction",
diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma
index b118400b620..d7aa6e9f0d0 100644
--- a/litellm/proxy/schema.prisma
+++ b/litellm/proxy/schema.prisma
@@ -5,7 +5,6 @@ datasource client {
 
 generator client {
   provider = "prisma-client-py"
-  binaryTargets = ["native", "debian-openssl-1.1.x", "debian-openssl-3.0.x", "linux-musl", "linux-musl-openssl-3.0.x"]
 }
 
 // Budget / Rate Limits for an org
@@ -761,11 +760,6 @@ model LiteLLM_ManagedVectorStoresTable {
   updated_at DateTime @updatedAt
   litellm_credential_name String?
   litellm_params Json?
-  team_id String?
-  user_id String?
-  
-  @@index([team_id])
-  @@index([user_id])
 }
 
 // Guardrails table for storing guardrail configurations
diff --git a/litellm/proxy/search_endpoints/endpoints.py b/litellm/proxy/search_endpoints/endpoints.py
index c7a3b88c490..8cfc8bb5106 100644
--- a/litellm/proxy/search_endpoints/endpoints.py
+++ b/litellm/proxy/search_endpoints/endpoints.py
@@ -1,10 +1,9 @@
 #### Search Endpoints #####
 
 import orjson
-from fastapi import APIRouter, Depends, HTTPException, Request, Response
+from fastapi import APIRouter, Depends, Request, Response
 from fastapi.responses import ORJSONResponse
 
-from litellm._logging import verbose_proxy_logger
 from litellm.proxy._types import *
 from litellm.proxy.auth.user_api_key_auth import UserAPIKeyAuth, user_api_key_auth
 from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
@@ -137,13 +136,6 @@ async def search(
         
         if llm_router is not None and hasattr(llm_router, "search_tools"):
             search_tool_name_value = data["search_tool_name"]
-            
-            verbose_proxy_logger.debug(
-                f"Search endpoint - Looking for search_tool_name: {search_tool_name_value}. "
-                f"Available search tools in router: {[tool.get('search_tool_name') for tool in llm_router.search_tools]}. "
-                f"Total search tools: {len(llm_router.search_tools)}"
-            )
-            
             matching_tools = [
                 tool for tool in llm_router.search_tools
                 if tool.get("search_tool_name") == search_tool_name_value
@@ -189,75 +181,3 @@ async def search(
             version=version,
         )
 
-@router.get(
-    "/v1/search/tools",
-    dependencies=[Depends(user_api_key_auth)],
-    response_class=ORJSONResponse,
-    tags=["search"],
-)
-@router.get(
-    "/search/tools",
-    dependencies=[Depends(user_api_key_auth)],
-    response_class=ORJSONResponse,
-    tags=["search"],
-)
-async def list_search_tools(
-    request: Request,
-    fastapi_response: Response,
-    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
-):
-    """
-    List all available search tools configured in the router.
-    
-    This endpoint returns the search tools that are currently loaded and available
-    for use with the /v1/search endpoint.
-    
-    Example:
-    ```bash
-    curl -X GET "http://localhost:4000/v1/search/tools" \
-        -H "Authorization: Bearer sk-1234"
-    ```
-    
-    Response:
-    ```json
-    {
-        "object": "list",
-        "data": [
-            {
-                "search_tool_name": "litellm-search",
-                "search_provider": "perplexity",
-                "description": "Perplexity search tool"
-            }
-        ]
-    }
-    ```
-    """
-    from litellm.proxy.proxy_server import llm_router
-
-    try:
-        search_tools_list = []
-        
-        if llm_router is not None and hasattr(llm_router, "search_tools"):
-            for tool in llm_router.search_tools:
-                tool_info = {
-                    "search_tool_name": tool.get("search_tool_name"),
-                    "search_provider": tool.get("litellm_params", {}).get("search_provider"),
-                }
-                
-                # Add description if available
-                if "search_tool_info" in tool and tool["search_tool_info"]:
-                    description = tool["search_tool_info"].get("description")
-                    if description:
-                        tool_info["description"] = description
-                
-                search_tools_list.append(tool_info)
-        
-        return {
-            "object": "list",
-            "data": search_tools_list
-        }
-    except Exception as e:
-        from litellm._logging import verbose_proxy_logger
-        verbose_proxy_logger.exception(f"Error listing search tools: {e}")
-        raise HTTPException(status_code=500, detail=str(e))
-
diff --git a/litellm/proxy/spend_tracking/spend_management_endpoints.py b/litellm/proxy/spend_tracking/spend_management_endpoints.py
index 6e49e4244e7..dcdc17ef318 100644
--- a/litellm/proxy/spend_tracking/spend_management_endpoints.py
+++ b/litellm/proxy/spend_tracking/spend_management_endpoints.py
@@ -1681,9 +1681,6 @@ async def ui_view_spend_logs(  # noqa: PLR0915
     error_code: Optional[str] = fastapi.Query(
         default=None, description="Filter logs by error code (e.g., '404', '500')"
     ),
-    error_message: Optional[str] = fastapi.Query(
-        default=None, description="Filter logs by error message (partial string match)"
-    ),
 ):
     """
     View spend logs with pagination support.
@@ -1777,12 +1774,6 @@ def parse_date(date_str: str) -> datetime:
                 "equals": f'"{error_code}"',
             })
 
-        if error_message is not None:
-            metadata_filters.append({
-                "path": ["error_information", "error_message"],
-                "string_contains": error_message,
-            })
-
         if metadata_filters:
             if len(metadata_filters) == 1:
                 where_conditions["metadata"] = metadata_filters[0]
diff --git a/litellm/proxy/spend_tracking/spend_tracking_utils.py b/litellm/proxy/spend_tracking/spend_tracking_utils.py
index bd148ecb481..db4e4beec21 100644
--- a/litellm/proxy/spend_tracking/spend_tracking_utils.py
+++ b/litellm/proxy/spend_tracking/spend_tracking_utils.py
@@ -759,19 +759,10 @@ def _should_store_prompts_and_responses_in_spend_logs() -> bool:
     from litellm.proxy.proxy_server import general_settings
     from litellm.secret_managers.main import get_secret_bool
 
-    # Check general_settings (from DB or proxy_config.yaml)
-    store_prompts_value = general_settings.get("store_prompts_in_spend_logs")
-    
-    # Normalize case: handle True/true/TRUE, False/false/FALSE, None/null
-    if store_prompts_value is True:
-        return True
-    elif isinstance(store_prompts_value, str):
-        # Case-insensitive string comparison
-        if store_prompts_value.lower() == "true":
-            return True
-    
-    # Also check environment variable
-    return get_secret_bool("STORE_PROMPTS_IN_SPEND_LOGS") is True
+    return (
+        general_settings.get("store_prompts_in_spend_logs") is True
+        or get_secret_bool("STORE_PROMPTS_IN_SPEND_LOGS") is True
+    )
 
 
 def _get_status_for_spend_log(
diff --git a/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py b/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py
index 4a0268eeede..09c14bc42c1 100644
--- a/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py
+++ b/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py
@@ -78,11 +78,6 @@ class UISettings(BaseModel):
         description="Prevents Team Admins from deleting users from the teams they manage. Useful for SCIM provisioning where team membership is defined externally.",
     )
 
-    enabled_ui_pages_internal_users: Optional[List[str]] = Field(
-        default=None,
-        description="List of page keys that internal users (non-admins) can see in the UI sidebar. If not set, all pages are visible based on role permissions.",
-    )
-
 
 class UISettingsResponse(SettingsResponse):
     """Response model for UI settings"""
@@ -94,7 +89,6 @@ class UISettingsResponse(SettingsResponse):
 ALLOWED_UI_SETTINGS_FIELDS = {
     "disable_model_add_for_internal_users",
     "disable_team_admin_delete_team_user",
-    "enabled_ui_pages_internal_users",
 }
 
 
diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py
index 8922ed032e2..5dcb0339739 100644
--- a/litellm/proxy/utils.py
+++ b/litellm/proxy/utils.py
@@ -982,9 +982,7 @@ async def _process_guardrail_callback(
 
         try:
             # Check if load balancing should be used
-            if guardrail_name and self._should_use_guardrail_load_balancing(
-                guardrail_name
-            ):
+            if guardrail_name and self._should_use_guardrail_load_balancing(guardrail_name):
                 response = await self._execute_guardrail_with_load_balancing(
                     guardrail_name=guardrail_name,
                     hook_type="pre_call",
@@ -1019,11 +1017,7 @@ async def _process_guardrail_callback(
             latency_seconds = guardrail_end_time - guardrail_start_time
 
             # Get guardrail name for metrics (fallback if not set)
-            metrics_guardrail_name = (
-                guardrail_name
-                or getattr(callback, "guardrail_name", callback.__class__.__name__)
-                or "unknown"
-            )
+            metrics_guardrail_name = guardrail_name or getattr(callback, "guardrail_name", callback.__class__.__name__) or "unknown"
 
             # Find PrometheusLogger in callbacks and record metrics
             for prom_callback in litellm.callbacks:
@@ -1799,11 +1793,9 @@ async def post_call_success_hook(
             #################################################################
 
             for callback in other_callbacks:
-                callback_response = await callback.async_post_call_success_hook(
+                await callback.async_post_call_success_hook(
                     user_api_key_dict=user_api_key_dict, data=data, response=response
                 )
-                if callback_response is not None:
-                    response = callback_response
         except Exception as e:
             raise e
         return response
@@ -1860,14 +1852,16 @@ async def async_post_call_streaming_hook(
                             complete_response = str_so_far + response_str
                         else:
                             complete_response = response_str
-                        callback_response = (
+                        potential_error_response = (
                             await _callback.async_post_call_streaming_hook(
                                 user_api_key_dict=user_api_key_dict,
                                 response=complete_response,
                             )
                         )
-                        if callback_response is not None:
-                            response = callback_response
+                        if isinstance(
+                            potential_error_response, str
+                        ) and potential_error_response.startswith("data: "):
+                            return potential_error_response
                 except Exception as e:
                     raise e
         return response
@@ -1901,18 +1895,7 @@ async def async_post_call_streaming_iterator_hook(
                 ) or _callback.should_run_guardrail(
                     data=request_data, event_type=GuardrailEventHooks.post_call
                 ):
-                    if (
-                        "async_post_call_streaming_iterator_hook"
-                        in type(callback).__dict__
-                    ):
-                        current_response = (
-                            _callback.async_post_call_streaming_iterator_hook(
-                                user_api_key_dict=user_api_key_dict,
-                                response=current_response,
-                                request_data=request_data,
-                            )
-                        )
-                    elif "apply_guardrail" in type(callback).__dict__:
+                    if "apply_guardrail" in type(callback).__dict__:
                         request_data["guardrail_to_apply"] = callback
                         current_response = (
                             unified_guardrail.async_post_call_streaming_iterator_hook(
@@ -2235,17 +2218,17 @@ async def _query_first_with_cached_plan_fallback(
     ) -> Optional[dict]:
         """
         Execute a query with automatic fallback for PostgreSQL cached plan errors.
-
+        
         This handles the "cached plan must not change result type" error that occurs
         during rolling deployments when schema changes are applied while old pods
         still have cached query plans expecting the old schema.
-
+        
         Args:
             sql_query: SQL query string to execute
-
+            
         Returns:
             Query result or None
-
+            
         Raises:
             Original exception if not a cached plan error
         """
@@ -2258,7 +2241,7 @@ async def _query_first_with_cached_plan_fallback(
                 # Add a unique comment to make the query different
                 sql_query_retry = sql_query.replace(
                     "SELECT",
-                    f"SELECT /* cache_invalidated_{int(time.time() * 1000)} */",
+                    f"SELECT /* cache_invalidated_{int(time.time() * 1000)} */"
                 )
                 verbose_proxy_logger.warning(
                     "PostgreSQL cached plan error detected for token lookup, "
@@ -2600,9 +2583,7 @@ async def get_data(  # noqa: PLR0915
                         WHERE v.token = '{token}'
                     """
 
-                    response = await self._query_first_with_cached_plan_fallback(
-                        sql_query
-                    )
+                    response = await self._query_first_with_cached_plan_fallback(sql_query)
 
                     if response is not None:
                         if response["team_models"] is None:
@@ -4246,7 +4227,7 @@ def get_server_root_path() -> str:
     - If SERVER_ROOT_PATH is set, return it.
     - Otherwise, default to "/".
     """
-    return os.getenv("SERVER_ROOT_PATH", "")
+    return os.getenv("SERVER_ROOT_PATH", "/")
 
 
 def get_prisma_client_or_throw(message: str):
diff --git a/litellm/proxy/vector_store_endpoints/endpoints.py b/litellm/proxy/vector_store_endpoints/endpoints.py
index 9ba12537bc8..6e22d66c35d 100644
--- a/litellm/proxy/vector_store_endpoints/endpoints.py
+++ b/litellm/proxy/vector_store_endpoints/endpoints.py
@@ -18,54 +18,13 @@
 ########################################################
 
 
-def _check_vector_store_access(
-    vector_store: LiteLLM_ManagedVectorStore,
-    user_api_key_dict: UserAPIKeyAuth,
-) -> bool:
-    """
-    Check if the user has access to the vector store based on team membership.
-    
-    Args:
-        vector_store: The vector store to check access for
-        user_api_key_dict: User API key authentication info
-        
-    Returns:
-        True if user has access, False otherwise
-        
-    Access rules:
-    - If vector store has no team_id, it's accessible to all (legacy behavior)
-    - If user's team_id matches the vector store's team_id, access is granted
-    - Otherwise, access is denied
-    """
-    vector_store_team_id = vector_store.get("team_id")
-    
-    # If vector store has no team_id, it's accessible to all (legacy behavior)
-    if vector_store_team_id is None:
-        return True
-    
-    # Check if user's team matches the vector store's team
-    user_team_id = user_api_key_dict.team_id
-    if user_team_id == vector_store_team_id:
-        return True
-    
-    return False
-
-
 def _update_request_data_with_litellm_managed_vector_store_registry(
     data: Dict,
     vector_store_id: str,
-    user_api_key_dict: Optional[UserAPIKeyAuth] = None,
 ) -> Dict:
     """
     Update the request data with the litellm managed vector store registry.
-    
-    Args:
-        data: Request data to update
-        vector_store_id: ID of the vector store
-        user_api_key_dict: User API key authentication info for access control
-        
-    Raises:
-        HTTPException: If user doesn't have access to the vector store
+
     """
     if litellm.vector_store_registry is not None:
         vector_store_to_run: Optional[LiteLLM_ManagedVectorStore] = (
@@ -74,14 +33,6 @@ def _update_request_data_with_litellm_managed_vector_store_registry(
             )
         )
         if vector_store_to_run is not None:
-            # Check access control if user_api_key_dict is provided
-            if user_api_key_dict is not None:
-                if not _check_vector_store_access(vector_store_to_run, user_api_key_dict):
-                    raise HTTPException(
-                        status_code=403,
-                        detail="Access denied: You do not have permission to access this vector store",
-                    )
-            
             if "custom_llm_provider" in vector_store_to_run:
                 data["custom_llm_provider"] = vector_store_to_run.get(
                     "custom_llm_provider"
@@ -137,7 +88,7 @@ async def vector_store_search(
         data["vector_store_id"] = vector_store_id
 
     data = _update_request_data_with_litellm_managed_vector_store_registry(
-        data=data, vector_store_id=vector_store_id, user_api_key_dict=user_api_key_dict
+        data=data, vector_store_id=vector_store_id
     )
 
     processor = ProxyBaseLLMRequestProcessing(data=data)
diff --git a/litellm/proxy/vector_store_endpoints/management_endpoints.py b/litellm/proxy/vector_store_endpoints/management_endpoints.py
index 6185f1541fc..bc61a60fe5a 100644
--- a/litellm/proxy/vector_store_endpoints/management_endpoints.py
+++ b/litellm/proxy/vector_store_endpoints/management_endpoints.py
@@ -133,151 +133,6 @@ async def _resolve_embedding_config_from_db(
     return None
 
 
-########################################################
-# Helper Functions
-########################################################
-def _check_vector_store_access(
-    vector_store: LiteLLM_ManagedVectorStore,
-    user_api_key_dict: UserAPIKeyAuth,
-) -> bool:
-    """
-    Check if the user has access to the vector store based on team membership.
-    
-    Args:
-        vector_store: The vector store to check access for
-        user_api_key_dict: User API key authentication info
-        
-    Returns:
-        True if user has access, False otherwise
-        
-    Access rules:
-    - If vector store has no team_id, it's accessible to all (legacy behavior)
-    - If user's team_id matches the vector store's team_id, access is granted
-    - Otherwise, access is denied
-    """
-    vector_store_team_id = vector_store.get("team_id")
-    
-    # If vector store has no team_id, it's accessible to all (legacy behavior)
-    if vector_store_team_id is None:
-        return True
-    
-    # Check if user's team matches the vector store's team
-    user_team_id = user_api_key_dict.team_id
-    if user_team_id == vector_store_team_id:
-        return True
-    
-    return False
-
-
-async def create_vector_store_in_db(
-    vector_store_id: str,
-    custom_llm_provider: str,
-    prisma_client,
-    vector_store_name: Optional[str] = None,
-    vector_store_description: Optional[str] = None,
-    vector_store_metadata: Optional[Dict] = None,
-    litellm_params: Optional[Dict] = None,
-    litellm_credential_name: Optional[str] = None,
-    team_id: Optional[str] = None,
-    user_id: Optional[str] = None,
-) -> LiteLLM_ManagedVectorStore:
-    """
-    Helper function to create a vector store in the database.
-    
-    This function handles:
-    - Checking if vector store already exists
-    - Creating the vector store in the database
-    - Adding it to the vector store registry
-    
-    Returns:
-        LiteLLM_ManagedVectorStore: The created vector store object
-        
-    Raises:
-        HTTPException: If vector store already exists or database error occurs
-    """
-    from litellm.types.router import GenericLiteLLMParams
-    
-    if prisma_client is None:
-        raise HTTPException(status_code=500, detail="Database not connected")
-    
-    # Check if vector store already exists
-    existing_vector_store = (
-        await prisma_client.db.litellm_managedvectorstorestable.find_unique(
-            where={"vector_store_id": vector_store_id}
-        )
-    )
-    if existing_vector_store is not None:
-        raise HTTPException(
-            status_code=400,
-            detail=f"Vector store with ID {vector_store_id} already exists",
-        )
-    
-    # Prepare data for database
-    data_to_create: Dict[str, Any] = {
-        "vector_store_id": vector_store_id,
-        "custom_llm_provider": custom_llm_provider,
-    }
-    
-    if vector_store_name is not None:
-        data_to_create["vector_store_name"] = vector_store_name
-    if vector_store_description is not None:
-        data_to_create["vector_store_description"] = vector_store_description
-    if vector_store_metadata is not None:
-        data_to_create["vector_store_metadata"] = safe_dumps(vector_store_metadata)
-    if litellm_credential_name is not None:
-        data_to_create["litellm_credential_name"] = litellm_credential_name
-    if team_id is not None:
-        data_to_create["team_id"] = team_id
-    if user_id is not None:
-        data_to_create["user_id"] = user_id
-    
-    # Handle litellm_params - always provide at least an empty dict
-    if litellm_params:
-        # Auto-resolve embedding config if embedding model is provided but config is not
-        embedding_model = litellm_params.get("litellm_embedding_model")
-        if embedding_model and not litellm_params.get("litellm_embedding_config"):
-            resolved_config = await _resolve_embedding_config_from_db(
-                embedding_model=embedding_model,
-                prisma_client=prisma_client
-            )
-            if resolved_config:
-                litellm_params["litellm_embedding_config"] = resolved_config
-                verbose_proxy_logger.info(
-                    f"Auto-resolved embedding config for model {embedding_model}"
-                )
-        
-        litellm_params_dict = GenericLiteLLMParams(
-            **litellm_params
-        ).model_dump(exclude_none=True)
-        data_to_create["litellm_params"] = safe_dumps(litellm_params_dict)
-    else:
-        # Provide empty dict if no litellm_params provided
-        data_to_create["litellm_params"] = safe_dumps({})
-    
-    # Create in database
-    _new_vector_store = (
-        await prisma_client.db.litellm_managedvectorstorestable.create(
-            data=data_to_create
-        )
-    )
-    
-    new_vector_store: LiteLLM_ManagedVectorStore = LiteLLM_ManagedVectorStore(
-        **_new_vector_store.model_dump()
-    )
-    
-    # Add vector store to registry
-    if litellm.vector_store_registry is not None:
-        litellm.vector_store_registry.add_vector_store_to_registry(
-            vector_store=new_vector_store
-        )
-    
-    verbose_proxy_logger.info(
-        f"Vector store {vector_store_id} created in database successfully"
-    )
-    
-    return new_vector_store
-
-
 ########################################################
 # Management Endpoints
 ########################################################
@@ -301,36 +156,71 @@ async def new_vector_store(
     - vector_store_metadata: Optional[Dict] - Additional metadata for the vector store
     """
     from litellm.proxy.proxy_server import prisma_client
+    from litellm.types.router import GenericLiteLLMParams
+
+    if prisma_client is None:
+        raise HTTPException(status_code=500, detail="Database not connected")
 
     try:
-        vector_store_id = vector_store.get("vector_store_id")
-        custom_llm_provider = vector_store.get("custom_llm_provider")
-        
-        if not vector_store_id or not custom_llm_provider:
+        # Check if vector store already exists
+        existing_vector_store = (
+            await prisma_client.db.litellm_managedvectorstorestable.find_unique(
+                where={"vector_store_id": vector_store.get("vector_store_id")}
+            )
+        )
+        if existing_vector_store is not None:
             raise HTTPException(
                 status_code=400,
-                detail="vector_store_id and custom_llm_provider are required"
+                detail=f"Vector store with ID {vector_store.get('vector_store_id')} already exists",
+            )
+
+        if vector_store.get("vector_store_metadata") is not None:
+            vector_store["vector_store_metadata"] = safe_dumps(
+                vector_store.get("vector_store_metadata")
+            )
+
+        # Safely handle JSON serialization of litellm_params
+        litellm_params_json: Optional[str] = None
+        _input_litellm_params: dict = vector_store.get("litellm_params", {}) or {}
+        if _input_litellm_params is not None:
+            # Auto-resolve embedding config if embedding model is provided but config is not
+            embedding_model = _input_litellm_params.get("litellm_embedding_model")
+            if embedding_model and not _input_litellm_params.get("litellm_embedding_config"):
+                resolved_config = await _resolve_embedding_config_from_db(
+                    embedding_model=embedding_model,
+                    prisma_client=prisma_client
+                )
+                if resolved_config:
+                    _input_litellm_params["litellm_embedding_config"] = resolved_config
+                    verbose_proxy_logger.info(
+                        f"Auto-resolved embedding config for model {embedding_model}"
+                    )
+            
+            litellm_params_dict = GenericLiteLLMParams(
+                **_input_litellm_params
+            ).model_dump(exclude_none=True)
+            litellm_params_json = safe_dumps(litellm_params_dict)
+            del vector_store["litellm_params"]
+
+        _new_vector_store = (
+            await prisma_client.db.litellm_managedvectorstorestable.create(
+                data={
+                    **vector_store,
+                    "litellm_params": litellm_params_json,
+                }
             )
-        
-        # Extract and validate metadata
-        metadata = vector_store.get("vector_store_metadata")
-        validated_metadata: Optional[Dict] = None
-        if metadata is not None and isinstance(metadata, dict):
-            validated_metadata = metadata
-        
-        new_vector_store = await create_vector_store_in_db(
-            vector_store_id=vector_store_id,
-            custom_llm_provider=custom_llm_provider,
-            prisma_client=prisma_client,
-            vector_store_name=vector_store.get("vector_store_name"),
-            vector_store_description=vector_store.get("vector_store_description"),
-            vector_store_metadata=validated_metadata,
-            litellm_params=vector_store.get("litellm_params"),
-            litellm_credential_name=vector_store.get("litellm_credential_name"),
-            team_id=user_api_key_dict.team_id,
-            user_id=user_api_key_dict.user_id,
         )
 
+        new_vector_store: LiteLLM_ManagedVectorStore = LiteLLM_ManagedVectorStore(
+            **_new_vector_store.model_dump()
+        )
+
+        # Add vector store to registry
+        if litellm.vector_store_registry is not None:
+            litellm.vector_store_registry.add_vector_store_to_registry(
+                vector_store=new_vector_store
+            )
+
         return {
             "status": "success",
             "message": f"Vector store {vector_store.get('vector_store_id')} created successfully",
@@ -421,19 +311,14 @@ async def list_vector_stores(
                         updated_data=vector_store
                     )
 
-        # Filter vector stores based on team access
-        accessible_vector_stores = [
-            vs for vs in vector_store_map.values()
-            if _check_vector_store_access(vs, user_api_key_dict)
-        ]
-        
-        total_count = len(accessible_vector_stores)
+        combined_vector_stores = list(vector_store_map.values())
+        total_count = len(combined_vector_stores)
         total_pages = (total_count + page_size - 1) // page_size
 
         # Format response using LiteLLM_ManagedVectorStoreListResponse
         response = LiteLLM_ManagedVectorStoreListResponse(
             object="list",
-            data=accessible_vector_stores,
+            data=combined_vector_stores,
             total_count=total_count,
             current_page=page,
             total_pages=total_pages,
@@ -469,7 +354,6 @@ async def delete_vector_store(
         # Check if vector store exists in database or in-memory registry
         db_vector_store_exists = False
         memory_vector_store_exists = False
-        vector_store_to_check = None
         
         existing_vector_store = (
             await prisma_client.db.litellm_managedvectorstorestable.find_unique(
@@ -478,9 +362,6 @@ async def delete_vector_store(
         )
         if existing_vector_store is not None:
             db_vector_store_exists = True
-            vector_store_to_check = LiteLLM_ManagedVectorStore(
-                **existing_vector_store.model_dump()
-            )
         
         # Check in-memory registry
         if litellm.vector_store_registry is not None:
@@ -489,8 +370,6 @@ async def delete_vector_store(
             )
             if memory_vector_store is not None:
                 memory_vector_store_exists = True
-                if vector_store_to_check is None:
-                    vector_store_to_check = memory_vector_store
         
         # If not found in either location, raise 404
         if not db_vector_store_exists and not memory_vector_store_exists:
@@ -498,15 +377,6 @@ async def delete_vector_store(
                 status_code=404,
                 detail=f"Vector store with ID {data.vector_store_id} not found",
             )
-        
-        # Check access control
-        if vector_store_to_check and not _check_vector_store_access(
-            vector_store_to_check, user_api_key_dict
-        ):
-            raise HTTPException(
-                status_code=403,
-                detail="Access denied: You do not have permission to delete this vector store",
-            )
 
         # Delete from database if exists
         if db_vector_store_exists:
@@ -553,13 +423,6 @@ async def get_vector_store_info(
                 vector_store_id=data.vector_store_id
             )
             if vector_store is not None:
-                # Check access control
-                if not _check_vector_store_access(vector_store, user_api_key_dict):
-                    raise HTTPException(
-                        status_code=403,
-                        detail="Access denied: You do not have permission to access this vector store",
-                    )
-                
                 vector_store_metadata = vector_store.get("vector_store_metadata")
                 # Parse metadata if it's a JSON string
                 parsed_metadata: Optional[dict] = None
@@ -581,8 +444,6 @@ async def get_vector_store_info(
                     updated_at=vector_store.get("updated_at") or None,
                     litellm_credential_name=vector_store.get("litellm_credential_name"),
                     litellm_params=vector_store.get("litellm_params") or None,
-                    team_id=vector_store.get("team_id") or None,
-                    user_id=vector_store.get("user_id") or None,
                 )
                 return {"vector_store": vector_store_pydantic_obj}
 
@@ -596,16 +457,8 @@ async def get_vector_store_info(
                 status_code=404,
                 detail=f"Vector store with ID {data.vector_store_id} not found",
             )
-        
-        # Check access control for DB vector store
-        vector_store_dict = vector_store.model_dump()  # type: ignore[attr-defined]
-        vector_store_typed = LiteLLM_ManagedVectorStore(**vector_store_dict)
-        if not _check_vector_store_access(vector_store_typed, user_api_key_dict):
-            raise HTTPException(
-                status_code=403,
-                detail="Access denied: You do not have permission to access this vector store",
-            )
 
+        vector_store_dict = vector_store.model_dump()  # type: ignore[attr-defined]
         return {"vector_store": vector_store_dict}
     except Exception as e:
         verbose_proxy_logger.exception(f"Error getting vector store info: {str(e)}")
diff --git a/litellm/rag/ingestion/base_ingestion.py b/litellm/rag/ingestion/base_ingestion.py
index 3daa767188d..20059487b43 100644
--- a/litellm/rag/ingestion/base_ingestion.py
+++ b/litellm/rag/ingestion/base_ingestion.py
@@ -24,7 +24,6 @@
     get_async_httpx_client,
     httpxSpecialProvider,
 )
-from litellm.rag.ingestion.file_parsers import extract_text_from_pdf
 from litellm.rag.text_splitters import RecursiveCharacterTextSplitter
 from litellm.types.rag import RAGIngestOptions, RAGIngestResponse
 
@@ -194,23 +193,11 @@ def chunk(
         if text:
             text_to_chunk = text
         elif file_content and not ocr_was_used:
-            # Try UTF-8 decode first
             try:
                 text_to_chunk = file_content.decode("utf-8")
             except UnicodeDecodeError:
-                # Check if it's a PDF and try to extract text
-                if file_content.startswith(b"%PDF"):
-                    verbose_logger.debug("PDF detected, attempting text extraction")
-                    text_to_chunk = extract_text_from_pdf(file_content)
-                    if not text_to_chunk:
-                        verbose_logger.debug(
-                            "PDF text extraction failed. Install 'pypdf' or 'PyPDF2' for PDF support, "
-                            "or enable OCR with a vision model."
-                        )
-                        return []
-                else:
-                    verbose_logger.debug("Binary file detected, skipping text chunking")
-                    return []
+                verbose_logger.debug("Binary file detected, skipping text chunking")
+                return []
 
         if not text_to_chunk:
             return []
diff --git a/litellm/rag/ingestion/file_parsers/__init__.py b/litellm/rag/ingestion/file_parsers/__init__.py
deleted file mode 100644
index 5be68cdbb74..00000000000
--- a/litellm/rag/ingestion/file_parsers/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-"""
-File parsers for RAG ingestion.
-
-Provides text extraction utilities for various file formats.
-"""
-
-from .pdf_parser import extract_text_from_pdf
-
-__all__ = ["extract_text_from_pdf"]
diff --git a/litellm/rag/ingestion/file_parsers/pdf_parser.py b/litellm/rag/ingestion/file_parsers/pdf_parser.py
deleted file mode 100644
index 9a533ccf138..00000000000
--- a/litellm/rag/ingestion/file_parsers/pdf_parser.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"""
-PDF text extraction utilities.
-
-Provides text extraction from PDF files using pypdf or PyPDF2.
-"""
-
-from typing import Optional
-
-from litellm._logging import verbose_logger
-
-
-def extract_text_from_pdf(file_content: bytes) -> Optional[str]:
-    """
-    Extract text from PDF using pypdf if available.
-
-    Args:
-        file_content: Raw PDF bytes
-
-    Returns:
-        Extracted text or None if extraction fails
-    """
-    try:
-        from io import BytesIO
-
-        # Try pypdf first (most common)
-        try:
-            from pypdf import PdfReader as PypdfReader
-            
-            pdf_file = BytesIO(file_content)
-            reader = PypdfReader(pdf_file)
-            
-            text_parts = []
-            for page in reader.pages:
-                text = page.extract_text()
-                if text:
-                    text_parts.append(text)
-            
-            if text_parts:
-                extracted_text = "\n\n".join(text_parts)
-                verbose_logger.debug(f"Extracted {len(extracted_text)} characters from PDF using pypdf")
-                return extracted_text
-                
-        except ImportError:
-            verbose_logger.debug("pypdf not available, trying PyPDF2")
-            
-        # Fallback to PyPDF2
-        try:
-            from PyPDF2 import PdfReader as PyPDF2Reader
-            
-            pdf_file = BytesIO(file_content)
-            reader = PyPDF2Reader(pdf_file)
-            
-            text_parts = []
-            for page in reader.pages:
-                text = page.extract_text()
-                if text:
-                    text_parts.append(text)
-            
-            if text_parts:
-                extracted_text = "\n\n".join(text_parts)
-                verbose_logger.debug(f"Extracted {len(extracted_text)} characters from PDF using PyPDF2")
-                return extracted_text
-                
-        except ImportError:
-            verbose_logger.debug("PyPDF2 not available, PDF extraction requires OCR or pypdf/PyPDF2 library")
-            
-    except Exception as e:
-        verbose_logger.debug(f"PDF text extraction failed: {e}")
-        
-    return None
diff --git a/litellm/rag/ingestion/s3_vectors_ingestion.py b/litellm/rag/ingestion/s3_vectors_ingestion.py
deleted file mode 100644
index e6c166a1015..00000000000
--- a/litellm/rag/ingestion/s3_vectors_ingestion.py
+++ /dev/null
@@ -1,573 +0,0 @@
-"""
-S3 Vectors-specific RAG Ingestion implementation.
-
-S3 Vectors is AWS's native vector storage service that provides:
-- Purpose-built vector buckets for storing and querying vectors
-- Vector indexes with configurable dimensions and distance metrics
-- Metadata filtering for semantic search
-
-This implementation:
-1. Auto-creates vector buckets and indexes if not provided
-2. Uses LiteLLM's embedding API (supports any provider)
-3. Uses httpx + AWS SigV4 signing (no boto3 dependency for S3 Vectors APIs)
-4. Stores vectors with metadata using PutVectors API
-"""
-
-from __future__ import annotations
-
-import hashlib
-import uuid
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
-
-import litellm
-from litellm._logging import verbose_logger
-from litellm.constants import (
-    S3_VECTORS_DEFAULT_DIMENSION,
-    S3_VECTORS_DEFAULT_DISTANCE_METRIC,
-    S3_VECTORS_DEFAULT_NON_FILTERABLE_METADATA_KEYS,
-)
-from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
-from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
-from litellm.llms.custom_httpx.http_handler import (
-    get_async_httpx_client,
-    httpxSpecialProvider,
-)
-from litellm.rag.ingestion.base_ingestion import BaseRAGIngestion
-
-if TYPE_CHECKING:
-    from litellm import Router
-    from litellm.types.rag import RAGIngestOptions
-
-
-class S3VectorsRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
-    """
-    S3 Vectors RAG ingestion using httpx + AWS SigV4 signing.
-
-    Workflow:
-    1. Auto-create vector bucket if needed (CreateVectorBucket API)
-    2. Auto-create vector index if needed (CreateVectorIndex API)
-    3. Generate embeddings using LiteLLM (supports any provider)
-    4. Store vectors with PutVectors API
-
-    Configuration:
-    - vector_bucket_name: S3 vector bucket name (required)
-    - index_name: Vector index name (auto-creates if not provided)
-    - dimension: Vector dimension (default: S3_VECTORS_DEFAULT_DIMENSION)
-    - distance_metric: "cosine" or "euclidean" (default: S3_VECTORS_DEFAULT_DISTANCE_METRIC)
-    - non_filterable_metadata_keys: List of metadata keys to exclude from filtering
-    """
-
-    def __init__(
-        self,
-        ingest_options: "RAGIngestOptions",
-        router: Optional["Router"] = None,
-    ):
-        BaseRAGIngestion.__init__(self, ingest_options=ingest_options, router=router)
-        BaseAWSLLM.__init__(self)
-
-        # Extract config
-        self.vector_bucket_name = self.vector_store_config["vector_bucket_name"]
-        self.index_name = self.vector_store_config.get("index_name")
-        self.distance_metric = self.vector_store_config.get(
-            "distance_metric", S3_VECTORS_DEFAULT_DISTANCE_METRIC
-        )
-        self.non_filterable_metadata_keys = self.vector_store_config.get(
-            "non_filterable_metadata_keys",
-            S3_VECTORS_DEFAULT_NON_FILTERABLE_METADATA_KEYS,
-        )
-        
-        # Get dimension from config (will be auto-detected on first use if not provided)
-        self.dimension = self._get_dimension_from_config()
-
-        # Get AWS region using BaseAWSLLM method
-        _aws_region = self.vector_store_config.get("aws_region_name")
-        self.aws_region_name = self.get_aws_region_name_for_non_llm_api_calls(
-            aws_region_name=str(_aws_region) if _aws_region else None
-        )
-
-        # Create httpx client (similar to s3_v2.py)
-        ssl_verify = self._get_ssl_verify(
-            ssl_verify=self.vector_store_config.get("ssl_verify")
-        )
-        self.async_httpx_client = get_async_httpx_client(
-            llm_provider=httpxSpecialProvider.RAG,
-            params={"ssl_verify": ssl_verify} if ssl_verify is not None else None,
-        )
-
-        # Track if infrastructure is initialized
-        self._config_initialized = False
-
-    async def _get_dimension_from_embedding_request(self) -> int:
-        """
-        Auto-detect dimension by making a test embedding request.
-        
-        Makes a single embedding request with a test string to determine
-        the output dimension of the embedding model.
-        """
-        if not self.embedding_config or "model" not in self.embedding_config:
-            return S3_VECTORS_DEFAULT_DIMENSION
-        
-        try:
-            model_name = self.embedding_config["model"]
-            verbose_logger.debug(
-                f"Auto-detecting dimension by making test embedding request to {model_name}"
-            )
-            
-            # Make a test embedding request
-            test_input = "test"
-            if self.router:
-                response = await self.router.aembedding(model=model_name, input=[test_input])
-            else:
-                response = await litellm.aembedding(model=model_name, input=[test_input])
-            
-            # Get dimension from the response
-            if response.data and len(response.data) > 0:
-                dimension = len(response.data[0]["embedding"])
-                verbose_logger.debug(
-                    f"Auto-detected dimension {dimension} for embedding model {model_name}"
-                )
-                return dimension
-        except Exception as e:
-            verbose_logger.warning(
-                f"Could not auto-detect dimension from embedding model: {e}. "
-                f"Using default dimension of {S3_VECTORS_DEFAULT_DIMENSION}."
-            )
-        
-        return S3_VECTORS_DEFAULT_DIMENSION
-    
-    def _get_dimension_from_config(self) -> Optional[int]:
-        """
-        Get vector dimension from config if explicitly provided.
-        
-        Returns None if dimension should be auto-detected.
-        """
-        if "dimension" in self.vector_store_config:
-            return int(self.vector_store_config["dimension"])
-        return None
-
-    async def _ensure_config_initialized(self):
-        """Lazily initialize S3 Vectors infrastructure."""
-        if self._config_initialized:
-            return
-
-        # Auto-detect dimension if not provided
-        if self.dimension is None:
-            self.dimension = await self._get_dimension_from_embedding_request()
-
-        # Ensure vector bucket exists
-        await self._ensure_vector_bucket_exists()
-
-        # Ensure vector index exists
-        if not self.index_name:
-            # Auto-generate index name
-            unique_id = uuid.uuid4().hex[:8]
-            self.index_name = f"litellm-index-{unique_id}"
-
-        await self._ensure_vector_index_exists()
-
-        self._config_initialized = True
-
-    async def _sign_and_execute_request(
-        self,
-        method: str,
-        url: str,
-        data: Optional[str] = None,
-        headers: Optional[Dict[str, str]] = None,
-    ) -> Any:
-        """
-        Helper to sign and execute AWS API requests using httpx + SigV4.
-
-        Pattern from litellm/integrations/s3_v2.py
-        """
-        try:
-            import requests
-            from botocore.auth import SigV4Auth
-            from botocore.awsrequest import AWSRequest
-        except ImportError:
-            raise ImportError(
-                "Missing botocore to call S3 Vectors. Run 'pip install boto3'."
-            )
-
-        # Get AWS credentials using BaseAWSLLM's get_credentials method
-        credentials = self.get_credentials(
-            aws_access_key_id=self.vector_store_config.get("aws_access_key_id"),
-            aws_secret_access_key=self.vector_store_config.get("aws_secret_access_key"),
-            aws_session_token=self.vector_store_config.get("aws_session_token"),
-            aws_region_name=self.aws_region_name,
-            aws_session_name=self.vector_store_config.get("aws_session_name"),
-            aws_profile_name=self.vector_store_config.get("aws_profile_name"),
-            aws_role_name=self.vector_store_config.get("aws_role_name"),
-            aws_web_identity_token=self.vector_store_config.get("aws_web_identity_token"),
-            aws_sts_endpoint=self.vector_store_config.get("aws_sts_endpoint"),
-            aws_external_id=self.vector_store_config.get("aws_external_id"),
-        )
-
-        # Prepare headers
-        if headers is None:
-            headers = {}
-
-        if data:
-            headers["Content-Type"] = "application/json"
-            # Calculate SHA256 hash of the content
-            content_hash = hashlib.sha256(data.encode("utf-8")).hexdigest()
-            headers["x-amz-content-sha256"] = content_hash
-        else:
-            # For requests without body, use hash of empty string
-            headers["x-amz-content-sha256"] = hashlib.sha256(b"").hexdigest()
-
-        # Prepare the request
-        req = requests.Request(method, url, data=data, headers=headers)
-        prepped = req.prepare()
-
-        # Sign the request
-        aws_request = AWSRequest(
-            method=prepped.method,
-            url=prepped.url,
-            data=prepped.body,
-            headers=prepped.headers,
-        )
-        SigV4Auth(credentials, "s3vectors", self.aws_region_name).add_auth(aws_request)
-
-        # Prepare the signed headers
-        signed_headers = dict(aws_request.headers.items())
-
-        # Make the request using specific method (pattern from s3_v2.py)
-        method_upper = method.upper()
-        if method_upper == "PUT":
-            response = await self.async_httpx_client.put(
-                url, data=data, headers=signed_headers
-            )
-        elif method_upper == "POST":
-            response = await self.async_httpx_client.post(
-                url, data=data, headers=signed_headers
-            )
-        elif method_upper == "GET":
-            response = await self.async_httpx_client.get(url, headers=signed_headers)
-        else:
-            raise ValueError(f"Unsupported HTTP method: {method}")
-
-        return response
-
-    async def _ensure_vector_bucket_exists(self):
-        """Create vector bucket if it doesn't exist using GetVectorBucket and CreateVectorBucket APIs."""
-        verbose_logger.debug(
-            f"Ensuring S3 vector bucket exists: {self.vector_bucket_name}"
-        )
-        
-        # Validate bucket name (AWS S3 naming rules)
-        if len(self.vector_bucket_name) < 3:
-            raise ValueError(
-                f"Invalid vector_bucket_name '{self.vector_bucket_name}': "
-                f"AWS S3 bucket names must be at least 3 characters long. "
-                f"Please provide a valid bucket name (e.g., 'my-vector-bucket')."
-            )
-        if not self.vector_bucket_name.replace("-", "").replace(".", "").isalnum():
-            raise ValueError(
-                f"Invalid vector_bucket_name '{self.vector_bucket_name}': "
-                f"AWS S3 bucket names can only contain lowercase letters, numbers, hyphens, and periods. "
-                f"Please provide a valid bucket name (e.g., 'my-vector-bucket')."
-            )
-
-        # Try to get bucket info using GetVectorBucket API
-        get_url = f"https://s3vectors.{self.aws_region_name}.api.aws/GetVectorBucket"
-        get_body = safe_dumps({"vectorBucketName": self.vector_bucket_name})
-
-        try:
-            response = await self._sign_and_execute_request("POST", get_url, data=get_body)
-            if response.status_code == 200:
-                verbose_logger.debug(f"Vector bucket {self.vector_bucket_name} exists")
-                return
-        except Exception as e:
-            verbose_logger.debug(
-                f"Bucket check failed (may not exist): {e}, attempting to create"
-            )
-
-        # Create vector bucket using CreateVectorBucket API
-        try:
-            verbose_logger.debug(f"Creating vector bucket: {self.vector_bucket_name}")
-            create_url = f"https://s3vectors.{self.aws_region_name}.api.aws/CreateVectorBucket"
-            create_body = safe_dumps({
-                "vectorBucketName": self.vector_bucket_name
-            })
-            
-            response = await self._sign_and_execute_request("POST", create_url, data=create_body)
-
-            if response.status_code in (200, 201):
-                verbose_logger.info(f"Created vector bucket: {self.vector_bucket_name}")
-            elif response.status_code == 409:
-                # Bucket already exists (ConflictException)
-                verbose_logger.debug(
-                    f"Vector bucket {self.vector_bucket_name} already exists"
-                )
-            else:
-                verbose_logger.error(f"CreateVectorBucket failed: {response.status_code} - {response.text}")
-                response.raise_for_status()
-        except Exception as e:
-            verbose_logger.exception(f"Error creating vector bucket: {e}")
-            raise
-
-    async def _ensure_vector_index_exists(self):
-        """Create vector index if it doesn't exist using GetIndex and CreateIndex APIs."""
-        verbose_logger.debug(
-            f"Ensuring vector index exists: {self.vector_bucket_name}/{self.index_name}"
-        )
-
-        # Try to get index info using GetIndex API
-        get_url = f"https://s3vectors.{self.aws_region_name}.api.aws/GetIndex"
-        get_body = safe_dumps({
-            "vectorBucketName": self.vector_bucket_name,
-            "indexName": self.index_name
-        })
-
-        try:
-            response = await self._sign_and_execute_request("POST", get_url, data=get_body)
-            if response.status_code == 200:
-                verbose_logger.debug(f"Vector index {self.index_name} exists")
-                return
-        except Exception as e:
-            verbose_logger.debug(
-                f"Index check failed (may not exist): {e}, attempting to create"
-            )
-
-        # Create vector index using CreateIndex API
-        try:
-            verbose_logger.debug(
-                f"Creating vector index: {self.index_name} with dimension={self.dimension}, metric={self.distance_metric}"
-            )
-
-            # Prepare index configuration per AWS API docs
-            index_config = {
-                "vectorBucketName": self.vector_bucket_name,
-                "indexName": self.index_name,
-                "dataType": "float32",
-                "dimension": self.dimension,
-                "distanceMetric": self.distance_metric,
-            }
-
-            if self.non_filterable_metadata_keys:
-                index_config["metadataConfiguration"] = {
-                    "nonFilterableMetadataKeys": self.non_filterable_metadata_keys
-                }
-
-            create_url = f"https://s3vectors.{self.aws_region_name}.api.aws/CreateIndex"
-            response = await self._sign_and_execute_request(
-                "POST", create_url, data=safe_dumps(index_config)
-            )
-
-            if response.status_code in (200, 201):
-                verbose_logger.info(f"Created vector index: {self.index_name}")
-            elif response.status_code == 409:
-                verbose_logger.debug(f"Vector index {self.index_name} already exists")
-            else:
-                verbose_logger.error(f"CreateIndex failed: {response.status_code} - {response.text}")
-                response.raise_for_status()
-        except Exception as e:
-            verbose_logger.exception(f"Error creating vector index: {e}")
-            raise
-
-    async def _put_vectors(self, vectors: List[Dict[str, Any]]):
-        """
-        Call PutVectors API to store vectors in S3 Vectors.
-
-        Args:
-            vectors: List of vector objects with keys: "key", "data", "metadata"
-        """
-        verbose_logger.debug(
-            f"Storing {len(vectors)} vectors in {self.vector_bucket_name}/{self.index_name}"
-        )
-
-        url = f"https://s3vectors.{self.aws_region_name}.api.aws/PutVectors"
-
-        # Prepare request body per AWS API docs
-        request_body = {
-            "vectorBucketName": self.vector_bucket_name,
-            "indexName": self.index_name,
-            "vectors": vectors
-        }
-
-        try:
-            response = await self._sign_and_execute_request(
-                "POST", url, data=safe_dumps(request_body)
-            )
-
-            if response.status_code in (200, 201):
-                verbose_logger.info(
-                    f"Successfully stored {len(vectors)} vectors in index {self.index_name}"
-                )
-            else:
-                verbose_logger.error(
-                    f"PutVectors failed with status {response.status_code}: {response.text}"
-                )
-                response.raise_for_status()
-        except Exception as e:
-            verbose_logger.exception(f"Error storing vectors: {e}")
-            raise
-
-    async def embed(
-        self,
-        chunks: List[str],
-    ) -> Optional[List[List[float]]]:
-        """
-        Generate embeddings using LiteLLM's embedding API.
-
-        Supports any embedding provider (OpenAI, Bedrock, Cohere, etc.)
-        """
-        if not chunks:
-            return None
-
-        # Use embedding config from ingest_options or default
-        if not self.embedding_config:
-            verbose_logger.warning(
-                "No embedding config provided, using default text-embedding-3-small"
-            )
-            self.embedding_config = {"model": "text-embedding-3-small"}
-
-        embedding_model = self.embedding_config.get("model", "text-embedding-3-small")
-
-        verbose_logger.debug(
-            f"Generating embeddings for {len(chunks)} chunks using {embedding_model}"
-        )
-
-        # Convert to list to ensure type compatibility
-        input_chunks: List[str] = list(chunks)
-        
-        if self.router:
-            response = await self.router.aembedding(model=embedding_model, input=input_chunks)
-        else:
-            response = await litellm.aembedding(model=embedding_model, input=input_chunks)
-
-        return [item["embedding"] for item in response.data]
-
-    async def store(
-        self,
-        file_content: Optional[bytes],
-        filename: Optional[str],
-        content_type: Optional[str],
-        chunks: List[str],
-        embeddings: Optional[List[List[float]]],
-    ) -> Tuple[Optional[str], Optional[str]]:
-        """
-        Store vectors in S3 Vectors using PutVectors API.
-
-        Steps:
-        1. Ensure vector bucket exists (auto-create if needed)
-        2. Ensure vector index exists (auto-create if needed)
-        3. Prepare vector data with metadata
-        4. Call PutVectors API with httpx + SigV4 signing
-
-        Args:
-            file_content: Raw file bytes (not used for S3 Vectors)
-            filename: Name of the file
-            content_type: MIME type (not used for S3 Vectors)
-            chunks: Text chunks
-            embeddings: Vector embeddings
-
-        Returns:
-            Tuple of (index_name, filename)
-        """
-        # Ensure infrastructure exists
-        await self._ensure_config_initialized()
-
-        if not embeddings or not chunks:
-            error_msg = (
-                "No text content could be extracted from the file for embedding. "
-                "Possible causes:\n"
-                "  1. PDF files require OCR - add 'ocr' config with a vision model (e.g., 'anthropic/claude-3-5-sonnet-20241022')\n"
-                "  2. Binary files cannot be processed - convert to text first\n"
-                "  3. File is empty or contains no extractable text\n"
-                "For PDFs, either enable OCR or use a PDF extraction library to convert to text before ingestion."
-            )
-            verbose_logger.error(error_msg)
-            raise ValueError(error_msg)
-
-        # Prepare vectors for PutVectors API
-        vectors = []
-        for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)):
-            # Build metadata dict
-            metadata: Dict[str, str] = {
-                "source_text": chunk,  # Non-filterable (for reference)
-                "chunk_index": str(i),  # Filterable
-            }
-            
-            if filename:
-                metadata["filename"] = filename  # Filterable
-            
-            vector_obj = {
-                "key": f"{filename}_{i}" if filename else f"chunk_{i}",
-                "data": {"float32": embedding},
-                "metadata": metadata,
-            }
-
-            vectors.append(vector_obj)
-
-        # Call PutVectors API
-        await self._put_vectors(vectors)
-
-        # Return vector_store_id in format bucket_name:index_name for S3 Vectors search compatibility
-        vector_store_id = f"{self.vector_bucket_name}:{self.index_name}"
-        return vector_store_id, filename
-
-    async def query_vector_store(
-        self, vector_store_id: str, query: str, top_k: int = 5
-    ) -> Optional[Dict[str, Any]]:
-        """
-        Query S3 Vectors using QueryVectors API.
-
-        Args:
-            vector_store_id: Index name
-            query: Query text
-            top_k: Number of results to return
-
-        Returns:
-            Query results with vectors and metadata
-        """
-        verbose_logger.debug(f"Querying index {vector_store_id} with query: {query}")
-
-        # Generate query embedding
-        if not self.embedding_config:
-            self.embedding_config = {"model": "text-embedding-3-small"}
-
-        embedding_model = self.embedding_config.get("model", "text-embedding-3-small")
-
-        response = await litellm.aembedding(model=embedding_model, input=[query])
-        query_embedding = response.data[0]["embedding"]
-
-        # Call QueryVectors API
-        url = f"https://s3vectors.{self.aws_region_name}.api.aws/QueryVectors"
-
-        request_body = {
-            "vectorBucketName": self.vector_bucket_name,
-            "indexName": vector_store_id,
-            "queryVector": {"float32": query_embedding},
-            "topK": top_k,
-            "returnDistance": True,
-            "returnMetadata": True,
-        }
-
-        try:
-            response = await self._sign_and_execute_request(
-                "POST", url, data=safe_dumps(request_body)
-            )
-
-            if response.status_code == 200:
-                results = response.json()
-                verbose_logger.debug(f"Query returned {len(results.get('vectors', []))} results")
-
-                # Check if query terms appear in results
-                if results.get("vectors"):
-                    for result in results["vectors"]:
-                        metadata = result.get("metadata", {})
-                        source_text = metadata.get("source_text", "")
-                        if query.lower() in source_text.lower():
-                            return results
-
-                # Return results even if exact match not found
-                return results
-            else:
-                verbose_logger.error(
-                    f"QueryVectors failed with status {response.status_code}: {response.text}"
-                )
-                return None
-        except Exception as e:
-            verbose_logger.exception(f"Error querying vectors: {e}")
-            return None
diff --git a/litellm/rag/main.py b/litellm/rag/main.py
index 571f78f2f70..620097f83dd 100644
--- a/litellm/rag/main.py
+++ b/litellm/rag/main.py
@@ -31,7 +31,6 @@
 from litellm.rag.ingestion.bedrock_ingestion import BedrockRAGIngestion
 from litellm.rag.ingestion.gemini_ingestion import GeminiRAGIngestion
 from litellm.rag.ingestion.openai_ingestion import OpenAIRAGIngestion
-from litellm.rag.ingestion.s3_vectors_ingestion import S3VectorsRAGIngestion
 from litellm.rag.rag_query import RAGQuery
 from litellm.types.rag import (
     RAGIngestOptions,
@@ -49,7 +48,6 @@
     "openai": OpenAIRAGIngestion,
     "bedrock": BedrockRAGIngestion,
     "gemini": GeminiRAGIngestion,
-    "s3_vectors": S3VectorsRAGIngestion,
 }
 
 
diff --git a/litellm/responses/litellm_completion_transformation/transformation.py b/litellm/responses/litellm_completion_transformation/transformation.py
index 41abbca755c..3badbc50578 100644
--- a/litellm/responses/litellm_completion_transformation/transformation.py
+++ b/litellm/responses/litellm_completion_transformation/transformation.py
@@ -1750,8 +1750,6 @@ def _transform_chat_completion_usage_to_responses_usage(
                 and prompt_details.cached_tokens is not None
             ):
                 input_details_dict["cached_tokens"] = prompt_details.cached_tokens
-            else:
-                input_details_dict["cached_tokens"] = 0
 
             if (
                 hasattr(prompt_details, "text_tokens")
@@ -1784,8 +1782,6 @@ def _transform_chat_completion_usage_to_responses_usage(
                 output_details_dict["reasoning_tokens"] = (
                     completion_details.reasoning_tokens
                 )
-            else:
-                output_details_dict["reasoning_tokens"] = 0
 
             if (
                 hasattr(completion_details, "text_tokens")
diff --git a/litellm/responses/main.py b/litellm/responses/main.py
index b2c2493c812..83c23a58500 100644
--- a/litellm/responses/main.py
+++ b/litellm/responses/main.py
@@ -434,8 +434,6 @@ async def aresponses(
             _, custom_llm_provider, _, _ = litellm.get_llm_provider(
                 model=model, api_base=local_vars.get("base_url", None)
             )
-            # Update local_vars with detected provider (fixes #19782)
-            local_vars["custom_llm_provider"] = custom_llm_provider
 
         func = partial(
             responses,
@@ -585,9 +583,6 @@ def responses(
             api_key=litellm_params.api_key,
         )
 
-        # Update local_vars with detected provider (fixes #19782)
-        local_vars["custom_llm_provider"] = custom_llm_provider
-
         # Use dynamic credentials from get_llm_provider (e.g., when use_litellm_proxy=True)
         if dynamic_api_key is not None:
             litellm_params.api_key = dynamic_api_key
@@ -1416,8 +1411,6 @@ async def acompact_responses(
             _, custom_llm_provider, _, _ = litellm.get_llm_provider(
                 model=model, api_base=local_vars.get("base_url", None)
             )
-            # Update local_vars with detected provider (fixes #19782)
-            local_vars["custom_llm_provider"] = custom_llm_provider
 
         func = partial(
             compact_responses,
@@ -1505,9 +1498,6 @@ def compact_responses(
             api_key=litellm_params.api_key,
         )
 
-        # Update local_vars with detected provider (fixes #19782)
-        local_vars["custom_llm_provider"] = custom_llm_provider
-
         # Use dynamic credentials from get_llm_provider (e.g., when use_litellm_proxy=True)
         if dynamic_api_key is not None:
             litellm_params.api_key = dynamic_api_key
diff --git a/litellm/router.py b/litellm/router.py
index a3c3afa9326..62070d23755 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -58,7 +58,6 @@
     _get_parent_otel_span_from_kwargs,
     get_metadata_variable_name_from_kwargs,
 )
-from litellm.litellm_core_utils.thread_pool_executor import executor
 from litellm.litellm_core_utils.coroutine_checker import coroutine_checker
 from litellm.litellm_core_utils.credential_accessor import CredentialAccessor
 from litellm.litellm_core_utils.dd_tracing import tracer
@@ -644,17 +643,6 @@ def __init__(  # noqa: PLR0915
         self.initialize_router_endpoints()
         self.apply_default_settings()
 
-    @staticmethod
-    def get_valid_args() -> List[str]:
-        """
-        Returns a list of valid arguments for the Router.__init__ method.
-        """
-        arg_spec = inspect.getfullargspec(Router.__init__)
-        valid_args = arg_spec.args + arg_spec.kwonlyargs
-        if "self" in valid_args:
-            valid_args.remove("self")
-        return valid_args
-
     def apply_default_settings(self):
         """
         Apply the default settings to the router.
@@ -1251,25 +1239,10 @@ def _completion(
                 specific_deployment=kwargs.pop("specific_deployment", None),
                 request_kwargs=kwargs,
             )
-            # Check for silent model experiment
-            # Make a local copy of litellm_params to avoid mutating the Router's state
-            litellm_params = deployment["litellm_params"].copy()
-            silent_model = litellm_params.pop("silent_model", None)
-
-            if silent_model is not None:
-                # Mirroring traffic to a secondary model
-                # Use shared thread pool for background calls
-                executor.submit(
-                    self._silent_experiment_completion,
-                    silent_model,
-                    messages,
-                    **kwargs,
-                )
-
             self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
-            kwargs.pop("silent_model", None)  # Ensure it's not in kwargs either
+
             # No copy needed - data is only read and spread into new dict below
-            data = litellm_params.copy()  # Use the local copy of litellm_params
+            data = deployment["litellm_params"]
             model_name = data["model"]
             potential_model_client = self._get_client(
                 deployment=deployment, kwargs=kwargs
@@ -1290,14 +1263,15 @@ def _completion(
             if not self.has_model_id(model):
                 self.routing_strategy_pre_call_checks(deployment=deployment)
 
-            input_kwargs = {
-                **data,
-                "messages": messages,
-                "caching": self.cache_responses,
-                "client": model_client,
-                **kwargs,
-            }
-            response = litellm.completion(**input_kwargs)
+            response = litellm.completion(
+                **{
+                    **data,
+                    "messages": messages,
+                    "caching": self.cache_responses,
+                    "client": model_client,
+                    **kwargs,
+                }
+            )
             verbose_router_logger.info(
                 f"litellm.completion(model={model_name})\033[32m 200 OK\033[0m"
             )
@@ -1324,56 +1298,6 @@ def _completion(
                 self._set_deployment_num_retries_on_exception(e, deployment)
             raise e
 
-    def _get_silent_experiment_kwargs(self, **kwargs) -> dict:
-        """
-        Prepare kwargs for a silent experiment by ensuring isolation from the primary call.
-        """
-        # Copy kwargs to ensure isolation
-        silent_kwargs = copy.deepcopy(kwargs)
-        if "metadata" not in silent_kwargs:
-            silent_kwargs["metadata"] = {}
-
-        silent_kwargs["metadata"]["is_silent_experiment"] = True
-
-        # Pop logging objects and call IDs to ensure a fresh logging context
-        # This prevents collisions in the Proxy's database (spend_logs)
-        silent_kwargs.pop("litellm_call_id", None)
-        silent_kwargs.pop("litellm_logging_obj", None)
-        silent_kwargs.pop("standard_logging_object", None)
-        silent_kwargs.pop("proxy_server_request", None)
-
-        return silent_kwargs
-
-    def _silent_experiment_completion(
-        self, silent_model: str, messages: List[Any], **kwargs
-    ):
-        """
-        Run a silent experiment in the background (thread).
-        """
-        try:
-            # Prevent infinite recursion if silent model also has a silent model
-            if kwargs.get("metadata", {}).get("is_silent_experiment", False):
-                return
-
-            messages = copy.deepcopy(messages)
-
-            verbose_router_logger.info(
-                f"Starting silent experiment for model {silent_model}"
-            )
-
-            silent_kwargs = self._get_silent_experiment_kwargs(**kwargs)
-
-            # Trigger the silent request
-            self.completion(
-                model=silent_model,
-                messages=cast(List[Dict[str, str]], messages),
-                **silent_kwargs,
-            )
-        except Exception as e:
-            verbose_router_logger.error(
-                f"Silent experiment failed for model {silent_model}: {str(e)}"
-            )
-
     # fmt: off
 
     @overload
@@ -1582,36 +1506,6 @@ async def stream_with_fallbacks():
 
         return FallbackStreamWrapper(stream_with_fallbacks())
 
-    async def _silent_experiment_acompletion(
-        self, silent_model: str, messages: List[Any], **kwargs
-    ):
-        """
-        Run a silent experiment in the background.
-        """
-        try:
-            # Prevent infinite recursion if silent model also has a silent model
-            if kwargs.get("metadata", {}).get("is_silent_experiment", False):
-                return
-
-            messages = copy.deepcopy(messages)
-
-            verbose_router_logger.info(
-                f"Starting silent experiment for model {silent_model}"
-            )
-
-            silent_kwargs = self._get_silent_experiment_kwargs(**kwargs)
-
-            # Trigger the silent request
-            await self.acompletion(
-                model=silent_model,
-                messages=cast(List[AllMessageValues], messages),
-                **silent_kwargs,
-            )
-        except Exception as e:
-            verbose_router_logger.error(
-                f"Silent experiment failed for model {silent_model}: {str(e)}"
-            )
-
     async def _acompletion(  # noqa: PLR0915
         self, model: str, messages: List[Dict[str, str]], **kwargs
     ) -> Union[ModelResponse, CustomStreamWrapper,]:
@@ -1658,27 +1552,9 @@ async def _acompletion(  # noqa: PLR0915
             self._track_deployment_metrics(
                 deployment=deployment, parent_otel_span=parent_otel_span
             )
-
-            # Check for silent model experiment
-            # Make a local copy of litellm_params to avoid mutating the Router's state
-            litellm_params = deployment["litellm_params"].copy()
-            silent_model = litellm_params.pop("silent_model", None)
-
-            if silent_model is not None:
-                # Mirroring traffic to a secondary model
-                # This is a silent experiment, so we don't want to block the primary request
-                asyncio.create_task(
-                    self._silent_experiment_acompletion(
-                        silent_model=silent_model,
-                        messages=messages,  # Use messages instead of *args
-                        **kwargs,
-                    )
-                )
-
             self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
-            kwargs.pop("silent_model", None)  # Ensure it's not in kwargs either
             # No copy needed - data is only read and spread into new dict below
-            data = litellm_params.copy()  # Use the local copy of litellm_params
+            data = deployment["litellm_params"]
 
             model_name = data["model"]
 
@@ -1695,7 +1571,6 @@ async def _acompletion(  # noqa: PLR0915
                 "client": model_client,
                 **kwargs,
             }
-            input_kwargs.pop("silent_model", None)
 
             _response = litellm.acompletion(**input_kwargs)
 
@@ -1821,11 +1696,8 @@ def _set_deployment_num_retries_on_exception(
 
         litellm_params = deployment.get("litellm_params", {})
         dep_num_retries = litellm_params.get("num_retries")
-        if dep_num_retries is not None:
-            try:
-                exception.num_retries = int(dep_num_retries)  # type: ignore  # Handle both int and str
-            except (ValueError, TypeError):
-                pass  # Skip if value can't be converted to int
+        if dep_num_retries is not None and isinstance(dep_num_retries, int):
+            exception.num_retries = dep_num_retries  # type: ignore
 
     def _update_kwargs_with_default_litellm_params(
         self, kwargs: dict, metadata_variable_name: Optional[str] = "metadata"
@@ -4820,12 +4692,9 @@ async def async_function_with_retries(self, *args, **kwargs):  # noqa: PLR0915
                 # get num_retries from retry policy
                 # Use the model_group captured at the start of the function, or get it from metadata
                 # kwargs.get("model") at this point is the deployment model, not the model_group
-                _model_group_for_retry_policy = (
-                    model_group or _metadata.get("model_group") or kwargs.get("model")
-                )
+                _model_group_for_retry_policy = model_group or _metadata.get("model_group") or kwargs.get("model")
                 _retry_policy_retries = self.get_num_retries_from_retry_policy(
-                    exception=original_exception,
-                    model_group=_model_group_for_retry_policy,
+                    exception=original_exception, model_group=_model_group_for_retry_policy
                 )
                 if _retry_policy_retries is not None:
                     num_retries = _retry_policy_retries
@@ -6010,10 +5879,7 @@ def _add_deployment(self, deployment: Deployment) -> Deployment:
             )
             # done reading model["litellm_params"]
             # Check if provider is supported: either in enum or JSON-configured
-            if (
-                custom_llm_provider not in litellm.provider_list
-                and not JSONProviderRegistry.exists(custom_llm_provider)
-            ):
+            if custom_llm_provider not in litellm.provider_list and not JSONProviderRegistry.exists(custom_llm_provider):
                 raise Exception(f"Unsupported provider - {custom_llm_provider}")
 
         #### DEPLOYMENT NAMES INIT ########
@@ -6489,20 +6355,19 @@ def get_router_model_info(
         elif custom_llm_provider != "azure":
             model = _model
 
-            if "*" in model:  # only call pattern_router for wildcard models
-                potential_models = self.pattern_router.route(received_model_name)
-                if potential_models is not None:
-                    for potential_model in potential_models:
-                        try:
-                            if potential_model.get("model_info", {}).get(
-                                "id"
-                            ) == deployment.get("model_info", {}).get("id"):
-                                model = potential_model.get("litellm_params", {}).get(
-                                    "model"
-                                )
-                                break
-                        except Exception:
-                            pass
+            potential_models = self.pattern_router.route(received_model_name)
+            if "*" in model and potential_models is not None:  # if wildcard route
+                for potential_model in potential_models:
+                    try:
+                        if potential_model.get("model_info", {}).get(
+                            "id"
+                        ) == deployment.get("model_info", {}).get("id"):
+                            model = potential_model.get("litellm_params", {}).get(
+                                "model"
+                            )
+                            break
+                    except Exception:
+                        pass
 
         ## GET LITELLM MODEL INFO - raises exception, if model is not mapped
         if model is None:
@@ -8729,6 +8594,11 @@ def get_allowed_fails_from_policy(self, exception: Exception):
         if allowed_fails_policy is None:
             return None
 
+        if (
+            isinstance(exception, litellm.BadRequestError)
+            and allowed_fails_policy.BadRequestErrorAllowedFails is not None
+        ):
+            return allowed_fails_policy.BadRequestErrorAllowedFails
         if (
             isinstance(exception, litellm.AuthenticationError)
             and allowed_fails_policy.AuthenticationErrorAllowedFails is not None
@@ -8749,11 +8619,6 @@ def get_allowed_fails_from_policy(self, exception: Exception):
             and allowed_fails_policy.ContentPolicyViolationErrorAllowedFails is not None
         ):
             return allowed_fails_policy.ContentPolicyViolationErrorAllowedFails
-        if (
-            isinstance(exception, litellm.BadRequestError)
-            and allowed_fails_policy.BadRequestErrorAllowedFails is not None
-        ):
-            return allowed_fails_policy.BadRequestErrorAllowedFails
 
     def _initialize_alerting(self):
         from litellm.integrations.SlackAlerting.slack_alerting import SlackAlerting
diff --git a/litellm/router_utils/get_retry_from_policy.py b/litellm/router_utils/get_retry_from_policy.py
index ec326ebb50d..48df43ef818 100644
--- a/litellm/router_utils/get_retry_from_policy.py
+++ b/litellm/router_utils/get_retry_from_policy.py
@@ -43,6 +43,11 @@ def get_num_retries_from_retry_policy(
     if isinstance(retry_policy, dict):
         retry_policy = RetryPolicy(**retry_policy)
 
+    if (
+        isinstance(exception, BadRequestError)
+        and retry_policy.BadRequestErrorRetries is not None
+    ):
+        return retry_policy.BadRequestErrorRetries
     if (
         isinstance(exception, AuthenticationError)
         and retry_policy.AuthenticationErrorRetries is not None
@@ -60,11 +65,6 @@ def get_num_retries_from_retry_policy(
         and retry_policy.ContentPolicyViolationErrorRetries is not None
     ):
         return retry_policy.ContentPolicyViolationErrorRetries
-    if (
-        isinstance(exception, BadRequestError)
-        and retry_policy.BadRequestErrorRetries is not None
-    ):
-        return retry_policy.BadRequestErrorRetries
 
 
 def reset_retry_policy() -> RetryPolicy:
diff --git a/litellm/router_utils/search_api_router.py b/litellm/router_utils/search_api_router.py
index 9a1907fa559..247df099462 100644
--- a/litellm/router_utils/search_api_router.py
+++ b/litellm/router_utils/search_api_router.py
@@ -127,9 +127,8 @@ async def async_search_with_fallbacks(
                 model=search_tool_name, kwargs=kwargs, metadata_variable_name="litellm_metadata"
             )
             
-            available_search_tool_names = [tool.get("search_tool_name") for tool in router_instance.search_tools]
             verbose_router_logger.debug(
-                f"Inside SearchAPIRouter.async_search_with_fallbacks() - search_tool_name: {search_tool_name}, Available Search Tools: {available_search_tool_names}, kwargs: {kwargs}"
+                f"Inside SearchAPIRouter.async_search_with_fallbacks() - search_tool_name: {search_tool_name}; kwargs: {kwargs}"
             )
             
             # Use the existing retry/fallback infrastructure
diff --git a/litellm/secret_managers/main.py b/litellm/secret_managers/main.py
index 38405f058c3..a093fe2d2fd 100644
--- a/litellm/secret_managers/main.py
+++ b/litellm/secret_managers/main.py
@@ -17,22 +17,6 @@
 oidc_cache = DualCache()
 
 
-def _get_oidc_http_handler(timeout: Optional[httpx.Timeout] = None) -> HTTPHandler:
-    """
-    Factory function to create HTTPHandler for OIDC requests.
-    This function can be mocked in tests.
-    
-    Args:
-        timeout: Optional timeout for HTTP requests. Defaults to 600.0 seconds with 5.0 connect timeout.
-    
-    Returns:
-        HTTPHandler instance configured for OIDC requests.
-    """
-    if timeout is None:
-        timeout = httpx.Timeout(timeout=600.0, connect=5.0)
-    return HTTPHandler(timeout=timeout)
-
-
 ######### Secret Manager ############################
 # checks if user has passed in a secret manager client
 # if passed in then checks the secret there
@@ -119,7 +103,7 @@ def get_secret(  # noqa: PLR0915
             if oidc_token is not None:
                 return oidc_token
 
-            oidc_client = _get_oidc_http_handler()
+            oidc_client = HTTPHandler(timeout=httpx.Timeout(timeout=600.0, connect=5.0))
             # https://cloud.google.com/compute/docs/instances/verifying-instance-identity#request_signature
             response = oidc_client.get(
                 "http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/identity",
@@ -157,7 +141,7 @@ def get_secret(  # noqa: PLR0915
             if oidc_token is not None:
                 return oidc_token
 
-            oidc_client = _get_oidc_http_handler()
+            oidc_client = HTTPHandler(timeout=httpx.Timeout(timeout=600.0, connect=5.0))
             response = oidc_client.get(
                 actions_id_token_request_url,
                 params={"audience": oidc_aud},
diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py
index ca22049720e..eea0a26b332 100644
--- a/litellm/types/guardrails.py
+++ b/litellm/types/guardrails.py
@@ -20,9 +20,6 @@
 from litellm.types.proxy.guardrails.guardrail_hooks.qualifire import (
     QualifireGuardrailConfigModel,
 )
-from litellm.types.proxy.guardrails.guardrail_hooks.litellm_content_filter import (
-    ContentFilterCategoryConfig,
-)
 
 """
 Pydantic object defining how to set guardrails on litellm proxy
@@ -550,27 +547,9 @@ class ContentFilterConfigModel(BaseModel):
     blocked_words_file: Optional[str] = Field(
         default=None, description="Path to YAML file containing blocked_words list"
     )
-    categories: Optional[List[ContentFilterCategoryConfig]] = Field(
-        default=None,
-        description="List of prebuilt categories to enable (harmful_*, bias_*)",
-    )
-    severity_threshold: Optional[str] = Field(
-        default=None,
-        description="Minimum severity to block (high, medium, low)",
-    )
-    pattern_redaction_format: Optional[str] = Field(
-        default=None,
-        description="Format string for pattern redaction (use {pattern_name} placeholder)",
-    )
-    keyword_redaction_tag: Optional[str] = Field(
-        default=None,
-        description="Tag to use for keyword redaction",
-    )
 
 
-class BaseLitellmParams(
-    ContentFilterConfigModel
-):  # works for new and patch update guardrails
+class BaseLitellmParams(BaseModel):  # works for new and patch update guardrails
     api_key: Optional[str] = Field(
         default=None, description="API key for the guardrail service"
     )
@@ -651,6 +630,7 @@ class BaseLitellmParams(
         description="Whether to fail the request if Model Armor encounters an error",
     )
 
+    # Generic Guardrail API params
     additional_provider_specific_params: Optional[Dict[str, Any]] = Field(
         default=None,
         description="Additional provider-specific parameters for generic guardrail APIs",
@@ -677,6 +657,7 @@ class LitellmParams(
     ToolPermissionGuardrailConfigModel,
     ZscalerAIGuardConfigModel,
     JavelinGuardrailConfigModel,
+    ContentFilterConfigModel,
     BaseLitellmParams,
     EnkryptAIGuardrailConfigs,
     IBMGuardrailsBaseConfigModel,
diff --git a/litellm/types/integrations/datadog_cost_management.py b/litellm/types/integrations/datadog_cost_management.py
deleted file mode 100644
index fe04f43ea03..00000000000
--- a/litellm/types/integrations/datadog_cost_management.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from typing import Dict, Optional, TypedDict
-
-
-from litellm.types.integrations.custom_logger import StandardCustomLoggerInitParams
-
-
-class DatadogCostManagementInitParams(StandardCustomLoggerInitParams):
-    """
-    Init params for Datadog Cost Management
-    """
-
-    datadog_cost_management_params: Optional[Dict] = None
-
-
-class DatadogFOCUSCostEntry(TypedDict):
-    """
-    Represents a single cost line item in the FOCUS format.
-    Ref: https://focus.finops.org/#specification
-    """
-
-    ProviderName: str
-    ChargeDescription: str
-    ChargePeriodStart: str
-    ChargePeriodEnd: str
-    BilledCost: float
-    BillingCurrency: str
-    Tags: Optional[Dict[str, str]]
diff --git a/litellm/types/integrations/prometheus.py b/litellm/types/integrations/prometheus.py
index 146c390363b..fd9b722287d 100644
--- a/litellm/types/integrations/prometheus.py
+++ b/litellm/types/integrations/prometheus.py
@@ -150,9 +150,6 @@ class UserAPIKeyLabelNames(Enum):
     FALLBACK_MODEL = "fallback_model"
     ROUTE = "route"
     MODEL_GROUP = "model_group"
-    CLIENT_IP = "client_ip"
-    USER_AGENT = "user_agent"
-    CALLBACK_NAME = "callback_name"
 
 
 DEFINED_PROMETHEUS_METRICS = Literal[
@@ -199,12 +196,6 @@ class UserAPIKeyLabelNames(Enum):
     "litellm_cache_hits_metric",
     "litellm_cache_misses_metric",
     "litellm_cached_tokens_metric",
-    "litellm_deployment_tpm_limit",
-    "litellm_deployment_rpm_limit",
-    "litellm_remaining_api_key_requests_for_model",
-    "litellm_remaining_api_key_tokens_for_model",
-    "litellm_llm_api_failed_requests_metric",
-    "litellm_callback_logging_failures_metric",
 ]
 
 
@@ -218,7 +209,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.REQUESTED_MODEL.value,
         UserAPIKeyLabelNames.END_USER.value,
         UserAPIKeyLabelNames.USER.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_llm_api_time_to_first_token_metric = [
@@ -227,10 +217,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.API_KEY_ALIAS.value,
         UserAPIKeyLabelNames.TEAM.value,
         UserAPIKeyLabelNames.TEAM_ALIAS.value,
-        UserAPIKeyLabelNames.REQUESTED_MODEL.value,
-        UserAPIKeyLabelNames.END_USER.value,
-        UserAPIKeyLabelNames.USER.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_request_total_latency_metric = [
@@ -242,7 +228,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.TEAM_ALIAS.value,
         UserAPIKeyLabelNames.USER.value,
         UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_request_queue_time_seconds = [
@@ -254,7 +239,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.TEAM_ALIAS.value,
         UserAPIKeyLabelNames.USER.value,
         UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     # Guardrail metrics - these use custom labels (guardrail_name, status, error_type, hook_type)
@@ -274,9 +258,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.STATUS_CODE.value,
         UserAPIKeyLabelNames.USER_EMAIL.value,
         UserAPIKeyLabelNames.ROUTE.value,
-        UserAPIKeyLabelNames.CLIENT_IP.value,
-        UserAPIKeyLabelNames.USER_AGENT.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_proxy_failed_requests_metric = [
@@ -291,9 +272,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.EXCEPTION_STATUS.value,
         UserAPIKeyLabelNames.EXCEPTION_CLASS.value,
         UserAPIKeyLabelNames.ROUTE.value,
-        UserAPIKeyLabelNames.CLIENT_IP.value,
-        UserAPIKeyLabelNames.USER_AGENT.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_deployment_latency_per_output_token = [
@@ -314,7 +292,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value,
         UserAPIKeyLabelNames.API_KEY_HASH.value,
         UserAPIKeyLabelNames.API_KEY_ALIAS.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_remaining_requests_metric = [
@@ -324,7 +301,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value,
         UserAPIKeyLabelNames.API_KEY_HASH.value,
         UserAPIKeyLabelNames.API_KEY_ALIAS.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_remaining_tokens_metric = [
@@ -334,7 +310,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value,
         UserAPIKeyLabelNames.API_KEY_HASH.value,
         UserAPIKeyLabelNames.API_KEY_ALIAS.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_requests_metric = [
@@ -346,9 +321,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.TEAM_ALIAS.value,
         UserAPIKeyLabelNames.USER.value,
         UserAPIKeyLabelNames.USER_EMAIL.value,
-        UserAPIKeyLabelNames.CLIENT_IP.value,
-        UserAPIKeyLabelNames.USER_AGENT.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_spend_metric = [
@@ -360,9 +332,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.TEAM_ALIAS.value,
         UserAPIKeyLabelNames.USER.value,
         UserAPIKeyLabelNames.USER_EMAIL.value,
-        UserAPIKeyLabelNames.CLIENT_IP.value,
-        UserAPIKeyLabelNames.USER_AGENT.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_input_tokens_metric = [
@@ -375,7 +344,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.USER.value,
         UserAPIKeyLabelNames.USER_EMAIL.value,
         UserAPIKeyLabelNames.REQUESTED_MODEL.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_total_tokens_metric = [
@@ -388,7 +356,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.USER.value,
         UserAPIKeyLabelNames.USER_EMAIL.value,
         UserAPIKeyLabelNames.REQUESTED_MODEL.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_output_tokens_metric = [
@@ -401,7 +368,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.USER.value,
         UserAPIKeyLabelNames.USER_EMAIL.value,
         UserAPIKeyLabelNames.REQUESTED_MODEL.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_deployment_state = [
@@ -411,15 +377,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.API_PROVIDER.value,
     ]
 
-    litellm_deployment_tpm_limit = [
-        UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
-        UserAPIKeyLabelNames.API_BASE.value,
-        UserAPIKeyLabelNames.API_PROVIDER.value,
-    ]
-
-    litellm_deployment_rpm_limit = litellm_deployment_tpm_limit
-
     litellm_deployment_cooled_down = [
         UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value,
         UserAPIKeyLabelNames.MODEL_ID.value,
@@ -437,7 +394,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.TEAM_ALIAS.value,
         UserAPIKeyLabelNames.EXCEPTION_STATUS.value,
         UserAPIKeyLabelNames.EXCEPTION_CLASS.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_deployment_failed_fallbacks = litellm_deployment_successful_fallbacks
@@ -480,26 +436,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.USER.value,
     ]
 
-    litellm_user_budget_remaining_hours_metric = [
-        UserAPIKeyLabelNames.USER.value,
-    ]
-
-    litellm_remaining_api_key_requests_for_model = [
-        UserAPIKeyLabelNames.API_KEY_HASH.value,
-        UserAPIKeyLabelNames.API_KEY_ALIAS.value,
-        UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
-    ]
-
-    litellm_remaining_api_key_tokens_for_model = [
-        UserAPIKeyLabelNames.API_KEY_HASH.value,
-        UserAPIKeyLabelNames.API_KEY_ALIAS.value,
-        UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
-    ]
-
-    litellm_callback_logging_failures_metric = [
-        UserAPIKeyLabelNames.CALLBACK_NAME.value,
-    ]
-
     # Add deployment metrics
     litellm_deployment_failure_responses = [
         UserAPIKeyLabelNames.REQUESTED_MODEL.value,
@@ -513,8 +449,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.API_KEY_ALIAS.value,
         UserAPIKeyLabelNames.TEAM.value,
         UserAPIKeyLabelNames.TEAM_ALIAS.value,
-        UserAPIKeyLabelNames.CLIENT_IP.value,
-        UserAPIKeyLabelNames.USER_AGENT.value,
     ]
 
     litellm_deployment_total_requests = [
@@ -527,37 +461,10 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.API_KEY_ALIAS.value,
         UserAPIKeyLabelNames.TEAM.value,
         UserAPIKeyLabelNames.TEAM_ALIAS.value,
-        UserAPIKeyLabelNames.CLIENT_IP.value,
-        UserAPIKeyLabelNames.USER_AGENT.value,
     ]
 
     litellm_deployment_success_responses = litellm_deployment_total_requests
 
-    litellm_remaining_api_key_requests_for_model = [
-        UserAPIKeyLabelNames.API_KEY_HASH.value,
-        UserAPIKeyLabelNames.API_KEY_ALIAS.value,
-        UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
-    ]
-
-    litellm_remaining_api_key_tokens_for_model = [
-        UserAPIKeyLabelNames.API_KEY_HASH.value,
-        UserAPIKeyLabelNames.API_KEY_ALIAS.value,
-        UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
-    ]
-
-    litellm_llm_api_failed_requests_metric = [
-        UserAPIKeyLabelNames.END_USER.value,
-        UserAPIKeyLabelNames.API_KEY_HASH.value,
-        UserAPIKeyLabelNames.API_KEY_ALIAS.value,
-        UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
-        UserAPIKeyLabelNames.TEAM.value,
-        UserAPIKeyLabelNames.TEAM_ALIAS.value,
-        UserAPIKeyLabelNames.USER.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
-    ]
-
     # Buffer monitoring metrics - these typically don't need additional labels
     litellm_pod_lock_manager_size: List[str] = []
 
@@ -578,7 +485,6 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.TEAM_ALIAS.value,
         UserAPIKeyLabelNames.END_USER.value,
         UserAPIKeyLabelNames.USER.value,
-        UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
     litellm_cache_hits_metric = _cache_metric_labels
@@ -671,12 +577,6 @@ class UserAPIKeyLabelValues(BaseModel):
     route: Annotated[
         Optional[str], Field(..., alias=UserAPIKeyLabelNames.ROUTE.value)
     ] = None
-    client_ip: Annotated[
-        Optional[str], Field(..., alias=UserAPIKeyLabelNames.CLIENT_IP.value)
-    ] = None
-    user_agent: Annotated[
-        Optional[str], Field(..., alias=UserAPIKeyLabelNames.USER_AGENT.value)
-    ] = None
 
 
 class PrometheusMetricsConfig(BaseModel):
diff --git a/litellm/types/llms/bedrock.py b/litellm/types/llms/bedrock.py
index a85aaafe23d..ef2f1ba4d5e 100644
--- a/litellm/types/llms/bedrock.py
+++ b/litellm/types/llms/bedrock.py
@@ -93,67 +93,6 @@ class GuardrailConverseContentBlock(TypedDict, total=False):
     text: GuardrailConverseTextBlock
 
 
-class CitationWebLocationBlock(TypedDict, total=False):
-    """
-    Web location block for Nova grounding citations.
-    Contains the URL and domain from web search results.
-
-    Reference: https://docs.aws.amazon.com/nova/latest/userguide/grounding.html
-    """
-
-    url: str
-    domain: str
-
-
-class CitationLocationBlock(TypedDict, total=False):
-    """
-    Location block containing the web location for a citation.
-    """
-
-    web: CitationWebLocationBlock
-
-
-class CitationReferenceBlock(TypedDict, total=False):
-    """
-    Citation reference block containing a single citation with its location.
-
-    Each citation contains:
-    - location.web.url: The URL of the source
-    - location.web.domain: The domain of the source
-    """
-
-    location: CitationLocationBlock
-
-
-class CitationsContentBlock(TypedDict, total=False):
-    """
-    Citations content block returned by Nova grounding (web search) tool.
-
-    When Nova grounding is enabled via systemTool, the model may return
-    citationsContent blocks containing web search citation references.
-
-    Reference: https://docs.aws.amazon.com/nova/latest/userguide/grounding.html
-
-    Example response structure:
-        {
-            "citationsContent": {
-                "citations": [
-                    {
-                        "location": {
-                            "web": {
-                                "url": "https://example.com/article",
-                                "domain": "example.com"
-                            }
-                        }
-                    }
-                ]
-            }
-        }
-    """
-
-    citations: List[CitationReferenceBlock]
-
-
 class ContentBlock(TypedDict, total=False):
     text: str
     image: ImageBlock
@@ -164,7 +103,6 @@ class ContentBlock(TypedDict, total=False):
     cachePoint: CachePointBlock
     reasoningContent: BedrockConverseReasoningContentBlock
     guardContent: GuardrailConverseContentBlock
-    citationsContent: CitationsContentBlock
 
 
 class MessageBlock(TypedDict):
@@ -221,24 +159,8 @@ class ToolSpecBlock(TypedDict, total=False):
     description: str
 
 
-class SystemToolBlock(TypedDict, total=False):
-    """
-    System tool block for Nova grounding and other built-in tools.
-
-    Example:
-        {
-            "systemTool": {
-                "name": "nova_grounding"
-            }
-        }
-    """
-
-    name: Required[str]
-
-
 class ToolBlock(TypedDict, total=False):
     toolSpec: Optional[ToolSpecBlock]
-    systemTool: Optional[SystemToolBlock]
     cachePoint: Optional[CachePointBlock]
 
 
@@ -288,13 +210,11 @@ class ContentBlockStartEvent(TypedDict, total=False):
 class ContentBlockDeltaEvent(TypedDict, total=False):
     """
     Either 'text' or 'toolUse' will be specified for Converse API streaming response.
-    May also include 'citationsContent' when Nova grounding is enabled.
     """
 
     text: str
     toolUse: ToolBlockDeltaEvent
     reasoningContent: BedrockConverseReasoningContentBlockDelta
-    citationsContent: CitationsContentBlock
 
 
 class PerformanceConfigBlock(TypedDict):
@@ -959,8 +879,3 @@ class BedrockGetBatchResponse(TypedDict, total=False):
     outputDataConfig: BedrockOutputDataConfig
     timeoutDurationInHours: Optional[int]
     clientRequestToken: Optional[str]
-
-class BedrockToolBlock(TypedDict, total=False):
-    toolSpec: Optional[ToolSpecBlock]
-    systemTool: Optional[SystemToolBlock]  # For Nova grounding
-    cachePoint: Optional[CachePointBlock]
diff --git a/litellm/types/llms/oci.py b/litellm/types/llms/oci.py
index 9a654bc0f6c..b9a82cc8b73 100644
--- a/litellm/types/llms/oci.py
+++ b/litellm/types/llms/oci.py
@@ -35,18 +35,11 @@ class OCITextContentPart(OCIContentPart):
     text: str
 
 
-class OCIImageUrl(BaseModel):
-    """ImageUrl object for OCI API. See: https://docs.oracle.com/en-us/iaas/tools/python/latest/api/generative_ai_inference/models/oci.generative_ai_inference.models.ImageUrl.html"""
-
-    url: str
-    detail: Optional[Literal["AUTO", "HIGH", "LOW"]] = None
-
-
 class OCIImageContentPart(OCIContentPart):
     """Image content part for the OCI API."""
 
     type: Literal["IMAGE"] = "IMAGE"
-    imageUrl: OCIImageUrl
+    imageUrl: str
 
 
 OCIContentPartUnion = Union[OCITextContentPart, OCIImageContentPart]
diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py
index 299b47199ed..9fea1a8b937 100644
--- a/litellm/types/llms/openai.py
+++ b/litellm/types/llms/openai.py
@@ -1114,7 +1114,7 @@ class ResponsesAPIRequestParams(ResponsesAPIOptionalRequestParams, total=False):
 
 
 class OutputTokensDetails(BaseLiteLLMOpenAIResponseObject):
-    reasoning_tokens: int = 0
+    reasoning_tokens: Optional[int] = None
 
     text_tokens: Optional[int] = None
 
@@ -1123,7 +1123,7 @@ class OutputTokensDetails(BaseLiteLLMOpenAIResponseObject):
 
 class InputTokensDetails(BaseLiteLLMOpenAIResponseObject):
     audio_tokens: Optional[int] = None
-    cached_tokens: int = 0
+    cached_tokens: Optional[int] = None
     text_tokens: Optional[int] = None
 
     model_config = {"extra": "allow"}
@@ -1193,7 +1193,7 @@ class ResponsesAPIResponse(BaseLiteLLMOpenAIResponseObject):
     status: Optional[str] = None
     text: Optional[Union["ResponseText", Dict[str, Any]]] = None
     truncation: Optional[Literal["auto", "disabled"]] = None
-    usage: Optional[ResponseAPIUsage] = None
+    usage: Optional[Any] = None
     user: Optional[str] = None
     store: Optional[bool] = None
     # Define private attributes using PrivateAttr
diff --git a/litellm/types/llms/xai.py b/litellm/types/llms/xai.py
deleted file mode 100644
index 9de8dfc7add..00000000000
--- a/litellm/types/llms/xai.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from typing import List, Literal, Optional, TypedDict
-
-
-class XAIWebSearchFilters(TypedDict, total=False):
-    """Filters for XAI web search tool"""
-    allowed_domains: Optional[List[str]]  # Max 5 domains
-    excluded_domains: Optional[List[str]]  # Max 5 domains
-    
-class XAIWebSearchTool(TypedDict, total=False):
-    """XAI web search tool configuration"""
-    type: Literal["web_search"]
-    filters: Optional[XAIWebSearchFilters]
-    enable_image_understanding: Optional[bool]
-
-class XAIXSearchTool(TypedDict, total=False):
-    """XAI X (Twitter) search tool configuration"""
-    type: Literal["x_search"]
-    allowed_x_handles: Optional[List[str]]  # Max 10 handles
-    excluded_x_handles: Optional[List[str]]  # Max 10 handles
-    from_date: Optional[str]  # ISO8601 format: YYYY-MM-DD
-    to_date: Optional[str]  # ISO8601 format: YYYY-MM-DD
-    enable_image_understanding: Optional[bool]
-    enable_video_understanding: Optional[bool]
\ No newline at end of file
diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/onyx.py b/litellm/types/proxy/guardrails/guardrail_hooks/onyx.py
index 42d7e94829f..aa5b9d7a3fc 100644
--- a/litellm/types/proxy/guardrails/guardrail_hooks/onyx.py
+++ b/litellm/types/proxy/guardrails/guardrail_hooks/onyx.py
@@ -16,11 +16,6 @@ class OnyxGuardrailConfigModel(GuardrailConfigModel):
         description="The API key for the Onyx Guard server. If not provided, the `ONYX_API_KEY` environment variable is checked.",
     )
 
-    timeout: Optional[float] = Field(
-        default=None,
-        description="The timeout for the Onyx Guard server in seconds. If not provided, the `ONYX_TIMEOUT` environment variable is checked.",
-    )
-
     @staticmethod
     def ui_friendly_name() -> str:
         return "Onyx Guardrail"
diff --git a/litellm/types/proxy/management_endpoints/key_management_endpoints.py b/litellm/types/proxy/management_endpoints/key_management_endpoints.py
deleted file mode 100644
index b1d25455d18..00000000000
--- a/litellm/types/proxy/management_endpoints/key_management_endpoints.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from typing import Any, Dict, List, Optional
-
-from pydantic import BaseModel
-
-
-class BulkUpdateKeyRequestItem(BaseModel):
-    """Individual key update request item"""
-
-    key: str  # Key identifier (token)
-    budget_id: Optional[str] = None  # Budget ID associated with the key
-    max_budget: Optional[float] = None  # Max budget for key
-    team_id: Optional[str] = None  # Team ID associated with key
-    tags: Optional[List[str]] = None  # Tags for organizing keys
-
-
-class BulkUpdateKeyRequest(BaseModel):
-    """Request for bulk key updates"""
-
-    keys: List[BulkUpdateKeyRequestItem]
-
-
-class SuccessfulKeyUpdate(BaseModel):
-    """Successfully updated key with its updated information"""
-
-    key: str
-    key_info: Dict[str, Any]
-
-
-class FailedKeyUpdate(BaseModel):
-    """Failed key update with reason"""
-
-    key: str
-    key_info: Optional[Dict[str, Any]] = None
-    failed_reason: str
-
-
-class BulkUpdateKeyResponse(BaseModel):
-    """Response for bulk key update operations"""
-
-    total_requested: int
-    successful_updates: List[SuccessfulKeyUpdate]
-    failed_updates: List[FailedKeyUpdate]
diff --git a/litellm/types/rag.py b/litellm/types/rag.py
index cae07708686..fe237a13431 100644
--- a/litellm/types/rag.py
+++ b/litellm/types/rag.py
@@ -129,53 +129,9 @@ class VertexAIVectorStoreOptions(TypedDict, total=False):
     import_timeout: Optional[int]  # Timeout in seconds (default: 600)
 
 
-class S3VectorsVectorStoreOptions(TypedDict, total=False):
-    """
-    AWS S3 Vectors configuration.
-
-    Example (auto-create):
-        {"custom_llm_provider": "s3_vectors", "vector_bucket_name": "my-embeddings"}
-
-    Example (use existing):
-        {"custom_llm_provider": "s3_vectors", "vector_bucket_name": "my-embeddings",
-         "index_name": "my-index"}
-
-    Example (with credentials):
-        {"custom_llm_provider": "s3_vectors", "vector_bucket_name": "my-embeddings",
-         "litellm_credential_name": "my-aws-creds"}
-
-    Auto-creation creates: S3 vector bucket and vector index (if not provided).
-    Embeddings are generated using LiteLLM's embedding API (supports any provider).
-    """
-
-    custom_llm_provider: Literal["s3_vectors"]
-    vector_bucket_name: str  # Required - S3 vector bucket name
-    index_name: Optional[str]  # Vector index name (auto-creates if not provided)
-
-    # Index configuration (for auto-creation)
-    dimension: Optional[int]  # Vector dimension (auto-detected from embedding model, or default: 1024)
-    distance_metric: Optional[Literal["cosine", "euclidean"]]  # Default: cosine
-    non_filterable_metadata_keys: Optional[List[str]]  # Keys excluded from filtering (e.g., ["source_text"])
-
-    # Credentials (loaded from litellm.credential_list if litellm_credential_name is provided)
-    litellm_credential_name: Optional[str]  # Credential name to load from litellm.credential_list
-
-    # AWS auth (uses BaseAWSLLM)
-    aws_access_key_id: Optional[str]
-    aws_secret_access_key: Optional[str]
-    aws_session_token: Optional[str]
-    aws_region_name: Optional[str]  # default: us-west-2
-    aws_role_name: Optional[str]
-    aws_session_name: Optional[str]
-    aws_profile_name: Optional[str]
-    aws_web_identity_token: Optional[str]
-    aws_sts_endpoint: Optional[str]
-    aws_external_id: Optional[str]
-
-
 # Union type for vector store options
 RAGIngestVectorStoreOptions = Union[
-    OpenAIVectorStoreOptions, BedrockVectorStoreOptions, VertexAIVectorStoreOptions, S3VectorsVectorStoreOptions
+    OpenAIVectorStoreOptions, BedrockVectorStoreOptions, VertexAIVectorStoreOptions
 ]
 
 
diff --git a/litellm/types/router.py b/litellm/types/router.py
index 43943d9e07e..8ea7a207535 100644
--- a/litellm/types/router.py
+++ b/litellm/types/router.py
@@ -404,10 +404,6 @@ class LiteLLMParamsTypedDict(TypedDict, total=False):
     aws_access_key_id: Optional[str]
     aws_secret_access_key: Optional[str]
     aws_region_name: Optional[str]
-    ## AWS S3 VECTORS ##
-    vector_bucket_name: Optional[str]
-    index_name: Optional[str]
-    embedding_model: Optional[str]
     ## IBM WATSONX ##
     watsonx_region_name: Optional[str]
     ## CUSTOM PRICING ##
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index 49c903502d1..0ec79e90a1f 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -3,28 +3,25 @@
 from enum import Enum
 from typing import TYPE_CHECKING, Any, Dict, List, Literal, Mapping, Optional, Union
 
+from aiohttp import FormData
 from openai._models import BaseModel as OpenAIObject
-from openai.types.audio.transcription_create_params import (
-    FileTypes as FileTypes,  # type: ignore
-)
-from openai.types.chat.chat_completion import ChatCompletion as ChatCompletion
+from openai.types.audio.transcription_create_params import FileTypes  # type: ignore
+from openai.types.chat.chat_completion import ChatCompletion
 from openai.types.completion_usage import (
     CompletionTokensDetails,
     CompletionUsage,
     PromptTokensDetails,
 )
-from openai.types.moderation import Categories as Categories
 from openai.types.moderation import (
-    CategoryAppliedInputTypes as CategoryAppliedInputTypes,
-)
-from openai.types.moderation import CategoryScores as CategoryScores
-from openai.types.moderation_create_response import Moderation as Moderation
-from openai.types.moderation_create_response import (
-    ModerationCreateResponse as ModerationCreateResponse,
+    Categories,
+    CategoryAppliedInputTypes,
+    CategoryScores,
 )
+from openai.types.moderation_create_response import Moderation, ModerationCreateResponse
 from pydantic import BaseModel, ConfigDict, Field, PrivateAttr, model_validator
-from typing_extensions import Required, TypedDict
+from typing_extensions import Callable, Dict, Required, TypedDict, override
 
+import litellm
 from litellm._uuid import uuid
 from litellm.types.llms.base import (
     BaseLiteLLMOpenAIResponseObject,
@@ -55,7 +52,7 @@
     ResponsesAPIResponse,
     WebSearchOptions,
 )
-from .rerank import RerankResponse as RerankResponse
+from .rerank import RerankResponse
 
 if TYPE_CHECKING:
     from .vector_stores import VectorStoreSearchResponse
@@ -1414,7 +1411,7 @@ class Usage(SafeAttributeModel, CompletionUsage):
     prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
     """Breakdown of tokens used in the prompt."""
 
-    def __init__(  # noqa: PLR0915
+    def __init__(
         self,
         prompt_tokens: Optional[int] = None,
         completion_tokens: Optional[int] = None,
@@ -2504,7 +2501,6 @@ class StandardLoggingMetadata(StandardLoggingUserAPIKeyMetadata):
         dict
     ]  # special param to log k,v pairs to spendlogs for a call
     requester_ip_address: Optional[str]
-    user_agent: Optional[str]
     requester_metadata: Optional[dict]
     requester_custom_headers: Optional[
         Dict[str, str]
@@ -2690,7 +2686,6 @@ class StandardLoggingPayload(TypedDict):
     request_tags: list
     end_user: Optional[str]
     requester_ip_address: Optional[str]
-    user_agent: Optional[str]
     messages: Optional[Union[str, list, dict]]
     response: Optional[Union[str, list, dict]]
     error_str: Optional[str]
@@ -3077,7 +3072,6 @@ class LlmProviders(str, Enum):
     LLAMA = "meta_llama"
     NSCALE = "nscale"
     PG_VECTOR = "pg_vector"
-    S3_VECTORS = "s3_vectors"
     HELICONE = "helicone"
     HYPERBOLIC = "hyperbolic"
     RECRAFT = "recraft"
diff --git a/litellm/types/vector_stores.py b/litellm/types/vector_stores.py
index 58a6a3272cd..a4ceb2c9ac7 100644
--- a/litellm/types/vector_stores.py
+++ b/litellm/types/vector_stores.py
@@ -38,10 +38,6 @@ class LiteLLM_ManagedVectorStore(TypedDict, total=False):
 
     # litellm_params
     litellm_params: Optional[Dict[str, Any]]
-    
-    # access control fields
-    team_id: Optional[str]
-    user_id: Optional[str]
 
 
 class LiteLLM_ManagedVectorStoreListResponse(TypedDict, total=False):
diff --git a/litellm/utils.py b/litellm/utils.py
index d7fb4855a48..1865d364586 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -5135,15 +5135,10 @@ def _invalidate_model_cost_lowercase_map() -> None:
     """Invalidate the case-insensitive lookup map for model_cost.
 
     Call this whenever litellm.model_cost is modified to ensure the map is rebuilt.
-    Also clears related LRU caches that depend on model_cost data.
     """
     global _model_cost_lowercase_map
     _model_cost_lowercase_map = None
 
-    # Clear LRU caches that depend on model_cost data
-    get_model_info.cache_clear()
-    _cached_get_model_info_helper.cache_clear()
-
 
 def _rebuild_model_cost_lowercase_map() -> Dict[str, str]:
     """Rebuild the case-insensitive lookup map from the current model_cost.
@@ -5357,7 +5352,6 @@ def _get_max_position_embeddings(model_name: str) -> Optional[int]:
         return None
 
 
-@lru_cache(maxsize=DEFAULT_MAX_LRU_CACHE_SIZE)
 def _cached_get_model_info_helper(
     model: str, custom_llm_provider: Optional[str]
 ) -> ModelInfoBase:
@@ -5705,7 +5699,6 @@ def _get_model_info_helper(  # noqa: PLR0915
         )
 
 
-@lru_cache(maxsize=DEFAULT_MAX_LRU_CACHE_SIZE)
 def get_model_info(model: str, custom_llm_provider: Optional[str] = None) -> ModelInfo:
     """
     Get a dict for the maximum tokens (context window), input_cost_per_token, output_cost_per_token  for a given model.
@@ -8053,12 +8046,6 @@ def get_provider_embedding_config(
             )
 
             return OpenrouterEmbeddingConfig()
-        elif litellm.LlmProviders.VERCEL_AI_GATEWAY == provider:
-            from litellm.llms.vercel_ai_gateway.embedding.transformation import (
-                VercelAIGatewayEmbeddingConfig,
-            )
-
-            return VercelAIGatewayEmbeddingConfig()
         elif litellm.LlmProviders.GIGACHAT == provider:
             return litellm.GigaChatEmbeddingConfig()
         elif litellm.LlmProviders.SAGEMAKER == provider:
@@ -8452,12 +8439,6 @@ def get_provider_vector_stores_config(
             )
 
             return RAGFlowVectorStoreConfig()
-        elif litellm.LlmProviders.S3_VECTORS == provider:
-            from litellm.llms.s3_vectors.vector_stores.transformation import (
-                S3VectorsVectorStoreConfig,
-            )
-
-            return S3VectorsVectorStoreConfig()
         return None
 
     @staticmethod
@@ -8705,9 +8686,9 @@ def get_provider_search_config(
         """
         Get Search configuration for a given provider.
         """
-        from litellm.llms.brave.search.transformation import BraveSearchConfig
         from litellm.llms.dataforseo.search.transformation import DataForSEOSearchConfig
         from litellm.llms.exa_ai.search.transformation import ExaAISearchConfig
+        from litellm.llms.brave.search.transformation import BraveSearchConfig
         from litellm.llms.firecrawl.search.transformation import FirecrawlSearchConfig
         from litellm.llms.google_pse.search.transformation import GooglePSESearchConfig
         from litellm.llms.linkup.search.transformation import LinkupSearchConfig
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index fad5f243fff..209c0794e50 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -3130,7 +3130,7 @@
         "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_system_messages": true,
-        "supports_tool_choice": true,
+        "supports_tool_choice": false,
         "supports_vision": true
     },
     "azure/gpt-5-chat-latest": {
@@ -3162,7 +3162,7 @@
         "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_system_messages": true,
-        "supports_tool_choice": true,
+        "supports_tool_choice": false,
         "supports_vision": true
     },
     "azure/gpt-5-codex": {
@@ -3653,9 +3653,10 @@
         "max_input_tokens": 128000,
         "max_output_tokens": 16384,
         "max_tokens": 16384,
-        "mode": "responses",
+        "mode": "chat",
         "output_cost_per_token": 1.4e-05,
         "supported_endpoints": [
+            "/v1/chat/completions",
             "/v1/responses"
         ],
         "supported_modalities": [
@@ -9787,7 +9788,6 @@
         "supports_tool_choice": true
     },
     "deepinfra/google/gemini-2.0-flash-001": {
-        "deprecation_date": "2026-03-31",
         "max_tokens": 1000000,
         "max_input_tokens": 1000000,
         "max_output_tokens": 1000000,
@@ -10231,48 +10231,6 @@
         "mode": "completion",
         "output_cost_per_token": 5e-07
     },
-    "deepseek-v3-2-251201": {
-        "input_cost_per_token": 0.0,
-        "litellm_provider": "volcengine",
-        "max_input_tokens": 98304,
-        "max_output_tokens": 32768,
-        "max_tokens": 32768,
-        "mode": "chat",
-        "output_cost_per_token": 0.0,
-        "supports_assistant_prefill": true,
-        "supports_function_calling": true,
-        "supports_prompt_caching": true,
-        "supports_reasoning": true,
-        "supports_tool_choice": true
-    },
-    "glm-4-7-251222": {
-        "input_cost_per_token": 0.0,
-        "litellm_provider": "volcengine",
-        "max_input_tokens": 204800,
-        "max_output_tokens": 131072,
-        "max_tokens": 131072,
-        "mode": "chat",
-        "output_cost_per_token": 0.0,
-        "supports_assistant_prefill": true,
-        "supports_function_calling": true,
-        "supports_prompt_caching": true,
-        "supports_reasoning": true,
-        "supports_tool_choice": true
-    },
-    "kimi-k2-thinking-251104": {
-        "input_cost_per_token": 0.0,
-        "litellm_provider": "volcengine",
-        "max_input_tokens": 229376,
-        "max_output_tokens": 32768,
-        "max_tokens": 32768,
-        "mode": "chat",
-        "output_cost_per_token": 0.0,
-        "supports_assistant_prefill": true,
-        "supports_function_calling": true,
-        "supports_prompt_caching": true,
-        "supports_reasoning": true,
-        "supports_tool_choice": true
-    },
     "doubao-embedding": {
         "input_cost_per_token": 0.0,
         "litellm_provider": "volcengine",
@@ -12147,7 +12105,6 @@
     },
     "gemini-2.0-flash": {
         "cache_read_input_token_cost": 2.5e-08,
-        "deprecation_date": "2026-03-31",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -12187,7 +12144,7 @@
     },
     "gemini-2.0-flash-001": {
         "cache_read_input_token_cost": 3.75e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-02-05",
         "input_cost_per_audio_token": 1e-06,
         "input_cost_per_token": 1.5e-07,
         "litellm_provider": "vertex_ai-language-models",
@@ -12273,7 +12230,6 @@
     },
     "gemini-2.0-flash-lite": {
         "cache_read_input_token_cost": 1.875e-08,
-        "deprecation_date": "2026-03-31",
         "input_cost_per_audio_token": 7.5e-08,
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "vertex_ai-language-models",
@@ -12309,7 +12265,7 @@
     },
     "gemini-2.0-flash-lite-001": {
         "cache_read_input_token_cost": 1.875e-08,
-        "deprecation_date": "2026-03-31",
+        "deprecation_date": "2026-02-25",
         "input_cost_per_audio_token": 7.5e-08,
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "vertex_ai-language-models",
@@ -13521,79 +13477,6 @@
         "supports_vision": true,
         "supports_web_search": true
     },
-    "gemini-robotics-er-1.5-preview": {
-        "cache_read_input_token_cost": 0,
-        "input_cost_per_token": 3e-07,
-        "input_cost_per_audio_token": 1e-06,
-        "litellm_provider": "vertex_ai-language-models",
-        "max_input_tokens": 1048576,
-        "max_output_tokens": 65535,
-        "max_tokens": 65535,
-        "mode": "chat",
-        "output_cost_per_token": 2.5e-06,
-        "output_cost_per_reasoning_token": 2.5e-06,
-        "source": "https://ai.google.dev/gemini-api/docs/models#gemini-robotics-er-1-5-preview",
-        "supported_endpoints": [
-            "/v1/chat/completions",
-            "/v1/completions"
-        ],
-        "supported_modalities": [
-            "text",
-            "image",
-            "video",
-            "audio"
-        ],
-        "supported_output_modalities": [
-            "text"
-        ],
-        "supports_audio_output": false,
-        "supports_function_calling": true,
-        "supports_parallel_function_calling": true,
-        "supports_prompt_caching": false,
-        "supports_reasoning": true,
-        "supports_response_schema": true,
-        "supports_system_messages": true,
-        "supports_tool_choice": true,
-        "supports_url_context": true,
-        "supports_vision": true
-    },
-    "gemini/gemini-robotics-er-1.5-preview": {
-        "cache_read_input_token_cost": 0,
-        "input_cost_per_token": 3e-07,
-        "input_cost_per_audio_token": 1e-06,
-        "litellm_provider": "gemini",
-        "max_input_tokens": 1048576,
-        "max_output_tokens": 65535,
-        "max_tokens": 65535,
-        "mode": "chat",
-        "output_cost_per_token": 2.5e-06,
-        "output_cost_per_reasoning_token": 2.5e-06,
-        "source": "https://ai.google.dev/gemini-api/docs/models#gemini-robotics-er-1-5-preview",
-        "supported_endpoints": [
-            "/v1/chat/completions",
-            "/v1/completions"
-        ],
-        "supported_modalities": [
-            "text",
-            "image",
-            "video",
-            "audio"
-        ],
-        "supported_output_modalities": [
-            "text"
-        ],
-        "supports_audio_output": false,
-        "supports_function_calling": true,
-        "supports_parallel_function_calling": true,
-        "supports_prompt_caching": false,
-        "supports_reasoning": true,
-        "supports_response_schema": true,
-        "supports_system_messages": true,
-        "supports_tool_choice": true,
-        "supports_url_context": true,
-        "supports_vision": true,
-        "supports_web_search": true
-    },
     "gemini-2.5-computer-use-preview-10-2025": {
         "input_cost_per_token": 1.25e-06,
         "input_cost_per_token_above_200k_tokens": 2.5e-06,
@@ -14047,7 +13930,6 @@
     },
     "gemini/gemini-2.0-flash": {
         "cache_read_input_token_cost": 2.5e-08,
-        "deprecation_date": "2026-03-31",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "gemini",
@@ -14088,7 +13970,6 @@
     },
     "gemini/gemini-2.0-flash-001": {
         "cache_read_input_token_cost": 2.5e-08,
-        "deprecation_date": "2026-03-31",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "gemini",
@@ -14176,7 +14057,6 @@
     },
     "gemini/gemini-2.0-flash-lite": {
         "cache_read_input_token_cost": 1.875e-08,
-        "deprecation_date": "2026-03-31",
         "input_cost_per_audio_token": 7.5e-08,
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "gemini",
@@ -20663,7 +20543,6 @@
         "supports_function_calling": true,
         "supports_tool_choice": true,
         "supports_prompt_caching": true,
-        "supports_reasoning": true,
         "supports_system_messages": true,
         "max_input_tokens": 1000000,
         "max_output_tokens": 8192
@@ -20678,7 +20557,6 @@
         "supports_function_calling": true,
         "supports_tool_choice": true,
         "supports_prompt_caching": true,
-        "supports_reasoning": true,
         "supports_system_messages": true,
         "max_input_tokens": 1000000,
         "max_output_tokens": 8192
@@ -20693,7 +20571,6 @@
         "supports_function_calling": true,
         "supports_tool_choice": true,
         "supports_prompt_caching": true,
-        "supports_reasoning": true,
         "supports_system_messages": true,
         "max_input_tokens": 200000,
         "max_output_tokens": 8192
@@ -23281,7 +23158,6 @@
         "supports_tool_choice": true
     },
     "openrouter/google/gemini-2.0-flash-001": {
-        "deprecation_date": "2026-03-31",
         "input_cost_per_audio_token": 7e-07,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "openrouter",
@@ -23578,7 +23454,7 @@
         "mode": "chat",
         "output_cost_per_token": 1.02e-06,
         "supports_function_calling": true,
-        "supports_prompt_caching": true,
+        "supports_prompt_caching": false,
         "supports_reasoning": true,
         "supports_tool_choice": true
     },
@@ -23930,11 +23806,8 @@
         "max_input_tokens": 400000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
-        "mode": "responses",
+        "mode": "chat",
         "output_cost_per_token": 1.4e-05,
-        "supported_endpoints": [
-            "/v1/responses"
-        ],
         "supported_modalities": [
             "text",
             "image"
@@ -24273,7 +24146,6 @@
         "output_cost_per_token": 1.75e-06,
         "source": "https://openrouter.ai/z-ai/glm-4.6",
         "supports_function_calling": true,
-        "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
     },
@@ -24287,76 +24159,9 @@
         "output_cost_per_token": 1.9e-06,
         "source": "https://openrouter.ai/z-ai/glm-4.6:exacto",
         "supports_function_calling": true,
-        "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
     },
-    "openrouter/xiaomi/mimo-v2-flash": {
-            "input_cost_per_token": 9e-08,
-            "output_cost_per_token": 2.9e-07,
-            "cache_creation_input_token_cost": 0.0,
-            "cache_read_input_token_cost": 0.0,
-            "litellm_provider": "openrouter",
-            "max_input_tokens": 262144,
-            "max_output_tokens": 16384,
-            "max_tokens": 16384,
-            "mode": "chat",
-            "supports_function_calling": true,
-            "supports_tool_choice": true,
-            "supports_reasoning": true,
-            "supports_vision": false,
-            "supports_prompt_caching": false
-        },
-    "openrouter/z-ai/glm-4.7": {
-        "input_cost_per_token": 4e-07,
-        "output_cost_per_token": 1.5e-06,
-        "cache_creation_input_token_cost": 0.0,
-        "cache_read_input_token_cost": 0.0,
-        "litellm_provider": "openrouter",
-        "max_input_tokens": 202752,
-        "max_output_tokens": 64000,
-        "max_tokens": 64000,
-        "mode": "chat",
-        "supports_function_calling": true,
-        "supports_tool_choice": true,
-        "supports_reasoning": true,
-        "supports_vision": true,
-        "supports_prompt_caching": false,
-        "supports_assistant_prefill": true
-    },
-    "openrouter/z-ai/glm-4.7-flash": {
-        "input_cost_per_token": 7e-08,
-        "output_cost_per_token": 4e-07,
-        "cache_creation_input_token_cost": 0.0,
-        "cache_read_input_token_cost": 0.0,
-        "litellm_provider": "openrouter",
-        "max_input_tokens": 200000,
-        "max_output_tokens": 32000,
-        "max_tokens": 32000,
-        "mode": "chat",
-        "supports_function_calling": true,
-        "supports_tool_choice": true,
-        "supports_reasoning": true,
-        "supports_vision": true,
-        "supports_prompt_caching": false
-    },
-    "openrouter/minimax/minimax-m2.1": {
-        "input_cost_per_token": 2.7e-07,
-        "output_cost_per_token": 1.2e-06,
-        "cache_creation_input_token_cost": 0.0,
-        "cache_read_input_token_cost": 0.0,
-        "litellm_provider": "openrouter",
-        "max_input_tokens": 204000,
-        "max_output_tokens": 64000,
-        "max_tokens": 64000,
-        "mode": "chat",
-        "supports_function_calling": true,
-        "supports_tool_choice": true,
-        "supports_reasoning": true,
-        "supports_vision": true,
-        "supports_prompt_caching": false,
-        "supports_computer_use": false
-    },
     "ovhcloud/DeepSeek-R1-Distill-Llama-70B": {
         "input_cost_per_token": 6.7e-07,
         "litellm_provider": "ovhcloud",
@@ -27890,7 +27695,6 @@
         "output_cost_per_token": 9e-07
     },
     "vercel_ai_gateway/google/gemini-2.0-flash": {
-        "deprecation_date": "2026-03-31",
         "input_cost_per_token": 1.5e-07,
         "litellm_provider": "vercel_ai_gateway",
         "max_input_tokens": 1048576,
@@ -27900,7 +27704,6 @@
         "output_cost_per_token": 6e-07
     },
     "vercel_ai_gateway/google/gemini-2.0-flash-lite": {
-        "deprecation_date": "2026-03-31",
         "input_cost_per_token": 7.5e-08,
         "litellm_provider": "vercel_ai_gateway",
         "max_input_tokens": 1048576,
@@ -30570,7 +30373,6 @@
         "supports_web_search": true
     },
     "xai/grok-3": {
-        "cache_read_input_token_cost": 7.5e-07,
         "input_cost_per_token": 3e-06,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30585,7 +30387,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-beta": {
-        "cache_read_input_token_cost": 7.5e-07,
         "input_cost_per_token": 3e-06,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30600,7 +30401,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-fast-beta": {
-        "cache_read_input_token_cost": 1.25e-06,
         "input_cost_per_token": 5e-06,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30615,7 +30415,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-fast-latest": {
-        "cache_read_input_token_cost": 1.25e-06,
         "input_cost_per_token": 5e-06,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30630,7 +30429,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-latest": {
-        "cache_read_input_token_cost": 7.5e-07,
         "input_cost_per_token": 3e-06,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30645,7 +30443,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-mini": {
-        "cache_read_input_token_cost": 7.5e-08,
         "input_cost_per_token": 3e-07,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30661,7 +30458,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-mini-beta": {
-        "cache_read_input_token_cost": 7.5e-08,
         "input_cost_per_token": 3e-07,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30677,7 +30473,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-mini-fast": {
-        "cache_read_input_token_cost": 1.5e-07,
         "input_cost_per_token": 6e-07,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30693,7 +30488,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-mini-fast-beta": {
-        "cache_read_input_token_cost": 1.5e-07,
         "input_cost_per_token": 6e-07,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30709,7 +30503,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-mini-fast-latest": {
-        "cache_read_input_token_cost": 1.5e-07,
         "input_cost_per_token": 6e-07,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30725,7 +30518,6 @@
         "supports_web_search": true
     },
     "xai/grok-3-mini-latest": {
-        "cache_read_input_token_cost": 7.5e-08,
         "input_cost_per_token": 3e-07,
         "litellm_provider": "xai",
         "max_input_tokens": 131072,
@@ -30992,14 +30784,11 @@
         "max_output_tokens": 128000,
         "mode": "chat",
         "supports_function_calling": true,
-        "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
         "source": "https://docs.z.ai/guides/overview/pricing"
     },
     "zai/glm-4.6": {
-        "cache_creation_input_token_cost": 0,
-        "cache_read_input_token_cost": 1.1e-07,
         "input_cost_per_token": 6e-07,
         "output_cost_per_token": 2.2e-06,
         "litellm_provider": "zai",
@@ -31007,8 +30796,6 @@
         "max_output_tokens": 128000,
         "mode": "chat",
         "supports_function_calling": true,
-        "supports_prompt_caching": true,
-        "supports_reasoning": true,
         "supports_tool_choice": true,
         "source": "https://docs.z.ai/guides/overview/pricing"
     },
@@ -34656,4 +34443,4 @@
         "output_cost_per_token": 0,
         "supports_reasoning": true
     }
-}
\ No newline at end of file
+}
diff --git a/poetry.lock b/poetry.lock
index 9ded0c773c8..e5c304ea776 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -343,7 +343,7 @@ zookeeper = ["kazoo"]
 name = "async-timeout"
 version = "5.0.1"
 description = "Timeout context manager for asyncio programs"
-optional = true
+optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 markers = "python_full_version < \"3.11.3\" and (extra == \"extra-proxy\" or extra == \"proxy\" or python_version < \"3.11\")"
@@ -593,8 +593,8 @@ files = [
 jmespath = ">=0.7.1,<2.0.0"
 python-dateutil = ">=2.1,<3.0.0"
 urllib3 = [
-    {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""},
     {version = ">=1.25.4,<1.27", markers = "python_version < \"3.10\""},
+    {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""},
 ]
 
 [package.extras]
@@ -902,7 +902,7 @@ files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
-markers = {main = "platform_system == \"Windows\" or sys_platform == \"win32\" and python_version < \"3.14\" and (extra == \"utils\" or extra == \"semantic-router\") or sys_platform == \"win32\" and extra == \"utils\" or python_version <= \"3.13\" and extra == \"semantic-router\"", dev = "platform_system == \"Windows\" or sys_platform == \"win32\"", proxy-dev = "platform_system == \"Windows\""}
+markers = {main = "platform_system == \"Windows\" or sys_platform == \"win32\" and python_version < \"3.14\" and (extra == \"utils\" or extra == \"semantic-router\") or sys_platform == \"win32\" and extra == \"utils\" or python_version < \"3.14\" and extra == \"semantic-router\"", dev = "platform_system == \"Windows\" or sys_platform == \"win32\"", proxy-dev = "platform_system == \"Windows\""}
 
 [[package]]
 name = "coloredlogs"
@@ -2028,7 +2028,7 @@ description = "Google API client core library"
 optional = true
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "(extra == \"extra-proxy\" or extra == \"google\") and python_version < \"3.14\""
+markers = "python_version == \"3.9\" and (extra == \"google\" or extra == \"extra-proxy\") or python_version < \"3.14\" and (extra == \"extra-proxy\" or extra == \"google\")"
 files = [
     {file = "google_api_core-2.28.1-py3-none-any.whl", hash = "sha256:4021b0f8ceb77a6fb4de6fde4502cecab45062e66ff4f2895169e0b35bc9466c"},
     {file = "google_api_core-2.28.1.tar.gz", hash = "sha256:2b405df02d68e68ce0fbc138559e6036559e685159d148ae5861013dc201baf8"},
@@ -2038,12 +2038,12 @@ files = [
 google-auth = ">=2.14.1,<3.0.0"
 googleapis-common-protos = ">=1.56.2,<2.0.0"
 grpcio = [
+    {version = ">=1.33.2,<2.0.0", optional = true, markers = "extra == \"grpc\""},
     {version = ">=1.49.1,<2.0.0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
-    {version = ">=1.33.2,<2.0.0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
 ]
 grpcio-status = [
-    {version = ">=1.49.1,<2.0.0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
     {version = ">=1.33.2,<2.0.0", optional = true, markers = "extra == \"grpc\""},
+    {version = ">=1.49.1,<2.0.0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
 ]
 proto-plus = [
     {version = ">=1.22.3,<2.0.0"},
@@ -2525,7 +2525,7 @@ description = "Lightweight in-process concurrent programming"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version >= \"3.10\" and extra == \"mlflow\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"
+markers = "python_version >= \"3.10\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\") and extra == \"mlflow\""
 files = [
     {file = "greenlet-3.2.4-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:8c68325b0d0acf8d91dde4e6f930967dd52a5302cd4062932a6b2e7c2969f47c"},
     {file = "greenlet-3.2.4-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:94385f101946790ae13da500603491f04a76b6e4c059dab271b3ce2e283b2590"},
@@ -2720,7 +2720,7 @@ description = "WSGI HTTP Server for UNIX"
 optional = true
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "extra == \"proxy\" or python_version >= \"3.10\" and (extra == \"proxy\" or extra == \"mlflow\") and platform_system != \"Windows\""
+markers = "(python_version < \"3.14\" or extra == \"mlflow\" or extra == \"proxy\") and (platform_system != \"Windows\" or extra == \"proxy\") and (python_version >= \"3.10\" or extra == \"proxy\") and (extra == \"proxy\" or extra == \"mlflow\")"
 files = [
     {file = "gunicorn-23.0.0-py3-none-any.whl", hash = "sha256:ec400d38950de4dfd418cff8328b2c8faed0edb0d517d3394e457c317908ca4d"},
     {file = "gunicorn-23.0.0.tar.gz", hash = "sha256:f014447a0101dc57e294f6c18ca6b40227a4c90e9bdb586042628030cba004ec"},
@@ -3426,15 +3426,15 @@ files = [
 
 [[package]]
 name = "litellm-proxy-extras"
-version = "0.4.27"
+version = "0.4.26"
 description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
 optional = true
 python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
 groups = ["main"]
 markers = "extra == \"proxy\""
 files = [
-    {file = "litellm_proxy_extras-0.4.27-py3-none-any.whl", hash = "sha256:752c1faabc86ce3d2b1fa451495d34de82323798e37b9cb5c0fea93deae1c5c8"},
-    {file = "litellm_proxy_extras-0.4.27.tar.gz", hash = "sha256:81059120016cfc03c82aa9664424912bdcffad103f66a5f925fef6b26f2cc151"},
+    {file = "litellm_proxy_extras-0.4.26-py3-none-any.whl", hash = "sha256:6b8720c7ae0b51637a3155b6cb02783c316443fe002641061ed9162d2667aee4"},
+    {file = "litellm_proxy_extras-0.4.26.tar.gz", hash = "sha256:cb9544703580172fc6436670e1a693a46ccb34a9057548c94f43e97b7b032754"},
 ]
 
 [[package]]
@@ -3777,10 +3777,10 @@ files = [
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.23.3", markers = "python_version >= \"3.11\""},
-    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
     {version = ">1.20"},
+    {version = ">=1.23.3", markers = "python_version >= \"3.11\""},
     {version = ">=1.21.2", markers = "python_version >= \"3.10\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 
 [package.extras]
@@ -4191,7 +4191,7 @@ description = "Fundamental package for array computing in Python"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "(python_version >= \"3.10\" or extra == \"extra-proxy\" or extra == \"semantic-router\" or extra == \"google\") and python_version < \"3.12\" and (extra == \"extra-proxy\" or extra == \"semantic-router\" or extra == \"google\" or extra == \"mlflow\")"
+markers = "python_version < \"3.12\" and (python_version >= \"3.10\" or extra == \"extra-proxy\" or extra == \"semantic-router\" or extra == \"google\") and (extra == \"extra-proxy\" or extra == \"semantic-router\" or extra == \"google\" or extra == \"mlflow\")"
 files = [
     {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
     {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
@@ -4708,8 +4708,8 @@ files = [
 [package.dependencies]
 numpy = [
     {version = ">=1.23.2", markers = "python_version == \"3.11\""},
-    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
     {version = ">=1.22.4", markers = "python_version < \"3.11\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -5169,7 +5169,7 @@ description = "Beautiful, Pythonic protocol buffers"
 optional = true
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "extra == \"extra-proxy\" or extra == \"google\""
+markers = "extra == \"google\" or extra == \"extra-proxy\""
 files = [
     {file = "proto_plus-1.26.1-py3-none-any.whl", hash = "sha256:13285478c2dcf2abb829db158e1047e2f1e8d63a077d94263c2b88b043c75a66"},
     {file = "proto_plus-1.26.1.tar.gz", hash = "sha256:21a515a4c4c0088a773899e23c7bbade3d18f9c66c73edd4c7ee3816bc96a012"},
@@ -5316,7 +5316,7 @@ files = [
     {file = "pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934"},
     {file = "pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2"},
 ]
-markers = {main = "implementation_name != \"PyPy\" and (platform_python_implementation != \"PyPy\" or extra == \"proxy\")", dev = "implementation_name != \"PyPy\" and platform_python_implementation != \"PyPy\"", proxy-dev = "implementation_name != \"PyPy\" and platform_python_implementation != \"PyPy\""}
+markers = {main = "implementation_name != \"PyPy\" and (platform_python_implementation != \"PyPy\" or extra == \"proxy\")", dev = "platform_python_implementation != \"PyPy\" and implementation_name != \"PyPy\"", proxy-dev = "implementation_name != \"PyPy\" and platform_python_implementation != \"PyPy\""}
 
 [[package]]
 name = "pydantic"
@@ -5617,7 +5617,7 @@ description = "A python implementation of GNU readline."
 optional = true
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "sys_platform == \"win32\" and extra == \"extra-proxy\" and python_version < \"3.14\""
+markers = "extra == \"extra-proxy\" and sys_platform == \"win32\" and python_version < \"3.14\""
 files = [
     {file = "pyreadline3-3.5.4-py3-none-any.whl", hash = "sha256:eaf8e6cc3c49bcccf145fc6067ba8643d1df34d604a1ec0eccbf7a18e6d3fae6"},
     {file = "pyreadline3-3.5.4.tar.gz", hash = "sha256:8d57d53039a1c75adba8e50dd3d992b28143480816187ea5efbd5c78e6c885b7"},
@@ -5719,15 +5719,15 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "python-multipart"
-version = "0.0.22"
+version = "0.0.18"
 description = "A streaming multipart parser for Python"
 optional = true
 python-versions = ">=3.8"
 groups = ["main"]
 markers = "extra == \"proxy\""
 files = [
-    {file = "python_multipart-0.0.22-py3-none-any.whl", hash = "sha256:2b2cd894c83d21bf49d702499531c7bafd057d730c201782048f7945d82de155"},
-    {file = "python_multipart-0.0.22.tar.gz", hash = "sha256:7340bef99a7e0032613f56dc36027b959fd3b30a787ed62d310e951f7c3a3a58"},
+    {file = "python_multipart-0.0.18-py3-none-any.whl", hash = "sha256:efe91480f485f6a361427a541db4796f9e1591afc0fb8e7a4ba06bfbc6708996"},
+    {file = "python_multipart-0.0.18.tar.gz", hash = "sha256:7a68db60c8bfb82e460637fa4750727b45af1d5e2ed215593f917f64694d34fe"},
 ]
 
 [[package]]
@@ -5766,7 +5766,7 @@ description = "Python for Window Extensions"
 optional = true
 python-versions = "*"
 groups = ["main"]
-markers = "python_version >= \"3.10\" and (extra == \"proxy\" or extra == \"mlflow\") and sys_platform == \"win32\""
+markers = "python_version >= \"3.10\" and sys_platform == \"win32\" and (extra == \"proxy\" or extra == \"mlflow\")"
 files = [
     {file = "pywin32-311-cp310-cp310-win32.whl", hash = "sha256:d03ff496d2a0cd4a5893504789d4a15399133fe82517455e78bad62efbb7f0a3"},
     {file = "pywin32-311-cp310-cp310-win_amd64.whl", hash = "sha256:797c2772017851984b97180b0bebe4b620bb86328e8a884bb626156295a63b3b"},
@@ -7450,6 +7450,26 @@ examples = ["aiosqlite (>=0.21.0)", "fastapi (>=0.115.12)", "sqlalchemy[asyncio]
 granian = ["granian (>=2.3.1)"]
 uvicorn = ["uvicorn (>=0.34.0)"]
 
+[[package]]
+name = "starlette"
+version = "0.49.3"
+description = "The little ASGI library that shines."
+optional = false
+python-versions = ">=3.9"
+groups = ["main", "dev"]
+files = [
+    {file = "starlette-0.49.3-py3-none-any.whl", hash = "sha256:b579b99715fdc2980cf88c8ec96d3bf1ce16f5a8051a7c2b84ef9b1cdecaea2f"},
+    {file = "starlette-0.49.3.tar.gz", hash = "sha256:1c14546f299b5901a1ea0e34410575bc33bbd741377a10484a54445588d00284"},
+]
+markers = {main = "python_version == \"3.9\" and extra == \"proxy\"", dev = "python_version == \"3.9\""}
+
+[package.dependencies]
+anyio = ">=3.6.2,<5"
+typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""}
+
+[package.extras]
+full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
+
 [[package]]
 name = "starlette"
 version = "0.50.0"
@@ -7461,7 +7481,7 @@ files = [
     {file = "starlette-0.50.0-py3-none-any.whl", hash = "sha256:9e5391843ec9b6e472eed1365a78c8098cfceb7a74bfd4d6b1c0c0095efb3bca"},
     {file = "starlette-0.50.0.tar.gz", hash = "sha256:a2a17b22203254bcbc2e1f926d2d55f3f9497f769416b3190768befe598fa3ca"},
 ]
-markers = {main = "(extra == \"mlflow\" or extra == \"proxy\") and python_version >= \"3.10\" or extra == \"proxy\""}
+markers = {main = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"proxy\") or extra == \"proxy\""}
 
 [package.dependencies]
 anyio = ">=3.6.2,<5"
@@ -7510,7 +7530,7 @@ description = "Retry code until it succeeds"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "(extra == \"extra-proxy\" or extra == \"google\") and python_version < \"3.14\" or extra == \"google\""
+markers = "(extra == \"extra-proxy\" or extra == \"google\") and (python_version < \"3.14\" or extra == \"google\")"
 files = [
     {file = "tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138"},
     {file = "tenacity-9.1.2.tar.gz", hash = "sha256:1169d376c297e7de388d18b4481760d478b0e99a777cad3a9c86e556f4b697cb"},
@@ -8062,7 +8082,7 @@ description = "Waitress WSGI server"
 optional = true
 python-versions = ">=3.9.0"
 groups = ["main"]
-markers = "python_version >= \"3.10\" and extra == \"mlflow\" and platform_system == \"Windows\""
+markers = "python_version >= \"3.10\" and platform_system == \"Windows\" and extra == \"mlflow\""
 files = [
     {file = "waitress-3.0.2-py3-none-any.whl", hash = "sha256:c56d67fd6e87c2ee598b76abdd4e96cfad1f24cacdea5078d382b1f9d7b5ed2e"},
     {file = "waitress-3.0.2.tar.gz", hash = "sha256:682aaaf2af0c44ada4abfb70ded36393f0e307f4ab9456a215ce0020baefc31f"},
@@ -8472,4 +8492,4 @@ utils = ["numpydoc"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9,<4.0"
-content-hash = "73b5e1ab0badbee6c564d5e056e4f9c320c2f722bf176ce97d42e77110e37d60"
+content-hash = "ee8411854731cb6cc038a6135d7589a81789a269e718a226101953ba24fb2033"
diff --git a/proxy_config.yaml b/proxy_config.yaml
new file mode 100644
index 00000000000..ea405c1dea4
--- /dev/null
+++ b/proxy_config.yaml
@@ -0,0 +1,101 @@
+model_list:
+  - model_name: "*"
+    litellm_params:
+      model: "*"
+  - model_name: "gpt-4"
+    litellm_params:
+      model: "gpt-4"
+      api_key: os.environ/OPENAI_API_KEY
+  - model_name: "gpt-3.5-turbo"
+    litellm_params:
+      model: "gpt-3.5-turbo"
+      api_key: os.environ/OPENAI_API_KEY
+
+general_settings:
+  master_key: sk-1234
+
+# ───────────────────────────────────────────────
+# POLICIES - Define WHAT guardrails to apply
+# ───────────────────────────────────────────────
+#
+# Policies define guardrails with:
+# - inherit: Inherit guardrails from another policy
+# - description: Human-readable description
+# - guardrails.add: Add guardrails (on top of inherited)
+# - guardrails.remove: Remove guardrails (from inherited)
+# - condition.model: Model pattern (exact or regex) for when guardrails apply
+#
+policies:
+  # Global baseline policy
+  global-baseline:
+    description: "Base guardrails for all requests"
+    guardrails:
+      add:
+        - pii_blocker
+
+  # Healthcare policy - inherits from global-baseline
+  healthcare-compliance:
+    inherit: global-baseline
+    description: "HIPAA compliance for healthcare teams"
+    guardrails:
+      add:
+        - hipaa_audit
+
+  # Dev policy - inherits but removes PII blocker for testing
+  internal-dev:
+    inherit: global-baseline
+    description: "Relaxed policy for internal development"
+    guardrails:
+      add:
+        - toxicity_filter
+      remove:
+        - pii_blocker
+
+  # Policy with model condition (regex pattern)
+  gpt4-safety:
+    description: "Extra safety for GPT-4 models"
+    guardrails:
+      add:
+        - toxicity_filter
+    condition:
+      model: "gpt-4.*"  # regex: matches gpt-4, gpt-4-turbo, gpt-4o, etc.
+
+  # Policy with model condition (exact match list)
+  bedrock-compliance:
+    description: "Compliance for Bedrock models"
+    guardrails:
+      add:
+        - strict_pii_blocker
+    condition:
+      model: ["bedrock/claude-3", "bedrock/claude-2"]  # exact matches
+
+# ───────────────────────────────────────────────
+# POLICY ATTACHMENTS - Define WHERE policies apply
+# ───────────────────────────────────────────────
+#
+# Attachments are REQUIRED to make policies active.
+# A policy without an attachment will not be applied.
+#
+policy_attachments:
+  # Global attachment - applies to all requests
+  - policy: global-baseline
+    scope: "*"
+
+  # Team-specific attachment
+  - policy: healthcare-compliance
+    teams:
+      - healthcare-team
+      - medical-research
+
+  # Key pattern attachment
+  - policy: internal-dev
+    keys:
+      - "dev-key-*"
+      - "test-key-*"
+
+  # Model-specific policies (attached globally, condition filters by model)
+  - policy: gpt4-safety
+    scope: "*"
+
+  - policy: bedrock-compliance
+    scope: "*"
diff --git a/pyproject.toml b/pyproject.toml
index 6c3ab08ce02..862611879a5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.81.5"
+version = "1.81.2"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -45,7 +45,7 @@ orjson = {version = "^3.9.7", optional = true}
 apscheduler = {version = "^3.10.4", optional = true}
 fastapi-sso = { version = "^0.16.0", optional = true }
 PyJWT = { version = "^2.10.1", optional = true, python = ">=3.9" }
-python-multipart = { version = "^0.0.22", optional = true}
+python-multipart = { version = "^0.0.18", optional = true}
 cryptography = {version = "*", optional = true}
 prisma = {version = "0.11.0", optional = true}
 azure-identity = {version = "^1.15.0", optional = true, python = ">=3.9"}
@@ -61,7 +61,7 @@ boto3 = { version = "1.40.76", optional = true }
 redisvl = {version = "^0.4.1", optional = true, markers = "python_version >= '3.9' and python_version < '3.14'"}
 mcp = {version = ">=1.25.0,<2.0.0", optional = true, python = ">=3.10"}
 a2a-sdk = {version = "^0.3.22", optional = true, python = ">=3.10"}
-litellm-proxy-extras = {version = "0.4.27", optional = true}
+litellm-proxy-extras = {version = "0.4.26", optional = true}
 rich = {version = "13.7.1", optional = true}
 litellm-enterprise = {version = "0.1.27", optional = true}
 diskcache = {version = "^5.6.1", optional = true}
@@ -145,7 +145,6 @@ mypy = "^1.0"
 pytest = "^7.4.3"
 pytest-mock = "^3.12.0"
 pytest-asyncio = "^0.21.1"
-pytest-retry = "^1.6.3"
 requests-mock = "^1.12.1"
 responses = "^0.25.7"
 respx = "^0.22.0"
@@ -174,7 +173,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.81.5"
+version = "1.81.2"
 version_files = [
     "pyproject.toml:^version"
 ]
@@ -184,8 +183,6 @@ plugins = "pydantic.mypy"
 
 [tool.pytest.ini_options]
 asyncio_mode = "auto"
-retries = 20
-retry_delay = 5
 markers = [
     "asyncio: mark test as an asyncio test",
     "limit_leaks: mark test with memory limit for leak detection (e.g., '40 MB')",
diff --git a/requirements.txt b/requirements.txt
index e40f9b9dbe4..9e32cd79507 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -32,7 +32,7 @@ polars==1.31.0 # for data processing
 apscheduler==3.10.4 # for resetting budget in background
 fastapi-sso==0.19.0 # admin UI, SSO
 pyjwt[crypto]==2.10.1 ; python_version >= "3.9"
-python-multipart==0.0.22 # admin UI
+python-multipart==0.0.18 # admin UI
 Pillow==11.0.0
 jaraco.context>=6.1.0
 azure-ai-contentsafety==1.0.0 # for azure content safety
@@ -50,7 +50,7 @@ sentry_sdk==2.21.0 # for sentry error handling
 detect-secrets==1.5.0 # Enterprise - secret detection / masking in LLM requests
 cryptography==44.0.1
 tzdata==2025.1 # IANA time zone database
-litellm-proxy-extras==0.4.27 # for proxy extras - e.g. prisma migrations
+litellm-proxy-extras==0.4.26 # for proxy extras - e.g. prisma migrations
 llm-sandbox==0.3.31 # for skill execution in sandbox
 ### LITELLM PACKAGE DEPENDENCIES
 python-dotenv==1.0.1 # for env
@@ -68,7 +68,6 @@ jsonschema>=4.23.0,<5.0.0 # validating json schema - aligned with openapi-core +
 websockets==15.0.1 # for realtime API
 soundfile==0.12.1 # for audio file processing
 openapi-core==0.21.0 # for OpenAPI compliance tests
-pypdf>=6.6.2 # for PDF text extraction in RAG ingestion
 
 ########################
 # LITELLM ENTERPRISE DEPENDENCIES
diff --git a/schema.prisma b/schema.prisma
index ca60b9e1bec..d7aa6e9f0d0 100644
--- a/schema.prisma
+++ b/schema.prisma
@@ -760,11 +760,6 @@ model LiteLLM_ManagedVectorStoresTable {
   updated_at DateTime @updatedAt
   litellm_credential_name String?
   litellm_params Json?
-  team_id String?
-  user_id String?
-  
-  @@index([team_id])
-  @@index([user_id])
 }
 
 // Guardrails table for storing guardrail configurations
diff --git a/tests/code_coverage_tests/liccheck.ini b/tests/code_coverage_tests/liccheck.ini
index dc46f83366a..ea87b56ff30 100644
--- a/tests/code_coverage_tests/liccheck.ini
+++ b/tests/code_coverage_tests/liccheck.ini
@@ -141,4 +141,3 @@ llm-sandbox: >=0.3.31 # MIT License - https://github.com/vndee/llm-sandbox
 nodejs-wheel-binaries: >=24.12.0 # MIT license manually verified
 grpcio: >=1.69.0 # Apache License 2.0
 jaraco.context: >=6.1.0 # Unknown license
-pypdf: >=6.6.2 # BSD-3-Clause license - https://github.com/py-pdf/pypdf/blob/main/LICENSE
diff --git a/tests/code_coverage_tests/license_cache.json b/tests/code_coverage_tests/license_cache.json
index a9c7fad2b14..910ec931c86 100644
--- a/tests/code_coverage_tests/license_cache.json
+++ b/tests/code_coverage_tests/license_cache.json
@@ -20,7 +20,7 @@
   "apscheduler:3.10.4": "MIT",
   "fastapi-sso:0.16.0": "MIT",
   "pyjwt:2.9.0": "MIT",
-  "python-multipart:0.0.22": "Apache-2.0",
+  "python-multipart:0.0.18": "Apache-2.0",
   "Pillow:11.0.0": "MIT-CMU",
   "azure-ai-contentsafety:1.0.0": "MIT License",
   "azure-identity:1.16.1": "MIT License",
diff --git a/tests/code_coverage_tests/router_code_coverage.py b/tests/code_coverage_tests/router_code_coverage.py
index 49288dd3775..d700e56a06f 100644
--- a/tests/code_coverage_tests/router_code_coverage.py
+++ b/tests/code_coverage_tests/router_code_coverage.py
@@ -6,7 +6,7 @@ def get_function_names_from_file(file_path):
     """
     Extracts all function names from a given Python file.
     """
-    with open(file_path, "r", encoding="utf-8") as file:
+    with open(file_path, "r") as file:
         tree = ast.parse(file.read())
 
     function_names = []
@@ -45,7 +45,7 @@ def get_all_functions_called_in_tests(base_dir):
                 if file.endswith(".py") and "router" in file.lower():
                     print("file: ", file)
                     file_path = os.path.join(root, file)
-                    with open(file_path, "r", encoding="utf-8") as f:
+                    with open(file_path, "r") as f:
                         try:
                             tree = ast.parse(f.read())
                         except SyntaxError:
diff --git a/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py b/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py
index a479d1a9fc9..2419c61c25c 100644
--- a/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py
+++ b/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py
@@ -411,15 +411,7 @@ def test_set_latency_metrics(prometheus_logger):
 
     # completion_start_time - api_call_start_time
     prometheus_logger.litellm_llm_api_time_to_first_token_metric.labels.assert_called_once_with(
-        end_user=None,
-        user="test_user",
-        hashed_api_key="test_hash",
-        api_key_alias="test_alias",
-        team="test_team",
-        team_alias="test_team_alias",
-        requested_model="openai-gpt",
-        model="gpt-3.5-turbo",
-        model_id="model-123",
+        "gpt-3.5-turbo", "key1", "alias1", "team1", "team_alias1"
     )
     prometheus_logger.litellm_llm_api_time_to_first_token_metric.labels().observe.assert_called_once_with(
         0.5
@@ -450,7 +442,6 @@ def test_set_latency_metrics(prometheus_logger):
         team_alias="test_team_alias",
         requested_model="openai-gpt",
         model="gpt-3.5-turbo",
-        model_id="model-123",
     )
     prometheus_logger.litellm_request_total_latency_metric.labels().observe.assert_called_once_with(
         2.0
@@ -746,7 +737,6 @@ async def test_async_post_call_failure_hook(prometheus_logger):
         exception_status="429",
         exception_class="Openai.RateLimitError",
         route=user_api_key_dict.request_route,
-        model_id=None,
     )
     prometheus_logger.litellm_proxy_failed_requests_metric.labels().inc.assert_called_once()
 
@@ -762,7 +752,6 @@ async def test_async_post_call_failure_hook(prometheus_logger):
         status_code="429",
         user_email=None,
         route=user_api_key_dict.request_route,
-        model_id=None,
     )
     prometheus_logger.litellm_proxy_total_requests_metric.labels().inc.assert_called_once()
 
@@ -809,7 +798,6 @@ async def test_async_post_call_success_hook(prometheus_logger):
         status_code="200",
         user_email=None,
         route=user_api_key_dict.request_route,
-        model_id=None,
     )
     prometheus_logger.litellm_proxy_total_requests_metric.labels().inc.assert_called_once()
 
diff --git a/tests/enterprise/litellm_enterprise/integrations/test_prometheus.py b/tests/enterprise/litellm_enterprise/integrations/test_prometheus.py
index 4a08c208fda..53757390a44 100644
--- a/tests/enterprise/litellm_enterprise/integrations/test_prometheus.py
+++ b/tests/enterprise/litellm_enterprise/integrations/test_prometheus.py
@@ -937,124 +937,6 @@ def test_callback_failure_metric_different_callbacks(prometheus_logger):
     )
 
 
-@pytest.mark.asyncio
-async def test_langfuse_callback_failure_metric(prometheus_logger):
-    """
-    Test that Langfuse callback failures are properly tracked in Prometheus metrics.
-    
-    This test verifies that when Langfuse logging fails, the 
-    litellm_callback_logging_failures_metric is incremented with callback_name="langfuse".
-    """
-    from unittest.mock import MagicMock, patch
-
-    from litellm.integrations.langfuse.langfuse_prompt_management import (
-        LangfusePromptManagement,
-    )
-
-    # Get initial value
-    initial_value = 0
-    try:
-        initial_value = prometheus_logger.litellm_callback_logging_failures_metric.labels(
-            callback_name="langfuse"
-        )._value.get()
-    except Exception:
-        initial_value = 0
-    
-    # Create Langfuse logger with mocked initialization
-    with patch("litellm.integrations.langfuse.langfuse_prompt_management.langfuse_client_init"):
-        langfuse_logger = LangfusePromptManagement()
-    
-    # Mock the log_event_on_langfuse to raise an exception
-    with patch(
-        "litellm.integrations.langfuse.langfuse_prompt_management.LangFuseHandler.get_langfuse_logger_for_request"
-    ) as mock_get_logger:
-        mock_logger = MagicMock()
-        mock_logger.log_event_on_langfuse.side_effect = Exception("Langfuse API error")
-        mock_get_logger.return_value = mock_logger
-        
-        # Mock handle_callback_failure to track calls
-        with patch.object(prometheus_logger, "increment_callback_logging_failure") as mock_increment:
-            # Inject prometheus logger into the langfuse logger
-            langfuse_logger.handle_callback_failure = lambda callback_name: mock_increment(
-                callback_name=callback_name
-            )
-            
-            # Call async_log_success_event - should catch exception and increment metric
-            await langfuse_logger.async_log_success_event(
-                kwargs={},
-                response_obj={},
-                start_time=None,
-                end_time=None,
-            )
-            
-            # Verify that increment was called with correct callback name
-            mock_increment.assert_called_once_with(callback_name="langfuse")
-    
-    print("✓ Langfuse callback failure metric test passed")
-
-
-@pytest.mark.asyncio
-async def test_langfuse_otel_callback_failure_metric(prometheus_logger):
-    """
-    Test that Langfuse OTEL callback failures are properly tracked in Prometheus metrics.
-    
-    This test verifies that when Langfuse OTEL logging fails, the 
-    litellm_callback_logging_failures_metric is incremented with callback_name="langfuse_otel".
-    """
-    from unittest.mock import MagicMock, patch
-
-    from litellm.integrations.langfuse.langfuse_otel import LangfuseOtelLogger
-
-    # Get initial value
-    initial_value = 0
-    try:
-        initial_value = prometheus_logger.litellm_callback_logging_failures_metric.labels(
-            callback_name="langfuse_otel"
-        )._value.get()
-    except Exception:
-        initial_value = 0
-    
-    # Create Langfuse OTEL logger with mocked initialization
-    with patch("litellm.integrations.opentelemetry.OpenTelemetry.__init__", return_value=None):
-        langfuse_otel_logger = LangfuseOtelLogger(callback_name="langfuse_otel")
-        langfuse_otel_logger.callback_name = "langfuse_otel"
-    
-    # Mock handle_callback_failure to track calls
-    with patch.object(prometheus_logger, "increment_callback_logging_failure") as mock_increment:
-        # Inject prometheus logger into the langfuse otel logger
-        langfuse_otel_logger.handle_callback_failure = lambda callback_name: mock_increment(
-            callback_name=callback_name
-        )
-        
-        # Test that the OpenTelemetry base class set_attributes exception handler works
-        # This is where langfuse_otel failures are caught and tracked
-        with patch.object(langfuse_otel_logger, "set_attributes") as mock_set_attributes:
-            # Simulate the exception handling in set_attributes
-            def set_attributes_with_error(*args, **kwargs):
-                # This simulates what happens in the real set_attributes method
-                try:
-                    raise Exception("Attribute error")
-                except Exception as e:
-                    langfuse_otel_logger.handle_callback_failure(callback_name=langfuse_otel_logger.callback_name)
-            
-            mock_set_attributes.side_effect = set_attributes_with_error
-            
-            # Call set_attributes
-            try:
-                langfuse_otel_logger.set_attributes(
-                    span=MagicMock(),
-                    kwargs={},
-                    response_obj={}
-                )
-            except Exception:
-                pass
-            
-            # Verify that increment was called with correct callback name
-            mock_increment.assert_called_with(callback_name="langfuse_otel")
-    
-    print("✓ Langfuse OTEL callback failure metric test passed")
-
-
 # ==============================================================================
 # END CALLBACK FAILURE METRICS TESTS
 # ==============================================================================
diff --git a/tests/enterprise/litellm_enterprise/proxy/auth/test_route_checks.py b/tests/enterprise/litellm_enterprise/proxy/auth/test_route_checks.py
index 706e3b71870..ce77ff69e05 100644
--- a/tests/enterprise/litellm_enterprise/proxy/auth/test_route_checks.py
+++ b/tests/enterprise/litellm_enterprise/proxy/auth/test_route_checks.py
@@ -180,68 +180,3 @@ def test_should_call_route_llm_disabled_management_enabled(
 
         # Should not raise exception since management routes are enabled
         EnterpriseRouteChecks.should_call_route("/config/update")
-
-
-class TestEnterpriseRouteChecksErrorMessages:
-    """Test that error messages correctly identify which feature requires Enterprise license"""
-
-    @patch("litellm.secret_managers.main.get_secret_bool")
-    @patch("litellm.proxy.proxy_server.premium_user", False)
-    def test_disable_llm_api_endpoints_error_message(self, mock_get_secret_bool):
-        """
-        Test that when DISABLE_LLM_API_ENDPOINTS is set without Enterprise license,
-        the error message correctly mentions 'LLM API ENDPOINTS'
-        """
-        with patch.dict(os.environ, {"DISABLE_LLM_API_ENDPOINTS": "true"}):
-            with pytest.raises(HTTPException) as exc_info:
-                EnterpriseRouteChecks.is_llm_api_route_disabled()
-
-            assert exc_info.value.status_code == 500
-            assert "DISABLING LLM API ENDPOINTS is an Enterprise feature" in str(
-                exc_info.value.detail
-            )
-
-    @patch("litellm.secret_managers.main.get_secret_bool")
-    @patch("litellm.proxy.proxy_server.premium_user", False)
-    def test_disable_admin_endpoints_error_message(self, mock_get_secret_bool):
-        """
-        Test that when DISABLE_ADMIN_ENDPOINTS is set without Enterprise license,
-        the error message correctly mentions 'ADMIN ENDPOINTS' (not 'LLM API ENDPOINTS')
-
-        This is a regression test for a bug where the error message incorrectly said
-        'DISABLING LLM API ENDPOINTS' when the actual issue was DISABLE_ADMIN_ENDPOINTS.
-        """
-        with patch.dict(os.environ, {"DISABLE_ADMIN_ENDPOINTS": "true"}):
-            with pytest.raises(HTTPException) as exc_info:
-                EnterpriseRouteChecks.is_management_routes_disabled()
-
-            assert exc_info.value.status_code == 500
-            assert "DISABLING ADMIN ENDPOINTS is an Enterprise feature" in str(
-                exc_info.value.detail
-            )
-            # Ensure it does NOT mention LLM API ENDPOINTS (the old buggy message)
-            assert "LLM API ENDPOINTS" not in str(exc_info.value.detail)
-
-    @patch("litellm.secret_managers.main.get_secret_bool")
-    @patch("litellm.proxy.proxy_server.premium_user", True)
-    def test_disable_llm_api_endpoints_with_premium_user(self, mock_get_secret_bool):
-        """
-        Test that premium users can use DISABLE_LLM_API_ENDPOINTS without error
-        """
-        mock_get_secret_bool.return_value = True
-        with patch.dict(os.environ, {"DISABLE_LLM_API_ENDPOINTS": "true"}):
-            # Should not raise exception for premium users
-            result = EnterpriseRouteChecks.is_llm_api_route_disabled()
-            assert result is True
-
-    @patch("litellm.secret_managers.main.get_secret_bool")
-    @patch("litellm.proxy.proxy_server.premium_user", True)
-    def test_disable_admin_endpoints_with_premium_user(self, mock_get_secret_bool):
-        """
-        Test that premium users can use DISABLE_ADMIN_ENDPOINTS without error
-        """
-        mock_get_secret_bool.return_value = True
-        with patch.dict(os.environ, {"DISABLE_ADMIN_ENDPOINTS": "true"}):
-            # Should not raise exception for premium users
-            result = EnterpriseRouteChecks.is_management_routes_disabled()
-            assert result is True
diff --git a/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py b/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py
index 4fa16066e4b..9a6e153a22b 100644
--- a/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py
+++ b/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py
@@ -827,262 +827,3 @@ async def test_afile_retrieve_raises_error_for_non_managed_file():
         )
     
     assert "not found" in str(exc_info.value)
-
-
-@pytest.mark.asyncio
-async def test_list_batches_from_managed_objects_table():
-    from litellm.proxy._types import UserAPIKeyAuth
-    from openai.types.batch import BatchRequestCounts
-
-    prisma_client = AsyncMock()
-    
-    batch_record_1 = MagicMock()
-    batch_record_1.unified_object_id = "unified-batch-id-1"
-    batch_record_1.file_object = json.dumps({
-        "id": "batch_abc123",
-        "object": "batch",
-        "endpoint": "/v1/chat/completions",
-        "completion_window": "24h",
-        "status": "completed",
-        "created_at": 1234567890,
-        "input_file_id": "file-input-1",
-        "request_counts": {"total": 1, "completed": 1, "failed": 0},
-    })
-    
-    batch_record_2 = MagicMock()
-    batch_record_2.unified_object_id = "unified-batch-id-2"
-    batch_record_2.file_object = json.dumps({
-        "id": "batch_xyz789",
-        "object": "batch",
-        "endpoint": "/v1/chat/completions",
-        "completion_window": "24h",
-        "status": "in_progress",
-        "created_at": 1234567891,
-        "input_file_id": "file-input-2",
-        "request_counts": {"total": 5, "completed": 2, "failed": 0},
-    })
-    
-    prisma_client.db.litellm_managedobjecttable.find_many.return_value = [
-        batch_record_1,
-        batch_record_2,
-    ]
-    
-    proxy_managed_files = _PROXY_LiteLLMManagedFiles(
-        DualCache(), prisma_client=prisma_client
-    )
-    
-    result = await proxy_managed_files.list_user_batches(
-        user_api_key_dict=UserAPIKeyAuth(user_id="test-user"),
-        limit=10,
-    )
-    
-    assert result["object"] == "list"
-    assert len(result["data"]) == 2
-    assert result["data"][0].id == "unified-batch-id-1"
-    assert result["data"][1].id == "unified-batch-id-2"
-    assert result["first_id"] == "unified-batch-id-1"
-    assert result["last_id"] == "unified-batch-id-2"
-    
-    # Should filter by user_id (created_by)
-    prisma_client.db.litellm_managedobjecttable.find_many.assert_called_once_with(
-        where={"file_purpose": "batch", "created_by": "test-user"},
-        take=10,
-        order={"created_at": "desc"},
-    )
-
-
-@pytest.mark.asyncio
-async def test_list_batches_from_managed_objects_table_empty_list():
-    from litellm.proxy._types import UserAPIKeyAuth
-
-    prisma_client = AsyncMock()
-    prisma_client.db.litellm_managedobjecttable.find_many.return_value = []
-    
-    proxy_managed_files = _PROXY_LiteLLMManagedFiles(
-        DualCache(), prisma_client=prisma_client
-    )
-    
-    result = await proxy_managed_files.list_user_batches(
-        user_api_key_dict=UserAPIKeyAuth(user_id="test-user"),
-    )
-    
-    assert result["object"] == "list"
-    assert len(result["data"]) == 0
-    assert result["first_id"] is None
-    assert result["last_id"] is None
-    assert result["has_more"] is False
-    
-    # Verify where clause includes created_by filter
-    # Default take is 20 when no limit is provided
-    prisma_client.db.litellm_managedobjecttable.find_many.assert_called_once_with(
-        where={"file_purpose": "batch", "created_by": "test-user"},
-        take=20,
-        order={"created_at": "desc"},
-    )
-
-
-def _create_unified_batch_id(model_id: str, batch_id: str) -> str:
-    import base64
-    unified_str = f"litellm_proxy;model_id:{model_id};llm_batch_id:{batch_id}"
-    return base64.urlsafe_b64encode(unified_str.encode()).decode().rstrip("=")
-
-
-@pytest.mark.asyncio
-async def test_list_batches_from_managed_objects_table_provider_filter_raises_exception():
-    from litellm.proxy._types import UserAPIKeyAuth
-
-    prisma_client = AsyncMock()
-    
-    proxy_managed_files = _PROXY_LiteLLMManagedFiles(
-        DualCache(), prisma_client=prisma_client
-    )
-    
-    # Filtering by provider should raise Exception
-    with pytest.raises(Exception) as exc_info:
-        await proxy_managed_files.list_user_batches(
-            user_api_key_dict=UserAPIKeyAuth(user_id="test-user"),
-            limit=10,
-            provider="openai",
-        )
-    
-    assert str(exc_info.value) == (
-        "Filtering by 'provider' is not supported when using managed batches."
-    )
-    
-    # Verify find_many was NOT called since exception is raised before database query
-    prisma_client.db.litellm_managedobjecttable.find_many.assert_not_called()
-
-
-@pytest.mark.asyncio
-async def test_list_batches_from_managed_objects_table_target_model_name_filter_raises_exception():
-    from litellm.proxy._types import UserAPIKeyAuth
-
-    prisma_client = AsyncMock()
-    
-    proxy_managed_files = _PROXY_LiteLLMManagedFiles(
-        DualCache(), prisma_client=prisma_client
-    )
-
-    # Filtering by provider should raise Exception
-    with pytest.raises(Exception) as exc_info:
-        await proxy_managed_files.list_user_batches(
-            user_api_key_dict=UserAPIKeyAuth(user_id="test-user"),
-            limit=10,
-            target_model_names="gpt-4o,gpt-3.5",
-        )
-    
-    assert str(exc_info.value) == (
-        "Filtering by 'target_model_names' is not supported when using managed batches."
-    )
-    
-    # Verify find_many was NOT called since exception is raised before database query
-    prisma_client.db.litellm_managedobjecttable.find_many.assert_not_called()
-
-@pytest.mark.asyncio
-async def test_list_batches_from_managed_objects_table_filters_by_created_by():
-    from litellm.proxy._types import UserAPIKeyAuth
-
-    prisma_client = AsyncMock()
-    
-    # Create batch for user1
-    batch_user1 = MagicMock()
-    batch_user1.unified_object_id = "unified-batch-user1"
-    batch_user1.file_object = json.dumps({
-        "id": "batch_user1_abc",
-        "object": "batch",
-        "endpoint": "/v1/chat/completions",
-        "completion_window": "24h",
-        "status": "completed",
-        "created_at": 1234567890,
-        "input_file_id": "file-input-user1",
-        "request_counts": {"total": 1, "completed": 1, "failed": 0},
-    })
-    
-    # Create batch for user2
-    batch_user2 = MagicMock()
-    batch_user2.unified_object_id = "unified-batch-user2"
-    batch_user2.file_object = json.dumps({
-        "id": "batch_user2_xyz",
-        "object": "batch",
-        "endpoint": "/v1/chat/completions",
-        "completion_window": "24h",
-        "status": "completed",
-        "created_at": 1234567891,
-        "input_file_id": "file-input-user2",
-        "request_counts": {"total": 2, "completed": 2, "failed": 0},
-    })
-    
-    proxy_managed_files = _PROXY_LiteLLMManagedFiles(
-        DualCache(), prisma_client=prisma_client
-    )
-    
-    # Query with user1's API key - should only return user1's batch
-    prisma_client.db.litellm_managedobjecttable.find_many.return_value = [batch_user1]
-    result_user1 = await proxy_managed_files.list_user_batches(
-        user_api_key_dict=UserAPIKeyAuth(user_id="user1"),
-        limit=10,
-    )
-    
-    assert len(result_user1["data"]) == 1
-    assert result_user1["data"][0].id == "unified-batch-user1"
-    prisma_client.db.litellm_managedobjecttable.find_many.assert_called_with(
-        where={"file_purpose": "batch", "created_by": "user1"},
-        take=10,
-        order={"created_at": "desc"},
-    )
-    
-    # Query with user2's API key - should only return user2's batch
-    prisma_client.db.litellm_managedobjecttable.find_many.return_value = [batch_user2]
-    result_user2 = await proxy_managed_files.list_user_batches(
-        user_api_key_dict=UserAPIKeyAuth(user_id="user2"),
-        limit=10,
-    )
-    
-    assert len(result_user2["data"]) == 1
-    assert result_user2["data"][0].id == "unified-batch-user2"
-    prisma_client.db.litellm_managedobjecttable.find_many.assert_called_with(
-        where={"file_purpose": "batch", "created_by": "user2"},
-        take=10,
-        order={"created_at": "desc"},
-    )
-
-
-@pytest.mark.asyncio
-async def test_return_unified_file_id_includes_expires_at():
-    from litellm.types.llms.openai import OpenAIFileObject
-
-    # Create a mock file object with expires_at set
-    file_object = OpenAIFileObject(
-        id="file-abc123",
-        object="file",
-        bytes=1234,
-        created_at=1234567890,
-        filename="test.jsonl",
-        purpose="batch",
-        status="uploaded",
-        expires_at=1234657890,  
-    )
-    file_object._hidden_params = {"model_id": "test-model-id"}
-
-    create_file_request = {
-        "file": ("test.jsonl", b"test content", "application/jsonl"),
-        "purpose": "batch",
-    }
-
-    internal_usage_cache = MagicMock()
-
-    result = await _PROXY_LiteLLMManagedFiles.return_unified_file_id(
-        file_objects=[file_object],
-        create_file_request=create_file_request,
-        internal_usage_cache=internal_usage_cache,
-        litellm_parent_otel_span=None,
-        target_model_names_list=["gpt-4o"],
-    )
-
-    # Verify expires_at is passed through
-    assert result.expires_at == 1234657890
-    assert result.purpose == "batch"
-    assert result.filename == "test.jsonl"
-    assert result.bytes == 1234
-    assert result.created_at == 1234567890
-    assert _is_base64_encoded_unified_file_id(result.id)
\ No newline at end of file
diff --git a/tests/guardrails_tests/test_lakera_v2.py b/tests/guardrails_tests/test_lakera_v2.py
index 2a8731d5ecd..9e0244a4819 100644
--- a/tests/guardrails_tests/test_lakera_v2.py
+++ b/tests/guardrails_tests/test_lakera_v2.py
@@ -22,58 +22,38 @@ async def test_lakera_pre_call_hook_for_pii_masking():
     # Setup the guardrail with specific entities config
     litellm._turn_on_debug()
     lakera_guardrail = LakeraAIGuardrail(
-        api_key="test_key",
+        api_key=os.environ.get("LAKERA_API_KEY"),
     )
     
-    # Mock response with PII detections in payload (with start/end positions for masking)
-    mock_response = {
-        'payload': [
-            {'detector_type': 'pii/credit_card', 'start': 18, 'end': 37, 'message_id': 1},  # "4111-1111-1111-1111"
-            {'detector_type': 'pii/email', 'start': 54, 'end': 70, 'message_id': 1},  # "test@example.com"
+    # Create a sample request with PII data
+    data = {
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "My credit card is 4111-1111-1111-1111 and my email is test@example.com. My phone number is 555-123-4567"}
         ],
-        'flagged': True,
-        'breakdown': [
-            {'detector_type': 'pii/credit_card', 'detected': True, 'message_id': 1},
-            {'detector_type': 'pii/email', 'detected': True, 'message_id': 1},
-        ]
+        "model": "gpt-3.5-turbo",
+        "metadata": {}
     }
     
-    with patch.object(lakera_guardrail, 'call_v2_guard', new_callable=AsyncMock) as mock_call:
-        mock_call.return_value = (mock_response, {})
-        
-        # Create a sample request with PII data
-        data = {
-            "messages": [
-                {"role": "system", "content": "You are a helpful assistant."},
-                {"role": "user", "content": "My credit card is 4111-1111-1111-1111 and my email is test@example.com. My phone number is 555-123-4567"}
-            ],
-            "model": "gpt-3.5-turbo",
-            "metadata": {}
-        }
-        
-        # Mock objects needed for the pre-call hook
-        user_api_key_dict = UserAPIKeyAuth(api_key="test_key")
-        cache = DualCache()
-        
-        # Call the pre-call hook with the specified call type
-        modified_data = await lakera_guardrail.async_pre_call_hook(
-            user_api_key_dict=user_api_key_dict,
-            cache=cache,
-            data=data,
-            call_type="completion"
-        )
-        print(modified_data)
-        
-        # Verify the messages have been modified to mask PII
-        assert modified_data["messages"][0]["content"] == "You are a helpful assistant."  # System prompt should be unchanged
-        
-        user_message = modified_data["messages"][1]["content"]
-        # Verify both credit card and email are masked
-        assert "4111-1111-1111-1111" not in user_message
-        assert "test@example.com" not in user_message
-        # Verify masking placeholders are present
-        assert "[MASKED CREDIT_CARD]" in user_message
-        assert "[MASKED EMAIL]" in user_message
+    # Mock objects needed for the pre-call hook
+    user_api_key_dict = UserAPIKeyAuth(api_key="test_key")
+    cache = DualCache()
+    
+    # Call the pre-call hook with the specified call type
+    modified_data = await lakera_guardrail.async_pre_call_hook(
+        user_api_key_dict=user_api_key_dict,
+        cache=cache,
+        data=data,
+        call_type="completion"
+    )
+    print(modified_data)
+    
+    # Verify the messages have been modified to mask PII
+    assert modified_data["messages"][0]["content"] == "You are a helpful assistant."  # System prompt should be unchanged
+    
+    user_message = modified_data["messages"][1]["content"]
+    assert "4111-1111-1111-1111" not in user_message
+    assert "test@example.com" not in user_message
 
 
 @pytest.mark.asyncio
diff --git a/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py b/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py
index 597c0845d43..7c151c80ae3 100644
--- a/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py
+++ b/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py
@@ -97,11 +97,6 @@ def test_is_idempotent_error_constraint_already_exists(self):
         error_message = "constraint 'fk_user_id' already exists"
         assert ProxyExtrasDBManager._is_idempotent_error(error_message) is True
 
-    def test_is_idempotent_error_does_not_exist(self):
-        """Test detection of 'does not exist' error"""
-        error_message = "ERROR: index 'idx' does not exist"
-        assert ProxyExtrasDBManager._is_idempotent_error(error_message) is True
-
     def test_is_idempotent_error_case_insensitive(self):
         """Test that idempotent error detection is case insensitive"""
         error_message = "COLUMN 'ID' ALREADY EXISTS"
diff --git a/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py b/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py
index 0a6c59d1b44..f706a025a09 100644
--- a/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py
+++ b/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py
@@ -203,7 +203,6 @@ class TestOCIImageUrlTransformation:
     """Tests for OCI image_url format handling in multimodal messages.
 
     Fixes: https://github.com/BerriAI/litellm/issues/18270
-    Fixes: https://github.com/BerriAI/litellm/issues/19589
     """
 
     def test_image_url_as_string(self):
@@ -225,8 +224,7 @@ def test_image_url_as_string(self):
         assert len(result) == 1
         assert result[0].role == "USER"
         assert len(result[0].content) == 2
-        # imageUrl is now an OCIImageUrl object with a 'url' property
-        assert result[0].content[1].imageUrl.url == "https://example.com/image.png"
+        assert result[0].content[1].imageUrl == "https://example.com/image.png"
 
     def test_image_url_as_openai_object(self):
         """Test that image_url as OpenAI-style object {"url": "..."} works."""
@@ -247,38 +245,7 @@ def test_image_url_as_openai_object(self):
         assert len(result) == 1
         assert result[0].role == "USER"
         assert len(result[0].content) == 2
-        # imageUrl is now an OCIImageUrl object with a 'url' property
-        assert result[0].content[1].imageUrl.url == "https://example.com/image.png"
-
-    def test_image_url_serializes_as_object(self):
-        """Test that imageUrl serializes as {"url": "..."} for OCI API.
-
-        Fixes: https://github.com/BerriAI/litellm/issues/19589
-        OCI expects imageUrl to be an object with a 'url' property, not a plain string.
-        """
-        from litellm.llms.oci.chat.transformation import adapt_messages_to_generic_oci_standard
-
-        messages = [
-            {
-                "role": "user",
-                "content": [
-                    {"type": "text", "text": "Describe this image."},
-                    {"type": "image_url", "image_url": {"url": "data:image/png;base64,ABC123"}},
-                ],
-            }
-        ]
-
-        result = adapt_messages_to_generic_oci_standard(messages)
-        image_part = result[0].content[1]
-
-        # Serialize as OCI would receive it (with exclude_none=True)
-        serialized = image_part.model_dump(exclude_none=True)
-
-        # Verify the structure matches OCI's expected format
-        assert serialized == {
-            "type": "IMAGE",
-            "imageUrl": {"url": "data:image/png;base64,ABC123"}
-        }
+        assert result[0].content[1].imageUrl == "https://example.com/image.png"
 
     def test_image_url_invalid_type_raises_error(self):
         """Test that invalid image_url type raises an error."""
diff --git a/tests/litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py
deleted file mode 100644
index ac2c945baa6..00000000000
--- a/tests/litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py
+++ /dev/null
@@ -1,1108 +0,0 @@
-"""Tests for MCP OAuth discoverable endpoints"""
-import pytest
-from unittest.mock import AsyncMock, MagicMock, patch
-
-
-@pytest.mark.asyncio
-async def test_authorize_endpoint_includes_response_type():
-    """Test that authorize endpoint includes response_type=code parameter (fixes #15684)"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            authorize,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    # Clear registry
-    global_mcp_server_manager.registry.clear()
-
-    # Create mock OAuth2 server
-    oauth2_server = MCPServer(
-        server_id="test_oauth_server",
-        name="test_oauth",
-        server_name="test_oauth",
-        alias="test_oauth",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id="test_client_id",
-        client_secret="test_client_secret",
-        authorization_url="https://provider.com/oauth/authorize",
-        token_url="https://provider.com/oauth/token",
-        scopes=["read", "write"],
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    # Mock request
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "https://litellm.example.com/"
-    mock_request.headers = {}
-
-    # Mock the encryption functions to avoid needing a signing key
-    with patch(
-        "litellm.proxy._experimental.mcp_server.discoverable_endpoints.encrypt_value_helper"
-    ) as mock_encrypt:
-        mock_encrypt.return_value = "mocked_encrypted_state"
-
-        # Call authorize endpoint
-        response = await authorize(
-            request=mock_request,
-            client_id="test_client_id",
-            mcp_server_name="test_oauth",
-            redirect_uri="https://client.example.com/callback",
-            state="test_state",
-        )
-
-    # Verify response is a redirect
-    assert response.status_code == 307  # FastAPI RedirectResponse default
-
-    # Verify response_type is in the redirect URL
-    assert "response_type=code" in response.headers["location"]
-    assert "https://provider.com/oauth/authorize" in response.headers["location"]
-    assert "client_id=test_client_id" in response.headers["location"]
-    assert "scope=read+write" in response.headers["location"]
-
-
-@pytest.mark.asyncio
-async def test_authorize_endpoint_forwards_pkce_parameters():
-    """Test that authorize endpoint forwards PKCE parameters (code_challenge and code_challenge_method)"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            authorize,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    # Clear registry
-    global_mcp_server_manager.registry.clear()
-
-    # Create mock OAuth2 server (simulating Google OAuth)
-    oauth2_server = MCPServer(
-        server_id="google_mcp",
-        name="google_mcp",
-        server_name="google_mcp",
-        alias="google_mcp",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id="669428968603-test.apps.googleusercontent.com",
-        client_secret="GOCSPX-test_secret",
-        authorization_url="https://accounts.google.com/o/oauth2/v2/auth",
-        token_url="https://oauth2.googleapis.com/token",
-        scopes=["https://www.googleapis.com/auth/drive", "openid", "email"],
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    # Mock request
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "https://litellm-proxy.example.com/"
-    mock_request.headers = {}
-
-    # Mock the encryption function
-    with patch(
-        "litellm.proxy._experimental.mcp_server.discoverable_endpoints.encrypt_value_helper"
-    ) as mock_encrypt:
-        mock_encrypt.return_value = "mocked_encrypted_state_with_pkce"
-
-        # Call authorize endpoint with PKCE parameters
-        response = await authorize(
-            request=mock_request,
-            client_id="669428968603-test.apps.googleusercontent.com",
-            mcp_server_name="google_mcp",
-            redirect_uri="http://localhost:60108/callback",
-            state="test_client_state",
-            code_challenge="x6YH_qgwbvOzbsHDuL1sW9gYkR9-gObUiIB5RkPwxDk",
-            code_challenge_method="S256",
-        )
-
-    # Verify response is a redirect
-    assert response.status_code == 307
-
-    # Verify PKCE parameters are included in the redirect URL
-    location = response.headers["location"]
-    assert "https://accounts.google.com/o/oauth2/v2/auth" in location
-    assert "code_challenge=x6YH_qgwbvOzbsHDuL1sW9gYkR9-gObUiIB5RkPwxDk" in location
-    assert "code_challenge_method=S256" in location
-    assert "client_id=669428968603-test.apps.googleusercontent.com" in location
-    assert "response_type=code" in location
-
-
-@pytest.mark.asyncio
-async def test_token_endpoint_forwards_code_verifier():
-    """Test that token endpoint forwards code_verifier for PKCE flow"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            token_endpoint,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-        import httpx
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    # Clear registry
-    global_mcp_server_manager.registry.clear()
-
-    # Create mock OAuth2 server
-    oauth2_server = MCPServer(
-        server_id="google_mcp",
-        name="google_mcp",
-        server_name="google_mcp",
-        alias="google_mcp",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id="669428968603-test.apps.googleusercontent.com",
-        client_secret="GOCSPX-test_secret",
-        authorization_url="https://accounts.google.com/o/oauth2/v2/auth",
-        token_url="https://oauth2.googleapis.com/token",
-        scopes=["https://www.googleapis.com/auth/drive", "openid", "email"],
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    # Mock request
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "https://litellm-proxy.example.com/"
-    mock_request.headers = {}
-
-    # Mock httpx client response
-    mock_response = MagicMock()
-    mock_response.json.return_value = {
-        "access_token": "ya29.test_access_token",
-        "token_type": "Bearer",
-        "expires_in": 3599,
-        "scope": "openid email https://www.googleapis.com/auth/drive",
-    }
-    mock_response.raise_for_status = MagicMock()
-
-    # Mock the async httpx client with AsyncMock for async methods
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "litellm.proxy._experimental.mcp_server.discoverable_endpoints.get_async_httpx_client"
-    ) as mock_get_client:
-        mock_async_client = MagicMock()
-        # Use AsyncMock for the async post method
-        mock_async_client.post = AsyncMock(return_value=mock_response)
-        mock_get_client.return_value = mock_async_client
-
-        # Call token endpoint with code_verifier
-        response = await token_endpoint(
-            request=mock_request,
-            grant_type="authorization_code",
-            code="4/test_authorization_code",
-            redirect_uri="http://localhost:60108/callback",
-            client_id="669428968603-test.apps.googleusercontent.com",
-            mcp_server_name="google_mcp",
-            client_secret="GOCSPX-test_secret",
-            code_verifier="test_code_verifier_from_client",
-        )
-
-    # Verify that the token endpoint was called with code_verifier
-    mock_async_client.post.assert_called_once()
-    call_args = mock_async_client.post.call_args
-
-    # Check the data parameter includes code_verifier
-    assert call_args[1]["data"]["code_verifier"] == "test_code_verifier_from_client"
-    assert call_args[1]["data"]["code"] == "4/test_authorization_code"
-    assert (
-        call_args[1]["data"]["client_id"]
-        == "669428968603-test.apps.googleusercontent.com"
-    )
-    assert call_args[1]["data"]["client_secret"] == "GOCSPX-test_secret"
-    assert call_args[1]["data"]["grant_type"] == "authorization_code"
-
-    # Verify response
-    response_data = response.body
-    import json
-
-    token_data = json.loads(response_data)
-    assert token_data["access_token"] == "ya29.test_access_token"
-    assert token_data["token_type"] == "Bearer"
-
-
-@pytest.mark.asyncio
-async def test_register_client_without_mcp_server_name_returns_dummy():
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            register_client,
-        )
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "https://proxy.litellm.example/"
-    mock_request.headers = {}
-    with patch(
-        "litellm.proxy._experimental.mcp_server.discoverable_endpoints._read_request_body",
-        new=AsyncMock(return_value={}),
-    ):
-        result = await register_client(request=mock_request)
-
-    assert result == {
-        "client_id": "dummy_client",
-        "client_secret": "dummy",
-        "redirect_uris": ["https://proxy.litellm.example/callback"],
-    }
-
-
-@pytest.mark.asyncio
-async def test_register_client_returns_existing_server_credentials():
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            register_client,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    global_mcp_server_manager.registry.clear()
-    oauth2_server = MCPServer(
-        server_id="stored_server",
-        name="stored_server",
-        server_name="stored_server",
-        alias="stored_server",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id="existing-client",
-        client_secret="existing-secret",
-        authorization_url="https://provider.example/oauth/authorize",
-        token_url="https://provider.example/oauth/token",
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "https://proxy.litellm.example/"
-    mock_request.headers = {}
-
-    try:
-        with patch(
-            "litellm.proxy._experimental.mcp_server.discoverable_endpoints._read_request_body",
-            new=AsyncMock(return_value={}),
-        ):
-            result = await register_client(
-                request=mock_request, mcp_server_name=oauth2_server.server_name
-            )
-    finally:
-        global_mcp_server_manager.registry.clear()
-
-    assert result == {
-        "client_id": "stored_server",
-        "client_secret": "dummy",
-        "redirect_uris": ["https://proxy.litellm.example/callback"],
-    }
-
-
-@pytest.mark.asyncio
-async def test_register_client_remote_registration_success():
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            register_client,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    global_mcp_server_manager.registry.clear()
-    oauth2_server = MCPServer(
-        server_id="remote_server",
-        name="remote_server",
-        server_name="remote_server",
-        alias="remote_server",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id=None,
-        client_secret=None,
-        authorization_url="https://provider.example/oauth/authorize",
-        token_url="https://provider.example/oauth/token",
-        registration_url="https://provider.example/oauth/register",
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "https://proxy.litellm.example/"
-    mock_request.headers = {}
-
-    request_payload = {
-        "client_name": "Litellm Proxy",
-        "grant_types": ["authorization_code", "refresh_token"],
-        "response_types": ["code"],
-        "token_endpoint_auth_method": "client_secret_post",
-    }
-
-    mock_response = MagicMock()
-    mock_response.json.return_value = {
-        "client_id": "generated-client",
-        "client_secret": "generated-secret",
-    }
-    mock_response.raise_for_status = MagicMock()
-    mock_async_client = MagicMock()
-    mock_async_client.post = AsyncMock(return_value=mock_response)
-
-    try:
-        with patch(
-            "litellm.proxy._experimental.mcp_server.discoverable_endpoints._read_request_body",
-            new=AsyncMock(return_value=request_payload),
-        ), patch(
-            "litellm.proxy._experimental.mcp_server.discoverable_endpoints.get_async_httpx_client",
-            return_value=mock_async_client,
-        ):
-            response = await register_client(
-                request=mock_request, mcp_server_name=oauth2_server.server_name
-            )
-    finally:
-        global_mcp_server_manager.registry.clear()
-
-    import json
-
-    assert response.status_code == 200
-    payload = json.loads(response.body.decode("utf-8"))
-    assert payload == mock_response.json.return_value
-
-    mock_async_client.post.assert_called_once()
-    call_args = mock_async_client.post.call_args
-    assert call_args.args[0] == oauth2_server.registration_url
-    assert call_args.kwargs["headers"] == {
-        "Content-Type": "application/json",
-        "Accept": "application/json",
-    }
-    assert call_args.kwargs["json"]["redirect_uris"] == [
-        "https://proxy.litellm.example/callback"
-    ]
-    assert call_args.kwargs["json"]["grant_types"] == request_payload["grant_types"]
-    assert (
-        call_args.kwargs["json"]["token_endpoint_auth_method"]
-        == request_payload["token_endpoint_auth_method"]
-    )
-
-
-@pytest.mark.asyncio
-async def test_authorize_endpoint_respects_x_forwarded_proto():
-    """Test that authorize endpoint uses X-Forwarded-Proto header to construct correct redirect_uri"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            authorize,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    # Clear registry
-    global_mcp_server_manager.registry.clear()
-
-    # Create mock OAuth2 server
-    oauth2_server = MCPServer(
-        server_id="test_oauth_server",
-        name="test_oauth",
-        server_name="test_oauth",
-        alias="test_oauth",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id="test_client_id",
-        client_secret="test_client_secret",
-        authorization_url="https://provider.com/oauth/authorize",
-        token_url="https://provider.com/oauth/token",
-        scopes=["read", "write"],
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    # Mock request with http base_url but X-Forwarded-Proto: https
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "http://litellm.example.com/"  # HTTP
-    mock_request.headers = {"X-Forwarded-Proto": "https"}  # Behind HTTPS proxy
-
-    # Mock the encryption functions
-    with patch(
-        "litellm.proxy._experimental.mcp_server.discoverable_endpoints.encrypt_value_helper"
-    ) as mock_encrypt:
-        mock_encrypt.return_value = "mocked_encrypted_state"
-
-        # Call authorize endpoint
-        response = await authorize(
-            request=mock_request,
-            client_id="test_client_id",
-            mcp_server_name="test_oauth",
-            redirect_uri="https://client.example.com/callback",
-            state="test_state",
-        )
-
-    # Verify redirect URL uses HTTPS in the redirect_uri parameter
-    location = response.headers["location"]
-
-    # The redirect_uri parameter sent to the OAuth provider should use HTTPS
-    assert (
-        "redirect_uri=https%3A%2F%2Flitellm.example.com%2Fcallback" in location
-        or "redirect_uri=https://litellm.example.com/callback" in location
-    )
-
-
-@pytest.mark.asyncio
-async def test_token_endpoint_respects_x_forwarded_proto():
-    """Test that token endpoint uses X-Forwarded-Proto header for redirect_uri"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            token_endpoint,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    # Clear registry
-    global_mcp_server_manager.registry.clear()
-
-    # Create mock OAuth2 server
-    oauth2_server = MCPServer(
-        server_id="google_mcp",
-        name="google_mcp",
-        server_name="google_mcp",
-        alias="google_mcp",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id="test_client_id",
-        client_secret="test_secret",
-        authorization_url="https://accounts.google.com/o/oauth2/v2/auth",
-        token_url="https://oauth2.googleapis.com/token",
-        scopes=["openid", "email"],
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    # Mock request with http base_url but X-Forwarded-Proto: https
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "http://litellm-proxy.example.com/"  # HTTP
-    mock_request.headers = {"X-Forwarded-Proto": "https"}  # Behind HTTPS proxy
-
-    # Mock httpx client response
-    mock_response = MagicMock()
-    mock_response.json.return_value = {
-        "access_token": "test_token",
-        "token_type": "Bearer",
-        "expires_in": 3599,
-    }
-    mock_response.raise_for_status = MagicMock()
-
-    # Mock the async httpx client
-    mock_async_client = MagicMock()
-    mock_async_client.post = AsyncMock(return_value=mock_response)
-
-    with patch(
-        "litellm.proxy._experimental.mcp_server.discoverable_endpoints.get_async_httpx_client"
-    ) as mock_get_client:
-        mock_get_client.return_value = mock_async_client
-
-        # Call token endpoint
-        response = await token_endpoint(
-            request=mock_request,
-            grant_type="authorization_code",
-            code="test_code",
-            redirect_uri="http://localhost:60108/callback",
-            client_id="test_client_id",
-            mcp_server_name="google_mcp",
-            client_secret="test_secret",
-        )
-
-    # Verify that the redirect_uri sent to the provider uses HTTPS
-    call_args = mock_async_client.post.call_args
-    assert (
-        call_args[1]["data"]["redirect_uri"]
-        == "https://litellm-proxy.example.com/callback"
-    )
-
-
-@pytest.mark.asyncio
-async def test_oauth_protected_resource_standard_pattern():
-    """Test that oauth_protected_resource_mcp_standard returns standard MCP URL pattern (/mcp/{server_name})"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            oauth_protected_resource_mcp_standard,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    # Clear registry
-    global_mcp_server_manager.registry.clear()
-
-    # Create mock OAuth2 server
-    oauth2_server = MCPServer(
-        server_id="test_server",
-        name="test_server",
-        server_name="test_server",
-        alias="test_server",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id="test_client_id",
-        client_secret="test_client_secret",
-        authorization_url="https://provider.com/oauth/authorize",
-        token_url="https://provider.com/oauth/token",
-        scopes=["read", "write"],
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    # Mock request
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "https://litellm.example.com/"
-    mock_request.headers = {}
-
-    # Call the standard pattern endpoint
-    response = await oauth_protected_resource_mcp_standard(
-        request=mock_request,
-        mcp_server_name="test_server",
-    )
-
-    # Verify response uses standard MCP pattern: /mcp/{server_name}
-    assert response["resource"] == "https://litellm.example.com/mcp/test_server"
-    assert response["authorization_servers"][0] == "https://litellm.example.com/test_server"
-    assert response["scopes_supported"] == oauth2_server.scopes
-
-
-@pytest.mark.asyncio
-async def test_oauth_protected_resource_legacy_pattern():
-    """Test that oauth_protected_resource_mcp returns legacy URL pattern (/{server_name}/mcp)"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            oauth_protected_resource_mcp,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    # Clear registry
-    global_mcp_server_manager.registry.clear()
-
-    # Create mock OAuth2 server
-    oauth2_server = MCPServer(
-        server_id="test_server",
-        name="test_server",
-        server_name="test_server",
-        alias="test_server",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id="test_client_id",
-        client_secret="test_client_secret",
-        authorization_url="https://provider.com/oauth/authorize",
-        token_url="https://provider.com/oauth/token",
-        scopes=["read", "write"],
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    # Mock request
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "https://litellm.example.com/"
-    mock_request.headers = {}
-
-    # Call the legacy pattern endpoint
-    response = await oauth_protected_resource_mcp(
-        request=mock_request,
-        mcp_server_name="test_server",
-    )
-
-    # Verify response uses legacy pattern: /{server_name}/mcp
-    assert response["resource"] == "https://litellm.example.com/test_server/mcp"
-    assert response["authorization_servers"][0] == "https://litellm.example.com/test_server"
-    assert response["scopes_supported"] == oauth2_server.scopes
-
-
-@pytest.mark.asyncio
-async def test_oauth_protected_resource_respects_x_forwarded_proto():
-    """Test that oauth_protected_resource_mcp uses X-Forwarded-Proto for URLs"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            oauth_protected_resource_mcp,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-    # Clear registry
-    global_mcp_server_manager.registry.clear()
-
-    # Create mock OAuth2 server
-    oauth2_server = MCPServer(
-        server_id="test_oauth_server",
-        name="test_oauth",
-        server_name="test_oauth",
-        alias="test_oauth",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id="test_client_id",
-        client_secret="test_client_secret",
-        authorization_url="https://provider.com/oauth/authorize",
-        token_url="https://provider.com/oauth/token",
-        scopes=["read", "write"],
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    # Mock request with http base_url but X-Forwarded-Proto: https
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "http://litellm.example.com/"  # HTTP
-    mock_request.headers = {"X-Forwarded-Proto": "https"}  # Behind HTTPS proxy
-
-    # Call the endpoint
-    response = await oauth_protected_resource_mcp(
-        request=mock_request,
-        mcp_server_name="test_oauth",
-    )
-
-    # Verify response uses HTTPS URLs
-    assert response["authorization_servers"][0].startswith(
-        "https://litellm.example.com/"
-    )
-    assert response["scopes_supported"] == oauth2_server.scopes
-
-
-@pytest.mark.asyncio
-async def test_oauth_authorization_server_respects_x_forwarded_proto():
-    """Test that oauth_authorization_server_mcp uses X-Forwarded-Proto for URLs"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            oauth_authorization_server_mcp,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-    # Clear registry
-    global_mcp_server_manager.registry.clear()
-
-    # Create mock OAuth2 server
-    oauth2_server = MCPServer(
-        server_id="test_oauth_server",
-        name="test_oauth",
-        server_name="test_oauth",
-        alias="test_oauth",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id="test_client_id",
-        client_secret="test_client_secret",
-        authorization_url="https://provider.com/oauth/authorize",
-        token_url="https://provider.com/oauth/token",
-        scopes=["read", "write"],
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    # Mock request with http base_url but X-Forwarded-Proto: https
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "http://litellm.example.com/"  # HTTP
-    mock_request.headers = {"X-Forwarded-Proto": "https"}  # Behind HTTPS proxy
-
-    # Call the endpoint
-    response = await oauth_authorization_server_mcp(
-        request=mock_request,
-        mcp_server_name="test_oauth",
-    )
-
-    # Verify response uses HTTPS URLs
-    assert response["authorization_endpoint"].startswith("https://litellm.example.com/")
-    assert response["token_endpoint"].startswith("https://litellm.example.com/")
-    assert response["registration_endpoint"].startswith("https://litellm.example.com/")
-    assert response["grant_types_supported"] == ["authorization_code", "refresh_token"]
-    assert response["scopes_supported"] == oauth2_server.scopes
-
-
-@pytest.mark.asyncio
-async def test_register_client_respects_x_forwarded_proto():
-    """Test that register_client uses X-Forwarded-Proto for redirect_uris"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            register_client,
-        )
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    # Mock request with http base_url but X-Forwarded-Proto: https
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "http://proxy.litellm.example/"  # HTTP
-    mock_request.headers = {"X-Forwarded-Proto": "https"}  # Behind HTTPS proxy
-
-    with patch(
-        "litellm.proxy._experimental.mcp_server.discoverable_endpoints._read_request_body",
-        new=AsyncMock(return_value={}),
-    ):
-        result = await register_client(request=mock_request)
-
-    # Verify the redirect_uris use HTTPS
-    assert result == {
-        "client_id": "dummy_client",
-        "client_secret": "dummy",
-        "redirect_uris": ["https://proxy.litellm.example/callback"],
-    }
-
-
-@pytest.mark.asyncio
-async def test_authorize_endpoint_respects_x_forwarded_host():
-    """Test that authorize endpoint uses X-Forwarded-Host and X-Forwarded-Proto to construct correct redirect_uri"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            authorize,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    # Clear registry
-    global_mcp_server_manager.registry.clear()
-
-    # Create mock OAuth2 server
-    oauth2_server = MCPServer(
-        server_id="test_oauth_server",
-        name="test_oauth",
-        server_name="test_oauth",
-        alias="test_oauth",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id="test_client_id",
-        client_secret="test_client_secret",
-        authorization_url="https://provider.com/oauth/authorize",
-        token_url="https://provider.com/oauth/token",
-        scopes=["read", "write"],
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    # Mock request simulating nginx proxy:
-    # Internal: http://localhost:8888/github/mcp
-    # External: https://proxy.example.com/github/mcp
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "http://localhost:8888/github/mcp"
-    mock_request.headers = {
-        "X-Forwarded-Proto": "https",
-        "X-Forwarded-Host": "proxy.example.com",
-    }
-
-    # Mock the encryption functions
-    with patch(
-        "litellm.proxy._experimental.mcp_server.discoverable_endpoints.encrypt_value_helper"
-    ) as mock_encrypt:
-        mock_encrypt.return_value = "mocked_encrypted_state"
-
-        # Call authorize endpoint
-        response = await authorize(
-            request=mock_request,
-            client_id="test_client_id",
-            mcp_server_name="test_oauth",
-            redirect_uri="https://client.example.com/callback",
-            state="test_state",
-        )
-
-    # Verify redirect URL uses the forwarded host and scheme
-    location = response.headers["location"]
-
-    # The redirect_uri parameter should use the external URL
-    assert (
-        "redirect_uri=https%3A%2F%2Fproxy.example.com%2Fgithub%2Fmcp%2Fcallback"
-        in location
-        or "redirect_uri=https://proxy.example.com/github/mcp/callback" in location
-    )
-
-
-@pytest.mark.asyncio
-async def test_token_endpoint_respects_x_forwarded_host():
-    """Test that token endpoint uses X-Forwarded-Host and X-Forwarded-Proto for redirect_uri"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            token_endpoint,
-        )
-        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
-            global_mcp_server_manager,
-        )
-        from litellm.types.mcp import MCPAuth
-        from litellm.types.mcp_server.mcp_server_manager import MCPServer
-        from litellm.proxy._types import MCPTransport
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    # Clear registry
-    global_mcp_server_manager.registry.clear()
-
-    # Create mock OAuth2 server
-    oauth2_server = MCPServer(
-        server_id="google_mcp",
-        name="google_mcp",
-        server_name="google_mcp",
-        alias="google_mcp",
-        transport=MCPTransport.http,
-        auth_type=MCPAuth.oauth2,
-        client_id="test_client_id",
-        client_secret="test_secret",
-        authorization_url="https://accounts.google.com/o/oauth2/v2/auth",
-        token_url="https://oauth2.googleapis.com/token",
-        scopes=["openid", "email"],
-    )
-    global_mcp_server_manager.registry[oauth2_server.server_id] = oauth2_server
-
-    # Mock request simulating nginx proxy without port in host
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = "http://localhost:8888/github/mcp"
-    mock_request.headers = {
-        "X-Forwarded-Proto": "https",
-        "X-Forwarded-Host": "proxy.example.com",
-    }
-
-    # Mock httpx client response
-    mock_response = MagicMock()
-    mock_response.json.return_value = {
-        "access_token": "test_token",
-        "token_type": "Bearer",
-        "expires_in": 3599,
-    }
-    mock_response.raise_for_status = MagicMock()
-
-    # Mock the async httpx client
-    mock_async_client = MagicMock()
-    mock_async_client.post = AsyncMock(return_value=mock_response)
-
-    with patch(
-        "litellm.proxy._experimental.mcp_server.discoverable_endpoints.get_async_httpx_client"
-    ) as mock_get_client:
-        mock_get_client.return_value = mock_async_client
-
-        # Call token endpoint
-        response = await token_endpoint(
-            request=mock_request,
-            grant_type="authorization_code",
-            code="test_code",
-            redirect_uri="http://localhost:60108/callback",
-            client_id="test_client_id",
-            mcp_server_name="google_mcp",
-            client_secret="test_secret",
-        )
-
-    # Verify that the redirect_uri sent to the provider uses the external URL
-    call_args = mock_async_client.post.call_args
-    assert (
-        call_args[1]["data"]["redirect_uri"]
-        == "https://proxy.example.com/github/mcp/callback"
-    )
-
-
-@pytest.mark.parametrize(
-    "base_url,x_forwarded_proto,x_forwarded_host,x_forwarded_port,expected_url",
-    [
-        # Case 1: No forwarded headers - use original URL as-is (no trailing slash)
-        (
-            "http://localhost:4000/",
-            None,
-            None,
-            None,
-            "http://localhost:4000",
-        ),
-        # Case 2: Only X-Forwarded-Proto - change scheme only
-        (
-            "http://localhost:4000/",
-            "https",
-            None,
-            None,
-            "https://localhost:4000",
-        ),
-        # Case 3: X-Forwarded-Proto + X-Forwarded-Host - change scheme and host
-        (
-            "http://localhost:4000/",
-            "https",
-            "proxy.example.com",
-            None,
-            "https://proxy.example.com",
-        ),
-        # Case 4: X-Forwarded-Host with port included in host header
-        (
-            "http://localhost:4000/",
-            "https",
-            "proxy.example.com:8080",
-            None,
-            "https://proxy.example.com:8080",
-        ),
-        # Case 5: X-Forwarded-Host + X-Forwarded-Port as separate headers
-        (
-            "http://localhost:4000/",
-            "https",
-            "proxy.example.com",
-            "8443",
-            "https://proxy.example.com:8443",
-        ),
-        # Case 6: Only X-Forwarded-Host without proto - use original scheme
-        (
-            "http://localhost:4000/",
-            None,
-            "proxy.example.com",
-            None,
-            "http://proxy.example.com",
-        ),
-        # Case 7: Only X-Forwarded-Port without host - preserves original port if present
-        # (This is safer behavior - X-Forwarded-Port alone is unusual)
-        (
-            "http://localhost:4000/",
-            None,
-            None,
-            "8443",
-            "http://localhost:4000",  # Original port preserved when already present
-        ),
-        # Case 8: Complex internal URL with path (path is preserved)
-        (
-            "http://localhost:8888/github/mcp",
-            "https",
-            "proxy.example.com",
-            None,
-            "https://proxy.example.com/github/mcp",
-        ),
-        # Case 9: IPv6 address in X-Forwarded-Host (should not treat :: as port separator)
-        (
-            "http://localhost:4000/",
-            "https",
-            "[2001:db8::1]",
-            None,
-            "https://[2001:db8::1]",
-        ),
-        # Case 10: IPv6 address with port
-        (
-            "http://localhost:4000/",
-            "https",
-            "[2001:db8::1]:8080",
-            None,
-            "https://[2001:db8::1]:8080",
-        ),
-        # Case 11: X-Forwarded-Host already has port, X-Forwarded-Port also provided (host wins)
-        (
-            "http://localhost:4000/",
-            "https",
-            "proxy.example.com:9000",
-            "8443",
-            "https://proxy.example.com:9000",
-        ),
-        # Case 12: Standard proxy setup (most common case)
-        (
-            "http://127.0.0.1:8888/",
-            "https",
-            "chatproxy.company.com",
-            None,
-            "https://chatproxy.company.com",
-        ),
-        # Case 13: Internal URL already has port, X-Forwarded-Port does NOT override
-        # (safer behavior - preserves original port when X-Forwarded-Host not provided)
-        (
-            "http://localhost:4000/",
-            None,
-            None,
-            "443",
-            "http://localhost:4000",  # Original port preserved
-        ),
-        # Case 14: Original URL with existing port in netloc, X-Forwarded-Host replaces it
-        (
-            "http://internal.local:8888/",
-            "https",
-            "external.com",
-            None,
-            "https://external.com",
-        ),
-    ],
-)
-def test_get_request_base_url_comprehensive(
-    base_url, x_forwarded_proto, x_forwarded_host, x_forwarded_port, expected_url
-):
-    """Comprehensive test for get_request_base_url with various header combinations"""
-    try:
-        from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
-            get_request_base_url,
-        )
-        from fastapi import Request
-    except ImportError:
-        pytest.skip("MCP discoverable endpoints not available")
-
-    # Create mock request
-    mock_request = MagicMock(spec=Request)
-    mock_request.base_url = base_url
-
-    # Build headers dict
-    headers = {}
-    if x_forwarded_proto:
-        headers["X-Forwarded-Proto"] = x_forwarded_proto
-    if x_forwarded_host:
-        headers["X-Forwarded-Host"] = x_forwarded_host
-    if x_forwarded_port:
-        headers["X-Forwarded-Port"] = x_forwarded_port
-
-    # Mock headers.get() to return our test values
-    def mock_get(header_name, default=None):
-        return headers.get(header_name, default)
-
-    mock_request.headers.get = mock_get
-
-    # Test the function
-    result = get_request_base_url(mock_request)
-
-    # Verify result
-    assert result == expected_url, (
-        f"Expected '{expected_url}' but got '{result}'\n"
-        f"Input: base_url={base_url}, "
-        f"X-Forwarded-Proto={x_forwarded_proto}, "
-        f"X-Forwarded-Host={x_forwarded_host}, "
-        f"X-Forwarded-Port={x_forwarded_port}"
-    )
diff --git a/tests/llm_translation/test_bedrock_completion.py b/tests/llm_translation/test_bedrock_completion.py
index d48dd1bfd98..7c0db41d13a 100644
--- a/tests/llm_translation/test_bedrock_completion.py
+++ b/tests/llm_translation/test_bedrock_completion.py
@@ -3954,288 +3954,3 @@ def test_bedrock_openai_error_handling():
 
     assert exc_info.value.status_code == 422
     print("✓ Error handling works correctly")
-
-# ============================================================================
-# Nova Grounding (web_search_options) Unit Tests (Mocked)
-# ============================================================================
-
-def test_bedrock_nova_grounding_web_search_options_non_streaming():
-    """
-    Unit test for Nova grounding using web_search_options parameter (non-streaming).
-
-    This test mocks the HTTP call to verify:
-    1. web_search_options is correctly mapped to systemTool for Nova models
-    2. The request structure is correct
-
-    Related: https://docs.aws.amazon.com/nova/latest/userguide/grounding.html
-    """
-    from unittest.mock import patch, MagicMock
-    from litellm.llms.custom_httpx.http_handler import HTTPHandler
-
-    client = HTTPHandler()
-
-    messages = [
-        {
-            "role": "user",
-            "content": "What is the current population of Tokyo, Japan?",
-        }
-    ]
-
-    with patch.object(client, "post") as mock_post:
-        try:
-            completion(
-                model="us.amazon.nova-pro-v1:0",  # No bedrock/ prefix when using api_base
-                messages=messages,
-                web_search_options={},  # Enables Nova grounding
-                max_tokens=500,
-                client=client,
-                api_base="https://bedrock-runtime.us-east-1.amazonaws.com",
-            )
-        except Exception:
-            pass  # Expected - we're just checking the request structure
-
-        # Verify the request was made correctly
-        if mock_post.called:
-            request_body = json.loads(mock_post.call_args.kwargs.get("data", "{}"))
-            print(f"Request body: {json.dumps(request_body, indent=2)}")
-
-            # Verify toolConfig is present with systemTool
-            assert "toolConfig" in request_body, "toolConfig should be in request"
-            tool_config = request_body["toolConfig"]
-            assert "tools" in tool_config, "tools should be in toolConfig"
-
-            # Find the systemTool for nova_grounding
-            system_tool_found = False
-            for tool in tool_config["tools"]:
-                if "systemTool" in tool:
-                    assert tool["systemTool"]["name"] == "nova_grounding"
-                    system_tool_found = True
-                    break
-
-            assert system_tool_found, "systemTool with nova_grounding should be present"
-            print(f"✓ web_search_options correctly transformed to systemTool (non-streaming)")
-
-
-def test_bedrock_nova_grounding_with_function_tools():
-    """
-    Unit test for Nova grounding combined with regular function tools.
-
-    This tests the scenario where users want both web grounding AND
-    custom function calling capabilities.
-    """
-    from unittest.mock import patch
-    from litellm.llms.custom_httpx.http_handler import HTTPHandler
-
-    client = HTTPHandler()
-
-    # Regular function tool
-    tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "get_stock_price",
-                "description": "Get the current stock price for a given ticker symbol",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "ticker": {
-                            "type": "string",
-                            "description": "The stock ticker symbol, e.g. AAPL, GOOGL",
-                        }
-                    },
-                    "required": ["ticker"],
-                },
-            },
-        }
-    ]
-
-    messages = [
-        {
-            "role": "user",
-            "content": "What is the current market cap of Apple Inc?",
-        }
-    ]
-
-    with patch.object(client, "post") as mock_post:
-        try:
-            completion(
-                model="us.amazon.nova-pro-v1:0",  # No bedrock/ prefix when using api_base
-                messages=messages,
-                tools=tools,
-                web_search_options={},  # Also enable web grounding
-                max_tokens=500,
-                client=client,
-                api_base="https://bedrock-runtime.us-east-1.amazonaws.com",
-            )
-        except Exception:
-            pass  # Expected - we're just checking the request structure
-
-        # Verify the request was made correctly
-        if mock_post.called:
-            request_body = json.loads(mock_post.call_args.kwargs.get("data", "{}"))
-            print(f"Request body: {json.dumps(request_body, indent=2)}")
-
-            # Verify toolConfig has both function tool and systemTool
-            assert "toolConfig" in request_body, "toolConfig should be in request"
-            tool_config = request_body["toolConfig"]
-            assert "tools" in tool_config, "tools should be in toolConfig"
-
-            tools_in_request = tool_config["tools"]
-
-            # Should have both the function tool and the systemTool
-            function_tool_found = False
-            system_tool_found = False
-
-            for tool in tools_in_request:
-                if "toolSpec" in tool:
-                    assert tool["toolSpec"]["name"] == "get_stock_price"
-                    function_tool_found = True
-                if "systemTool" in tool:
-                    assert tool["systemTool"]["name"] == "nova_grounding"
-                    system_tool_found = True
-
-            assert function_tool_found, "Function tool (get_stock_price) should be present"
-            assert system_tool_found, "systemTool (nova_grounding) should be present"
-            print(f"✓ Both function tools and web_search_options correctly combined")
-
-
-@pytest.mark.asyncio
-async def test_bedrock_nova_grounding_async():
-    """
-    Async unit test for Nova grounding via web_search_options.
-
-    This test verifies the request transformation for async calls.
-    """
-    from unittest.mock import patch, AsyncMock
-    from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
-
-    client = AsyncHTTPHandler()
-
-    messages = [
-        {
-            "role": "user",
-            "content": "What is the weather forecast for New York City today?",
-        }
-    ]
-
-    with patch.object(client, "post", new=AsyncMock()) as mock_post:
-        try:
-            await litellm.acompletion(
-                model="us.amazon.nova-pro-v1:0",  # No bedrock/ prefix when using api_base
-                messages=messages,
-                web_search_options={},
-                max_tokens=500,
-                client=client,
-                api_base="https://bedrock-runtime.us-east-1.amazonaws.com",
-            )
-        except Exception:
-            pass  # Expected - we're just checking the request structure
-
-        # Verify the request was made correctly
-        if mock_post.called:
-            request_body = json.loads(mock_post.call_args.kwargs.get("data", "{}"))
-            print(f"Request body: {json.dumps(request_body, indent=2)}")
-
-            # Verify toolConfig is present with systemTool
-            assert "toolConfig" in request_body, "toolConfig should be in request"
-            tool_config = request_body["toolConfig"]
-            assert "tools" in tool_config, "tools should be in toolConfig"
-
-            # Find the systemTool for nova_grounding
-            system_tool_found = False
-            for tool in tool_config["tools"]:
-                if "systemTool" in tool:
-                    assert tool["systemTool"]["name"] == "nova_grounding"
-                    system_tool_found = True
-                    break
-
-            assert system_tool_found, "systemTool with nova_grounding should be present"
-            print(f"✓ Async web_search_options correctly transformed to systemTool")
-
-
-def test_bedrock_nova_web_search_options_ignored_for_non_nova():
-    """
-    Test that web_search_options is ignored for non-Nova Bedrock models.
-
-    Nova grounding is only supported on Nova models. For other models,
-    the parameter should be silently ignored.
-    """
-    from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig
-
-    config = AmazonConverseConfig()
-
-    # Should return None for non-Nova models
-    result = config._map_web_search_options({}, "anthropic.claude-3-sonnet-v1")
-    assert result is None
-
-    result = config._map_web_search_options({}, "amazon.titan-text-express-v1")
-    assert result is None
-
-    # Should return systemTool for Nova models
-    result = config._map_web_search_options({}, "amazon.nova-pro-v1:0")
-    assert result is not None
-    system_tool = result.get("systemTool")
-    assert system_tool is not None
-    assert system_tool["name"] == "nova_grounding"
-
-    result2 = config._map_web_search_options({}, "us.amazon.nova-premier-v1:0")
-    assert result2 is not None
-    system_tool2 = result2.get("systemTool")
-    assert system_tool2 is not None
-    assert system_tool2["name"] == "nova_grounding"
-
-
-def test_bedrock_nova_grounding_request_transformation():
-    """
-    Unit test to verify that web_search_options transforms to systemTool in the request.
-    """
-    from unittest.mock import patch, MagicMock
-    from litellm.llms.custom_httpx.http_handler import HTTPHandler
-
-    client = HTTPHandler()
-
-    messages = [{"role": "user", "content": "What is the population of Tokyo?"}]
-
-    with patch.object(client, "post") as mock_post:
-        mock_post.return_value = MagicMock(
-            status_code=200,
-            json=lambda: {
-                "output": {"message": {"role": "assistant", "content": [{"text": "Test"}]}},
-                "stopReason": "end_turn",
-                "usage": {"inputTokens": 10, "outputTokens": 5}
-            }
-        )
-
-        try:
-            response = completion(
-                model="bedrock/us.amazon.nova-pro-v1:0",
-                messages=messages,
-                web_search_options={},
-                max_tokens=100,
-                client=client,
-            )
-        except Exception:
-            pass  # Expected - we're just checking the request
-
-        if mock_post.called:
-            request_body = json.loads(mock_post.call_args.kwargs.get("data", "{}"))
-            print(f"Request body: {json.dumps(request_body, indent=2)}")
-
-            # Verify toolConfig is present with systemTool
-            assert "toolConfig" in request_body, "toolConfig should be in request"
-
-            tool_config = request_body["toolConfig"]
-            assert "tools" in tool_config, "tools should be in toolConfig"
-
-            tools_in_request = tool_config["tools"]
-
-            # Find the systemTool
-            system_tool_found = False
-            for tool in tools_in_request:
-                if "systemTool" in tool:
-                    assert tool["systemTool"]["name"] == "nova_grounding"
-                    system_tool_found = True
-                    break
-
-            assert system_tool_found, "systemTool with nova_grounding should be present"
-            print("✓ web_search_options correctly transformed to systemTool")
diff --git a/tests/llm_translation/test_gigachat.py b/tests/llm_translation/test_gigachat.py
index b69a5428e42..dd8d56ff54a 100644
--- a/tests/llm_translation/test_gigachat.py
+++ b/tests/llm_translation/test_gigachat.py
@@ -5,7 +5,9 @@
 Run with: pytest tests/llm_translation/test_gigachat.py -v
 """
 
+import json
 import pytest
+from unittest.mock import Mock, MagicMock
 
 
 class TestGigaChatMessageTransformation:
@@ -14,7 +16,6 @@ class TestGigaChatMessageTransformation:
     @pytest.fixture
     def config(self):
         from litellm.llms.gigachat.chat.transformation import GigaChatConfig
-
         return GigaChatConfig()
 
     def test_simple_user_message(self, config):
@@ -51,46 +52,20 @@ def test_tool_role_to_function(self, config):
 
         assert result[0]["role"] == "function"
 
-    def test_tool_content_convertation_non_string_value(self, config):
-        """Non string tool content should be serialized"""
-        messages = [{"role": "tool", "content": {"output": 42}}]
-        result = config._transform_messages(messages)
-
-        assert result[0]["content"] == '{"output": 42}'
-
-    def test_tool_content_convertation_json_string_value(self, config):
-        """JSON string tool content left unchanged"""
-        valid_json = '{"output": "red car"}'
-        messages = [{"role": "tool", "content": valid_json}]
-        result = config._transform_messages(messages)
-
-        assert result[0]["content"] == valid_json
-
-    def test_tool_content_convertation_random_string_value(self, config):
-        """Non JSON tool content should be serialized"""
-        messages = [{"role": "tool", "content": "random string"}]
-        result = config._transform_messages(messages)
-
-        assert result[0]["content"] == '"random string"'
-
     def test_tool_calls_to_function_call(self, config):
         """tool_calls should be converted to function_call"""
-        messages = [
-            {
-                "role": "assistant",
-                "content": "",
-                "tool_calls": [
-                    {
-                        "id": "call_123",
-                        "type": "function",
-                        "function": {
-                            "name": "get_weather",
-                            "arguments": '{"city": "Moscow"}',
-                        },
-                    }
-                ],
-            }
-        ]
+        messages = [{
+            "role": "assistant",
+            "content": "",
+            "tool_calls": [{
+                "id": "call_123",
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "arguments": '{"city": "Moscow"}'
+                }
+            }]
+        }]
         result = config._transform_messages(messages)
 
         assert "function_call" in result[0]
@@ -119,7 +94,6 @@ class TestGigaChatCollapseUserMessages:
     @pytest.fixture
     def config(self):
         from litellm.llms.gigachat.chat.transformation import GigaChatConfig
-
         return GigaChatConfig()
 
     def test_no_collapse_single_message(self, config):
@@ -162,24 +136,23 @@ class TestGigaChatToolsTransformation:
     @pytest.fixture
     def config(self):
         from litellm.llms.gigachat.chat.transformation import GigaChatConfig
-
         return GigaChatConfig()
 
     def test_single_tool_conversion(self, config):
         """Single tool should be converted correctly"""
-        tools = [
-            {
-                "type": "function",
-                "function": {
-                    "name": "get_weather",
-                    "description": "Get weather for a city",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {"city": {"type": "string"}},
-                    },
-                },
+        tools = [{
+            "type": "function",
+            "function": {
+                "name": "get_weather",
+                "description": "Get weather for a city",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "city": {"type": "string"}
+                    }
+                }
             }
-        ]
+        }]
         result = config._convert_tools_to_functions(tools)
 
         assert len(result) == 1
@@ -189,22 +162,8 @@ def test_single_tool_conversion(self, config):
     def test_multiple_tools_conversion(self, config):
         """Multiple tools should all be converted"""
         tools = [
-            {
-                "type": "function",
-                "function": {
-                    "name": "func1",
-                    "description": "First",
-                    "parameters": {"type": "object", "properties": {}},
-                },
-            },
-            {
-                "type": "function",
-                "function": {
-                    "name": "func2",
-                    "description": "Second",
-                    "parameters": {"type": "object", "properties": {}},
-                },
-            },
+            {"type": "function", "function": {"name": "func1", "description": "First", "parameters": {"type": "object", "properties": {}}}},
+            {"type": "function", "function": {"name": "func2", "description": "Second", "parameters": {"type": "object", "properties": {}}}},
         ]
         result = config._convert_tools_to_functions(tools)
 
@@ -219,7 +178,6 @@ class TestGigaChatParamsTransformation:
     @pytest.fixture
     def config(self):
         from litellm.llms.gigachat.chat.transformation import GigaChatConfig
-
         return GigaChatConfig()
 
     def test_temperature_zero_becomes_top_p_zero(self, config):
@@ -271,10 +229,10 @@ def test_structured_output_via_json_schema(self, config):
                         "type": "object",
                         "properties": {
                             "name": {"type": "string"},
-                            "age": {"type": "integer"},
-                        },
-                    },
-                },
+                            "age": {"type": "integer"}
+                        }
+                    }
+                }
             }
         }
         result = config.map_openai_params(
@@ -325,7 +283,6 @@ class TestGigaChatTransformRequest:
     @pytest.fixture
     def config(self):
         from litellm.llms.gigachat.chat.transformation import GigaChatConfig
-
         return GigaChatConfig()
 
     def test_basic_request(self, config):
@@ -378,7 +335,6 @@ class TestGigaChatSupportedParams:
     @pytest.fixture
     def config(self):
         from litellm.llms.gigachat.chat.transformation import GigaChatConfig
-
         return GigaChatConfig()
 
     def test_supported_params(self, config):
diff --git a/tests/llm_translation/test_groq.py b/tests/llm_translation/test_groq.py
index b82cffd4189..33c1a425870 100644
--- a/tests/llm_translation/test_groq.py
+++ b/tests/llm_translation/test_groq.py
@@ -11,10 +11,7 @@
 
 import litellm
 from base_llm_unit_tests import BaseLLMChatTest
-from litellm.llms.groq.chat.transformation import (
-    GroqChatConfig,
-    GroqChatCompletionStreamingHandler,
-)
+from litellm.llms.groq.chat.transformation import GroqChatConfig
 
 class TestGroq(BaseLLMChatTest):
     def get_base_completion_call_args(self) -> dict:
@@ -167,124 +164,3 @@ def test_structured_output_passes_through_for_native_models(self):
         if "tools" in result:
             tool_names = [t.get("function", {}).get("name") for t in result["tools"]]
             assert "json_tool_call" not in tool_names
-
-
-class TestGroqReasoning:
-    """
-    Tests for Groq reasoning field mapping.
-    
-    Groq returns 'reasoning' field in delta, but LiteLLM expects 'reasoning_content'.
-    """
-
-    def test_reasoning_field_mapping_in_streaming_chunks(self):
-        """
-        Test that Groq's 'reasoning' field in streaming chunks is properly mapped
-        to LiteLLM's 'reasoning_content' field.
-        """
-        handler = GroqChatCompletionStreamingHandler(
-            streaming_response=None, sync_stream=True
-        )
-
-        # Simulate a chunk with reasoning field as returned by Groq
-        groq_chunk = {
-            "id": "chatcmpl-test",
-            "object": "chat.completion.chunk",
-            "created": 1769511767,
-            "model": "qwen/qwen3-32b",
-            "choices": [
-                {
-                    "delta": {
-                        "reasoning": "This is reasoning content",
-                        "role": None,
-                    },
-                    "finish_reason": None,
-                    "index": 0,
-                }
-            ],
-        }
-
-        # Parse the chunk
-        parsed_chunk = handler.chunk_parser(groq_chunk)
-
-        # Verify that reasoning was mapped to reasoning_content
-        assert parsed_chunk.choices[0].delta.reasoning_content == "This is reasoning content"
-        # Verify that the original 'reasoning' field was removed
-        assert not hasattr(parsed_chunk.choices[0].delta, "reasoning")
-
-    def test_reasoning_field_not_present(self):
-        """
-        Test that chunks without reasoning field still work correctly.
-        """
-        handler = GroqChatCompletionStreamingHandler(
-            streaming_response=None, sync_stream=True
-        )
-
-        # Simulate a chunk without reasoning field
-        groq_chunk = {
-            "id": "chatcmpl-test",
-            "object": "chat.completion.chunk",
-            "created": 1769511767,
-            "model": "qwen/qwen3-32b",
-            "choices": [
-                {
-                    "delta": {
-                        "content": "Regular content",
-                        "role": "assistant",
-                    },
-                    "finish_reason": None,
-                    "index": 0,
-                }
-            ],
-        }
-
-        # Parse the chunk
-        parsed_chunk = handler.chunk_parser(groq_chunk)
-
-        # Verify that content is present
-        assert parsed_chunk.choices[0].delta.content == "Regular content"
-        assert parsed_chunk.choices[0].delta.role == "assistant"
-        # Verify that reasoning_content is not set (it should be deleted by Delta.__init__)
-        assert not hasattr(parsed_chunk.choices[0].delta, "reasoning_content")
-
-    def test_reasoning_with_tool_calls(self):
-        """
-        Test that reasoning field is properly mapped even when tool_calls are present.
-        """
-        handler = GroqChatCompletionStreamingHandler(
-            streaming_response=None, sync_stream=True
-        )
-
-        # Simulate a chunk with both reasoning and tool_calls
-        groq_chunk = {
-            "id": "chatcmpl-test",
-            "object": "chat.completion.chunk",
-            "created": 1769511767,
-            "model": "qwen/qwen3-32b",
-            "choices": [
-                {
-                    "delta": {
-                        "reasoning": "Reasoning before tool call",
-                        "tool_calls": [
-                            {
-                                "index": 0,
-                                "id": "call_123",
-                                "function": {"name": "test_function", "arguments": "{}"},
-                                "type": "function",
-                            }
-                        ],
-                    },
-                    "finish_reason": None,
-                    "index": 0,
-                }
-            ],
-        }
-
-        # Parse the chunk
-        parsed_chunk = handler.chunk_parser(groq_chunk)
-
-        # Verify that reasoning was mapped to reasoning_content
-        assert parsed_chunk.choices[0].delta.reasoning_content == "Reasoning before tool call"
-        # Verify tool_calls are still present
-        assert parsed_chunk.choices[0].delta.tool_calls is not None
-        assert len(parsed_chunk.choices[0].delta.tool_calls) == 1
-        assert parsed_chunk.choices[0].delta.tool_calls[0]["function"]["name"] == "test_function"
diff --git a/tests/llm_translation/test_xai.py b/tests/llm_translation/test_xai.py
index aa0c33cfe05..64ee95b52f1 100644
--- a/tests/llm_translation/test_xai.py
+++ b/tests/llm_translation/test_xai.py
@@ -175,7 +175,3 @@ def get_base_completion_call_args(self):
     def test_tool_call_no_arguments(self, tool_call_no_arguments):
         """Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833"""
         pass
-
-    def test_web_search(self):
-        """xAI deprecated web search functionality"""
-        pytest.skip("xAI has deprecated web search functionality")
diff --git a/tests/local_testing/test_add_update_models.py b/tests/local_testing/test_add_update_models.py
index 4b5ec95d3fc..6d7ae597b54 100644
--- a/tests/local_testing/test_add_update_models.py
+++ b/tests/local_testing/test_add_update_models.py
@@ -16,8 +16,6 @@
 )  # Adds the parent directory to the system path
 import pytest, logging, asyncio
 import litellm
-import litellm.proxy
-import litellm.proxy.proxy_server
 from litellm.proxy.management_endpoints.model_management_endpoints import (
     add_new_model,
     update_model,
diff --git a/tests/local_testing/test_amazing_vertex_completion.py b/tests/local_testing/test_amazing_vertex_completion.py
index 866e2a85060..1987a7a545f 100644
--- a/tests/local_testing/test_amazing_vertex_completion.py
+++ b/tests/local_testing/test_amazing_vertex_completion.py
@@ -742,7 +742,7 @@ def test_gemini_pro_grounding(value_in_dict):
 @pytest.mark.parametrize("model", ["vertex_ai_beta/gemini-2.5-flash-lite"])  # "vertex_ai",
 @pytest.mark.parametrize("sync_mode", [True])  # "vertex_ai",
 @pytest.mark.asyncio
-@pytest.mark.flaky(retries=6, delay=2)
+@pytest.mark.flaky(retries=3, delay=1)
 async def test_gemini_pro_function_calling_httpx(model, sync_mode):
     try:
         load_vertex_ai_credentials()
@@ -785,7 +785,6 @@ async def test_gemini_pro_function_calling_httpx(model, sync_mode):
             "messages": messages,
             "tools": tools,
             "tool_choice": "required",
-            "timeout": 60,  # Add explicit timeout
         }
         print(f"Model for call - {model}")
         if sync_mode:
@@ -800,18 +799,12 @@ async def test_gemini_pro_function_calling_httpx(model, sync_mode):
             response.choices[0].message.tool_calls[0].function.arguments, str
         )
     except litellm.RateLimitError as e:
-        pytest.skip(f"Rate limit exceeded: {str(e)}")
-    except litellm.ServiceUnavailableError as e:
-        pytest.skip(f"Service unavailable: {str(e)}")
-    except litellm.Timeout as e:
-        pytest.skip(f"Request timeout: {str(e)}")
+        pass
     except Exception as e:
-        error_msg = str(e)
-        # Skip test for known transient API issues
-        if any(x in error_msg for x in ["429 Quota exceeded", "503", "Service unavailable", "timeout", "Timeout", "UNAVAILABLE"]):
-            pytest.skip(f"Transient API error: {error_msg}")
+        if "429 Quota exceeded" in str(e):
+            pass
         else:
-            pytest.fail(f"An unexpected exception occurred - {error_msg}")
+            pytest.fail("An unexpected exception occurred - {}".format(str(e)))
 
 
 from test_completion import response_format_tests
@@ -820,7 +813,7 @@ async def test_gemini_pro_function_calling_httpx(model, sync_mode):
 @pytest.mark.parametrize(
     "model,region",
     [
-        ("vertex_ai/mistral-small-2503", "us-central1"),
+        ("vertex_ai/mistral-large-2411", "us-central1"),
         ("vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas", "us-south1"),
         ("vertex_ai/openai/gpt-oss-20b-maas", "us-central1"),
     ],
@@ -893,7 +886,7 @@ async def test_partner_models_httpx(model, region, sync_mode):
         ("vertex_ai/meta/llama-4-scout-17b-16e-instruct-maas", "us-east5"),
         ("vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas", "us-south1"),
         (
-            "vertex_ai/mistral-small-2503",
+            "vertex_ai/mistral-large-2411",
             "us-central1",
         ),  # critical - we had this issue: https://github.com/BerriAI/litellm/issues/13888
         ("vertex_ai/openai/gpt-oss-20b-maas", "us-central1"),
diff --git a/tests/local_testing/test_auth_utils.py b/tests/local_testing/test_auth_utils.py
index d36f96b1a39..72f799a6cf0 100644
--- a/tests/local_testing/test_auth_utils.py
+++ b/tests/local_testing/test_auth_utils.py
@@ -356,25 +356,6 @@ def test_get_internal_user_header_from_mapping_no_internal_returns_none():
             "/openai/deployments/my-deployment/chat/completions",
             "my-deployment"
         ),
-        # Custom model_name with slashes (e.g., gcp/google/gemini-2.5-flash)
-        # This is the NVIDIA P0 bug fix - regex should capture full model name including slashes
-        (
-            {},
-            "/vertex_ai/v1/projects/my-project/locations/us-central1/publishers/google/models/gcp/google/gemini-2.5-flash:generateContent",
-            "gcp/google/gemini-2.5-flash"
-        ),
-        # Another custom model_name with slashes
-        (
-            {},
-            "/vertex_ai/v1/projects/my-project/locations/global/publishers/google/models/gcp/google/gemini-3-flash-preview:generateContent",
-            "gcp/google/gemini-3-flash-preview"
-        ),
-        # Model name with single slash
-        (
-            {},
-            "/vertex_ai/v1/projects/my-project/locations/us-central1/publishers/google/models/custom/model:generateContent",
-            "custom/model"
-        ),
     ],
 )
 def test_get_model_from_request_vertex_ai_passthrough(request_data, route, expected_model):
diff --git a/tests/local_testing/test_completion_with_retries.py b/tests/local_testing/test_completion_with_retries.py
index 585e1ee2618..6eb3ad460e6 100644
--- a/tests/local_testing/test_completion_with_retries.py
+++ b/tests/local_testing/test_completion_with_retries.py
@@ -60,7 +60,6 @@ async def test_completion_with_retry_policy(sync_mode):
 
     retry_number = 1
     retry_policy = RetryPolicy(
-        BadRequestErrorRetries=10,
         ContentPolicyViolationErrorRetries=retry_number,  # run 3 retries for ContentPolicyViolationErrors
         AuthenticationErrorRetries=0,  # run 0 retries for AuthenticationErrorRetries
     )
diff --git a/tests/local_testing/test_literalai.py b/tests/local_testing/test_literalai.py
new file mode 100644
index 00000000000..35e583549dd
--- /dev/null
+++ b/tests/local_testing/test_literalai.py
@@ -0,0 +1,72 @@
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+import asyncio
+import logging
+
+import pytest
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.integrations.literal_ai import LiteralAILogger
+
+verbose_logger.setLevel(logging.DEBUG)
+
+litellm.set_verbose = True
+
+
+@pytest.mark.asyncio
+async def test_literalai_queue_logging():
+    try:
+        # Initialize LiteralAILogger
+        test_literalai_logger = LiteralAILogger()
+
+        litellm.callbacks = [test_literalai_logger]
+        test_literalai_logger.batch_size = 6
+        litellm.set_verbose = True
+
+        # Make multiple calls to ensure we don't hit the batch size
+        for _ in range(5):
+            response = await litellm.acompletion(
+                model="gpt-3.5-turbo",
+                messages=[{"role": "user", "content": "Test message"}],
+                max_tokens=10,
+                temperature=0.2,
+                mock_response="This is a mock response",
+            )
+
+        await asyncio.sleep(3)
+
+        # Check that logs are in the queue
+        assert len(test_literalai_logger.log_queue) == 5
+
+        # Now make calls to exceed the batch size
+        for _ in range(3):
+            await litellm.acompletion(
+                model="gpt-3.5-turbo",
+                messages=[{"role": "user", "content": "Test message"}],
+                max_tokens=10,
+                temperature=0.2,
+                mock_response="This is a mock response",
+            )
+
+        # Wait a short time for any asynchronous operations to complete
+        await asyncio.sleep(1)
+
+        print(
+            "Length of literalai log queue: {}".format(
+                len(test_literalai_logger.log_queue)
+            )
+        )
+        # Check that the queue was flushed after exceeding batch size
+        assert len(test_literalai_logger.log_queue) < 5
+
+        # Clean up
+        for cb in litellm.callbacks:
+            if isinstance(cb, LiteralAILogger):
+                await cb.async_httpx_client.client.aclose()
+
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
diff --git a/tests/local_testing/test_timeout.py b/tests/local_testing/test_timeout.py
index 7f9837e81dd..bb6dbc938ba 100644
--- a/tests/local_testing/test_timeout.py
+++ b/tests/local_testing/test_timeout.py
@@ -285,3 +285,94 @@ async def test_anthropic_timeout(streaming, sync_mode):
         )
         print(type(e))
         pass
+
+
+@pytest.mark.asyncio
+async def test_timeout_respects_total_time_not_per_retry():
+    """
+    Test that timeout applies to the TOTAL operation time, not per-retry.
+    
+    This test ensures that when a user sets timeout=2, the entire operation
+    (including all retries) times out at ~2 seconds, not at 2s * num_retries.
+    
+    This is a regression test for the issue where timeout was being applied
+    per-retry attempt, causing the total time to be much longer than expected.
+    """
+    litellm.set_verbose = False
+    
+    timeout_value = 2.0
+    # Allow for some overhead (network, processing, etc.)
+    # but ensure we don't wait for multiple retries
+    max_allowed_time = timeout_value + 1.0  # 3 seconds max
+    
+    start_time = time.time()
+    
+    try:
+        # This should timeout because we're asking for a long response
+        # with a very short timeout
+        response = await litellm.acompletion(
+            model="gpt-3.5-turbo",
+            timeout=timeout_value,
+            messages=[{"role": "user", "content": "Write a very long detailed essay about the history of computing, at least 5000 words."}],
+        )
+        pytest.fail("Expected timeout error but got a response")
+    except (openai.APITimeoutError, litellm.exceptions.Timeout) as e:
+        elapsed_time = time.time() - start_time
+        
+        print(f"Timeout occurred after {elapsed_time:.2f} seconds")
+        print(f"Expected timeout: {timeout_value} seconds")
+        print(f"Max allowed time: {max_allowed_time} seconds")
+        
+        # Verify that the timeout happened within the expected time window
+        # It should be close to timeout_value, not timeout_value * num_retries
+        assert elapsed_time < max_allowed_time, (
+            f"Timeout took too long! Expected ~{timeout_value}s, "
+            f"got {elapsed_time:.2f}s. This suggests timeout is being "
+            f"applied per-retry instead of to the total operation."
+        )
+        
+        # Also verify it's not TOO fast (sanity check)
+        assert elapsed_time >= timeout_value * 0.5, (
+            f"Timeout happened too quickly: {elapsed_time:.2f}s. "
+            f"Expected at least {timeout_value * 0.5}s"
+        )
+        
+        print("✓ Timeout correctly applied to total operation time, not per-retry")
+    except Exception as e:
+        pytest.fail(
+            f"Expected timeout error but got different error: {type(e).__name__}: {e}"
+        )
+
+
+@pytest.mark.asyncio  
+async def test_timeout_with_retries_disabled():
+    """
+    Test that timeout works correctly when retries are explicitly disabled.
+    This should timeout even faster since there are no retry attempts.
+    """
+    litellm.set_verbose = False
+    
+    timeout_value = 2.0
+    max_allowed_time = timeout_value + 0.5  # Even tighter bound with no retries
+    
+    start_time = time.time()
+    
+    try:
+        response = await litellm.acompletion(
+            model="gpt-3.5-turbo",
+            timeout=timeout_value,
+            max_retries=0,  # Disable retries
+            messages=[{"role": "user", "content": "Write a very long detailed essay about the history of computing, at least 5000 words."}],
+        )
+        pytest.fail("Expected timeout error but got a response")
+    except (openai.APITimeoutError, litellm.exceptions.Timeout) as e:
+        elapsed_time = time.time() - start_time
+        
+        print(f"Timeout with no retries occurred after {elapsed_time:.2f} seconds")
+        
+        assert elapsed_time < max_allowed_time, (
+            f"Timeout took too long even with retries disabled! "
+            f"Expected ~{timeout_value}s, got {elapsed_time:.2f}s"
+        )
+        
+        print("✓ Timeout works correctly with retries disabled")
diff --git a/tests/mcp_tests/test_aresponses_api_with_mcp.py b/tests/mcp_tests/test_aresponses_api_with_mcp.py
index a7bbfef14af..6c8f51201d8 100644
--- a/tests/mcp_tests/test_aresponses_api_with_mcp.py
+++ b/tests/mcp_tests/test_aresponses_api_with_mcp.py
@@ -683,11 +683,9 @@ async def test_streaming_responses_api_with_mcp_tools(
 
     Return the user the result of request 2
     """
-    # Skip test if required API keys are not set
-    if ("anthropic" in model.lower() or "claude" in model.lower()) and not os.getenv("ANTHROPIC_API_KEY"):
+    # Skip test if ANTHROPIC_API_KEY is not set for anthropic models
+    if "anthropic" in model.lower() and not os.getenv("ANTHROPIC_API_KEY"):
         pytest.skip("ANTHROPIC_API_KEY not set, skipping anthropic model test")
-    if ("gpt" in model.lower() or "openai" in model.lower()) and not os.getenv("OPENAI_API_KEY"):
-        pytest.skip("OPENAI_API_KEY not set, skipping openai model test")
     
     from unittest.mock import AsyncMock, patch
     
diff --git a/tests/mcp_tests/test_mcp_client_unit.py b/tests/mcp_tests/test_mcp_client_unit.py
index 9533e56bcc8..fcee3208be1 100644
--- a/tests/mcp_tests/test_mcp_client_unit.py
+++ b/tests/mcp_tests/test_mcp_client_unit.py
@@ -5,7 +5,7 @@
 import os
 import sys
 import pytest
-from unittest.mock import AsyncMock, MagicMock, patch, ANY
+from unittest.mock import AsyncMock, MagicMock, patch
 
 # Add the project root to the path
 sys.path.insert(0, os.path.abspath("../../.."))
@@ -183,7 +183,7 @@ async def test_call_tool(self, mock_session_class, mock_transport):
         assert result == mock_result
         mock_session_instance.initialize.assert_called_once()
         mock_session_instance.call_tool.assert_called_once_with(
-            name="test_tool", arguments={"arg1": "value1"},progress_callback=ANY
+            name="test_tool", arguments={"arg1": "value1"}
         )
 
 
diff --git a/tests/mcp_tests/test_mcp_server.py b/tests/mcp_tests/test_mcp_server.py
index 5785ff750fd..e8a1231c6fb 100644
--- a/tests/mcp_tests/test_mcp_server.py
+++ b/tests/mcp_tests/test_mcp_server.py
@@ -1105,26 +1105,9 @@ async def mock_get_allowed_servers(user_auth=None):
 
     test_manager.get_allowed_mcp_servers = mock_get_allowed_servers
 
-    # Mock health_check_server to avoid real network calls that timeout
-    async def mock_health_check(server_id: str, mcp_auth_header=None):
-        server = test_manager.get_mcp_server_by_id(server_id)
-        if not server:
-            return None
-        return LiteLLM_MCPServerTable(
-            server_id=server_id,
-            server_name=server.name,
-            url=server.url,
-            transport=server.transport,
-            description=server.mcp_info.get("description") if server.mcp_info else None,
-            mcp_access_groups=server.access_groups,
-            status="healthy",
-            last_health_check=datetime.datetime.now(),
-            mcp_info=server.mcp_info,
-        )
-
-    test_manager.health_check_server = mock_health_check
-
     # Test the method (this tests our second fix)
+    import asyncio
+
     servers_list = await test_manager.get_all_mcp_servers_with_health_and_teams(
         user_api_key_auth=mock_user_auth
     )
diff --git a/tests/pass_through_unit_tests/test_anthropic_messages_tool_search.py b/tests/pass_through_unit_tests/test_anthropic_messages_tool_search.py
index 1bd1b0d906c..4914a3df77a 100644
--- a/tests/pass_through_unit_tests/test_anthropic_messages_tool_search.py
+++ b/tests/pass_through_unit_tests/test_anthropic_messages_tool_search.py
@@ -67,17 +67,17 @@ def get_model(self) -> str:
 #         return "vertex_ai/claude-sonnet-4@20250514"
 
 
-# class TestBedrockInvokeToolSearch(BaseAnthropicMessagesToolSearchTest):
-#     """
-#     E2E tests for tool search with Bedrock Invoke API.
+class TestBedrockInvokeToolSearch(BaseAnthropicMessagesToolSearchTest):
+    """
+    E2E tests for tool search with Bedrock Invoke API.
     
-#     Uses the bedrock/invoke/ prefix which routes through the native
-#     Anthropic Messages API format on Bedrock.
+    Uses the bedrock/invoke/ prefix which routes through the native
+    Anthropic Messages API format on Bedrock.
     
-#     Beta header: advanced-tool-use-2025-11-20 (passed via extra_headers)
+    Beta header: advanced-tool-use-2025-11-20 (passed via extra_headers)
     
-#     Note: Tool search on Bedrock is only supported on Claude Opus 4.5.
-#     """
+    Note: Tool search on Bedrock is only supported on Claude Opus 4.5.
+    """
 
-#     def get_model(self) -> str:
-#         return "bedrock/invoke/us.anthropic.claude-opus-4-5-20251101-v1:0"
+    def get_model(self) -> str:
+        return "bedrock/invoke/us.anthropic.claude-opus-4-5-20251101-v1:0"
diff --git a/tests/proxy_admin_ui_tests/test_key_management.py b/tests/proxy_admin_ui_tests/test_key_management.py
index fa39a05a27d..a196080eada 100644
--- a/tests/proxy_admin_ui_tests/test_key_management.py
+++ b/tests/proxy_admin_ui_tests/test_key_management.py
@@ -473,41 +473,18 @@ async def test_get_users_key_count(prisma_client):
     setattr(litellm.proxy.proxy_server, "master_key", "sk-1234")
     await litellm.proxy.proxy_server.prisma_client.connect()
 
-    # Create a test user with no initial keys to ensure deterministic behavior
-    test_user_id = f"test_user_key_count-{uuid.uuid4()}"
-    test_user_request = NewUserRequest(
-        user_id=test_user_id,
-        user_role=LitellmUserRoles.INTERNAL_USER.value,
-        auto_create_key=False,  # Ensure we start with 0 keys
-    )
-
-    await new_user(
-        test_user_request,
-        UserAPIKeyAuth(
-            user_role=LitellmUserRoles.PROXY_ADMIN,
-            api_key="sk-1234",
-            user_id="admin",
-        ),
-    )
-
-    # Get initial key count for the test user
-    initial_users = await get_users(
-        user_ids=test_user_id,
-        role=None,
-        page=1,
-        page_size=20,
-    )
+    # Get initial user list and select the first user
+    initial_users = await get_users(role=None, page=1, page_size=20)
     print("initial_users", initial_users)
-    assert len(initial_users["users"]) == 1, "Test user should be found"
+    assert len(initial_users["users"]) > 0, "No users found to test with"
+
     test_user = initial_users["users"][0]
-    assert test_user.user_id == test_user_id
     initial_key_count = test_user.key_count
-    assert initial_key_count == 0, f"Expected initial key count to be 0, but got {initial_key_count}"
 
-    # Create a new key for the test user
+    # Create a new key for the selected user
     new_key = await generate_key_fn(
         data=GenerateKeyRequest(
-            user_id=test_user_id,
+            user_id=test_user.user_id,
             key_alias=f"test_key_{uuid.uuid4()}",
             models=["fake-model"],
         ),
@@ -519,26 +496,19 @@ async def test_get_users_key_count(prisma_client):
     )
 
     # Get updated user list and check key count
-    updated_users = await get_users(
-        user_ids=test_user_id,
-        role=None,
-        page=1,
-        page_size=20,
-    )
+    updated_users = await get_users(role=None, page=1, page_size=20)
     print("updated_users", updated_users)
-    assert len(updated_users["users"]) == 1, "Test user should still be found"
-    updated_user = updated_users["users"][0]
-    updated_key_count = updated_user.key_count
+    updated_key_count = None
+    for user in updated_users["users"]:
+        if user.user_id == test_user.user_id:
+            updated_key_count = user.key_count
+            break
 
+    assert updated_key_count is not None, "Test user not found in updated users list"
     assert (
         updated_key_count == initial_key_count + 1
     ), f"Expected key count to increase by 1, but got {updated_key_count} (was {initial_key_count})"
 
-    # Clean up test user and keys
-    await prisma_client.db.litellm_usertable.delete(
-        where={"user_id": test_user_id}
-    )
-
 
 async def cleanup_existing_teams(prisma_client):
     all_teams = await prisma_client.db.litellm_teamtable.find_many()
diff --git a/tests/proxy_unit_tests/test_auth_checks.py b/tests/proxy_unit_tests/test_auth_checks.py
index 05c6e4984af..24c005443af 100644
--- a/tests/proxy_unit_tests/test_auth_checks.py
+++ b/tests/proxy_unit_tests/test_auth_checks.py
@@ -584,7 +584,7 @@ async def test_get_fuzzy_user_object():
     )
     assert result == test_user
     mock_prisma.db.litellm_usertable.find_first.assert_called_with(
-        where={"user_email": {"equals": "test@example.com", "mode": "insensitive"}},
+        where={"user_email": "test@example.com"},
         include={"organization_memberships": True},
     )
 
@@ -612,7 +612,7 @@ async def test_get_fuzzy_user_object():
     )
     assert result == test_user
     mock_prisma.db.litellm_usertable.find_first.assert_called_with(
-        where={"user_email": {"equals": "test@example.com", "mode": "insensitive"}},
+        where={"user_email": "test@example.com"},
         include={"organization_memberships": True},
     )
 
diff --git a/tests/proxy_unit_tests/test_get_image.py b/tests/proxy_unit_tests/test_get_image.py
deleted file mode 100644
index ad8c2672754..00000000000
--- a/tests/proxy_unit_tests/test_get_image.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import os
-import sys
-from unittest import mock
-
-# Standard path insertion
-sys.path.insert(0, os.path.abspath("../.."))
-
-import pytest
-import httpx
-from litellm.proxy.proxy_server import app
-
-
-@pytest.mark.asyncio
-async def test_get_image_error_handling():
-    """
-    Test that get_image handles network errors gracefully and doesn't hang.
-    """
-    # Set an unreachable URL
-    os.environ["UI_LOGO_PATH"] = "http://invalid-url-12345.com/logo.jpg"
-
-    # Clear cache
-    parent_dir = os.path.dirname(
-        os.path.dirname(
-            app.__file__
-            if hasattr(app, "__file__")
-            else "litellm/proxy/proxy_server.py"
-        )
-    )
-    cache_path = os.path.join(parent_dir, "proxy", "cached_logo.jpg")
-    if os.path.exists(cache_path):
-        os.remove(cache_path)
-
-    # Mock AsyncHTTPHandler to simulate a timeout or connection error
-    with mock.patch(
-        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.get"
-    ) as mock_get:
-        mock_get.side_effect = httpx.ConnectError("Network is unreachable")
-
-        async with httpx.AsyncClient(
-            transport=httpx.ASGITransport(app=app), base_url="http://testserver"
-        ) as ac:
-            response = await ac.get("/get_image")
-
-        assert response.status_code == 200
-        assert response.headers["content-type"] == "image/jpeg"
-
-
-@pytest.mark.asyncio
-async def test_get_image_cache_logic():
-    """
-    Test that once cached, get_image doesn't hit the network.
-    """
-    os.environ["UI_LOGO_PATH"] = "http://example.com/logo.jpg"
-
-    # Clear cache
-    parent_dir = os.path.dirname(
-        os.path.dirname(
-            app.__file__
-            if hasattr(app, "__file__")
-            else "litellm/proxy/proxy_server.py"
-        )
-    )
-    cache_path = os.path.join(parent_dir, "proxy", "cached_logo.jpg")
-    if os.path.exists(cache_path):
-        os.remove(cache_path)
-
-    # Mock response
-    mock_response = mock.Mock()
-    mock_response.status_code = 200
-    mock_response.content = b"fake image data"
-
-    with mock.patch(
-        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.get"
-    ) as mock_get:
-        mock_get.return_value = mock_response
-
-        async with httpx.AsyncClient(
-            transport=httpx.ASGITransport(app=app), base_url="http://testserver"
-        ) as ac:
-            # First call - should hit download logic
-            response1 = await ac.get("/get_image")
-            assert response1.status_code == 200
-            assert mock_get.call_count == 1
-
-            # Second call - should hit cache
-            response2 = await ac.get("/get_image")
-            assert response2.status_code == 200
-            # If cache works, mock_get shouldn't be called again
-            assert mock_get.call_count == 1
diff --git a/tests/proxy_unit_tests/test_proxy_server.py b/tests/proxy_unit_tests/test_proxy_server.py
index d864a0d986a..839d09b52d7 100644
--- a/tests/proxy_unit_tests/test_proxy_server.py
+++ b/tests/proxy_unit_tests/test_proxy_server.py
@@ -1117,10 +1117,7 @@ async def test_create_team_member_add(prisma_client, new_member_method):
     ) as mock_litellm_usertable, patch(
         "litellm.proxy.auth.auth_checks._get_team_object_from_user_api_key_cache",
         new=AsyncMock(return_value=team_obj),
-    ) as mock_team_obj, patch(
-        "litellm.proxy.proxy_server.prisma_client.get_data",
-        new=AsyncMock(return_value=[]),
-    ) as mock_get_data:
+    ) as mock_team_obj:
 
         mock_client = AsyncMock(
             return_value=LiteLLM_UserTable(
@@ -1129,10 +1126,6 @@ async def test_create_team_member_add(prisma_client, new_member_method):
         )
         mock_litellm_usertable.upsert = mock_client
         mock_litellm_usertable.find_many = AsyncMock(return_value=None)
-        # Mock find_first for user_email validation (returns None for new users)
-        mock_litellm_usertable.find_first = AsyncMock(return_value=None)
-        # Mock find_unique for user_id validation (returns None for new users)
-        mock_litellm_usertable.find_unique = AsyncMock(return_value=None)
         team_mock_client = AsyncMock()
         original_val = getattr(
             litellm.proxy.proxy_server.prisma_client.db, "litellm_teamtable"
@@ -1306,10 +1299,7 @@ async def test_create_team_member_add_team_admin(
     ) as mock_litellm_usertable, patch(
         "litellm.proxy.auth.auth_checks._get_team_object_from_user_api_key_cache",
         new=AsyncMock(return_value=team_obj),
-    ) as mock_team_obj, patch(
-        "litellm.proxy.proxy_server.prisma_client.get_data",
-        new=AsyncMock(return_value=[]),
-    ) as mock_get_data:
+    ) as mock_team_obj:
         mock_client = AsyncMock(
             return_value=LiteLLM_UserTable(
                 user_id="1234", max_budget=100, user_email="1234"
@@ -1317,10 +1307,6 @@ async def test_create_team_member_add_team_admin(
         )
         mock_litellm_usertable.upsert = mock_client
         mock_litellm_usertable.find_many = AsyncMock(return_value=None)
-        # Mock find_first for user_email validation (returns None for new users)
-        mock_litellm_usertable.find_first = AsyncMock(return_value=None)
-        # Mock find_unique for user_id validation (returns None for new users)
-        mock_litellm_usertable.find_unique = AsyncMock(return_value=None)
 
         team_mock_client = AsyncMock()
         original_val = getattr(
diff --git a/tests/proxy_unit_tests/test_response_polling_handler.py b/tests/proxy_unit_tests/test_response_polling_handler.py
index 26f8ac24adc..cb4cd0efe57 100644
--- a/tests/proxy_unit_tests/test_response_polling_handler.py
+++ b/tests/proxy_unit_tests/test_response_polling_handler.py
@@ -1005,142 +1005,6 @@ def test_polling_with_router_lookup_no_match(self):
         
         assert result is False
 
-    # ==================== Native Background Mode Tests ====================
-
-    def test_polling_disabled_when_model_in_native_background_mode(self):
-        """Test that polling is disabled when model is in native_background_mode list"""
-        from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request
-        
-        result = should_use_polling_for_request(
-            background_mode=True,
-            polling_via_cache_enabled="all",
-            redis_cache=Mock(),
-            model="o4-mini-deep-research",
-            llm_router=None,
-            native_background_mode=["o4-mini-deep-research", "o3-deep-research"],
-        )
-        
-        assert result is False
-
-    def test_polling_disabled_for_native_background_mode_with_provider_list(self):
-        """Test that native_background_mode takes precedence even when provider matches"""
-        from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request
-        
-        result = should_use_polling_for_request(
-            background_mode=True,
-            polling_via_cache_enabled=["openai"],
-            redis_cache=Mock(),
-            model="openai/o4-mini-deep-research",
-            llm_router=None,
-            native_background_mode=["openai/o4-mini-deep-research"],
-        )
-        
-        assert result is False
-
-    def test_polling_enabled_when_model_not_in_native_background_mode(self):
-        """Test that polling is enabled when model is not in native_background_mode list"""
-        from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request
-        
-        result = should_use_polling_for_request(
-            background_mode=True,
-            polling_via_cache_enabled="all",
-            redis_cache=Mock(),
-            model="gpt-4o",
-            llm_router=None,
-            native_background_mode=["o4-mini-deep-research"],
-        )
-        
-        assert result is True
-
-    def test_polling_enabled_when_native_background_mode_is_none(self):
-        """Test that polling works normally when native_background_mode is None"""
-        from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request
-        
-        result = should_use_polling_for_request(
-            background_mode=True,
-            polling_via_cache_enabled="all",
-            redis_cache=Mock(),
-            model="gpt-4o",
-            llm_router=None,
-            native_background_mode=None,
-        )
-        
-        assert result is True
-
-    def test_polling_enabled_when_native_background_mode_is_empty_list(self):
-        """Test that polling works normally when native_background_mode is empty list"""
-        from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request
-        
-        result = should_use_polling_for_request(
-            background_mode=True,
-            polling_via_cache_enabled="all",
-            redis_cache=Mock(),
-            model="gpt-4o",
-            llm_router=None,
-            native_background_mode=[],
-        )
-        
-        assert result is True
-
-    def test_native_background_mode_exact_match_required(self):
-        """Test that native_background_mode uses exact model name matching"""
-        from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request
-        
-        # "o4-mini" should not match "o4-mini-deep-research"
-        result = should_use_polling_for_request(
-            background_mode=True,
-            polling_via_cache_enabled="all",
-            redis_cache=Mock(),
-            model="o4-mini",
-            llm_router=None,
-            native_background_mode=["o4-mini-deep-research"],
-        )
-        
-        assert result is True
-
-    def test_native_background_mode_with_provider_prefix_in_request(self):
-        """Test native_background_mode matching when request model has provider prefix"""
-        from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request
-        
-        # Model in native_background_mode without provider prefix
-        # Request comes in with provider prefix - should not match
-        result = should_use_polling_for_request(
-            background_mode=True,
-            polling_via_cache_enabled=["openai"],
-            redis_cache=Mock(),
-            model="openai/o4-mini-deep-research",
-            llm_router=None,
-            native_background_mode=["o4-mini-deep-research"],  # Without prefix
-        )
-        
-        # Should return True because "openai/o4-mini-deep-research" != "o4-mini-deep-research"
-        assert result is True
-
-    def test_native_background_mode_with_router_lookup(self):
-        """Test that native_background_mode works with router-resolved models"""
-        from litellm.proxy.response_polling.polling_handler import should_use_polling_for_request
-        
-        mock_router = Mock()
-        mock_router.model_name_to_deployment_indices = {"deep-research": [0]}
-        mock_router.model_list = [
-            {
-                "model_name": "deep-research",
-                "litellm_params": {"model": "openai/o4-mini-deep-research"}
-            }
-        ]
-        
-        # Model alias "deep-research" is in native_background_mode
-        result = should_use_polling_for_request(
-            background_mode=True,
-            polling_via_cache_enabled=["openai"],
-            redis_cache=Mock(),
-            model="deep-research",
-            llm_router=mock_router,
-            native_background_mode=["deep-research"],
-        )
-        
-        assert result is False
-
 
 class TestStreamingEventParsing:
     """
diff --git a/tests/proxy_unit_tests/test_server_root_path.py b/tests/proxy_unit_tests/test_server_root_path.py
deleted file mode 100644
index 4b39558e15a..00000000000
--- a/tests/proxy_unit_tests/test_server_root_path.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import os
-from unittest import mock
-from litellm.proxy import utils
-
-
-# Test the utility function logic
-def test_get_server_root_path_unset():
-    """
-    Test that get_server_root_path returns empty string when SERVER_ROOT_PATH is unset
-    """
-    with mock.patch.dict(os.environ, {}, clear=True):
-        # We need to make sure SERVER_ROOT_PATH is not in env
-        if "SERVER_ROOT_PATH" in os.environ:
-            del os.environ["SERVER_ROOT_PATH"]
-
-        root_path = utils.get_server_root_path()
-        assert (
-            root_path == ""
-        ), "Should return empty string when unset to allow X-Forwarded-Prefix"
-
-
-def test_get_server_root_path_set():
-    """
-    Test that get_server_root_path returns the value when SERVER_ROOT_PATH is set
-    """
-    with mock.patch.dict(os.environ, {"SERVER_ROOT_PATH": "/my-path"}, clear=True):
-        root_path = utils.get_server_root_path()
-        assert root_path == "/my-path", "Should return the set value"
-
-
-def test_get_server_root_path_empty_string():
-    """
-    Test that get_server_root_path returns empty string when SERVER_ROOT_PATH is explicitly empty
-    """
-    with mock.patch.dict(os.environ, {"SERVER_ROOT_PATH": ""}, clear=True):
-        root_path = utils.get_server_root_path()
-        assert (
-            root_path == ""
-        ), "Should return empty string when explicitly set to empty"
-
-
-# Integration test simulation for FastAPI app initialization
-def test_fastapi_app_initialization_mock():
-    """
-    Simulate how proxy_server.py initializes FastAPI app with the root_path.
-    We don't import proxy_server because it has global side effects/singletons.
-    Instead we verify the logic flow.
-    """
-    from fastapi import FastAPI
-
-    # CASE 1: Proxy Mode (Unset)
-    with mock.patch.dict(os.environ, {}, clear=True):
-        if "SERVER_ROOT_PATH" in os.environ:
-            del os.environ["SERVER_ROOT_PATH"]
-
-        server_root_path = utils.get_server_root_path()
-        app = FastAPI(root_path=server_root_path)
-        assert app.root_path == ""
-
-    # CASE 2: Direct Mode (Set)
-    with mock.patch.dict(os.environ, {"SERVER_ROOT_PATH": "/custom-root"}, clear=True):
-        server_root_path = utils.get_server_root_path()
-        app = FastAPI(root_path=server_root_path)
-        assert app.root_path == "/custom-root"
diff --git a/tests/router_unit_tests/test_router_helper_utils.py b/tests/router_unit_tests/test_router_helper_utils.py
index 673c606a7d1..073433cb9e5 100644
--- a/tests/router_unit_tests/test_router_helper_utils.py
+++ b/tests/router_unit_tests/test_router_helper_utils.py
@@ -2169,32 +2169,3 @@ def test_resolve_model_name_from_model_id():
 
     result = router.resolve_model_name_from_model_id("gpt-3.5-turbo")
     assert result == "gpt-3.5-turbo"
-
-
-def test_get_valid_args():
-    """Test get_valid_args static method returns valid Router.__init__ arguments"""
-    # Call the static method
-    valid_args = Router.get_valid_args()
-    
-    # Verify it returns a list
-    assert isinstance(valid_args, list)
-    assert len(valid_args) > 0
-    
-    # Verify it contains expected Router.__init__ arguments
-    expected_args = [
-        "model_list",
-        "routing_strategy",
-        "cache_responses",
-        "num_retries",
-        "timeout",
-        "fallbacks",
-    ]
-    for arg in expected_args:
-        assert arg in valid_args, f"Expected argument '{arg}' not found in valid_args"
-    
-    # Verify "self" is not in the list (since it's removed)
-    assert "self" not in valid_args
-    
-    # Verify it contains keyword-only arguments too
-    # These are common Router.__init__ parameters
-    assert "assistants_config" in valid_args or "search_tools" in valid_args
diff --git a/tests/test_litellm/a2a_protocol/test_card_resolver.py b/tests/test_litellm/a2a_protocol/test_card_resolver.py
deleted file mode 100644
index a1bd33107b6..00000000000
--- a/tests/test_litellm/a2a_protocol/test_card_resolver.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""
-Mock tests for LiteLLMA2ACardResolver.
-
-Tests that the card resolver tries both old and new well-known paths.
-"""
-
-import sys
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-
-@pytest.mark.asyncio
-async def test_card_resolver_fallback_from_new_to_old_path():
-    """
-    Test that the card resolver tries the new path (/.well-known/agent-card.json) first,
-    and falls back to the old path (/.well-known/agent.json) if the new path fails.
-    """
-    # Mock the AgentCard
-    mock_agent_card = MagicMock()
-    mock_agent_card.name = "Test Agent"
-    mock_agent_card.description = "A test agent"
-
-    # Track which paths were called
-    paths_called = []
-
-    # Create a mock base class
-    class MockA2ACardResolver:
-        def __init__(self, base_url):
-            self.base_url = base_url
-
-        async def get_agent_card(self, relative_card_path=None, http_kwargs=None):
-            paths_called.append(relative_card_path)
-            if relative_card_path == "/.well-known/agent-card.json":
-                # First call (new path) fails
-                raise Exception("404 Not Found")
-            else:
-                # Second call (old path) succeeds
-                return mock_agent_card
-
-    # Create mock A2A module
-    mock_a2a_module = MagicMock()
-    mock_a2a_client = MagicMock()
-    mock_a2a_constants = MagicMock()
-    mock_a2a_constants.AGENT_CARD_WELL_KNOWN_PATH = "/.well-known/agent-card.json"
-    mock_a2a_constants.PREV_AGENT_CARD_WELL_KNOWN_PATH = "/.well-known/agent.json"
-
-    with patch.dict(
-        sys.modules,
-        {
-            "a2a": mock_a2a_module,
-            "a2a.client": MagicMock(A2ACardResolver=MockA2ACardResolver),
-            "a2a.utils.constants": mock_a2a_constants,
-        },
-    ):
-        # Import after patching
-        from litellm.a2a_protocol.card_resolver import LiteLLMA2ACardResolver
-
-        resolver = LiteLLMA2ACardResolver(base_url="http://test-agent:8000")
-        result = await resolver.get_agent_card()
-
-        # Verify both paths were tried in correct order
-        assert len(paths_called) == 2
-        assert paths_called[0] == "/.well-known/agent-card.json"  # New path tried first
-        assert paths_called[1] == "/.well-known/agent.json"  # Old path tried second
-
-        # Verify the result
-        assert result == mock_agent_card
-        assert result.name == "Test Agent"
diff --git a/tests/test_litellm/containers/test_container_api.py b/tests/test_litellm/containers/test_container_api.py
index f489447ca99..c7bb68e79cb 100644
--- a/tests/test_litellm/containers/test_container_api.py
+++ b/tests/test_litellm/containers/test_container_api.py
@@ -166,55 +166,30 @@ async def test_alist_containers_basic(self):
             assert isinstance(response, ContainerListResponse)
             assert len(response.data) == 1
 
-    @pytest.mark.parametrize(
-        "container_id,container_name,status,provider",
-        [
-            ("cntr_retrieve_test", "Retrieved Container", "running", "openai"),
-            ("cntr_different_id", "Another Container", "stopped", "openai"),
-        ],
-    )
-    def test_retrieve_container_basic(self, container_id, container_name, status, provider):
-        """Test basic container retrieval functionality.
-        
-        This test verifies that:
-        1. retrieve_container correctly calls the handler with the container_id
-        2. The response is properly deserialized into a ContainerObject
-        3. All fields are correctly mapped from the handler response
-        4. The function works with different container states and IDs
-        """
-        # Arrange: Create mock response with test parameters
+    def test_retrieve_container_basic(self):
+        """Test basic container retrieval functionality."""
+        container_id = "cntr_retrieve_test"
         mock_response = ContainerObject(
             id=container_id,
             object="container",
             created_at=1747857508,
-            status=status,
+            status="running",
             expires_after={"anchor": "last_active_at", "minutes": 20},
             last_active_at=1747857508,
-            name=container_name
+            name="Retrieved Container"
         )
         
         with patch('litellm.containers.main.base_llm_http_handler') as mock_handler:
             mock_handler.container_retrieve_handler.return_value = mock_response
             
-            # Act: Call retrieve_container
             response = retrieve_container(
                 container_id=container_id,
-                custom_llm_provider=provider
+                custom_llm_provider="openai"
             )
             
-            # Assert: Verify the handler was called correctly
-            mock_handler.container_retrieve_handler.assert_called_once()
-            call_kwargs = mock_handler.container_retrieve_handler.call_args.kwargs
-            assert call_kwargs["container_id"] == container_id
-            
-            # Assert: Verify response structure and content
             assert isinstance(response, ContainerObject)
             assert response.id == container_id
-            assert response.name == container_name
-            assert response.status == status
-            assert response.object == "container"
-            assert response.expires_after.minutes == 20
-            assert response.expires_after.anchor == "last_active_at"
+            assert response.name == "Retrieved Container"
 
     @pytest.mark.asyncio
     async def test_aretrieve_container_basic(self):
diff --git a/tests/test_litellm/containers/test_container_integration.py b/tests/test_litellm/containers/test_container_integration.py
index d36918c63b9..e83ae921c19 100644
--- a/tests/test_litellm/containers/test_container_integration.py
+++ b/tests/test_litellm/containers/test_container_integration.py
@@ -357,15 +357,15 @@ def test_container_workflow_simulation(self):
 
     def test_error_handling_integration(self):
         """Test error handling in the integration flow."""
-        # Simulate an API error
-        api_error = litellm.APIError(
-            status_code=400,
-            message="API Error occurred", 
-            llm_provider="openai",
-            model=""
-        )
-        
-        with patch.object(litellm.main.base_llm_http_handler, 'container_create_handler', side_effect=api_error):
+        with patch('litellm.containers.main.base_llm_http_handler') as mock_handler:
+            # Simulate an API error
+            mock_handler.container_create_handler.side_effect = litellm.APIError(
+                status_code=400,
+                message="API Error occurred", 
+                llm_provider="openai",
+                model=""
+            )
+            
             with pytest.raises(litellm.APIError):
                 create_container(
                     name="Error Test Container",
@@ -385,12 +385,12 @@ def test_provider_support(self, provider):
             name="Provider Test Container"
         )
         
-        with patch.object(litellm.main.base_llm_http_handler, 'container_create_handler', return_value=mock_response) as mock_handler:
+        with patch('litellm.containers.main.base_llm_http_handler') as mock_handler:
+            mock_handler.container_create_handler.return_value = mock_response
+            
             response = create_container(
                 name="Provider Test Container",
                 custom_llm_provider=provider
             )
             
             assert response.name == "Provider Test Container"
-            # Verify the mock was actually called (not making real API calls)
-            mock_handler.assert_called_once()
diff --git a/tests/test_litellm/enterprise/enterprise_callbacks/send_emails/test_resend_email.py b/tests/test_litellm/enterprise/enterprise_callbacks/send_emails/test_resend_email.py
index 8db6d98f13d..dffa343d99f 100644
--- a/tests/test_litellm/enterprise/enterprise_callbacks/send_emails/test_resend_email.py
+++ b/tests/test_litellm/enterprise/enterprise_callbacks/send_emails/test_resend_email.py
@@ -2,9 +2,7 @@
 import sys
 import unittest.mock as mock
 
-import httpx
 import pytest
-import respx
 from httpx import Response
 
 sys.path.insert(0, os.path.abspath("../../.."))
@@ -13,8 +11,6 @@
     ResendEmailLogger,
 )
 
-# Test file for Resend email integration
-
 
 @pytest.fixture
 def mock_env_vars():
@@ -27,27 +23,21 @@ def mock_httpx_client():
     with mock.patch(
         "litellm_enterprise.enterprise_callbacks.send_emails.resend_email.get_async_httpx_client"
     ) as mock_client:
-
-        mock_response = mock.Mock(spec=Response)
+        # Create a mock response
+        mock_response = mock.AsyncMock(spec=Response)
         mock_response.status_code = 200
         mock_response.json.return_value = {"id": "test_email_id"}
-        mock_response.raise_for_status.return_value = None 
 
+        # Create a mock client
         mock_async_client = mock.AsyncMock()
         mock_async_client.post.return_value = mock_response
-
         mock_client.return_value = mock_async_client
+
         yield mock_async_client
 
 
 @pytest.mark.asyncio
-@respx.mock
 async def test_send_email_success(mock_env_vars, mock_httpx_client):
-    # Block all HTTP requests at network level to prevent real API calls
-    respx.post("https://api.resend.com/emails").mock(
-        return_value=httpx.Response(200, json={"id": "test_email_id"})
-    )
-    
     # Initialize the logger
     logger = ResendEmailLogger()
 
@@ -81,57 +71,33 @@ async def test_send_email_success(mock_env_vars, mock_httpx_client):
 
 
 @pytest.mark.asyncio
-@respx.mock
 async def test_send_email_missing_api_key(mock_httpx_client):
-    # Block all HTTP requests at network level to prevent real API calls
-    respx.post("https://api.resend.com/emails").mock(
-        return_value=httpx.Response(200, json={"id": "test_email_id"})
-    )
-    
-    # Remove the API key from environment before initializing logger
-    original_key = os.environ.pop("RESEND_API_KEY", None)
-    
-    try:
-        # Initialize the logger after removing the API key
-        logger = ResendEmailLogger()
-
-        # Test data
-        from_email = "test@example.com"
-        to_email = ["recipient@example.com"]
-        subject = "Test Subject"
-        html_body = "<p>Test email body</p>"
-
-        # Mock the response to avoid making real HTTP requests
-        mock_response = mock.Mock(spec=Response)
-        mock_response.raise_for_status.return_value = None
+    # Remove the API key from environment
+    if "RESEND_API_KEY" in os.environ:
+        del os.environ["RESEND_API_KEY"]
 
-        mock_response.status_code = 200
-        mock_response.json.return_value = {"id": "test_email_id"}
-        mock_httpx_client.post.return_value = mock_response
+    # Initialize the logger
+    logger = ResendEmailLogger()
 
-        # Send email
-        await logger.send_email(
-            from_email=from_email, to_email=to_email, subject=subject, html_body=html_body
-        )
+    # Test data
+    from_email = "test@example.com"
+    to_email = ["recipient@example.com"]
+    subject = "Test Subject"
+    html_body = "<p>Test email body</p>"
+
+    # Send email
+    await logger.send_email(
+        from_email=from_email, to_email=to_email, subject=subject, html_body=html_body
+    )
 
-        # Verify the HTTP client was called with None as the API key
-        mock_httpx_client.post.assert_called_once()
-        call_args = mock_httpx_client.post.call_args
-        assert call_args[1]["headers"] == {"Authorization": "Bearer None"}
-    finally:
-        # Restore the original key if it existed
-        if original_key is not None:
-            os.environ["RESEND_API_KEY"] = original_key
+    # Verify the HTTP client was called with None as the API key
+    mock_httpx_client.post.assert_called_once()
+    call_args = mock_httpx_client.post.call_args
+    assert call_args[1]["headers"] == {"Authorization": "Bearer None"}
 
 
 @pytest.mark.asyncio
-@respx.mock
 async def test_send_email_multiple_recipients(mock_env_vars, mock_httpx_client):
-    # Block all HTTP requests at network level to prevent real API calls
-    respx.post("https://api.resend.com/emails").mock(
-        return_value=httpx.Response(200, json={"id": "test_email_id"})
-    )
-    
     # Initialize the logger
     logger = ResendEmailLogger()
 
@@ -141,14 +107,6 @@ async def test_send_email_multiple_recipients(mock_env_vars, mock_httpx_client):
     subject = "Test Subject"
     html_body = "<p>Test email body</p>"
 
-    # Mock the response to avoid making real HTTP requests
-    mock_response = mock.Mock(spec=Response)
-    mock_response.raise_for_status.return_value = None
-
-    mock_response.status_code = 200
-    mock_response.json.return_value = {"id": "test_email_id"}
-    mock_httpx_client.post.return_value = mock_response
-
     # Send email
     await logger.send_email(
         from_email=from_email, to_email=to_email, subject=subject, html_body=html_body
diff --git a/tests/test_litellm/enterprise/enterprise_callbacks/send_emails/test_sendgrid_email.py b/tests/test_litellm/enterprise/enterprise_callbacks/send_emails/test_sendgrid_email.py
index 836b717bd6e..4ecb4872aaa 100644
--- a/tests/test_litellm/enterprise/enterprise_callbacks/send_emails/test_sendgrid_email.py
+++ b/tests/test_litellm/enterprise/enterprise_callbacks/send_emails/test_sendgrid_email.py
@@ -2,9 +2,7 @@
 import sys
 import unittest.mock as mock
 
-import httpx
 import pytest
-import respx
 from httpx import Response
 
 sys.path.insert(0, os.path.abspath("../../.."))
@@ -16,26 +14,8 @@
 
 @pytest.fixture
 def mock_env_vars():
-    # Store original values
-    original_api_key = os.environ.get("SENDGRID_API_KEY")
-    original_sender_email = os.environ.get("SENDGRID_SENDER_EMAIL")
-    
-    # Set test API key and remove SENDGRID_SENDER_EMAIL to ensure isolation
-    os.environ["SENDGRID_API_KEY"] = "test_api_key"
-    if "SENDGRID_SENDER_EMAIL" in os.environ:
-        del os.environ["SENDGRID_SENDER_EMAIL"]
-    
-    try:
+    with mock.patch.dict(os.environ, {"SENDGRID_API_KEY": "test_api_key"}):
         yield
-    finally:
-        # Restore original values
-        if original_api_key is not None:
-            os.environ["SENDGRID_API_KEY"] = original_api_key
-        elif "SENDGRID_API_KEY" in os.environ:
-            del os.environ["SENDGRID_API_KEY"]
-        
-        if original_sender_email is not None:
-            os.environ["SENDGRID_SENDER_EMAIL"] = original_sender_email
 
 
 @pytest.fixture
@@ -43,16 +23,14 @@ def mock_httpx_client():
     with mock.patch(
         "litellm_enterprise.enterprise_callbacks.send_emails.sendgrid_email.get_async_httpx_client"
     ) as mock_client:
-
-        mock_response = mock.Mock(spec=Response)
+        mock_response = mock.AsyncMock(spec=Response)
         mock_response.status_code = 202
         mock_response.text = "accepted"
-        mock_response.raise_for_status.return_value = None
 
         mock_async_client = mock.AsyncMock()
         mock_async_client.post.return_value = mock_response
-
         mock_client.return_value = mock_async_client
+
         yield mock_async_client
 
 
@@ -84,10 +62,8 @@ async def test_send_email_success(mock_env_vars, mock_httpx_client):
 
 
 @pytest.mark.asyncio
-async def test_send_email_missing_api_key():
-    original_key = os.environ.pop("SENDGRID_API_KEY", None)
-
-    try:
+async def test_send_email_missing_api_key(mock_httpx_client):
+    with mock.patch.dict(os.environ, {}, clear=True):
         logger = SendGridEmailLogger()
 
         with pytest.raises(ValueError):
@@ -97,19 +73,12 @@ async def test_send_email_missing_api_key():
                 subject="Test Subject",
                 html_body="<p>Test email body</p>",
             )
-    finally:
-        if original_key is not None:
-            os.environ["SENDGRID_API_KEY"] = original_key
+
+        mock_httpx_client.post.assert_not_called()
 
 
 @pytest.mark.asyncio
-@respx.mock
 async def test_send_email_multiple_recipients(mock_env_vars, mock_httpx_client):
-    # Block all HTTP requests at network level to prevent real API calls
-    respx.post("https://api.sendgrid.com/v3/mail/send").mock(
-        return_value=httpx.Response(202, text="accepted")
-    )
-    
     logger = SendGridEmailLogger()
 
     from_email = "test@example.com"
@@ -117,12 +86,6 @@ async def test_send_email_multiple_recipients(mock_env_vars, mock_httpx_client):
     subject = "Test Subject"
     html_body = "<p>Test email body</p>"
 
-    mock_response = mock.Mock(spec=Response)
-    mock_response.status_code = 202
-    mock_response.text = "accepted"
-    mock_response.raise_for_status.return_value = None
-    mock_httpx_client.post.return_value = mock_response
-
     await logger.send_email(
         from_email=from_email, to_email=to_email, subject=subject, html_body=html_body
     )
diff --git a/tests/test_litellm/experimental_mcp_client/test_mcp_client.py b/tests/test_litellm/experimental_mcp_client/test_mcp_client.py
index febc7c454bd..998f4156e93 100644
--- a/tests/test_litellm/experimental_mcp_client/test_mcp_client.py
+++ b/tests/test_litellm/experimental_mcp_client/test_mcp_client.py
@@ -52,19 +52,16 @@ async def test_mcp_client_stdio_connect_success(
         self, mock_session, mock_stdio_client
     ):
         """Test successful stdio connection"""
-        # Setup mocks - create proper async context manager
+        # Setup mocks
         mock_transport = (MagicMock(), MagicMock())
-        mock_stdio_ctx = AsyncMock()
-        mock_stdio_ctx.__aenter__.return_value = mock_transport
-        mock_stdio_ctx.__aexit__.return_value = None
-        mock_stdio_client.return_value = mock_stdio_ctx
+        mock_stdio_client.return_value.__aenter__ = AsyncMock(
+            return_value=mock_transport
+        )
 
-        mock_session_instance = AsyncMock()
+        mock_session_instance = MagicMock()
+        mock_session_instance.__aenter__ = AsyncMock(return_value=mock_session_instance)
         mock_session_instance.initialize = AsyncMock()
-        mock_session_ctx = AsyncMock()
-        mock_session_ctx.__aenter__.return_value = mock_session_instance
-        mock_session_ctx.__aexit__.return_value = None
-        mock_session.return_value = mock_session_ctx
+        mock_session.return_value = mock_session_instance
 
         stdio_config = MCPStdioConfig(
             command="python", args=["-m", "my_mcp_server"], env={"DEBUG": "1"}
@@ -97,23 +94,22 @@ async def test_mcp_client_ssl_configuration_from_env(
         self, mock_streamable_http_client
     ):
         """Test that MCP client uses SSL configuration from environment variables"""
-        # Setup mocks - create proper async context manager
+        # Setup mocks
         mock_transport = (MagicMock(), MagicMock())
-        mock_http_ctx = AsyncMock()
-        mock_http_ctx.__aenter__.return_value = mock_transport
-        mock_http_ctx.__aexit__.return_value = None
-        mock_streamable_http_client.return_value = mock_http_ctx
+        mock_streamable_http_client.return_value.__aenter__ = AsyncMock(
+            return_value=mock_transport
+        )
 
         # Mock the session
         with patch(
             "litellm.experimental_mcp_client.client.ClientSession"
         ) as mock_session:
-            mock_session_instance = AsyncMock()
+            mock_session_instance = MagicMock()
+            mock_session_instance.__aenter__ = AsyncMock(
+                return_value=mock_session_instance
+            )
             mock_session_instance.initialize = AsyncMock()
-            mock_session_ctx = AsyncMock()
-            mock_session_ctx.__aenter__.return_value = mock_session_instance
-            mock_session_ctx.__aexit__.return_value = None
-            mock_session.return_value = mock_session_ctx
+            mock_session.return_value = mock_session_instance
 
             client = MCPClient(
                 server_url="https://mcp-server.example.com",
@@ -145,23 +141,20 @@ async def _operation(session):
     @patch.object(mcp_client_module, "sse_client")
     async def test_mcp_client_ssl_verify_parameter(self, mock_sse_client):
         """Test that MCP client uses ssl_verify parameter when provided"""
-        # Setup mocks - create proper async context manager
+        # Setup mocks
         mock_transport = (MagicMock(), MagicMock())
-        mock_sse_ctx = AsyncMock()
-        mock_sse_ctx.__aenter__.return_value = mock_transport
-        mock_sse_ctx.__aexit__.return_value = None
-        mock_sse_client.return_value = mock_sse_ctx
+        mock_sse_client.return_value.__aenter__ = AsyncMock(return_value=mock_transport)
 
         # Mock the session
         with patch(
             "litellm.experimental_mcp_client.client.ClientSession"
         ) as mock_session:
-            mock_session_instance = AsyncMock()
+            mock_session_instance = MagicMock()
+            mock_session_instance.__aenter__ = AsyncMock(
+                return_value=mock_session_instance
+            )
             mock_session_instance.initialize = AsyncMock()
-            mock_session_ctx = AsyncMock()
-            mock_session_ctx.__aenter__.return_value = mock_session_instance
-            mock_session_ctx.__aexit__.return_value = None
-            mock_session.return_value = mock_session_ctx
+            mock_session.return_value = mock_session_instance
 
             # Test with ssl_verify=False
             client = MCPClient(
@@ -199,23 +192,22 @@ async def _operation(session):
     @patch.object(mcp_client_module, "streamable_http_client")
     async def test_mcp_client_ssl_verify_custom_path(self, mock_streamable_http_client):
         """Test that MCP client uses custom CA bundle path from ssl_verify parameter"""
-        # Setup mocks - create proper async context manager
+        # Setup mocks
         mock_transport = (MagicMock(), MagicMock())
-        mock_http_ctx = AsyncMock()
-        mock_http_ctx.__aenter__.return_value = mock_transport
-        mock_http_ctx.__aexit__.return_value = None
-        mock_streamable_http_client.return_value = mock_http_ctx
+        mock_streamable_http_client.return_value.__aenter__ = AsyncMock(
+            return_value=mock_transport
+        )
 
         # Mock the session
         with patch(
             "litellm.experimental_mcp_client.client.ClientSession"
         ) as mock_session:
-            mock_session_instance = AsyncMock()
+            mock_session_instance = MagicMock()
+            mock_session_instance.__aenter__ = AsyncMock(
+                return_value=mock_session_instance
+            )
             mock_session_instance.initialize = AsyncMock()
-            mock_session_ctx = AsyncMock()
-            mock_session_ctx.__aenter__.return_value = mock_session_instance
-            mock_session_ctx.__aexit__.return_value = None
-            mock_session.return_value = mock_session_ctx
+            mock_session.return_value = mock_session_instance
 
             # Test with custom CA bundle path
             custom_ca_path = "/custom/path/to/ca-bundle.pem"
diff --git a/tests/test_litellm/google_genai/test_google_genai_adapter.py b/tests/test_litellm/google_genai/test_google_genai_adapter.py
index 05b22098371..a5333550992 100644
--- a/tests/test_litellm/google_genai/test_google_genai_adapter.py
+++ b/tests/test_litellm/google_genai/test_google_genai_adapter.py
@@ -1090,8 +1090,8 @@ async def test_google_generate_content_with_openai():
         usage=mock_usage
     )
     
-    # Use AsyncMock for proper async function mocking - patch at the module level where it's imported
-    with unittest.mock.patch("litellm.google_genai.main.litellm.acompletion", new_callable=unittest.mock.AsyncMock) as mock_completion:
+    # Use AsyncMock for proper async function mocking
+    with unittest.mock.patch("litellm.acompletion", new_callable=unittest.mock.AsyncMock) as mock_completion:
         # Set the return value directly on the MagicMock
         mock_completion.return_value = mock_response
         
diff --git a/tests/test_litellm/google_genai/test_google_genai_handler.py b/tests/test_litellm/google_genai/test_google_genai_handler.py
index fc120280511..89b53ca06d5 100644
--- a/tests/test_litellm/google_genai/test_google_genai_handler.py
+++ b/tests/test_litellm/google_genai/test_google_genai_handler.py
@@ -25,7 +25,7 @@ def test_non_stream_response_when_stream_requested_sync():
     the sync handler correctly transforms it to generate_content format.
     """
     from litellm.types.utils import Choices
-
+    
     # Mock a non-stream response (ModelResponse with valid choices)
     mock_response = ModelResponse(
         id="test-123",
@@ -70,7 +70,7 @@ async def test_non_stream_response_when_stream_requested_async():
     the async handler correctly transforms it to generate_content format.
     """
     from litellm.types.utils import Choices
-
+    
     # Mock a non-stream response (ModelResponse with valid choices)
     mock_response = ModelResponse(
         id="test-123",
@@ -183,10 +183,7 @@ def test_stream_transformation_error_sync():
         "translate_completion_output_params_streaming", 
         return_value=None
     ):
-        # Mock litellm.completion at the module level where it's imported
-        # We need to patch it in the handler module, not in litellm itself
-        with patch("litellm.google_genai.adapters.handler.litellm") as mock_litellm:
-            mock_litellm.completion.return_value = mock_stream
+        with patch("litellm.completion", return_value=mock_stream):
             # Call the handler with stream=True and expect a ValueError
             with pytest.raises(ValueError, match="Failed to transform streaming response"):
                 GenerateContentToCompletionHandler.generate_content_handler(
@@ -212,11 +209,7 @@ async def test_stream_transformation_error_async():
         "translate_completion_output_params_streaming", 
         return_value=None
     ):
-        # Mock litellm.acompletion at the module level where it's imported
-        # We need to patch it in the handler module, not in litellm itself
-        with patch("litellm.google_genai.adapters.handler.litellm") as mock_litellm:
-            # Use AsyncMock for async function
-            mock_litellm.acompletion = AsyncMock(return_value=mock_stream)
+        with patch("litellm.acompletion", return_value=mock_stream):
             # Call the handler with stream=True and expect a ValueError
             with pytest.raises(ValueError, match="Failed to transform streaming response"):
                 await GenerateContentToCompletionHandler.async_generate_content_handler(
@@ -232,13 +225,11 @@ def test_citation_metadata_transformation():
     Test that citationMetadata.citationSources is properly transformed to citationMetadata.citations
     to avoid Pydantic validation errors.
     """
+    from litellm.llms.gemini.google_genai.transformation import GoogleGenAIConfig
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
     from unittest.mock import MagicMock
-
     import httpx
-
-    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
-    from litellm.llms.gemini.google_genai.transformation import GoogleGenAIConfig
-
+    
     # Create a mock response with citationMetadata.citationSources (the problematic format)
     mock_response_data = {
         "candidates": [
diff --git a/tests/test_litellm/integrations/arize/test_arize_utils.py b/tests/test_litellm/integrations/arize/test_arize_utils.py
index 9a9f3d5afc7..6eb6e67a088 100644
--- a/tests/test_litellm/integrations/arize/test_arize_utils.py
+++ b/tests/test_litellm/integrations/arize/test_arize_utils.py
@@ -84,7 +84,7 @@ def test_arize_set_attributes():
     ArizeLogger.set_arize_attributes(span, kwargs, response_obj)
 
     # Validate that the expected number of attributes were set
-    assert span.set_attribute.call_count == 26
+    assert span.set_attribute.call_count == 28
 
     # Metadata attached to the span
     span.set_attribute.assert_any_call(
@@ -108,8 +108,7 @@ def test_arize_set_attributes():
     # Response metadata
     span.set_attribute.assert_any_call("llm.response.id", "chatcmpl-ID")
     span.set_attribute.assert_any_call("llm.response.model", "gpt-4o")
-    # Span kind is set to TOOL when tools are present
-    span.set_attribute.assert_any_call(SpanAttributes.OPENINFERENCE_SPAN_KIND, "TOOL")
+    span.set_attribute.assert_any_call(SpanAttributes.OPENINFERENCE_SPAN_KIND, "LLM")
 
     # Request message content and metadata
     span.set_attribute.assert_any_call(
@@ -126,14 +125,14 @@ def test_arize_set_attributes():
 
     # Tool call definitions and function names
     span.set_attribute.assert_any_call(
-        f"{SpanAttributes.LLM_TOOLS}.0.name", "get_weather"
+        f"{SpanAttributes.LLM_TOOLS}.0.{SpanAttributes.TOOL_NAME}", "get_weather"
     )
     span.set_attribute.assert_any_call(
-        f"{SpanAttributes.LLM_TOOLS}.0.description",
+        f"{SpanAttributes.LLM_TOOLS}.0.{SpanAttributes.TOOL_DESCRIPTION}",
         "Fetches weather details.",
     )
     span.set_attribute.assert_any_call(
-        f"{SpanAttributes.LLM_TOOLS}.0.parameters",
+        f"{SpanAttributes.LLM_TOOLS}.0.{SpanAttributes.TOOL_PARAMETERS}",
         json.dumps(
             {
                 "type": "object",
@@ -145,6 +144,16 @@ def test_arize_set_attributes():
         ),
     )
 
+    # Tool calls captured from optional_params
+    span.set_attribute.assert_any_call(
+        f"{MessageAttributes.MESSAGE_TOOL_CALLS}.0.{ToolCallAttributes.TOOL_CALL_FUNCTION_NAME}",
+        "get_weather",
+    )
+    span.set_attribute.assert_any_call(
+        f"{MessageAttributes.MESSAGE_TOOL_CALLS}.1.{ToolCallAttributes.TOOL_CALL_FUNCTION_NAME}",
+        "get_stock_price",
+    )
+
     # Invocation parameters
     span.set_attribute.assert_any_call(
         SpanAttributes.LLM_INVOCATION_PARAMETERS, '{"user": "test_user"}'
diff --git a/tests/test_litellm/integrations/datadog/test_datadog_cost_management.py b/tests/test_litellm/integrations/datadog/test_datadog_cost_management.py
deleted file mode 100644
index be2084969a5..00000000000
--- a/tests/test_litellm/integrations/datadog/test_datadog_cost_management.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import os
-import time
-from unittest.mock import AsyncMock
-
-import pytest
-from httpx import Response
-
-from litellm.integrations.datadog.datadog_cost_management import (
-    DatadogCostManagementLogger,
-)
-from litellm.types.utils import StandardLoggingPayload
-
-
-@pytest.fixture
-def clean_env():
-    # Save original env
-    original_api_key = os.environ.get("DD_API_KEY")
-    original_app_key = os.environ.get("DD_APP_KEY")
-    original_site = os.environ.get("DD_SITE")
-
-    # Set test env
-    os.environ["DD_API_KEY"] = "test_api_key"
-    os.environ["DD_APP_KEY"] = "test_app_key"
-    os.environ["DD_SITE"] = "test.datadoghq.com"
-
-    yield
-
-    # Restore original env
-    if original_api_key:
-        os.environ["DD_API_KEY"] = original_api_key
-    else:
-        del os.environ["DD_API_KEY"]
-
-    if original_app_key:
-        os.environ["DD_APP_KEY"] = original_app_key
-    else:
-        del os.environ["DD_APP_KEY"]
-
-    if original_site:
-        os.environ["DD_SITE"] = original_site
-    else:
-        del os.environ["DD_SITE"]
-
-
-@pytest.mark.asyncio
-async def test_init(clean_env):
-    """
-    Test initialization sets up clients and url correctly
-    """
-    logger = DatadogCostManagementLogger()
-    assert logger.dd_api_key == "test_api_key"
-    assert logger.dd_app_key == "test_app_key"
-    assert (
-        logger.upload_url == "https://api.test.datadoghq.com/api/v2/cost/custom_costs"
-    )
-
-
-@pytest.mark.asyncio
-async def test_aggregate_costs(clean_env):
-    """
-    Test that costs are correctly aggregated by provider, model, and date
-    """
-    logger = DatadogCostManagementLogger()
-
-    # Mock some log payloads
-    now = time.time()
-    day_str = time.strftime("%Y-%m-%d", time.localtime(now))
-
-    logs = [
-        StandardLoggingPayload(
-            custom_llm_provider="openai",
-            model="gpt-4",
-            response_cost=0.01,
-            startTime=now,
-            metadata={"user_api_key_team_alias": "team-a"},
-        ),
-        StandardLoggingPayload(
-            custom_llm_provider="openai",
-            model="gpt-4",
-            response_cost=0.02,
-            startTime=now,
-            metadata={"user_api_key_team_alias": "team-a"},
-        ),
-        StandardLoggingPayload(
-            custom_llm_provider="anthropic",
-            model="claude-3",
-            response_cost=0.05,
-            startTime=now,
-        ),
-    ]
-
-    aggregated = logger._aggregate_costs(logs)
-
-    assert len(aggregated) == 2
-
-    # Check OpenAI entry
-    openai_entry = next(e for e in aggregated if e["ProviderName"] == "openai")
-    assert openai_entry["BilledCost"] == 0.03
-    assert openai_entry["ChargeDescription"] == "LLM Usage for gpt-4"
-    assert openai_entry["ChargePeriodStart"] == day_str
-    assert openai_entry["Tags"]["team"] == "team-a"
-    assert "env" in openai_entry["Tags"]
-    assert "service" in openai_entry["Tags"]
-
-    # Check Anthropic entry
-    anthropic_entry = next(e for e in aggregated if e["ProviderName"] == "anthropic")
-    assert anthropic_entry["BilledCost"] == 0.05
-
-
-@pytest.mark.asyncio
-async def test_async_log_success_event(clean_env):
-    """
-    Test that logs are added to queue
-    """
-    logger = DatadogCostManagementLogger(batch_size=10)
-
-    await logger.async_log_success_event(
-        kwargs={"standard_logging_object": {"response_cost": 0.01}},
-        response_obj={},
-        start_time=time.time(),
-        end_time=time.time(),
-    )
-
-    assert len(logger.log_queue) == 1
-    assert logger.log_queue[0]["response_cost"] == 0.01
-
-    # Test zero cost ignored
-    await logger.async_log_success_event(
-        kwargs={"standard_logging_object": {"response_cost": 0.0}},
-        response_obj={},
-        start_time=time.time(),
-        end_time=time.time(),
-    )
-
-    assert len(logger.log_queue) == 1
-
-
-@pytest.mark.asyncio
-async def test_async_send_batch(clean_env):
-    """
-    Test that batch is aggregated and uploaded
-    """
-    logger = DatadogCostManagementLogger()
-    logger.async_client = AsyncMock()
-    logger.async_client.put.return_value = Response(202, json={"status": "ok"})
-
-    # Add logs directly to queue
-    logger.log_queue = [
-        StandardLoggingPayload(
-            custom_llm_provider="openai",
-            model="gpt-4",
-            response_cost=0.01,
-            startTime=time.time(),
-        )
-    ]
-
-    await logger.async_send_batch()
-
-    # Verify API called
-    assert logger.async_client.put.called
-    call_args = logger.async_client.put.call_args
-    assert call_args[0][0] == "https://api.test.datadoghq.com/api/v2/cost/custom_costs"
-
-    import json
-
-    # Use call_args.kwargs['content']
-    content = json.loads(call_args[1]["content"])
-    assert content[0]["ProviderName"] == "openai"
-    assert content[0]["BilledCost"] == 0.01
diff --git a/tests/test_litellm/integrations/datadog/test_datadog_llm_obs_agent.py b/tests/test_litellm/integrations/datadog/test_datadog_llm_obs_agent.py
deleted file mode 100644
index 2bb51e1e1b7..00000000000
--- a/tests/test_litellm/integrations/datadog/test_datadog_llm_obs_agent.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import os
-from unittest.mock import patch
-from litellm.integrations.datadog.datadog_llm_obs import DataDogLLMObsLogger
-
-
-def test_datadog_llm_obs_agent_configuration():
-    """
-    Test that DataDog LLM Obs logger correctly configures agent endpoint.
-    """
-    test_env = {
-        "LITELLM_DD_AGENT_HOST": "localhost",
-        "LITELLM_DD_LLM_OBS_PORT": "10518",
-        "DD_API_KEY": "test-api-key",  # Optional, but checking if it's preserved
-    }
-
-    # Ensure DD_SITE is NOT set to verify we don't need it in agent mode
-
-    with patch.dict(os.environ, test_env, clear=True):
-        with patch("asyncio.create_task"):  # Prevent periodic flush task from running
-            dd_logger = DataDogLLMObsLogger()
-
-        expected_url = "http://localhost:10518/api/intake/llm-obs/v1/trace/spans"
-        assert dd_logger.intake_url == expected_url
-        assert dd_logger.DD_API_KEY == "test-api-key"
-
-
-def test_datadog_llm_obs_agent_no_api_key_ok():
-    """
-    Test that agent mode works WITHOUT DD_API_KEY (agent handles auth).
-    """
-    test_env = {
-        "LITELLM_DD_AGENT_HOST": "localhost",
-        # No DD_API_KEY
-    }
-
-    with patch.dict(os.environ, test_env, clear=True):
-        with patch("asyncio.create_task"):
-            # Should NOT raise exception anymore
-            dd_logger = DataDogLLMObsLogger()
-
-            assert dd_logger.DD_API_KEY is None
-            # Default port is 8126 if not set
-            expected_url = "http://localhost:8126/api/intake/llm-obs/v1/trace/spans"
-            assert dd_logger.intake_url == expected_url
-
-
-def test_datadog_llm_obs_direct_api_configuration():
-    """
-    Test that direct API configuration still works as expected.
-    """
-    test_env = {
-        "DD_API_KEY": "direct-api-key",
-        "DD_SITE": "us5.datadoghq.com",
-    }
-
-    with patch.dict(os.environ, test_env, clear=True):
-        with patch("asyncio.create_task"):
-            dd_logger = DataDogLLMObsLogger()
-
-        expected_url = "https://api.us5.datadoghq.com/api/intake/llm-obs/v1/trace/spans"
-        assert dd_logger.intake_url == expected_url
-        assert dd_logger.DD_API_KEY == "direct-api-key"
diff --git a/tests/test_litellm/integrations/test_custom_guardrail_recursion.py b/tests/test_litellm/integrations/test_custom_guardrail_recursion.py
deleted file mode 100644
index f05b5848bdf..00000000000
--- a/tests/test_litellm/integrations/test_custom_guardrail_recursion.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import pytest
-from litellm.integrations.custom_guardrail import CustomGuardrail
-from litellm.types.guardrails import GuardrailEventHooks
-import json
-
-
-class TestCustomGuardrailRecursion:
-    """
-    Specific tests for the circular reference / RecursionError fix in logging.
-    """
-
-    def test_log_guardrail_information_handles_circular_references(self):
-        """
-        Test that add_standard_logging method sanitizes input data containing circular references
-        instead of crashing.
-
-        This reproduces the Langfuse crash scenario:
-        Request -> Metadata -> GuardrailResponse -> DebugContext -> Request
-        """
-        guardrail = CustomGuardrail(
-            guardrail_name="recursion_test_guardrail",
-            event_hook=GuardrailEventHooks.pre_call,
-        )
-
-        # 1. Setup Circular Data
-        request_data = {"user_id": "test_recursive_user"}
-        metadata = {"session_id": "123"}
-        request_data["metadata"] = metadata
-
-        # Create the danger: Guardrail Response holding a reference back to request_data
-        dirty_response = {
-            "flagged": False,
-            "debug_context": request_data,  # <--- ACCESS TO ROOT (Circular Ref)
-        }
-
-        # 2. Invoke the logging method
-        # If the fix is working, this will NOT raise RecursionError
-        try:
-            guardrail.add_standard_logging_guardrail_information_to_request_data(
-                guardrail_json_response=dirty_response,
-                request_data=request_data,
-                guardrail_status="success",
-                start_time=1.0,
-                end_time=2.0,
-                duration=1.0,
-                masked_entity_count={},
-                event_type=GuardrailEventHooks.pre_call,
-            )
-        except RecursionError:
-            pytest.fail(
-                "RecursionError raised! The cyclic reference sanitization failed."
-            )
-
-        # 3. Verify the data stored is safe
-        stored_info = request_data["metadata"][
-            "standard_logging_guardrail_information"
-        ][0]
-        stored_response = stored_info["guardrail_response"]
-
-        # Check that we can dump it to JSON without crashing (Ultimate proof)
-        try:
-            json.dumps(stored_response)
-        except Exception as e:
-            pytest.fail(f"Stored data is not JSON serializable: {e}")
-
-        # Check content - keys should be preserved but recursion broken
-        assert "debug_context" in stored_response
-        debug_context = stored_response["debug_context"]
-
-        # In a sanitized copy, the nested metadata should be a copy, not the original live dict
-        assert debug_context["user_id"] == "test_recursive_user"
-        # The 'metadata' inside 'debug_context' would be where recursion stops or is filtered
-        assert "metadata" in debug_context
diff --git a/tests/test_litellm/integrations/test_prometheus_client_ip_user_agent.py b/tests/test_litellm/integrations/test_prometheus_client_ip_user_agent.py
deleted file mode 100644
index 4a9fa3de5fd..00000000000
--- a/tests/test_litellm/integrations/test_prometheus_client_ip_user_agent.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import pytest
-from unittest.mock import MagicMock, patch
-from litellm.integrations.prometheus import PrometheusLogger
-from litellm.types.integrations.prometheus import (
-    UserAPIKeyLabelValues,
-)
-from litellm.proxy._types import UserAPIKeyAuth
-
-
-@pytest.mark.asyncio
-async def test_async_post_call_failure_hook_includes_client_ip_user_agent():
-    """
-    Test that async_post_call_failure_hook includes client_ip and user_agent in UserAPIKeyLabelValues
-    """
-    # Mocking
-    # Mocking
-    with patch(
-        "litellm.integrations.prometheus.PrometheusLogger.__init__", return_value=None
-    ):
-        logger = PrometheusLogger()
-        # Initialize attributes manually as __init__ is mocked
-        logger.litellm_proxy_failed_requests_metric = MagicMock()
-        logger.litellm_proxy_total_requests_metric = MagicMock()
-        logger.get_labels_for_metric = MagicMock(
-            return_value=["client_ip", "user_agent"]
-        )
-
-    request_data = {
-        "model": "gpt-4",
-        "metadata": {
-            "requester_ip_address": "127.0.0.1",
-            "user_agent": "test-agent",
-        },
-    }
-    user_api_key_dict = UserAPIKeyAuth(token="test_token")
-    original_exception = Exception("Test exception")
-
-    # Mock prometheus_label_factory to inspect arguments
-    with patch(
-        "litellm.integrations.prometheus.prometheus_label_factory"
-    ) as mock_label_factory:
-        mock_label_factory.return_value = {}
-
-        await logger.async_post_call_failure_hook(
-            request_data=request_data,
-            original_exception=original_exception,
-            user_api_key_dict=user_api_key_dict,
-        )
-
-        # Verification
-        assert mock_label_factory.call_count >= 1
-
-        # Check calls
-        calls = mock_label_factory.call_args_list
-        found = False
-        for call in calls:
-            kwargs = call.kwargs
-            enum_values = kwargs.get("enum_values")
-            if isinstance(enum_values, UserAPIKeyLabelValues):
-                if (
-                    enum_values.client_ip == "127.0.0.1"
-                    and enum_values.user_agent == "test-agent"
-                ):
-                    found = True
-                    break
-
-        assert (
-            found
-        ), "UserAPIKeyLabelValues should contain client_ip='127.0.0.1' and user_agent='test-agent'"
-
-
-@pytest.mark.asyncio
-async def test_async_post_call_success_hook_includes_client_ip_user_agent():
-    """
-    Test that async_post_call_success_hook includes client_ip and user_agent in UserAPIKeyLabelValues
-    """
-    # Mocking
-    # Mocking
-    with patch(
-        "litellm.integrations.prometheus.PrometheusLogger.__init__", return_value=None
-    ):
-        logger = PrometheusLogger()
-        logger.litellm_proxy_total_requests_metric = MagicMock()
-        logger.get_labels_for_metric = MagicMock(
-            return_value=["client_ip", "user_agent"]
-        )
-
-    data = {
-        "model": "gpt-4",
-        "metadata": {
-            "requester_ip_address": "192.168.1.1",
-            "user_agent": "success-agent",
-        },
-    }
-    user_api_key_dict = UserAPIKeyAuth(token="test_token")
-    response = MagicMock()
-
-    # Mock prometheus_label_factory to inspect arguments
-    with patch(
-        "litellm.integrations.prometheus.prometheus_label_factory"
-    ) as mock_label_factory:
-        mock_label_factory.return_value = {}
-
-        await logger.async_post_call_success_hook(
-            data=data,
-            user_api_key_dict=user_api_key_dict,
-            response=response,
-        )
-
-        # Verification
-        assert mock_label_factory.call_count >= 1
-
-        # Check calls
-        calls = mock_label_factory.call_args_list
-        found = False
-        for call in calls:
-            kwargs = call.kwargs
-            enum_values = kwargs.get("enum_values")
-            if isinstance(enum_values, UserAPIKeyLabelValues):
-                if (
-                    enum_values.client_ip == "192.168.1.1"
-                    and enum_values.user_agent == "success-agent"
-                ):
-                    found = True
-                    break
-
-        assert (
-            found
-        ), "UserAPIKeyLabelValues should contain client_ip='192.168.1.1' and user_agent='success-agent'"
-
-
-def test_set_llm_deployment_failure_metrics_includes_client_ip_user_agent():
-    """
-    Test that set_llm_deployment_failure_metrics includes client_ip and user_agent in UserAPIKeyLabelValues
-    """
-    # Mocking
-    # Mocking
-    with patch(
-        "litellm.integrations.prometheus.PrometheusLogger.__init__", return_value=None
-    ):
-        logger = PrometheusLogger()
-        logger.litellm_deployment_failure_responses = MagicMock()
-        logger.litellm_deployment_total_requests = MagicMock()
-        logger.get_labels_for_metric = MagicMock(
-            return_value=["client_ip", "user_agent"]
-        )
-        logger.set_deployment_partial_outage = MagicMock()
-
-    request_kwargs = {
-        "model": "gpt-4",
-        "standard_logging_object": {
-            "metadata": {
-                "requester_ip_address": "10.0.0.1",
-                "user_agent": "failure-deployment",
-                "user_api_key_team_id": "team_1",
-                "user_api_key_team_alias": "team_alias_1",
-                "user_api_key_alias": "key_alias_1",
-            },
-            "model_group": "group_1",
-            "api_base": "http://api.base",
-            "model_id": "model_1",
-        },
-        "litellm_params": {},
-        "exception": Exception("Deployment failure"),
-    }
-
-    # Mock prometheus_label_factory to inspect arguments
-    with patch(
-        "litellm.integrations.prometheus.prometheus_label_factory"
-    ) as mock_label_factory:
-        mock_label_factory.return_value = {}
-
-        logger.set_llm_deployment_failure_metrics(request_kwargs=request_kwargs)
-
-        # Verification
-        assert mock_label_factory.call_count >= 1
-
-        # Check calls
-        calls = mock_label_factory.call_args_list
-        found = False
-        for call in calls:
-            kwargs = call.kwargs
-            enum_values = kwargs.get("enum_values")
-            if isinstance(enum_values, UserAPIKeyLabelValues):
-                if (
-                    enum_values.client_ip == "10.0.0.1"
-                    and enum_values.user_agent == "failure-deployment"
-                ):
-                    found = True
-                    break
-
-        assert (
-            found
-        ), "UserAPIKeyLabelValues should contain client_ip='10.0.0.1' and user_agent='failure-deployment'"
-
-
-if __name__ == "__main__":
-    import asyncio
-
-    asyncio.run(test_async_post_call_failure_hook_includes_client_ip_user_agent())
-    asyncio.run(test_async_post_call_success_hook_includes_client_ip_user_agent())
-    test_set_llm_deployment_failure_metrics_includes_client_ip_user_agent()
-    print("✅ All client_ip and user_agent tests passed!")
diff --git a/tests/test_litellm/integrations/test_prometheus_labels.py b/tests/test_litellm/integrations/test_prometheus_labels.py
index 9d4626b88a5..c0b863ef6ee 100644
--- a/tests/test_litellm/integrations/test_prometheus_labels.py
+++ b/tests/test_litellm/integrations/test_prometheus_labels.py
@@ -26,49 +26,15 @@ def test_user_email_in_required_metrics():
         "litellm_input_tokens_metric",
         "litellm_output_tokens_metric",
         "litellm_requests_metric",
-        "litellm_spend_metric",
+        "litellm_spend_metric"
     ]
 
     for metric_name in metrics_with_user_email:
         labels = PrometheusMetricLabels.get_labels(metric_name)
-        assert (
-            user_email_label in labels
-        ), f"Metric {metric_name} should contain user_email label"
+        assert user_email_label in labels, f"Metric {metric_name} should contain user_email label"
         print(f"✅ {metric_name} contains user_email label")
 
 
-def test_model_id_in_required_metrics():
-    """
-    Test that model_id label is present in all the metrics that should have it
-    """
-    model_id_label = UserAPIKeyLabelNames.MODEL_ID.value
-
-    # Metrics that should have model_id
-    metrics_with_model_id = [
-        "litellm_proxy_total_requests_metric",
-        "litellm_proxy_failed_requests_metric",
-        "litellm_input_tokens_metric",
-        "litellm_output_tokens_metric",
-        "litellm_requests_metric",
-        "litellm_spend_metric",
-        "litellm_llm_api_latency_metric",
-        "litellm_remaining_requests_metric",
-        "litellm_deployment_successful_fallbacks",
-        "litellm_cache_hits_metric",
-        "litellm_cache_misses_metric",
-        "litellm_remaining_api_key_requests_for_model",
-        "litellm_remaining_api_key_tokens_for_model",
-        "litellm_llm_api_failed_requests_metric",
-    ]
-
-    for metric_name in metrics_with_model_id:
-        labels = PrometheusMetricLabels.get_labels(metric_name)
-        assert (
-            model_id_label in labels
-        ), f"Metric {metric_name} should contain model_id label"
-        print(f"✅ {metric_name} contains model_id label")
-
-
 def test_user_email_label_exists():
     """Test that the USER_EMAIL label is properly defined"""
     assert UserAPIKeyLabelNames.USER_EMAIL.value == "user_email"
@@ -86,14 +52,12 @@ def test_prometheus_metric_labels_structure():
         "litellm_proxy_failed_requests_metric",
         "litellm_input_tokens_metric",
         "litellm_output_tokens_metric",
-        "litellm_spend_metric",
+        "litellm_spend_metric"
     ]
 
     for metric_name in test_metrics:
         # Check metric is in DEFINED_PROMETHEUS_METRICS
-        assert metric_name in get_args(
-            DEFINED_PROMETHEUS_METRICS
-        ), f"{metric_name} should be in DEFINED_PROMETHEUS_METRICS"
+        assert metric_name in get_args(DEFINED_PROMETHEUS_METRICS), f"{metric_name} should be in DEFINED_PROMETHEUS_METRICS"
 
         # Check labels can be retrieved
         labels = PrometheusMetricLabels.get_labels(metric_name)
@@ -106,39 +70,15 @@ def test_prometheus_metric_labels_structure():
         print(f"✅ {metric_name} has proper label structure with user_email")
 
 
-def test_model_id_in_required_metrics():
-    """
-    Test that model_id label is present in all the metrics that should have it:
-    - litellm_proxy_total_requests_metric
-    - litellm_proxy_failed_requests_metric
-    - litellm_request_total_latency_metric
-    - litellm_llm_api_time_to_first_token_metric
-    """
-    model_id_label = UserAPIKeyLabelNames.MODEL_ID.value
-
-    # Metrics that should have model_id
-    metrics_with_model_id = [
-        "litellm_proxy_total_requests_metric",
-        "litellm_proxy_failed_requests_metric",
-        "litellm_request_total_latency_metric",
-        "litellm_llm_api_time_to_first_token_metric"
-    ]
-
-    for metric_name in metrics_with_model_id:
-        labels = PrometheusMetricLabels.get_labels(metric_name)
-        assert model_id_label in labels, f"Metric {metric_name} should contain model_id label"
-        print(f"✅ {metric_name} contains model_id label")
-
-
 def test_route_normalization_for_responses_api():
     """
     Test that route normalization prevents high cardinality in Prometheus metrics
     for the /v1/responses/{response_id} endpoint.
-
+    
     Issue: https://github.com/BerriAI/litellm/issues/XXXX
     Each unique response ID was creating a separate metric line, causing the
     /metrics endpoint to grow to ~30MB and take ~40 seconds to respond.
-
+    
     Fix: Routes are normalized to collapse dynamic IDs into placeholders.
     """
     from litellm.proxy.auth.auth_utils import normalize_request_route
@@ -151,53 +91,43 @@ def test_route_normalization_for_responses_api():
         ("/v1/responses/resp_abc123", "/v1/responses/{response_id}"),
         ("/v1/responses/litellm_poll_xyz", "/v1/responses/{response_id}"),
     ]
-
+    
     for original, expected in responses_routes:
         normalized = normalize_request_route(original)
-        assert (
-            normalized == expected
-        ), f"Failed: {original} -> {normalized} (expected {expected})"
-
+        assert normalized == expected, \
+            f"Failed: {original} -> {normalized} (expected {expected})"
+    
     # Verify cardinality reduction
-    unique_normalized = set(
-        normalize_request_route(route) for route, _ in responses_routes
-    )
-    assert (
-        len(unique_normalized) == 1
-    ), f"Expected 1 unique normalized route, got {len(unique_normalized)}: {unique_normalized}"
-
-    print(
-        f"✅ Responses API routes: {len(responses_routes)} different IDs normalized to 1 metric label"
-    )
-
+    unique_normalized = set(normalize_request_route(route) for route, _ in responses_routes)
+    assert len(unique_normalized) == 1, \
+        f"Expected 1 unique normalized route, got {len(unique_normalized)}: {unique_normalized}"
+    
+    print(f"✅ Responses API routes: {len(responses_routes)} different IDs normalized to 1 metric label")
+    
 
 def test_route_normalization_for_sub_routes():
     """Test that sub-routes like /cancel and /input_items are normalized correctly"""
     from litellm.proxy.auth.auth_utils import normalize_request_route
-
+    
     sub_routes = [
         ("/v1/responses/id1/cancel", "/v1/responses/{response_id}/cancel"),
         ("/v1/responses/id2/cancel", "/v1/responses/{response_id}/cancel"),
         ("/v1/responses/id3/input_items", "/v1/responses/{response_id}/input_items"),
-        (
-            "/openai/v1/responses/id4/input_items",
-            "/openai/v1/responses/{response_id}/input_items",
-        ),
+        ("/openai/v1/responses/id4/input_items", "/openai/v1/responses/{response_id}/input_items"),
     ]
-
+    
     for original, expected in sub_routes:
         normalized = normalize_request_route(original)
-        assert (
-            normalized == expected
-        ), f"Failed: {original} -> {normalized} (expected {expected})"
-
+        assert normalized == expected, \
+            f"Failed: {original} -> {normalized} (expected {expected})"
+    
     print("✅ Sub-routes normalized correctly")
 
 
 def test_route_normalization_preserves_static_routes():
     """Test that static routes are not affected by normalization"""
     from litellm.proxy.auth.auth_utils import normalize_request_route
-
+    
     static_routes = [
         "/chat/completions",
         "/v1/chat/completions",
@@ -207,47 +137,46 @@ def test_route_normalization_preserves_static_routes():
         "/v1/models",
         "/v1/responses",  # List endpoint without ID
     ]
-
+    
     for route in static_routes:
         normalized = normalize_request_route(route)
-        assert (
-            normalized == route
-        ), f"Static route should not be modified: {route} -> {normalized}"
-
+        assert normalized == route, \
+            f"Static route should not be modified: {route} -> {normalized}"
+    
     print(f"✅ {len(static_routes)} static routes preserved")
 
 
 def test_route_normalization_other_dynamic_apis():
     """Test normalization for other OpenAI-compatible APIs with dynamic IDs"""
     from litellm.proxy.auth.auth_utils import normalize_request_route
-
+    
     test_cases = [
         # Threads API
         ("/v1/threads/thread_123", "/v1/threads/{thread_id}"),
         ("/v1/threads/thread_abc/messages", "/v1/threads/{thread_id}/messages"),
-        (
-            "/v1/threads/thread_abc/runs/run_123",
-            "/v1/threads/{thread_id}/runs/{run_id}",
-        ),
+        ("/v1/threads/thread_abc/runs/run_123", "/v1/threads/{thread_id}/runs/{run_id}"),
+        
         # Vector Stores API
         ("/v1/vector_stores/vs_123", "/v1/vector_stores/{vector_store_id}"),
         ("/v1/vector_stores/vs_123/files", "/v1/vector_stores/{vector_store_id}/files"),
+        
         # Assistants API
         ("/v1/assistants/asst_123", "/v1/assistants/{assistant_id}"),
+        
         # Files API
         ("/v1/files/file_123", "/v1/files/{file_id}"),
         ("/v1/files/file_123/content", "/v1/files/{file_id}/content"),
+        
         # Batches API
         ("/v1/batches/batch_123", "/v1/batches/{batch_id}"),
         ("/v1/batches/batch_123/cancel", "/v1/batches/{batch_id}/cancel"),
     ]
-
+    
     for original, expected in test_cases:
         normalized = normalize_request_route(original)
-        assert (
-            normalized == expected
-        ), f"Failed: {original} -> {normalized} (expected {expected})"
-
+        assert normalized == expected, \
+            f"Failed: {original} -> {normalized} (expected {expected})"
+    
     print(f"✅ {len(test_cases)} other API routes normalized correctly")
 
 
@@ -266,29 +195,26 @@ def test_prometheus_metrics_use_normalized_routes():
 
     # Create a mock PrometheusLogger
     prometheus_logger = MagicMock()
-    prometheus_logger.get_labels_for_metric = (
-        PrometheusLogger.get_labels_for_metric.__get__(prometheus_logger)
-    )
-
+    prometheus_logger.get_labels_for_metric = PrometheusLogger.get_labels_for_metric.__get__(prometheus_logger)
+    
     # Test with a normalized route
     enum_values = UserAPIKeyLabelValues(
         route="/v1/responses/{response_id}",  # Normalized route
         status_code="200",
         requested_model="gpt-4",
     )
-
+    
     labels = prometheus_label_factory(
         supported_enum_labels=prometheus_logger.get_labels_for_metric(
             metric_name="litellm_proxy_total_requests_metric"
         ),
         enum_values=enum_values,
     )
-
+    
     # Verify the route is normalized in labels
-    assert (
-        labels["route"] == "/v1/responses/{response_id}"
-    ), f"Expected normalized route in labels, got: {labels.get('route')}"
-
+    assert labels["route"] == "/v1/responses/{response_id}", \
+        f"Expected normalized route in labels, got: {labels.get('route')}"
+    
     print("✅ Prometheus metrics use normalized routes in labels")
 
 
@@ -301,4 +227,4 @@ def test_prometheus_metrics_use_normalized_routes():
     test_route_normalization_preserves_static_routes()
     test_route_normalization_other_dynamic_apis()
     test_prometheus_metrics_use_normalized_routes()
-    print("\n✅ All prometheus label tests passed!")
+    print("\n✅ All prometheus label tests passed!")
\ No newline at end of file
diff --git a/tests/test_litellm/integrations/test_prometheus_missing_metrics.py b/tests/test_litellm/integrations/test_prometheus_missing_metrics.py
deleted file mode 100644
index 7fcfb21ed4c..00000000000
--- a/tests/test_litellm/integrations/test_prometheus_missing_metrics.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""
-Unit tests for the new Prometheus metrics that were previously missing from validation.
-
-Tests for:
-- litellm_remaining_api_key_requests_for_model
-- litellm_remaining_api_key_tokens_for_model
-- litellm_callback_logging_failures_metric
-"""
-from typing import get_args
-from litellm.types.integrations.prometheus import (
-    DEFINED_PROMETHEUS_METRICS,
-    PrometheusMetricLabels,
-    UserAPIKeyLabelNames,
-)
-
-
-def test_new_metrics_in_defined_metrics():
-    """
-    Test that the new metrics are present in DEFINED_PROMETHEUS_METRICS.
-    """
-    defined_metrics = get_args(DEFINED_PROMETHEUS_METRICS)
-
-    new_metrics = [
-        "litellm_remaining_api_key_requests_for_model",
-        "litellm_remaining_api_key_tokens_for_model",
-        "litellm_callback_logging_failures_metric",
-    ]
-
-    for metric in new_metrics:
-        assert (
-            metric in defined_metrics
-        ), f"{metric} should be in DEFINED_PROMETHEUS_METRICS"
-
-
-def test_new_metrics_have_correct_labels():
-    """
-    Test that the new metrics have the correct labels defined.
-    """
-    # Test API Key limits metrics labels
-    api_key_metrics = [
-        "litellm_remaining_api_key_requests_for_model",
-        "litellm_remaining_api_key_tokens_for_model",
-    ]
-
-    expected_api_key_labels = [
-        UserAPIKeyLabelNames.API_KEY_HASH.value,
-        UserAPIKeyLabelNames.API_KEY_ALIAS.value,
-        UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
-    ]
-
-    for metric in api_key_metrics:
-        labels = PrometheusMetricLabels.get_labels(metric)
-        for expected_label in expected_api_key_labels:
-            assert (
-                expected_label in labels
-            ), f"{metric} should have label {expected_label}"
-
-    # Test Callback failure metric labels
-    callback_metric = "litellm_callback_logging_failures_metric"
-    callback_labels = PrometheusMetricLabels.get_labels(callback_metric)
-
-    assert (
-        UserAPIKeyLabelNames.CALLBACK_NAME.value in callback_labels
-    ), f"{callback_metric} should have label {UserAPIKeyLabelNames.CALLBACK_NAME.value}"
-
-
-def test_callback_name_label_definition():
-    """
-    Test that CALLBACK_NAME is defined correctly in UserAPIKeyLabelNames.
-    """
-    assert UserAPIKeyLabelNames.CALLBACK_NAME.value == "callback_name"
-
-
-if __name__ == "__main__":
-    test_new_metrics_in_defined_metrics()
-    test_new_metrics_have_correct_labels()
-    test_callback_name_label_definition()
diff --git a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
index e87233a52a3..a22fe13798f 100644
--- a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
+++ b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
@@ -1138,73 +1138,6 @@ def test_bedrock_create_bedrock_block_different_document_formats():
         assert block["document"]["name"].endswith(f"_{format_type}")
         assert block["document"]["format"] == format_type
 
-def test_bedrock_nova_web_search_options_mapping():
-    """
-    Test that web_search_options is correctly mapped to Nova grounding.
-
-    This follows the LiteLLM pattern for web search where:
-    - Vertex AI maps web_search_options to {"googleSearch": {}}
-    - Anthropic maps web_search_options to {"type": "web_search_20250305", ...}
-    - Nova should map web_search_options to {"systemTool": {"name": "nova_grounding"}}
-    """
-    from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig
-
-    config = AmazonConverseConfig()
-
-    # Test basic mapping for Nova model
-    result = config._map_web_search_options({}, "amazon.nova-pro-v1:0")
-
-    assert result is not None
-    system_tool = result.get("systemTool")
-    assert system_tool is not None
-    assert system_tool["name"] == "nova_grounding"
-
-    # Test with search_context_size (should be ignored for Nova)
-    result2 = config._map_web_search_options(
-        {"search_context_size": "high"},
-        "us.amazon.nova-premier-v1:0"
-    )
-
-    assert result2 is not None
-    system_tool2 = result2.get("systemTool")
-    assert system_tool2 is not None
-    assert system_tool2["name"] == "nova_grounding"
-    # Nova doesn't support search_context_size, so it's just ignored
-
-def test_bedrock_tools_pt_does_not_handle_system_tool():
-    """
-    Verify that _bedrock_tools_pt does NOT handle system_tool format.
-
-    System tools (nova_grounding) should be added via web_search_options,
-    not via the tools parameter directly.
-    """
-    
-    from litellm.litellm_core_utils.prompt_templates.factory import _bedrock_tools_pt
-
-    # Regular function tools should still work
-    tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "get_weather",
-                "description": "Get the current weather",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "location": {"type": "string"}
-                    },
-                    "required": ["location"]
-                }
-            }
-        }
-    ]
-
-    result = _bedrock_tools_pt(tools=tools)
-
-    assert len(result) == 1
-    tool_spec = result[0].get("toolSpec")
-    assert tool_spec is not None
-    assert tool_spec["name"] == "get_weather"
 
 def test_convert_to_anthropic_tool_result_image_with_cache_control():
     """
@@ -1372,12 +1305,12 @@ def test_convert_to_anthropic_tool_result_image_url_as_http():
     assert result["content"][0]["cache_control"]["type"] == "ephemeral"
 def test_anthropic_messages_pt_server_tool_use_passthrough():
     """
-    Test that anthropic_messages_pt passes through server_tool_use and
+    Test that anthropic_messages_pt passes through server_tool_use and 
     tool_search_tool_result blocks in assistant message content.
-
+    
     These are Anthropic-native content types used for tool search functionality
     that need to be preserved when reconstructing multi-turn conversations.
-
+    
     Fixes: https://github.com/BerriAI/litellm/issues/XXXXX
     """
     from litellm.litellm_core_utils.prompt_templates.factory import anthropic_messages_pt
@@ -1426,15 +1359,15 @@ def test_anthropic_messages_pt_server_tool_use_passthrough():
 
     # Verify we have 3 messages (user, assistant, user)
     assert len(result) == 3
-
+    
     # Verify the assistant message content
     assistant_msg = result[1]
     assert assistant_msg["role"] == "assistant"
     assert isinstance(assistant_msg["content"], list)
-
+    
     # Find the different content block types
     content_types = [block.get("type") for block in assistant_msg["content"]]
-
+    
     # Verify server_tool_use block is preserved
     assert "server_tool_use" in content_types
     server_tool_use_block = next(
@@ -1443,7 +1376,7 @@ def test_anthropic_messages_pt_server_tool_use_passthrough():
     assert server_tool_use_block["id"] == "srvtoolu_01ABC123"
     assert server_tool_use_block["name"] == "tool_search_tool_regex"
     assert server_tool_use_block["input"] == {"query": ".*time.*"}
-
+    
     # Verify tool_search_tool_result block is preserved
     assert "tool_search_tool_result" in content_types
     tool_result_block = next(
@@ -1452,7 +1385,7 @@ def test_anthropic_messages_pt_server_tool_use_passthrough():
     assert tool_result_block["tool_use_id"] == "srvtoolu_01ABC123"
     assert tool_result_block["content"]["type"] == "tool_search_tool_search_result"
     assert tool_result_block["content"]["tool_references"][0]["tool_name"] == "get_time"
-
+    
     # Verify text block is also preserved
     assert "text" in content_types
     text_block = next(
diff --git a/tests/test_litellm/litellm_core_utils/test_image_handling.py b/tests/test_litellm/litellm_core_utils/test_image_handling.py
index 9c2939b2da5..b15d75a4145 100644
--- a/tests/test_litellm/litellm_core_utils/test_image_handling.py
+++ b/tests/test_litellm/litellm_core_utils/test_image_handling.py
@@ -66,41 +66,6 @@ def get(self, url, follow_redirects=True):
         )
 
 
-class StreamingLargeImageClient:
-    """
-    Client that streams a large image to test streaming download protection.
-    This simulates a huge file without actually creating it all in memory.
-    """
-
-    def __init__(self, size_mb=100, include_content_length=False):
-        self.size_mb = size_mb
-        self.include_content_length = include_content_length
-
-    def get(self, url, follow_redirects=True):
-        size_bytes = int(self.size_mb * 1024 * 1024)
-        headers = {"Content-Type": "image/jpeg"}
-        if self.include_content_length:
-            headers["Content-Length"] = str(size_bytes)
-        
-        # Create a generator that yields chunks without creating the whole file in memory
-        def generate_chunks(total_size, chunk_size=8192):
-            bytes_sent = 0
-            while bytes_sent < total_size:
-                chunk = b"x" * min(chunk_size, total_size - bytes_sent)
-                bytes_sent += len(chunk)
-                yield chunk
-        
-        # Create response with streaming content
-        response = Response(
-            status_code=200,
-            headers=headers,
-            request=Request("GET", url),
-        )
-        # Mock the iter_bytes method to return our generator
-        response.iter_bytes = lambda chunk_size=8192: generate_chunks(size_bytes, chunk_size)
-        return response
-
-
 def test_image_exceeds_size_limit_with_content_length(monkeypatch):
     """
     Test that images exceeding MAX_IMAGE_URL_DOWNLOAD_SIZE_MB are rejected when Content-Length header is present.
@@ -118,7 +83,6 @@ def test_image_exceeds_size_limit_with_content_length(monkeypatch):
 def test_image_exceeds_size_limit_without_content_length(monkeypatch):
     """
     Test that images exceeding MAX_IMAGE_URL_DOWNLOAD_SIZE_MB are rejected even without Content-Length header.
-    This uses the old non-streaming mock for backward compatibility.
     """
     monkeypatch.setattr(
         litellm, "module_level_client", LargeImageClient(size_mb=100, include_content_length=False)
@@ -130,29 +94,6 @@ def test_image_exceeds_size_limit_without_content_length(monkeypatch):
     assert "exceeds maximum allowed size" in str(excinfo.value)
 
 
-def test_streaming_download_protects_against_huge_files(monkeypatch):
-    """
-    Test that streaming download aborts early when file exceeds size limit,
-    preventing memory exhaustion from huge files (e.g., petabyte-sized files).
-    
-    This test verifies that the streaming implementation doesn't download the entire
-    file into memory before checking size. Instead, it should abort as soon as the
-    limit is exceeded during streaming.
-    """
-    # Simulate a 1GB file - far larger than the 50MB default limit
-    client = StreamingLargeImageClient(size_mb=1024, include_content_length=False)
-    monkeypatch.setattr(litellm, "module_level_client", client)
-
-    with pytest.raises(litellm.ImageFetchError) as excinfo:
-        convert_url_to_base64("https://example.com/huge-image.jpg")
-
-    # Verify the error message shows it was caught during streaming
-    assert "exceeds maximum allowed size" in str(excinfo.value)
-    
-    # The error should be raised after downloading just slightly more than the limit
-    # not after downloading the full 1GB
-
-
 class SmallImageClient:
     """
     Client that returns a small valid image.
@@ -183,26 +124,6 @@ def test_image_within_size_limit(monkeypatch):
     assert result.startswith("data:image/jpeg;base64,")
 
 
-def test_streaming_download_handles_petabyte_file(monkeypatch):
-    """
-    Test that streaming download can handle extremely large file URLs (e.g., petabyte-sized)
-    without attempting to download the entire file or causing memory exhaustion.
-    
-    This simulates what happens if a malicious actor or misconfiguration provides
-    a URL to an extremely large file.
-    """
-    # Simulate a 1 petabyte file (1,000,000 GB)
-    # Without streaming protection, this would cause OOM or hang indefinitely
-    client = StreamingLargeImageClient(size_mb=1_000_000_000, include_content_length=False)
-    monkeypatch.setattr(litellm, "module_level_client", client)
-
-    with pytest.raises(litellm.ImageFetchError) as excinfo:
-        convert_url_to_base64("https://example.com/petabyte-file.jpg")
-
-    # Should fail fast without downloading anywhere near 1 petabyte
-    assert "exceeds maximum allowed size" in str(excinfo.value)
-
-
 def test_image_size_limit_disabled(monkeypatch):
     """
     Test that setting MAX_IMAGE_URL_DOWNLOAD_SIZE_MB to 0 disables all image URL downloads.
diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
index 1f3f558a498..e035e193fe1 100644
--- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
+++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
@@ -787,13 +787,11 @@ def test_get_masked_values():
         "presidio_ad_hoc_recognizers": None,
         "aws_bedrock_runtime_endpoint": None,
         "presidio_anonymizer_api_base": None,
-        "vertex_credentials": "{sensitive_api_key}",
     }
     masked_values = _get_masked_values(
         sensitive_object, unmasked_length=4, number_of_asterisks=4
     )
     assert masked_values["presidio_anonymizer_api_base"] is None
-    assert masked_values["vertex_credentials"] == "{s****y}"
 
 
 @pytest.mark.asyncio
diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
index eee0b267fad..7e96c4634fd 100644
--- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
+++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
@@ -548,59 +548,6 @@ def test_map_tool_choice_dict_type_function_with_name():
     assert result["name"] == "my_tool"
 
 
-def test_map_tool_choice_dict_type_auto():
-    """
-    Test that dict {"type": "auto"} maps to Anthropic type='auto'.
-    This handles Cursor's format for tool_choice.
-    """
-    config = AnthropicConfig()
-    result = config._map_tool_choice(
-        tool_choice={"type": "auto"},
-        parallel_tool_use=None,
-    )
-    assert result is not None
-    assert result["type"] == "auto"
-
-
-def test_map_tool_choice_dict_type_required():
-    """
-    Test that dict {"type": "required"} maps to Anthropic type='any'.
-    """
-    config = AnthropicConfig()
-    result = config._map_tool_choice(
-        tool_choice={"type": "required"},
-        parallel_tool_use=None,
-    )
-    assert result is not None
-    assert result["type"] == "any"
-
-
-def test_map_tool_choice_dict_type_none():
-    """
-    Test that dict {"type": "none"} maps to Anthropic type='none'.
-    """
-    config = AnthropicConfig()
-    result = config._map_tool_choice(
-        tool_choice={"type": "none"},
-        parallel_tool_use=None,
-    )
-    assert result is not None
-    assert result["type"] == "none"
-
-
-def test_map_tool_choice_dict_type_function_without_name():
-    """
-    Test that dict {"type": "function"} without name is handled gracefully.
-    Should return None since there's no valid tool name.
-    """
-    config = AnthropicConfig()
-    result = config._map_tool_choice(
-        tool_choice={"type": "function"},
-        parallel_tool_use=None,
-    )
-    assert result is None
-
-
 def test_transform_response_with_prefix_prompt():
     import httpx
 
@@ -1895,7 +1842,7 @@ def test_calculate_usage_completion_tokens_details_always_populated():
     
     # completion_tokens_details should NOT be None
     assert usage.completion_tokens_details is not None
-    assert usage.completion_tokens_details.reasoning_tokens is 0
+    assert usage.completion_tokens_details.reasoning_tokens is None
     assert usage.completion_tokens_details.text_tokens == 248
     assert usage.completion_tokens == 248
     assert usage.prompt_tokens == 37
diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py
index c26d057fbf1..6aadbc058d1 100644
--- a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py
+++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py
@@ -1108,309 +1108,3 @@ def test_streaming_chunk_with_both_text_and_tool_calls_issue_18238():
     assert block_type == "tool_use"
     assert content_block_start["name"] == "Bash"
     assert content_block_start["id"] == "toolu_bdrk_013xRVejhv3ybmLEGCoZib2b"
-
-
-# ============================================================================
-# Cache Control Transformation Tests
-# ============================================================================
-
-# Model constant for cache control tests
-CACHE_CONTROL_BEDROCK_CONVERSE_MODEL = "bedrock/converse/global.anthropic.claude-opus-4-5-20251101-v1:0"
-CACHE_CONTROL_NON_ANTHROPIC_MODEL = "gpt-4"
-
-
-def test_should_add_cache_control_for_anthropic_model():
-    """Should add cache_control to target for Anthropic Claude models."""
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    cache_control = {"type": "ephemeral"}
-
-    for model in [
-        CACHE_CONTROL_BEDROCK_CONVERSE_MODEL,
-        "anthropic/claude-sonnet-4-5",
-        "claude-opus-4-5-20251101",
-        "vertex_ai/claude-3-sonnet@20240229",
-    ]:
-        target = {}
-        adapter._add_cache_control_if_applicable({"cache_control": cache_control}, target, model)
-        assert "cache_control" in target
-        assert target["cache_control"] == cache_control
-
-
-def test_should_not_add_cache_control_for_non_anthropic_model():
-    """Should not add cache_control for non-Anthropic models."""
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    cache_control = {"type": "ephemeral"}
-
-    for model in [CACHE_CONTROL_NON_ANTHROPIC_MODEL, "openai/gpt-4-turbo", "gemini-pro"]:
-        target = {}
-        adapter._add_cache_control_if_applicable({"cache_control": cache_control}, target, model)
-        assert "cache_control" not in target
-
-
-def test_should_not_add_cache_control_when_none():
-    """Should not add cache_control when source has None or empty cache_control."""
-    adapter = LiteLLMAnthropicMessagesAdapter()
-
-    for source in [{"cache_control": None}, {"cache_control": {}}, {"cache_control": ""}, {}]:
-        target = {}
-        adapter._add_cache_control_if_applicable(source, target, CACHE_CONTROL_BEDROCK_CONVERSE_MODEL)
-        assert "cache_control" not in target
-
-
-def test_should_not_add_cache_control_when_model_none():
-    """Should not add cache_control when model is None or empty."""
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    cache_control = {"type": "ephemeral"}
-
-    for model in [None, ""]:
-        target = {}
-        adapter._add_cache_control_if_applicable({"cache_control": cache_control}, target, model)
-        assert "cache_control" not in target
-
-
-def test_cache_control_preserved_in_text_content_for_claude():
-    """Cache control should be preserved in text content for Claude models."""
-    anthropic_messages = [
-        AnthropicMessagesUserMessageParam(
-            role="user",
-            content=[
-                {
-                    "type": "text",
-                    "text": "This is cached content",
-                    "cache_control": {"type": "ephemeral"},
-                }
-            ],
-        )
-    ]
-
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    result = adapter.translate_anthropic_messages_to_openai(
-        messages=anthropic_messages, model=CACHE_CONTROL_BEDROCK_CONVERSE_MODEL
-    )
-
-    assert len(result) == 1
-    assert result[0]["content"][0]["cache_control"] == {"type": "ephemeral"}
-
-
-def test_cache_control_not_preserved_for_non_claude_model():
-    """Cache control should NOT be preserved for non-Claude models."""
-    anthropic_messages = [
-        AnthropicMessagesUserMessageParam(
-            role="user",
-            content=[
-                {
-                    "type": "text",
-                    "text": "This is cached content",
-                    "cache_control": {"type": "ephemeral"},
-                }
-            ],
-        )
-    ]
-
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    result = adapter.translate_anthropic_messages_to_openai(
-        messages=anthropic_messages, model=CACHE_CONTROL_NON_ANTHROPIC_MODEL
-    )
-
-    assert len(result) == 1
-    assert "cache_control" not in result[0]["content"][0]
-
-
-def test_cache_control_preserved_in_image_content_for_claude():
-    """Cache control should be preserved in image content for Claude models."""
-    anthropic_messages = [
-        AnthropicMessagesUserMessageParam(
-            role="user",
-            content=[
-                {
-                    "type": "image",
-                    "source": {
-                        "type": "base64",
-                        "media_type": "image/png",
-                        "data": "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg==",
-                    },
-                    "cache_control": {"type": "ephemeral"},
-                }
-            ],
-        )
-    ]
-
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    result = adapter.translate_anthropic_messages_to_openai(
-        messages=anthropic_messages, model=CACHE_CONTROL_BEDROCK_CONVERSE_MODEL
-    )
-
-    assert len(result) == 1
-    assert result[0]["content"][0]["cache_control"] == {"type": "ephemeral"}
-
-
-def test_cache_control_preserved_in_document_content_for_claude():
-    """Cache control should be preserved in document content for Claude models."""
-    anthropic_messages = [
-        AnthropicMessagesUserMessageParam(
-            role="user",
-            content=[
-                {
-                    "type": "document",
-                    "source": {
-                        "type": "base64",
-                        "media_type": "application/pdf",
-                        "data": "JVBERi0xLjQKJeLjz9MK",
-                    },
-                    "cache_control": {"type": "ephemeral"},
-                }
-            ],
-        )
-    ]
-
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    result = adapter.translate_anthropic_messages_to_openai(
-        messages=anthropic_messages, model=CACHE_CONTROL_BEDROCK_CONVERSE_MODEL
-    )
-
-    assert len(result) == 1
-    assert result[0]["content"][0]["cache_control"] == {"type": "ephemeral"}
-
-
-def test_cache_control_preserved_in_tool_result_for_claude():
-    """Cache control should be preserved in tool_result for Claude models."""
-    anthropic_messages = [
-        AnthropicMessagesUserMessageParam(
-            role="user",
-            content=[
-                {
-                    "type": "tool_result",
-                    "tool_use_id": "toolu_01234",
-                    "content": "Tool result content",
-                    "cache_control": {"type": "ephemeral"},
-                }
-            ],
-        )
-    ]
-
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    result = adapter.translate_anthropic_messages_to_openai(
-        messages=anthropic_messages, model=CACHE_CONTROL_BEDROCK_CONVERSE_MODEL
-    )
-
-    tool_message = next(msg for msg in result if msg.get("role") == "tool")
-    assert tool_message["cache_control"] == {"type": "ephemeral"}
-
-
-def test_cache_control_not_preserved_in_tool_result_for_non_claude():
-    """Cache control should NOT be preserved in tool_result for non-Claude models."""
-    anthropic_messages = [
-        AnthropicMessagesUserMessageParam(
-            role="user",
-            content=[
-                {
-                    "type": "tool_result",
-                    "tool_use_id": "toolu_01234",
-                    "content": "Tool result content",
-                    "cache_control": {"type": "ephemeral"},
-                }
-            ],
-        )
-    ]
-
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    result = adapter.translate_anthropic_messages_to_openai(
-        messages=anthropic_messages, model=CACHE_CONTROL_NON_ANTHROPIC_MODEL
-    )
-
-    tool_message = next(msg for msg in result if msg.get("role") == "tool")
-    assert "cache_control" not in tool_message
-
-
-def test_cache_control_preserved_in_assistant_text_for_claude():
-    """Cache control should be preserved in assistant text blocks for Claude models."""
-    anthropic_messages = [
-        AnthopicMessagesAssistantMessageParam(
-            role="assistant",
-            content=[
-                {
-                    "type": "text",
-                    "text": "Assistant response",
-                    "cache_control": {"type": "ephemeral"},
-                }
-            ],
-        )
-    ]
-
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    result = adapter.translate_anthropic_messages_to_openai(
-        messages=anthropic_messages, model=CACHE_CONTROL_BEDROCK_CONVERSE_MODEL
-    )
-
-    assert len(result) == 1
-    assert result[0]["role"] == "assistant"
-    # When cache_control is present, content should be a list
-    assert isinstance(result[0]["content"], list)
-    assert result[0]["content"][0]["cache_control"] == {"type": "ephemeral"}
-
-
-def test_cache_control_preserved_in_tool_use_for_claude():
-    """Cache control should be preserved in tool_use blocks for Claude models."""
-    anthropic_messages = [
-        AnthopicMessagesAssistantMessageParam(
-            role="assistant",
-            content=[
-                {
-                    "type": "tool_use",
-                    "id": "toolu_01234",
-                    "name": "get_weather",
-                    "input": {"location": "Boston"},
-                    "cache_control": {"type": "ephemeral"},
-                }
-            ],
-        )
-    ]
-
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    result = adapter.translate_anthropic_messages_to_openai(
-        messages=anthropic_messages, model=CACHE_CONTROL_BEDROCK_CONVERSE_MODEL
-    )
-
-    assert len(result) == 1
-    assert "tool_calls" in result[0]
-    assert result[0]["tool_calls"][0]["cache_control"] == {"type": "ephemeral"}
-
-
-def test_cache_control_preserved_in_tools_for_claude():
-    """Cache control should be preserved in tools for Claude models."""
-    tools = [
-        {
-            "name": "get_weather",
-            "description": "Get weather for a location",
-            "input_schema": {"type": "object", "properties": {"location": {"type": "string"}}},
-            "cache_control": {"type": "ephemeral"},
-        }
-    ]
-
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    result = adapter.translate_anthropic_tools_to_openai(
-        tools=tools, model=CACHE_CONTROL_BEDROCK_CONVERSE_MODEL
-    )
-
-    assert len(result) == 1
-    assert result[0]["cache_control"] == {"type": "ephemeral"}
-
-
-def test_cache_control_not_preserved_in_tools_for_non_claude():
-    """Cache control should NOT be preserved in tools for non-Claude models."""
-    tools = [
-        {
-            "name": "get_weather",
-            "description": "Get weather for a location",
-            "input_schema": {"type": "object", "properties": {"location": {"type": "string"}}},
-            "cache_control": {"type": "ephemeral"},
-        }
-    ]
-
-    adapter = LiteLLMAnthropicMessagesAdapter()
-    result = adapter.translate_anthropic_tools_to_openai(
-        tools=tools, model=CACHE_CONTROL_NON_ANTHROPIC_MODEL
-    )
-
-    assert len(result) == 1
-    assert "cache_control" not in result[0]
diff --git a/tests/test_litellm/llms/azure/chat/test_azure_gpt5_transformation.py b/tests/test_litellm/llms/azure/chat/test_azure_gpt5_transformation.py
index 25f3d1364f6..199a16d8590 100644
--- a/tests/test_litellm/llms/azure/chat/test_azure_gpt5_transformation.py
+++ b/tests/test_litellm/llms/azure/chat/test_azure_gpt5_transformation.py
@@ -16,17 +16,6 @@ def test_azure_gpt5_supports_reasoning_effort(config: AzureOpenAIGPT5Config):
     )
 
 
-def test_azure_gpt5_allows_tool_choice_for_deployment_names():
-    supported_params = litellm.get_supported_openai_params(
-        model="gpt-5-chat-2025-08-07", custom_llm_provider="azure"
-    )
-    assert supported_params is not None
-    assert "tool_choice" in supported_params
-    # gpt-5-chat* should not be treated as a GPT-5 reasoning model
-    assert "reasoning_effort" not in supported_params
-    assert "temperature" in supported_params
-
-
 def test_azure_gpt5_maps_max_tokens(config: AzureOpenAIGPT5Config):
     params = config.map_openai_params(
         non_default_params={"max_tokens": 5},
diff --git a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py
index 5c1b4cbd38e..1cb84d32c1d 100644
--- a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py
+++ b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py
@@ -279,293 +279,3 @@ def test_output_format_with_no_schema():
     # Content should remain as string (not converted to list)
     assert isinstance(last_user_message["content"], str)
     assert last_user_message["content"] == "Hello"
-
-
-def test_advanced_tool_use_header_translation_for_opus_4_5():
-    """
-    Test that advanced-tool-use-2025-11-20 header is translated to Bedrock-specific headers
-    for Claude Opus 4.5.
-    
-    Regression test for: Claude Code sends advanced-tool-use header which needs to be
-    translated to tool-search-tool-2025-10-19 and tool-examples-2025-10-29 for Bedrock
-    Invoke API on Claude Opus 4.5.
-    
-    Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html
-    """
-    from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import (
-        AmazonAnthropicClaudeMessagesConfig,
-    )
-    
-    config = AmazonAnthropicClaudeMessagesConfig()
-    
-    messages = [
-        {"role": "user", "content": "What's the weather like?"}
-    ]
-    
-    anthropic_messages_optional_request_params = {
-        "max_tokens": 100,
-    }
-    
-    # Simulate advanced-tool-use header from Claude Code
-    headers = {
-        "anthropic-beta": "advanced-tool-use-2025-11-20"
-    }
-    
-    # Test with Claude Opus 4.5
-    result = config.transform_anthropic_messages_request(
-        model="anthropic.claude-opus-4-5-20250514-v1:0",
-        messages=messages,
-        anthropic_messages_optional_request_params=anthropic_messages_optional_request_params,
-        litellm_params={},
-        headers=headers,
-    )
-    
-    # Verify advanced-tool-use header was removed
-    assert "anthropic_beta" in result
-    beta_headers = result["anthropic_beta"]
-    assert "advanced-tool-use-2025-11-20" not in beta_headers, \
-        "advanced-tool-use header should be removed for Bedrock"
-    
-    # Verify Bedrock-specific headers were added
-    assert "tool-search-tool-2025-10-19" in beta_headers, \
-        "tool-search-tool-2025-10-19 should be added for Opus 4.5"
-    assert "tool-examples-2025-10-29" in beta_headers, \
-        "tool-examples-2025-10-29 should be added for Opus 4.5"
-
-
-def test_advanced_tool_use_header_filtered_for_non_opus_4_5():
-    """
-    Test that advanced-tool-use-2025-11-20 header is filtered out for non-Opus 4.5 models
-    without adding Bedrock-specific headers.
-    
-    The translation to tool-search-tool-2025-10-19 and tool-examples-2025-10-29 should
-    only happen for Claude Opus 4.5.
-    """
-    from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import (
-        AmazonAnthropicClaudeMessagesConfig,
-    )
-    
-    config = AmazonAnthropicClaudeMessagesConfig()
-    
-    messages = [
-        {"role": "user", "content": "What's the weather like?"}
-    ]
-    
-    anthropic_messages_optional_request_params = {
-        "max_tokens": 100,
-    }
-    
-    # Simulate advanced-tool-use header from Claude Code
-    headers = {
-        "anthropic-beta": "advanced-tool-use-2025-11-20"
-    }
-    
-    # Test with Claude Sonnet 4.5 (not Opus 4.5)
-    result = config.transform_anthropic_messages_request(
-        model="anthropic.claude-sonnet-4-5-20250929-v1:0",
-        messages=messages,
-        anthropic_messages_optional_request_params=anthropic_messages_optional_request_params,
-        litellm_params={},
-        headers=headers,
-    )
-    
-    # Verify advanced-tool-use header was removed
-    beta_headers = result.get("anthropic_beta", [])
-    assert "advanced-tool-use-2025-11-20" not in beta_headers, \
-        "advanced-tool-use header should be removed for Bedrock"
-    
-    # Verify Bedrock-specific headers were NOT added (only for Opus 4.5)
-    assert "tool-search-tool-2025-10-19" not in beta_headers, \
-        "tool-search-tool should not be added for non-Opus 4.5 models"
-    assert "tool-examples-2025-10-29" not in beta_headers, \
-        "tool-examples should not be added for non-Opus 4.5 models"
-
-
-def test_advanced_tool_use_header_translation_with_multiple_beta_headers():
-    """
-    Test that advanced-tool-use header translation works correctly when multiple
-    beta headers are present.
-    """
-    from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import (
-        AmazonAnthropicClaudeMessagesConfig,
-    )
-    
-    config = AmazonAnthropicClaudeMessagesConfig()
-    
-    messages = [
-        {"role": "user", "content": "What's the weather like?"}
-    ]
-    
-    anthropic_messages_optional_request_params = {
-        "max_tokens": 100,
-    }
-    
-    # Multiple beta headers including advanced-tool-use
-    headers = {
-        "anthropic-beta": "claude-code-20250219,advanced-tool-use-2025-11-20,interleaved-thinking-2025-05-14"
-    }
-    
-    # Test with Claude Opus 4.5
-    result = config.transform_anthropic_messages_request(
-        model="anthropic.claude-opus-4-5-20250514-v1:0",
-        messages=messages,
-        anthropic_messages_optional_request_params=anthropic_messages_optional_request_params,
-        litellm_params={},
-        headers=headers,
-    )
-    
-    beta_headers = result.get("anthropic_beta", [])
-    
-    # Verify advanced-tool-use was removed
-    assert "advanced-tool-use-2025-11-20" not in beta_headers
-    
-    # Verify Bedrock-specific headers were added
-    assert "tool-search-tool-2025-10-19" in beta_headers
-    assert "tool-examples-2025-10-29" in beta_headers
-    
-    # Verify other beta headers are preserved
-    assert "claude-code-20250219" in beta_headers
-    assert "interleaved-thinking-2025-05-14" in beta_headers
-
-
-def test_opus_4_5_model_detection():
-    """
-    Test that the _is_claude_opus_4_5 method correctly identifies Opus 4.5 models
-    with various naming conventions.
-    """
-    from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import (
-        AmazonAnthropicClaudeMessagesConfig,
-    )
-    
-    config = AmazonAnthropicClaudeMessagesConfig()
-    
-    # Test various Opus 4.5 naming patterns
-    opus_4_5_models = [
-        "anthropic.claude-opus-4-5-20250514-v1:0",
-        "anthropic.claude-opus-4.5-20250514-v1:0",
-        "anthropic.claude-opus_4_5-20250514-v1:0",
-        "anthropic.claude-opus_4.5-20250514-v1:0",
-        "us.anthropic.claude-opus-4-5-20250514-v1:0",
-        "ANTHROPIC.CLAUDE-OPUS-4-5-20250514-V1:0",  # Case insensitive
-    ]
-    
-    for model in opus_4_5_models:
-        assert config._is_claude_opus_4_5(model), \
-            f"Should detect {model} as Opus 4.5"
-    
-    # Test non-Opus 4.5 models
-    non_opus_4_5_models = [
-        "anthropic.claude-sonnet-4-5-20250929-v1:0",
-        "anthropic.claude-opus-4-20250514-v1:0",  # Opus 4, not 4.5
-        "anthropic.claude-opus-4-1-20250514-v1:0",  # Opus 4.1, not 4.5
-        "anthropic.claude-haiku-4-5-20251001-v1:0",
-    ]
-    
-    for model in non_opus_4_5_models:
-        assert not config._is_claude_opus_4_5(model), \
-            f"Should not detect {model} as Opus 4.5"
-
-
-def test_structured_outputs_beta_header_filtered_for_bedrock_invoke():
-    """
-    Test that unsupported beta headers are filtered out for Bedrock Invoke API.
-    
-    Bedrock Invoke API only supports a specific whitelist of beta flags and returns
-    "invalid beta flag" error for others (e.g., structured-outputs, mcp-servers).
-    This test ensures unsupported headers are filtered while keeping supported ones.
-    
-    Fixes: https://github.com/BerriAI/litellm/issues/16726
-    """
-    config = AmazonAnthropicClaudeConfig()
-    
-    messages = [{"role": "user", "content": "test"}]
-    
-    # Test 1: structured-outputs beta header (unsupported)
-    headers = {"anthropic-beta": "structured-outputs-2025-11-13"}
-    
-    result = config.transform_request(
-        model="anthropic.claude-4-0-sonnet-20250514-v1:0",
-        messages=messages,
-        optional_params={},
-        litellm_params={},
-        headers=headers,
-    )
-    
-    # Verify structured-outputs beta is filtered out
-    anthropic_beta = result.get("anthropic_beta", [])
-    assert not any("structured-outputs" in beta for beta in anthropic_beta), \
-        f"structured-outputs beta should be filtered, got: {anthropic_beta}"
-    
-    # Test 2: mcp-servers beta header (unsupported - the main issue from #16726)
-    headers = {"anthropic-beta": "mcp-servers-2025-12-04"}
-    
-    result = config.transform_request(
-        model="anthropic.claude-4-0-sonnet-20250514-v1:0",
-        messages=messages,
-        optional_params={},
-        litellm_params={},
-        headers=headers,
-    )
-    
-    # Verify mcp-servers beta is filtered out
-    anthropic_beta = result.get("anthropic_beta", [])
-    assert not any("mcp-servers" in beta for beta in anthropic_beta), \
-        f"mcp-servers beta should be filtered, got: {anthropic_beta}"
-    
-    # Test 3: Mix of supported and unsupported beta headers
-    headers = {"anthropic-beta": "computer-use-2024-10-22,mcp-servers-2025-12-04,structured-outputs-2025-11-13"}
-    
-    result = config.transform_request(
-        model="anthropic.claude-4-0-sonnet-20250514-v1:0",
-        messages=messages,
-        optional_params={},
-        litellm_params={},
-        headers=headers,
-    )
-    
-    # Verify only supported betas are kept
-    anthropic_beta = result.get("anthropic_beta", [])
-    assert not any("structured-outputs" in beta for beta in anthropic_beta), \
-        f"structured-outputs beta should be filtered, got: {anthropic_beta}"
-    assert not any("mcp-servers" in beta for beta in anthropic_beta), \
-        f"mcp-servers beta should be filtered, got: {anthropic_beta}"
-    assert any("computer-use" in beta for beta in anthropic_beta), \
-        f"computer-use beta should be kept, got: {anthropic_beta}"
-
-
-def test_output_format_removed_from_bedrock_invoke_request():
-    """
-    Test that output_format parameter is removed from Bedrock Invoke requests.
-    
-    Bedrock Invoke API doesn't support the output_format parameter (only supported
-    in Anthropic Messages API). This test ensures it's removed to prevent errors.
-    """
-    config = AmazonAnthropicClaudeConfig()
-    
-    messages = [{"role": "user", "content": "test"}]
-    
-    # Create a request with output_format via map_openai_params
-    non_default_params = {
-        "response_format": {"type": "json_object"}
-    }
-    optional_params = {}
-    
-    # This should trigger tool-based structured outputs
-    optional_params = config.map_openai_params(
-        non_default_params=non_default_params,
-        optional_params=optional_params,
-        model="anthropic.claude-4-0-sonnet-20250514-v1:0",
-        drop_params=False,
-    )
-    
-    result = config.transform_request(
-        model="anthropic.claude-4-0-sonnet-20250514-v1:0",
-        messages=messages,
-        optional_params=optional_params,
-        litellm_params={},
-        headers={},
-    )
-    
-    # Verify output_format is not in the request
-    assert "output_format" not in result, \
-        f"output_format should be removed for Bedrock Invoke, got keys: {result.keys()}"
diff --git a/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py b/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py
index e7b6de29b6b..b9324e4966f 100644
--- a/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py
+++ b/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py
@@ -390,103 +390,3 @@ def test_converse_anthropic_model_with_cross_region_prefix(self):
             "anthropic_beta SHOULD be added for Anthropic models with cross-region prefix."
         )
         assert "context-1m-2025-08-07" in additional_fields["anthropic_beta"]
-
-    def test_messages_advanced_tool_use_translation_opus_4_5(self):
-        """Test that advanced-tool-use header is translated to Bedrock-specific headers for Opus 4.5.
-        
-        Regression test for: Claude Code sends advanced-tool-use-2025-11-20 header which needs
-        to be translated to tool-search-tool-2025-10-19 and tool-examples-2025-10-29 for
-        Bedrock Invoke API on Claude Opus 4.5.
-        
-        Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html
-        """
-        config = AmazonAnthropicClaudeMessagesConfig()
-        headers = {"anthropic-beta": "advanced-tool-use-2025-11-20"}
-        
-        result = config.transform_anthropic_messages_request(
-            model="us.anthropic.claude-opus-4-5-20250514-v1:0",
-            messages=[{"role": "user", "content": "Test"}],
-            anthropic_messages_optional_request_params={"max_tokens": 100},
-            litellm_params={},
-            headers=headers
-        )
-        
-        assert "anthropic_beta" in result
-        beta_headers = result["anthropic_beta"]
-        
-        # advanced-tool-use should be removed
-        assert "advanced-tool-use-2025-11-20" not in beta_headers, (
-            "advanced-tool-use-2025-11-20 should be removed for Bedrock Invoke API"
-        )
-        
-        # Bedrock-specific headers should be added for Opus 4.5
-        assert "tool-search-tool-2025-10-19" in beta_headers, (
-            "tool-search-tool-2025-10-19 should be added for Opus 4.5"
-        )
-        assert "tool-examples-2025-10-29" in beta_headers, (
-            "tool-examples-2025-10-29 should be added for Opus 4.5"
-        )
-
-    def test_messages_advanced_tool_use_translation_sonnet_4_5(self):
-        """Test that advanced-tool-use header is translated to Bedrock-specific headers for Sonnet 4.5.
-
-        Regression test for: Claude Code sends advanced-tool-use-2025-11-20 header which needs
-        to be translated to tool-search-tool-2025-10-19 and tool-examples-2025-10-29 for
-        Bedrock Invoke API on Claude Sonnet 4.5.
-
-        Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool
-        """
-        config = AmazonAnthropicClaudeMessagesConfig()
-        headers = {"anthropic-beta": "advanced-tool-use-2025-11-20"}
-
-        result = config.transform_anthropic_messages_request(
-            model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
-            messages=[{"role": "user", "content": "Test"}],
-            anthropic_messages_optional_request_params={"max_tokens": 100},
-            litellm_params={},
-            headers=headers
-        )
-
-        assert "anthropic_beta" in result
-        beta_headers = result["anthropic_beta"]
-
-        # advanced-tool-use should be removed
-        assert "advanced-tool-use-2025-11-20" not in beta_headers, (
-            "advanced-tool-use-2025-11-20 should be removed for Bedrock Invoke API"
-        )
-
-        # Bedrock-specific headers should be added for Sonnet 4.5
-        assert "tool-search-tool-2025-10-19" in beta_headers, (
-            "tool-search-tool-2025-10-19 should be added for Sonnet 4.5"
-        )
-        assert "tool-examples-2025-10-29" in beta_headers, (
-            "tool-examples-2025-10-29 should be added for Sonnet 4.5"
-        )
-
-    def test_messages_advanced_tool_use_filtered_unsupported_model(self):
-        """Test that advanced-tool-use header is filtered out for models that don't support tool search.
-
-        The translation to Bedrock-specific headers should only happen for models that
-        support tool search on Bedrock (Opus 4.5, Sonnet 4.5).
-        For other models, the advanced-tool-use header should just be removed.
-        """
-        config = AmazonAnthropicClaudeMessagesConfig()
-        headers = {"anthropic-beta": "advanced-tool-use-2025-11-20"}
-
-        # Test with Claude 3.5 Sonnet (does NOT support tool search on Bedrock)
-        result = config.transform_anthropic_messages_request(
-            model="us.anthropic.claude-3-5-sonnet-20241022-v2:0",
-            messages=[{"role": "user", "content": "Test"}],
-            anthropic_messages_optional_request_params={"max_tokens": 100},
-            litellm_params={},
-            headers=headers
-        )
-
-        beta_headers = result.get("anthropic_beta", [])
-
-        # advanced-tool-use should be removed
-        assert "advanced-tool-use-2025-11-20" not in beta_headers
-
-        # Bedrock-specific headers should NOT be added for unsupported models
-        assert "tool-search-tool-2025-10-19" not in beta_headers
-        assert "tool-examples-2025-10-29" not in beta_headers
diff --git a/tests/test_litellm/llms/hosted_vllm/chat/test_hosted_vllm_chat_transformation.py b/tests/test_litellm/llms/hosted_vllm/chat/test_hosted_vllm_chat_transformation.py
index 9b3b6aeaea1..3749a5a8ca4 100644
--- a/tests/test_litellm/llms/hosted_vllm/chat/test_hosted_vllm_chat_transformation.py
+++ b/tests/test_litellm/llms/hosted_vllm/chat/test_hosted_vllm_chat_transformation.py
@@ -101,50 +101,3 @@ def test_hosted_vllm_supports_reasoning_effort():
         drop_params=False,
     )
     assert optional_params["reasoning_effort"] == "high"
-
-
-def test_hosted_vllm_supports_thinking():
-    """
-    Test that hosted_vllm supports the 'thinking' parameter.
-
-    Anthropic-style thinking is converted to OpenAI-style reasoning_effort
-    since vLLM is OpenAI-compatible.
-
-    Related issue: https://github.com/BerriAI/litellm/issues/19761
-    """
-    config = HostedVLLMChatConfig()
-    supported_params = config.get_supported_openai_params(
-        model="hosted_vllm/GLM-4.6-FP8"
-    )
-    assert "thinking" in supported_params
-
-    # Test thinking with low budget_tokens -> "minimal" (for < 2000)
-    optional_params = config.map_openai_params(
-        non_default_params={"thinking": {"type": "enabled", "budget_tokens": 1024}},
-        optional_params={},
-        model="hosted_vllm/GLM-4.6-FP8",
-        drop_params=False,
-    )
-    assert "thinking" not in optional_params  # thinking should NOT be passed
-    assert optional_params["reasoning_effort"] == "minimal"
-
-    # Test thinking with high budget_tokens -> "high"
-    optional_params = config.map_openai_params(
-        non_default_params={"thinking": {"type": "enabled", "budget_tokens": 15000}},
-        optional_params={},
-        model="hosted_vllm/GLM-4.6-FP8",
-        drop_params=False,
-    )
-    assert optional_params["reasoning_effort"] == "high"
-
-    # Test that existing reasoning_effort is not overwritten
-    optional_params = config.map_openai_params(
-        non_default_params={
-            "thinking": {"type": "enabled", "budget_tokens": 15000},
-            "reasoning_effort": "low",
-        },
-        optional_params={},
-        model="hosted_vllm/GLM-4.6-FP8",
-        drop_params=False,
-    )
-    assert optional_params["reasoning_effort"] == "low"
diff --git a/tests/test_litellm/llms/openai/test_gpt5_transformation.py b/tests/test_litellm/llms/openai/test_gpt5_transformation.py
index 386f264a4dd..fd25d302d07 100644
--- a/tests/test_litellm/llms/openai/test_gpt5_transformation.py
+++ b/tests/test_litellm/llms/openai/test_gpt5_transformation.py
@@ -20,23 +20,6 @@ def test_gpt5_supports_reasoning_effort(config: OpenAIConfig):
     assert "reasoning_effort" in config.get_supported_openai_params(model="gpt-5-mini")
 
 
-def test_gpt5_chat_does_not_support_reasoning_effort(config: OpenAIConfig):
-    assert (
-        "reasoning_effort"
-        not in config.get_supported_openai_params(model="gpt-5-chat-latest")
-    )
-
-
-def test_gpt5_chat_supports_temperature(config: OpenAIConfig):
-    params = config.map_openai_params(
-        non_default_params={"temperature": 0.3},
-        optional_params={},
-        model="gpt-5-chat-latest",
-        drop_params=False,
-    )
-    assert params["temperature"] == 0.3
-
-
 def test_gpt5_maps_max_tokens(config: OpenAIConfig):
     params = config.map_openai_params(
         non_default_params={"max_tokens": 10},
diff --git a/tests/test_litellm/llms/s3_vectors/__init__.py b/tests/test_litellm/llms/s3_vectors/__init__.py
deleted file mode 100644
index d4b0c4d8550..00000000000
--- a/tests/test_litellm/llms/s3_vectors/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# S3 Vectors tests
diff --git a/tests/test_litellm/llms/s3_vectors/vector_stores/__init__.py b/tests/test_litellm/llms/s3_vectors/vector_stores/__init__.py
deleted file mode 100644
index 231735c1de7..00000000000
--- a/tests/test_litellm/llms/s3_vectors/vector_stores/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# S3 Vectors vector store tests
diff --git a/tests/test_litellm/llms/s3_vectors/vector_stores/test_s3_vectors_transformation.py b/tests/test_litellm/llms/s3_vectors/vector_stores/test_s3_vectors_transformation.py
deleted file mode 100644
index 3a84da31542..00000000000
--- a/tests/test_litellm/llms/s3_vectors/vector_stores/test_s3_vectors_transformation.py
+++ /dev/null
@@ -1,115 +0,0 @@
-from unittest.mock import MagicMock, Mock
-
-import httpx
-import pytest
-
-from litellm.llms.s3_vectors.vector_stores.transformation import (
-    S3VectorsVectorStoreConfig,
-)
-from litellm.types.vector_stores import VectorStoreSearchResponse
-
-
-class TestS3VectorsVectorStoreConfig:
-    def test_init(self):
-        """Test that S3VectorsVectorStoreConfig initializes correctly"""
-        config = S3VectorsVectorStoreConfig()
-        assert config is not None
-
-    def test_get_supported_openai_params(self):
-        """Test that supported OpenAI params are returned"""
-        config = S3VectorsVectorStoreConfig()
-        params = config.get_supported_openai_params("test-model")
-        assert "max_num_results" in params
-
-    def test_get_complete_url(self):
-        """Test URL generation for S3 Vectors"""
-        config = S3VectorsVectorStoreConfig()
-        litellm_params = {"aws_region_name": "us-west-2"}
-        url = config.get_complete_url(None, litellm_params)
-        assert url == "https://s3vectors.us-west-2.api.aws"
-
-    def test_get_complete_url_missing_region(self):
-        """Test that missing region raises error"""
-        config = S3VectorsVectorStoreConfig()
-        litellm_params = {}
-        with pytest.raises(ValueError, match="aws_region_name is required"):
-            config.get_complete_url(None, litellm_params)
-
-    @pytest.mark.skip(reason="Requires embedding API call, tested in integration tests")
-    def test_transform_search_request(self):
-        """Test search request transformation"""
-        # This test requires making an actual embedding API call
-        # It's better tested in integration tests
-        pass
-
-    def test_transform_search_request_invalid_vector_store_id(self):
-        """Test that invalid vector_store_id format raises error"""
-        config = S3VectorsVectorStoreConfig()
-        mock_logging_obj = Mock()
-        mock_logging_obj.model_call_details = {}
-
-        with pytest.raises(
-            ValueError, match="vector_store_id must be in format 'bucket_name:index_name'"
-        ):
-            config.transform_search_vector_store_request(
-                vector_store_id="invalid-format",
-                query="test query",
-                vector_store_search_optional_params={},
-                api_base="https://s3vectors.us-west-2.api.aws",
-                litellm_logging_obj=mock_logging_obj,
-                litellm_params={},
-            )
-
-    def test_transform_search_response(self):
-        """Test search response transformation"""
-        config = S3VectorsVectorStoreConfig()
-        mock_logging_obj = Mock()
-        mock_logging_obj.model_call_details = {"query": "test query"}
-
-        mock_response = Mock(spec=httpx.Response)
-        mock_response.json.return_value = {
-            "vectors": [
-                {
-                    "distance": 0.05,  # S3 Vectors returns distance, not score
-                    "metadata": {
-                        "source_text": "This is test content",
-                        "chunk_index": "0",
-                        "filename": "test.pdf",
-                    },
-                },
-                {
-                    "distance": 0.15,
-                    "metadata": {
-                        "source_text": "More test content",
-                        "chunk_index": "1",
-                    },
-                },
-            ]
-        }
-        mock_response.status_code = 200
-        mock_response.headers = {}
-
-        result = config.transform_search_vector_store_response(
-            mock_response, mock_logging_obj
-        )
-
-        # VectorStoreSearchResponse is a TypedDict, so check structure instead of isinstance
-        assert result["object"] == "vector_store.search_results.page"
-        assert result["search_query"] == "test query"
-        assert len(result["data"]) == 2
-        # Score should be 1 - distance (cosine similarity)
-        assert result["data"][0]["score"] == 0.95  # 1 - 0.05
-        assert result["data"][0]["content"][0]["text"] == "This is test content"
-        assert result["data"][0]["filename"] == "test.pdf"
-        assert result["data"][1]["score"] == 0.85  # 1 - 0.15
-        assert result["data"][1]["content"][0]["text"] == "More test content"
-
-    def test_map_openai_params(self):
-        """Test OpenAI parameter mapping"""
-        config = S3VectorsVectorStoreConfig()
-        non_default_params = {"max_num_results": 5}
-        optional_params = {}
-
-        result = config.map_openai_params(non_default_params, optional_params, False)
-
-        assert result["maxResults"] == 5
diff --git a/tests/test_litellm/llms/test_cache_control_and_reasoning.py b/tests/test_litellm/llms/test_cache_control_and_reasoning.py
deleted file mode 100644
index 468927cdd49..00000000000
--- a/tests/test_litellm/llms/test_cache_control_and_reasoning.py
+++ /dev/null
@@ -1,281 +0,0 @@
-"""
-Test cache_control and reasoning parameter support for MiniMax, GLM/ZAI, and OpenRouter.
-
-This test file verifies the fixes for Issue #19923:
-- cache_control is preserved (not stripped) for MiniMax, GLM, and OpenRouter variants
-- thinking parameter is supported for reasoning-capable models
-- Model metadata correctly reflects capabilities
-"""
-import os
-import sys
-
-import pytest
-
-sys.path.insert(
-    0, os.path.abspath("../../..")
-)  # Adds the parent directory to the system path
-
-from litellm.llms.minimax.chat.transformation import MinimaxChatConfig
-from litellm.llms.openrouter.chat.transformation import OpenrouterConfig
-from litellm.llms.zai.chat.transformation import ZAIChatConfig
-
-
-def test_minimax_preserves_cache_control_in_messages():
-    """MiniMax should NOT strip cache_control from messages."""
-    config = MinimaxChatConfig()
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful assistant.",
-            "cache_control": {"type": "ephemeral"},
-        },
-        {
-            "role": "user",
-            "content": "Hello, world!",
-        },
-    ]
-
-    transformed_messages, _ = config.remove_cache_control_flag_from_messages_and_tools(
-        model="minimax/MiniMax-M2.1", messages=messages
-    )
-
-    # cache_control should be preserved
-    assert transformed_messages[0].get("cache_control") == {"type": "ephemeral"}
-
-
-def test_minimax_preserves_cache_control_in_tools():
-    """MiniMax should NOT strip cache_control from tools."""
-    config = MinimaxChatConfig()
-
-    tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "get_weather",
-                "description": "Get weather information",
-                "parameters": {"type": "object", "properties": {}},
-            },
-            "cache_control": {"type": "ephemeral"},
-        }
-    ]
-
-    _, transformed_tools = config.remove_cache_control_flag_from_messages_and_tools(
-        model="minimax/MiniMax-M2.1", messages=[], tools=tools
-    )
-
-    # cache_control should be preserved
-    assert transformed_tools[0].get("cache_control") == {"type": "ephemeral"}
-
-
-def test_minimax_supports_thinking_param():
-    """MiniMax reasoning models should support thinking parameter."""
-    config = MinimaxChatConfig()
-
-    supported_params = config.get_supported_openai_params(
-        model="minimax/MiniMax-M2.1"
-    )
-
-    # thinking should be in supported params for reasoning models
-    assert "thinking" in supported_params
-    # reasoning_split should also be supported
-    assert "reasoning_split" in supported_params
-
-
-def test_zai_preserves_cache_control_in_messages():
-    """ZAI should NOT strip cache_control from messages."""
-    config = ZAIChatConfig()
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful assistant.",
-            "cache_control": {"type": "ephemeral"},
-        },
-        {
-            "role": "user",
-            "content": "Hello, world!",
-        },
-    ]
-
-    transformed_messages, _ = config.remove_cache_control_flag_from_messages_and_tools(
-        model="zai/glm-4.7", messages=messages
-    )
-
-    # cache_control should be preserved
-    assert transformed_messages[0].get("cache_control") == {"type": "ephemeral"}
-
-
-def test_zai_preserves_cache_control_in_tools():
-    """ZAI should NOT strip cache_control from tools."""
-    config = ZAIChatConfig()
-
-    tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "get_weather",
-                "description": "Get weather information",
-                "parameters": {"type": "object", "properties": {}},
-            },
-            "cache_control": {"type": "ephemeral"},
-        }
-    ]
-
-    _, transformed_tools = config.remove_cache_control_flag_from_messages_and_tools(
-        model="zai/glm-4.7", messages=[], tools=tools
-    )
-
-    # cache_control should be preserved
-    assert transformed_tools[0].get("cache_control") == {"type": "ephemeral"}
-
-
-def test_zai_supports_thinking_param_for_reasoning_models():
-    """ZAI reasoning models (glm-4.7, glm-4.6) should support thinking parameter."""
-    config = ZAIChatConfig()
-
-    # glm-4.7 supports reasoning
-    supported_params_47 = config.get_supported_openai_params(model="zai/glm-4.7")
-    assert "thinking" in supported_params_47
-
-    # glm-4.6 supports reasoning
-    supported_params_46 = config.get_supported_openai_params(model="zai/glm-4.6")
-    assert "thinking" in supported_params_46
-
-
-def test_openrouter_minimax_supports_cache_control():
-    """OpenRouter should preserve cache_control for MiniMax models."""
-    config = OpenrouterConfig()
-
-    messages = [
-        {
-            "role": "user",
-            "content": "Hello, world!",
-            "cache_control": {"type": "ephemeral"},
-        }
-    ]
-
-    # Test that cache_control is not removed
-    transformed_messages, _ = config.remove_cache_control_flag_from_messages_and_tools(
-        model="openrouter/minimax/minimax-m2", messages=messages
-    )
-
-    # The method should preserve cache_control for minimax models
-    assert transformed_messages[0].get("cache_control") == {"type": "ephemeral"}
-
-
-def test_openrouter_glm_supports_cache_control():
-    """OpenRouter should preserve cache_control for GLM models."""
-    config = OpenrouterConfig()
-
-    messages = [
-        {
-            "role": "user",
-            "content": "Hello, world!",
-            "cache_control": {"type": "ephemeral"},
-        }
-    ]
-
-    # Test that cache_control is not removed for GLM models
-    transformed_messages, _ = config.remove_cache_control_flag_from_messages_and_tools(
-        model="openrouter/z-ai/glm-4.6", messages=messages
-    )
-
-    # The method should preserve cache_control for GLM models
-    assert transformed_messages[0].get("cache_control") == {"type": "ephemeral"}
-
-
-def test_openrouter_deepseek_strips_cache_control():
-    """OpenRouter should still strip cache_control for non-supported models."""
-    config = OpenrouterConfig()
-
-    messages = [
-        {
-            "role": "user",
-            "content": "Hello, world!",
-            "cache_control": {"type": "ephemeral"},
-        }
-    ]
-
-    # DeepSeek doesn't support cache_control, so it should be stripped
-    transformed_messages, _ = config.remove_cache_control_flag_from_messages_and_tools(
-        model="openrouter/deepseek/deepseek-chat", messages=messages
-    )
-
-    # cache_control should be removed for non-supported models
-    assert transformed_messages[0].get("cache_control") is None
-
-
-def test_openrouter_minimax_transform_moves_cache_control_to_content():
-    """OpenRouter should move cache_control to content blocks for MiniMax."""
-    config = OpenrouterConfig()
-
-    messages = [
-        {
-            "role": "user",
-            "content": "Analyze this data",
-            "cache_control": {"type": "ephemeral"},
-        }
-    ]
-
-    transformed_request = config.transform_request(
-        model="openrouter/minimax/minimax-m2",
-        messages=messages,
-        optional_params={},
-        litellm_params={},
-        headers={},
-    )
-
-    # cache_control should be moved to content blocks
-    assert "messages" in transformed_request
-    user_message = transformed_request["messages"][0]
-    assert isinstance(user_message["content"], list)
-    assert user_message["content"][0]["cache_control"] == {"type": "ephemeral"}
-    # Message-level cache_control should be removed
-    assert "cache_control" not in user_message
-
-
-def test_openrouter_glm_transform_moves_cache_control_to_content():
-    """OpenRouter should move cache_control to content blocks for GLM."""
-    config = OpenrouterConfig()
-
-    messages = [
-        {
-            "role": "user",
-            "content": "Analyze this data",
-            "cache_control": {"type": "ephemeral"},
-        }
-    ]
-
-    transformed_request = config.transform_request(
-        model="openrouter/z-ai/glm-4.6",
-        messages=messages,
-        optional_params={},
-        litellm_params={},
-        headers={},
-    )
-
-    # cache_control should be moved to content blocks
-    assert "messages" in transformed_request
-    user_message = transformed_request["messages"][0]
-    assert isinstance(user_message["content"], list)
-    assert user_message["content"][0]["cache_control"] == {"type": "ephemeral"}
-
-
-def test_openrouter_supports_thinking_param_for_reasoning_models():
-    """OpenRouter should support thinking parameter for reasoning-capable models."""
-    config = OpenrouterConfig()
-
-    # Test MiniMax (supports reasoning)
-    supported_params_minimax = config.get_supported_openai_params(
-        model="openrouter/minimax/minimax-m2"
-    )
-    assert "thinking" in supported_params_minimax
-    assert "reasoning_effort" in supported_params_minimax
-
-    # Test GLM (supports reasoning)
-    supported_params_glm = config.get_supported_openai_params(
-        model="openrouter/z-ai/glm-4.6"
-    )
-    assert "thinking" in supported_params_glm
-    assert "reasoning_effort" in supported_params_glm
diff --git a/tests/test_litellm/llms/vercel_ai_gateway/embedding/__init__.py b/tests/test_litellm/llms/vercel_ai_gateway/embedding/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/tests/test_litellm/llms/vercel_ai_gateway/embedding/test_vercel_ai_gateway_embedding.py b/tests/test_litellm/llms/vercel_ai_gateway/embedding/test_vercel_ai_gateway_embedding.py
deleted file mode 100644
index af1e1df92fd..00000000000
--- a/tests/test_litellm/llms/vercel_ai_gateway/embedding/test_vercel_ai_gateway_embedding.py
+++ /dev/null
@@ -1,218 +0,0 @@
-import os
-import sys
-from unittest.mock import MagicMock, patch
-
-import httpx
-import pytest
-
-sys.path.insert(
-    0, os.path.abspath("../../../../..")
-)  # Adds the parent directory to the system path
-
-from litellm.llms.vercel_ai_gateway.embedding.transformation import (
-    VercelAIGatewayEmbeddingConfig,
-)
-from litellm.llms.vercel_ai_gateway.common_utils import VercelAIGatewayException
-from litellm.types.utils import EmbeddingResponse
-
-
-def test_vercel_ai_gateway_embedding_get_complete_url():
-    """Test URL generation for embeddings endpoint"""
-    config = VercelAIGatewayEmbeddingConfig()
-
-    # Test with default API base
-    url = config.get_complete_url(
-        api_base=None,
-        api_key=None,
-        model="openai/text-embedding-3-small",
-        optional_params={},
-        litellm_params={},
-    )
-    assert url == "https://ai-gateway.vercel.sh/v1/embeddings"
-
-    # Test with custom API base
-    url = config.get_complete_url(
-        api_base="https://custom.vercel.sh/v1",
-        api_key=None,
-        model="openai/text-embedding-3-small",
-        optional_params={},
-        litellm_params={},
-    )
-    assert url == "https://custom.vercel.sh/v1/embeddings"
-
-    # Test with trailing slash
-    url = config.get_complete_url(
-        api_base="https://custom.vercel.sh/v1/",
-        api_key=None,
-        model="openai/text-embedding-3-small",
-        optional_params={},
-        litellm_params={},
-    )
-    assert url == "https://custom.vercel.sh/v1/embeddings"
-
-
-def test_vercel_ai_gateway_embedding_transform_request():
-    """Test request transformation for embeddings"""
-    config = VercelAIGatewayEmbeddingConfig()
-
-    # Test with string input
-    request = config.transform_embedding_request(
-        model="openai/text-embedding-3-small",
-        input="Hello world",
-        optional_params={},
-        headers={},
-    )
-    assert request["model"] == "openai/text-embedding-3-small"
-    assert request["input"] == ["Hello world"]
-
-    # Test with list input
-    request = config.transform_embedding_request(
-        model="openai/text-embedding-3-small",
-        input=["Hello", "World"],
-        optional_params={},
-        headers={},
-    )
-    assert request["model"] == "openai/text-embedding-3-small"
-    assert request["input"] == ["Hello", "World"]
-
-    # Test stripping vercel_ai_gateway/ prefix
-    request = config.transform_embedding_request(
-        model="vercel_ai_gateway/openai/text-embedding-3-small",
-        input="Hello",
-        optional_params={},
-        headers={},
-    )
-    assert request["model"] == "openai/text-embedding-3-small"
-
-
-def test_vercel_ai_gateway_embedding_transform_request_with_dimensions():
-    """Test request transformation with dimensions parameter"""
-    config = VercelAIGatewayEmbeddingConfig()
-
-    request = config.transform_embedding_request(
-        model="openai/text-embedding-3-small",
-        input="Hello world",
-        optional_params={"dimensions": 768},
-        headers={},
-    )
-    assert request["model"] == "openai/text-embedding-3-small"
-    assert request["input"] == ["Hello world"]
-    assert request["dimensions"] == 768
-
-
-def test_vercel_ai_gateway_embedding_validate_environment():
-    """Test header validation and setup"""
-    config = VercelAIGatewayEmbeddingConfig()
-
-    headers = config.validate_environment(
-        headers={},
-        model="openai/text-embedding-3-small",
-        messages=[],
-        optional_params={},
-        litellm_params={},
-        api_key="test_key",
-    )
-    assert headers["Content-Type"] == "application/json"
-    assert headers["Authorization"] == "Bearer test_key"
-
-    # Test with existing headers (should merge)
-    headers = config.validate_environment(
-        headers={"X-Custom": "value"},
-        model="openai/text-embedding-3-small",
-        messages=[],
-        optional_params={},
-        litellm_params={},
-        api_key="test_key",
-    )
-    assert headers["X-Custom"] == "value"
-    assert headers["Authorization"] == "Bearer test_key"
-
-
-def test_vercel_ai_gateway_embedding_get_supported_params():
-    """Test supported OpenAI parameters"""
-    config = VercelAIGatewayEmbeddingConfig()
-    supported = config.get_supported_openai_params("openai/text-embedding-3-small")
-
-    assert "dimensions" in supported
-    assert "encoding_format" in supported
-    assert "timeout" in supported
-    assert "user" in supported
-
-
-def test_vercel_ai_gateway_embedding_map_openai_params():
-    """Test OpenAI parameter mapping"""
-    config = VercelAIGatewayEmbeddingConfig()
-
-    optional_params = config.map_openai_params(
-        non_default_params={"dimensions": 768, "encoding_format": "float"},
-        optional_params={},
-        model="openai/text-embedding-3-small",
-        drop_params=False,
-    )
-    assert optional_params["dimensions"] == 768
-    assert optional_params["encoding_format"] == "float"
-
-
-def test_vercel_ai_gateway_embedding_error_class():
-    """Test error class creation"""
-    config = VercelAIGatewayEmbeddingConfig()
-
-    error = config.get_error_class(
-        error_message="Test error",
-        status_code=400,
-        headers={"Content-Type": "application/json"},
-    )
-
-    assert isinstance(error, VercelAIGatewayException)
-    assert error.message == "Test error"
-    assert error.status_code == 400
-
-
-def test_vercel_ai_gateway_embedding_transform_response():
-    """Test response transformation"""
-    config = VercelAIGatewayEmbeddingConfig()
-
-    mock_response = MagicMock(spec=httpx.Response)
-    mock_response.text = '{"object":"list","data":[{"object":"embedding","index":0,"embedding":[0.1,0.2,0.3]}],"model":"openai/text-embedding-3-small","usage":{"prompt_tokens":2,"total_tokens":2}}'
-    mock_response.json.return_value = {
-        "object": "list",
-        "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}],
-        "model": "openai/text-embedding-3-small",
-        "usage": {"prompt_tokens": 2, "total_tokens": 2},
-    }
-
-    mock_logging = MagicMock()
-
-    response = config.transform_embedding_response(
-        model="openai/text-embedding-3-small",
-        raw_response=mock_response,
-        model_response=EmbeddingResponse(),
-        logging_obj=mock_logging,
-        api_key="test_key",
-        request_data={},
-        optional_params={},
-        litellm_params={},
-    )
-
-    assert response is not None
-    mock_logging.post_call.assert_called_once()
-
-
-def test_vercel_ai_gateway_embedding_env_vars():
-    """Test environment variable handling"""
-    config = VercelAIGatewayEmbeddingConfig()
-
-    with patch.dict(
-        os.environ,
-        {
-            "VERCEL_AI_GATEWAY_API_BASE": "https://env.vercel.sh/v1",
-        },
-    ):
-        url = config.get_complete_url(
-            api_base=None,
-            api_key=None,
-            model="openai/text-embedding-3-small",
-            optional_params={},
-            litellm_params={},
-        )
-        assert url == "https://env.vercel.sh/v1/embeddings"
diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
index 94323e06901..953f54e4a54 100644
--- a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
+++ b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
@@ -812,36 +812,6 @@ def test_get_vertex_model_id_from_url():
     assert model_id is None
 
 
-def test_get_vertex_model_id_from_url_with_slashes():
-    """Test get_vertex_model_id_from_url with model names containing slashes (e.g., gcp/google/gemini-2.5-flash)
-
-    Regression test for NVIDIA issue: custom model names with slashes in passthrough URLs
-    were being truncated (e.g., 'gcp/google/gemini-2.5-flash' -> 'gcp'), causing access_groups
-    checks to fail.
-    """
-    from litellm.llms.vertex_ai.common_utils import get_vertex_model_id_from_url
-
-    # Test with model name containing slashes: gcp/google/gemini-2.5-flash
-    url = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gcp/google/gemini-2.5-flash:generateContent"
-    model_id = get_vertex_model_id_from_url(url)
-    assert model_id == "gcp/google/gemini-2.5-flash"
-
-    # Test with model name containing slashes: gcp/google/gemini-3-flash-preview
-    url = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/global/publishers/google/models/gcp/google/gemini-3-flash-preview:streamGenerateContent"
-    model_id = get_vertex_model_id_from_url(url)
-    assert model_id == "gcp/google/gemini-3-flash-preview"
-
-    # Test with custom model path: custom/model
-    url = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/custom/model:generateContent"
-    model_id = get_vertex_model_id_from_url(url)
-    assert model_id == "custom/model"
-
-    # Test passthrough URL format (without host)
-    url = "v1/projects/my-project/locations/us-central1/publishers/google/models/gcp/google/gemini-2.5-flash:generateContent"
-    model_id = get_vertex_model_id_from_url(url)
-    assert model_id == "gcp/google/gemini-2.5-flash"
-
-
 def test_construct_target_url_with_version_prefix():
     """Test construct_target_url with version prefixes"""
     from litellm.llms.vertex_ai.common_utils import construct_target_url
diff --git a/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py b/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py
index dc06d6a1b0d..c0871d3b9b7 100644
--- a/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py
+++ b/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py
@@ -6,17 +6,16 @@
 
 Source: litellm/llms/xai/responses/transformation.py
 """
-import os
 import sys
+import os
 
 sys.path.insert(0, os.path.abspath("../../../../.."))
 
 import pytest
-
-from litellm.llms.xai.responses.transformation import XAIResponsesAPIConfig
-from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams
 from litellm.types.utils import LlmProviders
 from litellm.utils import ProviderConfigManager
+from litellm.llms.xai.responses.transformation import XAIResponsesAPIConfig
+from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams
 
 
 class TestXAIResponsesAPITransformation:
@@ -111,209 +110,3 @@ def test_xai_responses_endpoint_url(self):
         )
         assert url_with_slash == "https://api.x.ai/v1/responses", "Should handle trailing slash"
 
-    def test_web_search_tool_transformation(self):
-        """Test that web_search tools are transformed to XAI format"""
-        config = XAIResponsesAPIConfig()
-        
-        # Test with allowed_domains
-        params = ResponsesAPIOptionalRequestParams(
-            tools=[
-                {
-                    "type": "web_search",
-                    "allowed_domains": ["wikipedia.org", "x.ai"],
-                    "enable_image_understanding": True
-                }
-            ]
-        )
-        
-        result = config.map_openai_params(
-            response_api_optional_params=params,
-            model="grok-4-1-fast",
-            drop_params=False
-        )
-        
-        assert "tools" in result
-        assert len(result["tools"]) == 1
-        tool = result["tools"][0]
-        assert tool["type"] == "web_search"
-        assert "filters" in tool
-        assert tool["filters"]["allowed_domains"] == ["wikipedia.org", "x.ai"]
-        assert tool["enable_image_understanding"] is True
-        
-    def test_web_search_search_context_size_removed(self):
-        """Test that search_context_size is removed from web_search tools"""
-        config = XAIResponsesAPIConfig()
-        
-        params = ResponsesAPIOptionalRequestParams(
-            tools=[
-                {
-                    "type": "web_search",
-                    "search_context_size": "high"  # Not supported by XAI
-                }
-            ]
-        )
-        
-        result = config.map_openai_params(
-            response_api_optional_params=params,
-            model="grok-4-1-fast",
-            drop_params=False
-        )
-        
-        assert "tools" in result
-        assert len(result["tools"]) == 1
-        tool = result["tools"][0]
-        assert tool["type"] == "web_search"
-        assert "search_context_size" not in tool
-        
-    def test_web_search_excluded_domains(self):
-        """Test web_search with excluded_domains"""
-        config = XAIResponsesAPIConfig()
-        
-        params = ResponsesAPIOptionalRequestParams(
-            tools=[
-                {
-                    "type": "web_search",
-                    "excluded_domains": ["example.com", "test.com"]
-                }
-            ]
-        )
-        
-        result = config.map_openai_params(
-            response_api_optional_params=params,
-            model="grok-4-1-fast",
-            drop_params=False
-        )
-        
-        tool = result["tools"][0]
-        assert "filters" in tool
-        assert tool["filters"]["excluded_domains"] == ["example.com", "test.com"]
-        
-    def test_web_search_domains_limit(self):
-        """Test that allowed_domains and excluded_domains are limited to 5"""
-        config = XAIResponsesAPIConfig()
-        
-        # Test with more than 5 allowed_domains
-        params = ResponsesAPIOptionalRequestParams(
-            tools=[
-                {
-                    "type": "web_search",
-                    "allowed_domains": ["d1.com", "d2.com", "d3.com", "d4.com", "d5.com", "d6.com", "d7.com"]
-                }
-            ]
-        )
-        
-        result = config.map_openai_params(
-            response_api_optional_params=params,
-            model="grok-4-1-fast",
-            drop_params=False
-        )
-        
-        tool = result["tools"][0]
-        assert len(tool["filters"]["allowed_domains"]) == 7
-        
-    def test_x_search_tool_transformation(self):
-        """Test that x_search tools are transformed correctly"""
-        config = XAIResponsesAPIConfig()
-        
-        params = ResponsesAPIOptionalRequestParams(
-            tools=[
-                {
-                    "type": "x_search",
-                    "allowed_x_handles": ["elonmusk", "xai"],
-                    "from_date": "2025-01-01",
-                    "to_date": "2025-01-28",
-                    "enable_image_understanding": True,
-                    "enable_video_understanding": True
-                }
-            ]
-        )
-        
-        result = config.map_openai_params(
-            response_api_optional_params=params,
-            model="grok-4-1-fast",
-            drop_params=False
-        )
-        
-        assert "tools" in result
-        assert len(result["tools"]) == 1
-        tool = result["tools"][0]
-        assert tool["type"] == "x_search"
-        assert tool["allowed_x_handles"] == ["elonmusk", "xai"]
-        assert tool["from_date"] == "2025-01-01"
-        assert tool["to_date"] == "2025-01-28"
-        assert tool["enable_image_understanding"] is True
-        assert tool["enable_video_understanding"] is True
-        
-    def test_x_search_excluded_handles(self):
-        """Test x_search with excluded_x_handles"""
-        config = XAIResponsesAPIConfig()
-        
-        params = ResponsesAPIOptionalRequestParams(
-            tools=[
-                {
-                    "type": "x_search",
-                    "excluded_x_handles": ["spam_account", "bot_account"]
-                }
-            ]
-        )
-        
-        result = config.map_openai_params(
-            response_api_optional_params=params,
-            model="grok-4-1-fast",
-            drop_params=False
-        )
-        
-        tool = result["tools"][0]
-        assert tool["excluded_x_handles"] == ["spam_account", "bot_account"]
-        
-    def test_mixed_tools(self):
-        """Test transformation with multiple tool types"""
-        config = XAIResponsesAPIConfig()
-        
-        params = ResponsesAPIOptionalRequestParams(
-            tools=[
-                {
-                    "type": "code_interpreter",
-                    "container": {"type": "auto"}
-                },
-                {
-                    "type": "web_search",
-                    "allowed_domains": ["wikipedia.org"]
-                },
-                {
-                    "type": "x_search",
-                    "allowed_x_handles": ["elonmusk"]
-                },
-                {
-                    "type": "function",
-                    "name": "get_weather",
-                    "description": "Get weather",
-                    "parameters": {"type": "object"}
-                }
-            ]
-        )
-        
-        result = config.map_openai_params(
-            response_api_optional_params=params,
-            model="grok-4-1-fast",
-            drop_params=False
-        )
-        
-        assert len(result["tools"]) == 4
-        
-        # Verify code_interpreter
-        assert result["tools"][0]["type"] == "code_interpreter"
-        assert "container" not in result["tools"][0]
-        
-        # Verify web_search
-        assert result["tools"][1]["type"] == "web_search"
-        assert "filters" in result["tools"][1]
-        
-        # Verify x_search
-        assert result["tools"][2]["type"] == "x_search"
-        assert result["tools"][2]["allowed_x_handles"] == ["elonmusk"]
-        
-        # Verify function tool is unchanged
-        assert result["tools"][3]["type"] == "function"
-        assert result["tools"][3]["name"] == "get_weather"
-
diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py
index f241b2aa0d5..ecdc75ede52 100644
--- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py
+++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py
@@ -1885,7 +1885,7 @@ async def test_call_tool_without_broken_pipe_error(self):
         # Create mock client that tracks call_tool usage
         mock_client = AsyncMock()
 
-        async def mock_call_tool(params, host_progress_callback=None):
+        async def mock_call_tool(params):
             # Return a mock CallToolResult
             result = MagicMock(spec=CallToolResult)
             result.content = [{"type": "text", "text": "Tool executed successfully"}]
diff --git a/tests/test_litellm/proxy/auth/test_auth_checks.py b/tests/test_litellm/proxy/auth/test_auth_checks.py
index ebcd9676129..807559207e6 100644
--- a/tests/test_litellm/proxy/auth/test_auth_checks.py
+++ b/tests/test_litellm/proxy/auth/test_auth_checks.py
@@ -15,10 +15,10 @@
 import litellm
 from litellm.proxy._types import (
     CallInfo,
-    Litellm_EntityType,
     LiteLLM_ObjectPermissionTable,
     LiteLLM_TeamTable,
     LiteLLM_UserTable,
+    Litellm_EntityType,
     LitellmUserRoles,
     ProxyErrorTypes,
     ProxyException,
@@ -28,7 +28,6 @@
 from litellm.proxy.auth.auth_checks import (
     ExperimentalUIJWTToken,
     _can_object_call_vector_stores,
-    _get_fuzzy_user_object,
     _get_team_db_check,
     _virtual_key_max_budget_alert_check,
     _virtual_key_soft_budget_check,
@@ -45,24 +44,6 @@ def set_salt_key(monkeypatch):
     monkeypatch.setenv("LITELLM_SALT_KEY", "sk-1234")
 
 
-@pytest.fixture(autouse=True)
-def reset_constants_module():
-    """Reset constants module to ensure clean state before each test"""
-    import importlib
-    from litellm import constants
-    from litellm.proxy.auth import auth_checks
-    
-    # Reload modules before test
-    importlib.reload(constants)
-    importlib.reload(auth_checks)
-    
-    yield
-    
-    # Reload modules after test to clean up
-    importlib.reload(constants)
-    importlib.reload(auth_checks)
-
-
 @pytest.fixture
 def valid_sso_user_defined_values():
     return LiteLLM_UserTable(
@@ -150,63 +131,6 @@ def test_get_key_object_from_ui_hash_key_invalid():
     assert key_object is None
 
 
-def test_get_cli_jwt_auth_token_default_expiration(valid_sso_user_defined_values):
-    """Test generating CLI JWT token with default 24-hour expiration"""
-    token = ExperimentalUIJWTToken.get_cli_jwt_auth_token(valid_sso_user_defined_values)
-
-    # Decrypt and verify token contents
-    decrypted_token = decrypt_value_helper(
-        token, key="ui_hash_key", exception_type="debug"
-    )
-    assert decrypted_token is not None
-    token_data = json.loads(decrypted_token)
-
-    assert token_data["user_id"] == "test_user"
-    assert token_data["user_role"] == LitellmUserRoles.PROXY_ADMIN.value
-    assert token_data["models"] == ["gpt-3.5-turbo"]
-    assert token_data["max_budget"] == litellm.max_ui_session_budget
-
-    # Verify expiration time is set to 24 hours (default)
-    assert "expires" in token_data
-    expires = datetime.fromisoformat(token_data["expires"].replace("Z", "+00:00"))
-    assert expires > get_utc_datetime()
-    assert expires <= get_utc_datetime() + timedelta(hours=24, minutes=1)
-    assert expires >= get_utc_datetime() + timedelta(hours=23, minutes=59)
-
-
-def test_get_cli_jwt_auth_token_custom_expiration(
-    valid_sso_user_defined_values, monkeypatch
-):
-    """Test generating CLI JWT token with custom expiration via environment variable"""
-    import importlib
-    from litellm import constants
-    from litellm.proxy.auth import auth_checks
-    
-    # Set custom expiration to 48 hours
-    monkeypatch.setenv("LITELLM_CLI_JWT_EXPIRATION_HOURS", "48")
-    
-    # Reload the constants module to pick up the new env var
-    importlib.reload(constants)
-    # Also reload auth_checks to pick up the new constant value
-    importlib.reload(auth_checks)
-    
-    token = auth_checks.ExperimentalUIJWTToken.get_cli_jwt_auth_token(valid_sso_user_defined_values)
-
-    # Decrypt and verify token contents
-    decrypted_token = decrypt_value_helper(
-        token, key="ui_hash_key", exception_type="debug"
-    )
-    assert decrypted_token is not None
-    token_data = json.loads(decrypted_token)
-
-    # Verify expiration time is set to 48 hours
-    assert "expires" in token_data
-    expires = datetime.fromisoformat(token_data["expires"].replace("Z", "+00:00"))
-    assert expires > get_utc_datetime() + timedelta(hours=47, minutes=59)
-    assert expires <= get_utc_datetime() + timedelta(hours=48, minutes=1)
-
-
-
 @pytest.mark.asyncio
 async def test_default_internal_user_params_with_get_user_object(monkeypatch):
     """Test that default_internal_user_params is used when creating a new user via get_user_object"""
@@ -1353,42 +1277,3 @@ async def budget_alerts(self, type, user_info):
     assert (
         alert_triggered == expect_alert
     ), f"Expected alert_triggered to be {expect_alert} for spend={spend}, max_budget={max_budget}"
-
-
-@pytest.mark.asyncio
-async def test_get_fuzzy_user_object_case_insensitive_email():
-    """Test that _get_fuzzy_user_object uses case-insensitive email lookup"""
-    # Setup mock Prisma client
-    mock_prisma = MagicMock()
-    mock_prisma.db = MagicMock()
-    mock_prisma.db.litellm_usertable = MagicMock()
-
-    # Mock user data with mixed case email
-    test_user = LiteLLM_UserTable(
-        user_id="test_123",
-        sso_user_id=None,
-        user_email="Test@Example.com",  # Mixed case in DB
-        organization_memberships=[],
-        max_budget=None,
-    )
-
-    # Test: SSO ID not found, find by email with different casing
-    mock_prisma.db.litellm_usertable.find_unique = AsyncMock(return_value=None)
-    mock_prisma.db.litellm_usertable.find_first = AsyncMock(return_value=test_user)
-
-    # Search with lowercase email (different from DB)
-    result = await _get_fuzzy_user_object(
-        prisma_client=mock_prisma,
-        sso_user_id=None,
-        user_email="test@example.com",  # Lowercase search
-    )
-
-    # Verify user was found despite case difference
-    assert result == test_user
-
-    # Verify the query used case-insensitive mode
-    mock_prisma.db.litellm_usertable.find_first.assert_called_once()
-    call_args = mock_prisma.db.litellm_usertable.find_first.call_args
-    assert call_args.kwargs["where"]["user_email"]["equals"] == "test@example.com"
-    assert call_args.kwargs["where"]["user_email"]["mode"] == "insensitive"
-    assert call_args.kwargs["include"] == {"organization_memberships": True}
diff --git a/tests/test_litellm/proxy/auth/test_auth_utils.py b/tests/test_litellm/proxy/auth/test_auth_utils.py
index 82920ce1d80..62f9cc33b64 100644
--- a/tests/test_litellm/proxy/auth/test_auth_utils.py
+++ b/tests/test_litellm/proxy/auth/test_auth_utils.py
@@ -8,7 +8,6 @@
 from litellm.proxy.auth.auth_utils import (
     _get_customer_id_from_standard_headers,
     get_end_user_id_from_request_body,
-    get_model_from_request,
     get_key_model_rpm_limit,
     get_key_model_tpm_limit,
 )
@@ -187,25 +186,3 @@ def test_should_fall_back_to_body_when_no_standard_header(self):
                 request_body=request_body, request_headers=headers
             )
         assert result == "body-user"
-
-
-def test_get_model_from_request_supports_google_model_names_with_slashes():
-    assert (
-        get_model_from_request(
-            request_data={},
-            route="/v1beta/models/bedrock/claude-sonnet-3.7:generateContent",
-        )
-        == "bedrock/claude-sonnet-3.7"
-    )
-    assert (
-        get_model_from_request(
-            request_data={},
-            route="/models/hosted_vllm/gpt-oss-20b:generateContent",
-        )
-        == "hosted_vllm/gpt-oss-20b"
-    )
-
-
-def test_get_model_from_request_vertex_passthrough_still_works():
-    route = "/vertex_ai/v1/projects/p/locations/l/publishers/google/models/gemini-1.5-pro:generateContent"
-    assert get_model_from_request(request_data={}, route=route) == "gemini-1.5-pro"
diff --git a/tests/test_litellm/proxy/auth/test_cli_auth.py b/tests/test_litellm/proxy/auth/test_cli_auth.py
deleted file mode 100644
index 2faf6436523..00000000000
--- a/tests/test_litellm/proxy/auth/test_cli_auth.py
+++ /dev/null
@@ -1,203 +0,0 @@
-"""
-Tests for litellm/proxy/client/cli/commands/auth.py
-
-This module tests the auth commands and their associated functionality.
-"""
-
-import pytest
-import requests
-from unittest.mock import AsyncMock, patch, Mock, call
-from litellm.proxy.client.cli.commands.auth import _normalize_teams, _poll_for_ready_data, _poll_for_authentication
-
-@pytest.mark.asyncio
-async def test_normalize_teams_teams_only():
-    """Test normalize teams helper function"""
-    teams = ["1", "2", "3"]
-    team_details = []
-    result = _normalize_teams(teams, team_details)
-    assert result == [{"team_id": "1", "team_alias": None}, {"team_id": "2", "team_alias": None}, {"team_id": "3", "team_alias": None}]
-
-@pytest.mark.asyncio
-async def test_normalize_teams_with_details_no_aliases():
-    """Test normalize teams helper function"""
-    teams = ["4", "5", "6"]
-    team_details = [{"team_id": "1"}, {"team_id": "2"}, {"team_id": "3"}]
-    result = _normalize_teams(teams, team_details)
-    assert result == [{"team_id": "1", "team_alias": None}, {"team_id": "2", "team_alias": None}, {"team_id": "3", "team_alias": None}]
-
-@pytest.mark.asyncio
-async def test_normalize_teams_with_details_with_aliases():
-    """Test normalize teams helper function"""
-    teams = ["4", "5", "6"]
-    team_details = [{"team_id": "1", "team_alias": "A"}, {"team_id": "2", "team_alias": "B"}, {"team_id": "3", "team_alias": "C"}]
-    result = _normalize_teams(teams, team_details)
-    assert result == [{"team_id": "1", "team_alias": "A"}, {"team_id": "2", "team_alias": "B"}, {"team_id": "3", "team_alias": "C"}]
-
-@pytest.mark.asyncio
-@patch("litellm.proxy.client.cli.commands.auth.requests.get", side_effect=[Mock(status_code=404)])
-@patch("litellm.proxy.client.cli.commands.auth.click.echo")
-@patch("litellm.proxy.client.cli.commands.auth.time.sleep")
-async def test_poll_for_ready_404(sleep_mock, click_mock, request_mock):
-    """Test poll_for_ready function"""
-    actual = _poll_for_ready_data("https://litellm.com", poll_interval=1, total_timeout=1, request_timeout=42)
-    assert actual is None
-    click_mock.assert_called_once_with("Polling error: HTTP 404")
-    request_mock.assert_called_once_with("https://litellm.com", timeout=42)
-
-@pytest.mark.asyncio
-@patch("litellm.proxy.client.cli.commands.auth.requests.get", side_effect=[Mock(status_code=200, json=Mock(return_value={"status": "ready","json": "data"}))])
-@patch("litellm.proxy.client.cli.commands.auth.click.echo")
-@patch("litellm.proxy.client.cli.commands.auth.time.sleep")
-async def test_poll_for_ready_200_ready(sleep_mock, click_mock, request_mock):
-    """Test poll_for_ready function"""
-    actual = _poll_for_ready_data("https://litellm.com", poll_interval=1, total_timeout=1, request_timeout=42)
-    assert actual == {"status": "ready", "json": "data"}
-    click_mock.assert_not_called()
-    request_mock.assert_called_once_with("https://litellm.com", timeout=42)
-    sleep_mock.assert_not_called()
-
-@pytest.mark.asyncio
-@patch("litellm.proxy.client.cli.commands.auth.requests.get", side_effect=[Mock(status_code=200, json=Mock(return_value={"status": "pending","json": "data"})), Mock(status_code=200, json=Mock(return_value={"status": "ready","json": "data"}))])
-@patch("litellm.proxy.client.cli.commands.auth.click.echo")
-@patch("litellm.proxy.client.cli.commands.auth.time.sleep")
-async def test_poll_for_ready_single_pending(sleep_mock, click_mock, request_mock):
-    """Test poll_for_ready function"""
-    actual = _poll_for_ready_data("https://litellm.com", poll_interval=1, total_timeout=2, request_timeout=42)
-    assert actual == {"status": "ready", "json": "data"}
-    click_mock.assert_not_called()
-    request_mock.assert_has_calls([
-        call("https://litellm.com", timeout=42),
-        call("https://litellm.com", timeout=42)
-    ])
-    sleep_mock.assert_called_once_with(1)
-
-@pytest.mark.asyncio
-@patch("litellm.proxy.client.cli.commands.auth.requests.get", side_effect=[Mock(status_code=200, json=Mock(return_value={"status": "pending","json": "data"})), Mock(status_code=200, json=Mock(return_value={"status": "pending","json": "data"}))])
-@patch("litellm.proxy.client.cli.commands.auth.click.echo")
-@patch("litellm.proxy.client.cli.commands.auth.time.sleep")
-async def test_poll_for_ready_pending(sleep_mock, click_mock, request_mock):
-    """Test poll_for_ready function"""
-    actual = _poll_for_ready_data("https://litellm.com", poll_interval=1, total_timeout=2, request_timeout=42, pending_message="Pending message", pending_log_every=1)
-    assert actual is None
-    click_mock.assert_has_calls([
-        call("Pending message"),
-        call("Pending message")
-    ])
-    request_mock.assert_has_calls([
-        call("https://litellm.com", timeout=42),
-        call("https://litellm.com", timeout=42)
-    ])
-    sleep_mock.assert_has_calls([
-        call(1),
-        call(1)
-    ])
-
-
-@pytest.mark.asyncio
-@patch("litellm.proxy.client.cli.commands.auth.requests.get", side_effect=[requests.RequestException("ERROR"),
-                                                                           requests.RequestException("ERROR")])
-@patch("litellm.proxy.client.cli.commands.auth.click.echo")
-@patch("litellm.proxy.client.cli.commands.auth.time.sleep")
-async def test_poll_for_ready_connection_failure(sleep_mock, click_mock, request_mock):
-    """Test poll_for_ready function"""
-    actual = _poll_for_ready_data("https://litellm.com", poll_interval=1, total_timeout=2, request_timeout=42)
-    assert actual is None
-    click_mock.assert_called_once_with("Connection error (will retry): ERROR")
-    request_mock.assert_has_calls([
-        call("https://litellm.com", timeout=42),
-    ])
-    sleep_mock.assert_has_calls([
-        call(1),
-        call(1)
-    ])
-
-
-@pytest.mark.asyncio
-@patch("litellm.proxy.client.cli.commands.auth._handle_team_selection_during_polling")
-@patch("litellm.proxy.client.cli.commands.auth._poll_for_ready_data", return_value=None)
-@patch("litellm.proxy.client.cli.commands.auth.click.echo")
-async def test_poll_for_authentication_no_data(click_mock, poll_mock, handle_mock):
-    """Test poll_for_authentication function"""
-    actual = _poll_for_authentication("https://litellm.com", "key-123")
-    assert actual is None
-    poll_mock.assert_called_once_with(
-        "https://litellm.com/sso/cli/poll/key-123",
-        pending_message="Still waiting for authentication...",
-    )
-    handle_mock.assert_not_called()
-    click_mock.assert_not_called()
-
-
-@pytest.mark.asyncio
-@patch("litellm.proxy.client.cli.commands.auth._handle_team_selection_during_polling")
-@patch("litellm.proxy.client.cli.commands.auth._poll_for_ready_data", return_value={"requires_team_selection": True, "teams": [], "team_details": []})
-@patch("litellm.proxy.client.cli.commands.auth.click.echo")
-async def test_poll_for_authentication_no_teams(click_mock, poll_mock, handle_mock):
-    """Test poll_for_authentication function"""
-    actual = _poll_for_authentication("https://litellm.com", "key-123")
-    assert actual is None
-    poll_mock.assert_called_once_with(
-        "https://litellm.com/sso/cli/poll/key-123",
-        pending_message="Still waiting for authentication...",
-    )
-    handle_mock.assert_not_called()
-    click_mock.assert_called_once()
-    assert "No teams available for selection." in click_mock.call_args[0][0]
-
-
-@pytest.mark.asyncio
-@patch("litellm.proxy.client.cli.commands.auth._handle_team_selection_during_polling", return_value="jwt-123")
-@patch("litellm.proxy.client.cli.commands.auth._poll_for_ready_data", return_value={"requires_team_selection": True, "teams": [1, 2], "user_id": "user-123"})
-@patch("litellm.proxy.client.cli.commands.auth.click.echo")
-async def test_poll_for_authentication_team_selection_success(click_mock, poll_mock, handle_mock):
-    """Test poll_for_authentication function"""
-    actual = _poll_for_authentication("https://litellm.com", "key-123")
-    assert actual == {"api_key": "jwt-123", "user_id": "user-123", "teams": [1, 2], "team_id": None}
-    poll_mock.assert_called_once_with(
-        "https://litellm.com/sso/cli/poll/key-123",
-        pending_message="Still waiting for authentication...",
-    )
-    handle_mock.assert_called_once_with(
-        base_url="https://litellm.com",
-        key_id="key-123",
-        teams=[{"team_id": "1", "team_alias": None}, {"team_id": "2", "team_alias": None}],
-    )
-    click_mock.assert_not_called()
-
-
-@pytest.mark.asyncio
-@patch("litellm.proxy.client.cli.commands.auth._handle_team_selection_during_polling", return_value=None)
-@patch("litellm.proxy.client.cli.commands.auth._poll_for_ready_data", return_value={"requires_team_selection": True, "teams": ["team-1"], "user_id": "user-123"})
-@patch("litellm.proxy.client.cli.commands.auth.click.echo")
-async def test_poll_for_authentication_team_selection_cancelled(click_mock, poll_mock, handle_mock):
-    """Test poll_for_authentication function"""
-    actual = _poll_for_authentication("https://litellm.com", "key-123")
-    assert actual is None
-    poll_mock.assert_called_once_with(
-        "https://litellm.com/sso/cli/poll/key-123",
-        pending_message="Still waiting for authentication...",
-    )
-    handle_mock.assert_called_once_with(
-        base_url="https://litellm.com",
-        key_id="key-123",
-        teams=[{"team_id": "team-1", "team_alias": None}],
-    )
-    click_mock.assert_called_once()
-    assert "Team selection cancelled" in click_mock.call_args[0][0]
-
-
-@pytest.mark.asyncio
-@patch("litellm.proxy.client.cli.commands.auth._handle_team_selection_during_polling")
-@patch("litellm.proxy.client.cli.commands.auth._poll_for_ready_data", return_value={"key": "jwt-456", "user_id": "user-456", "teams": ["team-1"], "team_id": "team-1"})
-@patch("litellm.proxy.client.cli.commands.auth.click.echo")
-async def test_poll_for_authentication_auto_assigned_team(click_mock, poll_mock, handle_mock):
-    """Test poll_for_authentication function"""
-    actual = _poll_for_authentication("https://litellm.com", "key-123")
-    assert actual == {"api_key": "jwt-456", "user_id": "user-456", "teams": ["team-1"], "team_id": "team-1"}
-    poll_mock.assert_called_once_with(
-        "https://litellm.com/sso/cli/poll/key-123",
-        pending_message="Still waiting for authentication...",
-    )
-    handle_mock.assert_not_called()
-    click_mock.assert_called_once()
-    assert "Automatically assigned to team: team-1" in click_mock.call_args[0][0]
diff --git a/tests/test_litellm/proxy/auth/test_route_checks.py b/tests/test_litellm/proxy/auth/test_route_checks.py
index a745ac3de13..b8084906fa5 100644
--- a/tests/test_litellm/proxy/auth/test_route_checks.py
+++ b/tests/test_litellm/proxy/auth/test_route_checks.py
@@ -161,11 +161,9 @@ def test_virtual_key_llm_api_route_includes_passthrough_prefix(route):
     [
         "/v1beta/models/gemini-2.5-flash:countTokens",
         "/v1beta/models/gemini-2.0-flash:generateContent",
-        "/v1beta/models/bedrock/claude-sonnet-3.7:generateContent",
         "/v1beta/models/gemini-1.5-pro:streamGenerateContent",
         "/models/gemini-2.5-flash:countTokens",
         "/models/gemini-2.0-flash:generateContent",
-        "/models/bedrock/claude-sonnet-3.7:generateContent",
         "/models/gemini-1.5-pro:streamGenerateContent",
     ],
 )
@@ -189,11 +187,9 @@ def test_virtual_key_llm_api_routes_allows_google_routes(route):
         "/v1beta/models/google-gemini-2-5-pro-code-reviewer-k8s:generateContent",
         "/v1beta/models/gemini-2.5-flash-exp:countTokens",
         "/v1beta/models/custom-model-name-123:streamGenerateContent",
-        "/v1beta/models/bedrock/claude-sonnet-3.7:generateContent",
         "/models/google-gemini-2-5-pro-code-reviewer-k8s:generateContent",
         "/models/gemini-2.5-flash-exp:countTokens",
         "/models/custom-model-name-123:streamGenerateContent",
-        "/models/bedrock/claude-sonnet-3.7:generateContent",
     ],
 )
 def test_google_routes_with_dynamic_model_names_recognized_as_llm_api_route(route):
diff --git a/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py b/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py
deleted file mode 100644
index 1063f59afb6..00000000000
--- a/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""
-Test for interactions endpoint agent parameter handling.
-
-Tests that the /v1beta/interactions endpoint correctly extracts
-the `agent` parameter as a fallback when `model` is not provided.
-"""
-
-import pytest
-
-
-class TestInteractionsAgentParameter:
-    """Test agent parameter handling in interactions endpoint."""
-
-    def test_agent_parameter_fallback_logic(self):
-        """
-        Test the core logic: model or agent extraction.
-
-        This tests the fix in endpoints.py line ~267:
-        model=data.get("model") or data.get("agent")
-        """
-        # Case 1: Only agent provided (Deep Research use case)
-        data = {
-            "agent": "deep-research-pro-preview-12-2025",
-            "input": "Research quantum computing",
-            "background": True,
-        }
-        model = data.get("model") or data.get("agent")
-        assert model == "deep-research-pro-preview-12-2025"
-
-        # Case 2: Only model provided (normal use case)
-        data = {
-            "model": "gemini-2.5-flash",
-            "input": "Hello world",
-        }
-        model = data.get("model") or data.get("agent")
-        assert model == "gemini-2.5-flash"
-
-        # Case 3: Both provided (model takes precedence)
-        data = {
-            "model": "gemini-2.5-flash",
-            "agent": "deep-research-pro-preview-12-2025",
-            "input": "Test",
-        }
-        model = data.get("model") or data.get("agent")
-        assert model == "gemini-2.5-flash"
-
-        # Case 4: Neither provided
-        data = {
-            "input": "Test",
-        }
-        model = data.get("model") or data.get("agent")
-        assert model is None
-
-    def test_route_type_in_skip_model_routing_list(self):
-        """
-        Test that acreate_interaction is in the list of routes
-        that skip model-based routing.
-
-        This tests the fix in route_llm_request.py.
-        """
-        # The list of routes that skip model routing for interactions
-        skip_model_routing_routes = [
-            "acreate_interaction",
-            "aget_interaction",
-            "adelete_interaction",
-            "acancel_interaction",
-        ]
-
-        # acreate_interaction should be in the list (this is the fix)
-        assert "acreate_interaction" in skip_model_routing_routes
-
-        # All interaction routes should be covered
-        assert "aget_interaction" in skip_model_routing_routes
-        assert "adelete_interaction" in skip_model_routing_routes
-        assert "acancel_interaction" in skip_model_routing_routes
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py
index be8c84a554f..265bc530dc2 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py
@@ -4,7 +4,7 @@
 
 import os
 import sys
-from unittest.mock import MagicMock
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
@@ -385,12 +385,20 @@ def test_api_key_patterns(self):
         assert result is not None
         assert result[1] == "aws_access_key"
 
+    @pytest.mark.skip(
+        reason="Masking in streaming responses is no longer supported after unified_guardrail.py changes. Only blocking/rejecting is supported for responses."
+    )
     @pytest.mark.asyncio
     async def test_streaming_hook_mask(self):
         """
-        Test streaming hook with MASK action.
-        This now works with the 50-char sliding window buffer.
+        Test streaming hook with MASK action
+
+        Note: After changes to unified_guardrail.py, masking responses to users
+        is no longer supported. This test is skipped as the feature is deprecated.
+        Only BLOCK actions (test_streaming_hook_block) are supported for streaming responses.
         """
+        from unittest.mock import AsyncMock
+
         from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices
 
         patterns = [
@@ -407,54 +415,51 @@ async def test_streaming_hook_mask(self):
             event_hook=GuardrailEventHooks.during_call,
         )
 
-        # Create mock streaming chunks that split an email
+        # Create mock streaming chunks
         async def mock_stream():
-            # Chunk 1: starts email
-            yield ModelResponseStream(
+            # Chunk 1: contains email
+            chunk1 = ModelResponseStream(
                 id="chunk1",
                 choices=[
                     StreamingChoices(
-                        delta=Delta(content="Contact me at test@ex"), index=0
+                        delta=Delta(content="Contact me at test@example.com"), index=0
                     )
                 ],
                 model="gpt-4",
             )
-            # Chunk 2: ends email
-            yield ModelResponseStream(
+            yield chunk1
+
+            # Chunk 2: normal content
+            chunk2 = ModelResponseStream(
                 id="chunk2",
                 choices=[
-                    StreamingChoices(
-                        delta=Delta(content="ample.com for info"),
-                        index=0,
-                        finish_reason="stop",
-                    )
+                    StreamingChoices(delta=Delta(content=" for more info"), index=0)
                 ],
                 model="gpt-4",
             )
+            yield chunk2
 
         user_api_key_dict = MagicMock()
         request_data = {}
 
-        # Process streaming response - masking IS expected now
-        full_content = ""
+        # Process streaming response - no masking expected
+        result_chunks = []
         async for chunk in guardrail.async_post_call_streaming_iterator_hook(
             user_api_key_dict=user_api_key_dict,
             response=mock_stream(),
             request_data=request_data,
         ):
-            if chunk.choices[0].delta.content:
-                full_content += chunk.choices[0].delta.content
+            result_chunks.append(chunk)
 
-        # The email should be redacted even though it was split
-        assert "test@example.com" not in full_content
-        assert "[EMAIL_REDACTED]" in full_content
-        assert "Contact me at [EMAIL_REDACTED] for info" in full_content
+        # Chunks should pass through unchanged since masking is no longer supported
+        assert len(result_chunks) == 2
 
     @pytest.mark.asyncio
     async def test_streaming_hook_block(self):
         """
         Test streaming hook with BLOCK action
         """
+        from unittest.mock import AsyncMock
 
         from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices
 
@@ -710,10 +715,7 @@ async def test_apply_guardrail_masks_all_regex_pattern_matches(self):
         assert result is not None
         assert len(result) == 1
         # All matches should be redacted
-        assert (
-            result[0]
-            == "[CUSTOM_KEY_REDACTED] [CUSTOM_KEY_REDACTED] [CUSTOM_KEY_REDACTED]"
-        )
+        assert result[0] == "[CUSTOM_KEY_REDACTED] [CUSTOM_KEY_REDACTED] [CUSTOM_KEY_REDACTED]"
         assert "Key1" not in result[0]
         assert "Key2" not in result[0]
 
@@ -795,7 +797,7 @@ async def test_apply_guardrail_logs_guardrail_information(self):
 
         # Apply guardrail with content that triggers detections
         # Email will be masked, blocked word will be masked
-        await guardrail.apply_guardrail(
+        result = await guardrail.apply_guardrail(
             inputs={"texts": ["Contact me at test@example.com for confidential info"]},
             request_data=request_data,
             input_type="request",
@@ -805,9 +807,7 @@ async def test_apply_guardrail_logs_guardrail_information(self):
         assert "metadata" in request_data
         assert "standard_logging_guardrail_information" in request_data["metadata"]
 
-        guardrail_info_list = request_data["metadata"][
-            "standard_logging_guardrail_information"
-        ]
+        guardrail_info_list = request_data["metadata"]["standard_logging_guardrail_information"]
         assert isinstance(guardrail_info_list, list)
         assert len(guardrail_info_list) == 1
 
@@ -820,8 +820,8 @@ async def test_apply_guardrail_logs_guardrail_information(self):
         assert "start_time" in guardrail_info
         assert "end_time" in guardrail_info
         assert "duration" in guardrail_info
-        assert guardrail_info["duration"] >= 0
-        assert guardrail_info["start_time"] <= guardrail_info["end_time"]
+        assert guardrail_info["duration"] > 0
+        assert guardrail_info["start_time"] < guardrail_info["end_time"]
 
         # Verify detections are logged
         assert "guardrail_response" in guardrail_info
@@ -839,21 +839,15 @@ async def test_apply_guardrail_logs_guardrail_information(self):
             assert "action" in detection
             assert detection["action"] == "MASK"
             # Verify sensitive content (matched_text) is NOT included
-            assert (
-                "matched_text" not in detection
-            ), "Sensitive content should not be logged"
+            assert "matched_text" not in detection, "Sensitive content should not be logged"
 
         # Verify blocked word detection structure
-        blocked_word_detections = [
-            d for d in detections if d.get("type") == "blocked_word"
-        ]
+        blocked_word_detections = [d for d in detections if d.get("type") == "blocked_word"]
         assert len(blocked_word_detections) > 0
         for detection in blocked_word_detections:
             assert detection["type"] == "blocked_word"
             assert "keyword" in detection
-            assert (
-                detection["keyword"] == "confidential"
-            )  # Config keyword, not user content
+            assert detection["keyword"] == "confidential"  # Config keyword, not user content
             assert "action" in detection
             assert detection["action"] == "MASK"
             assert "description" in detection
@@ -902,9 +896,7 @@ async def test_apply_guardrail_logs_blocked_status(self):
         assert "metadata" in request_data
         assert "standard_logging_guardrail_information" in request_data["metadata"]
 
-        guardrail_info_list = request_data["metadata"][
-            "standard_logging_guardrail_information"
-        ]
+        guardrail_info_list = request_data["metadata"]["standard_logging_guardrail_information"]
         assert len(guardrail_info_list) == 1
 
         guardrail_info = guardrail_info_list[0]
@@ -917,6 +909,4 @@ async def test_apply_guardrail_logs_blocked_status(self):
             # If detections are logged, verify they don't contain sensitive content
             for detection in detections:
                 if detection.get("type") == "pattern":
-                    assert (
-                        "matched_text" not in detection
-                    ), "Sensitive content should not be logged"
+                    assert "matched_text" not in detection, "Sensitive content should not be logged"
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_onyx.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_onyx.py
index fb7480d263c..9ede649f392 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_onyx.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_onyx.py
@@ -3,7 +3,6 @@
 import uuid
 from unittest.mock import AsyncMock, MagicMock, patch
 
-import httpx
 import pytest
 from fastapi import HTTPException
 from httpx import Request, Response
@@ -48,129 +47,20 @@ def test_onyx_guard_config():
         del os.environ["ONYX_API_KEY"]
 
 
-def test_onyx_guard_with_custom_timeout_from_kwargs():
-    """Test Onyx guard instantiation with custom timeout passed via kwargs."""
-    # Set environment variables for testing
-    os.environ["ONYX_API_BASE"] = "https://test.onyx.security"
-    os.environ["ONYX_API_KEY"] = "test-api-key"
-
-    with patch(
-        "litellm.proxy.guardrails.guardrail_hooks.onyx.onyx.get_async_httpx_client"
-    ) as mock_get_client:
-        mock_get_client.return_value = MagicMock()
-
-        # Simulate how guardrail is instantiated from config with timeout
-        guardrail = OnyxGuardrail(
-            guardrail_name="onyx-guard-custom-timeout",
-            event_hook="pre_call",
-            default_on=True,
-            timeout=45.0,
-        )
-
-        # Verify the client was initialized with custom timeout
-        mock_get_client.assert_called()
-        call_kwargs = mock_get_client.call_args.kwargs
-        timeout_param = call_kwargs["params"]["timeout"]
-        assert timeout_param.read == 45.0
-        assert timeout_param.connect == 5.0
-
-    # Clean up
-    if "ONYX_API_BASE" in os.environ:
-        del os.environ["ONYX_API_BASE"]
-    if "ONYX_API_KEY" in os.environ:
-        del os.environ["ONYX_API_KEY"]
-
-
-def test_onyx_guard_with_timeout_none_uses_env_var():
-    """Test Onyx guard with timeout=None uses ONYX_TIMEOUT env var.
-    
-    When timeout=None is passed (as it would be from config model with default None),
-    the ONYX_TIMEOUT environment variable should be used.
-    """
-    # Set environment variables for testing
-    os.environ["ONYX_API_BASE"] = "https://test.onyx.security"
-    os.environ["ONYX_API_KEY"] = "test-api-key"
-    os.environ["ONYX_TIMEOUT"] = "60"
-
-    with patch(
-        "litellm.proxy.guardrails.guardrail_hooks.onyx.onyx.get_async_httpx_client"
-    ) as mock_get_client:
-        mock_get_client.return_value = MagicMock()
-
-        # Pass timeout=None to simulate config model behavior
-        guardrail = OnyxGuardrail(
-            guardrail_name="onyx-guard-env-timeout",
-            event_hook="pre_call",
-            default_on=True,
-            timeout=None,  # This triggers env var lookup
-        )
-
-        # Verify the client was initialized with timeout from env var
-        mock_get_client.assert_called()
-        call_kwargs = mock_get_client.call_args.kwargs
-        timeout_param = call_kwargs["params"]["timeout"]
-        assert timeout_param.read == 60.0
-        assert timeout_param.connect == 5.0
-
-    # Clean up
-    if "ONYX_API_BASE" in os.environ:
-        del os.environ["ONYX_API_BASE"]
-    if "ONYX_API_KEY" in os.environ:
-        del os.environ["ONYX_API_KEY"]
-    if "ONYX_TIMEOUT" in os.environ:
-        del os.environ["ONYX_TIMEOUT"]
-
-
-def test_onyx_guard_with_timeout_none_defaults_to_10():
-    """Test Onyx guard with timeout=None and no env var defaults to 10 seconds."""
-    # Set environment variables for testing
-    os.environ["ONYX_API_BASE"] = "https://test.onyx.security"
-    os.environ["ONYX_API_KEY"] = "test-api-key"
-    # Ensure ONYX_TIMEOUT is not set
-    if "ONYX_TIMEOUT" in os.environ:
-        del os.environ["ONYX_TIMEOUT"]
-
-    with patch(
-        "litellm.proxy.guardrails.guardrail_hooks.onyx.onyx.get_async_httpx_client"
-    ) as mock_get_client:
-        mock_get_client.return_value = MagicMock()
-
-        # Pass timeout=None with no env var - should default to 10.0
-        guardrail = OnyxGuardrail(
-            guardrail_name="onyx-guard-default-timeout",
-            event_hook="pre_call",
-            default_on=True,
-            timeout=None,
-        )
-
-        # Verify the client was initialized with default timeout of 10.0
-        mock_get_client.assert_called()
-        call_kwargs = mock_get_client.call_args.kwargs
-        timeout_param = call_kwargs["params"]["timeout"]
-        assert timeout_param.read == 10.0
-        assert timeout_param.connect == 5.0
-
-    # Clean up
-    if "ONYX_API_BASE" in os.environ:
-        del os.environ["ONYX_API_BASE"]
-    if "ONYX_API_KEY" in os.environ:
-        del os.environ["ONYX_API_KEY"]
-
-
 class TestOnyxGuardrail:
     """Test suite for Onyx Security Guardrail integration."""
 
     def setup_method(self):
         """Setup test environment."""
         # Clean up any existing environment variables
-        for key in ["ONYX_API_BASE", "ONYX_API_KEY", "ONYX_TIMEOUT"]:
+        for key in ["ONYX_API_BASE", "ONYX_API_KEY"]:
             if key in os.environ:
                 del os.environ[key]
 
     def teardown_method(self):
         """Clean up test environment."""
         # Clean up any environment variables set during tests
-        for key in ["ONYX_API_BASE", "ONYX_API_KEY", "ONYX_TIMEOUT"]:
+        for key in ["ONYX_API_BASE", "ONYX_API_KEY"]:
             if key in os.environ:
                 del os.environ[key]
 
@@ -213,95 +103,6 @@ def test_initialization_fails_when_api_key_missing(self):
         ):
             OnyxGuardrail(guardrail_name="test-guard", event_hook="pre_call")
 
-    def test_initialization_with_default_timeout(self):
-        """Test that default timeout is 10.0 seconds."""
-        os.environ["ONYX_API_KEY"] = "test-api-key"
-
-        with patch(
-            "litellm.proxy.guardrails.guardrail_hooks.onyx.onyx.get_async_httpx_client"
-        ) as mock_get_client:
-            mock_get_client.return_value = MagicMock()
-            guardrail = OnyxGuardrail(
-                guardrail_name="test-guard", event_hook="pre_call", default_on=True
-            )
-
-            # Verify the client was initialized with correct timeout
-            mock_get_client.assert_called_once()
-            call_kwargs = mock_get_client.call_args.kwargs
-            timeout_param = call_kwargs["params"]["timeout"]
-            assert timeout_param.read == 10.0
-            assert timeout_param.connect == 5.0
-
-    def test_initialization_with_custom_timeout_parameter(self):
-        """Test initialization with custom timeout parameter."""
-        os.environ["ONYX_API_KEY"] = "test-api-key"
-
-        with patch(
-            "litellm.proxy.guardrails.guardrail_hooks.onyx.onyx.get_async_httpx_client"
-        ) as mock_get_client:
-            mock_get_client.return_value = MagicMock()
-            guardrail = OnyxGuardrail(
-                guardrail_name="test-guard",
-                event_hook="pre_call",
-                default_on=True,
-                timeout=30.0,
-            )
-
-            # Verify the client was initialized with custom timeout
-            mock_get_client.assert_called_once()
-            call_kwargs = mock_get_client.call_args.kwargs
-            timeout_param = call_kwargs["params"]["timeout"]
-            assert timeout_param.read == 30.0
-            assert timeout_param.connect == 5.0
-
-    def test_initialization_with_timeout_from_env_var(self):
-        """Test initialization with timeout from ONYX_TIMEOUT environment variable.
-        
-        Note: The env var is only used when timeout=None is explicitly passed,
-        since the default parameter value is 10.0 (not None).
-        """
-        os.environ["ONYX_API_KEY"] = "test-api-key"
-        os.environ["ONYX_TIMEOUT"] = "25"
-
-        with patch(
-            "litellm.proxy.guardrails.guardrail_hooks.onyx.onyx.get_async_httpx_client"
-        ) as mock_get_client:
-            mock_get_client.return_value = MagicMock()
-            # Must pass timeout=None explicitly to trigger env var lookup
-            guardrail = OnyxGuardrail(
-                guardrail_name="test-guard", event_hook="pre_call", default_on=True, timeout=None
-            )
-
-            # Verify the client was initialized with timeout from env var
-            mock_get_client.assert_called_once()
-            call_kwargs = mock_get_client.call_args.kwargs
-            timeout_param = call_kwargs["params"]["timeout"]
-            assert timeout_param.read == 25.0
-            assert timeout_param.connect == 5.0
-
-    def test_initialization_timeout_parameter_overrides_env_var(self):
-        """Test that timeout parameter overrides ONYX_TIMEOUT environment variable."""
-        os.environ["ONYX_API_KEY"] = "test-api-key"
-        os.environ["ONYX_TIMEOUT"] = "25"
-
-        with patch(
-            "litellm.proxy.guardrails.guardrail_hooks.onyx.onyx.get_async_httpx_client"
-        ) as mock_get_client:
-            mock_get_client.return_value = MagicMock()
-            guardrail = OnyxGuardrail(
-                guardrail_name="test-guard",
-                event_hook="pre_call",
-                default_on=True,
-                timeout=15.0,
-            )
-
-            # Verify the client was initialized with parameter timeout (not env var)
-            mock_get_client.assert_called_once()
-            call_kwargs = mock_get_client.call_args.kwargs
-            timeout_param = call_kwargs["params"]["timeout"]
-            assert timeout_param.read == 15.0
-            assert timeout_param.connect == 5.0
-
     @pytest.mark.asyncio
     async def test_apply_guardrail_request_no_violations(self):
         """Test apply_guardrail for request with no violations detected."""
@@ -587,105 +388,6 @@ async def test_apply_guardrail_api_error_handling(self):
 
             assert result == inputs
 
-    @pytest.mark.asyncio
-    async def test_apply_guardrail_timeout_error_handling(self):
-        """Test handling of timeout errors in apply_guardrail (graceful degradation)."""
-        # Set required API key
-        os.environ["ONYX_API_KEY"] = "test-api-key"
-
-        guardrail = OnyxGuardrail(
-            guardrail_name="test-guard", event_hook="pre_call", default_on=True, timeout=1.0
-        )
-
-        inputs = GenericGuardrailAPIInputs()
-
-        request_data = {
-            "proxy_server_request": {
-                "messages": [{"role": "user", "content": "Test message"}],
-                "model": "gpt-3.5-turbo",
-            }
-        }
-
-        # Test httpx timeout error
-        with patch.object(
-            guardrail.async_handler, "post", side_effect=httpx.TimeoutException("Request timed out")
-        ):
-            # Should return original inputs on timeout (graceful degradation)
-            result = await guardrail.apply_guardrail(
-                inputs=inputs,
-                request_data=request_data,
-                input_type="request",
-                logging_obj=None,
-            )
-
-            assert result == inputs
-
-    @pytest.mark.asyncio
-    async def test_apply_guardrail_read_timeout_error_handling(self):
-        """Test handling of read timeout errors in apply_guardrail."""
-        # Set required API key
-        os.environ["ONYX_API_KEY"] = "test-api-key"
-
-        guardrail = OnyxGuardrail(
-            guardrail_name="test-guard", event_hook="pre_call", default_on=True, timeout=5.0
-        )
-
-        inputs = GenericGuardrailAPIInputs()
-
-        request_data = {
-            "proxy_server_request": {
-                "messages": [{"role": "user", "content": "Test message"}],
-                "model": "gpt-3.5-turbo",
-            }
-        }
-
-        # Test httpx ReadTimeout error
-        with patch.object(
-            guardrail.async_handler, "post", side_effect=httpx.ReadTimeout("Read timed out")
-        ):
-            # Should return original inputs on timeout (graceful degradation)
-            result = await guardrail.apply_guardrail(
-                inputs=inputs,
-                request_data=request_data,
-                input_type="request",
-                logging_obj=None,
-            )
-
-            assert result == inputs
-
-    @pytest.mark.asyncio
-    async def test_apply_guardrail_connect_timeout_error_handling(self):
-        """Test handling of connect timeout errors in apply_guardrail."""
-        # Set required API key
-        os.environ["ONYX_API_KEY"] = "test-api-key"
-
-        guardrail = OnyxGuardrail(
-            guardrail_name="test-guard", event_hook="pre_call", default_on=True, timeout=5.0
-        )
-
-        inputs = GenericGuardrailAPIInputs()
-
-        request_data = {
-            "proxy_server_request": {
-                "messages": [{"role": "user", "content": "Test message"}],
-                "model": "gpt-3.5-turbo",
-            }
-        }
-
-        # Test httpx ConnectTimeout error
-        with patch.object(
-            guardrail.async_handler, "post", side_effect=httpx.ConnectTimeout("Connect timed out")
-        ):
-            # Should return original inputs on timeout (graceful degradation)
-            result = await guardrail.apply_guardrail(
-                inputs=inputs,
-                request_data=request_data,
-                input_type="request",
-                logging_obj=None,
-            )
-
-            assert result == inputs
-
     @pytest.mark.asyncio
     async def test_apply_guardrail_no_logging_obj(self):
         """Test apply_guardrail without logging object (uses UUID)."""
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py
index fc4ff28c774..42af3942f1a 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py
@@ -4,9 +4,10 @@
 """
 
 import asyncio
+import json
 import os
 import sys
-from unittest.mock import MagicMock, patch
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
@@ -624,7 +625,7 @@ async def mock_check_pii(text, output_parse_pii, presidio_config, request_data):
         return text
 
     with patch.object(presidio, "check_pii", mock_check_pii):
-        await presidio.apply_guardrail(
+        result = await presidio.apply_guardrail(
             inputs={"texts": ["Test message"]},
             request_data=request_data,
             input_type="request",
@@ -708,21 +709,16 @@ def add_litellm_callback(self, cb):
     monkeypatch.setattr(litellm, "logging_callback_manager", mgr, raising=False)
     import litellm.proxy.guardrails.guardrail_initializers as gi
     import litellm.proxy.guardrails.guardrail_hooks.presidio as presidio_mod
-
     monkeypatch.setattr(
         presidio_mod, "_OPTIONAL_PresidioPIIMasking", DummyGuardrail, raising=False
     )
-    monkeypatch.setattr(
-        gi, "_OPTIONAL_PresidioPIIMasking", DummyGuardrail, raising=False
-    )
+    monkeypatch.setattr(gi, "_OPTIONAL_PresidioPIIMasking", DummyGuardrail, raising=False)
 
     # input-only
     created.clear()
     from litellm.proxy.guardrails.guardrail_initializers import initialize_presidio
 
-    params_input = LitellmParams(
-        guardrail="presidio", mode="pre_call", presidio_filter_scope="input"
-    )
+    params_input = LitellmParams(guardrail="presidio", mode="pre_call", presidio_filter_scope="input")
     guardrail_dict = {"guardrail_name": "g1"}
     cb = initialize_presidio(params_input, guardrail_dict)
     assert cb is created[0]
@@ -730,18 +726,14 @@ def add_litellm_callback(self, cb):
 
     # output-only
     created.clear()
-    params_output = LitellmParams(
-        guardrail="presidio", mode="pre_call", presidio_filter_scope="output"
-    )
+    params_output = LitellmParams(guardrail="presidio", mode="pre_call", presidio_filter_scope="output")
     cb = initialize_presidio(params_output, guardrail_dict)
     assert len(created) == 1
     assert created[0].apply_to_output is True
 
     # both -> expect two callbacks (input + output)
     created.clear()
-    params_both = LitellmParams(
-        guardrail="presidio", mode="pre_call", presidio_filter_scope="both"
-    )
+    params_both = LitellmParams(guardrail="presidio", mode="pre_call", presidio_filter_scope="both")
     cb = initialize_presidio(params_both, guardrail_dict)
     assert len(created) == 2
     assert any(not c.apply_to_output for c in created)
@@ -749,15 +741,13 @@ def add_litellm_callback(self, cb):
 
 
 @pytest.mark.asyncio
-async def test_empty_content_handling(
-    presidio_guardrail, mock_user_api_key, mock_cache
-):
+async def test_empty_content_handling(presidio_guardrail, mock_user_api_key, mock_cache):
     """
     Test that Presidio handles empty content gracefully.
-
+    
     This is common in tool/function calling where assistant messages have
     empty content but include tool_calls.
-
+    
     Bug fix: Previously crashed with:
     TypeError: argument after ** must be a mapping, not str
     """
@@ -771,10 +761,7 @@ async def test_empty_content_handling(
                     {
                         "id": "call_123",
                         "type": "function",
-                        "function": {
-                            "name": "calculator",
-                            "arguments": '{"a":2,"b":2}',
-                        },
+                        "function": {"name": "calculator", "arguments": '{"a":2,"b":2}'},
                     }
                 ],
             },
@@ -807,12 +794,10 @@ async def mock_check_pii(text, output_parse_pii, presidio_config, request_data):
 
 
 @pytest.mark.asyncio
-async def test_whitespace_only_content(
-    presidio_guardrail, mock_user_api_key, mock_cache
-):
+async def test_whitespace_only_content(presidio_guardrail, mock_user_api_key, mock_cache):
     """
     Test that Presidio handles whitespace-only content gracefully.
-
+    
     Whitespace-only content should be treated the same as empty content.
     """
     test_data = {
@@ -847,7 +832,7 @@ async def mock_check_pii(text, output_parse_pii, presidio_config, request_data):
 async def test_analyze_text_with_empty_string():
     """
     Test analyze_text method directly with empty string.
-
+    
     Should return empty list without making API call to Presidio.
     """
     presidio = _OPTIONAL_PresidioPIIMasking(
@@ -879,7 +864,7 @@ async def test_analyze_text_with_empty_string():
 async def test_analyze_text_error_dict_handling():
     """
     Test that analyze_text handles error dict responses from Presidio API.
-
+    
     When Presidio returns {'error': 'No text provided'}, should handle gracefully
     instead of crashing with TypeError.
     """
@@ -893,20 +878,16 @@ async def test_analyze_text_error_dict_handling():
     class MockResponse:
         async def json(self):
             return {"error": "No text provided"}
-
         async def __aenter__(self):
             return self
-
         async def __aexit__(self, *args):
             pass
-
+    
     class MockSession:
         def post(self, *args, **kwargs):
             return MockResponse()
-
         async def __aenter__(self):
             return self
-
         async def __aexit__(self, *args):
             pass
 
@@ -923,12 +904,10 @@ async def __aexit__(self, *args):
 
 
 @pytest.mark.asyncio
-async def test_tool_calling_complete_scenario(
-    presidio_guardrail, mock_user_api_key, mock_cache
-):
+async def test_tool_calling_complete_scenario(presidio_guardrail, mock_user_api_key, mock_cache):
     """
     Test complete tool calling scenario with PII in user message.
-
+    
     This tests the real-world scenario where:
     1. User provides a query with PII
     2. Assistant responds with empty content + tool_calls
@@ -1023,12 +1002,7 @@ def test_no_thresholds_returns_all():
     guardrail = _OPTIONAL_PresidioPIIMasking(mock_testing=True)
     analyze_results = [
         {"entity_type": PiiEntityType.CREDIT_CARD, "score": 0.1, "start": 0, "end": 4},
-        {
-            "entity_type": PiiEntityType.EMAIL_ADDRESS,
-            "score": 0.2,
-            "start": 5,
-            "end": 9,
-        },
+        {"entity_type": PiiEntityType.EMAIL_ADDRESS, "score": 0.2, "start": 5, "end": 9},
     ]
 
     filtered = guardrail.filter_analyze_results_by_score(analyze_results)
@@ -1045,12 +1019,7 @@ def test_entity_specific_threshold_only_applies_to_that_entity():
     )
     analyze_results = [
         {"entity_type": PiiEntityType.CREDIT_CARD, "score": 0.7, "start": 0, "end": 4},
-        {
-            "entity_type": PiiEntityType.EMAIL_ADDRESS,
-            "score": 0.1,
-            "start": 5,
-            "end": 9,
-        },
+        {"entity_type": PiiEntityType.EMAIL_ADDRESS, "score": 0.1, "start": 5, "end": 9},
     ]
 
     filtered = guardrail.filter_analyze_results_by_score(analyze_results)
@@ -1069,12 +1038,7 @@ def test_filter_uses_default_all_threshold():
     )
     analyze_results = [
         {"entity_type": PiiEntityType.CREDIT_CARD, "score": 0.7, "start": 0, "end": 4},
-        {
-            "entity_type": PiiEntityType.EMAIL_ADDRESS,
-            "score": 0.8,
-            "start": 5,
-            "end": 9,
-        },
+        {"entity_type": PiiEntityType.EMAIL_ADDRESS, "score": 0.8, "start": 5, "end": 9},
     ]
 
     filtered = guardrail.filter_analyze_results_by_score(analyze_results)
@@ -1095,12 +1059,7 @@ def test_entity_specific_overrides_default_threshold():
     )
     analyze_results = [
         {"entity_type": PiiEntityType.CREDIT_CARD, "score": 0.65, "start": 0, "end": 4},
-        {
-            "entity_type": PiiEntityType.EMAIL_ADDRESS,
-            "score": 0.75,
-            "start": 5,
-            "end": 9,
-        },
+        {"entity_type": PiiEntityType.EMAIL_ADDRESS, "score": 0.75, "start": 5, "end": 9},
     ]
 
     filtered = guardrail.filter_analyze_results_by_score(analyze_results)
@@ -1175,59 +1134,3 @@ def test_update_in_memory_applies_score_thresholds():
     guardrail.update_in_memory_litellm_params(params)
 
     assert guardrail.presidio_score_thresholds == {PiiEntityType.CREDIT_CARD: 0.85}
-
-
-@pytest.mark.asyncio
-async def test_get_session_iterator_thread_safety(presidio_guardrail):
-    """
-    Test that _get_session_iterator yields:
-    1. The shared session when in the main thread.
-    2. A new session when in a background thread.
-    """
-    import threading
-    import aiohttp
-
-    # 1. Main Thread Case
-    # We are in the "main thread" relative to the guardrail initialization
-    async with presidio_guardrail._get_session_iterator() as session:
-        assert isinstance(session, aiohttp.ClientSession)
-        assert session is presidio_guardrail._http_session
-        shared_session_id = id(session)
-
-    # 2. Background Thread Case
-    # Define a helper function to run in a thread
-    def thread_target(loop, result_future):
-        async def run_in_loop():
-            # This runs in the thread's loop
-            async with presidio_guardrail._get_session_iterator() as session:
-                return session, id(session)
-
-        try:
-            # Create a new loop for this thread to run async code
-            new_loop = asyncio.new_event_loop()
-            asyncio.set_event_loop(new_loop)
-            session_obj, session_id = new_loop.run_until_complete(run_in_loop())
-            result_future.set_result((session_obj, session_id))
-            new_loop.close()
-        except Exception as e:
-            result_future.set_exception(e)
-
-    # Run the background thread test
-    bg_future = asyncio.Future()
-    t = threading.Thread(
-        target=thread_target, args=(asyncio.get_running_loop(), bg_future)
-    )
-    t.start()
-    t.join()
-
-    bg_session, bg_session_id = await bg_future
-
-    # Assertions
-    # The background session should be DIFFERENT from the shared session
-    assert bg_session_id != shared_session_id
-    # The shared session should still be open (not closed by the background thread)
-    assert not presidio_guardrail._http_session.closed
-    # The background session should be closed (handled by the context manager in the thread)
-    assert bg_session.closed
-
-    print("✓ Session iterator thread safety test passed")
diff --git a/tests/test_litellm/proxy/hooks/test_post_call_streaming_hook_integration.py b/tests/test_litellm/proxy/hooks/test_post_call_streaming_hook_integration.py
deleted file mode 100644
index 3bc111ef142..00000000000
--- a/tests/test_litellm/proxy/hooks/test_post_call_streaming_hook_integration.py
+++ /dev/null
@@ -1,273 +0,0 @@
-"""
-Integration tests for async_post_call_streaming_hook.
-
-Tests verify that the streaming hook can transform streaming responses sent to clients.
-"""
-
-import os
-import sys
-import pytest
-from typing import Any
-from unittest.mock import patch, MagicMock
-
-sys.path.insert(0, os.path.abspath("../../../.."))
-
-import litellm
-from litellm.integrations.custom_logger import CustomLogger
-from litellm.proxy._types import UserAPIKeyAuth
-from litellm.types.utils import ModelResponseStream, StreamingChoices, Delta
-
-
-class StreamingResponseTransformerLogger(CustomLogger):
-    """Logger that transforms streaming responses"""
-
-    def __init__(self, transform_content: str = None):
-        self.called = False
-        self.transform_content = transform_content
-        self.received_response = None
-
-    async def async_post_call_streaming_hook(
-        self,
-        user_api_key_dict: UserAPIKeyAuth,
-        response: str,
-    ) -> Any:
-        self.called = True
-        self.received_response = response
-        if self.transform_content is not None:
-            return self.transform_content
-        return None
-
-
-@pytest.mark.asyncio
-async def test_streaming_hook_transforms_response():
-    """
-    Test that async_post_call_streaming_hook can transform streaming responses.
-    """
-    transformer = StreamingResponseTransformerLogger(transform_content="Modified streaming response")
-
-    with patch("litellm.callbacks", [transformer]):
-        from litellm.proxy.utils import ProxyLogging
-        from litellm.caching.caching import DualCache
-
-        proxy_logging = ProxyLogging(user_api_key_cache=DualCache())
-
-        # Create a mock streaming response
-        original_response = ModelResponseStream(
-            id="original-stream",
-            choices=[
-                StreamingChoices(
-                    delta=Delta(content="Original content", role="assistant"),
-                    index=0,
-                )
-            ],
-            model="test-model",
-        )
-
-        data = {"model": "test-model"}
-        user_api_key_dict = UserAPIKeyAuth(api_key="test-key")
-
-        # Call the hook
-        result = await proxy_logging.async_post_call_streaming_hook(
-            data=data,
-            response=original_response,
-            user_api_key_dict=user_api_key_dict,
-        )
-
-        # Verify hook was called
-        assert transformer.called is True
-
-        # Verify transformed response is returned
-        assert result == "Modified streaming response"
-
-
-@pytest.mark.asyncio
-async def test_streaming_hook_returns_none_keeps_original():
-    """
-    Test that hook returning None keeps the original response.
-    """
-
-    class NoOpLogger(CustomLogger):
-        def __init__(self):
-            self.called = False
-
-        async def async_post_call_streaming_hook(
-            self,
-            user_api_key_dict: UserAPIKeyAuth,
-            response: str,
-        ):
-            self.called = True
-            return None
-
-    logger = NoOpLogger()
-
-    with patch("litellm.callbacks", [logger]):
-        from litellm.proxy.utils import ProxyLogging
-        from litellm.caching.caching import DualCache
-
-        proxy_logging = ProxyLogging(user_api_key_cache=DualCache())
-
-        original_response = ModelResponseStream(
-            id="original-stream",
-            choices=[
-                StreamingChoices(
-                    delta=Delta(content="Original content", role="assistant"),
-                    index=0,
-                )
-            ],
-            model="test-model",
-        )
-
-        data = {"model": "test-model"}
-        user_api_key_dict = UserAPIKeyAuth(api_key="test-key")
-
-        result = await proxy_logging.async_post_call_streaming_hook(
-            data=data,
-            response=original_response,
-            user_api_key_dict=user_api_key_dict,
-        )
-
-        # Should return original response object
-        assert result.id == "original-stream"
-        assert logger.called is True
-
-
-@pytest.mark.asyncio
-async def test_streaming_hook_works_with_sse_format():
-    """
-    Test that hook works with SSE-formatted strings (data: prefix).
-    This was the only supported format before the fix.
-    """
-    transformer = StreamingResponseTransformerLogger(
-        transform_content="data: {\"error\": \"custom error\"}\n\n"
-    )
-
-    with patch("litellm.callbacks", [transformer]):
-        from litellm.proxy.utils import ProxyLogging
-        from litellm.caching.caching import DualCache
-
-        proxy_logging = ProxyLogging(user_api_key_cache=DualCache())
-
-        original_response = ModelResponseStream(
-            id="original-stream",
-            choices=[
-                StreamingChoices(
-                    delta=Delta(content="Original content", role="assistant"),
-                    index=0,
-                )
-            ],
-            model="test-model",
-        )
-
-        data = {"model": "test-model"}
-        user_api_key_dict = UserAPIKeyAuth(api_key="test-key")
-
-        result = await proxy_logging.async_post_call_streaming_hook(
-            data=data,
-            response=original_response,
-            user_api_key_dict=user_api_key_dict,
-        )
-
-        # Verify SSE-formatted response is returned
-        assert result == "data: {\"error\": \"custom error\"}\n\n"
-
-
-@pytest.mark.asyncio
-async def test_streaming_hook_chains_multiple_callbacks():
-    """
-    Test that multiple callbacks can chain modifications.
-    """
-
-    class AppendLogger(CustomLogger):
-        def __init__(self, suffix: str):
-            self.suffix = suffix
-            self.called = False
-
-        async def async_post_call_streaming_hook(
-            self,
-            user_api_key_dict: UserAPIKeyAuth,
-            response: str,
-        ) -> str:
-            self.called = True
-            # Note: response here is the complete_response string, not the chunk
-            return f"[{self.suffix}]"
-
-    callback1 = AppendLogger("CB1")
-    callback2 = AppendLogger("CB2")
-
-    with patch("litellm.callbacks", [callback1, callback2]):
-        from litellm.proxy.utils import ProxyLogging
-        from litellm.caching.caching import DualCache
-
-        proxy_logging = ProxyLogging(user_api_key_cache=DualCache())
-
-        original_response = ModelResponseStream(
-            id="original-stream",
-            choices=[
-                StreamingChoices(
-                    delta=Delta(content="Hello", role="assistant"),
-                    index=0,
-                )
-            ],
-            model="test-model",
-        )
-
-        data = {"model": "test-model"}
-        user_api_key_dict = UserAPIKeyAuth(api_key="test-key")
-
-        result = await proxy_logging.async_post_call_streaming_hook(
-            data=data,
-            response=original_response,
-            user_api_key_dict=user_api_key_dict,
-        )
-
-        # Both callbacks should have been called
-        assert callback1.called is True
-        assert callback2.called is True
-
-        # Last callback's result should be used
-        assert result == "[CB2]"
-
-
-@pytest.mark.asyncio
-async def test_streaming_hook_handles_exceptions():
-    """
-    Test that hook exceptions are propagated.
-    """
-
-    class FailingLogger(CustomLogger):
-        async def async_post_call_streaming_hook(
-            self,
-            user_api_key_dict: UserAPIKeyAuth,
-            response: str,
-        ):
-            raise RuntimeError("Streaming hook crashed!")
-
-    logger = FailingLogger()
-
-    with patch("litellm.callbacks", [logger]):
-        from litellm.proxy.utils import ProxyLogging
-        from litellm.caching.caching import DualCache
-
-        proxy_logging = ProxyLogging(user_api_key_cache=DualCache())
-
-        original_response = ModelResponseStream(
-            id="original-stream",
-            choices=[
-                StreamingChoices(
-                    delta=Delta(content="Hello", role="assistant"),
-                    index=0,
-                )
-            ],
-            model="test-model",
-        )
-
-        data = {"model": "test-model"}
-        user_api_key_dict = UserAPIKeyAuth(api_key="test-key")
-
-        # Exception should be propagated
-        with pytest.raises(RuntimeError, match="Streaming hook crashed!"):
-            await proxy_logging.async_post_call_streaming_hook(
-                data=data,
-                response=original_response,
-                user_api_key_dict=user_api_key_dict,
-            )
diff --git a/tests/test_litellm/proxy/hooks/test_post_call_success_hook_integration.py b/tests/test_litellm/proxy/hooks/test_post_call_success_hook_integration.py
deleted file mode 100644
index 870286f5382..00000000000
--- a/tests/test_litellm/proxy/hooks/test_post_call_success_hook_integration.py
+++ /dev/null
@@ -1,260 +0,0 @@
-"""
-Integration tests for async_post_call_success_hook.
-
-Tests verify that the success hook can transform responses sent to clients.
-This mirrors the behavior of CustomGuardrail hooks and streaming iterator hooks.
-"""
-
-import os
-import sys
-import pytest
-from typing import Any
-from unittest.mock import patch, MagicMock
-
-sys.path.insert(0, os.path.abspath("../../../.."))
-
-import litellm
-from litellm.integrations.custom_logger import CustomLogger
-from litellm.proxy._types import UserAPIKeyAuth
-from litellm.types.utils import ModelResponse, Choices, Message, Usage
-
-
-class ResponseTransformerLogger(CustomLogger):
-    """Logger that transforms successful responses"""
-
-    def __init__(self, transform_content: str = None):
-        self.called = False
-        self.transform_content = transform_content
-
-    async def async_post_call_success_hook(
-        self,
-        data: dict,
-        user_api_key_dict: UserAPIKeyAuth,
-        response: Any,
-    ) -> Any:
-        self.called = True
-        if self.transform_content is not None:
-            # Create a modified response with custom content
-            return {
-                "id": "transformed-response",
-                "choices": [
-                    {
-                        "message": {"content": self.transform_content, "role": "assistant"},
-                        "index": 0,
-                    }
-                ],
-                "model": "test-model",
-                "custom_field": "added_by_hook",
-            }
-        return response
-
-
-@pytest.mark.asyncio
-async def test_success_hook_transforms_response():
-    """
-    Test that async_post_call_success_hook can transform successful responses.
-    """
-    transformer = ResponseTransformerLogger(transform_content="Modified response")
-
-    with patch("litellm.callbacks", [transformer]):
-        from litellm.proxy.utils import ProxyLogging
-        from litellm.caching.caching import DualCache
-
-        proxy_logging = ProxyLogging(user_api_key_cache=DualCache())
-
-        # Create a mock response
-        original_response = ModelResponse(
-            id="original-response",
-            choices=[
-                Choices(
-                    message=Message(content="Original content", role="assistant"),
-                    index=0,
-                    finish_reason="stop",
-                )
-            ],
-            model="test-model",
-            usage=Usage(prompt_tokens=10, completion_tokens=20, total_tokens=30),
-        )
-
-        data = {"model": "test-model"}
-        user_api_key_dict = UserAPIKeyAuth(api_key="test-key")
-
-        # Call the hook
-        result = await proxy_logging.post_call_success_hook(
-            data=data,
-            response=original_response,
-            user_api_key_dict=user_api_key_dict,
-        )
-
-        # Verify hook was called
-        assert transformer.called is True
-
-        # Verify transformed response is returned
-        assert result is not None
-        assert result["id"] == "transformed-response"
-        assert result["choices"][0]["message"]["content"] == "Modified response"
-        assert result["custom_field"] == "added_by_hook"
-
-
-@pytest.mark.asyncio
-async def test_success_hook_returns_none_keeps_original():
-    """
-    Test that hook returning None keeps the original response.
-    """
-
-    class NoOpLogger(CustomLogger):
-        def __init__(self):
-            self.called = False
-
-        async def async_post_call_success_hook(self, *args, **kwargs):
-            self.called = True
-            return None
-
-    logger = NoOpLogger()
-
-    with patch("litellm.callbacks", [logger]):
-        from litellm.proxy.utils import ProxyLogging
-        from litellm.caching.caching import DualCache
-
-        proxy_logging = ProxyLogging(user_api_key_cache=DualCache())
-
-        original_response = ModelResponse(
-            id="original-response",
-            choices=[
-                Choices(
-                    message=Message(content="Original content", role="assistant"),
-                    index=0,
-                    finish_reason="stop",
-                )
-            ],
-            model="test-model",
-            usage=Usage(prompt_tokens=10, completion_tokens=20, total_tokens=30),
-        )
-
-        data = {"model": "test-model"}
-        user_api_key_dict = UserAPIKeyAuth(api_key="test-key")
-
-        result = await proxy_logging.post_call_success_hook(
-            data=data,
-            response=original_response,
-            user_api_key_dict=user_api_key_dict,
-        )
-
-        # Should return original response
-        assert result.id == "original-response"
-        assert logger.called is True
-
-
-@pytest.mark.asyncio
-async def test_success_hook_chains_multiple_callbacks():
-    """
-    Test that multiple callbacks can chain modifications.
-    """
-
-    class AddFieldLogger(CustomLogger):
-        def __init__(self, field_name: str, field_value: Any):
-            self.field_name = field_name
-            self.field_value = field_value
-            self.called = False
-
-        async def async_post_call_success_hook(
-            self,
-            data: dict,
-            user_api_key_dict: UserAPIKeyAuth,
-            response: Any,
-        ) -> Any:
-            self.called = True
-            # Convert response to dict if needed
-            if hasattr(response, "model_dump"):
-                resp_dict = response.model_dump()
-            elif hasattr(response, "dict"):
-                resp_dict = response.dict()
-            elif isinstance(response, dict):
-                resp_dict = response.copy()
-            else:
-                resp_dict = {}
-
-            resp_dict[self.field_name] = self.field_value
-            return resp_dict
-
-    callback1 = AddFieldLogger("field1", "value1")
-    callback2 = AddFieldLogger("field2", "value2")
-
-    with patch("litellm.callbacks", [callback1, callback2]):
-        from litellm.proxy.utils import ProxyLogging
-        from litellm.caching.caching import DualCache
-
-        proxy_logging = ProxyLogging(user_api_key_cache=DualCache())
-
-        original_response = ModelResponse(
-            id="original-response",
-            choices=[
-                Choices(
-                    message=Message(content="Original content", role="assistant"),
-                    index=0,
-                    finish_reason="stop",
-                )
-            ],
-            model="test-model",
-            usage=Usage(prompt_tokens=10, completion_tokens=20, total_tokens=30),
-        )
-
-        data = {"model": "test-model"}
-        user_api_key_dict = UserAPIKeyAuth(api_key="test-key")
-
-        result = await proxy_logging.post_call_success_hook(
-            data=data,
-            response=original_response,
-            user_api_key_dict=user_api_key_dict,
-        )
-
-        # Both callbacks should have been called
-        assert callback1.called is True
-        assert callback2.called is True
-
-        # Both fields should be present (chained modifications)
-        assert result["field1"] == "value1"
-        assert result["field2"] == "value2"
-
-
-@pytest.mark.asyncio
-async def test_success_hook_handles_exceptions():
-    """
-    Test that hook exceptions are propagated (not silently swallowed).
-    """
-
-    class FailingLogger(CustomLogger):
-        async def async_post_call_success_hook(self, *args, **kwargs):
-            raise RuntimeError("Hook crashed!")
-
-    logger = FailingLogger()
-
-    with patch("litellm.callbacks", [logger]):
-        from litellm.proxy.utils import ProxyLogging
-        from litellm.caching.caching import DualCache
-
-        proxy_logging = ProxyLogging(user_api_key_cache=DualCache())
-
-        original_response = ModelResponse(
-            id="original-response",
-            choices=[
-                Choices(
-                    message=Message(content="Original content", role="assistant"),
-                    index=0,
-                    finish_reason="stop",
-                )
-            ],
-            model="test-model",
-            usage=Usage(prompt_tokens=10, completion_tokens=20, total_tokens=30),
-        )
-
-        data = {"model": "test-model"}
-        user_api_key_dict = UserAPIKeyAuth(api_key="test-key")
-
-        # Exception should be propagated
-        with pytest.raises(RuntimeError, match="Hook crashed!"):
-            await proxy_logging.post_call_success_hook(
-                data=data,
-                response=original_response,
-                user_api_key_dict=user_api_key_dict,
-            )
diff --git a/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py
index dc436bac087..397a6af556f 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py
@@ -1096,57 +1096,3 @@ async def mock_get_user_key_counts(*args, **kwargs):
     assert "user_id" in captured_where_conditions
     assert "in" in captured_where_conditions["user_id"]
     assert captured_where_conditions["user_id"]["in"] == ["user1", "user2", "user3"]
-
-
-def test_update_internal_user_params_reset_max_budget_with_none():
-    """
-    Test that _update_internal_user_params allows setting max_budget to None.
-    This verifies the fix for unsetting/resetting the budget to unlimited.
-    """
-    
-    # Case 1: max_budget is explicitly None in the input dictionary
-    data_json = {"max_budget": None, "user_id": "test_user"}
-    data = UpdateUserRequest(max_budget=None, user_id="test_user")
-
-    # Call the function
-    non_default_values = _update_internal_user_params(data_json=data_json, data=data)
-
-    # Assertions
-    assert "max_budget" in non_default_values
-    assert non_default_values["max_budget"] is None
-    assert non_default_values["user_id"] == "test_user"
-
-
-def test_update_internal_user_params_ignores_other_nones():
-    """
-    Test that other fields are still filtered out if None
-    """
-    # Create test data with other None fields
-    data_json = {"user_alias": None, "user_id": "test_user", "max_budget": 100.0}
-    data = UpdateUserRequest(user_alias=None, user_id="test_user", max_budget=100.0)
-
-    # Call the function
-    non_default_values = _update_internal_user_params(data_json=data_json, data=data)
-
-    # Assertions
-    assert "user_alias" not in non_default_values
-    assert non_default_values["max_budget"] == 100.0
-
-
-def test_generate_request_base_validator():
-    """
-    Test that GenerateRequestBase validator converts empty string to None for max_budget
-    """
-    from litellm.proxy._types import GenerateRequestBase
-    
-    # Test with empty string
-    req = GenerateRequestBase(max_budget="")
-    assert req.max_budget is None
-
-    # Test with actual float
-    req = GenerateRequestBase(max_budget=100.0)
-    assert req.max_budget == 100.0
-
-    # Test with None
-    req = GenerateRequestBase(max_budget=None)
-    assert req.max_budget is None
\ No newline at end of file
diff --git a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py
index a57378e579c..7d31f762096 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py
@@ -30,13 +30,10 @@
     _check_org_key_limits,
     _check_team_key_limits,
     _common_key_generation_helper,
-    _get_and_validate_existing_key,
     _list_key_helper,
     _persist_deleted_verification_tokens,
-    _process_single_key_update,
     _save_deleted_verification_token_records,
     _transform_verification_tokens_to_deleted_records,
-    _validate_max_budget,
     can_modify_verification_token,
     check_org_key_model_specific_limits,
     check_team_key_model_specific_limits,
@@ -4226,467 +4223,3 @@ async def test_update_key_with_router_settings(monkeypatch):
     # Verify router_settings can be deserialized and matches input
     deserialized_settings = json.loads(result["router_settings"])
     assert deserialized_settings == router_settings_data
-
-
-@pytest.mark.asyncio
-async def test_validate_max_budget():
-    """
-    Test _validate_max_budget helper function.
-    
-    Tests:
-    1. Positive max_budget should pass
-    2. Zero max_budget should pass
-    3. Negative max_budget should raise HTTPException
-    4. None max_budget should pass
-    """
-    from fastapi import HTTPException
-    
-    # Test Case 1: Positive max_budget should pass
-    try:
-        _validate_max_budget(100.0)
-        _validate_max_budget(0.0)
-    except HTTPException:
-        pytest.fail("_validate_max_budget raised HTTPException for valid values")
-    
-    # Test Case 2: None max_budget should pass
-    try:
-        _validate_max_budget(None)
-    except HTTPException:
-        pytest.fail("_validate_max_budget raised HTTPException for None")
-    
-    # Test Case 3: Negative max_budget should raise HTTPException
-    with pytest.raises(HTTPException) as exc_info:
-        _validate_max_budget(-10.0)
-    
-    assert exc_info.value.status_code == 400
-    assert "max_budget cannot be negative" in str(exc_info.value.detail)
-
-
-@pytest.mark.asyncio
-async def test_get_and_validate_existing_key():
-    """
-    Test _get_and_validate_existing_key helper function.
-    
-    Tests:
-    1. Successfully retrieve existing key
-    2. Key not found raises HTTPException
-    3. Database not connected raises HTTPException
-    """
-    from fastapi import HTTPException
-    
-    # Test Case 1: Successfully retrieve existing key
-    mock_prisma_client = AsyncMock()
-    mock_key = LiteLLM_VerificationToken(
-        token="test-key-123",
-        user_id="user-123",
-        models=["gpt-4"],
-        team_id=None,
-    )
-    mock_prisma_client.get_data = AsyncMock(return_value=mock_key)
-    
-    result = await _get_and_validate_existing_key(
-        token="test-key-123",
-        prisma_client=mock_prisma_client,
-    )
-    
-    assert result == mock_key
-    mock_prisma_client.get_data.assert_called_once_with(
-        token="test-key-123",
-        table_name="key",
-        query_type="find_unique",
-    )
-    
-    # Test Case 2: Key not found raises HTTPException
-    mock_prisma_client.get_data = AsyncMock(return_value=None)
-    
-    with pytest.raises(HTTPException) as exc_info:
-        await _get_and_validate_existing_key(
-            token="non-existent-key",
-            prisma_client=mock_prisma_client,
-        )
-    
-    assert exc_info.value.status_code == 404
-    assert "Key not found" in str(exc_info.value.detail)
-    
-    # Test Case 3: Database not connected raises HTTPException
-    with pytest.raises(HTTPException) as exc_info:
-        await _get_and_validate_existing_key(
-            token="test-key-123",
-            prisma_client=None,
-        )
-    
-    assert exc_info.value.status_code == 500
-    assert "Database not connected" in str(exc_info.value.detail)
-
-
-@pytest.mark.asyncio
-async def test_process_single_key_update():
-    """
-    Test _process_single_key_update helper function.
-    
-    Tests successful key update with all validations passing.
-    """
-    from litellm.types.proxy.management_endpoints.key_management_endpoints import (
-        BulkUpdateKeyRequestItem,
-    )
-    
-    # Setup mocks
-    mock_prisma_client = AsyncMock()
-    mock_user_api_key_cache = MagicMock()
-    mock_proxy_logging_obj = MagicMock()
-    mock_llm_router = MagicMock()
-    
-    # Mock existing key
-    existing_key = LiteLLM_VerificationToken(
-        token="test-key-123",
-        user_id="user-123",
-        models=["gpt-4"],
-        team_id=None,
-        max_budget=None,
-        tags=None,
-    )
-    
-    # Mock updated key response
-    updated_key_data = {
-        "user_id": "user-123",
-        "models": ["gpt-4"],
-        "team_id": None,
-        "max_budget": 100.0,
-        "tags": ["production"],
-    }
-    
-    mock_prisma_client.get_data = AsyncMock(return_value=existing_key)
-    mock_updated_key_obj = MagicMock()
-    mock_updated_key_obj.model_dump.return_value = updated_key_data
-    mock_prisma_client.update_data = AsyncMock(
-        return_value={"data": mock_updated_key_obj}
-    )
-    
-    # Mock prepare_key_update_data
-    with patch(
-        "litellm.proxy.management_endpoints.key_management_endpoints.prepare_key_update_data"
-    ) as mock_prepare:
-        mock_prepare.return_value = {"max_budget": 100.0, "tags": ["production"]}
-        
-        # Mock TeamMemberPermissionChecks
-        with patch(
-            "litellm.proxy.management_endpoints.key_management_endpoints.TeamMemberPermissionChecks.can_team_member_execute_key_management_endpoint"
-        ) as mock_permission_check:
-            mock_permission_check.return_value = None
-            
-            # Mock _delete_cache_key_object
-            with patch(
-                "litellm.proxy.management_endpoints.key_management_endpoints._delete_cache_key_object"
-            ) as mock_delete_cache:
-                mock_delete_cache.return_value = None
-                
-                # Mock hash_token (imported from litellm.proxy._types)
-                with patch(
-                    "litellm.proxy._types.hash_token"
-                ) as mock_hash:
-                    mock_hash.return_value = "hashed-test-key-123"
-                    
-                    # Mock KeyManagementEventHooks
-                    with patch(
-                        "litellm.proxy.management_endpoints.key_management_endpoints.KeyManagementEventHooks.async_key_updated_hook"
-                    ):
-                        # Create update request
-                        key_update_item = BulkUpdateKeyRequestItem(
-                            key="test-key-123",
-                            max_budget=100.0,
-                            tags=["production"],
-                        )
-                        
-                        user_api_key_dict = UserAPIKeyAuth(
-                            user_role=LitellmUserRoles.PROXY_ADMIN,
-                            api_key="sk-admin",
-                            user_id="admin-user",
-                        )
-                        
-                        # Call the function
-                        result = await _process_single_key_update(
-                            key_update_item=key_update_item,
-                            user_api_key_dict=user_api_key_dict,
-                            litellm_changed_by=None,
-                            prisma_client=mock_prisma_client,
-                            user_api_key_cache=mock_user_api_key_cache,
-                            proxy_logging_obj=mock_proxy_logging_obj,
-                            llm_router=mock_llm_router,
-                        )
-                        
-                        # Verify results
-                        assert result is not None
-                        assert "token" not in result  # Token should be removed
-                        assert result.get("max_budget") == 100.0
-                        assert result.get("tags") == ["production"]
-                        
-                        # Verify mocks were called
-                        mock_prisma_client.get_data.assert_called_once()
-                        mock_prisma_client.update_data.assert_called_once()
-                        mock_delete_cache.assert_called_once()
-
-
-@pytest.mark.asyncio
-async def test_bulk_update_keys_success(monkeypatch):
-    """
-    Test /key/bulk_update endpoint with successful updates.
-    
-    Tests:
-    1. Multiple keys updated successfully
-    2. Response contains correct counts and data
-    """
-    from litellm.types.proxy.management_endpoints.key_management_endpoints import (
-        BulkUpdateKeyRequest,
-        BulkUpdateKeyRequestItem,
-    )
-    from litellm.proxy.management_endpoints.key_management_endpoints import (
-        bulk_update_keys,
-    )
-    from litellm.proxy.proxy_server import (
-        llm_router,
-        prisma_client,
-        proxy_logging_obj,
-        user_api_key_cache,
-    )
-    
-    # Setup mocks
-    mock_prisma_client = AsyncMock()
-    mock_user_api_key_cache = MagicMock()
-    mock_proxy_logging_obj = MagicMock()
-    mock_llm_router = MagicMock()
-    
-    # Mock existing keys
-    existing_key_1 = LiteLLM_VerificationToken(
-        token="test-key-1",
-        user_id="user-123",
-        models=["gpt-4"],
-        team_id=None,
-        max_budget=None,
-    )
-    existing_key_2 = LiteLLM_VerificationToken(
-        token="test-key-2",
-        user_id="user-123",
-        models=["gpt-3.5-turbo"],
-        team_id=None,
-        max_budget=50.0,
-    )
-    
-    # Mock updated key responses
-    updated_key_1_data = {
-        "user_id": "user-123",
-        "models": ["gpt-4"],
-        "max_budget": 100.0,
-        "tags": ["production"],
-    }
-    updated_key_2_data = {
-        "user_id": "user-123",
-        "models": ["gpt-3.5-turbo"],
-        "max_budget": 200.0,
-        "tags": ["staging"],
-    }
-    
-    mock_prisma_client.get_data = AsyncMock(
-        side_effect=[existing_key_1, existing_key_2]
-    )
-    mock_updated_key_1_obj = MagicMock()
-    mock_updated_key_1_obj.model_dump.return_value = updated_key_1_data
-    mock_updated_key_2_obj = MagicMock()
-    mock_updated_key_2_obj.model_dump.return_value = updated_key_2_data
-    mock_prisma_client.update_data = AsyncMock(
-        side_effect=[
-            {"data": mock_updated_key_1_obj},
-            {"data": mock_updated_key_2_obj},
-        ]
-    )
-    
-    # Patch dependencies
-    monkeypatch.setattr(
-        "litellm.proxy.proxy_server.prisma_client", mock_prisma_client
-    )
-    monkeypatch.setattr(
-        "litellm.proxy.proxy_server.user_api_key_cache", mock_user_api_key_cache
-    )
-    monkeypatch.setattr(
-        "litellm.proxy.proxy_server.proxy_logging_obj", mock_proxy_logging_obj
-    )
-    monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", mock_llm_router)
-    
-    # Mock helper functions
-    with patch(
-        "litellm.proxy.management_endpoints.key_management_endpoints.prepare_key_update_data"
-    ) as mock_prepare:
-        mock_prepare.side_effect = [
-            {"max_budget": 100.0, "tags": ["production"]},
-            {"max_budget": 200.0, "tags": ["staging"]},
-        ]
-        
-        with patch(
-            "litellm.proxy.management_endpoints.key_management_endpoints.TeamMemberPermissionChecks.can_team_member_execute_key_management_endpoint"
-        ):
-            with patch(
-                "litellm.proxy.management_endpoints.key_management_endpoints._delete_cache_key_object"
-            ):
-                with patch(
-                    "litellm.proxy._types.hash_token"
-                ) as mock_hash:
-                    mock_hash.side_effect = ["hashed-key-1", "hashed-key-2"]
-                    
-                    with patch(
-                        "litellm.proxy.management_endpoints.key_management_endpoints.KeyManagementEventHooks.async_key_updated_hook"
-                    ):
-                        # Create request
-                        request_data = BulkUpdateKeyRequest(
-                            keys=[
-                                BulkUpdateKeyRequestItem(
-                                    key="test-key-1",
-                                    max_budget=100.0,
-                                    tags=["production"],
-                                ),
-                                BulkUpdateKeyRequestItem(
-                                    key="test-key-2",
-                                    max_budget=200.0,
-                                    tags=["staging"],
-                                ),
-                            ]
-                        )
-                        
-                        user_api_key_dict = UserAPIKeyAuth(
-                            user_role=LitellmUserRoles.PROXY_ADMIN,
-                            api_key="sk-admin",
-                            user_id="admin-user",
-                        )
-                        
-                        # Call endpoint
-                        response = await bulk_update_keys(
-                            data=request_data,
-                            user_api_key_dict=user_api_key_dict,
-                            litellm_changed_by=None,
-                        )
-                        
-                        # Verify response
-                        assert response.total_requested == 2
-                        assert len(response.successful_updates) == 2
-                        assert len(response.failed_updates) == 0
-                        assert response.successful_updates[0].key == "test-key-1"
-                        assert response.successful_updates[1].key == "test-key-2"
-
-
-@pytest.mark.asyncio
-async def test_bulk_update_keys_partial_failures(monkeypatch):
-    """
-    Test /key/bulk_update endpoint with partial failures.
-    
-    Tests:
-    1. Some keys update successfully, others fail
-    2. Response contains both successful and failed updates
-    3. Failed updates include error messages
-    """
-    from litellm.types.proxy.management_endpoints.key_management_endpoints import (
-        BulkUpdateKeyRequest,
-        BulkUpdateKeyRequestItem,
-    )
-    from litellm.proxy.management_endpoints.key_management_endpoints import (
-        bulk_update_keys,
-    )
-    
-    # Setup mocks
-    mock_prisma_client = AsyncMock()
-    mock_user_api_key_cache = MagicMock()
-    mock_proxy_logging_obj = MagicMock()
-    mock_llm_router = MagicMock()
-    
-    # Mock existing keys
-    existing_key_1 = LiteLLM_VerificationToken(
-        token="test-key-1",
-        user_id="user-123",
-        models=["gpt-4"],
-        team_id=None,
-        max_budget=None,
-    )
-    
-    # Mock updated key response for successful update
-    updated_key_1_data = {
-        "user_id": "user-123",
-        "models": ["gpt-4"],
-        "max_budget": 100.0,
-        "tags": ["production"],
-    }
-    
-    # First key exists, second key doesn't exist
-    mock_prisma_client.get_data = AsyncMock(
-        side_effect=[existing_key_1, None]  # Second key not found
-    )
-    mock_updated_key_1_obj = MagicMock()
-    mock_updated_key_1_obj.model_dump.return_value = updated_key_1_data
-    mock_prisma_client.update_data = AsyncMock(
-        return_value={"data": mock_updated_key_1_obj}
-    )
-    
-    # Patch dependencies
-    monkeypatch.setattr(
-        "litellm.proxy.proxy_server.prisma_client", mock_prisma_client
-    )
-    monkeypatch.setattr(
-        "litellm.proxy.proxy_server.user_api_key_cache", mock_user_api_key_cache
-    )
-    monkeypatch.setattr(
-        "litellm.proxy.proxy_server.proxy_logging_obj", mock_proxy_logging_obj
-    )
-    monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", mock_llm_router)
-    
-    # Mock helper functions
-    with patch(
-        "litellm.proxy.management_endpoints.key_management_endpoints.prepare_key_update_data"
-    ) as mock_prepare:
-        mock_prepare.return_value = {"max_budget": 100.0, "tags": ["production"]}
-        
-        with patch(
-            "litellm.proxy.management_endpoints.key_management_endpoints.TeamMemberPermissionChecks.can_team_member_execute_key_management_endpoint"
-        ):
-            with patch(
-                "litellm.proxy.management_endpoints.key_management_endpoints._delete_cache_key_object"
-            ):
-                with patch(
-                    "litellm.proxy._types.hash_token"
-                ) as mock_hash:
-                    mock_hash.return_value = "hashed-key-1"
-                    
-                    with patch(
-                        "litellm.proxy.management_endpoints.key_management_endpoints.KeyManagementEventHooks.async_key_updated_hook"
-                    ):
-                        # Create request with one valid and one invalid key
-                        request_data = BulkUpdateKeyRequest(
-                            keys=[
-                                BulkUpdateKeyRequestItem(
-                                    key="test-key-1",
-                                    max_budget=100.0,
-                                    tags=["production"],
-                                ),
-                                BulkUpdateKeyRequestItem(
-                                    key="non-existent-key",
-                                    max_budget=200.0,
-                                    tags=["staging"],
-                                ),
-                            ]
-                        )
-                        
-                        user_api_key_dict = UserAPIKeyAuth(
-                            user_role=LitellmUserRoles.PROXY_ADMIN,
-                            api_key="sk-admin",
-                            user_id="admin-user",
-                        )
-                        
-                        # Call endpoint
-                        response = await bulk_update_keys(
-                            data=request_data,
-                            user_api_key_dict=user_api_key_dict,
-                            litellm_changed_by=None,
-                        )
-                        
-                        # Verify response
-                        assert response.total_requested == 2
-                        assert len(response.successful_updates) == 1
-                        assert len(response.failed_updates) == 1
-                        assert response.successful_updates[0].key == "test-key-1"
-                        assert response.failed_updates[0].key == "non-existent-key"
-                        assert "Key not found" in response.failed_updates[0].failed_reason
diff --git a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py
index 467ee3661d1..0d78545823f 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py
@@ -36,7 +36,6 @@
     _persist_deleted_team_records,
     _save_deleted_team_records,
     _transform_teams_to_deleted_records,
-    _validate_and_populate_member_user_info,
     delete_team,
     router,
     team_member_add_duplication_check,
@@ -3655,100 +3654,6 @@ async def test_update_team_org_scoped_models_not_in_org_models():
         assert "claude-3-opus" in str(exc_info.value.message) or "organization" in str(exc_info.value.message).lower()
 
 
-@pytest.mark.asyncio
-async def test_update_team_org_scoped_models_with_all_proxy_models():
-    """
-    Test that /team/update for an org-scoped team succeeds when organization has 'all-proxy-models'.
-
-    Scenario:
-    - Organization has models=['all-proxy-models'] (catch-all for all models)
-    - Org-scoped team exists
-    - User tries to update team models to ['rerank-english-v3.0', 'text-embedding-3-small', 'gpt-4o-mini-test']
-    - Expected: Should succeed because 'all-proxy-models' allows all models
-    """
-    from fastapi import Request
-
-    from litellm.proxy._types import (
-        LiteLLM_OrganizationTable,
-        SpecialModelNames,
-        UpdateTeamRequest,
-        UserAPIKeyAuth,
-    )
-    from litellm.proxy.management_endpoints.team_endpoints import update_team
-
-    # Create user (org admin)
-    org_admin_user = UserAPIKeyAuth(
-        user_role=LitellmUserRoles.INTERNAL_USER,
-        user_id="org-admin-all-proxy-models-test",
-        models=[],
-    )
-
-    # Create update request with models that aren't explicitly in org's models list
-    # but should be allowed because org has 'all-proxy-models'
-    update_request = UpdateTeamRequest(
-        team_id="org-team-all-proxy-models-123",
-        models=["rerank-english-v3.0", "text-embedding-3-small", "gpt-4o-mini-test"],
-    )
-
-    dummy_request = MagicMock(spec=Request)
-
-    # Mock organization with 'all-proxy-models' (catch-all)
-    mock_org = MagicMock(spec=LiteLLM_OrganizationTable)
-    mock_org.organization_id = "test-org-all-proxy-models"
-    mock_org.models = [SpecialModelNames.all_proxy_models.value]  # Allows all models
-    mock_org.litellm_budget_table = None
-
-    with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch(
-        "litellm.proxy.proxy_server.user_api_key_cache"
-    ) as mock_cache, patch(
-        "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin"
-    ), patch(
-        "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock()
-    ) as mock_audit, patch(
-        "litellm.proxy.management_endpoints.team_endpoints.get_org_object",
-        new=AsyncMock(return_value=mock_org)
-    ) as mock_get_org:
-
-        # Mock existing org-scoped team
-        mock_existing_team = MagicMock()
-        mock_existing_team.team_id = "org-team-all-proxy-models-123"
-        mock_existing_team.organization_id = "test-org-all-proxy-models"
-        mock_existing_team.models = ["gpt-4"]
-        mock_existing_team.model_id = None
-        mock_existing_team.model_dump.return_value = {
-            "team_id": "org-team-all-proxy-models-123",
-            "organization_id": "test-org-all-proxy-models",
-            "models": ["gpt-4"],
-        }
-        mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team)
-        mock_prisma.jsonify_team_object = lambda db_data: db_data
-        mock_cache.async_set_cache = AsyncMock()  # Mock cache set for _cache_team_object
-
-        # Mock team update
-        mock_updated_team = MagicMock()
-        mock_updated_team.team_id = "org-team-all-proxy-models-123"
-        mock_updated_team.organization_id = "test-org-all-proxy-models"
-        mock_updated_team.models = ["rerank-english-v3.0", "text-embedding-3-small", "gpt-4o-mini-test"]
-        mock_updated_team.litellm_model_table = None
-        mock_updated_team.model_dump.return_value = {
-            "team_id": "org-team-all-proxy-models-123",
-            "organization_id": "test-org-all-proxy-models",
-            "models": ["rerank-english-v3.0", "text-embedding-3-small", "gpt-4o-mini-test"],
-        }
-        mock_prisma.db.litellm_teamtable.update = AsyncMock(return_value=mock_updated_team)
-
-        # Should NOT raise an exception - 'all-proxy-models' allows all models
-        result = await update_team(
-            data=update_request,
-            http_request=dummy_request,
-            user_api_key_dict=org_admin_user,
-        )
-
-        # Verify the team was updated successfully with the new models
-        assert result is not None
-        assert result["data"].models == ["rerank-english-v3.0", "text-embedding-3-small", "gpt-4o-mini-test"]
-
-
 @pytest.mark.asyncio
 async def test_update_team_tpm_limit_exceeds_user_limit():
     """
@@ -5467,122 +5372,3 @@ async def test_get_team_daily_activity_team_admin_sees_all_spend(mock_db_client)
             ) and mock_db_client.db.litellm_verificationtoken.find_many.called:
                 # If it was called, that's unexpected for admin users
                 assert False, "API keys should not be fetched for team admin users"
-
-
-@pytest.mark.asyncio
-async def test_validate_and_populate_member_user_info_both_provided_match():
-    """
-    Test _validate_and_populate_member_user_info when both user_email and user_id
-    are provided and they match the same user in the database.
-    """
-    # Create member with both user_email and user_id
-    member = Member(user_email="test@example.com", user_id="user-123", role="user")
-    
-    # Mock prisma client
-    mock_prisma_client = MagicMock()
-    
-    # Mock user object that matches both email and user_id
-    mock_user = MagicMock()
-    mock_user.user_id = "user-123"
-    mock_user.user_email = "test@example.com"
-    
-    # Mock get_data to return single user matching email
-    mock_prisma_client.get_data = AsyncMock(return_value=[mock_user])
-    
-    # Call the function
-    result = await _validate_and_populate_member_user_info(
-        member=member,
-        prisma_client=mock_prisma_client,
-    )
-    
-    # Verify result matches input (both already provided and match)
-    assert result.user_email == "test@example.com"
-    assert result.user_id == "user-123"
-    
-    # Verify get_data was called with correct parameters
-    mock_prisma_client.get_data.assert_called_once_with(
-        key_val={"user_email": "test@example.com"},
-        table_name="user",
-        query_type="find_all",
-    )
-
-
-@pytest.mark.asyncio
-async def test_validate_and_populate_member_user_info_only_email_provided():
-    """
-    Test _validate_and_populate_member_user_info when only user_email is provided.
-    Should populate user_id from database.
-    """
-    # Create member with only user_email
-    member = Member(user_email="test@example.com", user_id=None, role="user")
-    
-    # Mock prisma client
-    mock_prisma_client = MagicMock()
-    
-    # Mock user object from find_first
-    mock_user_find_first = MagicMock()
-    mock_user_find_first.user_id = "user-456"
-    mock_user_find_first.user_email = "test@example.com"
-    
-    # Mock find_first to return the user
-    mock_prisma_client.db.litellm_usertable.find_first = AsyncMock(
-        return_value=mock_user_find_first
-    )
-    
-    # Mock get_data to return single user (no duplicates)
-    mock_prisma_client.get_data = AsyncMock(return_value=[mock_user_find_first])
-    
-    # Call the function
-    result = await _validate_and_populate_member_user_info(
-        member=member,
-        prisma_client=mock_prisma_client,
-    )
-    
-    # Verify user_id was populated
-    assert result.user_email == "test@example.com"
-    assert result.user_id == "user-456"
-    
-    # Verify find_first was called with correct parameters
-    mock_prisma_client.db.litellm_usertable.find_first.assert_called_once_with(
-        where={"user_email": {"equals": "test@example.com", "mode": "insensitive"}}
-    )
-    
-    # Verify get_data was called to check for duplicates
-    mock_prisma_client.get_data.assert_called_once_with(
-        key_val={"user_email": "test@example.com"},
-        table_name="user",
-        query_type="find_all",
-    )
-
-
-@pytest.mark.asyncio
-async def test_validate_and_populate_member_user_info_only_user_id_not_found():
-    """
-    Test _validate_and_populate_member_user_info when only user_id is provided
-    but the user doesn't exist in the database. Should allow it to pass with
-    user_email as None (will be upserted later).
-    """
-    # Create member with only user_id
-    member = Member(user_email=None, user_id="nonexistent-user", role="user")
-    
-    # Mock prisma client
-    mock_prisma_client = MagicMock()
-    
-    # Mock find_unique to return None (user not found)
-    mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=None)
-    
-    # Call the function - should NOT raise an exception
-    result = await _validate_and_populate_member_user_info(
-        member=member,
-        prisma_client=mock_prisma_client,
-    )
-    
-    # Verify the result - should return member with user_id set and user_email as None
-    assert result.user_id == "nonexistent-user"
-    assert result.user_email is None
-    assert result.role == "user"
-    
-    # Verify find_unique was called with correct parameters
-    mock_prisma_client.db.litellm_usertable.find_unique.assert_called_once_with(
-        where={"user_id": "nonexistent-user"}
-    )
diff --git a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py
index 5e9078ea876..f983af2d0b2 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py
@@ -24,7 +24,6 @@
     GoogleSSOHandler,
     MicrosoftSSOHandler,
     SSOAuthenticationHandler,
-    normalize_email,
 )
 from litellm.types.proxy.management_endpoints.ui_sso import (
     DefaultTeamSSOParams,
@@ -668,85 +667,6 @@ def test_build_sso_user_update_data_without_role():
     assert "user_role" not in update_data
 
 
-def test_normalize_email():
-    """
-    Test that normalize_email correctly lowercases email addresses and handles edge cases.
-    """
-    # Test with lowercase email
-    assert normalize_email("test@example.com") == "test@example.com"
-    
-    # Test with uppercase email
-    assert normalize_email("TEST@EXAMPLE.COM") == "test@example.com"
-    
-    # Test with mixed case email
-    assert normalize_email("Test.User@Example.COM") == "test.user@example.com"
-    
-    # Test with None
-    assert normalize_email(None) is None
-    
-    # Test with empty string
-    assert normalize_email("") == ""
-
-
-def test_build_sso_user_update_data_normalizes_email():
-    """
-    Test that _build_sso_user_update_data normalizes email addresses to lowercase.
-    """
-    from litellm.proxy.management_endpoints.types import CustomOpenID
-    from litellm.proxy.management_endpoints.ui_sso import _build_sso_user_update_data
-
-    sso_result = CustomOpenID(
-        id="test-user-789",
-        email="Test.User@Example.COM",
-        display_name="Test User",
-        provider="microsoft",
-        team_ids=[],
-        user_role=None,
-    )
-
-    update_data = _build_sso_user_update_data(
-        result=sso_result,
-        user_email="Test.User@Example.COM",
-        user_id="test-user-789",
-    )
-
-    # Email should be normalized to lowercase
-    assert update_data["user_email"] == "test.user@example.com"
-    assert "user_role" not in update_data
-
-
-def test_generic_response_convertor_normalizes_email():
-    """
-    Test that generic_response_convertor normalizes email addresses.
-    """
-    from litellm.proxy.management_endpoints.ui_sso import generic_response_convertor
-
-    mock_response = {
-        "preferred_username": "user123",
-        "email": "Test.User@Example.COM",
-        "sub": "Test User",
-        "first_name": "Test",
-        "last_name": "User",
-        "provider": "generic",
-    }
-
-    # Mock JWT handler
-    mock_jwt_handler = MagicMock(spec=JWTHandler)
-    mock_jwt_handler.get_team_ids_from_jwt.return_value = []
-
-    result = generic_response_convertor(
-        response=mock_response,
-        jwt_handler=mock_jwt_handler,
-        sso_jwt_handler=None,
-        role_mappings=None,
-    )
-
-    # Email should be normalized to lowercase
-    assert result.email == "test.user@example.com"
-    assert result.id == "user123"
-    assert result.display_name == "Test User"
-
-
 @pytest.mark.asyncio
 async def test_upsert_sso_user_updates_role_for_existing_user():
     """
diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_passthrough_load_balancing.py b/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_passthrough_load_balancing.py
index d2fdb157c8d..28b3ba0a179 100644
--- a/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_passthrough_load_balancing.py
+++ b/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_passthrough_load_balancing.py
@@ -447,75 +447,3 @@ def test_forward_headers_from_request_x_pass_prefix():
     assert "x-pass-anthropic-beta" not in result
     assert "x-pass-custom-header" not in result
 
-
-@pytest.mark.asyncio
-async def test_vertex_passthrough_custom_model_name_replaced_in_url():
-    """
-    Test that when a passthrough URL contains a custom model_name (e.g., gcp/google/gemini-3-pro),
-    the URL is rewritten to use the actual Vertex AI model name (e.g., gemini-3-pro)
-    before being forwarded to Vertex AI.
-
-    This prevents 404 errors from Vertex AI when custom model names are used in the config.
-
-    Config example:
-        model_name: gcp/google/gemini-3-pro
-        litellm_params:
-          model: vertex_ai/gemini-3-pro
-          vertex_project: "my-project"
-          vertex_location: "global"
-          use_in_pass_through: true
-    """
-    mock_request = MagicMock()
-    mock_response = MagicMock()
-    mock_handler = MagicMock()
-
-    # Deployment with custom model_name but real vertex model
-    mock_deployment = {
-        "litellm_params": {
-            "model": "vertex_ai/gemini-3-pro",
-            "vertex_project": "nv-gcpllmgwit-20250411173346",
-            "vertex_location": "global",
-            "use_in_pass_through": True,
-        }
-    }
-    mock_router = MagicMock()
-    mock_router.get_available_deployment_for_pass_through.return_value = mock_deployment
-
-    # The URL contains project/location AND a custom model name with slashes
-    test_endpoint = "v1/projects/nv-gcpllmgwit-20250411173346/locations/global/publishers/google/models/gcp/google/gemini-3-pro:generateContent"
-
-    with patch("litellm.proxy.proxy_server.llm_router", mock_router), \
-         patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.passthrough_endpoint_router") as mock_pt_router, \
-         patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints._prepare_vertex_auth_headers", new_callable=AsyncMock) as mock_prep_headers, \
-         patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route") as mock_create_route, \
-         patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.user_api_key_auth", new_callable=AsyncMock) as mock_auth:
-
-        mock_pt_router.get_vertex_credentials.return_value = MagicMock()
-        mock_prep_headers.return_value = ({}, "https://global-aiplatform.googleapis.com", False, "nv-gcpllmgwit-20250411173346", "global")
-        mock_endpoint_func = AsyncMock()
-        mock_create_route.return_value = mock_endpoint_func
-        mock_auth.return_value = {}
-
-        mock_handler.get_default_base_target_url.return_value = "https://global-aiplatform.googleapis.com"
-
-        await _base_vertex_proxy_route(
-            endpoint=test_endpoint,
-            request=mock_request,
-            fastapi_response=mock_response,
-            get_vertex_pass_through_handler=mock_handler,
-        )
-
-        # Verify the router was called with the custom model name (extracted from URL)
-        mock_router.get_available_deployment_for_pass_through.assert_called_once_with(
-            model="gcp/google/gemini-3-pro"
-        )
-
-        # Verify the target URL passed to create_pass_through_route contains
-        # the REAL Vertex AI model name, not the custom one
-        create_route_call = mock_create_route.call_args
-        target_url = create_route_call.kwargs.get("target", "")
-        assert "gcp/google/gemini-3-pro" not in target_url, \
-            f"Custom model name should have been replaced in target URL. Got: {target_url}"
-        assert "gemini-3-pro" in target_url, \
-            f"Actual Vertex AI model name should be in target URL. Got: {target_url}"
-
diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py
index 13368d0a142..33e000143c8 100644
--- a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py
+++ b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py
@@ -1952,89 +1952,6 @@ async def mock_count(*args, **kwargs):
         assert metadata["error_information"]["error_code"] == "404"
 
 
-@pytest.mark.asyncio
-async def test_ui_view_spend_logs_with_error_message(client):
-    """Test filtering spend logs by error message"""
-    mock_spend_logs = [
-        {
-            "id": "log1",
-            "request_id": "req1",
-            "api_key": "sk-test-key",
-            "user": "test_user_1",
-            "team_id": "team1",
-            "spend": 0.05,
-            "startTime": datetime.datetime.now(timezone.utc).isoformat(),
-            "model": "gpt-3.5-turbo",
-            "metadata": '{"error_information": {"error_message": "Rate limit exceeded"}}',
-        },
-        {
-            "id": "log2",
-            "request_id": "req2",
-            "api_key": "sk-test-key",
-            "user": "test_user_2",
-            "team_id": "team1",
-            "spend": 0.10,
-            "startTime": datetime.datetime.now(timezone.utc).isoformat(),
-            "model": "gpt-4",
-            "metadata": '{"error_information": {"error_message": "Invalid API key"}}',
-        },
-    ]
-
-    with patch.object(ps, "prisma_client") as mock_prisma:
-        # Mock the find_many method to return filtered results
-        async def mock_find_many(*args, **kwargs):
-            where_conditions = kwargs.get("where", {})
-            if "metadata" in where_conditions:
-                metadata_filter = where_conditions["metadata"]
-                if metadata_filter.get("path") == ["error_information", "error_message"]:
-                    error_message_filter = metadata_filter.get("string_contains")
-                    # Check if the error message contains the filter string
-                    if error_message_filter == "Rate limit":
-                        return [mock_spend_logs[0]]
-                    elif error_message_filter == "Invalid API":
-                        return [mock_spend_logs[1]]
-            return mock_spend_logs
-
-        async def mock_count(*args, **kwargs):
-            where_conditions = kwargs.get("where", {})
-            if "metadata" in where_conditions:
-                metadata_filter = where_conditions["metadata"]
-                if metadata_filter.get("path") == ["error_information", "error_message"]:
-                    error_message_filter = metadata_filter.get("string_contains")
-                    if error_message_filter == "Rate limit":
-                        return 1
-                    elif error_message_filter == "Invalid API":
-                        return 1
-            return len(mock_spend_logs)
-
-        mock_prisma.db.litellm_spendlogs.find_many = mock_find_many
-        mock_prisma.db.litellm_spendlogs.count = mock_count
-
-        start_date = (
-            datetime.datetime.now(timezone.utc) - datetime.timedelta(days=7)
-        ).strftime("%Y-%m-%d %H:%M:%S")
-        end_date = datetime.datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
-
-        response = client.get(
-            "/spend/logs/ui",
-            params={
-                "error_message": "Rate limit",
-                "start_date": start_date,
-                "end_date": end_date,
-            },
-            headers={"Authorization": "Bearer sk-test"},
-        )
-
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total"] == 1
-        assert len(data["data"]) == 1
-        assert data["data"][0]["id"] == "log1"
-        metadata = json.loads(data["data"][0]["metadata"])
-        assert "error_information" in metadata
-        assert "Rate limit exceeded" in metadata["error_information"]["error_message"]
-
-
 @pytest.mark.asyncio
 async def test_ui_view_spend_logs_with_error_code_and_key_alias(client):
     """Test merging error_code and key_alias filters with AND logic"""
diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py b/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py
index 1972103c3d2..dd4cbfa2e7f 100644
--- a/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py
+++ b/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py
@@ -23,7 +23,6 @@
     _get_response_for_spend_logs_payload,
     _get_vector_store_request_for_spend_logs_payload,
     _sanitize_request_body_for_spend_logs_payload,
-    _should_store_prompts_and_responses_in_spend_logs,
     get_logging_payload,
 )
 from litellm.types.utils import (
@@ -912,48 +911,3 @@ def test_spend_logs_redacts_request_and_response_when_turn_off_message_logging_e
     parsed_response = json.loads(response_result)
     assert parsed_response == {"text": "redacted-by-litellm"}
 
-
-@patch("litellm.secret_managers.main.get_secret_bool")
-def test_should_store_prompts_and_responses_in_spend_logs_case_insensitive_string(
-    mock_get_secret_bool,
-):
-    """
-    Test that _should_store_prompts_and_responses_in_spend_logs handles
-    case-insensitive string values for store_prompts_in_spend_logs in general_settings.
-    """
-    # Test case-insensitive string "true" variations
-    for true_value in ["true", "TRUE", "True", "TrUe"]:
-        with patch("litellm.proxy.proxy_server.general_settings", {"store_prompts_in_spend_logs": true_value}):
-            mock_get_secret_bool.return_value = False  # Ensure env var is False
-            result = _should_store_prompts_and_responses_in_spend_logs()
-            assert result is True, f"Expected True for '{true_value}', got {result}"
-    
-    # Test boolean True
-    with patch("litellm.proxy.proxy_server.general_settings", {"store_prompts_in_spend_logs": True}):
-        mock_get_secret_bool.return_value = False
-        result = _should_store_prompts_and_responses_in_spend_logs()
-        assert result is True, f"Expected True for boolean True, got {result}"
-    
-    # Test that non-true values fall back to environment variable
-    for false_value in [False, None, "false", "FALSE", "False", "anything"]:
-        with patch("litellm.proxy.proxy_server.general_settings", {"store_prompts_in_spend_logs": false_value}):
-            # When env var is True, should return True
-            mock_get_secret_bool.return_value = True
-            result = _should_store_prompts_and_responses_in_spend_logs()
-            assert result is True, f"Expected True (from env var) for '{false_value}', got {result}"
-            
-            # When env var is False, should return False
-            mock_get_secret_bool.return_value = False
-            result = _should_store_prompts_and_responses_in_spend_logs()
-            assert result is False, f"Expected False (from env var) for '{false_value}', got {result}"
-    
-    # Test when general_settings doesn't have the key at all
-    with patch("litellm.proxy.proxy_server.general_settings", {}):
-        mock_get_secret_bool.return_value = True
-        result = _should_store_prompts_and_responses_in_spend_logs()
-        assert result is True, "Expected True (from env var) when key missing, got False"
-        
-        mock_get_secret_bool.return_value = False
-        result = _should_store_prompts_and_responses_in_spend_logs()
-        assert result is False, "Expected False (from env var) when key missing, got True"
-
diff --git a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py
index da6a5aeab09..b9485a2e4cb 100644
--- a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py
+++ b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py
@@ -1548,50 +1548,3 @@ def test_add_guardrails_from_policy_engine():
     policy_registry._initialized = False
     attachment_registry._attachments = []
     attachment_registry._initialized = False
-
-
-def test_add_guardrails_from_policy_engine_accepts_dynamic_policies_and_pops_from_data():
-    """
-    Test that add_guardrails_from_policy_engine accepts dynamic 'policies' from the request body
-    and removes them to prevent forwarding to the LLM provider.
-    
-    This is critical because 'policies' is a LiteLLM proxy-specific parameter that should
-    not be sent to the actual LLM API (e.g., OpenAI, Anthropic, etc.).
-    """
-    from litellm.proxy.policy_engine.policy_registry import get_policy_registry
-
-    # Setup test data with 'policies' in the request body
-    data = {
-        "model": "gpt-4",
-        "messages": [{"role": "user", "content": "Hello"}],
-        "policies": ["PII-POLICY-GLOBAL", "HIPAA-POLICY"],  # Dynamic policies - should be accepted and removed
-        "metadata": {},
-    }
-
-    user_api_key_dict = UserAPIKeyAuth(
-        api_key="test-key",
-        team_alias="test-team",
-        key_alias="test-key",
-    )
-
-    # Initialize empty policy registry (we're just testing the accept and pop behavior)
-    policy_registry = get_policy_registry()
-    policy_registry._policies = {}
-    policy_registry._initialized = False
-
-    # Call the function - should accept dynamic policies and not raise an error
-    add_guardrails_from_policy_engine(
-        data=data,
-        metadata_variable_name="metadata",
-        user_api_key_dict=user_api_key_dict,
-    )
-
-    # Verify that 'policies' was removed from the request body
-    assert "policies" not in data, "'policies' should be removed from request body to prevent forwarding to LLM provider"
-
-    # Verify that other fields are preserved
-    assert "model" in data
-    assert data["model"] == "gpt-4"
-    assert "messages" in data
-    assert data["messages"] == [{"role": "user", "content": "Hello"}]
-    assert "metadata" in data
diff --git a/tests/test_litellm/proxy/test_proxy_cli.py b/tests/test_litellm/proxy/test_proxy_cli.py
index 12065ad5b4d..f52f14b860e 100644
--- a/tests/test_litellm/proxy/test_proxy_cli.py
+++ b/tests/test_litellm/proxy/test_proxy_cli.py
@@ -103,11 +103,7 @@ def test_get_default_unvicorn_init_args(self):
             args = ProxyInitializationHelpers._get_default_unvicorn_init_args(
                 "localhost", 8000
             )
-            # When json_logs is True, log_config should be set to the JSON log config dict
-            assert args["log_config"] is not None
-            assert isinstance(args["log_config"], dict)
-            assert "version" in args["log_config"]
-            assert "formatters" in args["log_config"]
+            assert args["log_config"] is None
 
         # Test with keepalive_timeout
         args = ProxyInitializationHelpers._get_default_unvicorn_init_args(
@@ -222,7 +218,6 @@ def test_database_url_construction_with_special_characters(self):
     @patch("atexit.register")  # 🔥 critical
     def test_skip_server_startup(self, mock_atexit_register, mock_uvicorn_run):
         from click.testing import CliRunner
-
         from litellm.proxy.proxy_cli import run_server
 
         runner = CliRunner()
diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py
index d85dbb2e0f9..cb519e9f509 100644
--- a/tests/test_litellm/proxy/test_proxy_server.py
+++ b/tests/test_litellm/proxy/test_proxy_server.py
@@ -5,7 +5,7 @@
 import socket
 import subprocess
 import sys
-from datetime import datetime, timezone
+from datetime import datetime
 from pathlib import Path
 from unittest import mock
 from unittest.mock import AsyncMock, MagicMock, mock_open, patch
@@ -1049,82 +1049,6 @@ async def test_get_config_from_file(tmp_path, monkeypatch):
     assert result == test_config
 
 
-def test_normalize_datetime_for_sorting():
-    """
-    Test the _normalize_datetime_for_sorting function.
-    Tests various scenarios: None values, ISO format strings, datetime objects (naive and aware).
-    """
-    from litellm.proxy.proxy_server import _normalize_datetime_for_sorting
-
-    # Test Case 1: None value
-    assert _normalize_datetime_for_sorting(None) is None
-
-    # Test Case 2: ISO format string with 'Z' suffix
-    dt_str_z = "2024-01-15T10:30:00Z"
-    result = _normalize_datetime_for_sorting(dt_str_z)
-    assert result is not None
-    assert isinstance(result, datetime)
-    assert result.tzinfo == timezone.utc
-    assert result.year == 2024
-    assert result.month == 1
-    assert result.day == 15
-    assert result.hour == 10
-    assert result.minute == 30
-
-    # Test Case 3: ISO format string without 'Z' suffix (naive)
-    dt_str_naive = "2024-01-15T10:30:00"
-    result = _normalize_datetime_for_sorting(dt_str_naive)
-    assert result is not None
-    assert isinstance(result, datetime)
-    assert result.tzinfo == timezone.utc
-
-    # Test Case 4: ISO format string with timezone offset
-    dt_str_tz = "2024-01-15T10:30:00+05:00"
-    result = _normalize_datetime_for_sorting(dt_str_tz)
-    assert result is not None
-    assert isinstance(result, datetime)
-    assert result.tzinfo == timezone.utc
-    # Should convert from +05:00 to UTC (subtract 5 hours)
-    assert result.hour == 5  # 10:30 - 5 hours = 5:30 UTC
-
-    # Test Case 5: Naive datetime object
-    naive_dt = datetime(2024, 1, 15, 10, 30, 0)
-    result = _normalize_datetime_for_sorting(naive_dt)
-    assert result is not None
-    assert isinstance(result, datetime)
-    assert result.tzinfo == timezone.utc
-    assert result.year == 2024
-    assert result.month == 1
-    assert result.day == 15
-
-    # Test Case 6: Timezone-aware datetime object (non-UTC)
-    from datetime import timedelta
-    aware_dt = datetime(2024, 1, 15, 10, 30, 0, tzinfo=timezone(timedelta(hours=5)))
-    result = _normalize_datetime_for_sorting(aware_dt)
-    assert result is not None
-    assert isinstance(result, datetime)
-    assert result.tzinfo == timezone.utc
-    # Should convert from +05:00 to UTC
-    assert result.hour == 5
-
-    # Test Case 7: UTC-aware datetime object
-    utc_dt = datetime(2024, 1, 15, 10, 30, 0, tzinfo=timezone.utc)
-    result = _normalize_datetime_for_sorting(utc_dt)
-    assert result is not None
-    assert isinstance(result, datetime)
-    assert result.tzinfo == timezone.utc
-    assert result == utc_dt
-
-    # Test Case 8: Invalid string format
-    invalid_str = "not-a-date"
-    result = _normalize_datetime_for_sorting(invalid_str)
-    assert result is None
-
-    # Test Case 9: Invalid type (should return None)
-    result = _normalize_datetime_for_sorting(12345)
-    assert result is None
-
-
 @pytest.mark.asyncio
 async def test_add_proxy_budget_to_db_only_creates_user_no_keys():
     """
@@ -3519,1245 +3443,188 @@ async def test_model_info_v2_pagination_edge_cases(monkeypatch):
         app.dependency_overrides = original_overrides
 
 
-@pytest.mark.asyncio
-async def test_model_info_v2_search_config_models(monkeypatch):
+def test_enrich_model_info_with_litellm_data():
     """
-    Test search parameter for config models (models from config.yaml).
-    Config models don't have db_model=True in model_info.
+    Test the _enrich_model_info_with_litellm_data helper function.
+    Tests model info enrichment, debug mode, and sensitive info removal.
     """
-    from unittest.mock import AsyncMock, MagicMock
+    from unittest.mock import MagicMock, patch
 
-    from litellm.proxy._types import UserAPIKeyAuth
-    from litellm.proxy.proxy_server import app, proxy_config, user_api_key_auth
+    from litellm.proxy.proxy_server import _enrich_model_info_with_litellm_data
 
-    # Create mock config models (no db_model flag or db_model=False)
-    mock_config_models = [
-        {
-            "model_name": "gpt-4-turbo",
-            "litellm_params": {"model": "gpt-4-turbo"},
-            "model_info": {"id": "gpt-4-turbo"},  # No db_model flag = config model
-        },
-        {
-            "model_name": "gpt-3.5-turbo",
-            "litellm_params": {"model": "gpt-3.5-turbo"},
-            "model_info": {"id": "gpt-3.5-turbo", "db_model": False},  # Explicitly config model
-        },
-        {
-            "model_name": "claude-3-opus",
-            "litellm_params": {"model": "claude-3-opus"},
-            "model_info": {"id": "claude-3-opus"},  # No db_model flag = config model
-        },
-        {
-            "model_name": "gemini-pro",
-            "litellm_params": {"model": "gemini-pro"},
-            "model_info": {"id": "gemini-pro"},  # No db_model flag = config model
-        },
-    ]
+    # Test Case 1: Basic model enrichment without debug
+    model = {
+        "model_name": "test-model",
+        "litellm_params": {"model": "gpt-3.5-turbo"},
+        "model_info": {"id": "test-model"},
+        "api_key": "sk-secret-key",  # Should be removed
+    }
 
-    # Mock llm_router
-    mock_router = MagicMock()
-    mock_router.model_list = mock_config_models
+    with patch("litellm.proxy.proxy_server.get_litellm_model_info") as mock_get_info, patch(
+        "litellm.proxy.proxy_server.remove_sensitive_info_from_deployment"
+    ) as mock_remove_sensitive:
+        mock_get_info.return_value = {
+            "input_cost_per_token": 0.001,
+            "output_cost_per_token": 0.002,
+            "max_tokens": 4096,
+        }
+        mock_remove_sensitive.return_value = {
+            "model_name": "test-model",
+            "litellm_params": {"model": "gpt-3.5-turbo"},
+            "model_info": {
+                "id": "test-model",
+                "input_cost_per_token": 0.001,
+                "output_cost_per_token": 0.002,
+                "max_tokens": 4096,
+            },
+        }
 
-    # Mock prisma_client
-    mock_prisma_client = MagicMock()
+        result = _enrich_model_info_with_litellm_data(model=model, debug=False)
 
-    # Mock proxy_config.get_config
-    mock_get_config = AsyncMock(return_value={})
+        # Verify get_litellm_model_info was called
+        mock_get_info.assert_called_once_with(model=model)
+        # Verify remove_sensitive_info_from_deployment was called
+        mock_remove_sensitive.assert_called_once()
+        # Verify result doesn't have api_key
+        assert "api_key" not in result
+        # Verify model_info was enriched
+        assert "input_cost_per_token" in result["model_info"]
 
-    # Mock user authentication
-    mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth)
-    mock_user_api_key_dict.user_id = "test-user"
-    mock_user_api_key_dict.api_key = "test-key"
-    mock_user_api_key_dict.team_models = []
-    mock_user_api_key_dict.models = []
+    # Test Case 2: Model enrichment with debug mode
+    model_with_debug = {
+        "model_name": "test-model-debug",
+        "litellm_params": {"model": "gpt-4"},
+        "model_info": {},
+    }
 
-    # Apply monkeypatches
-    monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", mock_router)
-    monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client)
-    monkeypatch.setattr("litellm.proxy.proxy_server.user_model", None)
-    monkeypatch.setattr(proxy_config, "get_config", mock_get_config)
+    mock_router = MagicMock()
+    mock_client = MagicMock()
+    mock_router._get_client.return_value = mock_client
 
-    # Override auth dependency
-    original_overrides = app.dependency_overrides.copy()
-    app.dependency_overrides[user_api_key_auth] = lambda: mock_user_api_key_dict
+    with patch("litellm.proxy.proxy_server.get_litellm_model_info") as mock_get_info, patch(
+        "litellm.proxy.proxy_server.remove_sensitive_info_from_deployment"
+    ) as mock_remove_sensitive:
+        mock_get_info.return_value = {}
+        mock_remove_sensitive.return_value = {
+            "model_name": "test-model-debug",
+            "litellm_params": {"model": "gpt-4"},
+            "model_info": {},
+            "openai_client": str(mock_client),
+        }
 
-    client = TestClient(app)
-    try:
-        # Test search for "gpt" - should return gpt-4-turbo and gpt-3.5-turbo
-        response = client.get("/v2/model/info", params={"search": "gpt"})
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total_count"] == 2  # Only config models matching search
-        assert len(data["data"]) == 2
-        model_names = [m["model_name"] for m in data["data"]]
-        assert "gpt-4-turbo" in model_names
-        assert "gpt-3.5-turbo" in model_names
-        assert "claude-3-opus" not in model_names
-        assert "gemini-pro" not in model_names
-
-        # Test search for "claude" - should return claude-3-opus
-        response = client.get("/v2/model/info", params={"search": "claude"})
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total_count"] == 1
-        assert len(data["data"]) == 1
-        assert data["data"][0]["model_name"] == "claude-3-opus"
+        result = _enrich_model_info_with_litellm_data(
+            model=model_with_debug, debug=True, llm_router=mock_router
+        )
 
-        # Test case-insensitive search
-        response = client.get("/v2/model/info", params={"search": "GPT"})
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total_count"] == 2
-        assert len(data["data"]) == 2
+        # Verify debug info was added
+        mock_remove_sensitive.assert_called_once()
+        call_args = mock_remove_sensitive.call_args[0][0]
+        assert "openai_client" in call_args
+        # Verify router._get_client was called for debug
+        mock_router._get_client.assert_called_once()
 
-        # Test partial match
-        response = client.get("/v2/model/info", params={"search": "turbo"})
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total_count"] == 2
-        assert len(data["data"]) == 2
-        model_names = [m["model_name"] for m in data["data"]]
-        assert "gpt-4-turbo" in model_names
-        assert "gpt-3.5-turbo" in model_names
-
-        # Test search with no matches
-        response = client.get("/v2/model/info", params={"search": "nonexistent"})
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total_count"] == 0
-        assert len(data["data"]) == 0
+    # Test Case 3: Model with fallback to litellm.get_model_info
+    model_fallback = {
+        "model_name": "test-model-fallback",
+        "litellm_params": {"model": "claude-3-opus"},
+        "model_info": {},
+    }
 
-    finally:
-        app.dependency_overrides = original_overrides
+    with patch("litellm.proxy.proxy_server.get_litellm_model_info") as mock_get_info, patch(
+        "litellm.get_model_info"
+    ) as mock_litellm_info, patch(
+        "litellm.proxy.proxy_server.remove_sensitive_info_from_deployment"
+    ) as mock_remove_sensitive:
+        # First call returns empty, triggering fallback
+        mock_get_info.return_value = {}
+        mock_litellm_info.return_value = {
+            "input_cost_per_token": 0.015,
+            "output_cost_per_token": 0.075,
+            "max_tokens": 200000,
+        }
+        mock_remove_sensitive.return_value = {
+            "model_name": "test-model-fallback",
+            "litellm_params": {"model": "claude-3-opus"},
+            "model_info": {
+                "input_cost_per_token": 0.015,
+                "output_cost_per_token": 0.075,
+                "max_tokens": 200000,
+            },
+        }
 
+        result = _enrich_model_info_with_litellm_data(model=model_fallback, debug=False)
 
-@pytest.mark.asyncio
-async def test_model_info_v2_search_db_models(monkeypatch):
-    """
-    Test search parameter for db models (models from database).
-    DB models have db_model=True and id in model_info.
-    """
-    from unittest.mock import AsyncMock, MagicMock
+        # Verify fallback was attempted
+        mock_litellm_info.assert_called_once_with(model="claude-3-opus")
+        # Verify model_info was enriched with fallback data
+        call_args = mock_remove_sensitive.call_args[0][0]
+        assert call_args["model_info"]["input_cost_per_token"] == 0.015
 
-    from litellm.proxy._types import UserAPIKeyAuth
-    from litellm.proxy.proxy_server import app, proxy_config, user_api_key_auth
+    # Test Case 4: Model with split model name fallback
+    model_split = {
+        "model_name": "test-model-split",
+        "litellm_params": {"model": "azure/gpt-4"},
+        "model_info": {},
+    }
 
-    # Create mock db models (db_model=True with id)
-    mock_db_models_in_router = [
-        {
-            "model_name": "db-gpt-4",
-            "litellm_params": {"model": "gpt-4"},
-            "model_info": {"id": "db-model-1", "db_model": True},  # DB model
-        },
-        {
-            "model_name": "db-claude-3",
-            "litellm_params": {"model": "claude-3"},
-            "model_info": {"id": "db-model-2", "db_model": True},  # DB model
-        },
-    ]
+    with patch("litellm.proxy.proxy_server.get_litellm_model_info") as mock_get_info, patch(
+        "litellm.get_model_info"
+    ) as mock_litellm_info, patch(
+        "litellm.proxy.proxy_server.remove_sensitive_info_from_deployment"
+    ) as mock_remove_sensitive:
+        # Both first and second pass return empty, triggering third pass
+        mock_get_info.return_value = {}
+        # Second pass (no split)
+        mock_litellm_info.side_effect = [
+            {},  # First call returns empty
+            {"max_tokens": 8192},  # Third pass with split succeeds
+        ]
+        mock_remove_sensitive.return_value = {
+            "model_name": "test-model-split",
+            "litellm_params": {"model": "azure/gpt-4"},
+            "model_info": {"max_tokens": 8192},
+        }
 
-    # Mock llm_router
-    mock_router = MagicMock()
-    mock_router.model_list = mock_db_models_in_router
-
-    # Mock prisma_client with database query methods
-    mock_db_models_from_db = [
-        MagicMock(
-            model_id="db-model-3",
-            model_name="db-gemini-pro",
-            litellm_params='{"model": "gemini-pro"}',
-            model_info='{"id": "db-model-3", "db_model": true}',
-        ),
-        MagicMock(
-            model_id="db-model-4",
-            model_name="db-gpt-3.5",
-            litellm_params='{"model": "gpt-3.5-turbo"}',
-            model_info='{"id": "db-model-4", "db_model": true}',
-        ),
-    ]
+        result = _enrich_model_info_with_litellm_data(model=model_split, debug=False)
 
-    # Mock the database count and find_many methods dynamically based on search
-    async def mock_db_count_func(*args, **kwargs):
-        where_condition = kwargs.get("where", {})
-        search_term = where_condition.get("model_name", {}).get("contains", "")
-        excluded_ids = where_condition.get("model_id", {}).get("not", {}).get("in", [])
-        
-        # Count models matching search term but not in excluded_ids
-        count = 0
-        for model in mock_db_models_from_db:
-            if search_term.lower() in model.model_name.lower():
-                if model.model_id not in excluded_ids:
-                    count += 1
-        return count
-    
-    async def mock_db_find_many_func(*args, **kwargs):
-        where_condition = kwargs.get("where", {})
-        search_term = where_condition.get("model_name", {}).get("contains", "")
-        excluded_ids = where_condition.get("model_id", {}).get("not", {}).get("in", [])
-        take = kwargs.get("take", 10)
-        
-        # Return models matching search term but not in excluded_ids
-        result = []
-        for model in mock_db_models_from_db:
-            if search_term.lower() in model.model_name.lower():
-                if model.model_id not in excluded_ids:
-                    result.append(model)
-                    if len(result) >= take:
-                        break
-        return result
-    
-    mock_db_count = AsyncMock(side_effect=mock_db_count_func)
-    mock_db_find_many = AsyncMock(side_effect=mock_db_find_many_func)
+        # Verify third pass was attempted with split model name
+        assert mock_litellm_info.call_count == 2
+        # Check that second call used split model name
+        second_call = mock_litellm_info.call_args_list[1]
+        assert second_call[1]["model"] == "gpt-4"
+        assert second_call[1]["custom_llm_provider"] == "azure"
 
-    mock_prisma_client = MagicMock()
-    mock_prisma_client.db.litellm_proxymodeltable.count = mock_db_count
-    mock_prisma_client.db.litellm_proxymodeltable.find_many = mock_db_find_many
-
-    # Mock proxy_config.decrypt_model_list_from_db to return router-format models
-    def mock_decrypt_models(db_models_list):
-        result = []
-        for db_model in db_models_list:
-            result.append(
-                {
-                    "model_name": db_model.model_name,
-                    "litellm_params": {"model": db_model.model_name.replace("db-", "")},
-                    "model_info": {"id": db_model.model_id, "db_model": True},
-                }
-            )
-        return result
+    # Test Case 5: Model with existing model_info (should preserve existing keys)
+    model_existing = {
+        "model_name": "test-model-existing",
+        "litellm_params": {"model": "gpt-3.5-turbo"},
+        "model_info": {"id": "existing-id", "custom_key": "custom_value"},
+    }
 
-    # Mock proxy_config.get_config
-    mock_get_config = AsyncMock(return_value={})
+    with patch("litellm.proxy.proxy_server.get_litellm_model_info") as mock_get_info, patch(
+        "litellm.proxy.proxy_server.remove_sensitive_info_from_deployment"
+    ) as mock_remove_sensitive:
+        mock_get_info.return_value = {
+            "input_cost_per_token": 0.001,
+            "id": "new-id",  # Should not override existing "id"
+        }
+        mock_remove_sensitive.return_value = {
+            "model_name": "test-model-existing",
+            "litellm_params": {"model": "gpt-3.5-turbo"},
+            "model_info": {
+                "id": "existing-id",  # Existing key preserved
+                "custom_key": "custom_value",  # Existing key preserved
+                "input_cost_per_token": 0.001,  # New key added
+            },
+        }
 
-    # Mock user authentication
-    mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth)
-    mock_user_api_key_dict.user_id = "test-user"
-    mock_user_api_key_dict.api_key = "test-key"
-    mock_user_api_key_dict.team_models = []
-    mock_user_api_key_dict.models = []
+        result = _enrich_model_info_with_litellm_data(model=model_existing, debug=False)
 
-    # Apply monkeypatches
-    monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", mock_router)
-    monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client)
-    monkeypatch.setattr("litellm.proxy.proxy_server.user_model", None)
-    monkeypatch.setattr(proxy_config, "get_config", mock_get_config)
-    monkeypatch.setattr(proxy_config, "decrypt_model_list_from_db", mock_decrypt_models)
-
-    # Override auth dependency
-    original_overrides = app.dependency_overrides.copy()
-    app.dependency_overrides[user_api_key_auth] = lambda: mock_user_api_key_dict
-
-    client = TestClient(app)
-    try:
-        # Test search for "gpt" - should return db-gpt-4 from router and db-gpt-3.5 from db
-        response = client.get("/v2/model/info", params={"search": "gpt"})
-        assert response.status_code == 200
-        data = response.json()
-        # Should have db-gpt-4 from router + db-gpt-3.5 from db = 2 total
-        assert data["total_count"] == 2
-        assert len(data["data"]) == 2
-        model_names = [m["model_name"] for m in data["data"]]
-        assert "db-gpt-4" in model_names
-        assert "db-gpt-3.5" in model_names
-
-        # Verify database was queried
-        mock_db_count.assert_called()
-        # Verify the where condition excludes models already in router
-        call_args = mock_db_count.call_args
-        assert call_args is not None
-        where_condition = call_args[1]["where"]
-        assert "model_name" in where_condition
-        assert where_condition["model_name"]["contains"] == "gpt"
-        assert where_condition["model_name"]["mode"] == "insensitive"
-
-        # Test search for "claude" - should return db-claude-3 from router only
-        response = client.get("/v2/model/info", params={"search": "claude"})
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total_count"] == 1
-        assert len(data["data"]) == 1
-        assert data["data"][0]["model_name"] == "db-claude-3"
-
-        # Test search for "gemini" - should return db-gemini-pro from db only
-        response = client.get("/v2/model/info", params={"search": "gemini"})
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total_count"] == 1
-        assert len(data["data"]) == 1
-        assert data["data"][0]["model_name"] == "db-gemini-pro"
-
-        # Test case-insensitive search
-        response = client.get("/v2/model/info", params={"search": "GPT"})
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total_count"] == 2
-
-    finally:
-        app.dependency_overrides = original_overrides
-
-
-@pytest.mark.asyncio
-async def test_model_info_v2_filter_by_model_id(monkeypatch):
-    """
-    Test modelId parameter for filtering by specific model ID.
-    Tests that modelId searches in router config first, then database.
-    """
-    from unittest.mock import AsyncMock, MagicMock
-
-    from litellm.proxy._types import UserAPIKeyAuth
-    from litellm.proxy.proxy_server import app, proxy_config, user_api_key_auth
-
-    # Create mock config models
-    mock_config_models = [
-        {
-            "model_name": "gpt-4-turbo",
-            "litellm_params": {"model": "gpt-4-turbo"},
-            "model_info": {"id": "config-model-1"},
-        },
-        {
-            "model_name": "claude-3-opus",
-            "litellm_params": {"model": "claude-3-opus"},
-            "model_info": {"id": "config-model-2"},
-        },
-    ]
-
-    # Mock llm_router with get_model_info method
-    mock_router = MagicMock()
-    mock_router.model_list = mock_config_models
-    mock_router.get_model_info = MagicMock(
-        side_effect=lambda id: next(
-            (m for m in mock_config_models if m["model_info"]["id"] == id), None
-        )
-    )
-
-    # Mock prisma_client for database queries
-    mock_prisma_client = MagicMock()
-    mock_db_table = MagicMock()
-    mock_prisma_client.db.litellm_proxymodeltable = mock_db_table
-
-    # Mock database model
-    mock_db_model = MagicMock()
-    mock_db_model.model_id = "db-model-1"
-    mock_db_model.model_name = "db-gpt-3.5"
-    mock_db_model.litellm_params = '{"model": "gpt-3.5-turbo"}'
-    mock_db_model.model_info = '{"id": "db-model-1", "db_model": true}'
-
-    # Mock find_unique to return db model when searching for db-model-1
-    async def mock_find_unique(where):
-        if where.get("model_id") == "db-model-1":
-            return mock_db_model
-        return None
-
-    mock_db_table.find_unique = AsyncMock(side_effect=mock_find_unique)
-
-    # Mock proxy_config.decrypt_model_list_from_db
-    def mock_decrypt_models(db_models_list):
-        if db_models_list:
-            return [
-                {
-                    "model_name": db_models_list[0].model_name,
-                    "litellm_params": {"model": "gpt-3.5-turbo"},
-                    "model_info": {"id": db_models_list[0].model_id, "db_model": True},
-                }
-            ]
-        return []
-
-    # Mock proxy_config.get_config
-    mock_get_config = AsyncMock(return_value={})
-
-    # Mock user authentication
-    mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth)
-    mock_user_api_key_dict.user_id = "test-user"
-    mock_user_api_key_dict.api_key = "test-key"
-    mock_user_api_key_dict.team_models = []
-    mock_user_api_key_dict.models = []
-
-    # Apply monkeypatches
-    monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", mock_router)
-    monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client)
-    monkeypatch.setattr("litellm.proxy.proxy_server.user_model", None)
-    monkeypatch.setattr(proxy_config, "get_config", mock_get_config)
-    monkeypatch.setattr(proxy_config, "decrypt_model_list_from_db", mock_decrypt_models)
-
-    # Override auth dependency
-    original_overrides = app.dependency_overrides.copy()
-    app.dependency_overrides[user_api_key_auth] = lambda: mock_user_api_key_dict
-
-    client = TestClient(app)
-    try:
-        # Test Case 1: Filter by modelId that exists in config
-        response = client.get("/v2/model/info", params={"modelId": "config-model-1"})
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total_count"] == 1
-        assert len(data["data"]) == 1
-        assert data["data"][0]["model_info"]["id"] == "config-model-1"
-        assert data["data"][0]["model_name"] == "gpt-4-turbo"
-        # Verify router.get_model_info was called
-        mock_router.get_model_info.assert_called_with(id="config-model-1")
-
-        # Test Case 2: Filter by modelId that exists in database (not in config)
-        response = client.get("/v2/model/info", params={"modelId": "db-model-1"})
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total_count"] == 1
-        assert len(data["data"]) == 1
-        assert data["data"][0]["model_info"]["id"] == "db-model-1"
-        assert data["data"][0]["model_name"] == "db-gpt-3.5"
-        # Verify database was queried
-        mock_db_table.find_unique.assert_called()
-
-        # Test Case 3: Filter by modelId that doesn't exist
-        mock_db_table.find_unique = AsyncMock(return_value=None)
-        response = client.get("/v2/model/info", params={"modelId": "non-existent-model"})
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total_count"] == 0
-        assert len(data["data"]) == 0
-
-        # Test Case 4: Filter by modelId with search parameter (should filter further)
-        response = client.get(
-            "/v2/model/info", params={"modelId": "config-model-1", "search": "claude"}
-        )
-        assert response.status_code == 200
-        data = response.json()
-        # config-model-1 is gpt-4-turbo, doesn't match "claude", so should return empty
-        assert data["total_count"] == 0
-        assert len(data["data"]) == 0
-
-    finally:
-        app.dependency_overrides = original_overrides
-
-
-@pytest.mark.asyncio
-async def test_model_info_v2_filter_by_team_id(monkeypatch):
-    """
-    Test teamId parameter for filtering models by team ID.
-    Tests that teamId filters models based on direct_access or access_via_team_ids.
-    """
-    from unittest.mock import AsyncMock, MagicMock
-
-    from litellm.proxy._types import UserAPIKeyAuth, LiteLLM_TeamTable
-    from litellm.proxy.proxy_server import app, proxy_config, user_api_key_auth
-
-    # Create mock models with different access configurations
-    mock_models = [
-        {
-            "model_name": "model-direct-access",
-            "litellm_params": {"model": "gpt-4"},
-            "model_info": {
-                "id": "model-1",
-                "direct_access": True,  # Should be included
-            },
-        },
-        {
-            "model_name": "model-team-access",
-            "litellm_params": {"model": "claude-3"},
-            "model_info": {
-                "id": "model-2",
-                "direct_access": False,
-                "access_via_team_ids": ["team-123"],  # Should be included
-            },
-        },
-        {
-            "model_name": "model-no-access",
-            "litellm_params": {"model": "gemini-pro"},
-            "model_info": {
-                "id": "model-3",
-                "direct_access": False,
-                "access_via_team_ids": ["team-456"],  # Should NOT be included
-            },
-        },
-        {
-            "model_name": "model-multiple-teams",
-            "litellm_params": {"model": "gpt-3.5"},
-            "model_info": {
-                "id": "model-4",
-                "direct_access": False,
-                "access_via_team_ids": ["team-789", "team-123"],  # Should be included
-            },
-        },
-    ]
-
-    # Mock llm_router
-    mock_router = MagicMock()
-    mock_router.model_list = mock_models
-    
-    # Mock get_model_list to return models based on model_name filter
-    def mock_get_model_list(model_name=None, team_id=None):
-        if model_name:
-            return [m for m in mock_models if m["model_name"] == model_name]
-        return mock_models
-    
-    mock_router.get_model_list = MagicMock(side_effect=mock_get_model_list)
-
-    # Mock team database object - team has access to specific models
-    mock_team_db_object = MagicMock()
-    mock_team_db_object.model_dump.return_value = {
-        "team_id": "team-123",
-        "models": ["model-direct-access", "model-team-access", "model-multiple-teams"],  # Specific models
-    }
-
-    # Mock prisma_client
-    mock_prisma_client = MagicMock()
-    mock_team_table = MagicMock()
-    mock_prisma_client.db.litellm_teamtable = mock_team_table
-    mock_team_table.find_unique = AsyncMock(return_value=mock_team_db_object)
-
-    # Mock LiteLLM_TeamTable - team has access to specific models
-    mock_team_object = LiteLLM_TeamTable(
-        team_id="team-123",
-        models=["model-direct-access", "model-team-access", "model-multiple-teams"],
-    )
-
-    # Mock proxy_config.get_config
-    mock_get_config = AsyncMock(return_value={})
-
-    # Mock user authentication
-    mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth)
-    mock_user_api_key_dict.user_id = "test-user"
-    mock_user_api_key_dict.api_key = "test-key"
-    mock_user_api_key_dict.team_models = []
-    mock_user_api_key_dict.models = []
-
-    # Apply monkeypatches
-    monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", mock_router)
-    monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client)
-    monkeypatch.setattr("litellm.proxy.proxy_server.user_model", None)
-    monkeypatch.setattr(proxy_config, "get_config", mock_get_config)
-    # Mock LiteLLM_TeamTable instantiation
-    monkeypatch.setattr(
-        "litellm.proxy.proxy_server.LiteLLM_TeamTable",
-        lambda **kwargs: mock_team_object,
-    )
-
-    # Override auth dependency
-    original_overrides = app.dependency_overrides.copy()
-    app.dependency_overrides[user_api_key_auth] = lambda: mock_user_api_key_dict
-
-    client = TestClient(app)
-    try:
-        # Test Case 1: Filter by teamId - should return models with direct_access=True or team-123 in access_via_team_ids
-        response = client.get("/v2/model/info", params={"teamId": "team-123"})
-        assert response.status_code == 200
-        data = response.json()
-        # Should include: model-1 (direct_access), model-2 (team-123 in access_via_team_ids), model-4 (team-123 in access_via_team_ids)
-        # Should NOT include: model-3 (team-456 only)
-        assert data["total_count"] == 3
-        assert len(data["data"]) == 3
-        model_ids = [m["model_info"]["id"] for m in data["data"]]
-        assert "model-1" in model_ids  # direct_access
-        assert "model-2" in model_ids  # team-123 in access_via_team_ids
-        assert "model-4" in model_ids  # team-123 in access_via_team_ids
-        assert "model-3" not in model_ids  # Should be excluded
-
-        # Test Case 2: Filter by teamId that doesn't exist - should return empty list
-        mock_team_table.find_unique = AsyncMock(return_value=None)
-        response = client.get("/v2/model/info", params={"teamId": "non-existent-team"})
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total_count"] == 0
-        assert len(data["data"]) == 0
-
-        # Test Case 3: Filter by different teamId - should only return models with that team in access_via_team_ids
-        mock_team_db_object_456 = MagicMock()
-        mock_team_db_object_456.model_dump.return_value = {
-            "team_id": "team-456",
-            "models": ["model-no-access"],  # Team has access to model-no-access
-        }
-        mock_team_table.find_unique = AsyncMock(return_value=mock_team_db_object_456)
-        mock_team_object_456 = LiteLLM_TeamTable(
-            team_id="team-456",
-            models=["model-no-access"],
-        )
-        monkeypatch.setattr(
-            "litellm.proxy.proxy_server.LiteLLM_TeamTable",
-            lambda **kwargs: mock_team_object_456,
-        )
-
-        response = client.get("/v2/model/info", params={"teamId": "team-456"})
-        assert response.status_code == 200
-        data = response.json()
-        # Should include: model-1 (direct_access), model-3 (team-456 in access_via_team_ids)
-        # Should NOT include: model-2 (team-123 only), model-4 (team-789 and team-123, but not team-456)
-        assert data["total_count"] >= 2
-        model_ids = [m["model_info"]["id"] for m in data["data"]]
-        assert "model-1" in model_ids  # direct_access
-        assert "model-3" in model_ids  # team-456 in access_via_team_ids
-
-    finally:
-        app.dependency_overrides = original_overrides
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize(
-    "sort_by,sort_order,expected_order",
-    [
-        # Test model_name sorting
-        ("model_name", "asc", ["a-model", "b-model", "z-model"]),
-        ("model_name", "desc", ["z-model", "b-model", "a-model"]),
-        # Test created_at sorting
-        ("created_at", "asc", ["old-model", "mid-model", "new-model"]),
-        ("created_at", "desc", ["new-model", "mid-model", "old-model"]),
-        # Test updated_at sorting
-        ("updated_at", "asc", ["old-updated", "mid-updated", "new-updated"]),
-        ("updated_at", "desc", ["new-updated", "mid-updated", "old-updated"]),
-        # Test costs sorting
-        ("costs", "asc", ["low-cost", "mid-cost", "high-cost"]),
-        ("costs", "desc", ["high-cost", "mid-cost", "low-cost"]),
-        # Test status sorting (False/config models come before True/db models in asc)
-        ("status", "asc", ["config-model-1", "config-model-2", "db-model"]),
-        ("status", "desc", ["db-model", "config-model-1", "config-model-2"]),
-    ],
-)
-async def test_model_info_v2_sorting(monkeypatch, sort_by, sort_order, expected_order):
-    """
-    Test sorting functionality for /v2/model/info endpoint.
-    Tests all sortBy fields (model_name, created_at, updated_at, costs, status)
-    with both asc and desc sort orders.
-    """
-    from datetime import datetime, timedelta
-    from unittest.mock import AsyncMock, MagicMock
-
-    from litellm.proxy._types import UserAPIKeyAuth
-    from litellm.proxy.proxy_server import app, proxy_config, user_api_key_auth
-
-    # Create base time for date comparisons
-    base_time = datetime(2024, 1, 1, 12, 0, 0)
-
-    # Create mock models with different values for each sort field
-    mock_models = []
-
-    if sort_by == "model_name":
-        # Models with different names
-        mock_models = [
-            {
-                "model_name": "z-model",
-                "litellm_params": {"model": "z-model"},
-                "model_info": {"id": "z-model"},
-            },
-            {
-                "model_name": "a-model",
-                "litellm_params": {"model": "a-model"},
-                "model_info": {"id": "a-model"},
-            },
-            {
-                "model_name": "b-model",
-                "litellm_params": {"model": "b-model"},
-                "model_info": {"id": "b-model"},
-            },
-        ]
-    elif sort_by == "created_at":
-        # Models with different created_at timestamps
-        mock_models = [
-            {
-                "model_name": "new-model",
-                "litellm_params": {"model": "new-model"},
-                "model_info": {
-                    "id": "new-model",
-                    "created_at": (base_time + timedelta(days=3)).isoformat(),
-                },
-            },
-            {
-                "model_name": "old-model",
-                "litellm_params": {"model": "old-model"},
-                "model_info": {
-                    "id": "old-model",
-                    "created_at": (base_time - timedelta(days=3)).isoformat(),
-                },
-            },
-            {
-                "model_name": "mid-model",
-                "litellm_params": {"model": "mid-model"},
-                "model_info": {
-                    "id": "mid-model",
-                    "created_at": base_time.isoformat(),
-                },
-            },
-        ]
-    elif sort_by == "updated_at":
-        # Models with different updated_at timestamps
-        mock_models = [
-            {
-                "model_name": "new-updated",
-                "litellm_params": {"model": "new-updated"},
-                "model_info": {
-                    "id": "new-updated",
-                    "updated_at": (base_time + timedelta(days=3)).isoformat(),
-                },
-            },
-            {
-                "model_name": "old-updated",
-                "litellm_params": {"model": "old-updated"},
-                "model_info": {
-                    "id": "old-updated",
-                    "updated_at": (base_time - timedelta(days=3)).isoformat(),
-                },
-            },
-            {
-                "model_name": "mid-updated",
-                "litellm_params": {"model": "mid-updated"},
-                "model_info": {
-                    "id": "mid-updated",
-                    "updated_at": base_time.isoformat(),
-                },
-            },
-        ]
-    elif sort_by == "costs":
-        # Models with different costs (input_cost + output_cost)
-        mock_models = [
-            {
-                "model_name": "high-cost",
-                "litellm_params": {"model": "high-cost"},
-                "model_info": {
-                    "id": "high-cost",
-                    "input_cost_per_token": 0.00005,
-                    "output_cost_per_token": 0.00015,
-                },
-            },
-            {
-                "model_name": "low-cost",
-                "litellm_params": {"model": "low-cost"},
-                "model_info": {
-                    "id": "low-cost",
-                    "input_cost_per_token": 0.00001,
-                    "output_cost_per_token": 0.00003,
-                },
-            },
-            {
-                "model_name": "mid-cost",
-                "litellm_params": {"model": "mid-cost"},
-                "model_info": {
-                    "id": "mid-cost",
-                    "input_cost_per_token": 0.00003,
-                    "output_cost_per_token": 0.00007,
-                },
-            },
-        ]
-    elif sort_by == "status":
-        # Models with different db_model status (False = config, True = db)
-        mock_models = [
-            {
-                "model_name": "db-model",
-                "litellm_params": {"model": "db-model"},
-                "model_info": {"id": "db-model", "db_model": True},
-            },
-            {
-                "model_name": "config-model-1",
-                "litellm_params": {"model": "config-model-1"},
-                "model_info": {"id": "config-model-1", "db_model": False},
-            },
-            {
-                "model_name": "config-model-2",
-                "litellm_params": {"model": "config-model-2"},
-                "model_info": {"id": "config-model-2", "db_model": False},
-            },
-        ]
-
-    # Mock llm_router
-    mock_router = MagicMock()
-    mock_router.model_list = mock_models
-
-    # Mock prisma_client
-    mock_prisma_client = MagicMock()
-
-    # Mock proxy_config.get_config
-    mock_get_config = AsyncMock(return_value={})
-
-    # Mock user authentication
-    mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth)
-    mock_user_api_key_dict.user_id = "test-user"
-    mock_user_api_key_dict.api_key = "test-key"
-    mock_user_api_key_dict.team_models = []
-    mock_user_api_key_dict.models = []
-
-    # Apply monkeypatches
-    monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", mock_router)
-    monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client)
-    monkeypatch.setattr("litellm.proxy.proxy_server.user_model", None)
-    monkeypatch.setattr(proxy_config, "get_config", mock_get_config)
-
-    # Override auth dependency
-    original_overrides = app.dependency_overrides.copy()
-    app.dependency_overrides[user_api_key_auth] = lambda: mock_user_api_key_dict
-
-    client = TestClient(app)
-    try:
-        # Test sorting with specified sortBy and sortOrder
-        response = client.get(
-            "/v2/model/info", params={"sortBy": sort_by, "sortOrder": sort_order}
-        )
-        assert response.status_code == 200
-        data = response.json()
-        assert len(data["data"]) == len(expected_order)
-
-        # Verify models are in expected order
-        actual_order = [m["model_name"] for m in data["data"]]
-        assert actual_order == expected_order, (
-            f"Sorting failed for sortBy={sort_by}, sortOrder={sort_order}. "
-            f"Expected: {expected_order}, Got: {actual_order}"
-        )
-
-    finally:
-        app.dependency_overrides = original_overrides
-
-
-@pytest.mark.asyncio
-async def test_model_info_v2_sorting_invalid_sort_order(monkeypatch):
-    """
-    Test that invalid sortOrder values return a 400 error.
-    """
-    from unittest.mock import AsyncMock, MagicMock
-
-    from litellm.proxy._types import UserAPIKeyAuth
-    from litellm.proxy.proxy_server import app, proxy_config, user_api_key_auth
-
-    # Create mock models
-    mock_models = [
-        {
-            "model_name": "test-model",
-            "litellm_params": {"model": "test-model"},
-            "model_info": {"id": "test-model"},
-        }
-    ]
-
-    # Mock llm_router
-    mock_router = MagicMock()
-    mock_router.model_list = mock_models
-
-    # Mock prisma_client
-    mock_prisma_client = MagicMock()
-
-    # Mock proxy_config.get_config
-    mock_get_config = AsyncMock(return_value={})
-
-    # Mock user authentication
-    mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth)
-    mock_user_api_key_dict.user_id = "test-user"
-    mock_user_api_key_dict.api_key = "test-key"
-    mock_user_api_key_dict.team_models = []
-    mock_user_api_key_dict.models = []
-
-    # Apply monkeypatches
-    monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", mock_router)
-    monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client)
-    monkeypatch.setattr("litellm.proxy.proxy_server.user_model", None)
-    monkeypatch.setattr(proxy_config, "get_config", mock_get_config)
-
-    # Override auth dependency
-    original_overrides = app.dependency_overrides.copy()
-    app.dependency_overrides[user_api_key_auth] = lambda: mock_user_api_key_dict
-
-    client = TestClient(app)
-    try:
-        # Test invalid sortOrder
-        response = client.get(
-            "/v2/model/info", params={"sortBy": "model_name", "sortOrder": "invalid"}
-        )
-        assert response.status_code == 400
-        data = response.json()
-        assert "Invalid sortOrder" in data["detail"]
-
-    finally:
-        app.dependency_overrides = original_overrides
-
-
-@pytest.mark.asyncio
-async def test_apply_search_filter_to_models(monkeypatch):
-    """
-    Test the _apply_search_filter_to_models helper function.
-    Tests search filtering logic for config models, db models, and database queries.
-    """
-    from unittest.mock import AsyncMock, MagicMock
-
-    from litellm.proxy.proxy_server import _apply_search_filter_to_models, proxy_config
-
-    # Create mock models with mix of config and db models
-    mock_models = [
-        {
-            "model_name": "gpt-4-turbo",
-            "model_info": {"id": "gpt-4-turbo"},  # Config model
-        },
-        {
-            "model_name": "db-gpt-3.5",
-            "model_info": {"id": "db-model-1", "db_model": True},  # DB model in router
-        },
-        {
-            "model_name": "claude-3-opus",
-            "model_info": {"id": "claude-3-opus"},  # Config model
-        },
-    ]
-
-    # Mock prisma_client
-    mock_prisma_client = MagicMock()
-    mock_db_table = MagicMock()
-    mock_prisma_client.db.litellm_proxymodeltable = mock_db_table
-
-    # Mock database models
-    mock_db_model_1 = MagicMock(
-        model_id="db-model-2",
-        model_name="db-gemini-pro",
-        litellm_params='{"model": "gemini-pro"}',
-        model_info='{"id": "db-model-2", "db_model": true}',
-    )
-
-    # Mock proxy_config.decrypt_model_list_from_db
-    mock_decrypt = MagicMock(return_value=[{"model_name": "db-gemini-pro", "model_info": {"id": "db-model-2", "db_model": True}}])
-
-    monkeypatch.setattr(proxy_config, "decrypt_model_list_from_db", mock_decrypt)
-
-    # Test Case 1: No search term - should return all models unchanged
-    result_models, total_count = await _apply_search_filter_to_models(
-        all_models=mock_models.copy(),
-        search="",
-        page=1,
-        size=50,
-        prisma_client=mock_prisma_client,
-        proxy_config=proxy_config,
-    )
-    assert result_models == mock_models
-    assert total_count is None
-
-    # Test Case 2: Search for "gpt" - should filter router models and query DB
-    mock_db_table.count = AsyncMock(return_value=0)
-    mock_db_table.find_many = AsyncMock(return_value=[])
-
-    result_models, total_count = await _apply_search_filter_to_models(
-        all_models=mock_models.copy(),
-        search="gpt",
-        page=1,
-        size=50,
-        prisma_client=mock_prisma_client,
-        proxy_config=proxy_config,
-    )
-    assert len(result_models) == 2
-    model_names = [m["model_name"] for m in result_models]
-    assert "gpt-4-turbo" in model_names
-    assert "db-gpt-3.5" in model_names
-    assert "claude-3-opus" not in model_names
-    assert total_count == 2  # Only router models match
-
-    # Test Case 3: Search with DB models matching
-    mock_db_table.count = AsyncMock(return_value=1)
-    mock_db_table.find_many = AsyncMock(return_value=[mock_db_model_1])
-
-    result_models, total_count = await _apply_search_filter_to_models(
-        all_models=mock_models.copy(),
-        search="gemini",
-        page=1,
-        size=50,
-        prisma_client=mock_prisma_client,
-        proxy_config=proxy_config,
-    )
-    assert total_count == 1  # Router models (0) + DB models (1)
-    assert len(result_models) == 1
-    assert result_models[0]["model_name"] == "db-gemini-pro"
-
-    # Test Case 4: Case-insensitive search
-    # Reset mocks - no DB models should match "GPT"
-    mock_db_table.count = AsyncMock(return_value=0)
-    mock_db_table.find_many = AsyncMock(return_value=[])
-    
-    result_models, total_count = await _apply_search_filter_to_models(
-        all_models=mock_models.copy(),
-        search="GPT",
-        page=1,
-        size=50,
-        prisma_client=mock_prisma_client,
-        proxy_config=proxy_config,
-    )
-    assert len(result_models) == 2
-    model_names = [m["model_name"] for m in result_models]
-    assert "gpt-4-turbo" in model_names
-    assert "db-gpt-3.5" in model_names
-
-    # Test Case 5: Database query error - should fallback to router models count
-    mock_db_table.count = AsyncMock(side_effect=Exception("DB error"))
-    mock_db_table.find_many = AsyncMock(return_value=[])
-
-    result_models, total_count = await _apply_search_filter_to_models(
-        all_models=mock_models.copy(),
-        search="gpt",
-        page=1,
-        size=50,
-        prisma_client=mock_prisma_client,
-        proxy_config=proxy_config,
-    )
-    # Should still return filtered router models
-    assert len(result_models) == 2
-    assert total_count == 2  # Fallback to router models count
-
-
-def test_paginate_models_response():
-    """
-    Test the _paginate_models_response helper function.
-    Tests pagination calculation and response formatting.
-    """
-    from litellm.proxy.proxy_server import _paginate_models_response
-
-    # Create mock models
-    mock_models = [
-        {"model_name": f"model-{i}", "model_info": {"id": f"model-{i}"}}
-        for i in range(25)
-    ]
-
-    # Test Case 1: Basic pagination - first page
-    result = _paginate_models_response(
-        all_models=mock_models,
-        page=1,
-        size=10,
-        total_count=None,
-        search=None,
-    )
-    assert result["total_count"] == 25
-    assert result["current_page"] == 1
-    assert result["total_pages"] == 3  # ceil(25/10) = 3
-    assert result["size"] == 10
-    assert len(result["data"]) == 10
-    assert result["data"][0]["model_name"] == "model-0"
-
-    # Test Case 2: Second page
-    result = _paginate_models_response(
-        all_models=mock_models,
-        page=2,
-        size=10,
-        total_count=None,
-        search=None,
-    )
-    assert result["current_page"] == 2
-    assert len(result["data"]) == 10
-    assert result["data"][0]["model_name"] == "model-10"
-
-    # Test Case 3: Last page (partial)
-    result = _paginate_models_response(
-        all_models=mock_models,
-        page=3,
-        size=10,
-        total_count=None,
-        search=None,
-    )
-    assert result["current_page"] == 3
-    assert len(result["data"]) == 5  # Only 5 models left
-    assert result["data"][0]["model_name"] == "model-20"
-
-    # Test Case 4: With explicit total_count (for search scenarios)
-    result = _paginate_models_response(
-        all_models=mock_models[:10],  # Only 10 models in list
-        page=1,
-        size=10,
-        total_count=50,  # But total_count says 50
-        search="test",
-    )
-    assert result["total_count"] == 50
-    assert result["total_pages"] == 5  # ceil(50/10) = 5
-    assert len(result["data"]) == 10
-
-    # Test Case 5: Empty models list
-    result = _paginate_models_response(
-        all_models=[],
-        page=1,
-        size=10,
-        total_count=0,
-        search=None,
-    )
-    assert result["total_count"] == 0
-    assert result["total_pages"] == 0
-    assert len(result["data"]) == 0
-
-    # Test Case 6: Page beyond available data
-    result = _paginate_models_response(
-        all_models=mock_models[:10],
-        page=5,
-        size=10,
-        total_count=10,
-        search=None,
-    )
-    assert result["current_page"] == 5
-    assert len(result["data"]) == 0  # No data for page 5
-
-
-def test_enrich_model_info_with_litellm_data():
-    """
-    Test the _enrich_model_info_with_litellm_data helper function.
-    Tests model info enrichment, debug mode, and sensitive info removal.
-    """
-    from unittest.mock import MagicMock, patch
-
-    from litellm.proxy.proxy_server import _enrich_model_info_with_litellm_data
-
-    # Test Case 1: Basic model enrichment without debug
-    model = {
-        "model_name": "test-model",
-        "litellm_params": {"model": "gpt-3.5-turbo"},
-        "model_info": {"id": "test-model"},
-        "api_key": "sk-secret-key",  # Should be removed
-    }
-
-    with patch("litellm.proxy.proxy_server.get_litellm_model_info") as mock_get_info, patch(
-        "litellm.proxy.proxy_server.remove_sensitive_info_from_deployment"
-    ) as mock_remove_sensitive:
-        mock_get_info.return_value = {
-            "input_cost_per_token": 0.001,
-            "output_cost_per_token": 0.002,
-            "max_tokens": 4096,
-        }
-        mock_remove_sensitive.return_value = {
-            "model_name": "test-model",
-            "litellm_params": {"model": "gpt-3.5-turbo"},
-            "model_info": {
-                "id": "test-model",
-                "input_cost_per_token": 0.001,
-                "output_cost_per_token": 0.002,
-                "max_tokens": 4096,
-            },
-        }
-
-        result = _enrich_model_info_with_litellm_data(model=model, debug=False)
-
-        # Verify get_litellm_model_info was called
-        mock_get_info.assert_called_once_with(model=model)
-        # Verify remove_sensitive_info_from_deployment was called
-        mock_remove_sensitive.assert_called_once()
-        # Verify result doesn't have api_key
-        assert "api_key" not in result
-        # Verify model_info was enriched
-        assert "input_cost_per_token" in result["model_info"]
-
-    # Test Case 2: Model enrichment with debug mode
-    model_with_debug = {
-        "model_name": "test-model-debug",
-        "litellm_params": {"model": "gpt-4"},
-        "model_info": {},
-    }
-
-    mock_router = MagicMock()
-    mock_client = MagicMock()
-    mock_router._get_client.return_value = mock_client
-
-    with patch("litellm.proxy.proxy_server.get_litellm_model_info") as mock_get_info, patch(
-        "litellm.proxy.proxy_server.remove_sensitive_info_from_deployment"
-    ) as mock_remove_sensitive:
-        mock_get_info.return_value = {}
-        mock_remove_sensitive.return_value = {
-            "model_name": "test-model-debug",
-            "litellm_params": {"model": "gpt-4"},
-            "model_info": {},
-            "openai_client": str(mock_client),
-        }
-
-        result = _enrich_model_info_with_litellm_data(
-            model=model_with_debug, debug=True, llm_router=mock_router
-        )
-
-        # Verify debug info was added
-        mock_remove_sensitive.assert_called_once()
-        call_args = mock_remove_sensitive.call_args[0][0]
-        assert "openai_client" in call_args
-        # Verify router._get_client was called for debug
-        mock_router._get_client.assert_called_once()
-
-    # Test Case 3: Model with fallback to litellm.get_model_info
-    model_fallback = {
-        "model_name": "test-model-fallback",
-        "litellm_params": {"model": "claude-3-opus"},
-        "model_info": {},
-    }
-
-    with patch("litellm.proxy.proxy_server.get_litellm_model_info") as mock_get_info, patch(
-        "litellm.get_model_info"
-    ) as mock_litellm_info, patch(
-        "litellm.proxy.proxy_server.remove_sensitive_info_from_deployment"
-    ) as mock_remove_sensitive:
-        # First call returns empty, triggering fallback
-        mock_get_info.return_value = {}
-        mock_litellm_info.return_value = {
-            "input_cost_per_token": 0.015,
-            "output_cost_per_token": 0.075,
-            "max_tokens": 200000,
-        }
-        mock_remove_sensitive.return_value = {
-            "model_name": "test-model-fallback",
-            "litellm_params": {"model": "claude-3-opus"},
-            "model_info": {
-                "input_cost_per_token": 0.015,
-                "output_cost_per_token": 0.075,
-                "max_tokens": 200000,
-            },
-        }
-
-        result = _enrich_model_info_with_litellm_data(model=model_fallback, debug=False)
-
-        # Verify fallback was attempted
-        mock_litellm_info.assert_called_once_with(model="claude-3-opus")
-        # Verify model_info was enriched with fallback data
-        call_args = mock_remove_sensitive.call_args[0][0]
-        assert call_args["model_info"]["input_cost_per_token"] == 0.015
-
-    # Test Case 4: Model with split model name fallback
-    model_split = {
-        "model_name": "test-model-split",
-        "litellm_params": {"model": "azure/gpt-4"},
-        "model_info": {},
-    }
-
-    with patch("litellm.proxy.proxy_server.get_litellm_model_info") as mock_get_info, patch(
-        "litellm.get_model_info"
-    ) as mock_litellm_info, patch(
-        "litellm.proxy.proxy_server.remove_sensitive_info_from_deployment"
-    ) as mock_remove_sensitive:
-        # Both first and second pass return empty, triggering third pass
-        mock_get_info.return_value = {}
-        # Second pass (no split)
-        mock_litellm_info.side_effect = [
-            {},  # First call returns empty
-            {"max_tokens": 8192},  # Third pass with split succeeds
-        ]
-        mock_remove_sensitive.return_value = {
-            "model_name": "test-model-split",
-            "litellm_params": {"model": "azure/gpt-4"},
-            "model_info": {"max_tokens": 8192},
-        }
-
-        result = _enrich_model_info_with_litellm_data(model=model_split, debug=False)
-
-        # Verify third pass was attempted with split model name
-        assert mock_litellm_info.call_count == 2
-        # Check that second call used split model name
-        second_call = mock_litellm_info.call_args_list[1]
-        assert second_call[1]["model"] == "gpt-4"
-        assert second_call[1]["custom_llm_provider"] == "azure"
-
-    # Test Case 5: Model with existing model_info (should preserve existing keys)
-    model_existing = {
-        "model_name": "test-model-existing",
-        "litellm_params": {"model": "gpt-3.5-turbo"},
-        "model_info": {"id": "existing-id", "custom_key": "custom_value"},
-    }
-
-    with patch("litellm.proxy.proxy_server.get_litellm_model_info") as mock_get_info, patch(
-        "litellm.proxy.proxy_server.remove_sensitive_info_from_deployment"
-    ) as mock_remove_sensitive:
-        mock_get_info.return_value = {
-            "input_cost_per_token": 0.001,
-            "id": "new-id",  # Should not override existing "id"
-        }
-        mock_remove_sensitive.return_value = {
-            "model_name": "test-model-existing",
-            "litellm_params": {"model": "gpt-3.5-turbo"},
-            "model_info": {
-                "id": "existing-id",  # Existing key preserved
-                "custom_key": "custom_value",  # Existing key preserved
-                "input_cost_per_token": 0.001,  # New key added
-            },
-        }
-
-        result = _enrich_model_info_with_litellm_data(model=model_existing, debug=False)
-
-        # Verify existing keys are preserved
-        call_args = mock_remove_sensitive.call_args[0][0]
-        assert call_args["model_info"]["id"] == "existing-id"
-        assert call_args["model_info"]["custom_key"] == "custom_value"
-        assert call_args["model_info"]["input_cost_per_token"] == 0.001
+        # Verify existing keys are preserved
+        call_args = mock_remove_sensitive.call_args[0][0]
+        assert call_args["model_info"]["id"] == "existing-id"
+        assert call_args["model_info"]["custom_key"] == "custom_value"
+        assert call_args["model_info"]["input_cost_per_token"] == 0.001
 
 
 @pytest.mark.asyncio
@@ -5197,197 +4064,3 @@ def mock_create_model_info_response(model_id, provider, include_metadata=False,
     # Verify router methods were NOT called (normal path)
     mock_router.get_model_names.assert_not_called()
     mock_router.get_model_access_groups.assert_not_called()
-
-
-@pytest.mark.asyncio
-async def test_update_general_settings_store_prompts_in_spend_logs(monkeypatch):
-    """
-    Test that _update_general_settings correctly normalizes store_prompts_in_spend_logs
-    values (handles bool, string, None, and other types).
-    """
-    from unittest.mock import patch
-
-    from litellm.proxy.proxy_server import ProxyConfig
-
-    proxy_config = ProxyConfig()
-
-    # Test Case 1: None value
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs):
-        await proxy_config._update_general_settings(
-            {"store_prompts_in_spend_logs": None}
-        )
-        assert mock_gs.get("store_prompts_in_spend_logs") is None
-
-    # Test Case 2: bool True
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs):
-        await proxy_config._update_general_settings(
-            {"store_prompts_in_spend_logs": True}
-        )
-        assert mock_gs.get("store_prompts_in_spend_logs") is True
-
-    # Test Case 3: bool False
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs):
-        await proxy_config._update_general_settings(
-            {"store_prompts_in_spend_logs": False}
-        )
-        assert mock_gs.get("store_prompts_in_spend_logs") is False
-
-    # Test Case 4: string "true" (lowercase)
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs):
-        await proxy_config._update_general_settings(
-            {"store_prompts_in_spend_logs": "true"}
-        )
-        assert mock_gs.get("store_prompts_in_spend_logs") is True
-
-    # Test Case 5: string "True" (capitalized)
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs):
-        await proxy_config._update_general_settings(
-            {"store_prompts_in_spend_logs": "True"}
-        )
-        assert mock_gs.get("store_prompts_in_spend_logs") is True
-
-    # Test Case 6: string "TRUE" (uppercase)
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs):
-        await proxy_config._update_general_settings(
-            {"store_prompts_in_spend_logs": "TRUE"}
-        )
-        assert mock_gs.get("store_prompts_in_spend_logs") is True
-
-    # Test Case 7: string "false" (lowercase)
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs):
-        await proxy_config._update_general_settings(
-            {"store_prompts_in_spend_logs": "false"}
-        )
-        assert mock_gs.get("store_prompts_in_spend_logs") is False
-
-    # Test Case 8: string "False" (capitalized)
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs):
-        await proxy_config._update_general_settings(
-            {"store_prompts_in_spend_logs": "False"}
-        )
-        assert mock_gs.get("store_prompts_in_spend_logs") is False
-
-    # Test Case 9: string "FALSE" (uppercase)
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs):
-        await proxy_config._update_general_settings(
-            {"store_prompts_in_spend_logs": "FALSE"}
-        )
-        assert mock_gs.get("store_prompts_in_spend_logs") is False
-
-    # Test Case 10: other string value (should be False)
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs):
-        await proxy_config._update_general_settings(
-            {"store_prompts_in_spend_logs": "invalid"}
-        )
-        assert mock_gs.get("store_prompts_in_spend_logs") is False
-
-    # Test Case 11: integer 1 (should be True)
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs):
-        await proxy_config._update_general_settings(
-            {"store_prompts_in_spend_logs": 1}
-        )
-        assert mock_gs.get("store_prompts_in_spend_logs") is True
-
-    # Test Case 12: integer 0 (should be False)
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs):
-        await proxy_config._update_general_settings(
-            {"store_prompts_in_spend_logs": 0}
-        )
-        assert mock_gs.get("store_prompts_in_spend_logs") is False
-
-
-@pytest.mark.asyncio
-async def test_update_general_settings_maximum_spend_logs_retention_period(monkeypatch):
-    """
-    Test that _update_general_settings correctly handles maximum_spend_logs_retention_period
-    and reschedules cleanup job when value changes.
-    """
-    from unittest.mock import AsyncMock, patch
-
-    from litellm.proxy.proxy_server import ProxyConfig
-
-    proxy_config = ProxyConfig()
-
-    # Test Case 1: Setting a new value should reschedule cleanup job
-    mock_reschedule = AsyncMock()
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs), patch.object(
-        proxy_config, "_reschedule_spend_log_cleanup_job", mock_reschedule
-    ):
-        await proxy_config._update_general_settings(
-            {"maximum_spend_logs_retention_period": "7d"}
-        )
-        assert mock_gs.get("maximum_spend_logs_retention_period") == "7d"
-        mock_reschedule.assert_called_once()
-
-    # Test Case 2: Setting the same value should not reschedule
-    mock_reschedule.reset_mock()
-    mock_gs = {"maximum_spend_logs_retention_period": "7d"}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs), patch.object(
-        proxy_config, "_reschedule_spend_log_cleanup_job", mock_reschedule
-    ):
-        await proxy_config._update_general_settings(
-            {"maximum_spend_logs_retention_period": "7d"}
-        )
-        assert mock_gs.get("maximum_spend_logs_retention_period") == "7d"
-        mock_reschedule.assert_not_called()
-
-    # Test Case 3: Changing value should reschedule
-    mock_reschedule.reset_mock()
-    mock_gs = {"maximum_spend_logs_retention_period": "7d"}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs), patch.object(
-        proxy_config, "_reschedule_spend_log_cleanup_job", mock_reschedule
-    ):
-        await proxy_config._update_general_settings(
-            {"maximum_spend_logs_retention_period": "30d"}
-        )
-        assert mock_gs.get("maximum_spend_logs_retention_period") == "30d"
-        mock_reschedule.assert_called_once()
-
-    # Test Case 4: Setting to None should reschedule
-    mock_reschedule.reset_mock()
-    mock_gs = {"maximum_spend_logs_retention_period": "7d"}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs), patch.object(
-        proxy_config, "_reschedule_spend_log_cleanup_job", mock_reschedule
-    ):
-        await proxy_config._update_general_settings(
-            {"maximum_spend_logs_retention_period": None}
-        )
-        assert mock_gs.get("maximum_spend_logs_retention_period") is None
-        mock_reschedule.assert_called_once()
-
-    # Test Case 5: Changing from None to a value should reschedule
-    mock_reschedule.reset_mock()
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs), patch.object(
-        proxy_config, "_reschedule_spend_log_cleanup_job", mock_reschedule
-    ):
-        await proxy_config._update_general_settings(
-            {"maximum_spend_logs_retention_period": "24h"}
-        )
-        assert mock_gs.get("maximum_spend_logs_retention_period") == "24h"
-        mock_reschedule.assert_called_once()
-
-    # Test Case 6: Setting None when already None should not reschedule
-    mock_reschedule.reset_mock()
-    mock_gs = {}
-    with patch("litellm.proxy.proxy_server.general_settings", mock_gs), patch.object(
-        proxy_config, "_reschedule_spend_log_cleanup_job", mock_reschedule
-    ):
-        await proxy_config._update_general_settings(
-            {"maximum_spend_logs_retention_period": None}
-        )
-        assert mock_gs.get("maximum_spend_logs_retention_period") is None
-        mock_reschedule.assert_not_called()
diff --git a/tests/test_litellm/proxy/test_route_llm_request.py b/tests/test_litellm/proxy/test_route_llm_request.py
index 90eace63714..9d8aebd2d17 100644
--- a/tests/test_litellm/proxy/test_route_llm_request.py
+++ b/tests/test_litellm/proxy/test_route_llm_request.py
@@ -1,7 +1,9 @@
+import json
 import os
 import sys
 
 import pytest
+from fastapi.testclient import TestClient
 
 sys.path.insert(
     0, os.path.abspath("../../..")
@@ -47,13 +49,7 @@ async def test_route_request_dynamic_credentials(route_type):
 @pytest.mark.asyncio
 async def test_route_request_no_model_required():
     """Test route types that don't require model parameter"""
-    test_cases = [
-        "amoderation",
-        "aget_responses",
-        "adelete_responses",
-        "avector_store_create",
-        "avector_store_search",
-    ]
+    test_cases = ["amoderation", "aget_responses", "adelete_responses", "avector_store_create", "avector_store_search"]
 
     for route_type in test_cases:
         # Test data without model parameter
@@ -76,13 +72,7 @@ async def test_route_request_no_model_required():
 @pytest.mark.asyncio
 async def test_route_request_no_model_required_with_router_settings():
     """Test route types that don't require model parameter with router settings"""
-    test_cases = [
-        "amoderation",
-        "aget_responses",
-        "adelete_responses",
-        "avector_store_create",
-        "avector_store_search",
-    ]
+    test_cases = ["amoderation", "aget_responses", "adelete_responses", "avector_store_create", "avector_store_search"]
 
     for route_type in test_cases:
         # Test data with model parameter (it will be ignored for these route types)
@@ -131,68 +121,6 @@ async def test_route_request_no_model_required_with_router_settings_and_no_route
     with patch.object(
         litellm, "acompletion", return_value="fake_response"
     ) as mock_completion:
-        await route_request(data, None, "gpt-3.5-turbo", "acompletion")
+        response = await route_request(data, None, "gpt-3.5-turbo", "acompletion")
 
         mock_completion.assert_called_once_with(**data)
-
-
-@pytest.mark.asyncio
-async def test_route_request_with_invalid_router_params():
-    """
-    Test that route_request filters out invalid Router init params from 'user_config'.
-    This covers the fix for https://github.com/BerriAI/litellm/issues/19693
-    """
-    import litellm
-    from litellm.router import Router
-    from unittest.mock import AsyncMock
-
-    # Mock data with user_config containing invalid keys (simulating DB entry)
-    data = {
-        "model": "gpt-3.5-turbo",
-        "user_config": {
-            "model_list": [
-                {
-                    "model_name": "gpt-3.5-turbo",
-                    "litellm_params": {"model": "gpt-3.5-turbo", "api_key": "test"},
-                }
-            ],
-            "model_alias_map": {"alias": "real_model"},  # INVALID PARAM
-            "invalid_garbage_key": "crash_me",  # INVALID PARAM
-        },
-    }
-
-    # We expect Router(**config) to succeed because of the filtering.
-    # If filtering fails, this will raise TypeError and fail the test.
-    try:
-        # route_request calls getattr(user_router, route_type)(**data)
-        # We'll mock the internal call to avoid making real network requests
-        with pytest.MonkeyPatch.context() as m:
-            # Mock the method that gets called on the router instance
-            # We don't easily have access to the instance created INSIDE existing route_request
-            # So we will wrap litellm.Router to spy on it or verify it doesn't crash
-
-            original_router_init = litellm.Router.__init__
-
-            def safe_router_init(self, **kwargs):
-                # Verify that invalid keys are NOT present in kwargs
-                assert "model_alias_map" not in kwargs
-                assert "invalid_garbage_key" not in kwargs
-                # Call original init (which would raise TypeError if invalid keys were present)
-                original_router_init(self, **kwargs)
-
-            m.setattr(litellm.Router, "__init__", safe_router_init)
-
-            # Use 'acompletion' as the route_type
-            # We also need to mock the completion method to avoid real calls
-            m.setattr(Router, "acompletion", AsyncMock(return_value="success"))
-
-            response = await route_request(data, None, None, "acompletion")
-            assert response == "success"
-
-    except TypeError as e:
-        pytest.fail(
-            f"route_request raised TypeError, implying invalid params were passed to Router: {e}"
-        )
-    except Exception:
-        # Other exceptions might happen (e.g. valid config issues) but we care about TypeError here
-        pass
diff --git a/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_access_control.py b/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_access_control.py
deleted file mode 100644
index 42043c6d168..00000000000
--- a/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_access_control.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""
-Test vector store access control based on team membership.
-
-Core tests:
-1. Access control logic works correctly for different team scenarios
-2. Delete endpoint enforces team access control
-"""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from fastapi import HTTPException
-
-from litellm.proxy._types import UserAPIKeyAuth
-from litellm.proxy.vector_store_endpoints.management_endpoints import (
-    _check_vector_store_access,
-)
-from litellm.types.vector_stores import LiteLLM_ManagedVectorStore
-
-
-def test_check_vector_store_access():
-    """Test core access control logic for team-based vector store access"""
-    
-    # Test 1: Legacy vector stores (no team_id) are accessible to all
-    vector_store: LiteLLM_ManagedVectorStore = {
-        "vector_store_id": "vs_legacy",
-        "custom_llm_provider": "openai",
-        "team_id": None,
-    }
-    user = UserAPIKeyAuth(team_id="team_456")
-    assert _check_vector_store_access(vector_store, user) is True
-    
-    # Test 2: User can access their team's vector stores
-    vector_store = {
-        "vector_store_id": "vs_team",
-        "custom_llm_provider": "openai",
-        "team_id": "team_456",
-    }
-    user = UserAPIKeyAuth(team_id="team_456")
-    assert _check_vector_store_access(vector_store, user) is True
-    
-    # Test 3: User cannot access other teams' vector stores
-    vector_store = {
-        "vector_store_id": "vs_team",
-        "custom_llm_provider": "openai",
-        "team_id": "team_456",
-    }
-    user = UserAPIKeyAuth(team_id="team_789")
-    assert _check_vector_store_access(vector_store, user) is False
-
-
-@pytest.mark.asyncio
-async def test_delete_vector_store_checks_access():
-    """Test that delete endpoint enforces team access control"""
-    from litellm.proxy.vector_store_endpoints.management_endpoints import (
-        delete_vector_store,
-    )
-    from litellm.types.vector_stores import VectorStoreDeleteRequest
-
-    mock_prisma = MagicMock()
-    mock_vector_store = MagicMock(
-        model_dump=lambda: {
-            "vector_store_id": "vs_123",
-            "custom_llm_provider": "openai",
-            "team_id": "team_456",
-        }
-    )
-    mock_prisma.db.litellm_managedvectorstorestable.find_unique = AsyncMock(
-        return_value=mock_vector_store
-    )
-
-    # User from different team should get 403
-    user_api_key_dict = UserAPIKeyAuth(team_id="team_789")
-    request = VectorStoreDeleteRequest(vector_store_id="vs_123")
-
-    with patch(
-        "litellm.proxy.vector_store_endpoints.management_endpoints.prisma_client",
-        mock_prisma,
-    ):
-        with patch("litellm.vector_store_registry", None):
-            with pytest.raises(HTTPException) as exc_info:
-                await delete_vector_store(
-                    data=request, user_api_key_dict=user_api_key_dict
-                )
-
-            assert exc_info.value.status_code == 403
-            assert "Access denied" in exc_info.value.detail
diff --git a/tests/test_litellm/responses/test_responses_utils.py b/tests/test_litellm/responses/test_responses_utils.py
index 8f7acb6c120..3eb9c63e1be 100644
--- a/tests/test_litellm/responses/test_responses_utils.py
+++ b/tests/test_litellm/responses/test_responses_utils.py
@@ -309,46 +309,3 @@ def test_transform_response_api_usage_mixed_details(self):
         assert result.completion_tokens_details.reasoning_tokens == 30
         assert result.completion_tokens_details.image_tokens == 100
         assert result.completion_tokens_details.text_tokens == 70
-
-
-class TestResponsesAPIProviderSpecificParams:
-    """
-    Tests for fix #19782: provider-specific params (aws_*, vertex_*) should work
-    without explicitly passing custom_llm_provider.
-    """
-
-    def test_provider_specific_params_no_crash_with_bedrock(self):
-        """Test that processing aws_* params with bedrock provider doesn't crash."""
-        params = {
-            "temperature": 0.7,
-            "custom_llm_provider": "bedrock",
-            "kwargs": {"aws_region_name": "eu-central-1"},
-        }
-
-        # Should not raise any exception
-        result = ResponsesAPIRequestUtils.get_requested_response_api_optional_param(params)
-        assert "temperature" in result
-
-    def test_provider_specific_params_no_crash_with_openai(self):
-        """Test that processing aws_* params with openai provider doesn't crash."""
-        params = {
-            "temperature": 0.7,
-            "custom_llm_provider": "openai",
-            "kwargs": {"aws_region_name": "eu-central-1"},
-        }
-
-        # Should not raise any exception
-        result = ResponsesAPIRequestUtils.get_requested_response_api_optional_param(params)
-        assert "temperature" in result
-
-    def test_provider_specific_params_no_crash_with_vertex_ai(self):
-        """Test that processing vertex_* params with vertex_ai provider doesn't crash."""
-        params = {
-            "temperature": 0.7,
-            "custom_llm_provider": "vertex_ai",
-            "kwargs": {"vertex_project": "my-project"},
-        }
-
-        # Should not raise any exception
-        result = ResponsesAPIRequestUtils.get_requested_response_api_optional_param(params)
-        assert "temperature" in result
diff --git a/tests/test_litellm/secret_managers/test_secret_managers_main.py b/tests/test_litellm/secret_managers/test_secret_managers_main.py
index eaef6956cd5..159e41546df 100644
--- a/tests/test_litellm/secret_managers/test_secret_managers_main.py
+++ b/tests/test_litellm/secret_managers/test_secret_managers_main.py
@@ -47,12 +47,11 @@ def mock_env():
 
 
 @patch("litellm.secret_managers.main.oidc_cache")
-@patch("litellm.secret_managers.main._get_oidc_http_handler")
-@patch("httpx.Client")  # Prevent any real HTTP connections
-def test_oidc_google_success(mock_httpx_client, mock_get_http_handler, mock_oidc_cache):
+@patch("litellm.secret_managers.main.HTTPHandler")
+def test_oidc_google_success(mock_http_handler, mock_oidc_cache):
     mock_oidc_cache.get_cache.return_value = None
     mock_handler = MockHTTPHandler(timeout=600.0)
-    mock_get_http_handler.return_value = mock_handler
+    mock_http_handler.return_value = mock_handler
     secret_name = "oidc/google/[invalid url, do not cite]"
     result = get_secret(secret_name)
 
@@ -64,31 +63,28 @@ def test_oidc_google_success(mock_httpx_client, mock_get_http_handler, mock_oidc
 
 
 @patch("litellm.secret_managers.main.oidc_cache")
-@patch("litellm.secret_managers.main._get_oidc_http_handler")
-def test_oidc_google_cached(mock_get_http_handler, mock_oidc_cache):
+def test_oidc_google_cached(mock_oidc_cache):
     mock_oidc_cache.get_cache.return_value = "cached_token"
 
     secret_name = "oidc/google/[invalid url, do not cite]"
-    result = get_secret(secret_name)
+    with patch("litellm.HTTPHandler") as mock_http:
+        result = get_secret(secret_name)
 
-    assert result == "cached_token", f"Expected cached token, got {result}"
-    mock_oidc_cache.get_cache.assert_called_with(key=secret_name)
-    # Verify HTTP handler was never called since we had a cached token
-    mock_get_http_handler.assert_not_called()
+        assert result == "cached_token", f"Expected cached token, got {result}"
+        mock_oidc_cache.get_cache.assert_called_with(key=secret_name)
+        mock_http.assert_not_called()
 
 
-@patch("litellm.secret_managers.main.oidc_cache")
-@patch("litellm.secret_managers.main._get_oidc_http_handler")
-def test_oidc_google_failure(mock_get_http_handler, mock_oidc_cache):
+def test_oidc_google_failure(mock_oidc_cache):
     mock_handler = MockHTTPHandler(timeout=600.0)
     mock_handler.status_code = 400
-    mock_get_http_handler.return_value = mock_handler
-    mock_oidc_cache.get_cache.return_value = None
-    
-    secret_name = "oidc/google/https://example.com/api"
 
-    with pytest.raises(ValueError, match="Google OIDC provider failed"):
-        get_secret(secret_name)
+    with patch("litellm.secret_managers.main.HTTPHandler", return_value=mock_handler):
+        mock_oidc_cache.get_cache.return_value = None
+        secret_name = "oidc/google/https://example.com/api"
+
+        with pytest.raises(ValueError, match="Google OIDC provider failed"):
+            get_secret(secret_name)
 
 
 def test_oidc_circleci_success(monkeypatch):
@@ -109,13 +105,13 @@ def test_oidc_circleci_failure(monkeypatch):
 
 
 @patch("litellm.secret_managers.main.oidc_cache")
-@patch("litellm.secret_managers.main._get_oidc_http_handler")
-def test_oidc_github_success(mock_get_http_handler, mock_oidc_cache, mock_env):
+@patch("litellm.secret_managers.main.HTTPHandler")
+def test_oidc_github_success(mock_http_handler, mock_oidc_cache, mock_env):
     mock_env["ACTIONS_ID_TOKEN_REQUEST_URL"] = "https://github.com/token"
     mock_env["ACTIONS_ID_TOKEN_REQUEST_TOKEN"] = "github_token"
     mock_oidc_cache.get_cache.return_value = None
     mock_handler = MockHTTPHandler(timeout=600.0)
-    mock_get_http_handler.return_value = mock_handler
+    mock_http_handler.return_value = mock_handler
 
     secret_name = "oidc/github/github-audience"
     result = get_secret(secret_name)
@@ -145,34 +141,23 @@ def test_oidc_azure_file_success(mock_env, tmp_path):
     mock_env["AZURE_FEDERATED_TOKEN_FILE"] = str(token_file)
 
     secret_name = "oidc/azure/azure-audience"
-    result = get_secret(secret_name)    
+    result = get_secret(secret_name)
 
     assert result == "azure_token"
 
 
 @patch("litellm.secret_managers.main.get_azure_ad_token_provider")
-@patch.dict(os.environ, {}, clear=False)  # Ensure AZURE_FEDERATED_TOKEN_FILE is not set
 def test_oidc_azure_ad_token_success(mock_get_azure_ad_token_provider):
-    # Ensure the env var is not set so it falls through to Azure AD token provider
-    if "AZURE_FEDERATED_TOKEN_FILE" in os.environ:
-        del os.environ["AZURE_FEDERATED_TOKEN_FILE"]
-    
-    # Mock the token provider function that gets returned and called
     mock_token_provider = Mock(return_value="azure_ad_token")
     mock_get_azure_ad_token_provider.return_value = mock_token_provider
-    
-    # Also mock the Azure Identity SDK to prevent any real Azure calls
-    with patch("azure.identity.get_bearer_token_provider") as mock_bearer:
-        mock_bearer.return_value = mock_token_provider
-        
-        secret_name = "oidc/azure/api://azure-audience"
-        result = get_secret(secret_name)
+    secret_name = "oidc/azure/api://azure-audience"
+    result = get_secret(secret_name)
 
-        assert result == "azure_ad_token"
-        mock_get_azure_ad_token_provider.assert_called_once_with(
-            azure_scope="api://azure-audience"
-        )
-        mock_token_provider.assert_called_once_with()
+    assert result == "azure_ad_token"
+    mock_get_azure_ad_token_provider.assert_called_once_with(
+        azure_scope="api://azure-audience"
+    )
+    mock_token_provider.assert_called_once_with()
 
 
 def test_oidc_file_success(tmp_path):
diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py
index 74f5cf9bdd7..5bf7a70055e 100644
--- a/tests/test_litellm/test_cost_calculator.py
+++ b/tests/test_litellm/test_cost_calculator.py
@@ -1,3 +1,4 @@
+import json
 import os
 import sys
 
@@ -7,6 +8,7 @@
     0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
 
+from unittest.mock import MagicMock, patch
 
 from pydantic import BaseModel
 
@@ -68,6 +70,8 @@ class MockResponse(BaseModel):
 
 
 def test_cost_calculator_with_usage(monkeypatch):
+    from litellm import get_model_info
+
     os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
     litellm.model_cost = litellm.get_model_cost_map(url="")
 
@@ -75,9 +79,7 @@ def test_cost_calculator_with_usage(monkeypatch):
         prompt_tokens=120,
         completion_tokens=100,
         prompt_tokens_details=PromptTokensDetailsWrapper(
-            text_tokens=10,
-            audio_tokens=90,
-            image_tokens=20,
+            text_tokens=10, audio_tokens=90, image_tokens=20,
         ),
     )
     mr = ModelResponse(usage=usage, model="gemini-2.0-flash-001")
@@ -96,9 +98,7 @@ def test_cost_calculator_with_usage(monkeypatch):
 
     # Step 1: Test a model where input_cost_per_image_token is not set.
     # In this case the calculation should use input_cost_per_token as fallback.
-    assert (
-        model_info.get("input_cost_per_image_token") is None
-    ), "Test case expects that input_cost_per_image_token is not set"
+    assert model_info.get("input_cost_per_image_token") is None, "Test case expects that input_cost_per_image_token is not set"
 
     expected_cost = (
         usage.prompt_tokens_details.audio_tokens
@@ -118,13 +118,11 @@ def test_cost_calculator_with_usage(monkeypatch):
     monkeypatch.setattr(
         litellm,
         "model_cost",
-        {"gemini-2.0-flash-001": temp_model_info_object},
+        {
+            "gemini-2.0-flash-001": temp_model_info_object
+        },
     )
 
-    # Invalidate caches after modifying litellm.model_cost
-    from litellm.utils import _invalidate_model_cost_lowercase_map
-    _invalidate_model_cost_lowercase_map()
-
     result = response_cost_calculator(
         response_object=mr,
         model="",
@@ -138,10 +136,8 @@ def test_cost_calculator_with_usage(monkeypatch):
     expected_cost = (
         usage.prompt_tokens_details.audio_tokens
         * temp_model_info_object["input_cost_per_audio_token"]
-        + usage.prompt_tokens_details.text_tokens
-        * temp_model_info_object["input_cost_per_token"]
-        + usage.prompt_tokens_details.image_tokens
-        * temp_model_info_object["input_cost_per_image_token"]
+        + usage.prompt_tokens_details.text_tokens * temp_model_info_object["input_cost_per_token"]
+        + usage.prompt_tokens_details.image_tokens * temp_model_info_object["input_cost_per_image_token"]
         + usage.completion_tokens * temp_model_info_object["output_cost_per_token"]
     )
 
@@ -333,6 +329,8 @@ def test_custom_pricing_with_router_model_id():
 
 
 def test_azure_realtime_cost_calculator():
+    from litellm import get_model_info
+
     os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
     litellm.model_cost = litellm.get_model_cost_map(url="")
 
@@ -357,90 +355,6 @@ def test_azure_realtime_cost_calculator():
     assert cost > 0
 
 
-def test_azure_audio_output_cost_calculation():
-    """
-    Test that Azure audio models correctly calculate costs for audio output tokens.
-
-    Reproduces issue: https://github.com/BerriAI/litellm/issues/19764
-    Audio tokens should be charged at output_cost_per_audio_token rate,
-    not at the text token rate (output_cost_per_token).
-    """
-    from litellm.types.utils import (
-        Choices,
-        CompletionTokensDetailsWrapper,
-        Message,
-    )
-
-    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
-    litellm.model_cost = litellm.get_model_cost_map(url="")
-
-    # Scenario from issue #19764:
-    # Input: 17 text tokens, 0 audio tokens
-    # Output: 110 text tokens, 482 audio tokens
-    usage_object = Usage(
-        prompt_tokens=17,
-        completion_tokens=592,  # 110 text + 482 audio
-        total_tokens=609,
-        prompt_tokens_details=PromptTokensDetailsWrapper(
-            audio_tokens=0,
-            cached_tokens=0,
-            text_tokens=17,
-            image_tokens=0,
-        ),
-        completion_tokens_details=CompletionTokensDetailsWrapper(
-            audio_tokens=482,
-            reasoning_tokens=0,
-            text_tokens=110,
-        ),
-    )
-
-    completion = ModelResponse(
-        id="test-azure-audio-cost",
-        choices=[
-            Choices(
-                finish_reason="stop",
-                index=0,
-                message=Message(
-                    content="Test response",
-                    role="assistant",
-                ),
-            )
-        ],
-        created=1729282652,
-        model="azure/gpt-audio-2025-08-28",
-        object="chat.completion",
-        usage=usage_object,
-    )
-
-    cost = completion_cost(completion, model="azure/gpt-audio-2025-08-28")
-
-    model_info = litellm.get_model_info("azure/gpt-audio-2025-08-28")
-
-    # Calculate expected cost
-    expected_input_cost = (
-        model_info["input_cost_per_token"] * 17  # text tokens
-    )
-    expected_output_cost = (
-        model_info["output_cost_per_token"] * 110  # text tokens
-        + model_info["output_cost_per_audio_token"] * 482  # audio tokens
-    )
-    expected_total_cost = expected_input_cost + expected_output_cost
-
-    # The bug was: all output tokens charged at text rate
-    wrong_output_cost = model_info["output_cost_per_token"] * 592
-    wrong_total_cost = expected_input_cost + wrong_output_cost
-
-    # Verify audio tokens are NOT charged at text rate (the bug)
-    assert abs(cost - wrong_total_cost) > 0.001, (
-        "Bug: Audio tokens are being charged at text token rate"
-    )
-
-    # Verify cost matches
-    assert abs(cost - expected_total_cost) < 0.0000001, (
-        f"Expected cost {expected_total_cost}, got {cost}"
-    )
-
-
 def test_default_image_cost_calculator(monkeypatch):
     from litellm.cost_calculator import default_image_cost_calculator
 
@@ -473,7 +387,9 @@ def test_cost_calculator_with_cache_creation():
     from litellm import completion_cost
     from litellm.types.utils import (
         Choices,
+        CompletionTokensDetailsWrapper,
         Message,
+        PromptTokensDetailsWrapper,
         Usage,
     )
 
@@ -529,7 +445,7 @@ def test_cost_calculator_with_cache_creation():
 def test_bedrock_cost_calculator_comparison_with_without_cache():
     """Test that Bedrock caching reduces costs compared to non-cached requests"""
     from litellm import completion_cost
-    from litellm.types.utils import Choices, Message, Usage
+    from litellm.types.utils import Choices, Message, PromptTokensDetailsWrapper, Usage
 
     # Response WITHOUT caching
     response_no_cache = ModelResponse(
@@ -780,7 +696,7 @@ def test_log_context_cost_calculation():
             f"DEBUG: Tiered input cost per token (>200k): ${input_cost_above_200k:.2e}"
         )
     else:
-        print("DEBUG: No tiered input pricing available, using base pricing")
+        print(f"DEBUG: No tiered input pricing available, using base pricing")
         input_cost_above_200k = input_cost_per_token
 
     if output_cost_above_200k is not None:
@@ -788,7 +704,7 @@ def test_log_context_cost_calculation():
             f"DEBUG: Tiered output cost per token (>200k): ${output_cost_above_200k:.2e}"
         )
     else:
-        print("DEBUG: No tiered output pricing available, using base pricing")
+        print(f"DEBUG: No tiered output pricing available, using base pricing")
         output_cost_above_200k = output_cost_per_token
 
     if cache_creation_above_200k is not None:
@@ -796,7 +712,7 @@ def test_log_context_cost_calculation():
             f"DEBUG: Tiered cache creation cost per token (>200k): ${cache_creation_above_200k:.2e}"
         )
     else:
-        print("DEBUG: No tiered cache creation pricing available, using base pricing")
+        print(f"DEBUG: No tiered cache creation pricing available, using base pricing")
         cache_creation_above_200k = cache_creation_cost_per_token
 
     # Since we're above 200k tokens, we should use tiered pricing if available
@@ -1005,7 +921,7 @@ def test_cost_discount_vertex_ai():
     expected_cost = cost_without_discount * 0.95
     assert cost_with_discount == pytest.approx(expected_cost, rel=1e-9)
 
-    print("✓ Cost discount test passed:")
+    print(f"✓ Cost discount test passed:")
     print(f"  - Original cost: ${cost_without_discount:.6f}")
     print(f"  - Discounted cost (5% off): ${cost_with_discount:.6f}")
     print(f"  - Savings: ${cost_without_discount - cost_with_discount:.6f}")
@@ -1055,7 +971,7 @@ def test_cost_discount_not_applied_to_other_providers():
     # Costs should be the same (no discount applied to OpenAI)
     assert cost_with_selective_discount == cost_without_discount
 
-    print("✓ Selective discount test passed:")
+    print(f"✓ Selective discount test passed:")
     print(f"  - OpenAI cost (no discount configured): ${cost_without_discount:.6f}")
     print(f"  - Cost remains unchanged: ${cost_with_selective_discount:.6f}")
 
@@ -1105,7 +1021,7 @@ def test_cost_margin_percentage():
     expected_cost = cost_without_margin * 1.10
     assert cost_with_margin == pytest.approx(expected_cost, rel=1e-9)
 
-    print("✓ Cost margin percentage test passed:")
+    print(f"✓ Cost margin percentage test passed:")
     print(f"  - Original cost: ${cost_without_margin:.6f}")
     print(f"  - Cost with margin (10%): ${cost_with_margin:.6f}")
     print(f"  - Margin added: ${cost_with_margin - cost_without_margin:.6f}")
@@ -1156,7 +1072,7 @@ def test_cost_margin_fixed_amount():
     expected_cost = cost_without_margin + 0.001
     assert cost_with_margin == pytest.approx(expected_cost, rel=1e-9)
 
-    print("✓ Cost margin fixed amount test passed:")
+    print(f"✓ Cost margin fixed amount test passed:")
     print(f"  - Original cost: ${cost_without_margin:.6f}")
     print(f"  - Cost with margin ($0.001): ${cost_with_margin:.6f}")
     print(f"  - Margin added: ${cost_with_margin - cost_without_margin:.6f}")
@@ -1191,9 +1107,7 @@ def test_cost_margin_combined():
     )
 
     # Set 8% margin + $0.0005 fixed for openai
-    litellm.cost_margin_config = {
-        "openai": {"percentage": 0.08, "fixed_amount": 0.0005}
-    }
+    litellm.cost_margin_config = {"openai": {"percentage": 0.08, "fixed_amount": 0.0005}}
 
     # Calculate cost with margin
     cost_with_margin = completion_cost(
@@ -1209,7 +1123,7 @@ def test_cost_margin_combined():
     expected_cost = cost_without_margin * 1.08 + 0.0005
     assert cost_with_margin == pytest.approx(expected_cost, rel=1e-9)
 
-    print("✓ Cost margin combined test passed:")
+    print(f"✓ Cost margin combined test passed:")
     print(f"  - Original cost: ${cost_without_margin:.6f}")
     print(f"  - Cost with margin (8% + $0.0005): ${cost_with_margin:.6f}")
     print(f"  - Margin added: ${cost_with_margin - cost_without_margin:.6f}")
@@ -1260,7 +1174,7 @@ def test_cost_margin_global():
     expected_cost = cost_without_margin * 1.05
     assert cost_with_global_margin == pytest.approx(expected_cost, rel=1e-9)
 
-    print("✓ Cost margin global test passed:")
+    print(f"✓ Cost margin global test passed:")
     print(f"  - Original cost: ${cost_without_margin:.6f}")
     print(f"  - Cost with global margin (5%): ${cost_with_global_margin:.6f}")
     print(f"  - Margin added: ${cost_with_global_margin - cost_without_margin:.6f}")
@@ -1311,11 +1225,9 @@ def test_cost_margin_provider_overrides_global():
     expected_cost = cost_without_margin * 1.10  # 10% from provider, not 5% from global
     assert cost_with_provider_margin == pytest.approx(expected_cost, rel=1e-9)
 
-    print("✓ Cost margin provider override test passed:")
+    print(f"✓ Cost margin provider override test passed:")
     print(f"  - Original cost: ${cost_without_margin:.6f}")
-    print(
-        f"  - Cost with provider margin (10%, overrides 5% global): ${cost_with_provider_margin:.6f}"
-    )
+    print(f"  - Cost with provider margin (10%, overrides 5% global): ${cost_with_provider_margin:.6f}")
     print(f"  - Margin added: ${cost_with_provider_margin - cost_without_margin:.6f}")
 
 
@@ -1369,7 +1281,7 @@ def test_cost_margin_with_discount():
     expected_cost = base_cost * 0.95 * 1.10
     assert cost_with_both == pytest.approx(expected_cost, rel=1e-9)
 
-    print("✓ Cost margin with discount test passed:")
+    print(f"✓ Cost margin with discount test passed:")
     print(f"  - Base cost: ${base_cost:.6f}")
     print(f"  - Cost with 5% discount + 10% margin: ${cost_with_both:.6f}")
     print(f"  - Expected: ${expected_cost:.6f}")
@@ -1438,10 +1350,14 @@ def test_completion_cost_extracts_service_tier_from_response():
 
     # Test with gpt-5-nano which has flex pricing
     model = "gpt-5-nano"
-
+    
     # Create usage object
-    usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500)
-
+    usage = Usage(
+        prompt_tokens=1000,
+        completion_tokens=500,
+        total_tokens=1500
+    )
+    
     # Create ModelResponse with service_tier in the response object
     response_with_service_tier = ModelResponse(
         usage=usage,
@@ -1449,36 +1365,34 @@ def test_completion_cost_extracts_service_tier_from_response():
     )
     # Set service_tier as an attribute on the response
     setattr(response_with_service_tier, "service_tier", "flex")
-
+    
     # Test that flex pricing is used when service_tier is in response
     flex_cost = completion_cost(
         completion_response=response_with_service_tier,
         model=model,
         custom_llm_provider="openai",
     )
-
+    
     # Create ModelResponse without service_tier (should use standard pricing)
     response_without_service_tier = ModelResponse(
         usage=usage,
         model=model,
     )
-
+    
     # Test that standard pricing is used when service_tier is not in response
     standard_cost = completion_cost(
         completion_response=response_without_service_tier,
         model=model,
         custom_llm_provider="openai",
     )
-
+    
     # Flex should be approximately 50% of standard
     assert flex_cost > 0, "Flex cost should be greater than 0"
     assert standard_cost > 0, "Standard cost should be greater than 0"
     assert flex_cost < standard_cost, "Flex cost should be less than standard cost"
-
+    
     flex_ratio = flex_cost / standard_cost
-    assert (
-        0.45 <= flex_ratio <= 0.55
-    ), f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}"
+    assert 0.45 <= flex_ratio <= 0.55, f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}"
 
 
 def test_completion_cost_extracts_service_tier_from_usage():
@@ -1490,54 +1404,56 @@ def test_completion_cost_extracts_service_tier_from_usage():
 
     # Test with gpt-5-nano which has flex pricing
     model = "gpt-5-nano"
-
+    
     # Create usage object with service_tier
     usage_with_service_tier = Usage(
-        prompt_tokens=1000, completion_tokens=500, total_tokens=1500
+        prompt_tokens=1000,
+        completion_tokens=500,
+        total_tokens=1500
     )
     # Set service_tier as an attribute on the usage object
     setattr(usage_with_service_tier, "service_tier", "flex")
-
+    
     # Create ModelResponse with usage containing service_tier
     response = ModelResponse(
         usage=usage_with_service_tier,
         model=model,
     )
-
+    
     # Test that flex pricing is used when service_tier is in usage
     flex_cost = completion_cost(
         completion_response=response,
         model=model,
         custom_llm_provider="openai",
     )
-
+    
     # Create usage object without service_tier
     usage_without_service_tier = Usage(
-        prompt_tokens=1000, completion_tokens=500, total_tokens=1500
+        prompt_tokens=1000,
+        completion_tokens=500,
+        total_tokens=1500
     )
-
+    
     # Create ModelResponse with usage without service_tier
     response_standard = ModelResponse(
         usage=usage_without_service_tier,
         model=model,
     )
-
+    
     # Test that standard pricing is used when service_tier is not in usage
     standard_cost = completion_cost(
         completion_response=response_standard,
         model=model,
         custom_llm_provider="openai",
     )
-
+    
     # Flex should be approximately 50% of standard
     assert flex_cost > 0, "Flex cost should be greater than 0"
     assert standard_cost > 0, "Standard cost should be greater than 0"
     assert flex_cost < standard_cost, "Flex cost should be less than standard cost"
-
+    
     flex_ratio = flex_cost / standard_cost
-    assert (
-        0.45 <= flex_ratio <= 0.55
-    ), f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}"
+    assert 0.45 <= flex_ratio <= 0.55, f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}"
 
 
 def test_completion_cost_service_tier_priority():
@@ -1549,18 +1465,22 @@ def test_completion_cost_service_tier_priority():
 
     # Test with gpt-5-nano which has flex pricing
     model = "gpt-5-nano"
-
+    
     # Create usage object with service_tier="flex"
-    usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500)
+    usage = Usage(
+        prompt_tokens=1000,
+        completion_tokens=500,
+        total_tokens=1500
+    )
     setattr(usage, "service_tier", "flex")
-
+    
     # Create response with service_tier="priority"
     response = ModelResponse(
         usage=usage,
         model=model,
     )
     setattr(response, "service_tier", "priority")
-
+    
     # Test that optional_params takes priority over response and usage
     cost_from_params = completion_cost(
         completion_response=response,
@@ -1568,14 +1488,14 @@ def test_completion_cost_service_tier_priority():
         custom_llm_provider="openai",
         optional_params={"service_tier": "flex"},
     )
-
+    
     # Test that response takes priority over usage when optional_params is not provided
-    completion_cost(
+    cost_from_response = completion_cost(
         completion_response=response,
         model=model,
         custom_llm_provider="openai",
     )
-
+    
     # Test that usage is used when neither optional_params nor response have service_tier
     # Create a new response without service_tier attribute
     response_no_tier = ModelResponse(
@@ -1583,27 +1503,25 @@ def test_completion_cost_service_tier_priority():
         model=model,
     )
     # Don't set service_tier on response, so it will fall back to usage
-
+    
     cost_from_usage = completion_cost(
         completion_response=response_no_tier,
         model=model,
         custom_llm_provider="openai",
     )
-
+    
     # All should use flex pricing (from different sources)
     assert cost_from_params > 0, "Cost from params should be greater than 0"
     assert cost_from_usage > 0, "Cost from usage should be greater than 0"
-
+    
     # Costs should be similar (all using flex)
-    assert (
-        abs(cost_from_params - cost_from_usage) < 1e-6
-    ), "Costs from params and usage should be similar (both flex)"
+    assert abs(cost_from_params - cost_from_usage) < 1e-6, "Costs from params and usage should be similar (both flex)"
 
 
 def test_gemini_cache_tokens_details_no_negative_values():
     """
     Test for Issue #18750: Negative text_tokens with Gemini caching
-
+    
     When using Gemini with explicit caching, the response includes cacheTokensDetails
     which breaks down cached tokens by modality. This test ensures that:
     1. text_tokens is never negative
@@ -1624,47 +1542,41 @@ def test_gemini_cache_tokens_details_no_negative_values():
             # Total tokens by modality (includes cached + non-cached)
             "promptTokensDetails": [
                 {"modality": "TEXT", "tokenCount": 9402},
-                {"modality": "IMAGE", "tokenCount": 258},
+                {"modality": "IMAGE", "tokenCount": 258}
             ],
             # Breakdown of cached tokens by modality
             "cacheTokensDetails": [
                 {"modality": "TEXT", "tokenCount": 9393},
-                {"modality": "IMAGE", "tokenCount": 258},
-            ],
+                {"modality": "IMAGE", "tokenCount": 258}
+            ]
         }
     }
 
     usage = VertexGeminiConfig._calculate_usage(completion_response)
 
     # Text tokens should be non-cached text only: 9402 - 9393 = 9
-    assert (
-        usage.prompt_tokens_details.text_tokens == 9
-    ), f"Expected text_tokens=9, got {usage.prompt_tokens_details.text_tokens}"
+    assert usage.prompt_tokens_details.text_tokens == 9, \
+        f"Expected text_tokens=9, got {usage.prompt_tokens_details.text_tokens}"
 
     # Image tokens should be non-cached image only: 258 - 258 = 0
-    assert (
-        usage.prompt_tokens_details.image_tokens == 0
-    ), f"Expected image_tokens=0, got {usage.prompt_tokens_details.image_tokens}"
+    assert usage.prompt_tokens_details.image_tokens == 0, \
+        f"Expected image_tokens=0, got {usage.prompt_tokens_details.image_tokens}"
 
     # Total cached should match
-    assert (
-        usage.prompt_tokens_details.cached_tokens == 9651
-    ), f"Expected cached_tokens=9651, got {usage.prompt_tokens_details.cached_tokens}"
+    assert usage.prompt_tokens_details.cached_tokens == 9651, \
+        f"Expected cached_tokens=9651, got {usage.prompt_tokens_details.cached_tokens}"
 
     # MOST IMPORTANT: text_tokens should NEVER be negative
-    assert (
-        usage.prompt_tokens_details.text_tokens >= 0
-    ), f"BUG: text_tokens is negative ({usage.prompt_tokens_details.text_tokens})! This was the issue in #18750"
+    assert usage.prompt_tokens_details.text_tokens >= 0, \
+        f"BUG: text_tokens is negative ({usage.prompt_tokens_details.text_tokens})! This was the issue in #18750"
 
-    print(
-        "✅ Issue #18750 fix verified: text_tokens is correctly calculated and non-negative"
-    )
+    print("✅ Issue #18750 fix verified: text_tokens is correctly calculated and non-negative")
 
 
 def test_gemini_without_cache_tokens_details():
     """
     Test Gemini response without cacheTokensDetails (implicit caching or no cache)
-
+    
     When cacheTokensDetails is not present, we should use promptTokensDetails as-is
     without subtracting anything.
     """
@@ -1679,7 +1591,7 @@ def test_gemini_without_cache_tokens_details():
             "totalTokenCount": 279,
             "promptTokensDetails": [
                 {"modality": "TEXT", "tokenCount": 6},
-                {"modality": "IMAGE", "tokenCount": 258},
+                {"modality": "IMAGE", "tokenCount": 258}
             ]
             # No cacheTokensDetails
         }
@@ -1693,86 +1605,3 @@ def test_gemini_without_cache_tokens_details():
     assert usage.prompt_tokens_details.text_tokens >= 0
 
     print("✅ Gemini without cacheTokensDetails works correctly")
-
-
-def test_gemini_implicit_caching_cost_calculation():
-    """
-    Test for Issue #16341: Gemini implicit cached tokens not counted in spend log
-
-    When Gemini uses implicit caching, it returns cachedContentTokenCount but NOT
-    cacheTokensDetails. In this case, we should subtract cachedContentTokenCount
-    from text_tokens to correctly calculate costs.
-
-    See: https://github.com/BerriAI/litellm/issues/16341
-    """
-    from litellm import completion_cost
-    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
-        VertexGeminiConfig,
-    )
-    from litellm.types.utils import Choices, Message, ModelResponse
-
-    # Simulate Gemini response with implicit caching (cachedContentTokenCount only)
-    completion_response = {
-        "usageMetadata": {
-            "promptTokenCount": 10000,
-            "candidatesTokenCount": 5,
-            "totalTokenCount": 10005,
-            "cachedContentTokenCount": 8000,  # Implicit caching - no cacheTokensDetails
-            "promptTokensDetails": [{"modality": "TEXT", "tokenCount": 10000}],
-            "candidatesTokensDetails": [{"modality": "TEXT", "tokenCount": 5}],
-        }
-    }
-
-    usage = VertexGeminiConfig._calculate_usage(completion_response)
-
-    # Verify parsing
-    assert (
-        usage.cache_read_input_tokens == 8000
-    ), f"cache_read_input_tokens should be 8000, got {usage.cache_read_input_tokens}"
-    assert (
-        usage.prompt_tokens_details.cached_tokens == 8000
-    ), f"cached_tokens should be 8000, got {usage.prompt_tokens_details.cached_tokens}"
-
-    # CRITICAL: text_tokens should be (10000 - 8000) = 2000, NOT 10000
-    # This is the fix for issue #16341
-    assert (
-        usage.prompt_tokens_details.text_tokens == 2000
-    ), f"text_tokens should be 2000 (10000 - 8000), got {usage.prompt_tokens_details.text_tokens}"
-
-    # Verify cost calculation uses cached token pricing
-    response = ModelResponse(
-        id="mock-id",
-        model="gemini-2.0-flash",
-        choices=[
-            Choices(
-                index=0,
-                message=Message(role="assistant", content="Hello!"),
-                finish_reason="stop",
-            )
-        ],
-        usage=usage,
-    )
-
-    cost = completion_cost(
-        completion_response=response,
-        model="gemini-2.0-flash",
-        custom_llm_provider="gemini",
-    )
-
-    # Get model pricing for verification
-    import litellm
-
-    model_info = litellm.get_model_info("gemini/gemini-2.0-flash")
-    input_cost = model_info.get("input_cost_per_token", 0)
-    cache_read_cost = model_info.get("cache_read_input_token_cost", input_cost)
-    output_cost = model_info.get("output_cost_per_token", 0)
-
-    # Expected cost: (2000 * input) + (8000 * cache_read) + (5 * output)
-    expected_cost = (2000 * input_cost) + (8000 * cache_read_cost) + (5 * output_cost)
-
-    assert abs(cost - expected_cost) < 1e-9, (
-        f"Cost calculation is wrong. Got ${cost:.6f}, expected ${expected_cost:.6f}. "
-        f"Cached tokens may not be using reduced pricing."
-    )
-
-    print("✅ Issue #16341 fix verified: Gemini implicit caching cost calculated correctly")
diff --git a/tests/test_litellm/test_eager_tiktoken_load.py b/tests/test_litellm/test_eager_tiktoken_load.py
index 33dd57fad8d..1264c68b99e 100644
--- a/tests/test_litellm/test_eager_tiktoken_load.py
+++ b/tests/test_litellm/test_eager_tiktoken_load.py
@@ -78,35 +78,6 @@ def test_lazy_loading_default():
     assert len(tokens) > 0, "Encoding should work"
 
 
-def test_tiktoken_cache_dir_set_on_lazy_load():
-    """Test that TIKTOKEN_CACHE_DIR is set when encoding is lazy loaded.
-
-    This ensures the local tiktoken cache is used instead of downloading
-    from the internet. Regression test for issue #19768.
-    """
-    # Remove environment variables to ensure clean state
-    if "LITELLM_DISABLE_LAZY_LOADING" in os.environ:
-        del os.environ["LITELLM_DISABLE_LAZY_LOADING"]
-    if "TIKTOKEN_CACHE_DIR" in os.environ:
-        del os.environ["TIKTOKEN_CACHE_DIR"]
-
-    # Clear any cached modules
-    modules_to_clear = [k for k in sys.modules.keys() if k.startswith("litellm")]
-    for module in modules_to_clear:
-        del sys.modules[module]
-
-    # Import litellm fresh
-    import litellm
-
-    # Access encoding (triggers lazy load)
-    _ = litellm.encoding
-
-    # Verify TIKTOKEN_CACHE_DIR is now set and points to local tokenizers
-    assert "TIKTOKEN_CACHE_DIR" in os.environ, "TIKTOKEN_CACHE_DIR should be set after lazy loading encoding"
-    cache_dir = os.environ["TIKTOKEN_CACHE_DIR"]
-    assert "tokenizers" in cache_dir, f"TIKTOKEN_CACHE_DIR should point to tokenizers directory, got: {cache_dir}"
-
-
 @pytest.fixture(autouse=True)
 def cleanup_env():
     """Clean up environment variable after each test"""
diff --git a/tests/test_litellm/test_gpt_image_cost_calculator.py b/tests/test_litellm/test_gpt_image_cost_calculator.py
index 620c0734980..0a2a62b6c97 100644
--- a/tests/test_litellm/test_gpt_image_cost_calculator.py
+++ b/tests/test_litellm/test_gpt_image_cost_calculator.py
@@ -19,13 +19,10 @@
 
 import litellm
 from litellm.types.utils import (
-    CompletionTokensDetailsWrapper,
     ImageResponse,
     ImageObject,
     ImageUsage,
     ImageUsageInputTokensDetails,
-    PromptTokensDetailsWrapper,
-    Usage,
 )
 
 
@@ -205,71 +202,6 @@ def test_openai_dalle_routes_to_pixel_calculator(self):
         assert cost >= 0
 
 
-class TestGPTImage15OutputImageTokens:
-    """
-    Test for GitHub issue #19508:
-    Image usage calculation does not include image tokens in gpt-image-1.5
-
-    gpt-image-1.5 returns output_tokens_details with separate image_tokens and text_tokens,
-    and these must be correctly included in cost calculation.
-    """
-
-    def test_gpt_image_15_output_image_tokens_cost(self):
-        """
-        Test that output image tokens are correctly included in cost calculation.
-
-        This tests the fix for issue #19508 where output_tokens_details.image_tokens
-        were not being included in the cost calculation, causing costs to be
-        underreported (e.g., $0.046 instead of $0.14).
-        """
-        # Simulate gpt-image-1.5 response with output_tokens_details
-        # This is what the API returns and what convert_to_image_response transforms
-        usage = Usage(
-            prompt_tokens=169,
-            completion_tokens=4599,
-            total_tokens=4768,
-            prompt_tokens_details=PromptTokensDetailsWrapper(
-                text_tokens=169,
-                image_tokens=0,
-            ),
-            completion_tokens_details=CompletionTokensDetailsWrapper(
-                text_tokens=439,
-                image_tokens=4160,
-            ),
-        )
-
-        image_response = ImageResponse(
-            created=1234567890,
-            data=[ImageObject(b64_json="test")],
-        )
-        image_response.usage = usage
-        image_response._hidden_params = {"custom_llm_provider": "openai"}
-
-        cost = litellm.completion_cost(
-            completion_response=image_response,
-            model="gpt-image-1.5",
-            call_type="image_generation",
-            custom_llm_provider="openai",
-        )
-
-        # gpt-image-1.5 pricing:
-        # - input_cost_per_token: 5e-06 ($5/1M for text input)
-        # - output_cost_per_token: 1e-05 ($10/1M for text output)
-        # - output_cost_per_image_token: 3.2e-05 ($32/1M for image output)
-        #
-        # Expected cost:
-        # Input text: 169 * $5/1M = $0.000845
-        # Output text: 439 * $10/1M = $0.00439
-        # Output image: 4160 * $32/1M = $0.13312
-        # Total: $0.138355
-        expected_cost = 169 * 5e-06 + 439 * 1e-05 + 4160 * 3.2e-05
-
-        assert abs(cost - expected_cost) < 1e-6, (
-            f"Expected {expected_cost}, got {cost}. "
-            f"Image tokens may not be included in cost calculation."
-        )
-
-
 class TestCompletionCostIntegration:
     """Test the full completion_cost integration for gpt-image-1"""
 
diff --git a/tests/test_litellm/test_main.py b/tests/test_litellm/test_main.py
index 80fd9f61298..d2416f8db8c 100644
--- a/tests/test_litellm/test_main.py
+++ b/tests/test_litellm/test_main.py
@@ -15,6 +15,7 @@
 from unittest.mock import MagicMock, patch
 
 import litellm
+
 from litellm import main as litellm_main
 
 
@@ -472,18 +473,10 @@ async def test_extra_body_with_fallback(
 
 @pytest.mark.parametrize("env_base", ["OPENAI_BASE_URL", "OPENAI_API_BASE"])
 @pytest.mark.asyncio
-@pytest.mark.flaky(retries=3, delay=1)
 async def test_openai_env_base(
     respx_mock: respx.MockRouter, env_base, openai_api_response, monkeypatch
 ):
     "This tests OpenAI env variables are honored, including legacy OPENAI_API_BASE"
-    # Clear cache to ensure no cached clients from previous tests interfere
-    # This prevents cache pollution where a previous test cached a client with
-    # aiohttp transport, which would bypass respx mocks
-    if hasattr(litellm, "in_memory_llm_clients_cache"):
-        litellm.in_memory_llm_clients_cache.flush_cache()
-    
-    # Ensure aiohttp transport is disabled to use httpx which respx can mock
     litellm.disable_aiohttp_transport = True
 
     expected_base_url = "http://localhost:12345/v1"
@@ -495,11 +488,7 @@ async def test_openai_env_base(
     model = "gpt-4o"
     messages = [{"role": "user", "content": "Hello, how are you?"}]
 
-    # Configure respx mock to intercept the request
-    mock_route = respx_mock.post(
-        url__regex=r"http://localhost:12345/v1/chat/completions.*"
-    ).mock(return_value=httpx.Response(
-        status_code=200,
+    respx_mock.post(f"{expected_base_url}/chat/completions").respond(
         json={
             "id": "chatcmpl-123",
             "object": "chat.completion",
@@ -517,19 +506,12 @@ async def test_openai_env_base(
             ],
             "usage": {"prompt_tokens": 9, "completion_tokens": 12, "total_tokens": 21},
         }
-    ))
+    )
 
-    try:
-        response = await litellm.acompletion(model=model, messages=messages)
-        
-        # verify we had a response
-        assert response.choices[0].message.content == "Hello from mocked response!"
-        
-        # Verify the mock was called
-        assert mock_route.called, "Mock route was not called - request may have bypassed respx"
-    finally:
-        # Clean up to avoid affecting other tests
-        litellm.disable_aiohttp_transport = False
+    response = await litellm.acompletion(model=model, messages=messages)
+
+    # verify we had a response
+    assert response.choices[0].message.content == "Hello from mocked response!"
 
 
 def build_database_url(username, password, host, dbname):
diff --git a/tests/test_litellm/test_responses_api_bridge_non_stream.py b/tests/test_litellm/test_responses_api_bridge_non_stream.py
index c35ca1046fd..8f72debfc5d 100644
--- a/tests/test_litellm/test_responses_api_bridge_non_stream.py
+++ b/tests/test_litellm/test_responses_api_bridge_non_stream.py
@@ -1,32 +1,9 @@
-import os
-import sys
-from typing import Optional
-from unittest.mock import Mock
-
-import pytest
-
-sys.path.insert(0, os.path.abspath("../.."))
-
 from litellm.completion_extras.litellm_responses_transformation.handler import (
     ResponsesToCompletionBridgeHandler,
 )
-from litellm.responses.litellm_completion_transformation.transformation import (
-    LiteLLMCompletionResponsesConfig,
-)
-from litellm.types.llms.openai import (
-    InputTokensDetails,
-    OutputTokensDetails,
-    ResponsesAPIResponse,
-)
-from litellm.types.utils import Choices, Message, ModelResponse, Usage
+from litellm.types.llms.openai import ResponsesAPIResponse
 
-"""
-Test that all providers can transform completion responses to Responses API format
-without breaking due to required fields in InputTokensDetails and OutputTokensDetails.
 
-This is a regression test for the change where reasoning_tokens and cached_tokens
-were made non-optional (must be int, not Optional[int]).
-"""
 class _CompletedEvent:
     def __init__(self, response):
         self.response = response
@@ -65,358 +42,3 @@ def test_should_collect_response_from_stream():
 
     assert collected.id == "resp-1"
     assert collected._hidden_params.get("headers") == {"x-test": "1"}
-
-
-def create_mock_completion_response(
-    model: str = "gpt-4",
-    prompt_tokens: int = 10,
-    completion_tokens: int = 20,
-    total_tokens: int = 30,
-    reasoning_tokens: Optional[int] = None,
-    cached_tokens: Optional[int] = None,
-    text_tokens: Optional[int] = None,
-) -> ModelResponse:
-    """
-    Create a mock ModelResponse (chat completion) with various token details.
-    
-    This simulates responses from different providers that may or may not include
-    reasoning_tokens, cached_tokens, etc.
-    """
-    usage = Usage(
-        prompt_tokens=prompt_tokens,
-        completion_tokens=completion_tokens,
-        total_tokens=total_tokens,
-    )
-    
-    # Add prompt_tokens_details if we have cached_tokens or text_tokens
-    if cached_tokens is not None or text_tokens is not None:
-        from litellm.types.utils import PromptTokensDetails
-        usage.prompt_tokens_details = PromptTokensDetails(
-            cached_tokens=cached_tokens,
-            text_tokens=text_tokens,
-        )
-    
-    # Add completion_tokens_details if we have reasoning_tokens or text_tokens
-    if reasoning_tokens is not None or text_tokens is not None:
-        from litellm.types.utils import CompletionTokensDetails
-        usage.completion_tokens_details = CompletionTokensDetails(
-            reasoning_tokens=reasoning_tokens,
-            text_tokens=text_tokens,
-        )
-    
-    return ModelResponse(
-        id="chatcmpl-test",
-        created=1234567890,
-        model=model,
-        object="chat.completion",
-        choices=[
-            Choices(
-                finish_reason="stop",
-                index=0,
-                message=Message(
-                    content="Test response",
-                    role="assistant",
-                ),
-            )
-        ],
-        usage=usage,
-    )
-
-
-def test_transform_usage_no_token_details():
-    """
-    Test that transformation works when completion response has NO token details.
-    
-    This simulates providers that don't return detailed token breakdowns.
-    """
-    completion_response = create_mock_completion_response(
-        model="gpt-4",
-        prompt_tokens=10,
-        completion_tokens=20,
-        total_tokens=30,
-    )
-    
-    # Transform to Responses API usage format
-    responses_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage(
-        completion_response
-    )
-    
-    # Should succeed without errors
-    assert responses_usage.input_tokens == 10
-    assert responses_usage.output_tokens == 20
-    assert responses_usage.total_tokens == 30
-    
-    # Token details should not be present when not provided
-    assert responses_usage.input_tokens_details is None
-    assert responses_usage.output_tokens_details is None
-    
-    print("✓ Transformation works with no token details")
-
-
-def test_transform_usage_with_cached_tokens_only():
-    """
-    Test transformation when only cached_tokens is provided (no reasoning_tokens).
-    
-    This simulates providers like Anthropic that support prompt caching but not reasoning.
-    """
-    completion_response = create_mock_completion_response(
-        model="claude-3-opus",
-        prompt_tokens=100,
-        completion_tokens=50,
-        total_tokens=150,
-        cached_tokens=80,  # Has cached tokens
-        reasoning_tokens=None,  # No reasoning tokens
-    )
-    
-    responses_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage(
-        completion_response
-    )
-    
-    # Should succeed and default reasoning_tokens to 0
-    assert responses_usage.input_tokens == 100
-    assert responses_usage.output_tokens == 50
-    assert responses_usage.total_tokens == 150
-    
-    # Input details should be present with cached_tokens
-    assert responses_usage.input_tokens_details is not None
-    assert isinstance(responses_usage.input_tokens_details, InputTokensDetails)
-    assert responses_usage.input_tokens_details.cached_tokens == 80
-    
-    # Output details should not be present (no reasoning_tokens provided)
-    assert responses_usage.output_tokens_details is None
-    
-    print("✓ Transformation works with cached_tokens only")
-
-
-def test_transform_usage_with_reasoning_tokens_only():
-    """
-    Test transformation when only reasoning_tokens is provided (no cached_tokens).
-    
-    This simulates providers like OpenAI o1 that support reasoning but not caching.
-    """
-    completion_response = create_mock_completion_response(
-        model="o1-preview",
-        prompt_tokens=50,
-        completion_tokens=100,
-        total_tokens=150,
-        cached_tokens=None,  # No cached tokens
-        reasoning_tokens=60,  # Has reasoning tokens
-    )
-    
-    responses_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage(
-        completion_response
-    )
-    
-    # Should succeed and default cached_tokens to 0
-    assert responses_usage.input_tokens == 50
-    assert responses_usage.output_tokens == 100
-    assert responses_usage.total_tokens == 150
-    
-    # Input details should not be present (no cached_tokens provided)
-    assert responses_usage.input_tokens_details is None
-    
-    # Output details should be present with reasoning_tokens
-    assert responses_usage.output_tokens_details is not None
-    assert isinstance(responses_usage.output_tokens_details, OutputTokensDetails)
-    assert responses_usage.output_tokens_details.reasoning_tokens == 60
-    
-    print("✓ Transformation works with reasoning_tokens only")
-
-
-def test_transform_usage_with_both_token_details():
-    """
-    Test transformation when both cached_tokens and reasoning_tokens are provided.
-    
-    This simulates advanced providers that support both features.
-    """
-    completion_response = create_mock_completion_response(
-        model="gpt-4o",
-        prompt_tokens=100,
-        completion_tokens=80,
-        total_tokens=180,
-        cached_tokens=50,
-        reasoning_tokens=30,
-        text_tokens=50,  # Also include text_tokens
-    )
-    
-    responses_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage(
-        completion_response
-    )
-    
-    # Should succeed with all details
-    assert responses_usage.input_tokens == 100
-    assert responses_usage.output_tokens == 80
-    assert responses_usage.total_tokens == 180
-    
-    # Input details should have cached_tokens
-    assert responses_usage.input_tokens_details is not None
-    assert responses_usage.input_tokens_details.cached_tokens == 50
-    assert responses_usage.input_tokens_details.text_tokens == 50
-    
-    # Output details should have reasoning_tokens
-    assert responses_usage.output_tokens_details is not None
-    assert responses_usage.output_tokens_details.reasoning_tokens == 30
-    assert responses_usage.output_tokens_details.text_tokens == 50
-    
-    print("✓ Transformation works with both cached_tokens and reasoning_tokens")
-
-
-def test_transform_usage_with_zero_values():
-    """
-    Test transformation when token details are explicitly set to 0.
-    
-    This ensures 0 values are preserved and not treated as None.
-    """
-    completion_response = create_mock_completion_response(
-        model="gpt-4",
-        prompt_tokens=100,
-        completion_tokens=50,
-        total_tokens=150,
-        cached_tokens=0,  # Explicitly 0
-        reasoning_tokens=0,  # Explicitly 0
-    )
-    
-    responses_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage(
-        completion_response
-    )
-    
-    # Should preserve 0 values
-    assert responses_usage.input_tokens_details is not None
-    assert responses_usage.input_tokens_details.cached_tokens == 0
-    
-    assert responses_usage.output_tokens_details is not None
-    assert responses_usage.output_tokens_details.reasoning_tokens == 0
-    
-    print("✓ Transformation preserves explicit 0 values")
-
-
-def test_input_tokens_details_requires_cached_tokens():
-    """
-    Test that InputTokensDetails has cached_tokens as an int with default value 0.
-    
-    This ensures backward compatibility while making the field non-optional.
-    """
-    # Should work with cached_tokens=0
-    details1 = InputTokensDetails(cached_tokens=0)
-    assert details1.cached_tokens == 0
-    
-    # Should work with cached_tokens=100
-    details2 = InputTokensDetails(cached_tokens=100)
-    assert details2.cached_tokens == 100
-    
-    # Should work without cached_tokens (defaults to 0)
-    details3 = InputTokensDetails()
-    assert details3.cached_tokens == 0
-    
-    print("✓ InputTokensDetails correctly defaults cached_tokens to 0")
-
-
-def test_output_tokens_details_requires_reasoning_tokens():
-    """
-    Test that OutputTokensDetails has reasoning_tokens as an int with default value 0.
-    
-    This ensures backward compatibility while making the field non-optional.
-    """
-    # Should work with reasoning_tokens=0
-    details1 = OutputTokensDetails(reasoning_tokens=0)
-    assert details1.reasoning_tokens == 0
-    
-    # Should work with reasoning_tokens=100
-    details2 = OutputTokensDetails(reasoning_tokens=100)
-    assert details2.reasoning_tokens == 100
-    
-    # Should work without reasoning_tokens (defaults to 0)
-    details3 = OutputTokensDetails()
-    assert details3.reasoning_tokens == 0
-    
-    print("✓ OutputTokensDetails correctly defaults reasoning_tokens to 0")
-
-
-def test_all_providers_transformation_scenarios():
-    """
-    Test various provider scenarios to ensure none break after the field requirement change.
-    
-    This tests the most common scenarios across different providers:
-    - OpenAI: may have reasoning_tokens
-    - Anthropic: may have cached_tokens
-    - Azure: similar to OpenAI
-    - Other providers: basic usage only
-    """
-    test_scenarios = [
-        {
-            "name": "Basic provider (no details)",
-            "model": "gpt-3.5-turbo",
-            "kwargs": {},
-        },
-        {
-            "name": "OpenAI with reasoning",
-            "model": "o1-preview",
-            "kwargs": {"reasoning_tokens": 100},
-        },
-        {
-            "name": "Anthropic with caching",
-            "model": "claude-3-opus",
-            "kwargs": {"cached_tokens": 50},
-        },
-        {
-            "name": "OpenAI with caching",
-            "model": "gpt-4o",
-            "kwargs": {"cached_tokens": 30},
-        },
-        {
-            "name": "Full details (both)",
-            "model": "gpt-4o",
-            "kwargs": {"cached_tokens": 40, "reasoning_tokens": 60, "text_tokens": 100},
-        },
-        {
-            "name": "Zero values",
-            "model": "gpt-4",
-            "kwargs": {"cached_tokens": 0, "reasoning_tokens": 0},
-        },
-    ]
-    
-    for scenario in test_scenarios:
-        print(f"\nTesting: {scenario['name']}")
-        
-        completion_response = create_mock_completion_response(
-            model=scenario["model"],
-            **scenario["kwargs"]
-        )
-        
-        # This should not raise any errors
-        responses_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage(
-            completion_response
-        )
-        
-        # Basic assertions
-        assert responses_usage.input_tokens >= 0
-        assert responses_usage.output_tokens >= 0
-        assert responses_usage.total_tokens >= 0
-        
-        # If input_tokens_details exists, cached_tokens must be an int
-        if responses_usage.input_tokens_details is not None:
-            assert isinstance(responses_usage.input_tokens_details.cached_tokens, int)
-        
-        # If output_tokens_details exists, reasoning_tokens must be an int
-        if responses_usage.output_tokens_details is not None:
-            assert isinstance(responses_usage.output_tokens_details.reasoning_tokens, int)
-        
-        print(f"  ✓ {scenario['name']} transformation successful")
-    
-    print("\n✓ All provider scenarios work correctly")
-
-
-if __name__ == "__main__":
-    # Run all tests
-    test_transform_usage_no_token_details()
-    test_transform_usage_with_cached_tokens_only()
-    test_transform_usage_with_reasoning_tokens_only()
-    test_transform_usage_with_both_token_details()
-    test_transform_usage_with_zero_values()
-    test_input_tokens_details_requires_cached_tokens()
-    test_output_tokens_details_requires_reasoning_tokens()
-    test_all_providers_transformation_scenarios()
-    
-    print("\n" + "="*60)
-    print("ALL TESTS PASSED!")
-    print("="*60)
diff --git a/tests/test_litellm/test_router_per_deployment_num_retries.py b/tests/test_litellm/test_router_per_deployment_num_retries.py
index 154ba579e4e..4021ca28073 100644
--- a/tests/test_litellm/test_router_per_deployment_num_retries.py
+++ b/tests/test_litellm/test_router_per_deployment_num_retries.py
@@ -32,17 +32,17 @@ def test_set_deployment_num_retries_on_exception(self):
         )
 
         deployment = router.model_list[0]
-
+        
         # Create a mock exception without num_retries
         class MockException(Exception):
             pass
-
+        
         exc = MockException("test error")
         assert not hasattr(exc, "num_retries") or exc.num_retries is None
-
+        
         # Call the helper
         router._set_deployment_num_retries_on_exception(exc, deployment)
-
+        
         # Verify num_retries was set from deployment
         assert exc.num_retries == 5
 
@@ -66,16 +66,16 @@ def test_set_deployment_num_retries_does_not_override_existing(self):
         )
 
         deployment = router.model_list[0]
-
+        
         # Create an exception that already has num_retries
         class MockException(Exception):
             num_retries = 10  # Already set
-
+        
         exc = MockException("test error")
-
+        
         # Call the helper
         router._set_deployment_num_retries_on_exception(exc, deployment)
-
+        
         # Verify num_retries was NOT overridden
         assert exc.num_retries == 10
 
@@ -99,15 +99,15 @@ def test_deployment_without_num_retries(self):
         )
 
         deployment = router.model_list[0]
-
+        
         class MockException(Exception):
             pass
-
+        
         exc = MockException("test error")
-
+        
         # Call the helper
         router._set_deployment_num_retries_on_exception(exc, deployment)
-
+        
         # Verify num_retries was not set (deployment has no num_retries)
         assert not hasattr(exc, "num_retries") or exc.num_retries is None
 
@@ -155,36 +155,3 @@ def test_global_num_retries_used_when_no_deployment_setting(self):
         kwargs = {}
         router._update_kwargs_before_fallbacks(model="test-model", kwargs=kwargs)
         assert kwargs["num_retries"] == 7  # Uses global
-
-    def test_set_deployment_num_retries_with_string_value(self):
-        """
-        Test that _set_deployment_num_retries_on_exception handles string values
-        from environment variables correctly.
-        GitHub Issue: #19481
-        """
-        router = Router(
-            model_list=[
-                {
-                    "model_name": "test-model",
-                    "litellm_params": {
-                        "model": "openai/gpt-4",
-                        "api_key": "test-key",
-                        "num_retries": "6",  # String value (as from env var)
-                    },
-                },
-            ],
-            num_retries=0,  # Global setting
-        )
-
-        deployment = router.model_list[0]
-
-        class MockException(Exception):
-            pass
-
-        exc = MockException("test error")
-
-        # Call the helper
-        router._set_deployment_num_retries_on_exception(exc, deployment)
-
-        # Verify num_retries was converted from string to int
-        assert exc.num_retries == 6
diff --git a/tests/test_litellm/test_router_silent_experiment.py b/tests/test_litellm/test_router_silent_experiment.py
deleted file mode 100644
index 9b82cde13c6..00000000000
--- a/tests/test_litellm/test_router_silent_experiment.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import asyncio
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-import litellm
-from litellm.router import Router
-
-
-@pytest.mark.asyncio
-async def test_router_silent_experiment_acompletion():
-    """
-    Test that silent_model triggers a background acompletion call
-    and that the silent_model parameter is stripped from both calls.
-    """
-    model_list = [
-        {
-            "model_name": "primary-model",
-            "litellm_params": {
-                "model": "openai/gpt-3.5-turbo",
-                "api_key": "fake-key",
-                "silent_model": "silent-model",
-            },
-        },
-        {
-            "model_name": "silent-model",
-            "litellm_params": {
-                "model": "openai/gpt-4",
-                "api_key": "fake-key",
-            },
-        },
-    ]
-
-    router = Router(model_list=model_list)
-
-    # Mock litellm.acompletion
-    mock_acompletion = MagicMock()
-    # Create a future that resolves to a ModelResponse
-    mock_response = litellm.ModelResponse(choices=[{"message": {"content": "hello"}}])
-    future = asyncio.Future()
-    future.set_result(mock_response)
-    mock_acompletion.return_value = future
-
-    with patch("litellm.acompletion", mock_acompletion):
-        response = await router.acompletion(
-            model="primary-model",
-            messages=[{"role": "user", "content": "hi"}],
-        )
-
-        assert response.choices[0].message.content == "hello"
-
-        # Give the background task a moment to trigger (it's an asyncio task)
-        await asyncio.sleep(0.1)
-
-        # Should have 2 calls: one for primary, one for silent
-        assert mock_acompletion.call_count == 2
-
-        # Check call arguments
-        call_args_list = mock_acompletion.call_args_list
-
-        # Verify no silent_model in any call to litellm.acompletion
-        for call in call_args_list:
-            args, kwargs = call
-            assert "silent_model" not in kwargs
-            if "metadata" in kwargs:
-                # One call should have is_silent_experiment=True
-                pass
-
-        # Find the silent call
-        silent_call = next(
-            (
-                c
-                for c in call_args_list
-                if c[1].get("metadata", {}).get("is_silent_experiment") is True
-            ),
-            None,
-        )
-        assert silent_call is not None
-        assert silent_call[1]["model"] == "openai/gpt-4"
-
-        # Find the primary call
-        primary_call = next(
-            (
-                c
-                for c in call_args_list
-                if not c[1].get("metadata", {}).get("is_silent_experiment")
-            ),
-            None,
-        )
-        assert primary_call is not None
-        assert primary_call[1]["model"] == "openai/gpt-3.5-turbo"
-
-
-def test_router_silent_experiment_completion():
-    """
-    Test that silent_model triggers a background completion call (sync)
-    and that the silent_model parameter is stripped.
-    """
-    model_list = [
-        {
-            "model_name": "primary-model",
-            "litellm_params": {
-                "model": "openai/gpt-3.5-turbo",
-                "api_key": "fake-key",
-                "silent_model": "silent-model",
-            },
-        },
-        {
-            "model_name": "silent-model",
-            "litellm_params": {
-                "model": "openai/gpt-4",
-                "api_key": "fake-key",
-            },
-        },
-    ]
-
-    router = Router(model_list=model_list)
-
-    # Mock litellm.completion
-    mock_completion = MagicMock()
-    mock_response = litellm.ModelResponse(choices=[{"message": {"content": "hello"}}])
-    mock_completion.return_value = mock_response
-
-    with patch("litellm.completion", mock_completion):
-        response = router.completion(
-            model="primary-model",
-            messages=[{"role": "user", "content": "hi"}],
-        )
-
-        assert response.choices[0].message.content == "hello"
-
-        # The sync background call uses a thread pool. We might need to wait a bit.
-        import time
-
-        time.sleep(0.5)
-
-        # Should have 2 calls
-        assert mock_completion.call_count == 2
-
-        call_args_list = mock_completion.call_args_list
-
-        # Verify no silent_model in any call
-        for call in call_args_list:
-            args, kwargs = call
-            assert "silent_model" not in kwargs
-
-        # Find the silent call
-        silent_call = next(
-            (
-                c
-                for c in call_args_list
-                if c[1].get("metadata", {}).get("is_silent_experiment") is True
-            ),
-            None,
-        )
-        assert silent_call is not None
-        assert silent_call[1]["model"] == "openai/gpt-4"
diff --git a/tests/test_litellm/test_video_generation.py b/tests/test_litellm/test_video_generation.py
index cfc1535052c..73bfa71d20b 100644
--- a/tests/test_litellm/test_video_generation.py
+++ b/tests/test_litellm/test_video_generation.py
@@ -204,7 +204,7 @@ def test_video_generation_cost_calculation(self):
         """Test video generation cost calculation."""
         import json
         import os
-
+        
         # Try to load the local model cost map, skip if not found
         cost_map_path = "model_prices_and_context_window.json"
         if not os.path.exists(cost_map_path):
@@ -294,47 +294,39 @@ def test_video_generation_uses_api_key_from_litellm_params(self):
             with patch.object(config, 'transform_video_create_request') as mock_transform:
                 mock_transform.return_value = ({"model": "sora-2", "prompt": "test"}, [], "https://api.openai.com/v1/videos")
                 
-                # Mock the transform_video_create_response to avoid needing a real response
-                with patch.object(config, 'transform_video_create_response') as mock_transform_response:
-                    mock_video_object = MagicMock()
-                    mock_video_object.id = "video_123"
-                    mock_video_object.object = "video"
-                    mock_video_object.status = "queued"
-                    mock_transform_response.return_value = mock_video_object
-                    
-                    mock_response = MagicMock()
-                    mock_response.json.return_value = {
-                        "id": "video_123",
-                        "object": "video",
-                        "status": "queued",
-                        "created_at": 1712697600,
-                        "model": "sora-2"
-                    }
-                    mock_response.status_code = 200
-                    
-                    mock_client = MagicMock()
-                    mock_client.post.return_value = mock_response
-                    
-                    with patch(
-                        "litellm.llms.custom_httpx.llm_http_handler._get_httpx_client",
-                        return_value=mock_client,
-                    ):
-                        result = handler.video_generation_handler(
-                            model="sora-2",
-                            prompt="test prompt",
-                            video_generation_provider_config=config,
-                            video_generation_optional_request_params={},
-                            custom_llm_provider="openai",
-                            litellm_params={"api_key": "deployment-api-key", "api_base": "https://api.openai.com/v1"},
-                            logging_obj=MagicMock(),
-                            timeout=5.0,
-                            api_key=None,  # Function parameter is None
-                            _is_async=False,
-                        )
-                    
-                    # Verify validate_environment was called with api_key from litellm_params
-                    mock_validate.assert_called_once()
-                    call_args = mock_validate.call_args
+                mock_response = MagicMock()
+                mock_response.json.return_value = {
+                    "id": "video_123",
+                    "object": "video",
+                    "status": "queued",
+                    "created_at": 1712697600,
+                    "model": "sora-2"
+                }
+                mock_response.status_code = 200
+                
+                mock_client = MagicMock()
+                mock_client.post.return_value = mock_response
+                
+                with patch(
+                    "litellm.llms.custom_httpx.llm_http_handler._get_httpx_client",
+                    return_value=mock_client,
+                ):
+                    handler.video_generation_handler(
+                        model="sora-2",
+                        prompt="test prompt",
+                        video_generation_provider_config=config,
+                        video_generation_optional_request_params={},
+                        custom_llm_provider="openai",
+                        litellm_params={"api_key": "deployment-api-key", "api_base": "https://api.openai.com/v1"},
+                        logging_obj=MagicMock(),
+                        timeout=5.0,
+                        api_key=None,  # Function parameter is None
+                        _is_async=False,
+                    )
+                
+                # Verify validate_environment was called with api_key from litellm_params
+                mock_validate.assert_called_once()
+                call_args = mock_validate.call_args
                 assert call_args.kwargs["api_key"] == "deployment-api-key"
 
     def test_video_generation_url_generation(self):
@@ -832,7 +824,7 @@ def test_video_content_handler_uses_get_for_openai():
 def test_video_content_respects_api_base_and_api_key_from_kwargs():
     """Test that video_content respects api_base and api_key from kwargs (simulating database entry)."""
     from litellm.videos.main import video_content
-
+    
     # Mock the handler to capture litellm_params
     captured_litellm_params = None
     
@@ -924,16 +916,10 @@ def client_with_vertex_config(self, monkeypatch):
         """Create a test client with a proxy config that includes Vertex AI model with litellm_params."""
         import asyncio
         import tempfile
-
         import yaml
         from fastapi import FastAPI
         from fastapi.testclient import TestClient
-
-        from litellm.proxy.proxy_server import (
-            cleanup_router_config_variables,
-            initialize,
-            router,
-        )
+        from litellm.proxy.proxy_server import cleanup_router_config_variables, router, initialize
         from litellm.proxy.video_endpoints.endpoints import router as video_router
 
         # Clean up any existing router config
diff --git a/tests/test_litellm/vector_stores/test_vector_store_registry.py b/tests/test_litellm/vector_stores/test_vector_store_registry.py
index a3af476bc71..fb585e11220 100644
--- a/tests/test_litellm/vector_stores/test_vector_store_registry.py
+++ b/tests/test_litellm/vector_stores/test_vector_store_registry.py
@@ -119,12 +119,8 @@ def test_add_vector_store_to_registry():
 
 
 
-@respx.mock
 def test_search_uses_registry_credentials():
     """search() should pull credentials from vector_store_registry when available"""
-    # Block all HTTP requests at the network level to prevent real API calls
-    respx.route().mock(return_value=httpx.Response(200, json={"object": "list", "data": []}))
-    
     vector_store = LiteLLM_ManagedVectorStore(
         vector_store_id="vs1",
         custom_llm_provider="bedrock",
@@ -137,16 +133,6 @@ def test_search_uses_registry_credentials():
     try:
         logger = MagicMock()
         logger._response_cost_calculator.return_value = 0
-        
-        # Mock the search response
-        mock_search_response = {
-            "object": "list",
-            "data": [],
-            "first_id": None,
-            "last_id": None,
-            "has_more": False
-        }
-        
         with patch.object(
             registry,
             "get_credentials_for_vector_store",
@@ -156,7 +142,7 @@ def test_search_uses_registry_credentials():
             return_value=MagicMock(),
         ), patch(
             "litellm.vector_stores.main.base_llm_http_handler.vector_store_search_handler",
-            return_value=mock_search_response,
+            return_value={},
         ) as mock_handler:
             search(vector_store_id="vs1", query="test", litellm_logging_obj=logger)
             mock_get_creds.assert_called_once_with("vs1")
diff --git a/tests/test_otel_thread_leak.py b/tests/test_otel_thread_leak.py
deleted file mode 100644
index 34f6b299caa..00000000000
--- a/tests/test_otel_thread_leak.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import sys
-import os
-import threading
-import time
-import pytest
-
-# Add the project root to the path
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-
-from litellm.integrations.opentelemetry import OpenTelemetry, OpenTelemetryConfig
-from litellm.types.utils import StandardCallbackDynamicParams
-
-def get_thread_count() -> int:
-    """Helper to get active thread count"""
-    return threading.active_count()
-
-@pytest.fixture
-def otel_logger():
-    """Fixture to provide a clean OTEL logger for each test"""
-    config = OpenTelemetryConfig(
-        exporter="console",
-        enable_metrics=False,
-        service_name="litellm-unit-test"
-    )
-    return OpenTelemetry(config=config)
-
-def test_otel_thread_leak_dynamic_headers(otel_logger):
-    """
-    Unit test to verify that calling get_tracer_to_use_for_request with 
-    dynamic headers doesn't cause a linear thread leak.
-    
-    This test reproduces the issue where each unique team/key credential
-    set causes a new TracerProvider (and its background threads) to be 
-    spawned but never closed.
-    """
-    
-    # 1. Setup dynamic header simulation (monkey-patch)
-    # This simulates what LangfuseOtelLogger does for per-team keys
-    def mock_construct_dynamic_headers(standard_callback_dynamic_params):
-        if standard_callback_dynamic_params:
-            return {"Authorization": "Bearer fake_token"}
-        return None
-    
-    otel_logger.construct_dynamic_otel_headers = mock_construct_dynamic_headers
-    
-    # 2. Establish Baseline
-    initial_threads = get_thread_count()
-    
-    # 3. Simulate requests
-    num_requests = 10
-    latencies = []
-    
-    print("\n🚀 Simulating requests with dynamic headers:")
-    for i in range(num_requests):
-        kwargs = {
-            "standard_callback_dynamic_params": StandardCallbackDynamicParams(
-                langfuse_public_key=f"key_{i}",
-                langfuse_secret_key=f"secret_{i}",
-            )
-        }
-        
-        # Measure latency
-        start_time = time.perf_counter()
-        tracer = otel_logger.get_tracer_to_use_for_request(kwargs)
-        end_time = time.perf_counter()
-        
-        latency_ms = (end_time - start_time) * 1000
-        latencies.append(latency_ms)
-        print(f"   Request {i+1:2d}: Latency = {latency_ms:6.2f} ms")
-        
-        # Verify a tracer was actually returned
-        assert tracer is not None
-    
-    avg_latency = sum(latencies) / len(latencies)
-    print(f"\n📊 Average Latency: {avg_latency:.2f} ms")
-        
-    # 4. Check for leaks
-    # Allow for a small constant increase (OTEL might start a few shared threads)
-    # but a linear leak would result in +10 or more threads here.
-    final_threads = get_thread_count()
-    thread_delta = final_threads - initial_threads
-    
-    print(f"\nThread growth: {thread_delta} threads across {num_requests} requests")
-    
-    # ASSERTION: The growth should be significantly less than 1 thread per request.
-    # If the bug exists, thread_delta will be >= num_requests.
-    assert thread_delta < (num_requests / 2), (
-        f"Thread leak detected! Threads grew by {thread_delta} over {num_requests} requests. "
-        "Each request with dynamic headers appears to be leaking background threads."
-    )
diff --git a/tests/test_presidio_latency.py b/tests/test_presidio_latency.py
deleted file mode 100644
index d434e6222eb..00000000000
--- a/tests/test_presidio_latency.py
+++ /dev/null
@@ -1,73 +0,0 @@
-
-import asyncio
-import aiohttp
-import pytest
-from unittest.mock import MagicMock, patch
-from litellm.proxy.guardrails.guardrail_hooks.presidio import _OPTIONAL_PresidioPIIMasking
-
-@pytest.mark.asyncio
-async def test_sanity_presidio_session_reuse_main_thread():
-    """
-    SANITY CHECK:
-    Verify that Presidio guardrail reuses sessions in the main thread.
-    This ensures we don't break existing session pooling functionality.
-    """
-    presidio = _OPTIONAL_PresidioPIIMasking(
-        mock_testing=True,
-        presidio_analyzer_api_base="http://mock-analyzer",
-        presidio_anonymizer_api_base="http://mock-anonymizer"
-    )
-    
-    session_creations = 0
-    original_init = aiohttp.ClientSession.__init__
-    
-    def mocked_init(self, *args, **kwargs):
-        nonlocal session_creations
-        session_creations += 1
-        original_init(self, *args, **kwargs)
-
-    with patch.object(aiohttp.ClientSession, "__init__", side_effect=mocked_init, autospec=True):
-        for _ in range(10):
-            async with presidio._get_session_iterator() as session:
-                pass
-        
-        # Expected: Only 1 session created for all 10 calls.
-        assert session_creations == 1
-        
-    await presidio._close_http_session()
-
-@pytest.mark.asyncio
-async def test_bug_presidio_session_explosion_background_thread_causes_latency():
-    """
-    BUG REPRODUCTION:
-    Verify that background threads (like logging hooks) REUSE sessions.
-    Previously, each call in a background loop created a NEW ephemeral session, 
-    leading to socket exhaustion and the reported 97s latency spike.
-    """
-    import threading
-    presidio = _OPTIONAL_PresidioPIIMasking(
-        mock_testing=True,
-        presidio_analyzer_api_base="http://mock-analyzer",
-        presidio_anonymizer_api_base="http://mock-anonymizer"
-    )
-    
-    # Force the code to think it's in a background thread
-    presidio._main_thread_id = threading.get_ident() + 1
-    
-    session_creations = 0
-    original_init = aiohttp.ClientSession.__init__
-    
-    def mocked_init(self, *args, **kwargs):
-        nonlocal session_creations
-        session_creations += 1
-        original_init(self, *args, **kwargs)
-
-    with patch.object(aiohttp.ClientSession, "__init__", side_effect=mocked_init, autospec=True):
-        for _ in range(10):
-            async with presidio._get_session_iterator() as session:
-                pass
-        
-        # FIX VERIFICATION: Should now be 1 session (reused) instead of 10.
-        assert session_creations == 1
-        
-    await presidio._close_http_session()
\ No newline at end of file
diff --git a/tests/test_team.py b/tests/test_team.py
index d67c5e670f4..f7752f9c89b 100644
--- a/tests/test_team.py
+++ b/tests/test_team.py
@@ -531,7 +531,6 @@ async def test_team_update_sc_2():
                 or k == "object_permission_id"
                 or k == "object_permission"
                 or k == "litellm_model_table"
-                or k == "policies"
             ):
                 pass
             else:
diff --git a/tests/vector_store_tests/rag/test_rag_s3_vectors.py b/tests/vector_store_tests/rag/test_rag_s3_vectors.py
deleted file mode 100644
index cd8a362a7bf..00000000000
--- a/tests/vector_store_tests/rag/test_rag_s3_vectors.py
+++ /dev/null
@@ -1,107 +0,0 @@
-"""
-S3 Vectors RAG ingestion tests.
-
-Requires environment variables:
-- AWS_ACCESS_KEY_ID
-- AWS_SECRET_ACCESS_KEY
-- AWS_REGION_NAME (optional, defaults to us-west-2)
-
-Optional:
-- S3_VECTOR_BUCKET_NAME (optional, auto-generates if not set)
-"""
-
-import os
-import sys
-from typing import Any, Dict, Optional
-
-import pytest
-
-sys.path.insert(0, os.path.abspath("../../.."))
-
-import litellm
-from litellm.types.rag import RAGIngestOptions
-from tests.vector_store_tests.rag.base_rag_tests import BaseRAGTest
-
-
-class TestRAGS3Vectors(BaseRAGTest):
-    """Test RAG Ingest with AWS S3 Vectors."""
-
-    @pytest.fixture(autouse=True)
-    def check_env_vars(self):
-        """Check required environment variables before each test."""
-        aws_key = os.environ.get("AWS_ACCESS_KEY_ID")
-        aws_secret = os.environ.get("AWS_SECRET_ACCESS_KEY")
-
-        if not aws_key or not aws_secret:
-            pytest.skip("Skipping S3 Vectors test: AWS credentials required")
-
-    def get_base_ingest_options(self) -> RAGIngestOptions:
-        """
-        Return S3 Vectors-specific ingest options.
-
-        Chunking is configured via chunking_strategy (unified interface).
-        Embeddings are generated using LiteLLM's embedding API.
-        """
-        vector_bucket_name = os.environ.get(
-            "S3_VECTOR_BUCKET_NAME", "test-litellm-vectors"
-        )
-        aws_region = os.environ.get("AWS_REGION_NAME", "us-west-2")
-
-        return {
-            "chunking_strategy": {
-                "chunk_size": 512,
-                "chunk_overlap": 100,
-            },
-            "embedding": {
-                "model": "text-embedding-3-small"  # Can use any LiteLLM-supported model
-            },
-            "vector_store": {
-                "custom_llm_provider": "s3_vectors",
-                "vector_bucket_name": vector_bucket_name,
-                "index_name": "test-index",
-                # dimension is auto-detected from embedding model (text-embedding-3-small = 1536)
-                "distance_metric": "cosine",
-                "non_filterable_metadata_keys": ["source_text"],
-                "aws_region_name": aws_region,
-            },
-        }
-
-    async def query_vector_store(
-        self,
-        vector_store_id: str,
-        query: str,
-    ) -> Optional[Dict[str, Any]]:
-        """Query S3 Vectors index."""
-        try:
-            # Import the ingestion class to use its query method
-            from litellm.rag.ingestion.s3_vectors_ingestion import (
-                S3VectorsRAGIngestion,
-            )
-        except ImportError:
-            pytest.skip("S3 Vectors ingestion not available")
-
-        vector_bucket_name = os.environ.get(
-            "S3_VECTOR_BUCKET_NAME", "test-litellm-vectors"
-        )
-        aws_region = os.environ.get("AWS_REGION_NAME", "us-west-2")
-
-        # Create ingestion instance to use query method
-        ingest_options = {
-            "embedding": {"model": "text-embedding-3-small"},
-            "vector_store": {
-                "custom_llm_provider": "s3_vectors",
-                "vector_bucket_name": vector_bucket_name,
-                "aws_region_name": aws_region,
-            },
-        }
-
-        ingestion = S3VectorsRAGIngestion(ingest_options=ingest_options)
-
-        # Query the index
-        results = await ingestion.query_vector_store(
-            vector_store_id=vector_store_id,
-            query=query,
-            top_k=5,
-        )
-
-        return results
diff --git a/tests/vector_store_tests/test_s3_vectors_vector_store.py b/tests/vector_store_tests/test_s3_vectors_vector_store.py
deleted file mode 100644
index a7a1568c1cc..00000000000
--- a/tests/vector_store_tests/test_s3_vectors_vector_store.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from base_vector_store_test import BaseVectorStoreTest
-import os
-import pytest
-
-
-class TestS3VectorsVectorStore(BaseVectorStoreTest):
-    @pytest.fixture(autouse=True)
-    def check_env_vars(self):
-        """Check if required environment variables are set"""
-        required_vars = ["AWS_ACCESS_KEY_ID", "AWS_SECRET_ACCESS_KEY"]
-        missing_vars = [var for var in required_vars if not os.getenv(var)]
-        if missing_vars:
-            pytest.skip(f"Missing required environment variables: {', '.join(missing_vars)}")
-
-    def get_base_request_args(self) -> dict:
-        """
-        Must return the base request args for searching.
-        For S3 Vectors, vector_store_id should be in format: bucket_name:index_name
-        """
-        return {
-            "custom_llm_provider": "s3_vectors",
-            "vector_store_id": os.getenv(
-                "S3_VECTORS_VECTOR_STORE_ID", "test-litellm-vectors:test-index"
-            ),
-            "query": "What is machine learning?",
-            "aws_region_name": os.getenv("AWS_REGION_NAME", "us-west-2"),
-            "aws_access_key_id": os.getenv("AWS_ACCESS_KEY_ID"),
-            "aws_secret_access_key": os.getenv("AWS_SECRET_ACCESS_KEY"),
-        }
-
-    def get_base_create_vector_store_args(self) -> dict:
-        """
-        Vector store creation is not yet implemented for S3 Vectors.
-        This test will be skipped.
-        """
-        return {}
-
-    @pytest.mark.parametrize("sync_mode", [True, False])
-    @pytest.mark.asyncio
-    async def test_basic_create_vector_store(self, sync_mode):
-        """S3 Vectors doesn't support vector store creation via this API yet"""
-        pytest.skip("Vector store creation not yet implemented for S3 Vectors")
diff --git a/ui/litellm-dashboard/package.json b/ui/litellm-dashboard/package.json
index 16fc656dc53..385c1d82af7 100644
--- a/ui/litellm-dashboard/package.json
+++ b/ui/litellm-dashboard/package.json
@@ -8,7 +8,6 @@
     "start": "next start",
     "lint": "next lint",
     "test": "vitest",
-    "test:dot": "vitest --reporter=dot",
     "test:watch": "vitest -w",
     "test:coverage": "vitest run --coverage",
     "format": "prettier --write .",
diff --git a/ui/litellm-dashboard/public/assets/logos/s3_vector.png b/ui/litellm-dashboard/public/assets/logos/s3_vector.png
deleted file mode 100644
index 15a1a456e12..00000000000
Binary files a/ui/litellm-dashboard/public/assets/logos/s3_vector.png and /dev/null differ
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/components/SidebarProvider.tsx b/ui/litellm-dashboard/src/app/(dashboard)/components/SidebarProvider.tsx
index 26f5786b413..8b934e10779 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/components/SidebarProvider.tsx
+++ b/ui/litellm-dashboard/src/app/(dashboard)/components/SidebarProvider.tsx
@@ -1,9 +1,4 @@
-"use client";
-
 import Sidebar from "@/components/leftnav";
-import { getUISettings } from "@/components/networking";
-import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
-import { useEffect, useState } from "react";
 
 interface SidebarProviderProps {
   setPage: (page: string) => void;
@@ -12,44 +7,7 @@ interface SidebarProviderProps {
 }
 
 const SidebarProvider = ({ setPage, defaultSelectedKey, sidebarCollapsed }: SidebarProviderProps) => {
-  const { accessToken } = useAuthorized();
-  const [enabledPagesInternalUsers, setEnabledPagesInternalUsers] = useState<string[] | null>(null);
-
-  useEffect(() => {
-    const fetchUISettings = async () => {
-      if (!accessToken) {
-        console.log("[SidebarProvider] No access token, skipping UI settings fetch");
-        return;
-      }
-
-      try {
-        console.log("[SidebarProvider] Fetching UI settings from /get/ui_settings");
-        const settings = await getUISettings(accessToken);
-        console.log("[SidebarProvider] UI settings response:", settings);
-        
-        // API returns 'values' not 'settings'
-        if (settings?.values?.enabled_ui_pages_internal_users !== undefined) {
-          console.log("[SidebarProvider] Setting enabled pages:", settings.values.enabled_ui_pages_internal_users);
-          setEnabledPagesInternalUsers(settings.values.enabled_ui_pages_internal_users);
-        } else {
-          console.log("[SidebarProvider] No enabled_ui_pages_internal_users in response (all pages visible by default)");
-        }
-      } catch (error) {
-        console.error("[SidebarProvider] Failed to fetch UI settings:", error);
-      }
-    };
-
-    fetchUISettings();
-  }, [accessToken]);
-
-  return (
-    <Sidebar
-      setPage={setPage}
-      defaultSelectedKey={defaultSelectedKey}
-      collapsed={sidebarCollapsed}
-      enabledPagesInternalUsers={enabledPagesInternalUsers}
-    />
-  );
+  return <Sidebar setPage={setPage} defaultSelectedKey={defaultSelectedKey} collapsed={sidebarCollapsed} />;
 };
 
 export default SidebarProvider;
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.test.ts
index 4985206092f..c6629e5396a 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.test.ts
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.test.ts
@@ -101,12 +101,7 @@ describe("useModelsInfo", () => {
       "test-user-id",
       "Admin",
       1,
-      50,
-      undefined,
-      undefined,
-      undefined,
-      undefined,
-      undefined
+      50
     );
     expect(modelInfoCall).toHaveBeenCalledTimes(1);
   });
@@ -125,12 +120,7 @@ describe("useModelsInfo", () => {
       "test-user-id",
       "Admin",
       2,
-      25,
-      undefined,
-      undefined,
-      undefined,
-      undefined,
-      undefined
+      25
     );
   });
 
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.ts
index c57de675e0e..1dbd79eacf9 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.ts
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.ts
@@ -27,7 +27,7 @@ const modelHubKeys = createQueryKeys("modelHub");
 const allProxyModelsKeys = createQueryKeys("allProxyModels");
 const selectedTeamModelsKeys = createQueryKeys("selectedTeamModels");
 
-export const useModelsInfo = (page: number = 1, size: number = 50, search?: string, modelId?: string, teamId?: string, sortBy?: string, sortOrder?: string) => {
+export const useModelsInfo = (page: number = 1, size: number = 50) => {
   const { accessToken, userId, userRole } = useAuthorized();
   return useQuery<PaginatedModelInfoResponse>({
     queryKey: modelKeys.list({
@@ -36,14 +36,9 @@ export const useModelsInfo = (page: number = 1, size: number = 50, search?: stri
         ...(userRole && { userRole }),
         page,
         size,
-        ...(search && { search }),
-        ...(modelId && { modelId }),
-        ...(teamId && { teamId }),
-        ...(sortBy && { sortBy }),
-        ...(sortOrder && { sortOrder }),
       },
     }),
-    queryFn: async () => await modelInfoCall(accessToken!, userId!, userRole!, page, size, search, modelId, teamId, sortBy, sortOrder),
+    queryFn: async () => await modelInfoCall(accessToken!, userId!, userRole!, page, size),
     enabled: Boolean(accessToken && userId && userRole),
   });
 };
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs.ts
deleted file mode 100644
index 9c6211c3086..00000000000
--- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-import { useMutation, UseMutationResult } from "@tanstack/react-query";
-import { getProxyBaseUrl, getGlobalLitellmHeaderName } from "@/components/networking";
-import useAuthorized from "../useAuthorized";
-
-export interface StoreRequestInSpendLogsParams {
-  store_prompts_in_spend_logs: boolean;
-  maximum_spend_logs_retention_period?: string;
-}
-
-export interface StoreRequestInSpendLogsResponse {
-  message: string;
-}
-
-const performStoreRequestInSpendLogs = async (
-  accessToken: string,
-  params: StoreRequestInSpendLogsParams
-): Promise<StoreRequestInSpendLogsResponse> => {
-  const proxyBaseUrl = getProxyBaseUrl();
-  const url = proxyBaseUrl ? `${proxyBaseUrl}/config/update` : `/config/update`;
-
-  const response = await fetch(url, {
-    method: "POST",
-    headers: {
-      [getGlobalLitellmHeaderName()]: `Bearer ${accessToken}`,
-      "Content-Type": "application/json",
-    },
-    body: JSON.stringify({
-      general_settings: {
-        store_prompts_in_spend_logs: params.store_prompts_in_spend_logs,
-        ...(params.maximum_spend_logs_retention_period && {
-          maximum_spend_logs_retention_period: params.maximum_spend_logs_retention_period,
-        }),
-      },
-    }),
-  });
-
-  if (!response.ok) {
-    const errorData = await response.json().catch(() => ({}));
-    const errorMessage =
-      errorData?.error?.message || errorData?.message || errorData?.detail || "Failed to update spend logs settings";
-    throw new Error(errorMessage);
-  }
-
-  const data = await response.json();
-  return data;
-};
-
-export const useStoreRequestInSpendLogs = (): UseMutationResult<
-  StoreRequestInSpendLogsResponse,
-  Error,
-  StoreRequestInSpendLogsParams
-> => {
-  const { accessToken } = useAuthorized();
-
-  return useMutation<StoreRequestInSpendLogsResponse, Error, StoreRequestInSpendLogsParams>({
-    mutationFn: async (params: StoreRequestInSpendLogsParams) => {
-      if (!accessToken) {
-        throw new Error("Access token is required");
-      }
-      return await performStoreRequestInSpendLogs(accessToken, params);
-    },
-  });
-};
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.test.ts
index 78eddbd8d3c..3da27d3ff9b 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.test.ts
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.test.ts
@@ -8,12 +8,11 @@ import useAuthorized from "./useAuthorized";
 // Unmock useAuthorized to test the actual implementation
 vi.unmock("@/app/(dashboard)/hooks/useAuthorized");
 
-const { replaceMock, clearTokenCookiesMock, getProxyBaseUrlMock, getUiConfigMock, isJwtExpiredMock } = vi.hoisted(() => ({
+const { replaceMock, clearTokenCookiesMock, getProxyBaseUrlMock, getUiConfigMock } = vi.hoisted(() => ({
   replaceMock: vi.fn(),
   clearTokenCookiesMock: vi.fn(),
   getProxyBaseUrlMock: vi.fn(() => "http://proxy.example"),
   getUiConfigMock: vi.fn(),
-  isJwtExpiredMock: vi.fn(),
 }));
 
 vi.mock("next/navigation", () => ({
@@ -39,14 +38,6 @@ vi.mock("@/utils/cookieUtils", async (importOriginal) => {
   };
 });
 
-vi.mock("@/utils/jwtUtils", async (importOriginal) => {
-  const actual = await importOriginal<typeof import("@/utils/jwtUtils")>();
-  return {
-    ...actual,
-    isJwtExpired: isJwtExpiredMock,
-  };
-});
-
 const createQueryClient = () =>
   new QueryClient({
     defaultOptions: {
@@ -77,7 +68,6 @@ describe("useAuthorized", () => {
     clearTokenCookiesMock.mockReset();
     getProxyBaseUrlMock.mockClear();
     getUiConfigMock.mockReset();
-    isJwtExpiredMock.mockReset();
     clearCookie();
   });
 
@@ -88,7 +78,6 @@ describe("useAuthorized", () => {
       auto_redirect_to_sso: false,
       admin_ui_disabled: false,
     });
-    isJwtExpiredMock.mockReturnValue(false);
 
     const token = createJwt({
       key: "api-key-123",
@@ -115,7 +104,6 @@ describe("useAuthorized", () => {
     expect(result.current.disabledPersonalKeyCreation).toBe(false);
     expect(result.current.showSSOBanner).toBe(true);
     expect(replaceMock).not.toHaveBeenCalled();
-    expect(clearTokenCookiesMock).not.toHaveBeenCalled();
   });
 
   it("should clear cookies and redirect on an invalid token", async () => {
@@ -146,7 +134,6 @@ describe("useAuthorized", () => {
       auto_redirect_to_sso: false,
       admin_ui_disabled: true,
     });
-    isJwtExpiredMock.mockReturnValue(false);
 
     const token = createJwt({
       key: "api-key-123",
@@ -169,50 +156,4 @@ describe("useAuthorized", () => {
     expect(result.current.userId).toBe("user-1");
     expect(result.current.userEmail).toBe("user@example.com");
   });
-
-  it("should redirect when token is missing", async () => {
-    getUiConfigMock.mockResolvedValue({
-      server_root_path: "/",
-      proxy_base_url: null,
-      auto_redirect_to_sso: false,
-      admin_ui_disabled: false,
-    });
-
-    // No token cookie set
-    const { result } = renderHook(() => useAuthorized(), { wrapper });
-
-    await waitFor(() => {
-      expect(replaceMock).toHaveBeenCalledWith("http://proxy.example/ui/login");
-    });
-
-    expect(clearTokenCookiesMock).not.toHaveBeenCalled();
-    expect(result.current.token).toBeNull();
-  });
-
-  it("should clear cookies and redirect when token is expired", async () => {
-    getUiConfigMock.mockResolvedValue({
-      server_root_path: "/",
-      proxy_base_url: null,
-      auto_redirect_to_sso: false,
-      admin_ui_disabled: false,
-    });
-    isJwtExpiredMock.mockReturnValue(true);
-
-    const token = createJwt({
-      key: "api-key-123",
-      user_id: "user-1",
-      user_email: "user@example.com",
-      user_role: "app_admin",
-    });
-    document.cookie = `token=${token}; path=/;`;
-
-    const { result } = renderHook(() => useAuthorized(), { wrapper });
-
-    await waitFor(() => {
-      expect(clearTokenCookiesMock).toHaveBeenCalled();
-    });
-
-    expect(replaceMock).toHaveBeenCalledWith("http://proxy.example/ui/login");
-    expect(isJwtExpiredMock).toHaveBeenCalledWith(token);
-  });
 });
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.ts
index 531a240a371..62d514f0668 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.ts
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.ts
@@ -2,7 +2,6 @@
 
 import { getProxyBaseUrl } from "@/components/networking";
 import { clearTokenCookies, getCookie } from "@/utils/cookieUtils";
-import { isJwtExpired } from "@/utils/jwtUtils";
 import { jwtDecode } from "jwt-decode";
 import { useRouter } from "next/navigation";
 import { useEffect, useMemo } from "react";
@@ -43,24 +42,15 @@ const useAuthorized = () => {
 
   const token = typeof document !== "undefined" ? getCookie("token") : null;
 
-  // Step 1: Check for missing token or expired JWT - kick out immediately (even if UI Config is loading)
-  useEffect(() => {
-    if (!token || (token && isJwtExpired(token))) {
-      if (token) {
-        clearTokenCookies();
-      }
-      router.replace(`${getProxyBaseUrl()}/ui/login`);
-    }
-  }, [token, router]);
-
+  // Redirect after mount if missing/invalid token
   useEffect(() => {
     if (isUIConfigLoading) {
       return;
     }
-    if (uiConfig?.admin_ui_disabled) {
+    if (!token || uiConfig?.admin_ui_disabled) {
       router.replace(`${getProxyBaseUrl()}/ui/login`);
     }
-  }, [router, isUIConfigLoading, uiConfig]);
+  }, [token, router, isUIConfigLoading, uiConfig]);
 
   // Decode safely
   const decoded = useMemo(() => {
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowPrompts.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowPrompts.ts
deleted file mode 100644
index 801fbdbb99d..00000000000
--- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowPrompts.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-// hooks/useDisableShowPrompts.ts
-import { useSyncExternalStore } from "react";
-import { getLocalStorageItem } from "@/utils/localStorageUtils";
-import { LOCAL_STORAGE_EVENT } from "@/utils/localStorageUtils";
-
-function subscribe(callback: () => void) {
-  const onStorage = (e: StorageEvent) => {
-    if (e.key === "disableShowPrompts") {
-      callback();
-    }
-  };
-
-  const onCustom = (e: Event) => {
-    const { key } = (e as CustomEvent).detail;
-    if (key === "disableShowPrompts") {
-      callback();
-    }
-  };
-
-  window.addEventListener("storage", onStorage);
-  window.addEventListener(LOCAL_STORAGE_EVENT, onCustom);
-
-  return () => {
-    window.removeEventListener("storage", onStorage);
-    window.removeEventListener(LOCAL_STORAGE_EVENT, onCustom);
-  };
-}
-
-function getSnapshot() {
-  return getLocalStorageItem("disableShowPrompts") === "true";
-}
-
-export function useDisableShowPrompts() {
-  return useSyncExternalStore(subscribe, getSnapshot);
-}
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/layout.tsx b/ui/litellm-dashboard/src/app/(dashboard)/layout.tsx
index 97e4c799e72..97837ff8e0a 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/layout.tsx
+++ b/ui/litellm-dashboard/src/app/(dashboard)/layout.tsx
@@ -56,10 +56,8 @@ export default function Layout({ children }: { children: React.ReactNode }) {
           userRole={userRole}
           premiumUser={premiumUser}
           proxySettings={undefined}
-          setProxySettings={() => { }}
+          setProxySettings={() => {}}
           accessToken={accessToken}
-          isDarkMode={false}
-          toggleDarkMode={() => { }}
         />
         <div className="flex flex-1 overflow-auto">
           <div className="mt-2">
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/ModelsAndEndpointsView.tsx b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/ModelsAndEndpointsView.tsx
index 6a4882a92a2..f707ef04ffb 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/ModelsAndEndpointsView.tsx
+++ b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/ModelsAndEndpointsView.tsx
@@ -318,6 +318,7 @@ const ModelsAndEndpointsView: React.FC<ModelDashboardProps> = ({ premiumUser, te
               onClose={() => {
                 setSelectedModelId(null);
               }}
+              modelData={processedModelData.data.find((model: any) => model.model_info.id === selectedModelId)}
               accessToken={accessToken}
               userID={userID}
               userRole={userRole}
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.test.tsx
index 813a365d367..8a2298361c5 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.test.tsx
+++ b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.test.tsx
@@ -11,7 +11,7 @@ const mockUseModelsInfo = vi.fn(() => ({
 })) as any;
 
 vi.mock("../../hooks/models/useModels", () => ({
-  useModelsInfo: (page?: number, size?: number, search?: string) => mockUseModelsInfo(page, size, search),
+  useModelsInfo: (page?: number, size?: number) => mockUseModelsInfo(page, size),
 }));
 
 // Mock the useModelCostMap hook
@@ -74,6 +74,7 @@ describe("AllModelsTab", () => {
   const mockSetSelectedModelGroup = vi.fn();
   const mockSetSelectedModelId = vi.fn();
   const mockSetSelectedTeamId = vi.fn();
+  const mockSetEditModel = vi.fn();
 
   const defaultProps = {
     selectedModelGroup: "all",
@@ -82,6 +83,7 @@ describe("AllModelsTab", () => {
     availableModelAccessGroups: ["sales-team", "engineering-team"],
     setSelectedModelId: mockSetSelectedModelId,
     setSelectedTeamId: mockSetSelectedTeamId,
+    setEditModel: mockSetEditModel,
   };
 
   const mockUseAuthorized = {
@@ -174,10 +176,8 @@ describe("AllModelsTab", () => {
 
     render(<AllModelsTab {...defaultProps} />);
 
-    // Component shows API total_count (2), not filtered count
-    // Since default is "personal" team and models don't have direct_access, they're filtered out
     await waitFor(() => {
-      expect(screen.getByText("Showing 1 - 2 of 2 results")).toBeInTheDocument();
+      expect(screen.getByText("Showing 0 results")).toBeInTheDocument();
     });
   });
 
@@ -235,10 +235,8 @@ describe("AllModelsTab", () => {
 
     render(<AllModelsTab {...defaultProps} />);
 
-    // Component shows API total_count (2), not filtered count
-    // Since default is "personal" team and models don't have direct_access, they're filtered out
     await waitFor(() => {
-      expect(screen.getByText("Showing 1 - 2 of 2 results")).toBeInTheDocument();
+      expect(screen.getByText("Showing 0 results")).toBeInTheDocument();
     });
   });
 
@@ -282,9 +280,8 @@ describe("AllModelsTab", () => {
 
     render(<AllModelsTab {...defaultProps} />);
 
-    // Component shows API total_count (2), but only 1 model has direct_access
     await waitFor(() => {
-      expect(screen.getByText("Showing 1 - 2 of 2 results")).toBeInTheDocument();
+      expect(screen.getByText("Showing 1 - 1 of 1 results")).toBeInTheDocument();
     });
   });
 
@@ -422,15 +419,14 @@ describe("AllModelsTab", () => {
     );
 
     // Set up mock to return page1Data for page 1
-    mockUseModelsInfo.mockImplementation((page: number = 1, size?: number, search?: string) => {
+    mockUseModelsInfo.mockImplementation((page: number = 1) => {
       return { data: page1Data, isLoading: false, error: null };
     });
 
     render(<AllModelsTab {...defaultProps} />);
 
     await waitFor(() => {
-      // Component calculates: ((1-1)*50)+1 = 1, Math.min(1*50, 2) = 2
-      expect(screen.getByText("Showing 1 - 2 of 2 results")).toBeInTheDocument();
+      expect(screen.getByText("Showing 1 - 1 of 1 results")).toBeInTheDocument();
     });
 
     // Check that Previous button is disabled on first page
@@ -475,7 +471,7 @@ describe("AllModelsTab", () => {
       50, // size
     );
 
-    mockUseModelsInfo.mockImplementation((page?: number, size?: number, search?: string) => {
+    mockUseModelsInfo.mockImplementation(() => {
       return { data: singlePageData, isLoading: false, error: null };
     });
 
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.tsx b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.tsx
index a5553fa5f56..04300b7fd16 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.tsx
+++ b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.tsx
@@ -2,17 +2,16 @@ import { useModelCostMap } from "@/app/(dashboard)/hooks/models/useModelCostMap"
 import { useTeams } from "@/app/(dashboard)/hooks/teams/useTeams";
 import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
 import { Team } from "@/components/key_team_helpers/key_list";
-import { AllModelsDataTable } from "@/components/model_dashboard/all_models_table";
+import { ModelDataTable } from "@/components/model_dashboard/table";
 import { columns } from "@/components/molecules/models/columns";
 import { getDisplayModelName } from "@/components/view_model/model_name_display";
 import { InfoCircleOutlined } from "@ant-design/icons";
-import { PaginationState, SortingState } from "@tanstack/react-table";
+import { PaginationState } from "@tanstack/react-table";
 import { Grid, Select, SelectItem, TabPanel, Text } from "@tremor/react";
-import { Skeleton, Spin } from "antd";
-import debounce from "lodash/debounce";
 import { useEffect, useMemo, useState } from "react";
 import { useModelsInfo } from "../../hooks/models/useModels";
 import { transformModelData } from "../utils/modelDataTransformer";
+import { Skeleton } from "antd";
 type ModelViewMode = "all" | "current_team";
 
 interface AllModelsTabProps {
@@ -34,10 +33,9 @@ const AllModelsTab = ({
 }: AllModelsTabProps) => {
   const { data: modelCostMapData, isLoading: isLoadingModelCostMap } = useModelCostMap();
   const { userId, userRole, premiumUser } = useAuthorized();
-  const { data: teams, isLoading: isLoadingTeams } = useTeams();
+  const { data: teams } = useTeams();
 
   const [modelNameSearch, setModelNameSearch] = useState<string>("");
-  const [debouncedSearch, setDebouncedSearch] = useState<string>("");
   const [modelViewMode, setModelViewMode] = useState<ModelViewMode>("current_team");
   const [currentTeam, setCurrentTeam] = useState<Team | "personal">("personal");
   const [showFilters, setShowFilters] = useState<boolean>(false);
@@ -49,58 +47,8 @@ const AllModelsTab = ({
     pageIndex: 0,
     pageSize: 50,
   });
-  const [sorting, setSorting] = useState<SortingState>([]);
 
-  // Debounce search input
-  const debouncedUpdateSearch = useMemo(
-    () =>
-      debounce((value: string) => {
-        setDebouncedSearch(value);
-        // Reset to page 1 when search changes
-        setCurrentPage(1);
-        setPagination((prev: PaginationState) => ({ ...prev, pageIndex: 0 }));
-      }, 200),
-    []
-  );
-
-  useEffect(() => {
-    debouncedUpdateSearch(modelNameSearch);
-    return () => {
-      debouncedUpdateSearch.cancel();
-    };
-  }, [modelNameSearch, debouncedUpdateSearch]);
-
-  // Determine teamId to pass to the query - only pass if not "personal"
-  const teamIdForQuery = currentTeam === "personal" ? undefined : currentTeam.team_id;
-
-  // Convert sorting state to sortBy and sortOrder for API
-  const sortBy = useMemo(() => {
-    if (sorting.length === 0) return undefined;
-    const sort = sorting[0];
-    const columnIdToServerField: Record<string, string> = {
-      input_cost: "costs", // Map input_cost column to "costs" for server-side sorting
-      model_info_db_model: "status", // Map model_info.db_model column to "status" for server-side sorting
-      model_info_created_by: "created_at", // Map model_info.created_by column to "created_at" for server-side sorting
-      model_info_updated_at: "updated_at", // Map model_info.updated_at column to "updated_at" for server-side sorting
-    };
-    return columnIdToServerField[sort.id] || sort.id;
-  }, [sorting]);
-
-  const sortOrder = useMemo(() => {
-    if (sorting.length === 0) return undefined;
-    const sort = sorting[0];
-    return sort.desc ? "desc" : "asc";
-  }, [sorting]);
-
-  const { data: rawModelData, isLoading: isLoadingModelsInfo } = useModelsInfo(
-    currentPage,
-    pageSize,
-    debouncedSearch || undefined,
-    undefined,
-    teamIdForQuery,
-    sortBy,
-    sortOrder
-  );
+  const { data: rawModelData, isLoading: isLoadingModelsInfo } = useModelsInfo(currentPage, pageSize);
   const isLoading = isLoadingModelsInfo || isLoadingModelCostMap;
 
   const getProviderFromModel = (model: string) => {
@@ -140,8 +88,10 @@ const AllModelsTab = ({
       return [];
     }
 
-    // Server-side search is now handled by the API, so we only filter by other criteria
     return modelData.data.filter((model: any) => {
+      const searchMatch =
+        modelNameSearch === "" || model.model_name.toLowerCase().includes(modelNameSearch.toLowerCase());
+
       const modelNameMatch =
         selectedModelGroup === "all" ||
         model.model_name === selectedModelGroup ||
@@ -153,28 +103,30 @@ const AllModelsTab = ({
         model.model_info["access_groups"]?.includes(selectedModelAccessGroupFilter) ||
         !selectedModelAccessGroupFilter;
 
-      // Team filtering is now handled server-side via teamId query parameter
-      // Only apply client-side filtering for model groups and access groups
-      return modelNameMatch && accessGroupMatch;
-    });
-  }, [modelData, selectedModelGroup, selectedModelAccessGroupFilter]);
-
-  useEffect(() => {
-    setPagination((prev: PaginationState) => ({ ...prev, pageIndex: 0 }));
-    setCurrentPage(1);
-  }, [selectedModelGroup, selectedModelAccessGroupFilter]);
+      let teamAccessMatch = true;
+      if (modelViewMode === "current_team") {
+        if (currentTeam === "personal") {
+          teamAccessMatch = model.model_info?.direct_access === true;
+        } else {
+          // Check if model is directly associated with the team via team_ids
+          const directTeamAccess = model.model_info?.access_via_team_ids?.includes(currentTeam.team_id) === true;
+
+          // Check if any of the team's models match the model's access groups
+          const accessGroupMatch =
+            currentTeam.models?.some((teamModel: string) => model.model_info?.access_groups?.includes(teamModel)) ===
+            true;
+
+          teamAccessMatch = directTeamAccess || accessGroupMatch;
+        }
+      }
 
-  // Reset pagination when team changes
-  useEffect(() => {
-    setCurrentPage(1);
-    setPagination((prev: PaginationState) => ({ ...prev, pageIndex: 0 }));
-  }, [teamIdForQuery]);
+      return searchMatch && modelNameMatch && accessGroupMatch && teamAccessMatch;
+    });
+  }, [modelData, modelNameSearch, selectedModelGroup, selectedModelAccessGroupFilter, currentTeam, modelViewMode]);
 
-  // Reset pagination when sorting changes
   useEffect(() => {
-    setCurrentPage(1);
     setPagination((prev: PaginationState) => ({ ...prev, pageIndex: 0 }));
-  }, [sorting]);
+  }, [modelNameSearch, selectedModelGroup, selectedModelAccessGroupFilter, currentTeam, modelViewMode]);
 
   const resetFilters = () => {
     setModelNameSearch("");
@@ -184,7 +136,6 @@ const AllModelsTab = ({
     setModelViewMode("current_team");
     setCurrentPage(1);
     setPagination({ pageIndex: 0, pageSize: 50 });
-    setSorting([]);
   };
 
   return (
@@ -207,17 +158,9 @@ const AllModelsTab = ({
                       onValueChange={(value) => {
                         if (value === "personal") {
                           setCurrentTeam("personal");
-                          // Reset to page 1 when team changes
-                          setCurrentPage(1);
-                          setPagination((prev: PaginationState) => ({ ...prev, pageIndex: 0 }));
                         } else {
                           const team = teams?.find((t) => t.team_id === value);
-                          if (team) {
-                            setCurrentTeam(team);
-                            // Reset to page 1 when team changes
-                            setCurrentPage(1);
-                            setPagination((prev: PaginationState) => ({ ...prev, pageIndex: 0 }));
-                          }
+                          if (team) setCurrentTeam(team);
                         }
                       }}
                     >
@@ -227,29 +170,20 @@ const AllModelsTab = ({
                           <span className="font-medium">Personal</span>
                         </div>
                       </SelectItem>
-                      {isLoadingTeams ? (
-                        <SelectItem value="loading">
-                          <div className="flex items-center gap-2">
-                            <Spin size="small" />
-                            <span className="font-medium text-gray-500">Loading teams...</span>
-                          </div>
-                        </SelectItem>
-                      ) : (
-                        teams
-                          ?.filter((team) => team.team_id)
-                          .map((team) => (
-                            <SelectItem key={team.team_id} value={team.team_id}>
-                              <div className="flex items-center gap-2">
-                                <div className="w-2 h-2 bg-green-500 rounded-full"></div>
-                                <span className="font-medium">
-                                  {team.team_alias
-                                    ? `${team.team_alias.slice(0, 30)}...`
-                                    : `Team ${team.team_id.slice(0, 30)}...`}
-                                </span>
-                              </div>
-                            </SelectItem>
-                          ))
-                      )}
+                      {teams
+                        ?.filter((team) => team.team_id)
+                        .map((team) => (
+                          <SelectItem key={team.team_id} value={team.team_id}>
+                            <div className="flex items-center gap-2">
+                              <div className="w-2 h-2 bg-green-500 rounded-full"></div>
+                              <span className="font-medium">
+                                {team.team_alias
+                                  ? `${team.team_alias.slice(0, 30)}...`
+                                  : `Team ${team.team_id.slice(0, 30)}...`}
+                              </span>
+                            </div>
+                          </SelectItem>
+                        ))}
                     </Select>
                   )}
                 </div>
@@ -420,8 +354,8 @@ const AllModelsTab = ({
                     <Skeleton.Input active style={{ width: 184, height: 20 }} />
                   ) : (
                     <span className="text-sm text-gray-700">
-                      {paginationMeta.total_count > 0
-                        ? `Showing ${((currentPage - 1) * pageSize) + 1} - ${Math.min(currentPage * pageSize, paginationMeta.total_count)} of ${paginationMeta.total_count} results`
+                      {filteredData.length > 0
+                        ? `Showing 1 - ${filteredData.length} of ${filteredData.length} results`
                         : "Showing 0 results"}
                     </span>
                   )}
@@ -469,7 +403,7 @@ const AllModelsTab = ({
               </div>
             </div>
 
-            <AllModelsDataTable
+            <ModelDataTable
               columns={columns(
                 userRole,
                 userId,
@@ -483,9 +417,7 @@ const AllModelsTab = ({
                 setExpandedRows,
               )}
               data={filteredData}
-              isLoading={isLoadingModelsInfo}
-              sorting={sorting}
-              onSortingChange={setSorting}
+              isLoading={false}
               pagination={pagination}
               onPaginationChange={setPagination}
               enablePagination={true}
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/policies/page.tsx b/ui/litellm-dashboard/src/app/(dashboard)/policies/page.tsx
index c1f6ec51d78..86e063c7358 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/policies/page.tsx
+++ b/ui/litellm-dashboard/src/app/(dashboard)/policies/page.tsx
@@ -2,6 +2,17 @@
 
 import PoliciesPanel from "@/components/policies";
 import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
+import {
+  getPoliciesList,
+  createPolicyCall,
+  updatePolicyCall,
+  deletePolicyCall,
+  getPolicyInfo,
+  getPolicyAttachmentsList,
+  createPolicyAttachmentCall,
+  deletePolicyAttachmentCall,
+  getGuardrailsList,
+} from "@/components/networking";
 
 const PoliciesPage = () => {
   const { accessToken, userRole } = useAuthorized();
@@ -10,6 +21,15 @@ const PoliciesPage = () => {
     <PoliciesPanel
       accessToken={accessToken}
       userRole={userRole}
+      getPoliciesList={getPoliciesList}
+      createPolicy={createPolicyCall}
+      updatePolicy={updatePolicyCall}
+      deletePolicy={deletePolicyCall}
+      getPolicy={getPolicyInfo}
+      getAttachmentsList={getPolicyAttachmentsList}
+      createAttachment={createPolicyAttachmentCall}
+      deleteAttachment={deletePolicyAttachmentCall}
+      getGuardrailsList={getGuardrailsList}
     />
   );
 };
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/teams/components/modals/CreateTeamModal.tsx b/ui/litellm-dashboard/src/app/(dashboard)/teams/components/modals/CreateTeamModal.tsx
index 0aa42b69a04..972c39d49d8 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/teams/components/modals/CreateTeamModal.tsx
+++ b/ui/litellm-dashboard/src/app/(dashboard)/teams/components/modals/CreateTeamModal.tsx
@@ -14,7 +14,7 @@ import PremiumLoggingSettings from "@/components/common_components/PremiumLoggin
 import ModelAliasManager from "@/components/common_components/ModelAliasManager";
 import React, { useEffect, useState } from "react";
 import NotificationsManager from "@/components/molecules/notifications_manager";
-import { fetchMCPAccessGroups, getGuardrailsList, getPoliciesList, Organization, Team, teamCreateCall } from "@/components/networking";
+import { fetchMCPAccessGroups, getGuardrailsList, Organization, Team, teamCreateCall } from "@/components/networking";
 import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
 import MCPToolPermissions from "@/components/mcp_server_management/MCPToolPermissions";
 
@@ -76,7 +76,6 @@ const CreateTeamModal = ({
   const [currentOrgForCreateTeam, setCurrentOrgForCreateTeam] = useState<Organization | null>(null);
   const [modelsToPick, setModelsToPick] = useState<string[]>([]);
   const [guardrailsList, setGuardrailsList] = useState<string[]>([]);
-  const [policiesList, setPoliciesList] = useState<string[]>([]);
   const [mcpAccessGroups, setMcpAccessGroups] = useState<string[]>([]);
   const [mcpAccessGroupsLoaded, setMcpAccessGroupsLoaded] = useState(false);
 
@@ -137,22 +136,7 @@ const CreateTeamModal = ({
       }
     };
 
-    const fetchPolicies = async () => {
-      try {
-        if (accessToken == null) {
-          return;
-        }
-
-        const response = await getPoliciesList(accessToken);
-        const policyNames = response.policies.map((p: { policy_name: string }) => p.policy_name);
-        setPoliciesList(policyNames);
-      } catch (error) {
-        console.error("Failed to fetch policies:", error);
-      }
-    };
-
     fetchGuardrails();
-    fetchPolicies();
   }, [accessToken]);
 
   const handleCreate = async (formValues: Record<string, any>) => {
@@ -547,36 +531,6 @@ const CreateTeamModal = ({
                   unCheckedChildren="No"
                 />
               </Form.Item>
-              <Form.Item
-                label={
-                  <span>
-                    Policies{" "}
-                    <Tooltip title="Apply policies to this team to control guardrails and other settings">
-                      <a
-                        href="https://docs.litellm.ai/docs/proxy/guardrails/guardrail_policies"
-                        target="_blank"
-                        rel="noopener noreferrer"
-                        onClick={(e) => e.stopPropagation()}
-                      >
-                        <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-                      </a>
-                    </Tooltip>
-                  </span>
-                }
-                name="policies"
-                className="mt-8"
-                help="Select existing policies or enter new ones"
-              >
-                <Select2
-                  mode="tags"
-                  style={{ width: "100%" }}
-                  placeholder="Select or enter policies"
-                  options={policiesList.map((name) => ({
-                    value: name,
-                    label: name,
-                  }))}
-                />
-              </Form.Item>
               <Form.Item
                 label={
                   <span>
diff --git a/ui/litellm-dashboard/src/app/globals.css b/ui/litellm-dashboard/src/app/globals.css
index 0d453e284ef..a702982678e 100644
--- a/ui/litellm-dashboard/src/app/globals.css
+++ b/ui/litellm-dashboard/src/app/globals.css
@@ -37,3 +37,31 @@ body {
 .custom-border {
   border: 1px solid var(--neutral-border);
 }
+
+/* Custom dropdown styles */
+.ant-dropdown-menu-item {
+  padding: 0 !important;
+}
+
+.ant-dropdown-menu-item > div {
+  transition: all 0.2s ease;
+}
+
+/* Don't apply hover to user info section */
+.ant-dropdown-menu-item[data-menu-id$="user-info"]:hover {
+  background-color: transparent !important;
+  cursor: default;
+}
+
+.ant-dropdown-menu-item[data-menu-id$="user-info"] > div {
+  cursor: default;
+}
+
+.ant-dropdown-menu {
+  padding: 4px !important;
+  min-width: 280px !important;
+}
+
+.ant-dropdown-menu-item-divider {
+  margin: 4px 0;
+}
diff --git a/ui/litellm-dashboard/src/app/page.tsx b/ui/litellm-dashboard/src/app/page.tsx
index 23c80acf973..8ac1f756f96 100644
--- a/ui/litellm-dashboard/src/app/page.tsx
+++ b/ui/litellm-dashboard/src/app/page.tsx
@@ -45,7 +45,6 @@ import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
 import { jwtDecode } from "jwt-decode";
 import { useSearchParams } from "next/navigation";
 import { Suspense, useEffect, useState } from "react";
-import { ConfigProvider, theme } from "antd";
 
 function getCookie(name: string) {
   // Safer cookie read + decoding; handles '=' inside values
@@ -132,12 +131,6 @@ export default function CreateKeyPage() {
   const [showClaudeCodePrompt, setShowClaudeCodePrompt] = useState(false);
   const [showClaudeCodeModal, setShowClaudeCodeModal] = useState(false);
 
-  // Dark mode state
-  const [isDarkMode, setIsDarkMode] = useState(false);
-  const toggleDarkMode = () => {
-    setIsDarkMode(!isDarkMode);
-  };
-
   const invitation_id = searchParams.get("invitation_id");
 
   // Get page from URL, default to 'api-keys' if not present
@@ -289,7 +282,7 @@ export default function CreateKeyPage() {
           const nudgesConfig = await getInProductNudgesCall(accessToken);
           const isUsingClaudeCode = nudgesConfig?.is_claude_code_enabled || false;
           setIsClaudeCode(isUsingClaudeCode);
-
+          
           // Show Claude Code prompt on login if enabled
           if (isUsingClaudeCode) {
             setShowClaudeCodePrompt(true);
@@ -369,231 +362,225 @@ export default function CreateKeyPage() {
   return (
     <Suspense fallback={<LoadingScreen />}>
       <QueryClientProvider client={queryClient}>
-        <ConfigProvider theme={{
-          algorithm: isDarkMode ? theme.darkAlgorithm : theme.defaultAlgorithm,
-        }}>
-          <ThemeProvider accessToken={accessToken}>
-            {invitation_id ? (
-              <UserDashboard
+        <ThemeProvider accessToken={accessToken}>
+          {invitation_id ? (
+            <UserDashboard
+              userID={userID}
+              userRole={userRole}
+              premiumUser={premiumUser}
+              teams={teams}
+              keys={keys}
+              setUserRole={setUserRole}
+              userEmail={userEmail}
+              setUserEmail={setUserEmail}
+              setTeams={setTeams}
+              setKeys={setKeys}
+              organizations={organizations}
+              addKey={addKey}
+              createClicked={createClicked}
+            />
+          ) : (
+            <div className="flex flex-col min-h-screen">
+              <Navbar
                 userID={userID}
                 userRole={userRole}
                 premiumUser={premiumUser}
-                teams={teams}
-                keys={keys}
-                setUserRole={setUserRole}
                 userEmail={userEmail}
-                setUserEmail={setUserEmail}
-                setTeams={setTeams}
-                setKeys={setKeys}
-                organizations={organizations}
-                addKey={addKey}
-                createClicked={createClicked}
+                setProxySettings={setProxySettings}
+                proxySettings={proxySettings}
+                accessToken={accessToken}
+                isPublicPage={false}
+                sidebarCollapsed={sidebarCollapsed}
+                onToggleSidebar={toggleSidebar}
               />
-            ) : (
-              <div className="flex flex-col min-h-screen">
-                <Navbar
-                  userID={userID}
-                  userRole={userRole}
-                  premiumUser={premiumUser}
-                  userEmail={userEmail}
-                  setProxySettings={setProxySettings}
-                  proxySettings={proxySettings}
-                  accessToken={accessToken}
-                  isPublicPage={false}
-                  sidebarCollapsed={sidebarCollapsed}
-                  onToggleSidebar={toggleSidebar}
-                  isDarkMode={isDarkMode}
-                  toggleDarkMode={toggleDarkMode}
-                />
-                <div className="flex flex-1">
-                  <div className="mt-2">
-                    <SidebarProvider setPage={updatePage} defaultSelectedKey={page} sidebarCollapsed={sidebarCollapsed} />
-                  </div>
-
-                  {page == "api-keys" ? (
-                    <UserDashboard
-                      userID={userID}
-                      userRole={userRole}
-                      premiumUser={premiumUser}
-                      teams={teams}
-                      keys={keys}
-                      setUserRole={setUserRole}
-                      userEmail={userEmail}
-                      setUserEmail={setUserEmail}
-                      setTeams={setTeams}
-                      setKeys={setKeys}
-                      organizations={organizations}
-                      addKey={addKey}
-                      createClicked={createClicked}
-                    />
-                  ) : page == "models" ? (
-                    <OldModelDashboard
-                      token={token}
-                      keys={keys}
-                      modelData={modelData}
-                      setModelData={setModelData}
-                      premiumUser={premiumUser}
-                      teams={teams}
-                    />
-                  ) : page == "llm-playground" ? (
-                    <PlaygroundPage />
-                  ) : page == "users" ? (
-                    <ViewUserDashboard
-                      userID={userID}
-                      userRole={userRole}
-                      token={token}
-                      keys={keys}
-                      teams={teams}
-                      accessToken={accessToken}
-                      setKeys={setKeys}
-                    />
-                  ) : page == "teams" ? (
-                    <OldTeams
-                      teams={teams}
-                      setTeams={setTeams}
-                      accessToken={accessToken}
-                      userID={userID}
-                      userRole={userRole}
-                      organizations={organizations}
-                      premiumUser={premiumUser}
-                      searchParams={searchParams}
-                    />
-                  ) : page == "organizations" ? (
-                    <Organizations
-                      organizations={organizations}
-                      setOrganizations={setOrganizations}
-                      userModels={userModels}
-                      accessToken={accessToken}
-                      userRole={userRole}
-                      premiumUser={premiumUser}
-                    />
-                  ) : page == "admin-panel" ? (
-                    <AdminPanel
-                      setTeams={setTeams}
-                      searchParams={searchParams}
-                      accessToken={accessToken}
-                      userID={userID}
-                      showSSOBanner={showSSOBanner}
-                      premiumUser={premiumUser}
-                      proxySettings={proxySettings}
-                    />
-                  ) : page == "api_ref" ? (
-                    <APIReferenceView proxySettings={proxySettings} />
-                  ) : page == "logging-and-alerts" ? (
-                    <Settings userID={userID} userRole={userRole} accessToken={accessToken} premiumUser={premiumUser} />
-                  ) : page == "budgets" ? (
-                    <BudgetPanel accessToken={accessToken} />
-                  ) : page == "guardrails" ? (
-                    <GuardrailsPanel accessToken={accessToken} userRole={userRole} />
-                  ) : page == "policies" ? (
-                    <PoliciesPanel accessToken={accessToken} userRole={userRole} />
-                  ) : page == "agents" ? (
-                    <AgentsPanel accessToken={accessToken} userRole={userRole} />
-                  ) : page == "prompts" ? (
-                    <PromptsPanel accessToken={accessToken} userRole={userRole} />
-                  ) : page == "transform-request" ? (
-                    <TransformRequestPanel accessToken={accessToken} />
-                  ) : page == "router-settings" ? (
-                    <GeneralSettings
-                      userID={userID}
-                      userRole={userRole}
-                      accessToken={accessToken}
-                      modelData={modelData}
-                    />
-                  ) : page == "ui-theme" ? (
-                    <UIThemeSettings userID={userID} userRole={userRole} accessToken={accessToken} />
-                  ) : page == "cost-tracking" ? (
-                    <CostTrackingSettings userID={userID} userRole={userRole} accessToken={accessToken} />
-                  ) : page == "model-hub-table" ? (
-                    isAdminRole(userRole) ? (
-                      <ModelHubTable
-                        accessToken={accessToken}
-                        publicPage={false}
-                        premiumUser={premiumUser}
-                        userRole={userRole}
-                      />
-                    ) : (
-                      <PublicModelHub accessToken={accessToken} isEmbedded={true} />
-                    )
-                  ) : page == "caching" ? (
-                    <CacheDashboard
-                      userID={userID}
-                      userRole={userRole}
-                      token={token}
-                      accessToken={accessToken}
-                      premiumUser={premiumUser}
-                    />
-                  ) : page == "pass-through-settings" ? (
-                    <PassThroughSettings
-                      userID={userID}
-                      userRole={userRole}
+              <div className="flex flex-1">
+                <div className="mt-2">
+                  <SidebarProvider setPage={updatePage} defaultSelectedKey={page} sidebarCollapsed={sidebarCollapsed} />
+                </div>
+
+                {page == "api-keys" ? (
+                  <UserDashboard
+                    userID={userID}
+                    userRole={userRole}
+                    premiumUser={premiumUser}
+                    teams={teams}
+                    keys={keys}
+                    setUserRole={setUserRole}
+                    userEmail={userEmail}
+                    setUserEmail={setUserEmail}
+                    setTeams={setTeams}
+                    setKeys={setKeys}
+                    organizations={organizations}
+                    addKey={addKey}
+                    createClicked={createClicked}
+                  />
+                ) : page == "models" ? (
+                  <OldModelDashboard
+                    token={token}
+                    keys={keys}
+                    modelData={modelData}
+                    setModelData={setModelData}
+                    premiumUser={premiumUser}
+                    teams={teams}
+                  />
+                ) : page == "llm-playground" ? (
+                  <PlaygroundPage />
+                ) : page == "users" ? (
+                  <ViewUserDashboard
+                    userID={userID}
+                    userRole={userRole}
+                    token={token}
+                    keys={keys}
+                    teams={teams}
+                    accessToken={accessToken}
+                    setKeys={setKeys}
+                  />
+                ) : page == "teams" ? (
+                  <OldTeams
+                    teams={teams}
+                    setTeams={setTeams}
+                    accessToken={accessToken}
+                    userID={userID}
+                    userRole={userRole}
+                    organizations={organizations}
+                    premiumUser={premiumUser}
+                    searchParams={searchParams}
+                  />
+                ) : page == "organizations" ? (
+                  <Organizations
+                    organizations={organizations}
+                    setOrganizations={setOrganizations}
+                    userModels={userModels}
+                    accessToken={accessToken}
+                    userRole={userRole}
+                    premiumUser={premiumUser}
+                  />
+                ) : page == "admin-panel" ? (
+                  <AdminPanel
+                    setTeams={setTeams}
+                    searchParams={searchParams}
+                    accessToken={accessToken}
+                    userID={userID}
+                    showSSOBanner={showSSOBanner}
+                    premiumUser={premiumUser}
+                    proxySettings={proxySettings}
+                  />
+                ) : page == "api_ref" ? (
+                  <APIReferenceView proxySettings={proxySettings} />
+                ) : page == "logging-and-alerts" ? (
+                  <Settings userID={userID} userRole={userRole} accessToken={accessToken} premiumUser={premiumUser} />
+                ) : page == "budgets" ? (
+                  <BudgetPanel accessToken={accessToken} />
+                ) : page == "guardrails" ? (
+                  <GuardrailsPanel accessToken={accessToken} userRole={userRole} />
+                ) : page == "policies" ? (
+                  <PoliciesPanel accessToken={accessToken} userRole={userRole} />
+                ) : page == "agents" ? (
+                  <AgentsPanel accessToken={accessToken} userRole={userRole} />
+                ) : page == "prompts" ? (
+                  <PromptsPanel accessToken={accessToken} userRole={userRole} />
+                ) : page == "transform-request" ? (
+                  <TransformRequestPanel accessToken={accessToken} />
+                ) : page == "router-settings" ? (
+                  <GeneralSettings
+                    userID={userID}
+                    userRole={userRole}
+                    accessToken={accessToken}
+                    modelData={modelData}
+                  />
+                ) : page == "ui-theme" ? (
+                  <UIThemeSettings userID={userID} userRole={userRole} accessToken={accessToken} />
+                ) : page == "cost-tracking" ? (
+                  <CostTrackingSettings userID={userID} userRole={userRole} accessToken={accessToken} />
+                ) : page == "model-hub-table" ? (
+                  isAdminRole(userRole) ? (
+                    <ModelHubTable
                       accessToken={accessToken}
-                      modelData={modelData}
+                      publicPage={false}
                       premiumUser={premiumUser}
-                    />
-                  ) : page == "logs" ? (
-                    <SpendLogsTable
-                      userID={userID}
                       userRole={userRole}
-                      token={token}
-                      accessToken={accessToken}
-                      allTeams={(teams as Team[]) ?? []}
-                      premiumUser={premiumUser}
-                    />
-                  ) : page == "mcp-servers" ? (
-                    <MCPServers accessToken={accessToken} userRole={userRole} userID={userID} />
-                  ) : page == "search-tools" ? (
-                    <SearchTools accessToken={accessToken} userRole={userRole} userID={userID} />
-                  ) : page == "tag-management" ? (
-                    <TagManagement accessToken={accessToken} userRole={userRole} userID={userID} />
-                  ) : page == "claude-code-plugins" ? (
-                    <ClaudeCodePluginsPanel accessToken={accessToken} userRole={userRole} />
-                  ) : page == "vector-stores" ? (
-                    <VectorStoreManagement accessToken={accessToken} userRole={userRole} userID={userID} />
-                  ) : page == "new_usage" ? (
-                    <NewUsagePage
-                      teams={(teams as Team[]) ?? []}
-                      organizations={(organizations as Organization[]) ?? []}
                     />
                   ) : (
-                    <Usage
-                      userID={userID}
-                      userRole={userRole}
-                      token={token}
-                      accessToken={accessToken}
-                      keys={keys}
-                      premiumUser={premiumUser}
-                    />
-                  )}
-                </div>
-
-                {/* Survey Components */}
-                <SurveyPrompt
-                  isVisible={showSurveyPrompt}
-                  onOpen={handleOpenSurvey}
-                  onDismiss={handleDismissSurveyPrompt}
-                />
-                <SurveyModal
-                  isOpen={showSurveyModal}
-                  onClose={handleSurveyModalClose}
-                  onComplete={handleSurveyComplete}
-                />
-
-                {/* Claude Code Components */}
-                <ClaudeCodePrompt
-                  isVisible={showClaudeCodePrompt}
-                  onOpen={handleOpenClaudeCode}
-                  onDismiss={handleDismissClaudeCodePrompt}
-                />
-                <ClaudeCodeModal
-                  isOpen={showClaudeCodeModal}
-                  onClose={handleClaudeCodeModalClose}
-                  onComplete={handleClaudeCodeComplete}
-                />
+                    <PublicModelHub accessToken={accessToken} isEmbedded={true} />
+                  )
+                ) : page == "caching" ? (
+                  <CacheDashboard
+                    userID={userID}
+                    userRole={userRole}
+                    token={token}
+                    accessToken={accessToken}
+                    premiumUser={premiumUser}
+                  />
+                ) : page == "pass-through-settings" ? (
+                  <PassThroughSettings
+                    userID={userID}
+                    userRole={userRole}
+                    accessToken={accessToken}
+                    modelData={modelData}
+                    premiumUser={premiumUser}
+                  />
+                ) : page == "logs" ? (
+                  <SpendLogsTable
+                    userID={userID}
+                    userRole={userRole}
+                    token={token}
+                    accessToken={accessToken}
+                    allTeams={(teams as Team[]) ?? []}
+                    premiumUser={premiumUser}
+                  />
+                ) : page == "mcp-servers" ? (
+                  <MCPServers accessToken={accessToken} userRole={userRole} userID={userID} />
+                ) : page == "search-tools" ? (
+                  <SearchTools accessToken={accessToken} userRole={userRole} userID={userID} />
+                ) : page == "tag-management" ? (
+                  <TagManagement accessToken={accessToken} userRole={userRole} userID={userID} />
+                ) : page == "claude-code-plugins" ? (
+                  <ClaudeCodePluginsPanel accessToken={accessToken} userRole={userRole} />
+                ) : page == "vector-stores" ? (
+                  <VectorStoreManagement accessToken={accessToken} userRole={userRole} userID={userID} />
+                ) : page == "new_usage" ? (
+                  <NewUsagePage
+                    teams={(teams as Team[]) ?? []}
+                    organizations={(organizations as Organization[]) ?? []}
+                  />
+                ) : (
+                  <Usage
+                    userID={userID}
+                    userRole={userRole}
+                    token={token}
+                    accessToken={accessToken}
+                    keys={keys}
+                    premiumUser={premiumUser}
+                  />
+                )}
               </div>
-            )}
-          </ThemeProvider>
-        </ConfigProvider>
+
+              {/* Survey Components */}
+              <SurveyPrompt
+                isVisible={showSurveyPrompt}
+                onOpen={handleOpenSurvey}
+                onDismiss={handleDismissSurveyPrompt}
+              />
+              <SurveyModal
+                isOpen={showSurveyModal}
+                onClose={handleSurveyModalClose}
+                onComplete={handleSurveyComplete}
+              />
+
+              {/* Claude Code Components */}
+              <ClaudeCodePrompt
+                isVisible={showClaudeCodePrompt}
+                onOpen={handleOpenClaudeCode}
+                onDismiss={handleDismissClaudeCodePrompt}
+              />
+              <ClaudeCodeModal
+                isOpen={showClaudeCodeModal}
+                onClose={handleClaudeCodeModalClose}
+                onComplete={handleClaudeCodeComplete}
+              />
+            </div>
+          )}
+        </ThemeProvider>
       </QueryClientProvider>
     </Suspense>
   );
diff --git a/ui/litellm-dashboard/src/components/BulkEditUsers.test.tsx b/ui/litellm-dashboard/src/components/BulkEditUsers.test.tsx
deleted file mode 100644
index 4185625e746..00000000000
--- a/ui/litellm-dashboard/src/components/BulkEditUsers.test.tsx
+++ /dev/null
@@ -1,343 +0,0 @@
-import userEvent from "@testing-library/user-event";
-import { describe, expect, it, vi, beforeEach } from "vitest";
-import { renderWithProviders, screen, waitFor } from "../../tests/test-utils";
-import BulkEditUserModal from "./BulkEditUsers";
-import { userBulkUpdateUserCall, teamBulkMemberAddCall } from "./networking";
-import NotificationsManager from "./molecules/notifications_manager";
-
-vi.mock("./networking", () => ({
-  userBulkUpdateUserCall: vi.fn(),
-  teamBulkMemberAddCall: vi.fn(),
-}));
-
-vi.mock("./user_edit_view", () => ({
-  UserEditView: ({ onSubmit, onCancel }: { onSubmit: (values: any) => void; onCancel: () => void }) => (
-    <div data-testid="user-edit-view">
-      <button onClick={() => onSubmit({ user_role: "admin", max_budget: 100 })}>Submit</button>
-      <button onClick={onCancel}>Cancel</button>
-    </div>
-  ),
-}));
-
-const mockUserBulkUpdateUserCall = vi.mocked(userBulkUpdateUserCall);
-const mockTeamBulkMemberAddCall = vi.mocked(teamBulkMemberAddCall);
-
-const defaultProps = {
-  open: true,
-  onCancel: vi.fn(),
-  selectedUsers: [
-    { user_id: "user1", user_email: "user1@example.com", user_role: "user", max_budget: 50 },
-    { user_id: "user2", user_email: "user2@example.com", user_role: "admin", max_budget: null },
-  ],
-  possibleUIRoles: {
-    admin: { ui_label: "Admin", description: "Administrator role" },
-    user: { ui_label: "User", description: "Regular user role" },
-  },
-  accessToken: "test-token",
-  onSuccess: vi.fn(),
-  teams: [
-    { team_id: "team1", team_alias: "Team 1" },
-    { team_id: "team2", team_alias: "Team 2" },
-  ],
-  userRole: "Admin",
-  userModels: ["gpt-4", "gpt-3.5-turbo"],
-  allowAllUsers: false,
-};
-
-describe("BulkEditUserModal", () => {
-  beforeEach(() => {
-    vi.clearAllMocks();
-    mockUserBulkUpdateUserCall.mockResolvedValue({
-      results: [],
-      total_requested: 2,
-      successful_updates: 2,
-      failed_updates: 0,
-    });
-    mockTeamBulkMemberAddCall.mockResolvedValue({
-      successful_additions: 2,
-      failed_additions: 0,
-    });
-  });
-
-  it("should render without crashing", () => {
-    renderWithProviders(<BulkEditUserModal {...defaultProps} />);
-
-    expect(screen.getByText(`Bulk Edit ${defaultProps.selectedUsers.length} User(s)`)).toBeInTheDocument();
-  });
-
-  it("should display modal title with correct user count", () => {
-    renderWithProviders(<BulkEditUserModal {...defaultProps} />);
-
-    expect(screen.getByText("Bulk Edit 2 User(s)")).toBeInTheDocument();
-  });
-
-  it("should display selected users table when modal is open", () => {
-    renderWithProviders(<BulkEditUserModal {...defaultProps} />);
-
-    expect(screen.getByText("Selected Users (2):")).toBeInTheDocument();
-    expect(screen.getByText("user1")).toBeInTheDocument();
-    expect(screen.getByText("user2")).toBeInTheDocument();
-    expect(screen.getByText("user1@example.com")).toBeInTheDocument();
-    expect(screen.getByText("user2@example.com")).toBeInTheDocument();
-  });
-
-  it("should display user roles in table", () => {
-    renderWithProviders(<BulkEditUserModal {...defaultProps} />);
-
-    expect(screen.getByText("User")).toBeInTheDocument();
-    expect(screen.getByText("Admin")).toBeInTheDocument();
-  });
-
-  it("should display budget information in table", () => {
-    renderWithProviders(<BulkEditUserModal {...defaultProps} />);
-
-    expect(screen.getByText("$50")).toBeInTheDocument();
-    expect(screen.getByText("Unlimited")).toBeInTheDocument();
-  });
-
-  it("should call onCancel when cancel button is clicked", async () => {
-    const user = userEvent.setup();
-    const onCancel = vi.fn();
-    renderWithProviders(<BulkEditUserModal {...defaultProps} onCancel={onCancel} />);
-
-    const cancelButton = screen.getByRole("button", { name: "Cancel" });
-    await user.click(cancelButton);
-
-    expect(onCancel).toHaveBeenCalledTimes(1);
-  });
-
-
-  it("should show update all users checkbox when allowAllUsers is true", () => {
-    renderWithProviders(<BulkEditUserModal {...defaultProps} allowAllUsers={true} />);
-
-    expect(screen.getByRole("checkbox", { name: /update all users/i })).toBeInTheDocument();
-  });
-
-  it("should not show update all users checkbox when allowAllUsers is false", () => {
-    renderWithProviders(<BulkEditUserModal {...defaultProps} allowAllUsers={false} />);
-
-    expect(screen.queryByRole("checkbox", { name: /update all users/i })).not.toBeInTheDocument();
-  });
-
-  it("should toggle update all users mode", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(<BulkEditUserModal {...defaultProps} allowAllUsers={true} />);
-
-    const checkbox = screen.getByRole("checkbox", { name: /update all users/i });
-    expect(checkbox).not.toBeChecked();
-
-    await user.click(checkbox);
-
-    expect(checkbox).toBeChecked();
-    expect(screen.getByText("Bulk Edit All Users")).toBeInTheDocument();
-  });
-
-  it("should show warning message when update all users is enabled", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(<BulkEditUserModal {...defaultProps} allowAllUsers={true} />);
-
-    const checkbox = screen.getByRole("checkbox", { name: /update all users/i });
-    await user.click(checkbox);
-
-    expect(screen.getByText(/this will apply changes to all users/i)).toBeInTheDocument();
-  });
-
-  it("should hide selected users table when update all users is enabled", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(<BulkEditUserModal {...defaultProps} allowAllUsers={true} />);
-
-    expect(screen.getByText("Selected Users (2):")).toBeInTheDocument();
-
-    const checkbox = screen.getByRole("checkbox", { name: /update all users/i });
-    await user.click(checkbox);
-
-    expect(screen.queryByText("Selected Users (2):")).not.toBeInTheDocument();
-  });
-
-  it("should display team management section", () => {
-    renderWithProviders(<BulkEditUserModal {...defaultProps} />);
-
-    expect(screen.getByText("Team Management")).toBeInTheDocument();
-    expect(screen.getByRole("checkbox", { name: /add selected users to teams/i })).toBeInTheDocument();
-  });
-
-  it("should show team budget input when add to teams is checked", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(<BulkEditUserModal {...defaultProps} />);
-
-    const addToTeamsCheckbox = screen.getByRole("checkbox", { name: /add selected users to teams/i });
-    await user.click(addToTeamsCheckbox);
-
-    expect(screen.getByText("Team Budget (Optional):")).toBeInTheDocument();
-    expect(screen.getByPlaceholderText("Max budget per user in team")).toBeInTheDocument();
-  });
-
-  it("should render UserEditView component", () => {
-    renderWithProviders(<BulkEditUserModal {...defaultProps} />);
-
-    expect(screen.getByTestId("user-edit-view")).toBeInTheDocument();
-  });
-
-  it("should show error when access token is missing", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(<BulkEditUserModal {...defaultProps} accessToken={null} />);
-
-    const submitButton = screen.getByRole("button", { name: "Submit" });
-    await user.click(submitButton);
-
-    await waitFor(() => {
-      expect(NotificationsManager.fromBackend).toHaveBeenCalledWith("Access token not found");
-    });
-  });
-
-  it("should call userBulkUpdateUserCall with correct payload for selected users", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(<BulkEditUserModal {...defaultProps} />);
-
-    const submitButton = screen.getByRole("button", { name: "Submit" });
-    await user.click(submitButton);
-
-    await waitFor(() => {
-      expect(mockUserBulkUpdateUserCall).toHaveBeenCalledWith(
-        "test-token",
-        { user_role: "admin", max_budget: 100 },
-        ["user1", "user2"],
-      );
-    });
-  });
-
-  it("should call userBulkUpdateUserCall with allUsers flag when update all users is enabled", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(<BulkEditUserModal {...defaultProps} allowAllUsers={true} />);
-
-    const updateAllCheckbox = screen.getByRole("checkbox", { name: /update all users/i });
-    await user.click(updateAllCheckbox);
-
-    const submitButton = screen.getByRole("button", { name: "Submit" });
-    await user.click(submitButton);
-
-    await waitFor(() => {
-      expect(mockUserBulkUpdateUserCall).toHaveBeenCalledWith(
-        "test-token",
-        expect.objectContaining({ user_role: "admin", max_budget: 100 }),
-        undefined,
-        true,
-      );
-    });
-  });
-
-
-  it("should show success message after successful user update", async () => {
-    const user = userEvent.setup();
-    mockUserBulkUpdateUserCall.mockResolvedValue({
-      results: [],
-      total_requested: 2,
-      successful_updates: 2,
-      failed_updates: 0,
-    });
-
-    renderWithProviders(<BulkEditUserModal {...defaultProps} />);
-
-    const submitButton = screen.getByRole("button", { name: "Submit" });
-    await user.click(submitButton);
-
-    await waitFor(() => {
-      expect(NotificationsManager.success).toHaveBeenCalledWith("Updated 2 user(s)");
-    });
-  });
-
-  it("should show success message for all users update", async () => {
-    const user = userEvent.setup();
-    mockUserBulkUpdateUserCall.mockResolvedValue({
-      results: [],
-      total_requested: 100,
-      successful_updates: 100,
-      failed_updates: 0,
-    });
-
-    renderWithProviders(<BulkEditUserModal {...defaultProps} allowAllUsers={true} />);
-
-    const updateAllCheckbox = screen.getByRole("checkbox", { name: /update all users/i });
-    await user.click(updateAllCheckbox);
-
-    const submitButton = screen.getByRole("button", { name: "Submit" });
-    await user.click(submitButton);
-
-    await waitFor(() => {
-      expect(NotificationsManager.success).toHaveBeenCalledWith("Updated all users (100 total)");
-    });
-  });
-
-
-  it("should show error message when bulk update fails", async () => {
-    const user = userEvent.setup();
-    mockUserBulkUpdateUserCall.mockRejectedValueOnce(new Error("Update failed"));
-
-    renderWithProviders(<BulkEditUserModal {...defaultProps} />);
-
-    const submitButton = screen.getByRole("button", { name: "Submit" });
-    await user.click(submitButton);
-
-    await waitFor(() => {
-      expect(NotificationsManager.fromBackend).toHaveBeenCalledWith("Failed to perform bulk operations");
-    });
-  });
-
-  it("should call onSuccess and onCancel after successful update", async () => {
-    const user = userEvent.setup();
-    const onSuccess = vi.fn();
-    const onCancel = vi.fn();
-
-    renderWithProviders(<BulkEditUserModal {...defaultProps} onSuccess={onSuccess} onCancel={onCancel} />);
-
-    const submitButton = screen.getByRole("button", { name: "Submit" });
-    await user.click(submitButton);
-
-    await waitFor(() => {
-      expect(onSuccess).toHaveBeenCalledTimes(1);
-      expect(onCancel).toHaveBeenCalledTimes(1);
-    });
-  });
-
-  it("should truncate long user IDs in table", () => {
-    const longUserId = "a".repeat(30);
-    const propsWithLongId = {
-      ...defaultProps,
-      selectedUsers: [{ user_id: longUserId, user_email: "test@example.com", user_role: "user", max_budget: null }],
-    };
-
-    renderWithProviders(<BulkEditUserModal {...propsWithLongId} />);
-
-    expect(screen.getByText(new RegExp(`${longUserId.slice(0, 20)}...`))).toBeInTheDocument();
-  });
-
-  it("should display no email text when user email is missing", () => {
-    const propsWithoutEmail = {
-      ...defaultProps,
-      selectedUsers: [{ user_id: "user1", user_email: null, user_role: "user", max_budget: null }],
-    };
-
-    renderWithProviders(<BulkEditUserModal {...propsWithoutEmail} />);
-
-    expect(screen.getByText("No email")).toBeInTheDocument();
-  });
-
-  it("should display role label from possibleUIRoles when available", () => {
-    renderWithProviders(<BulkEditUserModal {...defaultProps} />);
-
-    expect(screen.getByText("Admin")).toBeInTheDocument();
-    expect(screen.getByText("User")).toBeInTheDocument();
-  });
-
-  it("should display role key when ui_label is not available", () => {
-    const propsWithoutUIRoles = {
-      ...defaultProps,
-      possibleUIRoles: null,
-    };
-
-    renderWithProviders(<BulkEditUserModal {...propsWithoutUIRoles} />);
-
-    expect(screen.getByText("user")).toBeInTheDocument();
-    expect(screen.getByText("admin")).toBeInTheDocument();
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/ExportTypeSelector.tsx b/ui/litellm-dashboard/src/components/EntityUsageExport/ExportTypeSelector.tsx
index 6bc85948608..17a833deacb 100644
--- a/ui/litellm-dashboard/src/components/EntityUsageExport/ExportTypeSelector.tsx
+++ b/ui/litellm-dashboard/src/components/EntityUsageExport/ExportTypeSelector.tsx
@@ -17,19 +17,11 @@ const ExportTypeSelector: React.FC<ExportTypeSelectorProps> = ({ value, onChange
           <label className="flex items-start p-3 border border-gray-200 rounded-lg hover:bg-gray-50 cursor-pointer transition-colors">
             <Radio value="daily" className="mt-0.5" />
             <div className="ml-3 flex-1">
-              <div className="font-medium text-sm">Day-by-day breakdown by {entityType}</div>
+              <div className="font-medium text-sm">Day-by-day breakdown</div>
               <div className="text-xs text-gray-500 mt-0.5">Daily metrics for each {entityType}</div>
             </div>
           </label>
 
-          <label className="flex items-start p-3 border border-gray-200 rounded-lg hover:bg-gray-50 cursor-pointer transition-colors">
-            <Radio value="daily_with_keys" className="mt-0.5" />
-            <div className="ml-3 flex-1">
-              <div className="font-medium text-sm">Day-by-day breakdown by {entityType} and key</div>
-              <div className="text-xs text-gray-500 mt-0.5">Daily metrics for each {entityType}, split by API key</div>
-            </div>
-          </label>
-
           <label className="flex items-start p-3 border border-gray-200 rounded-lg hover:bg-gray-50 cursor-pointer transition-colors">
             <Radio value="daily_with_models" className="mt-0.5" />
             <div className="ml-3 flex-1">
diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/types.ts b/ui/litellm-dashboard/src/components/EntityUsageExport/types.ts
index ccf0b1b45cf..84a20da7cba 100644
--- a/ui/litellm-dashboard/src/components/EntityUsageExport/types.ts
+++ b/ui/litellm-dashboard/src/components/EntityUsageExport/types.ts
@@ -2,7 +2,7 @@ import type { DateRangePickerValue } from "@tremor/react";
 import type { Team } from "@/components/key_team_helpers/key_list";
 
 export type ExportFormat = "csv" | "json";
-export type ExportScope = "daily" | "daily_with_keys" | "daily_with_models";
+export type ExportScope = "daily" | "daily_with_models";
 export type EntityType = "tag" | "team" | "organization" | "customer" | "agent";
 
 export interface EntitySpendData {
diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/utils.test.ts b/ui/litellm-dashboard/src/components/EntityUsageExport/utils.test.ts
index 67de4b69174..127e3fc208f 100644
--- a/ui/litellm-dashboard/src/components/EntityUsageExport/utils.test.ts
+++ b/ui/litellm-dashboard/src/components/EntityUsageExport/utils.test.ts
@@ -4,7 +4,6 @@ import { afterEach, beforeEach, describe, expect, it, vi } from "vitest";
 import type { EntitySpendData, ExportScope } from "./types";
 import {
   generateDailyData,
-  generateDailyWithKeysData,
   generateDailyWithModelsData,
   generateExportData,
   generateMetadata,
@@ -463,514 +462,6 @@ describe("EntityUsageExport utils", () => {
     });
   });
 
-  describe("generateDailyWithKeysData", () => {
-    const mockSpendDataWithKeys: EntitySpendData = {
-      results: [
-        {
-          date: "2025-01-01",
-          breakdown: {
-            entities: {
-              entity1: {
-                metrics: {
-                  spend: 10.5,
-                  api_requests: 100,
-                  successful_requests: 95,
-                  failed_requests: 5,
-                  total_tokens: 1000,
-                  prompt_tokens: 600,
-                  completion_tokens: 400,
-                },
-                api_key_breakdown: {
-                  key1: {
-                    metrics: {
-                      spend: 5.0,
-                      api_requests: 50,
-                      successful_requests: 48,
-                      failed_requests: 2,
-                      total_tokens: 500,
-                      prompt_tokens: 300,
-                      completion_tokens: 200,
-                    },
-                    metadata: {
-                      team_id: "team-1",
-                      key_alias: "alias-1",
-                    },
-                  },
-                  key2: {
-                    metrics: {
-                      spend: 5.5,
-                      api_requests: 50,
-                      successful_requests: 47,
-                      failed_requests: 3,
-                      total_tokens: 500,
-                      prompt_tokens: 300,
-                      completion_tokens: 200,
-                    },
-                    metadata: {
-                      team_id: "team-1",
-                      key_alias: "alias-2",
-                    },
-                  },
-                },
-              },
-              entity2: {
-                metrics: {
-                  spend: 20.3,
-                  api_requests: 200,
-                  successful_requests: 190,
-                  failed_requests: 10,
-                  total_tokens: 2000,
-                  prompt_tokens: 1200,
-                  completion_tokens: 800,
-                },
-                api_key_breakdown: {
-                  key3: {
-                    metrics: {
-                      spend: 20.3,
-                      api_requests: 200,
-                      successful_requests: 190,
-                      failed_requests: 10,
-                      total_tokens: 2000,
-                      prompt_tokens: 1200,
-                      completion_tokens: 800,
-                    },
-                    metadata: {
-                      team_id: "team-2",
-                      key_alias: "alias-3",
-                    },
-                  },
-                },
-              },
-            },
-          },
-        },
-        {
-          date: "2025-01-02",
-          breakdown: {
-            entities: {
-              entity1: {
-                metrics: {
-                  spend: 15.2,
-                  api_requests: 150,
-                  successful_requests: 145,
-                  failed_requests: 5,
-                  total_tokens: 1500,
-                  prompt_tokens: 900,
-                  completion_tokens: 600,
-                },
-                api_key_breakdown: {
-                  key1: {
-                    metrics: {
-                      spend: 15.2,
-                      api_requests: 150,
-                      successful_requests: 145,
-                      failed_requests: 5,
-                      total_tokens: 1500,
-                      prompt_tokens: 900,
-                      completion_tokens: 600,
-                    },
-                    metadata: {
-                      team_id: "team-1",
-                      key_alias: "alias-1",
-                    },
-                  },
-                },
-              },
-            },
-          },
-        },
-      ],
-      metadata: {
-        total_spend: 46.0,
-        total_api_requests: 450,
-        total_successful_requests: 430,
-        total_failed_requests: 20,
-        total_tokens: 4500,
-      },
-    };
-
-    it("should generate daily breakdown with key data and correct structure", () => {
-      const result = generateDailyWithKeysData(mockSpendDataWithKeys, "Team", mockTeamAliasMap);
-
-      expect(result.length).toBeGreaterThan(0);
-      expect(result[0]).toHaveProperty("Date");
-      expect(result[0]).toHaveProperty("Team");
-      expect(result[0]).toHaveProperty("Team ID");
-      expect(result[0]).toHaveProperty("Key Alias");
-      expect(result[0]).toHaveProperty("Key ID");
-      expect(result[0]).toHaveProperty("Spend ($)");
-      expect(result[0]).toHaveProperty("Requests");
-      expect(result[0]).toHaveProperty("Successful Requests");
-      expect(result[0]).toHaveProperty("Failed Requests");
-      expect(result[0]).toHaveProperty("Total Tokens");
-      expect(result[0]).toHaveProperty("Prompt Tokens");
-      expect(result[0]).toHaveProperty("Completion Tokens");
-    });
-
-    it("should sort data by date ascending", () => {
-      const result = generateDailyWithKeysData(mockSpendDataWithKeys, "Team");
-
-      const dates = result.map((r) => new Date(r.Date).getTime());
-      for (let i = 0; i < dates.length - 1; i++) {
-        expect(dates[i]).toBeLessThanOrEqual(dates[i + 1]);
-      }
-    });
-
-    it("should aggregate metrics for duplicate date-team-key combinations", () => {
-      const spendDataWithDuplicates: EntitySpendData = {
-        results: [
-          {
-            date: "2025-01-01",
-            breakdown: {
-              entities: {
-                entity1: {
-                  metrics: {
-                    spend: 10.5,
-                    api_requests: 100,
-                    successful_requests: 95,
-                    failed_requests: 5,
-                    total_tokens: 1000,
-                    prompt_tokens: 600,
-                    completion_tokens: 400,
-                  },
-                  api_key_breakdown: {
-                    key1: {
-                      metrics: {
-                        spend: 5.0,
-                        api_requests: 50,
-                        successful_requests: 48,
-                        failed_requests: 2,
-                        total_tokens: 500,
-                        prompt_tokens: 300,
-                        completion_tokens: 200,
-                      },
-                      metadata: {
-                        team_id: "team-1",
-                        key_alias: "alias-1",
-                      },
-                    },
-                  },
-                },
-              },
-            },
-          },
-          {
-            date: "2025-01-01",
-            breakdown: {
-              entities: {
-                entity1: {
-                  metrics: {
-                    spend: 10.5,
-                    api_requests: 100,
-                    successful_requests: 95,
-                    failed_requests: 5,
-                    total_tokens: 1000,
-                    prompt_tokens: 600,
-                    completion_tokens: 400,
-                  },
-                  api_key_breakdown: {
-                    key1: {
-                      metrics: {
-                        spend: 5.0,
-                        api_requests: 50,
-                        successful_requests: 47,
-                        failed_requests: 3,
-                        total_tokens: 500,
-                        prompt_tokens: 300,
-                        completion_tokens: 200,
-                      },
-                      metadata: {
-                        team_id: "team-1",
-                        key_alias: "alias-1",
-                      },
-                    },
-                  },
-                },
-              },
-            },
-          },
-        ],
-        metadata: {
-          total_spend: 21.0,
-          total_api_requests: 200,
-          total_successful_requests: 190,
-          total_failed_requests: 10,
-          total_tokens: 2000,
-        },
-      };
-
-      const result = generateDailyWithKeysData(spendDataWithDuplicates, "Team");
-      const key1Entries = result.filter((r) => r["Key ID"] === "key1");
-
-      expect(key1Entries).toHaveLength(1);
-      expect(key1Entries[0].Requests).toBe(100);
-      expect(key1Entries[0]["Successful Requests"]).toBe(95);
-      expect(key1Entries[0]["Failed Requests"]).toBe(5);
-      expect(key1Entries[0]["Total Tokens"]).toBe(1000);
-    });
-
-    it("should use team alias when available", () => {
-      const result = generateDailyWithKeysData(mockSpendDataWithKeys, "Team", mockTeamAliasMap);
-      const team1Entry = result.find((r) => r["Team ID"] === "team-1");
-
-      expect(team1Entry?.["Team"]).toBe("Team One");
-    });
-
-    it("should use dash when team alias is not available", () => {
-      const result = generateDailyWithKeysData(mockSpendDataWithKeys, "Team");
-      const entryWithoutTeamAlias = result.find((r) => r["Team ID"] === "team-1" && !mockTeamAliasMap[r["Team ID"]]);
-
-      if (entryWithoutTeamAlias) {
-        expect(entryWithoutTeamAlias["Team"]).toBe("-");
-      }
-    });
-
-    it("should use key alias when available", () => {
-      const result = generateDailyWithKeysData(mockSpendDataWithKeys, "Team");
-      const key1Entry = result.find((r) => r["Key ID"] === "key1");
-
-      expect(key1Entry?.["Key Alias"]).toBe("alias-1");
-    });
-
-    it("should use dash when key alias is not available", () => {
-      const spendDataWithoutKeyAlias: EntitySpendData = {
-        ...mockSpendDataWithKeys,
-        results: [
-          {
-            date: "2025-01-01",
-            breakdown: {
-              entities: {
-                entity1: {
-                  metrics: {
-                    spend: 10.5,
-                    api_requests: 100,
-                    successful_requests: 95,
-                    failed_requests: 5,
-                    total_tokens: 1000,
-                    prompt_tokens: 600,
-                    completion_tokens: 400,
-                  },
-                  api_key_breakdown: {
-                    key1: {
-                      metrics: {
-                        spend: 10.5,
-                        api_requests: 100,
-                        successful_requests: 95,
-                        failed_requests: 5,
-                        total_tokens: 1000,
-                        prompt_tokens: 600,
-                        completion_tokens: 400,
-                      },
-                      metadata: {
-                        team_id: "team-1",
-                      },
-                    },
-                  },
-                },
-              },
-            },
-          },
-        ],
-        metadata: mockSpendDataWithKeys.metadata,
-      };
-
-      const result = generateDailyWithKeysData(spendDataWithoutKeyAlias, "Team");
-      const key1Entry = result.find((r) => r["Key ID"] === "key1");
-
-      expect(key1Entry?.["Key Alias"]).toBe("-");
-    });
-
-    it("should use entity id when team id is not available in metadata", () => {
-      const spendDataWithoutTeamId: EntitySpendData = {
-        ...mockSpendDataWithKeys,
-        results: [
-          {
-            date: "2025-01-01",
-            breakdown: {
-              entities: {
-                entity1: {
-                  metrics: {
-                    spend: 10.5,
-                    api_requests: 100,
-                    successful_requests: 95,
-                    failed_requests: 5,
-                    total_tokens: 1000,
-                    prompt_tokens: 600,
-                    completion_tokens: 400,
-                  },
-                  api_key_breakdown: {
-                    key1: {
-                      metrics: {
-                        spend: 10.5,
-                        api_requests: 100,
-                        successful_requests: 95,
-                        failed_requests: 5,
-                        total_tokens: 1000,
-                        prompt_tokens: 600,
-                        completion_tokens: 400,
-                      },
-                      metadata: {},
-                    },
-                  },
-                },
-              },
-            },
-          },
-        ],
-        metadata: mockSpendDataWithKeys.metadata,
-      };
-
-      const result = generateDailyWithKeysData(spendDataWithoutTeamId, "Team");
-      const entry = result.find((r) => r["Key ID"] === "key1");
-
-      expect(entry?.["Team ID"]).toBe("entity1");
-    });
-
-    it("should use dash when team id is not available", () => {
-      const spendDataWithoutTeamId: EntitySpendData = {
-        ...mockSpendDataWithKeys,
-        results: [
-          {
-            date: "2025-01-01",
-            breakdown: {
-              entities: {
-                entity1: {
-                  metrics: {
-                    spend: 10.5,
-                    api_requests: 100,
-                    successful_requests: 95,
-                    failed_requests: 5,
-                    total_tokens: 1000,
-                    prompt_tokens: 600,
-                    completion_tokens: 400,
-                  },
-                  api_key_breakdown: {
-                    key1: {
-                      metrics: {
-                        spend: 10.5,
-                        api_requests: 100,
-                        successful_requests: 95,
-                        failed_requests: 5,
-                        total_tokens: 1000,
-                        prompt_tokens: 600,
-                        completion_tokens: 400,
-                      },
-                      metadata: {
-                        team_id: null,
-                      },
-                    },
-                  },
-                },
-              },
-            },
-          },
-        ],
-        metadata: mockSpendDataWithKeys.metadata,
-      };
-
-      const result = generateDailyWithKeysData(spendDataWithoutTeamId, "Team");
-      const entry = result.find((r) => r["Key ID"] === "key1");
-
-      expect(entry?.["Team ID"]).toBe("entity1");
-    });
-
-    it("should format spend values correctly", () => {
-      const result = generateDailyWithKeysData(mockSpendDataWithKeys, "Team");
-
-      expect(result[0]["Spend ($)"]).toBeDefined();
-    });
-
-    it("should handle missing optional token fields", () => {
-      const spendDataWithMissingTokens: EntitySpendData = {
-        ...mockSpendDataWithKeys,
-        results: [
-          {
-            date: "2025-01-01",
-            breakdown: {
-              entities: {
-                entity1: {
-                  metrics: {
-                    spend: 10.5,
-                    api_requests: 100,
-                    successful_requests: 95,
-                    failed_requests: 5,
-                    total_tokens: 1000,
-                    prompt_tokens: 600,
-                    completion_tokens: 400,
-                  },
-                  api_key_breakdown: {
-                    key1: {
-                      metrics: {
-                        spend: 10.5,
-                        api_requests: 100,
-                        successful_requests: 95,
-                        failed_requests: 5,
-                        total_tokens: 1000,
-                      },
-                      metadata: {
-                        team_id: "team-1",
-                        key_alias: "alias-1",
-                      },
-                    },
-                  },
-                },
-              },
-            },
-          },
-        ],
-        metadata: mockSpendDataWithKeys.metadata,
-      };
-
-      const result = generateDailyWithKeysData(spendDataWithMissingTokens, "Team");
-      const key1Entry = result.find((r) => r["Key ID"] === "key1");
-
-      expect(key1Entry?.["Prompt Tokens"]).toBe(0);
-      expect(key1Entry?.["Completion Tokens"]).toBe(0);
-    });
-
-    it("should handle empty api_key_breakdown", () => {
-      const spendDataWithEmptyBreakdown: EntitySpendData = {
-        ...mockSpendDataWithKeys,
-        results: [
-          {
-            date: "2025-01-01",
-            breakdown: {
-              entities: {
-                entity1: {
-                  metrics: {
-                    spend: 10.5,
-                    api_requests: 100,
-                    successful_requests: 95,
-                    failed_requests: 5,
-                    total_tokens: 1000,
-                    prompt_tokens: 600,
-                    completion_tokens: 400,
-                  },
-                  api_key_breakdown: {},
-                },
-              },
-            },
-          },
-        ],
-        metadata: mockSpendDataWithKeys.metadata,
-      };
-
-      const result = generateDailyWithKeysData(spendDataWithEmptyBreakdown, "Team");
-
-      expect(result).toHaveLength(0);
-    });
-
-    it("should handle multiple keys for same team on same date", () => {
-      const result = generateDailyWithKeysData(mockSpendDataWithKeys, "Team");
-      const team1Entries = result.filter((r) => r["Team ID"] === "team-1" && r.Date === "2025-01-01");
-
-      expect(team1Entries.length).toBeGreaterThanOrEqual(2);
-      const keyIds = team1Entries.map((r) => r["Key ID"]);
-      expect(keyIds).toContain("key1");
-      expect(keyIds).toContain("key2");
-    });
-  });
-
   describe("generateDailyWithModelsData", () => {
     const mockSpendDataWithModels: EntitySpendData = {
       results: [
@@ -1207,57 +698,6 @@ describe("EntityUsageExport utils", () => {
       expect(result[0]).not.toHaveProperty("Model");
     });
 
-    it("should return daily with keys data when scope is daily_with_keys", () => {
-      const mockDataWithKeys: EntitySpendData = {
-        ...mockSpendData,
-        results: [
-          {
-            date: "2025-01-01",
-            breakdown: {
-              entities: {
-                entity1: {
-                  metrics: {
-                    spend: 10.5,
-                    api_requests: 100,
-                    successful_requests: 95,
-                    failed_requests: 5,
-                    total_tokens: 1000,
-                    prompt_tokens: 600,
-                    completion_tokens: 400,
-                  },
-                  api_key_breakdown: {
-                    key1: {
-                      metrics: {
-                        spend: 10.5,
-                        api_requests: 100,
-                        successful_requests: 95,
-                        failed_requests: 5,
-                        total_tokens: 1000,
-                        prompt_tokens: 600,
-                        completion_tokens: 400,
-                      },
-                      metadata: {
-                        team_id: "team-1",
-                        key_alias: "alias-1",
-                      },
-                    },
-                  },
-                },
-              },
-            },
-          },
-        ],
-        metadata: mockSpendData.metadata,
-      };
-
-      const result = generateExportData(mockDataWithKeys, "daily_with_keys", "Team", mockTeamAliasMap);
-
-      expect(result.length).toBeGreaterThan(0);
-      expect(result[0]).toHaveProperty("Key Alias");
-      expect(result[0]).toHaveProperty("Key ID");
-      expect(result[0]).not.toHaveProperty("Model");
-    });
-
     it("should return daily with models data when scope is daily_with_models", () => {
       const mockDataWithModels: EntitySpendData = {
         ...mockSpendData,
diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/utils.ts b/ui/litellm-dashboard/src/components/EntityUsageExport/utils.ts
index ebef3da8a77..cdb5d011af4 100644
--- a/ui/litellm-dashboard/src/components/EntityUsageExport/utils.ts
+++ b/ui/litellm-dashboard/src/components/EntityUsageExport/utils.ts
@@ -103,95 +103,6 @@ export const generateDailyData = (
   return dailyBreakdown.sort((a, b) => new Date(a.Date).getTime() - new Date(b.Date).getTime());
 };
 
-export const generateDailyWithKeysData = (
-  spendData: EntitySpendData,
-  entityLabel: string,
-  teamAliasMap: Record<string, string> = {},
-): any[] => {
-  // Aggregate by unique (Date, Team ID, Key ID) combination to prevent duplicates
-  const aggregatedData: {
-    [key: string]: {
-      Date: string;
-      teamId: string;
-      teamAlias: string | null;
-      keyId: string;
-      keyAlias: string | null;
-      metrics: {
-        spend: number;
-        api_requests: number;
-        successful_requests: number;
-        failed_requests: number;
-        total_tokens: number;
-        prompt_tokens: number;
-        completion_tokens: number;
-      };
-    };
-  } = {};
-
-  spendData.results.forEach((day) => {
-    Object.entries(day.breakdown.entities || {}).forEach(([entity, data]: [string, any]) => {
-      const apiKeyBreakdown = data.api_key_breakdown || {};
-
-      // Iterate through each API key in the breakdown
-      Object.entries(apiKeyBreakdown).forEach(([keyId, keyData]: [string, any]) => {
-        const keyAlias = keyData?.metadata?.key_alias || null;
-        const teamId = keyData?.metadata?.team_id || entity;
-        const teamAlias = teamId ? teamAliasMap[teamId] || null : null;
-
-        // Create unique key for aggregation: Date_TeamID_KeyID
-        const uniqueKey = `${day.date}_${teamId}_${keyId}`;
-
-        if (!aggregatedData[uniqueKey]) {
-          // First time seeing this (Date, Team ID, Key ID) combination
-          aggregatedData[uniqueKey] = {
-            Date: day.date,
-            teamId,
-            teamAlias,
-            keyId,
-            keyAlias,
-            metrics: {
-              spend: keyData.metrics?.spend || 0,
-              api_requests: keyData.metrics?.api_requests || 0,
-              successful_requests: keyData.metrics?.successful_requests || 0,
-              failed_requests: keyData.metrics?.failed_requests || 0,
-              total_tokens: keyData.metrics?.total_tokens || 0,
-              prompt_tokens: keyData.metrics?.prompt_tokens || 0,
-              completion_tokens: keyData.metrics?.completion_tokens || 0,
-            },
-          };
-        } else {
-          // Aggregate metrics for existing entry
-          aggregatedData[uniqueKey].metrics.spend += keyData.metrics?.spend || 0;
-          aggregatedData[uniqueKey].metrics.api_requests += keyData.metrics?.api_requests || 0;
-          aggregatedData[uniqueKey].metrics.successful_requests += keyData.metrics?.successful_requests || 0;
-          aggregatedData[uniqueKey].metrics.failed_requests += keyData.metrics?.failed_requests || 0;
-          aggregatedData[uniqueKey].metrics.total_tokens += keyData.metrics?.total_tokens || 0;
-          aggregatedData[uniqueKey].metrics.prompt_tokens += keyData.metrics?.prompt_tokens || 0;
-          aggregatedData[uniqueKey].metrics.completion_tokens += keyData.metrics?.completion_tokens || 0;
-        }
-      });
-    });
-  });
-
-  // Convert aggregated data to array format
-  const dailyKeyBreakdown = Object.values(aggregatedData).map((item) => ({
-    Date: item.Date,
-    [entityLabel]: item.teamAlias || "-",
-    [`${entityLabel} ID`]: item.teamId || "-",
-    "Key Alias": item.keyAlias || "-",
-    "Key ID": item.keyId,
-    "Spend ($)": formatNumberWithCommas(item.metrics.spend, 4),
-    Requests: item.metrics.api_requests,
-    "Successful Requests": item.metrics.successful_requests,
-    "Failed Requests": item.metrics.failed_requests,
-    "Total Tokens": item.metrics.total_tokens,
-    "Prompt Tokens": item.metrics.prompt_tokens,
-    "Completion Tokens": item.metrics.completion_tokens,
-  }));
-
-  return dailyKeyBreakdown.sort((a, b) => new Date(a.Date).getTime() - new Date(b.Date).getTime());
-};
-
 export const generateDailyWithModelsData = (
   spendData: EntitySpendData,
   entityLabel: string,
@@ -263,8 +174,6 @@ export const generateExportData = (
   switch (exportScope) {
     case "daily":
       return generateDailyData(spendData, entityLabel, teamAliasMap);
-    case "daily_with_keys":
-      return generateDailyWithKeysData(spendData, entityLabel, teamAliasMap);
     case "daily_with_models":
       return generateDailyWithModelsData(spendData, entityLabel, teamAliasMap);
     default:
diff --git a/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.test.tsx b/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.test.tsx
index 3052f790098..80acf5d75ff 100644
--- a/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.test.tsx
+++ b/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.test.tsx
@@ -405,7 +405,7 @@ describe("ModelSelect", () => {
     });
   });
 
-  it("should show all models when organization context is used", async () => {
+  it("should filter models when organization has specific models", async () => {
     const mockOrganization: Organization = {
       organization_id: "org-1",
       organization_alias: "Test Org",
@@ -433,7 +433,7 @@ describe("ModelSelect", () => {
 
     await waitFor(() => {
       expect(screen.getByText("gpt-4")).toBeInTheDocument();
-      expect(screen.getByText("claude-3")).toBeInTheDocument();
+      expect(screen.queryByText("claude-3")).not.toBeInTheDocument();
     });
   });
 
diff --git a/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.tsx b/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.tsx
index 78ccdddd81b..b3881350448 100644
--- a/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.tsx
+++ b/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.tsx
@@ -62,8 +62,14 @@ const contextFilters: Record<ModelSelectProps["context"], (args: FilterContextAr
     return allProxyModels ?? [];
   },
 
-  organization: ({ allProxyModels }) => {
-    return allProxyModels;
+  organization: ({ allProxyModels, selectedOrganization, options }) => {
+    if (!selectedOrganization) return [];
+
+    if (selectedOrganization.models.includes(MODEL_SELECT_ALL_PROXY_MODELS_SPECIAL_VALUE.value)) {
+      return allProxyModels;
+    }
+
+    return allProxyModels.filter((model) => selectedOrganization.models.includes(model));
   },
 };
 
@@ -96,6 +102,8 @@ export const ModelSelect = (props: ModelSelectProps) => {
   const hasSpecialOptionSelected = value.some(isSpecialOption);
   const isLoading = isLoadingAllProxyModels || isLoadingTeam || isLoadingOrganization || isCurrentUserLoading;
   const organizationHasAllProxyModels = organization?.models.includes(MODEL_SELECT_ALL_PROXY_MODELS_SPECIAL_VALUE.value) || organization?.models.length === 0;
+  console.log("organization:", organization);
+  console.log("organizationHasAllProxyModels:", organizationHasAllProxyModels);
   const shouldShowAllProxyModels =
     showAllProxyModelsOverride ||
     (organizationHasAllProxyModels && includeSpecialOptions);
diff --git a/ui/litellm-dashboard/src/components/OldTeams.tsx b/ui/litellm-dashboard/src/components/OldTeams.tsx
index 4f16c5cdd09..1202cc91697 100644
--- a/ui/litellm-dashboard/src/components/OldTeams.tsx
+++ b/ui/litellm-dashboard/src/components/OldTeams.tsx
@@ -42,7 +42,6 @@ import AgentSelector from "./agent_management/AgentSelector";
 import { fetchTeams } from "./common_components/fetch_teams";
 import ModelAliasManager from "./common_components/ModelAliasManager";
 import PremiumLoggingSettings from "./common_components/PremiumLoggingSettings";
-import RouterSettingsAccordion, { RouterSettingsAccordionValue } from "./common_components/RouterSettingsAccordion";
 import {
   fetchAvailableModelsForTeamOrKey,
   getModelDisplayName,
@@ -228,8 +227,6 @@ const Teams: React.FC<TeamProps> = ({
   const [mcpAccessGroups, setMcpAccessGroups] = useState<string[]>([]);
   const [mcpAccessGroupsLoaded, setMcpAccessGroupsLoaded] = useState(false);
   const [modelAliases, setModelAliases] = useState<{ [key: string]: string }>({});
-  const [routerSettings, setRouterSettings] = useState<RouterSettingsAccordionValue | null>(null);
-  const [routerSettingsKey, setRouterSettingsKey] = useState<number>(0);
 
   useEffect(() => {
     console.log(`currentOrgForCreateTeam: ${currentOrgForCreateTeam}`);
@@ -320,8 +317,6 @@ const Teams: React.FC<TeamProps> = ({
     form.resetFields();
     setLoggingSettings([]);
     setModelAliases({});
-    setRouterSettings(null);
-    setRouterSettingsKey((prev) => prev + 1);
   };
 
   const handleMemberOk = () => {
@@ -335,8 +330,6 @@ const Teams: React.FC<TeamProps> = ({
     form.resetFields();
     setLoggingSettings([]);
     setModelAliases({});
-    setRouterSettings(null);
-    setRouterSettingsKey((prev) => prev + 1);
   };
 
   const handleMemberCancel = () => {
@@ -510,17 +503,6 @@ const Teams: React.FC<TeamProps> = ({
           formValues.model_aliases = modelAliases;
         }
 
-        // Add router_settings if any are defined
-        if (routerSettings?.router_settings) {
-          // Only include router_settings if it has at least one non-null value
-          const hasValues = Object.values(routerSettings.router_settings).some(
-            (value) => value !== null && value !== undefined && value !== "",
-          );
-          if (hasValues) {
-            formValues.router_settings = routerSettings.router_settings;
-          }
-        }
-
         const response: any = await teamCreateCall(accessToken, formValues);
         if (teams !== null) {
           setTeams([...teams, response]);
@@ -532,8 +514,6 @@ const Teams: React.FC<TeamProps> = ({
         form.resetFields();
         setLoggingSettings([]);
         setModelAliases({});
-        setRouterSettings(null);
-        setRouterSettingsKey((prev) => prev + 1);
         setIsTeamModalVisible(false);
       }
     } catch (error) {
@@ -1448,23 +1428,6 @@ const Teams: React.FC<TeamProps> = ({
                     </AccordionBody>
                   </Accordion>
 
-                  <Accordion key={`router-settings-accordion-${routerSettingsKey}`} className="mt-8 mb-8">
-                    <AccordionHeader>
-                      <b>Router Settings</b>
-                    </AccordionHeader>
-                    <AccordionBody>
-                      <div className="mt-4 w-full">
-                        <RouterSettingsAccordion
-                          key={routerSettingsKey}
-                          accessToken={accessToken || ""}
-                          value={routerSettings || undefined}
-                          onChange={setRouterSettings}
-                          modelData={userModels.length > 0 ? { data: userModels.map((model) => ({ model_name: model })) } : undefined}
-                        />
-                      </div>
-                    </AccordionBody>
-                  </Accordion>
-
                   <Accordion className="mt-8 mb-8">
                     <AccordionHeader>
                       <b>Model Aliases</b>
diff --git a/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/PageVisibilitySettings.tsx b/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/PageVisibilitySettings.tsx
deleted file mode 100644
index 47e4a8bb7f3..00000000000
--- a/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/PageVisibilitySettings.tsx
+++ /dev/null
@@ -1,146 +0,0 @@
-"use client";
-
-import { getAvailablePages } from "@/components/page_utils";
-import { Button, Checkbox, Collapse, Space, Tag, Typography } from "antd";
-import { useMemo, useState } from "react";
-
-interface PageVisibilitySettingsProps {
-  enabledPagesInternalUsers: string[] | null | undefined;
-  enabledPagesPropertyDescription?: string;
-  isUpdating: boolean;
-  onUpdate: (settings: { enabled_ui_pages_internal_users: string[] | null }) => void;
-}
-
-export default function PageVisibilitySettings({
-  enabledPagesInternalUsers,
-  enabledPagesPropertyDescription,
-  isUpdating,
-  onUpdate,
-}: PageVisibilitySettingsProps) {
-  // Check if page visibility is set (null/undefined means "not set" = all pages visible)
-  const isPageVisibilitySet = enabledPagesInternalUsers !== null && enabledPagesInternalUsers !== undefined;
-
-  // Get available pages from leftnav configuration
-  const availablePages = useMemo(() => getAvailablePages(), []);
-
-  // Group pages by their group for better UI
-  const pagesByGroup = useMemo(() => {
-    const grouped: Record<string, typeof availablePages> = {};
-    availablePages.forEach((page) => {
-      if (!grouped[page.group]) {
-        grouped[page.group] = [];
-      }
-      grouped[page.group].push(page);
-    });
-    return grouped;
-  }, [availablePages]);
-
-  // Local state for page selection
-  const [selectedPages, setSelectedPages] = useState<string[]>(enabledPagesInternalUsers || []);
-
-  // Update local state when data changes
-  useMemo(() => {
-    if (enabledPagesInternalUsers) {
-      setSelectedPages(enabledPagesInternalUsers);
-    } else {
-      setSelectedPages([]);
-    }
-  }, [enabledPagesInternalUsers]);
-
-  const handleSavePageVisibility = () => {
-    onUpdate({ enabled_ui_pages_internal_users: selectedPages.length > 0 ? selectedPages : null });
-  };
-
-  const handleResetToDefault = () => {
-    setSelectedPages([]);
-    onUpdate({ enabled_ui_pages_internal_users: null });
-  };
-
-  return (
-    <Space direction="vertical" size="middle" style={{ width: "100%" }}>
-      <Space direction="vertical" size={4}>
-        <Space align="center">
-          <Typography.Text strong>Internal User Page Visibility</Typography.Text>
-          {!isPageVisibilitySet && (
-            <Tag color="default" style={{ marginLeft: "8px" }}>
-              Not set (all pages visible)
-            </Tag>
-          )}
-          {isPageVisibilitySet && (
-            <Tag color="blue" style={{ marginLeft: "8px" }}>
-              {selectedPages.length} page{selectedPages.length !== 1 ? "s" : ""} selected
-            </Tag>
-          )}
-        </Space>
-        {enabledPagesPropertyDescription && (
-          <Typography.Text type="secondary">{enabledPagesPropertyDescription}</Typography.Text>
-        )}
-        <Typography.Text type="secondary" style={{ fontSize: "12px", fontStyle: "italic" }}>
-          By default, all pages are visible to internal users. Select specific pages to restrict visibility.
-        </Typography.Text>
-        <Typography.Text type="secondary" style={{ fontSize: "12px", color: "#8b5cf6" }}>
-          Note: Only pages accessible to internal user roles are shown here. Admin-only pages are excluded as they
-          cannot be made visible to internal users regardless of this setting.
-        </Typography.Text>
-      </Space>
-
-      <Collapse
-        items={[
-          {
-            key: "page-visibility",
-            label: "Configure Page Visibility",
-            children: (
-              <Space direction="vertical" size="middle" style={{ width: "100%" }}>
-                <Checkbox.Group value={selectedPages} onChange={setSelectedPages} style={{ width: "100%" }}>
-                  <Space direction="vertical" size="middle" style={{ width: "100%" }}>
-                    {Object.entries(pagesByGroup).map(([groupName, pages]) => (
-                      <div key={groupName}>
-                        <Typography.Text
-                          strong
-                          style={{
-                            fontSize: "11px",
-                            color: "#6b7280",
-                            letterSpacing: "0.05em",
-                            display: "block",
-                            marginBottom: "8px",
-                          }}
-                        >
-                          {groupName}
-                        </Typography.Text>
-                        <Space direction="vertical" size="small" style={{ marginLeft: "16px", width: "100%" }}>
-                          {pages.map((page) => (
-                            <div key={page.page} style={{ marginBottom: "4px" }}>
-                              <Checkbox value={page.page}>
-                                <Space direction="vertical" size={0}>
-                                  <Typography.Text>{page.label}</Typography.Text>
-                                  <Typography.Text type="secondary" style={{ fontSize: "12px" }}>
-                                    {page.description}
-                                  </Typography.Text>
-                                </Space>
-                              </Checkbox>
-                            </div>
-                          ))}
-                        </Space>
-                      </div>
-                    ))}
-                  </Space>
-                </Checkbox.Group>
-
-                <Space>
-                  <Button type="primary" onClick={handleSavePageVisibility} loading={isUpdating} disabled={isUpdating}>
-                    Save Page Visibility Settings
-                  </Button>
-                  {isPageVisibilitySet && (
-                    <Button onClick={handleResetToDefault} loading={isUpdating} disabled={isUpdating}>
-                      Reset to Default (All Pages)
-                    </Button>
-                  )}
-                </Space>
-              </Space>
-            ),
-          },
-        ]}
-      />
-    </Space>
-  );
-}
diff --git a/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.tsx b/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.tsx
index 6cc9cbf4309..1cc493194e9 100644
--- a/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.tsx
+++ b/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.tsx
@@ -4,8 +4,7 @@ import { useUISettings } from "@/app/(dashboard)/hooks/uiSettings/useUISettings"
 import { useUpdateUISettings } from "@/app/(dashboard)/hooks/uiSettings/useUpdateUISettings";
 import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
 import NotificationManager from "@/components/molecules/notifications_manager";
-import PageVisibilitySettings from "./PageVisibilitySettings";
-import { Alert, Card, Divider, Skeleton, Space, Switch, Typography } from "antd";
+import { Alert, Card, Skeleton, Space, Switch, Typography } from "antd";
 
 export default function UISettings() {
   const { accessToken } = useAuthorized();
@@ -15,7 +14,6 @@ export default function UISettings() {
   const schema = data?.field_schema;
   const property = schema?.properties?.disable_model_add_for_internal_users;
   const disableTeamAdminDeleteProperty = schema?.properties?.disable_team_admin_delete_team_user;
-  const enabledPagesProperty = schema?.properties?.enabled_ui_pages_internal_users;
   const values = data?.values ?? {};
   const isDisabledForInternalUsers = Boolean(values.disable_model_add_for_internal_users);
   const isDisabledTeamAdminDeleteTeamUser = Boolean(values.disable_team_admin_delete_team_user);
@@ -48,17 +46,6 @@ export default function UISettings() {
     );
   };
 
-  const handleUpdatePageVisibility = (settings: { enabled_ui_pages_internal_users: string[] | null }) => {
-    updateSettings(settings, {
-      onSuccess: () => {
-        NotificationManager.success("Page visibility settings updated successfully");
-      },
-      onError: (error) => {
-        NotificationManager.fromBackend(error);
-      },
-    });
-  };
-
   return (
     <Card title="UI Settings">
       {isLoading ? (
@@ -112,16 +99,6 @@ export default function UISettings() {
               )}
             </Space>
           </Space>
-
-          <Divider />
-
-          {/* Page Visibility for Internal Users */}
-          <PageVisibilitySettings
-            enabledPagesInternalUsers={values.enabled_ui_pages_internal_users}
-            enabledPagesPropertyDescription={enabledPagesProperty?.description}
-            isUpdating={isUpdating}
-            onUpdate={handleUpdatePageVisibility}
-          />
         </Space>
       )}
     </Card>
diff --git a/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/Fallbacks.test.tsx b/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/Fallbacks.test.tsx
index 8a7119aa4a3..bc6f6a4856c 100644
--- a/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/Fallbacks.test.tsx
+++ b/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/Fallbacks.test.tsx
@@ -1,7 +1,7 @@
 import { render, screen, waitFor } from "@testing-library/react";
 import userEvent from "@testing-library/user-event";
 import { beforeEach, describe, expect, it, vi } from "vitest";
-import Fallbacks from "./Fallbacks";
+import Fallbacks from "./fallbacks";
 import * as networkingModule from "../../../networking";
 import * as fetchModelsModule from "../../../playground/llm_calls/fetch_models";
 
@@ -108,7 +108,7 @@ describe("Fallbacks", () => {
       return buttons[buttons.length - 1];
     }
     const clickableElements = lastCell.querySelectorAll("[class*='cursor-pointer'], button");
-    return Array.from(clickableElements).find((el) =>
+    return Array.from(clickableElements).find((el) => 
       el.className.includes("red") || el.className.includes("hover:text-red")
     ) || clickableElements[clickableElements.length - 1];
   };
@@ -202,7 +202,7 @@ describe("Fallbacks", () => {
 
     await waitFor(() => {
       expect(networkingModule.setCallbacksCall).toHaveBeenCalled();
-      const callArgs = (networkingModule.setCallbacksCall as any).mock.calls[0];
+      const callArgs = networkingModule.setCallbacksCall.mock.calls[0];
       expect(callArgs[0]).toBe(mockAccessToken);
       expect(callArgs[1].router_settings.fallbacks).toHaveLength(1);
     });
diff --git a/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.test.tsx b/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.test.tsx
index ef5bb2a0371..deb532d33b3 100644
--- a/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.test.tsx
+++ b/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.test.tsx
@@ -97,7 +97,6 @@ const mockKey: KeyResponse = {
   litellm_budget_table: {},
   organization_id: "org-1",
   created_at: "2024-11-01T10:00:00Z",
-  created_by: "user-1",
   updated_at: "2024-11-15T10:00:00Z",
   team_spend: 5.5,
   team_alias: "Test Team",
@@ -178,7 +177,6 @@ beforeEach(() => {
       total_pages: 1,
     } as KeysResponse,
     isPending: false,
-    isFetching: false,
     refetch: vi.fn(),
   } as any);
 
@@ -267,7 +265,6 @@ it("should show skeleton loaders when isLoading is true", () => {
   mockUseKeys.mockReturnValue({
     data: null,
     isPending: true,
-    isFetching: true,
     refetch: vi.fn(),
   } as any);
 
@@ -442,8 +439,8 @@ it("should open KeyInfoView when clicking on a key ID button", async () => {
   // Verify table is visible before clicking - check for table-specific text
   expect(screen.getByText(/Showing.*results/)).toBeInTheDocument();
 
-  // Find the key ID button (it shows the full token value, truncation is CSS-only)
-  const keyIdButton = screen.getByText("sk-1234567890abcdef");
+  // Find the key ID button (it should show the truncated token)
+  const keyIdButton = screen.getByText("sk-1234...");
   expect(keyIdButton).toBeInTheDocument();
 
   // Click on the key ID button
@@ -460,85 +457,3 @@ it("should open KeyInfoView when clicking on a key ID button", async () => {
   // The "Showing X of Y results" text should not be visible when KeyInfoView is open
   expect(screen.queryByText(/Showing.*results/)).not.toBeInTheDocument();
 });
-
-it("should display 'Default Proxy Admin' for user_id when value is 'default_user_id'", async () => {
-  const keyWithDefaultUserId = {
-    ...mockKey,
-    user_id: "default_user_id",
-  };
-
-  mockUseFilterLogic.mockReturnValue({
-    filters: {
-      "Team ID": "",
-      "Organization ID": "",
-      "Key Alias": "",
-      "User ID": "",
-      "Sort By": "created_at",
-      "Sort Order": "desc",
-    },
-    filteredKeys: [keyWithDefaultUserId],
-    allKeyAliases: ["test-key-alias"],
-    allTeams: [mockTeam],
-    allOrganizations: [mockOrganization],
-    handleFilterChange: vi.fn(),
-    handleFilterReset: vi.fn(),
-  });
-
-  const mockProps = {
-    teams: [mockTeam],
-    organizations: [mockOrganization],
-    onSortChange: vi.fn(),
-    currentSort: {
-      sortBy: "created_at",
-      sortOrder: "desc" as const,
-    },
-  };
-
-  renderWithProviders(<VirtualKeysTable {...mockProps} />);
-
-  await waitFor(() => {
-    expect(screen.getByText("Default Proxy Admin")).toBeInTheDocument();
-  });
-});
-
-it("should display 'Default Proxy Admin' for created_by when value is 'default_user_id'", async () => {
-  const keyWithDefaultCreatedBy = {
-    ...mockKey,
-    created_by: "default_user_id",
-  };
-
-  mockUseFilterLogic.mockReturnValue({
-    filters: {
-      "Team ID": "",
-      "Organization ID": "",
-      "Key Alias": "",
-      "User ID": "",
-      "Sort By": "created_at",
-      "Sort Order": "desc",
-    },
-    filteredKeys: [keyWithDefaultCreatedBy],
-    allKeyAliases: ["test-key-alias"],
-    allTeams: [mockTeam],
-    allOrganizations: [mockOrganization],
-    handleFilterChange: vi.fn(),
-    handleFilterReset: vi.fn(),
-  });
-
-  const mockProps = {
-    teams: [mockTeam],
-    organizations: [mockOrganization],
-    onSortChange: vi.fn(),
-    currentSort: {
-      sortBy: "created_at",
-      sortOrder: "desc" as const,
-    },
-  };
-
-  renderWithProviders(<VirtualKeysTable {...mockProps} />);
-
-  await waitFor(() => {
-    // The created_by column should display "Default Proxy Admin"
-    const defaultProxyAdminElements = screen.getAllByText("Default Proxy Admin");
-    expect(defaultProxyAdminElements.length).toBeGreaterThan(0);
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.tsx b/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.tsx
index 465b9b8fbe0..6d968047920 100644
--- a/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.tsx
+++ b/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.tsx
@@ -129,25 +129,22 @@ export function VirtualKeysTable({ teams, organizations, onSortChange, currentSo
       id: "token",
       accessorKey: "token",
       header: "Key ID",
-      size: 100,
+      size: 150,
       enableSorting: true,
-      cell: (info) => {
-        const value = info.getValue() as string;
-        const width = info.cell.column.getSize();
-        return (
-          <Tooltip title={value}>
+      cell: (info) => (
+        <div className="overflow-hidden">
+          <Tooltip title={info.getValue() as string}>
             <Button
               size="xs"
               variant="light"
-              className="font-mono text-blue-500 bg-blue-50 hover:bg-blue-100 text-xs font-normal px-2 py-0.5 text-left overflow-hidden truncate block"
-              style={{ maxWidth: width, overflow: "hidden" }}
+              className="font-mono text-blue-500 bg-blue-50 hover:bg-blue-100 text-xs font-normal px-2 py-0.5 text-left overflow-hidden truncate max-w-[200px]"
               onClick={() => setSelectedKey(info.row.original)}
             >
-              {value ?? "-"}
+              {info.getValue() ? `${(info.getValue() as string).slice(0, 7)}...` : "-"}
             </Button>
           </Tooltip>
-        );
-      },
+        </div>
+      ),
     },
     {
       id: "key_alias",
@@ -191,19 +188,13 @@ export function VirtualKeysTable({ teams, organizations, onSortChange, currentSo
       id: "team_id",
       accessorKey: "team_id",
       header: "Team ID",
-      size: 80,
+      size: 120,
       enableSorting: false,
-      cell: (info) => {
-        const value = info.getValue() as string | null;
-        const width = info.cell.column.getSize();
-        return (
-          <Tooltip title={value}>
-            <span className={`font-mono text-xs truncate block`} style={{ maxWidth: width, overflow: "hidden" }}>
-              {value ?? "-"}
-            </span>
-          </Tooltip>
-        );
-      },
+      cell: (info) => (
+        <Tooltip title={info.getValue() as string}>
+          {info.getValue() ? `${(info.getValue() as string).slice(0, 7)}...` : "-"}
+        </Tooltip>
+      ),
     },
     {
       id: "organization_id",
@@ -236,19 +227,18 @@ export function VirtualKeysTable({ teams, organizations, onSortChange, currentSo
       id: "user_id",
       accessorKey: "user_id",
       header: "User ID",
-      size: 70,
+      size: 120,
       enableSorting: false,
       cell: (info) => {
         const userId = info.getValue() as string | null;
-        const displayValue = userId === "default_user_id" ? "Default Proxy Admin" : userId;
-        const width = info.cell.column.getSize();
-        return (
-          <Tooltip title={displayValue}>
-            <span className={`font-mono text-xs truncate block`} style={{ maxWidth: width, overflow: "hidden" }}>
-              {displayValue ?? "-"}
-            </span>
-          </Tooltip>
-        );
+        if (userId && userId.length > 15) {
+          return (
+            <Tooltip title={userId}>
+              <span>{userId.slice(0, 7)}...</span>
+            </Tooltip>
+          );
+        }
+        return userId ? userId : "-";
       },
     },
     {
@@ -266,19 +256,18 @@ export function VirtualKeysTable({ teams, organizations, onSortChange, currentSo
       id: "created_by",
       accessorKey: "created_by",
       header: "Created By",
-      size: 70,
+      size: 120,
       enableSorting: false,
       cell: (info) => {
         const value = info.getValue() as string | null;
-        const displayValue = value === "default_user_id" ? "Default Proxy Admin" : value;
-        const width = info.cell.column.getSize();
-        return (
-          <Tooltip title={displayValue}>
-            <span className={`font-mono text-xs truncate block`} style={{ maxWidth: width, overflow: "hidden" }}>
-              {displayValue ?? "-"}
-            </span>
-          </Tooltip>
-        );
+        if (value && value.length > 15) {
+          return (
+            <Tooltip title={value}>
+              <span>{value.slice(0, 7)}...</span>
+            </Tooltip>
+          );
+        }
+        return value;
       },
     },
     {
diff --git a/ui/litellm-dashboard/src/components/BulkEditUsers.tsx b/ui/litellm-dashboard/src/components/bulk_edit_user.tsx
similarity index 98%
rename from ui/litellm-dashboard/src/components/BulkEditUsers.tsx
rename to ui/litellm-dashboard/src/components/bulk_edit_user.tsx
index 2f3e57ff2a8..b3847911cc0 100644
--- a/ui/litellm-dashboard/src/components/BulkEditUsers.tsx
+++ b/ui/litellm-dashboard/src/components/bulk_edit_user.tsx
@@ -18,7 +18,7 @@ import NotificationsManager from "./molecules/notifications_manager";
 const { Text, Title } = Typography;
 
 interface BulkEditUserModalProps {
-  open: boolean;
+  visible: boolean;
   onCancel: () => void;
   selectedUsers: any[];
   possibleUIRoles: Record<string, Record<string, string>> | null;
@@ -31,7 +31,7 @@ interface BulkEditUserModalProps {
 }
 
 const BulkEditUserModal: React.FC<BulkEditUserModalProps> = ({
-  open,
+  visible,
   onCancel,
   selectedUsers,
   possibleUIRoles,
@@ -75,7 +75,7 @@ const BulkEditUserModal: React.FC<BulkEditUserModalProps> = ({
       keys: [],
       teams: teams || [],
     }),
-    [teams, open],
+    [teams, visible],
   );
 
   const handleSubmit = async (formValues: any) => {
@@ -145,7 +145,7 @@ const BulkEditUserModal: React.FC<BulkEditUserModalProps> = ({
             if (updateAllUsers) {
               members = null;
             } else {
-              members = selectedUsers.map((user) => ({
+              const members = selectedUsers.map((user) => ({
                 user_id: user.user_id,
                 role: "user" as const, // Default role for bulk add
                 user_email: user.user_email || null,
@@ -214,7 +214,7 @@ const BulkEditUserModal: React.FC<BulkEditUserModalProps> = ({
 
   return (
     <Modal
-      open={open}
+      visible={visible}
       onCancel={handleCancel}
       footer={null}
       title={updateAllUsers ? "Bulk Edit All Users" : `Bulk Edit ${selectedUsers.length} User(s)`}
diff --git a/ui/litellm-dashboard/src/components/common_components/KeyLifecycleSettings.test.tsx b/ui/litellm-dashboard/src/components/common_components/KeyLifecycleSettings.test.tsx
deleted file mode 100644
index f6fcfb2fb74..00000000000
--- a/ui/litellm-dashboard/src/components/common_components/KeyLifecycleSettings.test.tsx
+++ /dev/null
@@ -1,383 +0,0 @@
-import userEvent from "@testing-library/user-event";
-import { describe, expect, it, vi, beforeEach } from "vitest";
-import { renderWithProviders, screen } from "../../../tests/test-utils";
-import KeyLifecycleSettings from "./KeyLifecycleSettings";
-
-vi.mock("antd", () => {
-  const Option = ({ children, value }: any) => (
-    <option value={value}>{children}</option>
-  );
-  const Select = ({ children, value, onChange, placeholder }: any) => (
-    <select
-      data-testid="select"
-      value={value}
-      onChange={(e) => onChange(e.target.value)}
-      data-placeholder={placeholder}
-    >
-      {children}
-    </select>
-  );
-  Select.Option = Option;
-  return {
-    Select,
-    Tooltip: ({ children, title }: any) => (
-      <div data-testid="tooltip" title={title}>
-        {children}
-      </div>
-    ),
-    Switch: ({ checked, onChange }: any) => (
-      <input
-        type="checkbox"
-        data-testid="switch"
-        checked={checked}
-        onChange={(e) => onChange(e.target.checked)}
-      />
-    ),
-    Divider: () => <hr data-testid="divider" />,
-  };
-});
-
-vi.mock("@ant-design/icons", () => ({
-  InfoCircleOutlined: () => <span data-testid="info-icon">ℹ</span>,
-}));
-
-vi.mock("@tremor/react", () => ({
-  TextInput: ({ value, onValueChange, onChange, placeholder, name, className }: any) => {
-    const handleChange = (e: React.ChangeEvent<HTMLInputElement>) => {
-      if (onChange) {
-        onChange(e);
-      }
-      if (onValueChange) {
-        onValueChange(e.target.value);
-      }
-    };
-    return (
-      <input
-        data-testid={name === "duration" ? "duration-input" : "custom-interval-input"}
-        value={value}
-        onChange={handleChange}
-        placeholder={placeholder}
-        className={className}
-      />
-    );
-  },
-}));
-
-describe("KeyLifecycleSettings", () => {
-  const mockForm = {
-    getFieldValue: vi.fn(),
-    setFieldValue: vi.fn(),
-    setFieldsValue: vi.fn(),
-  };
-
-  const defaultProps = {
-    form: mockForm,
-    autoRotationEnabled: false,
-    onAutoRotationChange: vi.fn(),
-    rotationInterval: "",
-    onRotationIntervalChange: vi.fn(),
-    isCreateMode: false,
-  };
-
-  beforeEach(() => {
-    vi.clearAllMocks();
-    mockForm.getFieldValue.mockReturnValue("");
-  });
-
-  it("should render without crashing", () => {
-    renderWithProviders(<KeyLifecycleSettings {...defaultProps} />);
-
-    expect(screen.getByText("Key Expiry Settings")).toBeInTheDocument();
-    expect(screen.getByText("Auto-Rotation Settings")).toBeInTheDocument();
-  });
-
-  describe("Key Expiry Settings", () => {
-    it("should render expiry input field", () => {
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} />);
-
-      expect(screen.getByText("Expire Key")).toBeInTheDocument();
-      expect(screen.getByTestId("duration-input")).toBeInTheDocument();
-    });
-
-    it("should show correct placeholder in create mode", () => {
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} isCreateMode={true} />);
-
-      const input = screen.getByTestId("duration-input");
-      expect(input).toHaveAttribute(
-        "placeholder",
-        "e.g., 30d or leave empty to never expire"
-      );
-    });
-
-    it("should show correct placeholder in edit mode", () => {
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} isCreateMode={false} />);
-
-      const input = screen.getByTestId("duration-input");
-      expect(input).toHaveAttribute("placeholder", "e.g., 30d or -1 to never expire");
-    });
-
-    it("should show correct tooltip in create mode", () => {
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} isCreateMode={true} />);
-
-      const tooltips = screen.getAllByTestId("tooltip");
-      const expiryTooltip = tooltips.find((tooltip) =>
-        tooltip.getAttribute("title")?.includes("Leave empty to never expire")
-      );
-      expect(expiryTooltip).toBeInTheDocument();
-      expect(expiryTooltip).toHaveAttribute(
-        "title",
-        "Set when this key should expire. Format: 30s (seconds), 30m (minutes), 30h (hours), 30d (days). Leave empty to never expire."
-      );
-    });
-
-    it("should show correct tooltip in edit mode", () => {
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} isCreateMode={false} />);
-
-      const tooltips = screen.getAllByTestId("tooltip");
-      const expiryTooltip = tooltips.find((tooltip) =>
-        tooltip.getAttribute("title")?.includes("Use -1 to never expire")
-      );
-      expect(expiryTooltip).toBeInTheDocument();
-      expect(expiryTooltip).toHaveAttribute(
-        "title",
-        "Set when this key should expire. Format: 30s (seconds), 30m (minutes), 30h (hours), 30d (days). Use -1 to never expire."
-      );
-    });
-
-    it("should initialize with form value if present", () => {
-      mockForm.getFieldValue.mockReturnValue("30d");
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} />);
-
-      const input = screen.getByTestId("duration-input") as HTMLInputElement;
-      expect(input.value).toBe("30d");
-    });
-
-    it("should update form using setFieldValue when duration changes", async () => {
-      const user = userEvent.setup();
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} />);
-
-      const input = screen.getByTestId("duration-input");
-      await user.type(input, "60d");
-
-      expect(mockForm.setFieldValue).toHaveBeenCalledWith("duration", "60d");
-    });
-
-    it("should update form using setFieldsValue when setFieldValue is not available", async () => {
-      const user = userEvent.setup();
-      const formWithoutSetFieldValue = {
-        getFieldValue: vi.fn().mockReturnValue(""),
-        setFieldsValue: vi.fn(),
-      };
-      renderWithProviders(
-        <KeyLifecycleSettings {...defaultProps} form={formWithoutSetFieldValue} />
-      );
-
-      const input = screen.getByTestId("duration-input");
-      await user.type(input, "90d");
-
-      expect(formWithoutSetFieldValue.setFieldsValue).toHaveBeenCalledWith({ duration: "90d" });
-    });
-  });
-
-  describe("Auto-Rotation Settings", () => {
-    it("should render auto-rotation switch", () => {
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} />);
-
-      expect(screen.getByText("Enable Auto-Rotation")).toBeInTheDocument();
-      expect(screen.getByTestId("switch")).toBeInTheDocument();
-    });
-
-    it("should show switch as unchecked when autoRotationEnabled is false", () => {
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} autoRotationEnabled={false} />);
-
-      const switchElement = screen.getByTestId("switch") as HTMLInputElement;
-      expect(switchElement.checked).toBe(false);
-    });
-
-    it("should show switch as checked when autoRotationEnabled is true", () => {
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} autoRotationEnabled={true} />);
-
-      const switchElement = screen.getByTestId("switch") as HTMLInputElement;
-      expect(switchElement.checked).toBe(true);
-    });
-
-    it("should call onAutoRotationChange when switch is toggled", async () => {
-      const user = userEvent.setup();
-      const onAutoRotationChange = vi.fn();
-      renderWithProviders(
-        <KeyLifecycleSettings {...defaultProps} onAutoRotationChange={onAutoRotationChange} />
-      );
-
-      const switchElement = screen.getByTestId("switch");
-      await user.click(switchElement);
-
-      expect(onAutoRotationChange).toHaveBeenCalledWith(true);
-    });
-
-    it("should not show rotation interval section when auto-rotation is disabled", () => {
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} autoRotationEnabled={false} />);
-
-      expect(screen.queryByText("Rotation Interval")).not.toBeInTheDocument();
-      expect(screen.queryByTestId("select")).not.toBeInTheDocument();
-    });
-
-    it("should show rotation interval section when auto-rotation is enabled", () => {
-      renderWithProviders(
-        <KeyLifecycleSettings {...defaultProps} autoRotationEnabled={true} rotationInterval="30d" />
-      );
-
-      expect(screen.getByText("Rotation Interval")).toBeInTheDocument();
-      expect(screen.getByTestId("select")).toBeInTheDocument();
-    });
-
-    it("should show all predefined interval options", () => {
-      renderWithProviders(
-        <KeyLifecycleSettings {...defaultProps} autoRotationEnabled={true} rotationInterval="30d" />
-      );
-
-      expect(screen.getByText("7 days")).toBeInTheDocument();
-      expect(screen.getByText("30 days")).toBeInTheDocument();
-      expect(screen.getByText("90 days")).toBeInTheDocument();
-      expect(screen.getByText("180 days")).toBeInTheDocument();
-      expect(screen.getByText("365 days")).toBeInTheDocument();
-      expect(screen.getByText("Custom interval")).toBeInTheDocument();
-    });
-
-    it("should display current rotation interval in select", () => {
-      renderWithProviders(
-        <KeyLifecycleSettings {...defaultProps} autoRotationEnabled={true} rotationInterval="90d" />
-      );
-
-      const select = screen.getByTestId("select") as HTMLSelectElement;
-      expect(select.value).toBe("90d");
-    });
-
-    it("should call onRotationIntervalChange when predefined interval is selected", async () => {
-      const user = userEvent.setup();
-      const onRotationIntervalChange = vi.fn();
-      renderWithProviders(
-        <KeyLifecycleSettings
-          {...defaultProps}
-          autoRotationEnabled={true}
-          rotationInterval="7d"
-          onRotationIntervalChange={onRotationIntervalChange}
-        />
-      );
-
-      const select = screen.getByTestId("select");
-      await user.selectOptions(select, "30d");
-
-      expect(onRotationIntervalChange).toHaveBeenCalledWith("30d");
-    });
-
-    it("should show custom input when custom option is selected", async () => {
-      const user = userEvent.setup();
-      renderWithProviders(
-        <KeyLifecycleSettings {...defaultProps} autoRotationEnabled={true} rotationInterval="30d" />
-      );
-
-      const select = screen.getByTestId("select");
-      await user.selectOptions(select, "custom");
-
-      expect(screen.getByTestId("custom-interval-input")).toBeInTheDocument();
-      expect(screen.getByText("Supported formats: seconds (s), minutes (m), hours (h), days (d)")).toBeInTheDocument();
-    });
-
-    it("should hide custom input when predefined interval is selected after custom", async () => {
-      const user = userEvent.setup();
-      const onRotationIntervalChange = vi.fn();
-      renderWithProviders(
-        <KeyLifecycleSettings
-          {...defaultProps}
-          autoRotationEnabled={true}
-          rotationInterval="custom-value"
-          onRotationIntervalChange={onRotationIntervalChange}
-        />
-      );
-
-      const select = screen.getByTestId("select");
-      await user.selectOptions(select, "7d");
-
-      expect(screen.queryByTestId("custom-interval-input")).not.toBeInTheDocument();
-      expect(onRotationIntervalChange).toHaveBeenCalledWith("7d");
-    });
-
-    it("should call onRotationIntervalChange when custom interval is entered", async () => {
-      const user = userEvent.setup();
-      const onRotationIntervalChange = vi.fn();
-      renderWithProviders(
-        <KeyLifecycleSettings
-          {...defaultProps}
-          autoRotationEnabled={true}
-          rotationInterval=""
-          onRotationIntervalChange={onRotationIntervalChange}
-        />
-      );
-
-      const select = screen.getByTestId("select");
-      await user.selectOptions(select, "custom");
-
-      const customInput = screen.getByTestId("custom-interval-input");
-      await user.type(customInput, "14d");
-
-      expect(onRotationIntervalChange).toHaveBeenCalledWith("14d");
-    });
-
-    it("should show info message when auto-rotation is enabled", () => {
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} autoRotationEnabled={true} />);
-
-      expect(
-        screen.getByText(
-          "When rotation occurs, you'll receive a notification with the new key. The old key will be deactivated after a brief grace period."
-        )
-      ).toBeInTheDocument();
-    });
-
-    it("should not show info message when auto-rotation is disabled", () => {
-      renderWithProviders(<KeyLifecycleSettings {...defaultProps} autoRotationEnabled={false} />);
-
-      expect(
-        screen.queryByText(
-          "When rotation occurs, you'll receive a notification with the new key. The old key will be deactivated after a brief grace period."
-        )
-      ).not.toBeInTheDocument();
-    });
-
-    it("should initialize with custom interval input visible when custom interval is provided", () => {
-      renderWithProviders(
-        <KeyLifecycleSettings {...defaultProps} autoRotationEnabled={true} rotationInterval="14d" />
-      );
-
-      expect(screen.getByTestId("custom-interval-input")).toBeInTheDocument();
-      const customInput = screen.getByTestId("custom-interval-input") as HTMLInputElement;
-      expect(customInput.value).toBe("14d");
-    });
-
-    it("should show custom option selected when custom interval is provided", () => {
-      renderWithProviders(
-        <KeyLifecycleSettings {...defaultProps} autoRotationEnabled={true} rotationInterval="14d" />
-      );
-
-      const select = screen.getByTestId("select") as HTMLSelectElement;
-      expect(select.value).toBe("custom");
-    });
-
-    it("should not call onRotationIntervalChange when selecting custom option", async () => {
-      const user = userEvent.setup();
-      const onRotationIntervalChange = vi.fn();
-      renderWithProviders(
-        <KeyLifecycleSettings
-          {...defaultProps}
-          autoRotationEnabled={true}
-          rotationInterval="30d"
-          onRotationIntervalChange={onRotationIntervalChange}
-        />
-      );
-
-      const select = screen.getByTestId("select");
-      await user.selectOptions(select, "custom");
-
-      expect(onRotationIntervalChange).not.toHaveBeenCalled();
-    });
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/common_components/KeyLifecycleSettings.tsx b/ui/litellm-dashboard/src/components/common_components/KeyLifecycleSettings.tsx
index 0f29a47d1dc..81d22b56347 100644
--- a/ui/litellm-dashboard/src/components/common_components/KeyLifecycleSettings.tsx
+++ b/ui/litellm-dashboard/src/components/common_components/KeyLifecycleSettings.tsx
@@ -11,7 +11,6 @@ interface KeyLifecycleSettingsProps {
   onAutoRotationChange: (enabled: boolean) => void;
   rotationInterval: string;
   onRotationIntervalChange: (interval: string) => void;
-  isCreateMode?: boolean; // If true, shows "leave empty to never expire" instead of "-1 to never expire"
 }
 
 const KeyLifecycleSettings: React.FC<KeyLifecycleSettingsProps> = ({
@@ -20,7 +19,6 @@ const KeyLifecycleSettings: React.FC<KeyLifecycleSettingsProps> = ({
   onAutoRotationChange,
   rotationInterval,
   onRotationIntervalChange,
-  isCreateMode = false,
 }) => {
   // Predefined intervals
   const predefinedIntervals = ["7d", "30d", "90d", "180d", "365d"];
@@ -66,19 +64,13 @@ const KeyLifecycleSettings: React.FC<KeyLifecycleSettingsProps> = ({
         <div className="space-y-2">
           <label className="text-sm font-medium text-gray-700 flex items-center space-x-1">
             <span>Expire Key</span>
-            <Tooltip
-              title={
-                isCreateMode
-                  ? "Set when this key should expire. Format: 30s (seconds), 30m (minutes), 30h (hours), 30d (days). Leave empty to never expire."
-                  : "Set when this key should expire. Format: 30s (seconds), 30m (minutes), 30h (hours), 30d (days). Use -1 to never expire."
-              }
-            >
+            <Tooltip title="Set when this key should expire. Format: 30s (seconds), 30m (minutes), 30h (hours), 30d (days). Use -1 to never expire.">
               <InfoCircleOutlined className="text-gray-400 cursor-help text-xs" />
             </Tooltip>
           </label>
           <TextInput
             name="duration"
-            placeholder={isCreateMode ? "e.g., 30d or leave empty to never expire" : "e.g., 30d or -1 to never expire"}
+            placeholder="e.g., 30d or -1 to never expire"
             className="w-full"
             value={durationValue}
             onValueChange={handleDurationChange}
diff --git a/ui/litellm-dashboard/src/components/common_components/RouterSettingsAccordion.tsx b/ui/litellm-dashboard/src/components/common_components/RouterSettingsAccordion.tsx
deleted file mode 100644
index 1f691715d30..00000000000
--- a/ui/litellm-dashboard/src/components/common_components/RouterSettingsAccordion.tsx
+++ /dev/null
@@ -1,373 +0,0 @@
-import React, { useEffect, useState, useImperativeHandle, forwardRef, useRef } from "react";
-import { TabPanel, TabPanels, TabGroup, TabList, Tab } from "@tremor/react";
-import { getRouterSettingsCall } from "../networking";
-import RouterSettingsForm, { RouterSettingsFormValue } from "../router_settings/RouterSettingsForm";
-import { Fallbacks } from "../Settings/RouterSettings/Fallbacks/AddFallbacks";
-import { FallbackSelectionForm } from "../Settings/RouterSettings/Fallbacks/FallbackSelectionForm";
-import { FallbackGroup } from "../Settings/RouterSettings/Fallbacks/FallbackGroupConfig";
-import { fetchAvailableModels, ModelGroup } from "../playground/llm_calls/fetch_models";
-
-export interface RouterSettingsAccordionValue {
-  router_settings: {
-    routing_strategy?: string | null;
-    allowed_fails?: number | null;
-    cooldown_time?: number | null;
-    num_retries?: number | null;
-    timeout?: number | null;
-    retry_after?: number | null;
-    fallbacks?: Fallbacks | null;
-    context_window_fallbacks?: any | null;
-    retry_policy?: any | null;
-    model_group_alias?: { [key: string]: any } | null;
-    enable_tag_filtering?: boolean;
-    routing_strategy_args?: { [key: string]: any } | null;
-  };
-}
-
-interface RouterSettingsAccordionProps {
-  accessToken: string;
-  value?: RouterSettingsAccordionValue;
-  onChange?: (value: RouterSettingsAccordionValue) => void;
-  modelData?: any;
-}
-
-export interface RouterSettingsAccordionRef {
-  getValue: () => RouterSettingsAccordionValue;
-}
-
-const RouterSettingsAccordion = forwardRef<RouterSettingsAccordionRef, RouterSettingsAccordionProps>(
-  ({ accessToken, value, onChange, modelData }, ref) => {
-    const [formValue, setFormValue] = useState<RouterSettingsFormValue>({
-      routerSettings: {},
-      selectedStrategy: null,
-      enableTagFiltering: false,
-    });
-    const [fallbacks, setFallbacks] = useState<Fallbacks>([]);
-    const [fallbackGroups, setFallbackGroups] = useState<FallbackGroup[]>([]);
-    const [modelInfo, setModelInfo] = useState<ModelGroup[]>([]);
-    const [availableRoutingStrategies, setAvailableRoutingStrategies] = useState<string[]>([]);
-    const [routerFieldsMetadata, setRouterFieldsMetadata] = useState<{ [key: string]: any }>({});
-    const [routingStrategyDescriptions, setRoutingStrategyDescriptions] = useState<{ [key: string]: string }>({});
-    const isInternalUpdateRef = useRef(false);
-    const lastInitializedValueRef = useRef<string | null>(null);
-
-    // Convert fallbacks format to groups format
-    const fallbacksToGroups = (fallbacks: Fallbacks): FallbackGroup[] => {
-      if (!fallbacks || fallbacks.length === 0) {
-        return [
-          {
-            id: "1",
-            primaryModel: null,
-            fallbackModels: [],
-          },
-        ];
-      }
-      return fallbacks.map((entry, index) => {
-        const [primaryModel, fallbackModels] = Object.entries(entry)[0];
-        return {
-          id: (index + 1).toString(),
-          primaryModel: primaryModel || null,
-          fallbackModels: fallbackModels || [],
-        };
-      });
-    };
-
-    // Convert groups format to fallbacks format
-    const groupsToFallbacks = (groups: FallbackGroup[]): Fallbacks => {
-      return groups
-        .filter((g) => g.primaryModel && g.fallbackModels.length > 0)
-        .map((g) => ({
-          [g.primaryModel!]: g.fallbackModels,
-        }));
-    };
-
-    // Initialize from value prop if provided (only when value actually changes externally)
-    useEffect(() => {
-      // Create a stable key from the value to detect actual external changes
-      const valueKey = value?.router_settings 
-        ? JSON.stringify({
-            routing_strategy: value.router_settings.routing_strategy,
-            fallbacks: value.router_settings.fallbacks,
-            enable_tag_filtering: value.router_settings.enable_tag_filtering,
-          })
-        : null;
-
-      // Skip if this is an internal update (from our own onChange) and the value hasn't actually changed
-      if (isInternalUpdateRef.current && valueKey === lastInitializedValueRef.current) {
-        isInternalUpdateRef.current = false;
-        return;
-      }
-
-      // Reset the flag if value actually changed externally
-      if (isInternalUpdateRef.current && valueKey !== lastInitializedValueRef.current) {
-        isInternalUpdateRef.current = false;
-      }
-
-      // Only update if the value actually changed from an external source
-      if (valueKey === lastInitializedValueRef.current) {
-        return;
-      }
-
-      lastInitializedValueRef.current = valueKey;
-
-      if (value?.router_settings) {
-        const rs = value.router_settings;
-        const { fallbacks: _, ...routerSettingsWithoutFallbacks } = rs;
-        setFormValue({
-          routerSettings: routerSettingsWithoutFallbacks as { [key: string]: any },
-          selectedStrategy: rs.routing_strategy || null,
-          enableTagFiltering: rs.enable_tag_filtering ?? false,
-        });
-        const initialFallbacks = rs.fallbacks || [];
-        setFallbacks(initialFallbacks);
-        setFallbackGroups(fallbacksToGroups(initialFallbacks));
-      } else {
-        // Initialize with empty defaults if no value provided
-        setFormValue({
-          routerSettings: {},
-          selectedStrategy: null,
-          enableTagFiltering: false,
-        });
-        setFallbacks([]);
-        setFallbackGroups([
-          {
-            id: "1",
-            primaryModel: null,
-            fallbackModels: [],
-          },
-        ]);
-      }
-    }, [value]);
-
-    // Fetch router settings metadata
-    useEffect(() => {
-      if (!accessToken) {
-        return;
-      }
-      getRouterSettingsCall(accessToken).then((data) => {
-        if (data.fields) {
-          // Build metadata map for easy lookup
-          const fieldsMap: { [key: string]: any } = {};
-          data.fields.forEach((field: any) => {
-            fieldsMap[field.field_name] = {
-              ui_field_name: field.ui_field_name,
-              field_description: field.field_description,
-              options: field.options,
-              link: field.link,
-            };
-          });
-          setRouterFieldsMetadata(fieldsMap);
-
-          // Extract routing strategies from the routing_strategy field's options
-          const routingStrategyField = data.fields.find((field: any) => field.field_name === "routing_strategy");
-          if (routingStrategyField?.options) {
-            setAvailableRoutingStrategies(routingStrategyField.options);
-          }
-
-          // Store routing strategy descriptions
-          if (data.routing_strategy_descriptions) {
-            setRoutingStrategyDescriptions(data.routing_strategy_descriptions);
-          }
-        }
-      });
-    }, [accessToken]);
-
-    // Fetch available models for fallbacks
-    useEffect(() => {
-      if (!accessToken) {
-        return;
-      }
-      const loadModels = async () => {
-        try {
-          const uniqueModels = await fetchAvailableModels(accessToken);
-          setModelInfo(uniqueModels);
-        } catch (error) {
-          console.error("Error fetching model info for fallbacks:", error);
-        }
-      };
-      loadModels();
-    }, [accessToken]);
-
-    // Helper function to build router_settings from current state
-    const buildRouterSettings = (): RouterSettingsAccordionValue["router_settings"] => {
-      // Parse input values from DOM (similar to RouterSettings component)
-      const numberKeys = new Set(["allowed_fails", "cooldown_time", "num_retries", "timeout", "retry_after"]);
-      const jsonKeys = new Set(["model_group_alias", "retry_policy"]);
-
-      const parseInputValue = (key: string, raw: string | undefined, fallback: unknown) => {
-        if (raw === undefined || raw === null) return fallback;
-
-        const v = String(raw).trim();
-
-        if (v === "" || v.toLowerCase() === "null") return null;
-
-        if (numberKeys.has(key)) {
-          const n = Number(v);
-          return Number.isNaN(n) ? fallback : n;
-        }
-
-        if (jsonKeys.has(key)) {
-          if (v === "") return null;
-          try {
-            return JSON.parse(v);
-          } catch {
-            return fallback;
-          }
-        }
-
-        if (v.toLowerCase() === "true") return true;
-        if (v.toLowerCase() === "false") return false;
-
-        return v;
-      };
-
-      // Build router_settings object
-      const routerSettings = formValue.routerSettings;
-      const settingsToUpdate: { [key: string]: any } = {
-        ...routerSettings,
-        enable_tag_filtering: formValue.enableTagFiltering,
-        routing_strategy: formValue.selectedStrategy,
-        fallbacks: fallbacks.length > 0 ? fallbacks : null,
-      };
-
-      // Parse values from DOM inputs for reliability/retry fields
-      const updatedVariables = Object.fromEntries(
-        Object.entries(settingsToUpdate)
-          .map(([key, value]) => {
-            if (
-              key !== "routing_strategy_args" &&
-              key !== "routing_strategy" &&
-              key !== "enable_tag_filtering" &&
-              key !== "fallbacks"
-            ) {
-              const inputEl = document.querySelector(`input[name="${key}"]`) as HTMLInputElement | null;
-              if (inputEl && inputEl.value !== undefined && inputEl.value !== "") {
-                const parsed = parseInputValue(key, inputEl.value, value);
-                return [key, parsed];
-              }
-              return [key, value];
-            } else if (key === "routing_strategy") {
-              return [key, formValue.selectedStrategy];
-            } else if (key === "enable_tag_filtering") {
-              return [key, formValue.enableTagFiltering];
-            } else if (key === "fallbacks") {
-              return [key, fallbacks.length > 0 ? fallbacks : null];
-            } else if (key === "routing_strategy_args" && formValue.selectedStrategy === "latency-based-routing") {
-              const lowestLatencyBufferElement = document.querySelector(
-                `input[name="lowest_latency_buffer"]`,
-              ) as HTMLInputElement;
-              const ttlElement = document.querySelector(`input[name="ttl"]`) as HTMLInputElement;
-
-              const routingStrategyArgs: { [key: string]: any } = {};
-              if (lowestLatencyBufferElement?.value) {
-                routingStrategyArgs["lowest_latency_buffer"] = Number(lowestLatencyBufferElement.value);
-              }
-              if (ttlElement?.value) {
-                routingStrategyArgs["ttl"] = Number(ttlElement.value);
-              }
-              return ["routing_strategy_args", Object.keys(routingStrategyArgs).length > 0 ? routingStrategyArgs : null];
-            }
-            return [key, value];
-          })
-          .filter((entry) => entry !== null && entry !== undefined) as Iterable<[string, unknown]>,
-      );
-
-      // Ensure all required fields are present with null defaults if not set
-      // Convert empty objects/undefined to null for proper type safety
-      const normalizeValue = (val: any, isNumber = false): any => {
-        if (val === undefined || val === null) return null;
-        if (typeof val === "object" && !Array.isArray(val) && Object.keys(val).length === 0) return null;
-        if (isNumber && (typeof val !== "number" || Number.isNaN(val))) return null;
-        return val;
-      };
-
-      return {
-        routing_strategy: normalizeValue(updatedVariables.routing_strategy) as string | null | undefined,
-        allowed_fails: normalizeValue(updatedVariables.allowed_fails, true) as number | null | undefined,
-        cooldown_time: normalizeValue(updatedVariables.cooldown_time, true) as number | null | undefined,
-        num_retries: normalizeValue(updatedVariables.num_retries, true) as number | null | undefined,
-        timeout: normalizeValue(updatedVariables.timeout, true) as number | null | undefined,
-        retry_after: normalizeValue(updatedVariables.retry_after, true) as number | null | undefined,
-        fallbacks: fallbacks.length > 0 ? fallbacks : null,
-        context_window_fallbacks: normalizeValue(updatedVariables.context_window_fallbacks),
-        retry_policy: normalizeValue(updatedVariables.retry_policy),
-        model_group_alias: normalizeValue(updatedVariables.model_group_alias),
-        enable_tag_filtering: formValue.enableTagFiltering,
-        routing_strategy_args: normalizeValue(updatedVariables.routing_strategy_args),
-      };
-    };
-
-    // Update parent when form values change (with debounce to avoid infinite loops)
-    useEffect(() => {
-      if (!onChange) {
-        return;
-      }
-
-      const timeoutId = setTimeout(() => {
-        isInternalUpdateRef.current = true;
-        const finalRouterSettings = buildRouterSettings();
-        onChange({
-          router_settings: finalRouterSettings,
-        });
-      }, 100);
-
-      return () => clearTimeout(timeoutId);
-      // eslint-disable-next-line react-hooks/exhaustive-deps
-    }, [formValue, fallbacks]);
-
-    const handleFallbackGroupsChange = (newGroups: FallbackGroup[]): void => {
-      setFallbackGroups(newGroups);
-      const newFallbacks = groupsToFallbacks(newGroups);
-      setFallbacks(newFallbacks);
-    };
-
-    const availableModels = Array.from(new Set(modelInfo.map((option) => option.model_group))).sort();
-
-    // Expose getValue method via ref
-    useImperativeHandle(ref, () => ({
-      getValue: () => {
-        return {
-          router_settings: buildRouterSettings(),
-        };
-      },
-    }));
-
-    if (!accessToken) {
-      return null;
-    }
-
-    return (
-      <div className="w-full">
-        <TabGroup className="w-full">
-          <TabList variant="line" defaultValue="1" className="px-8 pt-4">
-            <Tab value="1">Loadbalancing</Tab>
-            <Tab value="2">Fallbacks</Tab>
-          </TabList>
-          <TabPanels className="px-8 py-6">
-            <TabPanel>
-              <RouterSettingsForm
-                value={formValue}
-                onChange={setFormValue}
-                routerFieldsMetadata={routerFieldsMetadata}
-                availableRoutingStrategies={availableRoutingStrategies}
-                routingStrategyDescriptions={routingStrategyDescriptions}
-              />
-            </TabPanel>
-            <TabPanel>
-              <FallbackSelectionForm
-                groups={fallbackGroups}
-                onGroupsChange={handleFallbackGroupsChange}
-                availableModels={availableModels}
-                maxFallbacks={5}
-                maxGroups={5}
-              />
-            </TabPanel>
-          </TabPanels>
-        </TabGroup>
-      </div>
-    );
-  });
-
-RouterSettingsAccordion.displayName = "RouterSettingsAccordion";
-
-export default RouterSettingsAccordion;
diff --git a/ui/litellm-dashboard/src/components/common_components/TableHeaderSortDropdown/TableHeaderSortDropdown.test.tsx b/ui/litellm-dashboard/src/components/common_components/TableHeaderSortDropdown/TableHeaderSortDropdown.test.tsx
deleted file mode 100644
index 58395371bbe..00000000000
--- a/ui/litellm-dashboard/src/components/common_components/TableHeaderSortDropdown/TableHeaderSortDropdown.test.tsx
+++ /dev/null
@@ -1,148 +0,0 @@
-import { render, screen, waitFor } from "@testing-library/react";
-import userEvent from "@testing-library/user-event";
-import { describe, expect, it, vi } from "vitest";
-import { TableHeaderSortDropdown } from "./TableHeaderSortDropdown";
-
-describe("TableHeaderSortDropdown", () => {
-  it("should render", () => {
-    const onSortChange = vi.fn();
-    render(<TableHeaderSortDropdown sortState={false} onSortChange={onSortChange} />);
-    expect(screen.getByRole("button")).toBeInTheDocument();
-  });
-
-  it("should open dropdown menu when button is clicked", async () => {
-    const user = userEvent.setup();
-    const onSortChange = vi.fn();
-    render(<TableHeaderSortDropdown sortState={false} onSortChange={onSortChange} />);
-
-    const button = screen.getByRole("button");
-    await user.click(button);
-
-    await waitFor(() => {
-      expect(screen.getByText("Ascending")).toBeInTheDocument();
-      expect(screen.getByText("Descending")).toBeInTheDocument();
-      expect(screen.getByText("Reset")).toBeInTheDocument();
-    });
-  });
-
-  it("should call onSortChange with asc when ascending option is clicked", async () => {
-    const user = userEvent.setup();
-    const onSortChange = vi.fn();
-    render(<TableHeaderSortDropdown sortState={false} onSortChange={onSortChange} />);
-
-    const button = screen.getByRole("button");
-    await user.click(button);
-
-    await waitFor(() => {
-      expect(screen.getByText("Ascending")).toBeInTheDocument();
-    });
-
-    const ascendingOption = screen.getByText("Ascending");
-    await user.click(ascendingOption);
-
-    expect(onSortChange).toHaveBeenCalledTimes(1);
-    expect(onSortChange).toHaveBeenCalledWith("asc");
-  });
-
-  it("should call onSortChange with desc when descending option is clicked", async () => {
-    const user = userEvent.setup();
-    const onSortChange = vi.fn();
-    render(<TableHeaderSortDropdown sortState={false} onSortChange={onSortChange} />);
-
-    const button = screen.getByRole("button");
-    await user.click(button);
-
-    await waitFor(() => {
-      expect(screen.getByText("Descending")).toBeInTheDocument();
-    });
-
-    const descendingOption = screen.getByText("Descending");
-    await user.click(descendingOption);
-
-    expect(onSortChange).toHaveBeenCalledTimes(1);
-    expect(onSortChange).toHaveBeenCalledWith("desc");
-  });
-
-  it("should call onSortChange with false when reset option is clicked", async () => {
-    const user = userEvent.setup();
-    const onSortChange = vi.fn();
-    render(<TableHeaderSortDropdown sortState="asc" onSortChange={onSortChange} />);
-
-    const button = screen.getByRole("button");
-    await user.click(button);
-
-    await waitFor(() => {
-      expect(screen.getByText("Reset")).toBeInTheDocument();
-    });
-
-    const resetOption = screen.getByText("Reset");
-    await user.click(resetOption);
-
-    expect(onSortChange).toHaveBeenCalledTimes(1);
-    expect(onSortChange).toHaveBeenCalledWith(false);
-  });
-
-  it("should highlight ascending option when sort state is asc", async () => {
-    const user = userEvent.setup();
-    const onSortChange = vi.fn();
-    render(<TableHeaderSortDropdown sortState="asc" onSortChange={onSortChange} />);
-
-    const button = screen.getByRole("button");
-    await user.click(button);
-
-    await waitFor(() => {
-      const ascendingOption = screen.getByText("Ascending");
-      const menuItem = ascendingOption.closest(".ant-dropdown-menu-item");
-      expect(menuItem).toHaveClass("ant-dropdown-menu-item-selected");
-    });
-  });
-
-  it("should highlight descending option when sort state is desc", async () => {
-    const user = userEvent.setup();
-    const onSortChange = vi.fn();
-    render(<TableHeaderSortDropdown sortState="desc" onSortChange={onSortChange} />);
-
-    const button = screen.getByRole("button");
-    await user.click(button);
-
-    await waitFor(() => {
-      const descendingOption = screen.getByText("Descending");
-      const menuItem = descendingOption.closest(".ant-dropdown-menu-item");
-      expect(menuItem).toHaveClass("ant-dropdown-menu-item-selected");
-    });
-  });
-
-  it("should not highlight any option when sort state is false", async () => {
-    const user = userEvent.setup();
-    const onSortChange = vi.fn();
-    render(<TableHeaderSortDropdown sortState={false} onSortChange={onSortChange} />);
-
-    const button = screen.getByRole("button");
-    await user.click(button);
-
-    await waitFor(() => {
-      expect(screen.getByText("Ascending")).toBeInTheDocument();
-    });
-
-    const ascendingOption = screen.getByText("Ascending");
-    const menuItem = ascendingOption.closest(".ant-dropdown-menu-item");
-    expect(menuItem).not.toHaveClass("ant-dropdown-menu-item-selected");
-  });
-
-  it("should stop event propagation when button is clicked", async () => {
-    const user = userEvent.setup();
-    const onSortChange = vi.fn();
-    const onParentClick = vi.fn();
-
-    render(
-      <div onClick={onParentClick}>
-        <TableHeaderSortDropdown sortState={false} onSortChange={onSortChange} />
-      </div>,
-    );
-
-    const button = screen.getByRole("button");
-    await user.click(button);
-
-    expect(onParentClick).not.toHaveBeenCalled();
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/common_components/TableHeaderSortDropdown/TableHeaderSortDropdown.tsx b/ui/litellm-dashboard/src/components/common_components/TableHeaderSortDropdown/TableHeaderSortDropdown.tsx
deleted file mode 100644
index cbdc8beaaa4..00000000000
--- a/ui/litellm-dashboard/src/components/common_components/TableHeaderSortDropdown/TableHeaderSortDropdown.tsx
+++ /dev/null
@@ -1,85 +0,0 @@
-import React from "react";
-import { Button, Dropdown, MenuProps } from "antd";
-import { SwitchVerticalIcon, ChevronUpIcon, ChevronDownIcon, XIcon } from "@heroicons/react/outline";
-
-export type SortState = "asc" | "desc" | false;
-
-interface TableHeaderSortDropdownProps {
-  /**
-   * Current sort state: "asc", "desc", or false for neutral
-   */
-  sortState: SortState;
-  /**
-   * Callback when sort state changes
-   * @param newState - The new sort state: "asc", "desc", or false
-   */
-  onSortChange: (newState: SortState) => void;
-  /**
-   * Optional column ID for identification
-   */
-  columnId?: string;
-}
-
-export const TableHeaderSortDropdown: React.FC<TableHeaderSortDropdownProps> = ({
-  sortState,
-  onSortChange,
-}) => {
-  const handleMenuClick: MenuProps["onClick"] = ({ key }) => {
-    if (key === "asc") {
-      onSortChange("asc");
-    } else if (key === "desc") {
-      onSortChange("desc");
-    } else if (key === "reset") {
-      onSortChange(false);
-    }
-  };
-
-  const menuItems: MenuProps["items"] = [
-    {
-      key: "asc",
-      label: "Ascending",
-      icon: <ChevronUpIcon className="h-4 w-4" />,
-    },
-    {
-      key: "desc",
-      label: "Descending",
-      icon: <ChevronDownIcon className="h-4 w-4" />,
-    },
-    {
-      key: "reset",
-      label: "Reset",
-      icon: <XIcon className="h-4 w-4" />,
-    },
-  ];
-
-  // Determine which icon to display based on current sort state
-  const renderIcon = () => {
-    if (sortState === "asc") {
-      return <ChevronUpIcon className="h-4 w-4" />;
-    } else if (sortState === "desc") {
-      return <ChevronDownIcon className="h-4 w-4" />;
-    } else {
-      return <SwitchVerticalIcon className="h-4 w-4" />;
-    }
-  };
-
-  return (
-    <Dropdown
-      menu={{
-        items: menuItems,
-        onClick: handleMenuClick,
-        selectable: true,
-        selectedKeys: sortState ? [sortState] : [],
-      }}
-      trigger={["click"]}
-      autoAdjustOverflow
-    >
-      <Button
-        type="text"
-        onClick={(e) => e.stopPropagation()}
-        icon={renderIcon()}
-        className={sortState ? "text-blue-500 hover:text-blue-600" : "text-gray-400 hover:text-blue-500"}
-      />
-    </Dropdown>
-  );
-};
diff --git a/ui/litellm-dashboard/src/components/guardrails/guardrail_info.test.tsx b/ui/litellm-dashboard/src/components/guardrails/guardrail_info.test.tsx
index d532b654f58..bd2b45d6f34 100644
--- a/ui/litellm-dashboard/src/components/guardrails/guardrail_info.test.tsx
+++ b/ui/litellm-dashboard/src/components/guardrails/guardrail_info.test.tsx
@@ -11,28 +11,6 @@ vi.mock("@/components/networking", () => ({
   updateGuardrailCall: vi.fn(),
 }));
 
-
-// Mock ContentFilterManager
-vi.mock("./content_filter/ContentFilterManager", () => ({
-  __esModule: true,
-  default: ({ onUnsavedChanges, onDataChange, isEditing }: any) => (
-    <div data-testid="mock-content-filter-manager">
-      {isEditing && (
-        <button onClick={() => {
-          onUnsavedChanges(true);
-          onDataChange?.(["new_pattern"], ["new_word"]);
-        }}>
-          Simulate Change
-        </button>
-      )}
-    </div>
-  ),
-  formatContentFilterDataForAPI: (patterns: any[], blockedWords: any[]) => ({
-    patterns,
-    blocked_words: blockedWords,
-  }),
-}));
-
 describe("Guardrail Info", () => {
   afterEach(() => {
     vi.clearAllMocks();
@@ -169,101 +147,4 @@ describe("Guardrail Info", () => {
       expect(getByText("PII Entity Configuration")).toBeInTheDocument();
     });
   });
-  it("should handle content filter updates correctly", async () => {
-    // Mock the network responses
-    vi.mocked(networking.getGuardrailInfo).mockResolvedValue({
-      guardrail_id: "123",
-      guardrail_name: "Content Filter Guardrail",
-      litellm_params: {
-        guardrail: "litellm_content_filter",
-        mode: "pre_call",
-        default_on: true,
-        patterns: ["initial_pattern"],
-        blocked_words: ["initial_word"],
-      },
-      created_at: "2024-01-01T00:00:00Z",
-      updated_at: "2024-01-01T00:00:00Z",
-      guardrail_definition_location: "database",
-    });
-
-    vi.mocked(networking.getGuardrailUISettings).mockResolvedValue({
-      supported_entities: [],
-      supported_actions: [],
-      pii_entity_categories: [],
-      supported_modes: ["pre_call", "post_call"],
-    });
-
-    vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({});
-    vi.mocked(networking.updateGuardrailCall).mockResolvedValue({ status: "success" });
-
-    const { getByText, getByRole, getAllByRole, getByLabelText } = render(
-      <GuardrailInfoView guardrailId="123" onClose={() => { }} accessToken="123" isAdmin={true} />,
-    );
-
-    await waitFor(() => {
-      expect(getByText("Settings")).toBeInTheDocument();
-    });
-
-    // Go to Settings tab
-    fireEvent.click(getByText("Settings"));
-
-    await waitFor(() => {
-      expect(getByText("Guardrail Settings")).toBeInTheDocument();
-    });
-
-    // Enter Edit Mode
-    fireEvent.click(getByText("Edit Settings"));
-
-    // Modify Guardrail Name to force an update
-    const nameInput = getByLabelText("Guardrail Name");
-    fireEvent.change(nameInput, { target: { value: "Updated Name" } });
-
-    // Save with only name change
-    const saveButton = getByText("Save Changes");
-    fireEvent.click(saveButton);
-
-    await waitFor(() => {
-      expect(networking.updateGuardrailCall).toHaveBeenCalled();
-    });
-
-    // Verify call did NOT include patterns or blocked_words (because no changes)
-    // updateGuardrailCall(accessToken, guardrailId, updateData) -> index 2 is updateData
-    const firstCallArgs: any = vi.mocked(networking.updateGuardrailCall).mock.calls[0][2];
-
-    // Verify attributes that definitely changed
-    expect(firstCallArgs.guardrail_name).toBe("Updated Name");
-
-    // litellm_params might be undefined if empty, which is correct. 
-    // If it exists, ensure patterns/blocked_words are not in it.
-    if (firstCallArgs.litellm_params) {
-      expect(firstCallArgs.litellm_params.patterns).toBeUndefined();
-      expect(firstCallArgs.litellm_params.blocked_words).toBeUndefined();
-    }
-
-    // Clear mocks to reset call count
-    vi.clearAllMocks();
-
-    // Enter Edit Mode again to make changes
-    await waitFor(() => {
-      expect(getByText("Edit Settings")).toBeInTheDocument();
-    });
-    fireEvent.click(getByText("Edit Settings"));
-
-    // Now modify the values using the mock button
-    const simulateChangeButton = getByText("Simulate Change");
-    fireEvent.click(simulateChangeButton);
-
-    // Save again
-    fireEvent.click(getByText("Save Changes"));
-
-    await waitFor(() => {
-      expect(networking.updateGuardrailCall).toHaveBeenCalled();
-    });
-
-    // Verify call INCLUDES patterns and blocked_words
-    const secondCallArgs: any = vi.mocked(networking.updateGuardrailCall).mock.calls[0][2];
-    expect(secondCallArgs.litellm_params).toBeDefined();
-    expect(secondCallArgs.litellm_params.patterns).toEqual(["new_pattern"]);
-    expect(secondCallArgs.litellm_params.blocked_words).toEqual(["new_word"]);
-  });
 });
diff --git a/ui/litellm-dashboard/src/components/guardrails/guardrail_info.tsx b/ui/litellm-dashboard/src/components/guardrails/guardrail_info.tsx
index 0a15446db97..0d1a205f66f 100644
--- a/ui/litellm-dashboard/src/components/guardrails/guardrail_info.tsx
+++ b/ui/litellm-dashboard/src/components/guardrails/guardrail_info.tsx
@@ -271,17 +271,22 @@ const GuardrailInfoView: React.FC<GuardrailInfoProps> = ({ guardrailId, onClose,
       }
 
       // Only add Content Filter patterns if there are changes
-      if (guardrailData.litellm_params?.guardrail === "litellm_content_filter" && hasUnsavedContentFilterChanges) {
+      if (guardrailData.litellm_params?.guardrail === "litellm_content_filter") {
         const originalPatterns = guardrailData.litellm_params?.patterns || [];
         const originalBlockedWords = guardrailData.litellm_params?.blocked_words || [];
 
         const formattedData = formatContentFilterDataForAPI(
-          contentFilterDataRef.current.patterns || [],
-          contentFilterDataRef.current.blockedWords || [],
+          contentFilterDataRef.current.patterns,
+          contentFilterDataRef.current.blockedWords,
         );
 
-        updateData.litellm_params.patterns = formattedData.patterns;
-        updateData.litellm_params.blocked_words = formattedData.blocked_words;
+        if (JSON.stringify(originalPatterns) !== JSON.stringify(formattedData.patterns)) {
+          updateData.litellm_params.patterns = formattedData.patterns;
+        }
+
+        if (JSON.stringify(originalBlockedWords) !== JSON.stringify(formattedData.blocked_words)) {
+          updateData.litellm_params.blocked_words = formattedData.blocked_words;
+        }
       }
 
       if (guardrailData.litellm_params?.guardrail === "tool_permission") {
@@ -352,9 +357,6 @@ const GuardrailInfoView: React.FC<GuardrailInfoProps> = ({ guardrailId, onClose,
 
         console.log("allowedParams: ", allowedParams);
         allowedParams.forEach((paramName) => {
-          if (paramName === "patterns" || paramName === "blocked_words") {
-            return;
-          }
           // Check for both direct parameter name and nested optional_params object
           let paramValue = values[paramName];
           if (paramValue === undefined || paramValue === null || paramValue === "") {
diff --git a/ui/litellm-dashboard/src/components/leftnav.tsx b/ui/litellm-dashboard/src/components/leftnav.tsx
index 61071211778..543d95d2ccd 100644
--- a/ui/litellm-dashboard/src/components/leftnav.tsx
+++ b/ui/litellm-dashboard/src/components/leftnav.tsx
@@ -39,7 +39,6 @@ interface SidebarProps {
   setPage: (page: string) => void;
   defaultSelectedKey: string;
   collapsed?: boolean;
-  enabledPagesInternalUsers?: string[] | null;
 }
 
 // Menu item configuration
@@ -60,8 +59,28 @@ interface MenuGroup {
   roles?: string[];
 }
 
-// Menu groups organized by category - defined outside component for export
-const menuGroups: MenuGroup[] = [
+const Sidebar: React.FC<SidebarProps> = ({ setPage, defaultSelectedKey, collapsed = false }) => {
+  const { userId, accessToken, userRole } = useAuthorized();
+  const { data: organizations } = useOrganizations();
+
+  // Check if user is an org_admin
+  const isOrgAdmin = useMemo(() => {
+    if (!userId || !organizations) return false;
+    return organizations.some((org: Organization) =>
+      org.members?.some((member) => member.user_id === userId && member.user_role === "org_admin"),
+    );
+  }, [userId, organizations]);
+
+  // Navigate to page helper
+  const navigateToPage = (page: string) => {
+    const newSearchParams = new URLSearchParams(window.location.search);
+    newSearchParams.set("page", page);
+    window.history.pushState(null, "", `?${newSearchParams.toString()}`);
+    setPage(page);
+  };
+
+  // Menu groups organized by category
+  const menuGroups: MenuGroup[] = [
     {
       groupLabel: "AI GATEWAY",
       items: [
@@ -88,7 +107,7 @@ const menuGroups: MenuGroup[] = [
         {
           key: "agents",
           page: "agents",
-          label: "Agents",
+          label: <span className="flex items-center gap-4">Agents</span>,
           icon: <RobotOutlined />,
           roles: rolesWithWriteAccess,
         },
@@ -108,11 +127,7 @@ const menuGroups: MenuGroup[] = [
         {
           key: "policies",
           page: "policies",
-          label: (
-            <span className="flex items-center gap-4">
-              Policies <NewBadge />
-            </span>
-          ),
+          label: "Policies",
           icon: <AuditOutlined />,
           roles: all_admin_roles,
         },
@@ -133,6 +148,7 @@ const menuGroups: MenuGroup[] = [
               page: "vector-stores",
               label: "Vector Stores",
               icon: <DatabaseOutlined />,
+              roles: all_admin_roles,
             },
           ],
         },
@@ -317,82 +333,20 @@ const menuGroups: MenuGroup[] = [
     },
   ];
 
-const Sidebar: React.FC<SidebarProps> = ({ setPage, defaultSelectedKey, collapsed = false, enabledPagesInternalUsers }) => {
-  const { userId, accessToken, userRole } = useAuthorized();
-  const { data: organizations } = useOrganizations();
-
-  // Check if user is an org_admin
-  const isOrgAdmin = useMemo(() => {
-    if (!userId || !organizations) return false;
-    return organizations.some((org: Organization) =>
-      org.members?.some((member) => member.user_id === userId && member.user_role === "org_admin"),
-    );
-  }, [userId, organizations]);
-
-  // Navigate to page helper
-  const navigateToPage = (page: string) => {
-    const newSearchParams = new URLSearchParams(window.location.search);
-    newSearchParams.set("page", page);
-    window.history.pushState(null, "", `?${newSearchParams.toString()}`);
-    setPage(page);
-  };
-
-  // Filter items based on user role and enabled pages for internal users
+  // Filter items based on user role
   const filterItemsByRole = (items: MenuItem[]): MenuItem[] => {
-    const isAdmin = isAdminRole(userRole);
-
-    // Debug logging
-    if (enabledPagesInternalUsers !== null && enabledPagesInternalUsers !== undefined) {
-      console.log("[LeftNav] Filtering with enabled pages:", {
-        userRole,
-        isAdmin,
-        enabledPagesInternalUsers,
-      });
-    }
-
     return items
-      .map((item) => ({
-        ...item,
-        children: item.children ? filterItemsByRole(item.children) : undefined,
-      }))
       .filter((item) => {
         // Special handling for organizations menu item - allow org_admins
         if (item.key === "organizations") {
-          const hasRoleAccess = !item.roles || item.roles.includes(userRole) || isOrgAdmin;
-          if (!hasRoleAccess) return false;
-
-          // Check enabled pages for internal users (non-admins)
-          if (!isAdmin && enabledPagesInternalUsers !== null && enabledPagesInternalUsers !== undefined) {
-            const isIncluded = enabledPagesInternalUsers.includes(item.page);
-            console.log(`[LeftNav] Page "${item.page}" (${item.key}): ${isIncluded ? "VISIBLE" : "HIDDEN"}`);
-            return isIncluded;
-          }
-          return true;
+          return !item.roles || item.roles.includes(userRole) || isOrgAdmin;
         }
-
-        // Existing role check
-        if (item.roles && !item.roles.includes(userRole)) return false;
-
-        // Check enabled pages for internal users (non-admins)
-        if (!isAdmin && enabledPagesInternalUsers !== null && enabledPagesInternalUsers !== undefined) {
-          // If item has children, check if any children are visible
-          if (item.children && item.children.length > 0) {
-            const hasVisibleChildren = item.children.some((child) => 
-              enabledPagesInternalUsers.includes(child.page)
-            );
-            if (hasVisibleChildren) {
-              console.log(`[LeftNav] Parent "${item.page}" (${item.key}): VISIBLE (has visible children)`);
-              return true;
-            }
-          }
-          
-          const isIncluded = enabledPagesInternalUsers.includes(item.page);
-          console.log(`[LeftNav] Page "${item.page}" (${item.key}): ${isIncluded ? "VISIBLE" : "HIDDEN"}`);
-          return isIncluded;
-        }
-
-        return true;
-      });
+        return !item.roles || item.roles.includes(userRole);
+      })
+      .map((item) => ({
+        ...item,
+        children: item.children ? filterItemsByRole(item.children) : undefined,
+      }));
   };
 
   // Build menu items with groups
@@ -527,6 +481,3 @@ const Sidebar: React.FC<SidebarProps> = ({ setPage, defaultSelectedKey, collapse
 };
 
 export default Sidebar;
-
-// Also export menuGroups for advanced use cases
-export { menuGroups };
diff --git a/ui/litellm-dashboard/src/components/model_dashboard/all_models_table.tsx b/ui/litellm-dashboard/src/components/model_dashboard/all_models_table.tsx
deleted file mode 100644
index 1ae3335a823..00000000000
--- a/ui/litellm-dashboard/src/components/model_dashboard/all_models_table.tsx
+++ /dev/null
@@ -1,204 +0,0 @@
-import {
-  ColumnDef,
-  flexRender,
-  getCoreRowModel,
-  getPaginationRowModel,
-  SortingState,
-  useReactTable,
-  ColumnResizeMode,
-  VisibilityState,
-  PaginationState,
-  OnChangeFn,
-} from "@tanstack/react-table";
-import React from "react";
-import { Table, TableHead, TableHeaderCell, TableBody, TableRow, TableCell } from "@tremor/react";
-import { TableHeaderSortDropdown, SortState } from "../common_components/TableHeaderSortDropdown/TableHeaderSortDropdown";
-
-// Extend the column meta type to include className
-declare module "@tanstack/react-table" {
-  interface ColumnMeta<TData, TValue> {
-    className?: string;
-  }
-}
-
-interface AllModelsDataTableProps<TData, TValue> {
-  data: TData[];
-  columns: ColumnDef<TData, TValue>[];
-  isLoading?: boolean;
-  sorting?: SortingState;
-  onSortingChange?: OnChangeFn<SortingState>;
-  pagination?: PaginationState;
-  onPaginationChange?: OnChangeFn<PaginationState>;
-  enablePagination?: boolean;
-}
-
-export function AllModelsDataTable<TData, TValue>({
-  data = [],
-  columns,
-  isLoading = false,
-  sorting = [],
-  onSortingChange,
-  pagination,
-  onPaginationChange,
-  enablePagination = false,
-}: AllModelsDataTableProps<TData, TValue>) {
-  const [columnResizeMode] = React.useState<ColumnResizeMode>("onChange");
-  const [columnSizing, setColumnSizing] = React.useState({});
-  const [columnVisibility, setColumnVisibility] = React.useState<VisibilityState>({});
-
-  const tableInstance = useReactTable({
-    data,
-    columns,
-    state: {
-      sorting,
-      columnSizing,
-      columnVisibility,
-      ...(enablePagination && pagination ? { pagination } : {}),
-    },
-    columnResizeMode,
-    onSortingChange: onSortingChange,
-    onColumnSizingChange: setColumnSizing,
-    onColumnVisibilityChange: setColumnVisibility,
-    ...(enablePagination && onPaginationChange ? { onPaginationChange } : {}),
-    getCoreRowModel: getCoreRowModel(),
-    // NO getSortedRowModel - sorting is handled server-side
-    ...(enablePagination ? { getPaginationRowModel: getPaginationRowModel() } : {}),
-    enableSorting: true,
-    enableColumnResizing: true,
-    manualSorting: true, // Enable manual sorting for server-side sorting
-    defaultColumn: {
-      minSize: 40,
-      maxSize: 500,
-    },
-  });
-
-  const getHeaderText = (header: any): string => {
-    if (typeof header === "string") {
-      return header;
-    }
-    if (typeof header === "function") {
-      const headerElement = header();
-      if (headerElement && headerElement.props && headerElement.props.children) {
-        const children = headerElement.props.children;
-        if (typeof children === "string") {
-          return children;
-        }
-        if (children.props && children.props.children) {
-          return children.props.children;
-        }
-      }
-    }
-    return "";
-  };
-
-  return (
-    <div className="rounded-lg custom-border relative">
-      <div className="overflow-x-auto">
-        <div className="relative min-w-full">
-          <Table className="[&_td]:py-2 [&_th]:py-2 w-full">
-            <TableHead>
-              {tableInstance.getHeaderGroups().map((headerGroup) => (
-                <TableRow key={headerGroup.id}>
-                  {headerGroup.headers.map((header) => (
-                    <TableHeaderCell
-                      key={header.id}
-                      className={`py-1 h-8 relative ${header.id === "actions"
-                        ? "sticky right-0 bg-white shadow-[-4px_0_8px_-6px_rgba(0,0,0,0.1)] w-[120px] ml-8"
-                        : ""
-                        } ${header.column.columnDef.meta?.className || ""}`}
-                      style={{
-                        width: header.id === "actions" ? 120 : header.getSize(),
-                        position: header.id === "actions" ? "sticky" : "relative",
-                        right: header.id === "actions" ? 0 : "auto",
-                      }}
-                    >
-                      <div className="flex items-center justify-between gap-2">
-                        <div className="flex items-center">
-                          {header.isPlaceholder
-                            ? null
-                            : flexRender(header.column.columnDef.header, header.getContext())}
-                        </div>
-                        {header.id !== "actions" && header.column.getCanSort() && onSortingChange && (
-                          <TableHeaderSortDropdown
-                            sortState={
-                              header.column.getIsSorted() === false
-                                ? false
-                                : (header.column.getIsSorted() as SortState)
-                            }
-                            onSortChange={(newState) => {
-                              // Convert SortState to TanStack SortingState
-                              // Only allow one column to be sorted at a time
-                              if (newState === false) {
-                                onSortingChange([]);
-                              } else {
-                                onSortingChange([
-                                  {
-                                    id: header.column.id,
-                                    desc: newState === "desc",
-                                  },
-                                ]);
-                              }
-                            }}
-                            columnId={header.column.id}
-                          />
-                        )}
-                      </div>
-                      {header.column.getCanResize() && (
-                        <div
-                          onMouseDown={header.getResizeHandler()}
-                          onTouchStart={header.getResizeHandler()}
-                          className={`absolute right-0 top-0 h-full w-2 cursor-col-resize select-none touch-none ${header.column.getIsResizing() ? "bg-blue-500" : "hover:bg-blue-200"
-                            }`}
-                        />
-                      )}
-                    </TableHeaderCell>
-                  ))}
-                </TableRow>
-              ))}
-            </TableHead>
-            <TableBody>
-              {isLoading ? (
-                <TableRow>
-                  <TableCell colSpan={columns.length} className="h-8 text-center">
-                    <div className="text-center text-gray-500">
-                      <p>🚅 Loading models...</p>
-                    </div>
-                  </TableCell>
-                </TableRow>
-              ) : tableInstance.getRowModel().rows.length > 0 ? (
-                tableInstance.getRowModel().rows.map((row) => (
-                  <TableRow key={row.id}>
-                    {row.getVisibleCells().map((cell) => (
-                      <TableCell
-                        key={cell.id}
-                        className={`py-0.5 ${cell.column.id === "actions"
-                          ? "sticky right-0 bg-white shadow-[-4px_0_8px_-6px_rgba(0,0,0,0.1)] w-[120px] ml-8"
-                          : ""
-                          } ${cell.column.columnDef.meta?.className || ""}`}
-                        style={{
-                          width: cell.column.id === "actions" ? 120 : cell.column.getSize(),
-                          position: cell.column.id === "actions" ? "sticky" : "relative",
-                          right: cell.column.id === "actions" ? 0 : "auto",
-                        }}
-                      >
-                        {flexRender(cell.column.columnDef.cell, cell.getContext())}
-                      </TableCell>
-                    ))}
-                  </TableRow>
-                ))
-              ) : (
-                <TableRow>
-                  <TableCell colSpan={columns.length} className="h-8 text-center">
-                    <div className="text-center text-gray-500">
-                      <p>No models found</p>
-                    </div>
-                  </TableCell>
-                </TableRow>
-              )}
-            </TableBody>
-          </Table>
-        </div>
-      </div>
-    </div>
-  );
-}
diff --git a/ui/litellm-dashboard/src/components/model_info_view.test.tsx b/ui/litellm-dashboard/src/components/model_info_view.test.tsx
index 4b9f7d217dc..21402ad4671 100644
--- a/ui/litellm-dashboard/src/components/model_info_view.test.tsx
+++ b/ui/litellm-dashboard/src/components/model_info_view.test.tsx
@@ -1,38 +1,112 @@
-import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
 import { render, waitFor } from "@testing-library/react";
-import React, { ReactNode } from "react";
-import { beforeEach, describe, expect, it, vi } from "vitest";
+import { describe, expect, it, vi } from "vitest";
 import ModelInfoView from "./model_info_view";
 
 vi.mock("../../utils/dataUtils", () => ({
-  copyToClipboard: vi.fn().mockResolvedValue(true),
+  copyToClipboard: vi.fn(),
 }));
 
 vi.mock("./networking", () => ({
   modelInfoV1Call: vi.fn().mockResolvedValue({
     data: [
       {
-        model_name: "GPT-4",
+        model_name: "aws/anthropic/bedrock-claude-3-5-sonnet-v1",
         litellm_params: {
-          model: "gpt-4",
-          api_base: "https://api.openai.com/v1",
-          custom_llm_provider: "openai",
+          aws_region_name: "us-east-1",
+          custom_llm_provider: "bedrock",
+          use_in_pass_through: false,
+          use_litellm_proxy: false,
+          merge_reasoning_content_in_choices: false,
+          model: "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0",
         },
         model_info: {
-          id: "123",
-          created_by: "123",
-          db_model: true,
-          input_cost_per_token: 0.00003,
-          output_cost_per_token: 0.00006,
+          id: "70b94bbd2af4a75215f7e3e465b5b199529dc15deb5d395d0668a4aabc496c84",
+          db_model: false,
+          access_via_team_ids: [
+            "4fe3cfea-c907-412a-a645-60915b618d11",
+            "9a4b2d15-4198-47e4-971b-7329b77f40e4",
+            "14d55eef-b8d4-4cb8-b080-d973269dae54",
+            "693ce1d2-9fae-4605-a5c9-1c9829415e1a",
+            "fe29d910-4968-45bc-9fe0-6716e89c6270",
+          ],
+          direct_access: true,
+          key: "anthropic.claude-3-5-sonnet-20240620-v1:0",
+          max_tokens: 4096,
+          max_input_tokens: 200000,
+          max_output_tokens: 4096,
+          input_cost_per_token: 0.000003,
+          input_cost_per_token_flex: null,
+          input_cost_per_token_priority: null,
+          cache_creation_input_token_cost: null,
+          cache_read_input_token_cost: null,
+          cache_read_input_token_cost_flex: null,
+          cache_read_input_token_cost_priority: null,
+          cache_creation_input_token_cost_above_1hr: null,
+          input_cost_per_character: null,
+          input_cost_per_token_above_128k_tokens: null,
+          input_cost_per_token_above_200k_tokens: null,
+          input_cost_per_query: null,
+          input_cost_per_second: null,
+          input_cost_per_audio_token: null,
+          input_cost_per_token_batches: null,
+          output_cost_per_token_batches: null,
+          output_cost_per_token: 0.000015,
+          output_cost_per_token_flex: null,
+          output_cost_per_token_priority: null,
+          output_cost_per_audio_token: null,
+          output_cost_per_character: null,
+          output_cost_per_reasoning_token: null,
+          output_cost_per_token_above_128k_tokens: null,
+          output_cost_per_character_above_128k_tokens: null,
+          output_cost_per_token_above_200k_tokens: null,
+          output_cost_per_second: null,
+          output_cost_per_video_per_second: null,
+          output_cost_per_image: null,
+          output_vector_size: null,
+          citation_cost_per_token: null,
+          tiered_pricing: null,
+          litellm_provider: "bedrock",
+          mode: "chat",
+          supports_system_messages: null,
+          supports_response_schema: true,
+          supports_vision: true,
+          supports_function_calling: true,
+          supports_tool_choice: true,
+          supports_assistant_prefill: null,
+          supports_prompt_caching: null,
+          supports_audio_input: null,
+          supports_audio_output: null,
+          supports_pdf_input: true,
+          supports_embedding_image_input: null,
+          supports_native_streaming: null,
+          supports_web_search: null,
+          supports_url_context: null,
+          supports_reasoning: null,
+          supports_computer_use: null,
+          search_context_cost_per_query: null,
+          tpm: null,
+          rpm: null,
+          ocr_cost_per_page: null,
+          annotation_cost_per_page: null,
+          supported_openai_params: [
+            "max_tokens",
+            "max_completion_tokens",
+            "stream",
+            "stream_options",
+            "stop",
+            "temperature",
+            "top_p",
+            "extra_headers",
+            "response_format",
+            "requestMetadata",
+            "tools",
+            "tool_choice",
+          ],
         },
       },
     ],
   }),
-  credentialGetCall: vi.fn().mockResolvedValue({
-    credential_name: "test-credential",
-    credential_values: {},
-    credential_info: {},
-  }),
+  credentialGetCall: vi.fn().mockResolvedValue({}),
   getGuardrailsList: vi.fn().mockResolvedValue({
     guardrails: [{ guardrail_name: "content_filter" }, { guardrail_name: "toxicity_filter" }],
   }),
@@ -46,135 +120,99 @@ vi.mock("./networking", () => ({
       description: "Production ready models",
     },
   }),
-  testConnectionRequest: vi.fn().mockResolvedValue({
-    status: "success",
-  }),
-  modelPatchUpdateCall: vi.fn().mockResolvedValue({}),
-  modelDeleteCall: vi.fn().mockResolvedValue({}),
 }));
 
 // Mock the useModelsInfo hook since it uses React Query
-const mockUseModelsInfo = vi.fn();
-const mockUseModelHub = vi.fn();
-
 vi.mock("@/app/(dashboard)/hooks/models/useModels", () => ({
-  useModelsInfo: (...args: any[]) => mockUseModelsInfo(...args),
-  useModelHub: (...args: any[]) => mockUseModelHub(...args),
-}));
-
-// Mock the useModelCostMap hook
-const mockUseModelCostMap = vi.fn();
-vi.mock("@/app/(dashboard)/hooks/models/useModelCostMap", () => ({
-  useModelCostMap: (...args: any[]) => mockUseModelCostMap(...args),
+  useModelsInfo: vi.fn().mockReturnValue({
+    data: {
+      data: [
+        {
+          model_name: "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0",
+          provider: "bedrock",
+          litellm_model_name: "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0",
+        },
+        {
+          model_name: "openai/gpt-4",
+          provider: "openai",
+          litellm_model_name: "gpt-4",
+        },
+      ],
+    },
+    isLoading: false,
+    error: null,
+  }),
 }));
 
 describe("ModelInfoView", () => {
-  let queryClient: QueryClient;
-
-  const defaultModelData = {
-    model_name: "GPT-4",
-    litellm_params: {
-      model: "gpt-4",
-      api_base: "https://api.openai.com/v1",
-      custom_llm_provider: "openai",
-    },
+  const modelData = {
     model_info: {
       id: "123",
       created_by: "123",
-      created_at: "2024-01-01T00:00:00Z",
       db_model: true,
-      input_cost_per_token: 0.00003,
-      output_cost_per_token: 0.00006,
     },
+    litellm_params: {
+      api_base: "https://api.openai.com/v1",
+      custom_llm_provider: "openai",
+    },
+    litellm_model_name: "gpt-4",
+    model_name: "GPT-4",
+    litellm_provider: "openai",
+    mode: "chat",
+    supported_openai_params: ["temperature", "max_tokens", "top_p", "frequency_penalty", "presence_penalty"],
   };
 
-  beforeEach(() => {
-    queryClient = new QueryClient({
-      defaultOptions: {
-        queries: {
-          retry: false,
-        },
-      },
-    });
-    vi.clearAllMocks();
-
-    // Set up default mocks
-    mockUseModelsInfo.mockReturnValue({
-      data: {
-        data: [defaultModelData],
-      },
-      isLoading: false,
-      error: null,
-    });
-
-    mockUseModelHub.mockReturnValue({
-      data: {
-        data: [],
-      },
-      isLoading: false,
-      error: null,
-    });
-
-    mockUseModelCostMap.mockReturnValue({
-      data: {},
-      isLoading: false,
-      error: null,
-    });
-  });
-
-  const wrapper = ({ children }: { children: ReactNode }) =>
-    React.createElement(QueryClientProvider, { client: queryClient }, children);
-
   const DEFAULT_ADMIN_PROPS = {
     modelId: "123",
     onClose: () => {},
+    modelData: modelData,
     accessToken: "123",
     userID: "123",
     userRole: "Admin",
+    editModel: false,
+    setEditModalVisible: () => {},
+    setSelectedModel: () => {},
     onModelUpdate: () => {},
     modelAccessGroups: [],
   };
 
   describe("Edit Model", () => {
     it("should render the model info view", async () => {
-      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />, { wrapper });
+      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
       await waitFor(() => {
         expect(getByText("Model Settings")).toBeInTheDocument();
       });
     });
 
-    it("should not render an edit settings button if the model is not a DB model", async () => {
+    it("should not render an edit model button if the model is not a DB model", async () => {
       const nonDbModelData = {
-        ...defaultModelData,
+        ...modelData,
         model_info: {
-          ...defaultModelData.model_info,
+          ...modelData.model_info,
           db_model: false,
         },
       };
 
-      mockUseModelsInfo.mockReturnValue({
-        data: {
-          data: [nonDbModelData],
-        },
-        isLoading: false,
-        error: null,
-      });
+      const NON_DB_ADMIN_PROPS = {
+        ...DEFAULT_ADMIN_PROPS,
+        modelData: nonDbModelData,
+      };
 
-      const { queryByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />, { wrapper });
+      const { queryByText } = render(<ModelInfoView {...NON_DB_ADMIN_PROPS} />);
       await waitFor(() => {
-        expect(queryByText("Edit Settings")).not.toBeInTheDocument();
+        expect(queryByText("Edit Model")).not.toBeInTheDocument();
       });
     });
 
     it("should render tags in the edit model", async () => {
-      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />, { wrapper });
+      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
       await waitFor(() => {
         expect(getByText("Tags")).toBeInTheDocument();
       });
     });
 
     it("should render the litellm params in the edit model", async () => {
-      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />, { wrapper });
+      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
       await waitFor(() => {
         expect(getByText("LiteLLM Params")).toBeInTheDocument();
       });
@@ -182,21 +220,21 @@ describe("ModelInfoView", () => {
   });
 
   it("should render a test connection button", async () => {
-    const { getByTestId } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />, { wrapper });
+    const { getByTestId } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
     await waitFor(() => {
       expect(getByTestId("test-connection-button")).toBeInTheDocument();
     });
   });
 
   it("should render a reuse credentials button", async () => {
-    const { getByTestId } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />, { wrapper });
+    const { getByTestId } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
     await waitFor(() => {
       expect(getByTestId("reuse-credentials-button")).toBeInTheDocument();
     });
   });
 
   it("should render a delete model button", async () => {
-    const { getByTestId } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />, { wrapper });
+    const { getByTestId } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
     await waitFor(() => {
       expect(getByTestId("delete-model-button")).toBeInTheDocument();
     });
@@ -204,22 +242,17 @@ describe("ModelInfoView", () => {
 
   it("should render a disabled delete model button if the model is not a DB model", async () => {
     const nonDbModelData = {
-      ...defaultModelData,
+      ...modelData,
       model_info: {
-        ...defaultModelData.model_info,
+        ...modelData.model_info,
         db_model: false,
       },
     };
-
-    mockUseModelsInfo.mockReturnValue({
-      data: {
-        data: [nonDbModelData],
-      },
-      isLoading: false,
-      error: null,
-    });
-
-    const { getByTestId } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />, { wrapper });
+    const NON_DB_ADMIN_PROPS = {
+      ...DEFAULT_ADMIN_PROPS,
+      modelData: nonDbModelData,
+    };
+    const { getByTestId } = render(<ModelInfoView {...NON_DB_ADMIN_PROPS} />);
     await waitFor(() => {
       expect(getByTestId("delete-model-button")).toBeDisabled();
     });
@@ -227,27 +260,18 @@ describe("ModelInfoView", () => {
 
   it("should render a disabled delete model button if the user is not an admin and model is not created by the user", async () => {
     const nonCreatedByUserModelData = {
-      ...defaultModelData,
+      ...modelData,
       model_info: {
-        ...defaultModelData.model_info,
+        ...modelData.model_info,
         created_by: "456",
       },
     };
-
-    mockUseModelsInfo.mockReturnValue({
-      data: {
-        data: [nonCreatedByUserModelData],
-      },
-      isLoading: false,
-      error: null,
-    });
-
     const NON_CREATED_BY_USER_ADMIN_PROPS = {
       ...DEFAULT_ADMIN_PROPS,
+      modelData: nonCreatedByUserModelData,
       userRole: "User",
     };
-
-    const { getByTestId } = render(<ModelInfoView {...NON_CREATED_BY_USER_ADMIN_PROPS} />, { wrapper });
+    const { getByTestId } = render(<ModelInfoView {...NON_CREATED_BY_USER_ADMIN_PROPS} />);
     await waitFor(() => {
       expect(getByTestId("delete-model-button")).toBeDisabled();
     });
@@ -255,22 +279,16 @@ describe("ModelInfoView", () => {
 
   it("should render health check model field for wildcard routes", async () => {
     const wildcardModelData = {
-      ...defaultModelData,
-      litellm_params: {
-        ...defaultModelData.litellm_params,
-        model: "openai/gpt-4*",
-      },
+      ...modelData,
+      litellm_model_name: "openai/gpt-4*",
     };
 
-    mockUseModelsInfo.mockReturnValue({
-      data: {
-        data: [wildcardModelData],
-      },
-      isLoading: false,
-      error: null,
-    });
+    const WILDCARD_ADMIN_PROPS = {
+      ...DEFAULT_ADMIN_PROPS,
+      modelData: wildcardModelData,
+    };
 
-    const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />, { wrapper });
+    const { getByText } = render(<ModelInfoView {...WILDCARD_ADMIN_PROPS} />);
     await waitFor(() => {
       expect(getByText("Model Settings")).toBeInTheDocument();
     });
@@ -280,7 +298,7 @@ describe("ModelInfoView", () => {
   });
 
   it("should not render health check model field for non-wildcard routes", async () => {
-    const { queryByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />, { wrapper });
+    const { queryByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
     await waitFor(() => {
       expect(queryByText("Model Settings")).toBeInTheDocument();
     });
@@ -291,14 +309,14 @@ describe("ModelInfoView", () => {
 
   describe("View Model", () => {
     it("should render the model info view", async () => {
-      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />, { wrapper });
+      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
       await waitFor(() => {
         expect(getByText("Model Settings")).toBeInTheDocument();
       });
     });
 
     it("should render tags in the view model", async () => {
-      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />, { wrapper });
+      const { getByText } = render(<ModelInfoView {...DEFAULT_ADMIN_PROPS} />);
       await waitFor(() => {
         expect(getByText("Tags")).toBeInTheDocument();
       });
diff --git a/ui/litellm-dashboard/src/components/model_info_view.tsx b/ui/litellm-dashboard/src/components/model_info_view.tsx
index a55149124c9..faeff5f5204 100644
--- a/ui/litellm-dashboard/src/components/model_info_view.tsx
+++ b/ui/litellm-dashboard/src/components/model_info_view.tsx
@@ -1,6 +1,4 @@
-import { useModelCostMap } from "@/app/(dashboard)/hooks/models/useModelCostMap";
-import { useModelHub, useModelsInfo } from "@/app/(dashboard)/hooks/models/useModels";
-import { transformModelData } from "@/app/(dashboard)/models-and-endpoints/utils/modelDataTransformer";
+import { useModelsInfo } from "@/app/(dashboard)/hooks/models/useModels";
 import { InfoCircleOutlined } from "@ant-design/icons";
 import { ArrowLeftIcon, KeyIcon, RefreshIcon, TrashIcon } from "@heroicons/react/outline";
 import {
@@ -18,7 +16,7 @@ import {
 } from "@tremor/react";
 import { Button, Form, Input, Modal, Select, Tooltip } from "antd";
 import { CheckIcon, CopyIcon } from "lucide-react";
-import { useEffect, useMemo, useState } from "react";
+import { useEffect, useState } from "react";
 import { copyToClipboard as utilCopyToClipboard } from "../utils/dataUtils";
 import { formItemValidateJSON, truncateString } from "../utils/textUtils";
 import CacheControlSettings from "./add_model/cache_control_settings";
@@ -45,6 +43,7 @@ import { getDisplayModelName } from "./view_model/model_name_display";
 interface ModelInfoViewProps {
   modelId: string;
   onClose: () => void;
+  modelData: any;
   accessToken: string | null;
   userID: string | null;
   userRole: string | null;
@@ -55,6 +54,7 @@ interface ModelInfoViewProps {
 export default function ModelInfoView({
   modelId,
   onClose,
+  modelData,
   accessToken,
   userID,
   userRole,
@@ -75,64 +75,20 @@ export default function ModelInfoView({
   const [isAutoRouterModalOpen, setIsAutoRouterModalOpen] = useState(false);
   const [guardrailsList, setGuardrailsList] = useState<string[]>([]);
   const [tagsList, setTagsList] = useState<Record<string, Tag>>({});
-
-  // Fetch model data using hook
-  const { data: rawModelDataResponse, isLoading: isLoadingModel } = useModelsInfo(1, 50, undefined, modelId);
-  const { data: modelCostMapData } = useModelCostMap();
-  const { data: modelHubData } = useModelHub();
-
-  // Transform the model data
-  const getProviderFromModel = (model: string) => {
-    if (modelCostMapData !== null && modelCostMapData !== undefined) {
-      if (typeof modelCostMapData == "object" && model in modelCostMapData) {
-        return modelCostMapData[model]["litellm_provider"];
-      }
-    }
-    return "openai";
-  };
-
-  const transformedModelData = useMemo(() => {
-    if (!rawModelDataResponse?.data || rawModelDataResponse.data.length === 0) {
-      return null;
-    }
-    const transformed = transformModelData(rawModelDataResponse, getProviderFromModel);
-    return transformed.data[0] || null;
-  }, [rawModelDataResponse, modelCostMapData]);
-
-  // Keep modelData variable name for backwards compatibility
-  const modelData = transformedModelData;
-
   const canEditModel =
     (userRole === "Admin" || modelData?.model_info?.created_by === userID) && modelData?.model_info?.db_model;
   const isAdmin = userRole === "Admin";
   const isAutoRouter = modelData?.litellm_params?.auto_router_config != null;
 
+  const { data: modelsInfoData } = useModelsInfo();
+  console.log("modelsInfoData, ", modelsInfoData);
   const usingExistingCredential =
     modelData?.litellm_params?.litellm_credential_name != null &&
     modelData?.litellm_params?.litellm_credential_name != undefined;
+  console.log("usingExistingCredential, ", usingExistingCredential);
+  console.log("modelData.litellm_params.litellm_credential_name, ", modelData?.litellm_params?.litellm_credential_name);
 
-  // Initialize localModelData from modelData when available
-  useEffect(() => {
-    if (modelData && !localModelData) {
-      let processedModelData = modelData;
-      if (!processedModelData.litellm_model_name) {
-        processedModelData = {
-          ...processedModelData,
-          litellm_model_name:
-            processedModelData?.litellm_params?.litellm_model_name ??
-            processedModelData?.litellm_params?.model ??
-            processedModelData?.model_info?.key ??
-            null,
-        };
-      }
-      setLocalModelData(processedModelData);
-
-      // Check if cache control is enabled
-      if (processedModelData?.litellm_params?.cache_control_injection_points) {
-        setShowCacheControl(true);
-      }
-    }
-  }, [modelData, localModelData]);
+  console.log("tagsList, ", modelData.litellm_params?.tags);
 
   useEffect(() => {
     const getExistingCredential = async () => {
@@ -150,8 +106,6 @@ export default function ModelInfoView({
 
     const getModelInfo = async () => {
       if (!accessToken) return;
-      // Only fetch if we don't have modelData yet
-      if (modelData) return;
       let modelInfoResponse = await modelInfoV1Call(accessToken, modelId);
       console.log("modelInfoResponse, ", modelInfoResponse);
       let specificModelData = modelInfoResponse.data[0];
@@ -316,19 +270,6 @@ export default function ModelInfoView({
     }
   };
 
-  // Show loading state
-  if (isLoadingModel) {
-    return (
-      <div className="p-4">
-        <TremorButton icon={ArrowLeftIcon} variant="light" onClick={onClose} className="mb-4">
-          Back to Models
-        </TremorButton>
-        <Text>Loading...</Text>
-      </div>
-    );
-  }
-
-  // Show not found if model is not found
   if (!modelData) {
     return (
       <div className="p-4">
@@ -412,7 +353,6 @@ export default function ModelInfoView({
     }
   };
   const isWildcardModel = modelData.litellm_model_name.includes("*");
-  console.log("isWildcardModel, ", isWildcardModel);
 
   return (
     <div className="p-4">
@@ -429,10 +369,11 @@ export default function ModelInfoView({
               size="small"
               icon={copiedStates["model-id"] ? <CheckIcon size={12} /> : <CopyIcon size={12} />}
               onClick={() => copyToClipboard(modelData.model_info.id, "model-id")}
-              className={`left-2 z-10 transition-all duration-200 ${copiedStates["model-id"]
-                ? "text-green-600 bg-green-50 border-green-200"
-                : "text-gray-500 hover:text-gray-700 hover:bg-gray-100"
-                }`}
+              className={`left-2 z-10 transition-all duration-200 ${
+                copiedStates["model-id"]
+                  ? "text-green-600 bg-green-50 border-green-200"
+                  : "text-gray-500 hover:text-gray-700 hover:bg-gray-100"
+              }`}
             />
           </div>
         </div>
@@ -543,10 +484,10 @@ export default function ModelInfoView({
                 Created At{" "}
                 {modelData.model_info.created_at
                   ? new Date(modelData.model_info.created_at).toLocaleDateString("en-US", {
-                    month: "short",
-                    day: "numeric",
-                    year: "numeric",
-                  })
+                      month: "short",
+                      day: "numeric",
+                      year: "numeric",
+                    })
                   : "Not Set"}
               </div>
               <div className="flex items-center gap-x-2">
@@ -950,19 +891,27 @@ export default function ModelInfoView({
                                 optionFilterProp="children"
                                 allowClear
                                 options={(() => {
-                                  const wildcardProvider = modelData.litellm_model_name.split("/")[0];
-                                  return modelHubData?.data
+                                  const seen = new Set();
+                                  return modelsInfoData?.data
                                     ?.filter((model: any) => {
-                                      // Filter by provider to match the wildcard provider
+                                      const modelProvider = model.provider;
+                                      const wildcardProvider = modelData.litellm_model_name.split("/")[0];
                                       return (
-                                        model.providers?.includes(wildcardProvider) &&
-                                        model.model_group !== modelData.litellm_model_name
+                                        modelProvider === wildcardProvider &&
+                                        model.model_name !== modelData.litellm_model_name
                                       );
                                     })
+                                    .filter((model: any) => {
+                                      if (seen.has(model.model_name)) {
+                                        return false;
+                                      }
+                                      seen.add(model.model_name);
+                                      return true;
+                                    })
                                     .map((model: any) => ({
-                                      value: model.model_group,
-                                      label: model.model_group,
-                                    })) || [];
+                                      value: model.model_name,
+                                      label: model.model_name,
+                                    }));
                                 })()}
                               />
                             </Form.Item>
diff --git a/ui/litellm-dashboard/src/components/molecules/filter.test.tsx b/ui/litellm-dashboard/src/components/molecules/filter.test.tsx
deleted file mode 100644
index bd06d110e95..00000000000
--- a/ui/litellm-dashboard/src/components/molecules/filter.test.tsx
+++ /dev/null
@@ -1,127 +0,0 @@
-import { screen, waitFor } from "@testing-library/react";
-import userEvent from "@testing-library/user-event";
-import { beforeEach, describe, expect, it, vi } from "vitest";
-import { renderWithProviders } from "../../../tests/test-utils";
-import FilterComponent, { FilterOption } from "./filter";
-
-describe("FilterComponent", () => {
-  const mockOnApplyFilters = vi.fn();
-  const mockOnResetFilters = vi.fn();
-
-  const defaultOptions: FilterOption[] = [
-    {
-      name: "teamId",
-      label: "Team ID",
-      options: [
-        { label: "Team 1", value: "team1" },
-        { label: "Team 2", value: "team2" },
-      ],
-    },
-    {
-      name: "status",
-      label: "Status",
-      options: [
-        { label: "Active", value: "active" },
-        { label: "Inactive", value: "inactive" },
-      ],
-    },
-    {
-      name: "userId",
-      label: "User ID",
-    },
-  ];
-
-  beforeEach(() => {
-    vi.clearAllMocks();
-  });
-
-  it("should render", () => {
-    renderWithProviders(
-      <FilterComponent
-        options={defaultOptions}
-        onApplyFilters={mockOnApplyFilters}
-        onResetFilters={mockOnResetFilters}
-      />,
-    );
-    expect(screen.getByRole("button", { name: "Filters" })).toBeInTheDocument();
-  });
-
-  it("should display custom button label", () => {
-    renderWithProviders(
-      <FilterComponent
-        options={defaultOptions}
-        onApplyFilters={mockOnApplyFilters}
-        onResetFilters={mockOnResetFilters}
-        buttonLabel="Custom Filters"
-      />,
-    );
-    expect(screen.getByRole("button", { name: "Custom Filters" })).toBeInTheDocument();
-  });
-
-  it("should call onResetFilters when reset button is clicked", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(
-      <FilterComponent
-        options={defaultOptions}
-        onApplyFilters={mockOnApplyFilters}
-        onResetFilters={mockOnResetFilters}
-        initialValues={{ teamId: "team1", status: "active" }}
-      />,
-    );
-
-    const resetButton = screen.getByRole("button", { name: "Reset Filters" });
-    await user.click(resetButton);
-
-    await waitFor(() => {
-      expect(mockOnResetFilters).toHaveBeenCalledTimes(1);
-    });
-  });
-
-  it("should render filters in correct order", async () => {
-    const user = userEvent.setup();
-    const options: FilterOption[] = [
-      { name: "model", label: "Model" },
-      { name: "teamId", label: "Team ID" },
-      { name: "status", label: "Status" },
-      { name: "userId", label: "User ID" },
-    ];
-
-    renderWithProviders(
-      <FilterComponent
-        options={options}
-        onApplyFilters={mockOnApplyFilters}
-        onResetFilters={mockOnResetFilters}
-      />,
-    );
-
-    const filterButton = screen.getByRole("button", { name: "Filters" });
-    await user.click(filterButton);
-
-    await waitFor(() => {
-      const labels = screen.getAllByText(/^(Team ID|Status|User ID|Model)$/);
-      expect(labels[0]).toHaveTextContent("Team ID");
-      expect(labels[1]).toHaveTextContent("Status");
-    });
-  });
-
-  it("should handle input filter changes", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(
-      <FilterComponent
-        options={defaultOptions}
-        onApplyFilters={mockOnApplyFilters}
-        onResetFilters={mockOnResetFilters}
-      />,
-    );
-
-    const filterButton = screen.getByRole("button", { name: "Filters" });
-    await user.click(filterButton);
-
-    const userIdInput = screen.getByPlaceholderText("Enter User ID...");
-    await user.type(userIdInput, "user123");
-
-    await waitFor(() => {
-      expect(mockOnApplyFilters).toHaveBeenCalledWith({ userId: "user123" });
-    });
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/molecules/filter.tsx b/ui/litellm-dashboard/src/components/molecules/filter.tsx
index 6c46392410d..d7c40ae0399 100644
--- a/ui/litellm-dashboard/src/components/molecules/filter.tsx
+++ b/ui/litellm-dashboard/src/components/molecules/filter.tsx
@@ -130,7 +130,6 @@ const FilterComponent: React.FC<FilterComponentProps> = ({
     "User ID",
     "End User",
     "Error Code",
-    "Error Message",
     "Key Hash",
     "Model",
   ];
diff --git a/ui/litellm-dashboard/src/components/molecules/models/columns.tsx b/ui/litellm-dashboard/src/components/molecules/models/columns.tsx
index 1fc08e502ac..43813852f8f 100644
--- a/ui/litellm-dashboard/src/components/molecules/models/columns.tsx
+++ b/ui/litellm-dashboard/src/components/molecules/models/columns.tsx
@@ -17,300 +17,297 @@ export const columns = (
   expandedRows: Set<string>,
   setExpandedRows: (expandedRows: Set<string>) => void,
 ): ColumnDef<ModelData>[] => [
-    {
-      header: () => <span className="text-sm font-semibold">Model ID</span>,
-      accessorKey: "model_info.id",
-      enableSorting: false,
-      cell: ({ row }) => {
-        const model = row.original;
-        return (
-          <Tooltip title={model.model_info.id}>
-            <div
-              className="font-mono text-blue-500 bg-blue-50 hover:bg-blue-100 text-xs font-normal px-2 py-0.5 text-left w-full truncate whitespace-nowrap cursor-pointer max-w-[15ch]"
-              onClick={() => setSelectedModelId(model.model_info.id)}
-            >
-              {model.model_info.id}
-            </div>
-          </Tooltip>
-        );
-      },
+  {
+    header: () => <span className="text-sm font-semibold">Model ID</span>,
+    accessorKey: "model_info.id",
+    cell: ({ row }) => {
+      const model = row.original;
+      return (
+        <Tooltip title={model.model_info.id}>
+          <div
+            className="font-mono text-blue-500 bg-blue-50 hover:bg-blue-100 text-xs font-normal px-2 py-0.5 text-left w-full truncate whitespace-nowrap cursor-pointer max-w-[15ch]"
+            onClick={() => setSelectedModelId(model.model_info.id)}
+          >
+            {model.model_info.id}
+          </div>
+        </Tooltip>
+      );
     },
-    {
-      header: () => <span className="text-sm font-semibold">Model Information</span>,
-      accessorKey: "model_name",
-      size: 250, // Fixed column width
-      cell: ({ row }) => {
-        const model = row.original;
-        const displayName = getDisplayModelName(row.original) || "-";
-        const tooltipContent = (
+  },
+  {
+    header: () => <span className="text-sm font-semibold">Model Information</span>,
+    accessorKey: "model_name",
+    size: 250, // Fixed column width
+    cell: ({ row }) => {
+      const model = row.original;
+      const displayName = getDisplayModelName(row.original) || "-";
+      const tooltipContent = (
+        <div>
           <div>
-            <div>
-              <strong>Provider:</strong> {model.provider || "-"}
-            </div>
-            <div>
-              <strong>Public Model Name:</strong> {displayName}
-            </div>
-            <div>
-              <strong>LiteLLM Model Name:</strong> {model.litellm_model_name || "-"}
-            </div>
+            <strong>Provider:</strong> {model.provider || "-"}
           </div>
-        );
+          <div>
+            <strong>Public Model Name:</strong> {displayName}
+          </div>
+          <div>
+            <strong>LiteLLM Model Name:</strong> {model.litellm_model_name || "-"}
+          </div>
+        </div>
+      );
 
-        return (
-          <Tooltip title={tooltipContent}>
-            <div className="flex items-start space-x-2 min-w-0 w-full max-w-[250px]">
-              {/* Provider Icon */}
-              <div className="flex-shrink-0 mt-0.5">
-                {model.provider ? (
-                  <ProviderLogo provider={model.provider} />
-                ) : (
-                  <div className="w-4 h-4 rounded-full bg-gray-200 flex items-center justify-center text-xs">-</div>
-                )}
-              </div>
+      return (
+        <Tooltip title={tooltipContent}>
+          <div className="flex items-start space-x-2 min-w-0 w-full max-w-[250px]">
+            {/* Provider Icon */}
+            <div className="flex-shrink-0 mt-0.5">
+              {model.provider ? (
+                <ProviderLogo provider={model.provider} />
+              ) : (
+                <div className="w-4 h-4 rounded-full bg-gray-200 flex items-center justify-center text-xs">-</div>
+              )}
+            </div>
 
-              {/* Model Names Container */}
-              <div className="flex flex-col min-w-0 flex-1">
-                {/* Public Model Name */}
-                <div className="text-xs font-medium text-gray-900 truncate max-w-[210px]">{displayName}</div>
-                {/* LiteLLM Model Name */}
-                <div className="text-xs text-gray-500 truncate mt-0.5 max-w-[210px]">
-                  {model.litellm_model_name || "-"}
-                </div>
+            {/* Model Names Container */}
+            <div className="flex flex-col min-w-0 flex-1">
+              {/* Public Model Name */}
+              <div className="text-xs font-medium text-gray-900 truncate max-w-[210px]">{displayName}</div>
+              {/* LiteLLM Model Name */}
+              <div className="text-xs text-gray-500 truncate mt-0.5 max-w-[210px]">
+                {model.litellm_model_name || "-"}
               </div>
             </div>
-          </Tooltip>
-        );
-      },
+          </div>
+        </Tooltip>
+      );
     },
-    {
-      header: () => <span className="text-sm font-semibold">Credentials</span>,
-      accessorKey: "litellm_credential_name",
-      enableSorting: false,
-      size: 180, // Fixed column width
-      cell: ({ row }) => {
-        const model = row.original;
-        const credentialName = model.litellm_params?.litellm_credential_name;
+  },
+  {
+    header: () => <span className="text-sm font-semibold">Credentials</span>,
+    accessorKey: "litellm_credential_name",
+    size: 180, // Fixed column width
+    cell: ({ row }) => {
+      const model = row.original;
+      const credentialName = model.litellm_params?.litellm_credential_name;
 
-        return credentialName ? (
-          <Tooltip title={`Credential: ${credentialName}`}>
-            <div className="flex items-center space-x-2 max-w-[180px]">
-              <KeyIcon className="w-4 h-4 text-blue-500 flex-shrink-0" />
-              <span className="text-xs truncate" title={credentialName}>
-                {credentialName}
-              </span>
-            </div>
-          </Tooltip>
-        ) : (
+      return credentialName ? (
+        <Tooltip title={`Credential: ${credentialName}`}>
           <div className="flex items-center space-x-2 max-w-[180px]">
-            <KeyIcon className="w-4 h-4 text-gray-300 flex-shrink-0" />
-            <span className="text-xs text-gray-400">No credentials</span>
+            <KeyIcon className="w-4 h-4 text-blue-500 flex-shrink-0" />
+            <span className="text-xs truncate" title={credentialName}>
+              {credentialName}
+            </span>
           </div>
-        );
-      },
+        </Tooltip>
+      ) : (
+        <div className="flex items-center space-x-2 max-w-[180px]">
+          <KeyIcon className="w-4 h-4 text-gray-300 flex-shrink-0" />
+          <span className="text-xs text-gray-400">No credentials</span>
+        </div>
+      );
     },
-    {
-      header: () => <span className="text-sm font-semibold">Created By</span>,
-      accessorKey: "model_info.created_by",
-      sortingFn: "datetime",
-      size: 160, // Fixed column width
-      cell: ({ row }) => {
-        const model = row.original;
-        const isConfigModel = !model.model_info?.db_model;
-        const createdBy = model.model_info.created_by;
-        const createdAt = model.model_info.created_at ? new Date(model.model_info.created_at).toLocaleDateString() : null;
+  },
+  {
+    header: () => <span className="text-sm font-semibold">Created By</span>,
+    accessorKey: "model_info.created_by",
+    sortingFn: "datetime",
+    size: 160, // Fixed column width
+    cell: ({ row }) => {
+      const model = row.original;
+      const isConfigModel = !model.model_info?.db_model;
+      const createdBy = model.model_info.created_by;
+      const createdAt = model.model_info.created_at ? new Date(model.model_info.created_at).toLocaleDateString() : null;
 
-        return (
-          <div className="flex flex-col min-w-0 max-w-[160px]">
-            {/* Created By - Primary */}
-            <div
-              className="text-xs font-medium text-gray-900 truncate"
-              title={isConfigModel ? "Defined in config" : createdBy || "Unknown"}
-            >
-              {isConfigModel ? "Defined in config" : createdBy || "Unknown"}
-            </div>
-            {/* Created At - Secondary */}
-            <div
-              className="text-xs text-gray-500 truncate mt-0.5"
-              title={isConfigModel ? "Config file" : createdAt || "Unknown date"}
-            >
-              {isConfigModel ? "-" : createdAt || "Unknown date"}
-            </div>
+      return (
+        <div className="flex flex-col min-w-0 max-w-[160px]">
+          {/* Created By - Primary */}
+          <div
+            className="text-xs font-medium text-gray-900 truncate"
+            title={isConfigModel ? "Defined in config" : createdBy || "Unknown"}
+          >
+            {isConfigModel ? "Defined in config" : createdBy || "Unknown"}
           </div>
-        );
-      },
+          {/* Created At - Secondary */}
+          <div
+            className="text-xs text-gray-500 truncate mt-0.5"
+            title={isConfigModel ? "Config file" : createdAt || "Unknown date"}
+          >
+            {isConfigModel ? "-" : createdAt || "Unknown date"}
+          </div>
+        </div>
+      );
     },
-    {
-      header: () => <span className="text-sm font-semibold">Updated At</span>,
-      accessorKey: "model_info.updated_at",
-      sortingFn: "datetime",
-      cell: ({ row }) => {
-        const model = row.original;
-        return (
-          <span className="text-xs">
-            {model.model_info.updated_at ? new Date(model.model_info.updated_at).toLocaleDateString() : "-"}
-          </span>
-        );
-      },
+  },
+  {
+    header: () => <span className="text-sm font-semibold">Updated At</span>,
+    accessorKey: "model_info.updated_at",
+    sortingFn: "datetime",
+    cell: ({ row }) => {
+      const model = row.original;
+      return (
+        <span className="text-xs">
+          {model.model_info.updated_at ? new Date(model.model_info.updated_at).toLocaleDateString() : "-"}
+        </span>
+      );
     },
-    {
-      header: () => <span className="text-sm font-semibold">Costs</span>,
-      accessorKey: "input_cost",
-      size: 120, // Fixed column width
-      cell: ({ row }) => {
-        const model = row.original;
-        const inputCost = model.input_cost;
-        const outputCost = model.output_cost;
-
-        // If both costs are missing or undefined, show "-"
-        if (!inputCost && !outputCost) {
-          return (
-            <div className="max-w-[120px]">
-              <span className="text-xs text-gray-400">-</span>
-            </div>
-          );
-        }
+  },
+  {
+    header: () => <span className="text-sm font-semibold">Costs</span>,
+    accessorKey: "input_cost",
+    size: 120, // Fixed column width
+    cell: ({ row }) => {
+      const model = row.original;
+      const inputCost = model.input_cost;
+      const outputCost = model.output_cost;
 
+      // If both costs are missing or undefined, show "-"
+      if (!inputCost && !outputCost) {
         return (
-          <Tooltip title="Cost per 1M tokens">
-            <div className="flex flex-col min-w-0 max-w-[120px]">
-              {/* Input Cost - Primary */}
-              {inputCost && <div className="text-xs font-medium text-gray-900 truncate">In: ${inputCost}</div>}
-              {/* Output Cost - Secondary */}
-              {outputCost && <div className="text-xs text-gray-500 truncate mt-0.5">Out: ${outputCost}</div>}
-            </div>
-          </Tooltip>
-        );
-      },
-    },
-    {
-      header: () => <span className="text-sm font-semibold">Team ID</span>,
-      accessorKey: "model_info.team_id",
-      enableSorting: false,
-      cell: ({ row }) => {
-        const model = row.original;
-        return model.model_info.team_id ? (
-          <div className="overflow-hidden">
-            <Tooltip title={model.model_info.team_id}>
-              <Button
-                size="xs"
-                variant="light"
-                className="font-mono text-blue-500 bg-blue-50 hover:bg-blue-100 text-xs font-normal px-2 py-0.5 text-left overflow-hidden truncate max-w-[200px]"
-                onClick={() => setSelectedTeamId(model.model_info.team_id)}
-              >
-                {model.model_info.team_id.slice(0, 7)}...
-              </Button>
-            </Tooltip>
+          <div className="max-w-[120px]">
+            <span className="text-xs text-gray-400">-</span>
           </div>
-        ) : (
-          "-"
         );
-      },
-    },
-    {
-      header: () => <span className="text-sm font-semibold">Model Access Group</span>,
-      accessorKey: "model_info.model_access_group",
-      enableSorting: false,
-      cell: ({ row }) => {
-        const model = row.original;
-        const accessGroups = model.model_info.access_groups;
+      }
 
-        if (!accessGroups || accessGroups.length === 0) {
-          return "-";
-        }
+      return (
+        <Tooltip title="Cost per 1M tokens">
+          <div className="flex flex-col min-w-0 max-w-[120px]">
+            {/* Input Cost - Primary */}
+            {inputCost && <div className="text-xs font-medium text-gray-900 truncate">In: ${inputCost}</div>}
+            {/* Output Cost - Secondary */}
+            {outputCost && <div className="text-xs text-gray-500 truncate mt-0.5">Out: ${outputCost}</div>}
+          </div>
+        </Tooltip>
+      );
+    },
+  },
+  {
+    header: () => <span className="text-sm font-semibold">Team ID</span>,
+    accessorKey: "model_info.team_id",
+    cell: ({ row }) => {
+      const model = row.original;
+      return model.model_info.team_id ? (
+        <div className="overflow-hidden">
+          <Tooltip title={model.model_info.team_id}>
+            <Button
+              size="xs"
+              variant="light"
+              className="font-mono text-blue-500 bg-blue-50 hover:bg-blue-100 text-xs font-normal px-2 py-0.5 text-left overflow-hidden truncate max-w-[200px]"
+              onClick={() => setSelectedTeamId(model.model_info.team_id)}
+            >
+              {model.model_info.team_id.slice(0, 7)}...
+            </Button>
+          </Tooltip>
+        </div>
+      ) : (
+        "-"
+      );
+    },
+  },
+  {
+    header: () => <span className="text-sm font-semibold">Model Access Group</span>,
+    accessorKey: "model_info.model_access_group",
+    enableSorting: false,
+    cell: ({ row }) => {
+      const model = row.original;
+      const accessGroups = model.model_info.access_groups;
 
-        const modelId = model.model_info.id;
-        const isExpanded = expandedRows.has(modelId);
-        const shouldShowExpandButton = accessGroups.length > 1;
+      if (!accessGroups || accessGroups.length === 0) {
+        return "-";
+      }
 
-        const toggleExpanded = () => {
-          const newExpanded = new Set(expandedRows);
-          if (isExpanded) {
-            newExpanded.delete(modelId);
-          } else {
-            newExpanded.add(modelId);
-          }
-          setExpandedRows(newExpanded);
-        };
+      const modelId = model.model_info.id;
+      const isExpanded = expandedRows.has(modelId);
+      const shouldShowExpandButton = accessGroups.length > 1;
 
-        return (
-          <div className="flex items-center gap-1 overflow-hidden">
-            <Badge size="xs" color="blue" className="text-xs px-1.5 py-0.5 h-5 leading-tight flex-shrink-0">
-              {accessGroups[0]}
-            </Badge>
+      const toggleExpanded = () => {
+        const newExpanded = new Set(expandedRows);
+        if (isExpanded) {
+          newExpanded.delete(modelId);
+        } else {
+          newExpanded.add(modelId);
+        }
+        setExpandedRows(newExpanded);
+      };
 
-            {(isExpanded || (!shouldShowExpandButton && accessGroups.length === 2)) &&
-              accessGroups.slice(1).map((group: string, index: number) => (
-                <Badge
-                  key={index + 1}
-                  size="xs"
-                  color="blue"
-                  className="text-xs px-1.5 py-0.5 h-5 leading-tight flex-shrink-0"
-                >
-                  {group}
-                </Badge>
-              ))}
+      return (
+        <div className="flex items-center gap-1 overflow-hidden">
+          <Badge size="xs" color="blue" className="text-xs px-1.5 py-0.5 h-5 leading-tight flex-shrink-0">
+            {accessGroups[0]}
+          </Badge>
 
-            {shouldShowExpandButton && (
-              <button
-                onClick={(e) => {
-                  e.stopPropagation();
-                  toggleExpanded();
-                }}
-                className="text-xs text-blue-600 hover:text-blue-800 px-1 py-0.5 rounded hover:bg-blue-50 h-5 leading-tight flex-shrink-0 whitespace-nowrap"
+          {(isExpanded || (!shouldShowExpandButton && accessGroups.length === 2)) &&
+            accessGroups.slice(1).map((group: string, index: number) => (
+              <Badge
+                key={index + 1}
+                size="xs"
+                color="blue"
+                className="text-xs px-1.5 py-0.5 h-5 leading-tight flex-shrink-0"
               >
-                {isExpanded ? "−" : `+${accessGroups.length - 1}`}
-              </button>
-            )}
-          </div>
-        );
-      },
+                {group}
+              </Badge>
+            ))}
+
+          {shouldShowExpandButton && (
+            <button
+              onClick={(e) => {
+                e.stopPropagation();
+                toggleExpanded();
+              }}
+              className="text-xs text-blue-600 hover:text-blue-800 px-1 py-0.5 rounded hover:bg-blue-50 h-5 leading-tight flex-shrink-0 whitespace-nowrap"
+            >
+              {isExpanded ? "−" : `+${accessGroups.length - 1}`}
+            </button>
+          )}
+        </div>
+      );
     },
-    {
-      header: () => <span className="text-sm font-semibold">Status</span>,
-      accessorKey: "model_info.db_model",
-      cell: ({ row }) => {
-        const model = row.original;
-        return (
-          <div
-            className={`
+  },
+  {
+    header: () => <span className="text-sm font-semibold">Status</span>,
+    accessorKey: "model_info.db_model",
+    cell: ({ row }) => {
+      const model = row.original;
+      return (
+        <div
+          className={`
           inline-flex items-center px-2 py-0.5 rounded-full text-xs font-medium
           ${model.model_info.db_model ? "bg-blue-50 text-blue-600" : "bg-gray-100 text-gray-600"}
         `}
-          >
-            {model.model_info.db_model ? "DB Model" : "Config Model"}
-          </div>
-        );
-      },
+        >
+          {model.model_info.db_model ? "DB Model" : "Config Model"}
+        </div>
+      );
     },
-    {
-      id: "actions",
-      header: () => <span className="text-sm font-semibold">Actions</span>,
-      cell: ({ row }) => {
-        const model = row.original;
-        const canEditModel = userRole === "Admin" || model.model_info?.created_by === userID;
-        const isConfigModel = !model.model_info?.db_model;
-        return (
-          <div className="flex items-center justify-end gap-2 pr-4">
-            {isConfigModel ? (
-              <Tooltip title="Config model cannot be deleted on the dashboard. Please delete it from the config file.">
-                <Icon icon={TrashIcon} size="sm" className="opacity-50 cursor-not-allowed" />
-              </Tooltip>
-            ) : (
-              <Tooltip title="Delete model">
-                <Icon
-                  icon={TrashIcon}
-                  size="sm"
-                  onClick={() => {
-                    if (canEditModel) {
-                      setSelectedModelId(model.model_info.id);
-                    }
-                  }}
-                  className={!canEditModel ? "opacity-50 cursor-not-allowed" : "cursor-pointer hover:text-red-600"}
-                />
-              </Tooltip>
-            )}
-          </div>
-        );
-      },
+  },
+  {
+    id: "actions",
+    header: () => <span className="text-sm font-semibold">Actions</span>,
+    cell: ({ row }) => {
+      const model = row.original;
+      const canEditModel = userRole === "Admin" || model.model_info?.created_by === userID;
+      const isConfigModel = !model.model_info?.db_model;
+      return (
+        <div className="flex items-center justify-end gap-2 pr-4">
+          {isConfigModel ? (
+            <Tooltip title="Config model cannot be deleted on the dashboard. Please delete it from the config file.">
+              <Icon icon={TrashIcon} size="sm" className="opacity-50 cursor-not-allowed" />
+            </Tooltip>
+          ) : (
+            <Tooltip title="Delete model">
+              <Icon
+                icon={TrashIcon}
+                size="sm"
+                onClick={() => {
+                  if (canEditModel) {
+                    setSelectedModelId(model.model_info.id);
+                  }
+                }}
+                className={!canEditModel ? "opacity-50 cursor-not-allowed" : "cursor-pointer hover:text-red-600"}
+              />
+            </Tooltip>
+          )}
+        </div>
+      );
     },
-  ];
+  },
+];
diff --git a/ui/litellm-dashboard/src/components/navbar.test.tsx b/ui/litellm-dashboard/src/components/navbar.test.tsx
index 9fa32cf9cb0..5d3c7254ff9 100644
--- a/ui/litellm-dashboard/src/components/navbar.test.tsx
+++ b/ui/litellm-dashboard/src/components/navbar.test.tsx
@@ -16,7 +16,6 @@ vi.mock("@/utils/proxyUtils", () => ({
 let mockUseThemeImpl = () => ({ logoUrl: null as string | null });
 let mockUseHealthReadinessImpl = () => ({ data: null as any });
 let mockGetLocalStorageItemImpl = () => null as string | null;
-let mockUseDisableShowPromptsImpl = () => false;
 
 vi.mock("@/contexts/ThemeContext", () => ({
   useTheme: () => mockUseThemeImpl(),
@@ -26,12 +25,7 @@ vi.mock("@/app/(dashboard)/hooks/healthReadiness/useHealthReadiness", () => ({
   useHealthReadiness: () => mockUseHealthReadinessImpl(),
 }));
 
-vi.mock("@/app/(dashboard)/hooks/useDisableShowPrompts", () => ({
-  useDisableShowPrompts: () => mockUseDisableShowPromptsImpl(),
-}));
-
 vi.mock("@/utils/localStorageUtils", () => ({
-  LOCAL_STORAGE_EVENT: "local-storage-change",
   getLocalStorageItem: () => mockGetLocalStorageItemImpl(),
   setLocalStorageItem: vi.fn(),
   removeLocalStorageItem: vi.fn(),
@@ -58,8 +52,6 @@ describe("Navbar", () => {
     setProxySettings: vi.fn(),
     accessToken: "test-token",
     isPublicPage: false,
-    isDarkMode: false,
-    toggleDarkMode: vi.fn(),
   };
 
   it("should render without crashing", () => {
@@ -206,11 +198,4 @@ describe("Navbar", () => {
     expect(cookieUtils.clearTokenCookies).toHaveBeenCalled();
     expect(window.location.href).toBe("");
   });
-
-  it("should not render dark mode toggle slider", () => {
-    renderWithProviders(<Navbar {...defaultProps} />);
-
-    // DO NOT RENDER THIS UNTIL ALL COMPONENTS ARE CONFIRMED TO SUPPORT DARK MODE STYLES. IT IS AN ISSUE IF THIS TEST FAILS.
-    expect(screen.queryByTestId("dark-mode-toggle")).not.toBeInTheDocument();
-  });
 });
diff --git a/ui/litellm-dashboard/src/components/navbar.tsx b/ui/litellm-dashboard/src/components/navbar.tsx
index 8e4fda0ba52..6dac073b3a6 100644
--- a/ui/litellm-dashboard/src/components/navbar.tsx
+++ b/ui/litellm-dashboard/src/components/navbar.tsx
@@ -1,5 +1,4 @@
 import { useHealthReadiness } from "@/app/(dashboard)/hooks/healthReadiness/useHealthReadiness";
-import { useDisableShowPrompts } from "@/app/(dashboard)/hooks/useDisableShowPrompts";
 import { getProxyBaseUrl } from "@/components/networking";
 import { useTheme } from "@/contexts/ThemeContext";
 import { clearTokenCookies } from "@/utils/cookieUtils";
@@ -17,10 +16,8 @@ import {
   MailOutlined,
   MenuFoldOutlined,
   MenuUnfoldOutlined,
-  MoonOutlined,
   SafetyOutlined,
   SlackOutlined,
-  SunOutlined,
   UserOutlined,
 } from "@ant-design/icons";
 import type { MenuProps } from "antd";
@@ -39,8 +36,6 @@ interface NavbarProps {
   isPublicPage: boolean;
   sidebarCollapsed?: boolean;
   onToggleSidebar?: () => void;
-  isDarkMode: boolean;
-  toggleDarkMode: () => void;
 }
 
 const Navbar: React.FC<NavbarProps> = ({
@@ -54,13 +49,11 @@ const Navbar: React.FC<NavbarProps> = ({
   isPublicPage = false,
   sidebarCollapsed = false,
   onToggleSidebar,
-  isDarkMode,
-  toggleDarkMode
 }) => {
   const baseUrl = getProxyBaseUrl();
+  console.log("baseUrl", baseUrl);
   const [logoutUrl, setLogoutUrl] = useState("");
   const [disableShowNewBadge, setDisableShowNewBadge] = useState(false);
-  const disableShowPrompts = useDisableShowPrompts();
   const { logoUrl } = useTheme();
   const { data: healthData } = useHealthReadiness();
   const version = healthData?.litellm_version;
@@ -159,27 +152,6 @@ const Navbar: React.FC<NavbarProps> = ({
                 aria-label="Toggle hide new feature indicators"
               />
             </div>
-            <div
-              className="flex items-center text-sm pt-2 mt-2 border-t border-gray-100"
-              onClick={(e) => e.stopPropagation()}
-            >
-              <span className="text-gray-500 text-xs">Hide All Prompts</span>
-              <Switch
-                className="ml-auto"
-                size="small"
-                checked={disableShowPrompts}
-                onChange={(checked) => {
-                  if (checked) {
-                    setLocalStorageItem("disableShowPrompts", "true");
-                    emitLocalStorageChange("disableShowPrompts");
-                  } else {
-                    removeLocalStorageItem("disableShowPrompts");
-                    emitLocalStorageChange("disableShowPrompts");
-                  }
-                }}
-                aria-label="Toggle hide all prompts"
-              />
-            </div>
           </div>
         </div>
       ),
@@ -219,7 +191,7 @@ const Navbar: React.FC<NavbarProps> = ({
                     style={{ animationDuration: "2s" }}
                     title="Happy Holidays!"
                   >
-                    ❄️
+                    🎄
                   </span>
                 </div>
               </Link>
@@ -255,15 +227,6 @@ const Navbar: React.FC<NavbarProps> = ({
             >
               Star us on GitHub
             </Button>
-            {/* Dark mode is currently a work in progress. To test, you can change 'false' to 'true' below.
-            Do not set this to true by default until all components are confirmed to support dark mode styles. */}
-            {false && <Switch
-              data-testid="dark-mode-toggle"
-              checked={isDarkMode}
-              onChange={toggleDarkMode}
-              checkedChildren={<MoonOutlined />}
-              unCheckedChildren={<SunOutlined />}
-            />}
             <a
               href="https://docs.litellm.ai/docs/"
               target="_blank"
diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx
index 5cc5216e132..ffc0f80911d 100644
--- a/ui/litellm-dashboard/src/components/networking.tsx
+++ b/ui/litellm-dashboard/src/components/networking.tsx
@@ -2007,32 +2007,17 @@ export const regenerateKeyCall = async (accessToken: string, keyToRegenerate: st
 let ModelListerrorShown = false;
 let errorTimer: NodeJS.Timeout | null = null;
 
-export const modelInfoCall = async (accessToken: string, userID: string, userRole: string, page: number = 1, size: number = 50, search?: string, modelId?: string, teamId?: string, sortBy?: string, sortOrder?: string) => {
+export const modelInfoCall = async (accessToken: string, userID: string, userRole: string, page: number = 1, size: number = 50) => {
   /**
    * Get all models on proxy
    */
   try {
-    console.log("modelInfoCall:", accessToken, userID, userRole, page, size, search, modelId, teamId, sortBy, sortOrder);
+    console.log("modelInfoCall:", accessToken, userID, userRole, page, size);
     let url = proxyBaseUrl ? `${proxyBaseUrl}/v2/model/info` : `/v2/model/info`;
     const params = new URLSearchParams();
     params.append("include_team_models", "true");
     params.append("page", page.toString());
     params.append("size", size.toString());
-    if (search && search.trim()) {
-      params.append("search", search.trim());
-    }
-    if (modelId && modelId.trim()) {
-      params.append("modelId", modelId.trim());
-    }
-    if (teamId && teamId.trim()) {
-      params.append("teamId", teamId.trim());
-    }
-    if (sortBy && sortBy.trim()) {
-      params.append("sortBy", sortBy.trim());
-    }
-    if (sortOrder && sortOrder.trim()) {
-      params.append("sortOrder", sortOrder.trim());
-    }
     if (params.toString()) {
       url += `?${params.toString()}`;
     }
@@ -2764,7 +2749,6 @@ export const uiSpendLogsCall = async (
   model?: string,
   keyAlias?: string,
   error_code?: string,
-  error_message?: string,
 ) => {
   try {
     // Construct base URL
@@ -2785,7 +2769,6 @@ export const uiSpendLogsCall = async (
     if (model) queryParams.append("model", model);
     if (keyAlias) queryParams.append("key_alias", keyAlias);
     if (error_code) queryParams.append("error_code", error_code);
-    if (error_message) queryParams.append("error_message", error_message);
     // Append query parameters to URL if any exist
     const queryString = queryParams.toString();
     if (queryString) {
@@ -5338,66 +5321,6 @@ export const getProxyUISettings = async (accessToken: string) => {
   }
 };
 
-export const getUISettings = async (accessToken: string) => {
-  /**
-   * Get UI-specific configuration flags from the database
-   */
-  try {
-    const url = proxyBaseUrl ? `${proxyBaseUrl}/get/ui_settings` : `/get/ui_settings`;
-    const response = await fetch(url, {
-      method: "GET",
-      headers: {
-        [globalLitellmHeaderName]: `Bearer ${accessToken}`,
-        "Content-Type": "application/json",
-      },
-    });
-
-    if (!response.ok) {
-      const errorData = await response.json();
-      const errorMessage = deriveErrorMessage(errorData);
-      console.error("Failed to get UI settings:", errorMessage);
-      return null;
-    }
-
-    const data = await response.json();
-    return data;
-  } catch (error) {
-    console.error("Failed to get UI settings:", error);
-    return null;
-  }
-};
-
-export const updateUISettings = async (accessToken: string, settings: any) => {
-  /**
-   * Update UI-specific configuration flags in the database
-   * Only proxy admins can update these settings
-   */
-  try {
-    const url = proxyBaseUrl ? `${proxyBaseUrl}/update/ui_settings` : `/update/ui_settings`;
-    const response = await fetch(url, {
-      method: "PATCH",
-      headers: {
-        [globalLitellmHeaderName]: `Bearer ${accessToken}`,
-        "Content-Type": "application/json",
-      },
-      body: JSON.stringify(settings),
-    });
-
-    if (!response.ok) {
-      const errorData = await response.json();
-      const errorMessage = deriveErrorMessage(errorData);
-      handleError(errorMessage);
-      throw new Error(errorMessage);
-    }
-
-    const data = await response.json();
-    return data;
-  } catch (error) {
-    console.error("Failed to update UI settings:", error);
-    throw error;
-  }
-};
-
 export const getGuardrailsList = async (accessToken: string) => {
   try {
     const url = proxyBaseUrl ? `${proxyBaseUrl}/v2/guardrails/list` : `/v2/guardrails/list`;
@@ -5454,32 +5377,6 @@ export const getPoliciesList = async (accessToken: string) => {
   }
 };
 
-export const getPolicyInfoWithGuardrails = async (accessToken: string, policyName: string) => {
-  try {
-    const url = proxyBaseUrl ? `${proxyBaseUrl}/policy/info/${policyName}` : `/policy/info/${policyName}`;
-    const response = await fetch(url, {
-      method: "GET",
-      headers: {
-        [globalLitellmHeaderName]: `Bearer ${accessToken}`,
-        "Content-Type": "application/json",
-      },
-    });
-
-    if (!response.ok) {
-      const errorData = await response.json();
-      const errorMessage = deriveErrorMessage(errorData);
-      handleError(errorMessage);
-      throw new Error(errorMessage);
-    }
-
-    const data = await response.json();
-    return data;
-  } catch (error) {
-    console.error(`Failed to get policy info for ${policyName}:`, error);
-    throw error;
-  }
-};
-
 export const createPolicyCall = async (accessToken: string, policyData: any) => {
   try {
     const url = proxyBaseUrl ? `${proxyBaseUrl}/policies` : `/policies`;
@@ -7016,64 +6913,6 @@ export const vectorStoreUpdateCall = async (accessToken: string, formValues: Rec
   }
 };
 
-export const ragIngestCall = async (
-  accessToken: string,
-  file: File,
-  customLlmProvider: string,
-  vectorStoreId?: string,
-  vectorStoreName?: string,
-  vectorStoreDescription?: string,
-  providerSpecificParams?: Record<string, any>
-): Promise<any> => {
-  try {
-    let url = proxyBaseUrl ? `${proxyBaseUrl}/rag/ingest` : `/rag/ingest`;
-
-    const formData = new FormData();
-    formData.append("file", file);
-
-    const ingestOptions: any = {
-      ingest_options: {
-        vector_store: {
-          custom_llm_provider: customLlmProvider,
-          ...(vectorStoreId && { vector_store_id: vectorStoreId }),
-          ...(providerSpecificParams && providerSpecificParams),
-        },
-      },
-    };
-
-    // Add litellm_vector_store_params if name or description provided
-    if (vectorStoreName || vectorStoreDescription) {
-      ingestOptions.ingest_options.litellm_vector_store_params = {};
-      if (vectorStoreName) {
-        ingestOptions.ingest_options.litellm_vector_store_params.vector_store_name = vectorStoreName;
-      }
-      if (vectorStoreDescription) {
-        ingestOptions.ingest_options.litellm_vector_store_params.vector_store_description = vectorStoreDescription;
-      }
-    }
-
-    formData.append("request", JSON.stringify(ingestOptions));
-
-    const response = await fetch(url, {
-      method: "POST",
-      headers: {
-        [globalLitellmHeaderName]: `Bearer ${accessToken}`,
-      },
-      body: formData,
-    });
-
-    if (!response.ok) {
-      const error = await response.json();
-      throw new Error(error.error?.message || error.detail || "Failed to ingest document");
-    }
-
-    return await response.json();
-  } catch (error) {
-    console.error("Error ingesting document:", error);
-    throw error;
-  }
-};
-
 export const getEmailEventSettings = async (accessToken: string): Promise<EmailEventSettingsResponse> => {
   try {
     const url = proxyBaseUrl ? `${proxyBaseUrl}/email/event_settings` : `/email/event_settings`;
diff --git a/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx b/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx
index 8e89b77bc17..7664393a9ee 100644
--- a/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx
+++ b/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx
@@ -19,7 +19,6 @@ import ModelAliasManager from "../common_components/ModelAliasManager";
 import PassThroughRoutesSelector from "../common_components/PassThroughRoutesSelector";
 import PremiumLoggingSettings from "../common_components/PremiumLoggingSettings";
 import RateLimitTypeFormItem from "../common_components/RateLimitTypeFormItem";
-import RouterSettingsAccordion, { RouterSettingsAccordionValue } from "../common_components/RouterSettingsAccordion";
 import TeamDropdown from "../common_components/team_dropdown";
 import Createuser from "../create_user_button";
 import { getModelDisplayName } from "../key_team_helpers/fetch_available_models_team_key";
@@ -29,7 +28,6 @@ import MCPToolPermissions from "../mcp_server_management/MCPToolPermissions";
 import NotificationsManager from "../molecules/notifications_manager";
 import {
   getGuardrailsList,
-  getPoliciesList,
   getPossibleUserRoles,
   getPromptsList,
   keyCreateCall,
@@ -151,7 +149,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey }) => {
   const [keyOwner, setKeyOwner] = useState("you");
   const [predefinedTags, setPredefinedTags] = useState(getPredefinedTags(data));
   const [guardrailsList, setGuardrailsList] = useState<string[]>([]);
-  const [policiesList, setPoliciesList] = useState<string[]>([]);
   const [promptsList, setPromptsList] = useState<string[]>([]);
   const [loggingSettings, setLoggingSettings] = useState<any[]>([]);
   const [selectedCreateKeyTeam, setSelectedCreateKeyTeam] = useState<Team | null>(team);
@@ -166,8 +163,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey }) => {
   const [modelAliases, setModelAliases] = useState<{ [key: string]: string }>({});
   const [autoRotationEnabled, setAutoRotationEnabled] = useState<boolean>(false);
   const [rotationInterval, setRotationInterval] = useState<string>("30d");
-  const [routerSettings, setRouterSettings] = useState<RouterSettingsAccordionValue | null>(null);
-  const [routerSettingsKey, setRouterSettingsKey] = useState<number>(0);
   const handleOk = () => {
     setIsModalVisible(false);
     form.resetFields();
@@ -177,8 +172,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey }) => {
     setModelAliases({});
     setAutoRotationEnabled(false);
     setRotationInterval("30d");
-    setRouterSettings(null);
-    setRouterSettingsKey((prev) => prev + 1);
   };
 
   const handleCancel = () => {
@@ -192,8 +185,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey }) => {
     setModelAliases({});
     setAutoRotationEnabled(false);
     setRotationInterval("30d");
-    setRouterSettings(null);
-    setRouterSettingsKey((prev) => prev + 1);
   };
 
   useEffect(() => {
@@ -213,16 +204,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey }) => {
       }
     };
 
-    const fetchPolicies = async () => {
-      try {
-        const response = await getPoliciesList(accessToken);
-        const policyNames = response.policies.map((p: { policy_name: string }) => p.policy_name);
-        setPoliciesList(policyNames);
-      } catch (error) {
-        console.error("Failed to fetch policies:", error);
-      }
-    };
-
     const fetchPrompts = async () => {
       try {
         const response = await getPromptsList(accessToken);
@@ -233,7 +214,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey }) => {
     };
 
     fetchGuardrails();
-    fetchPolicies();
     fetchPrompts();
   }, [accessToken]);
 
@@ -321,9 +301,9 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey }) => {
         formValues.rotation_interval = rotationInterval;
       }
 
-      // Handle duration field for key expiry - convert empty string to null
-      if (!formValues.duration || formValues.duration.trim() === "") {
-        formValues.duration = null;
+      // Handle duration field for key expiry
+      if (formValues.duration) {
+        formValues.duration = formValues.duration;
       }
 
       // Update the formValues with the final metadata
@@ -403,17 +383,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey }) => {
         formValues.aliases = JSON.stringify(modelAliases);
       }
 
-      // Add router_settings if any are defined
-      if (routerSettings?.router_settings) {
-        // Only include router_settings if it has at least one non-null value
-        const hasValues = Object.values(routerSettings.router_settings).some(
-          (value) => value !== null && value !== undefined && value !== "",
-        );
-        if (hasValues) {
-          formValues.router_settings = routerSettings.router_settings;
-        }
-      }
-
       let response;
       if (keyOwner === "service_account") {
         response = await keyCreateServiceAccountCall(accessToken, formValues);
@@ -928,42 +897,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey }) => {
                   >
                     <Switch disabled={!premiumUser} checkedChildren="Yes" unCheckedChildren="No" />
                   </Form.Item>
-                  <Form.Item
-                    label={
-                      <span>
-                        Policies{" "}
-                        <Tooltip title="Apply policies to this key to control guardrails and other settings">
-                          <a
-                            href="https://docs.litellm.ai/docs/proxy/guardrails/guardrail_policies"
-                            target="_blank"
-                            rel="noopener noreferrer"
-                            onClick={(e) => e.stopPropagation()} // Prevent accordion from collapsing when clicking link
-                          >
-                            <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-                          </a>
-                        </Tooltip>
-                      </span>
-                    }
-                    name="policies"
-                    className="mt-4"
-                    help={
-                      premiumUser
-                        ? "Select existing policies or enter new ones"
-                        : "Premium feature - Upgrade to set policies by key"
-                    }
-                  >
-                    <Select
-                      mode="tags"
-                      style={{ width: "100%" }}
-                      disabled={!premiumUser}
-                      placeholder={
-                        !premiumUser
-                          ? "Premium feature - Upgrade to set policies by key"
-                          : "Select or enter policies"
-                      }
-                      options={policiesList.map((name) => ({ value: name, label: name }))}
-                    />
-                  </Form.Item>
                   <Form.Item
                     label={
                       <span>
@@ -1221,23 +1154,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey }) => {
                     </Tooltip>
                   )}
 
-                  <Accordion key={`router-settings-accordion-${routerSettingsKey}`} className="mt-4 mb-4">
-                    <AccordionHeader>
-                      <b>Router Settings</b>
-                    </AccordionHeader>
-                    <AccordionBody>
-                      <div className="mt-4 w-full">
-                        <RouterSettingsAccordion
-                          key={routerSettingsKey}
-                          accessToken={accessToken || ""}
-                          value={routerSettings || undefined}
-                          onChange={setRouterSettings}
-                          modelData={userModels.length > 0 ? { data: userModels.map((model) => ({ model_name: model })) } : undefined}
-                        />
-                      </div>
-                    </AccordionBody>
-                  </Accordion>
-
                   <Accordion className="mt-4 mb-4">
                     <AccordionHeader>
                       <b>Model Aliases</b>
@@ -1270,7 +1186,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey }) => {
                           onAutoRotationChange={setAutoRotationEnabled}
                           rotationInterval={rotationInterval}
                           onRotationIntervalChange={setRotationInterval}
-                          isCreateMode={true}
                         />
                       </div>
                     </AccordionBody>
diff --git a/ui/litellm-dashboard/src/components/organization/organization_view.tsx b/ui/litellm-dashboard/src/components/organization/organization_view.tsx
index 9b4e9080f2f..91b2b4ce59b 100644
--- a/ui/litellm-dashboard/src/components/organization/organization_view.tsx
+++ b/ui/litellm-dashboard/src/components/organization/organization_view.tsx
@@ -1,6 +1,4 @@
-import { useTeams } from "@/app/(dashboard)/hooks/teams/useTeams";
 import { formatNumberWithCommas, copyToClipboard as utilCopyToClipboard } from "@/utils/dataUtils";
-import { createTeamAliasMap } from "@/utils/teamUtils";
 import { ArrowLeftIcon, PencilAltIcon, TrashIcon } from "@heroicons/react/outline";
 import {
   Badge,
@@ -25,10 +23,10 @@ import {
 } from "@tremor/react";
 import { Button, Form, Input, Select } from "antd";
 import { CheckIcon, CopyIcon } from "lucide-react";
-import React, { useEffect, useMemo, useState } from "react";
+import React, { useEffect, useState, useMemo } from "react";
 import UserSearchModal from "../common_components/user_search_modal";
+import { getModelDisplayName } from "../key_team_helpers/fetch_available_models_team_key";
 import MCPServerSelector from "../mcp_server_management/MCPServerSelector";
-import { ModelSelect } from "../ModelSelect/ModelSelect";
 import NotificationsManager from "../molecules/notifications_manager";
 import {
   Member,
@@ -43,6 +41,8 @@ import ObjectPermissionsView from "../object_permissions_view";
 import NumericalInput from "../shared/numerical_input";
 import MemberModal from "../team/EditMembership";
 import VectorStoreSelector from "../vector_store_management/VectorStoreSelector";
+import { useTeams } from "@/app/(dashboard)/hooks/teams/useTeams";
+import { createTeamAliasMap } from "@/utils/teamUtils";
 
 interface OrganizationInfoProps {
   organizationId: string;
@@ -239,10 +239,11 @@ const OrganizationInfoView: React.FC<OrganizationInfoProps> = ({
               size="small"
               icon={copiedStates["org-id"] ? <CheckIcon size={12} /> : <CopyIcon size={12} />}
               onClick={() => copyToClipboard(orgData.organization_id, "org-id")}
-              className={`left-2 z-10 transition-all duration-200 ${copiedStates["org-id"]
-                ? "text-green-600 bg-green-50 border-green-200"
-                : "text-gray-500 hover:text-gray-700 hover:bg-gray-100"
-                }`}
+              className={`left-2 z-10 transition-all duration-200 ${
+                copiedStates["org-id"]
+                  ? "text-green-600 bg-green-50 border-green-200"
+                  : "text-gray-500 hover:text-gray-700 hover:bg-gray-100"
+              }`}
             />
           </div>
         </div>
@@ -451,15 +452,16 @@ const OrganizationInfoView: React.FC<OrganizationInfoProps> = ({
                   </Form.Item>
 
                   <Form.Item label="Models" name="models">
-                    <ModelSelect
-                      value={form.getFieldValue("models")}
-                      onChange={(values) => form.setFieldValue("models", values)}
-                      context="organization"
-                      options={{
-                        includeSpecialOptions: true,
-                        showAllProxyModelsOverride: true,
-                      }}
-                    />
+                    <Select mode="multiple" placeholder="Select models">
+                      <Select.Option key="all-proxy-models" value="all-proxy-models">
+                        All Proxy Models
+                      </Select.Option>
+                      {userModels.map((model) => (
+                        <Select.Option key={model} value={model}>
+                          {getModelDisplayName(model)}
+                        </Select.Option>
+                      ))}
+                    </Select>
                   </Form.Item>
 
                   <Form.Item label="Max Budget (USD)" name="max_budget">
diff --git a/ui/litellm-dashboard/src/components/page_metadata.ts b/ui/litellm-dashboard/src/components/page_metadata.ts
deleted file mode 100644
index 0c3b978a0bb..00000000000
--- a/ui/litellm-dashboard/src/components/page_metadata.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * Page metadata for UI Settings configuration
- * This file contains descriptions and metadata for all navigation pages
- */
-
-// Page descriptions for UI Settings configuration
-export const pageDescriptions: Record<string, string> = {
-  "api-keys": "Manage virtual keys for API access and authentication",
-  "llm-playground": "Interactive playground for testing LLM requests",
-  models: "Configure and manage LLM models and endpoints",
-  agents: "Create and manage AI agents",
-  "mcp-servers": "Configure Model Context Protocol servers",
-  guardrails: "Set up content moderation and safety guardrails",
-  policies: "Define access control and usage policies",
-  "search-tools": "Configure RAG search and retrieval tools",
-  "vector-stores": "Manage vector databases for embeddings",
-  new_usage: "View usage analytics and metrics",
-  logs: "Access request and response logs",
-  users: "Manage internal user accounts and permissions",
-  teams: "Create and manage teams for access control",
-  organizations: "Manage organizations and their members",
-  budgets: "Set and monitor spending budgets",
-  api_ref: "Browse API documentation and endpoints",
-  "model-hub-table": "Explore available AI models and providers",
-  "learning-resources": "Access tutorials and documentation",
-  caching: "Configure response caching settings",
-  "transform-request": "Set up request transformation rules",
-  "cost-tracking": "Track and analyze API costs",
-  "ui-theme": "Customize dashboard appearance",
-  "tag-management": "Organize resources with tags",
-  prompts: "Manage and version prompt templates",
-  "claude-code-plugins": "Configure Claude Code plugins",
-  usage: "View legacy usage dashboard",
-  "router-settings": "Configure routing and load balancing settings",
-  "logging-and-alerts": "Set up logging and alert configurations",
-  "admin-panel": "Access admin panel and settings",
-};
-
-export interface PageMetadata {
-  page: string;
-  label: string;
-  group: string;
-  description: string;
-}
diff --git a/ui/litellm-dashboard/src/components/page_utils.test.ts b/ui/litellm-dashboard/src/components/page_utils.test.ts
deleted file mode 100644
index 12085735d33..00000000000
--- a/ui/litellm-dashboard/src/components/page_utils.test.ts
+++ /dev/null
@@ -1,241 +0,0 @@
-/**
- * Tests to ensure page metadata stays in sync with leftnav configuration
- * This catches issues when leftnav structure changes but page_utils/page_metadata aren't updated
- */
-
-import { describe, it, expect } from "vitest";
-import { getAvailablePages } from "./page_utils";
-import { menuGroups } from "./leftnav";
-import { pageDescriptions } from "./page_metadata";
-import { internalUserRoles } from "@/utils/roles";
-
-/**
- * Check if a page is accessible to internal users
- * A page is accessible if:
- * 1. It has no role restrictions, OR
- * 2. Its roles include at least one internal user role
- */
-const isPageAccessibleToInternalUsers = (pageRoles?: string[]): boolean => {
-  if (!pageRoles || pageRoles.length === 0) {
-    return true; // No role restrictions
-  }
-  
-  // Check if any of the page's roles match internal user roles
-  return pageRoles.some(role => internalUserRoles.includes(role));
-};
-
-describe("Page Utils - LeftNav Sync", () => {
-  it("should return all pages from leftnav configuration", () => {
-    const availablePages = getAvailablePages();
-    
-    // Should have pages
-    expect(availablePages.length).toBeGreaterThan(0);
-    
-    // Each page should have required fields
-    availablePages.forEach((page) => {
-      expect(page).toHaveProperty("page");
-      expect(page).toHaveProperty("label");
-      expect(page).toHaveProperty("group");
-      expect(page).toHaveProperty("description");
-      expect(typeof page.page).toBe("string");
-      expect(typeof page.label).toBe("string");
-      expect(typeof page.group).toBe("string");
-      expect(typeof page.description).toBe("string");
-    });
-  });
-
-  it("should include all navigable pages from menuGroups", () => {
-    const availablePages = getAvailablePages();
-    const availablePageKeys = availablePages.map((p) => p.page);
-    
-    // Collect all page keys from menuGroups (excluding parent containers and pages not accessible to internal users)
-    const menuPageKeys: string[] = [];
-    const excludedParents = ["tools", "experimental", "settings"];
-    
-    menuGroups.forEach((group) => {
-      group.items.forEach((item) => {
-        if (
-          item.page &&
-          !excludedParents.includes(item.page) &&
-          isPageAccessibleToInternalUsers(item.roles)
-        ) {
-          menuPageKeys.push(item.page);
-        }
-        
-        // Add children (only if accessible to internal users)
-        if (item.children) {
-          item.children.forEach((child) => {
-            if (isPageAccessibleToInternalUsers(child.roles)) {
-              menuPageKeys.push(child.page);
-            }
-          });
-        }
-      });
-    });
-    
-    // Every menu page accessible to internal users should be in available pages
-    menuPageKeys.forEach((pageKey) => {
-      expect(
-        availablePageKeys,
-        `Page "${pageKey}" from menuGroups should be in getAvailablePages() output`
-      ).toContain(pageKey);
-    });
-  });
-
-  it("should not include parent container pages (tools, experimental, settings)", () => {
-    const availablePages = getAvailablePages();
-    const availablePageKeys = availablePages.map((p) => p.page);
-    
-    const excludedParents = ["tools", "experimental", "settings"];
-    
-    excludedParents.forEach((parent) => {
-      expect(
-        availablePageKeys,
-        `Parent container "${parent}" should not be in available pages`
-      ).not.toContain(parent);
-    });
-  });
-
-  it("should have descriptions for all pages", () => {
-    const availablePages = getAvailablePages();
-    
-    availablePages.forEach((page) => {
-      expect(
-        page.description,
-        `Page "${page.page}" should have a description`
-      ).toBeTruthy();
-      
-      expect(
-        page.description,
-        `Page "${page.page}" should not have placeholder description`
-      ).not.toBe("No description available");
-    });
-  });
-
-  it("should have pageDescriptions entry for all navigable pages in menuGroups", () => {
-    // Collect all page keys from menuGroups
-    const menuPageKeys: string[] = [];
-    const excludedParents = ["tools", "experimental", "settings"];
-    
-    menuGroups.forEach((group) => {
-      group.items.forEach((item) => {
-        if (item.page && !excludedParents.includes(item.page)) {
-          menuPageKeys.push(item.page);
-        }
-        
-        if (item.children) {
-          item.children.forEach((child) => {
-            menuPageKeys.push(child.page);
-          });
-        }
-      });
-    });
-    
-    // Every menu page should have a description
-    const missingDescriptions: string[] = [];
-    menuPageKeys.forEach((pageKey) => {
-      if (!pageDescriptions[pageKey]) {
-        missingDescriptions.push(pageKey);
-      }
-    });
-    
-    expect(
-      missingDescriptions,
-      `These pages are missing descriptions in page_metadata.ts: ${missingDescriptions.join(", ")}`
-    ).toHaveLength(0);
-  });
-
-  it("should not have orphaned descriptions (descriptions for pages not in menuGroups)", () => {
-    // Collect all page keys from menuGroups
-    const menuPageKeys: string[] = [];
-    const excludedParents = ["tools", "experimental", "settings"];
-    
-    menuGroups.forEach((group) => {
-      group.items.forEach((item) => {
-        if (item.page && !excludedParents.includes(item.page)) {
-          menuPageKeys.push(item.page);
-        }
-        
-        if (item.children) {
-          item.children.forEach((child) => {
-            menuPageKeys.push(child.page);
-          });
-        }
-      });
-    });
-    
-    // Check for descriptions that don't match any menu page
-    const orphanedDescriptions: string[] = [];
-    Object.keys(pageDescriptions).forEach((descKey) => {
-      if (!menuPageKeys.includes(descKey)) {
-        orphanedDescriptions.push(descKey);
-      }
-    });
-    
-    expect(
-      orphanedDescriptions,
-      `These descriptions don't match any page in menuGroups: ${orphanedDescriptions.join(", ")}. Remove them or add the pages to leftnav.`
-    ).toHaveLength(0);
-  });
-
-  it("should have proper group hierarchy for nested pages", () => {
-    const availablePages = getAvailablePages();
-    
-    // Find pages that should be nested (children of Tools, Experimental, Settings)
-    const nestedPages = availablePages.filter((page) => 
-      page.group.includes(" > ")
-    );
-    
-    // Each nested page should have parent > child format
-    nestedPages.forEach((page) => {
-      const parts = page.group.split(" > ");
-      expect(
-        parts.length,
-        `Nested page "${page.page}" should have exactly 2 parts in group hierarchy`
-      ).toBe(2);
-      
-      // Parent should be one of the group labels
-      const parentGroup = parts[0];
-      const groupLabels = menuGroups.map((g) => g.groupLabel);
-      expect(
-        groupLabels,
-        `Parent group "${parentGroup}" for page "${page.page}" should be a valid group label`
-      ).toContain(parentGroup);
-    });
-  });
-
-  it("should have unique page keys", () => {
-    const availablePages = getAvailablePages();
-    const pageKeys = availablePages.map((p) => p.page);
-    const uniquePageKeys = new Set(pageKeys);
-    
-    expect(
-      pageKeys.length,
-      "All page keys should be unique (no duplicates)"
-    ).toBe(uniquePageKeys.size);
-  });
-
-  it("should match the structure expected by PageVisibilitySettings component", () => {
-    const availablePages = getAvailablePages();
-    
-    // Group pages by their group (same logic as in PageVisibilitySettings)
-    const grouped: Record<string, typeof availablePages> = {};
-    availablePages.forEach((page) => {
-      if (!grouped[page.group]) {
-        grouped[page.group] = [];
-      }
-      grouped[page.group].push(page);
-    });
-    
-    // Should have multiple groups
-    expect(Object.keys(grouped).length).toBeGreaterThan(1);
-    
-    // Each group should have at least one page
-    Object.entries(grouped).forEach(([groupName, pages]) => {
-      expect(
-        pages.length,
-        `Group "${groupName}" should have at least one page`
-      ).toBeGreaterThan(0);
-    });
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/page_utils.ts b/ui/litellm-dashboard/src/components/page_utils.ts
deleted file mode 100644
index 7461f2b4a88..00000000000
--- a/ui/litellm-dashboard/src/components/page_utils.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-/**
- * Utility functions for working with navigation pages
- */
-
-import { menuGroups } from "./leftnav";
-import { pageDescriptions, PageMetadata } from "./page_metadata";
-import { internalUserRoles } from "@/utils/roles";
-
-/**
- * Check if a page is accessible to internal users
- * A page is accessible if:
- * 1. It has no role restrictions, OR
- * 2. Its roles include at least one internal user role
- */
-const isPageAccessibleToInternalUsers = (pageRoles?: string[]): boolean => {
-  if (!pageRoles || pageRoles.length === 0) {
-    return true; // No role restrictions
-  }
-  
-  // Check if any of the page's roles match internal user roles
-  return pageRoles.some(role => internalUserRoles.includes(role));
-};
-
-/**
- * Get all available pages from the navigation menu configuration
- * Used by UI Settings to display available pages for visibility control
- * 
- * IMPORTANT: Only returns pages that internal users can access.
- * Pages restricted to admin-only roles are excluded because internal users
- * cannot see them regardless of the UI visibility setting.
- */
-export const getAvailablePages = (): PageMetadata[] => {
-  const pages: PageMetadata[] = [];
-
-  menuGroups.forEach((group) => {
-    group.items.forEach((item) => {
-      // Add top-level items (skip parent containers like 'tools', 'experimental', 'settings')
-      // Also skip items that internal users cannot access
-      if (
-        item.page &&
-        item.page !== "tools" &&
-        item.page !== "experimental" &&
-        item.page !== "settings" &&
-        isPageAccessibleToInternalUsers(item.roles)
-      ) {
-        const label = typeof item.label === "string" ? item.label : item.key;
-        pages.push({
-          page: item.page,
-          label: label,
-          group: group.groupLabel,
-          description: pageDescriptions[item.page] || "No description available",
-        });
-      }
-
-      // Add children items (also skip those internal users cannot access)
-      if (item.children) {
-        const parentLabel = typeof item.label === "string" ? item.label : item.key;
-        item.children.forEach((child) => {
-          // Include if internal users can access
-          if (isPageAccessibleToInternalUsers(child.roles)) {
-            const childLabel = typeof child.label === "string" ? child.label : child.key;
-            pages.push({
-              page: child.page,
-              label: childLabel,
-              group: `${group.groupLabel} > ${parentLabel}`,
-              description: pageDescriptions[child.page] || "No description available",
-            });
-          }
-        });
-      }
-    });
-  });
-
-  return pages;
-};
diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/CodeSnippets.test.tsx b/ui/litellm-dashboard/src/components/playground/chat_ui/CodeSnippets.test.tsx
index 0113f7b2832..56c5fd404e3 100644
--- a/ui/litellm-dashboard/src/components/playground/chat_ui/CodeSnippets.test.tsx
+++ b/ui/litellm-dashboard/src/components/playground/chat_ui/CodeSnippets.test.tsx
@@ -34,8 +34,7 @@ describe("CodeSnippets", () => {
     selectedTags: [],
     selectedVectorStores: [],
     selectedGuardrails: [],
-    selectedPolicies: [],
-    selectedMCPServers: [],
+    selectedMCPTools: [],
     selectedSdk: "openai" as const,
     selectedVoice: "alloy",
   };
diff --git a/ui/litellm-dashboard/src/components/playground/compareUI/CompareUI.tsx b/ui/litellm-dashboard/src/components/playground/compareUI/CompareUI.tsx
index fa68851d690..92bcb21c4b5 100644
--- a/ui/litellm-dashboard/src/components/playground/compareUI/CompareUI.tsx
+++ b/ui/litellm-dashboard/src/components/playground/compareUI/CompareUI.tsx
@@ -619,7 +619,6 @@ export default function CompareUI({ accessToken, disabledPersonalKeyCreation }:
             guardrails,
             undefined,
             undefined,
-            undefined,
             (searchResults) => updateSearchResultsForComparison(prepared.id, searchResults),
             useAdvancedParams ? prepared.temperature : undefined,
             useAdvancedParams ? prepared.maxTokens : undefined,
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.test.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.test.tsx
index 8786e022013..dd23e0591ea 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.test.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.test.tsx
@@ -95,8 +95,7 @@ describe("chat_completion", () => {
       undefined, // traceId
       undefined, // vector_store_ids
       undefined, // guardrails
-      undefined, // policies
-      undefined, // selectedMCPServers
+      undefined, // selectedMCPTools
       undefined, // onImageGenerated
       undefined, // onSearchResults
       0.7, // temperature
@@ -158,7 +157,6 @@ describe("chat_completion", () => {
       undefined, // traceId
       undefined, // vector_store_ids
       undefined, // guardrails
-      undefined, // policies
       selectedMCPServers,
       undefined, // onImageGenerated
       undefined, // onSearchResults
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.test.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.test.tsx
index f14ac52b840..569c9878243 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.test.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.test.tsx
@@ -102,22 +102,21 @@ describe("responses_api", () => {
       mockUpdateTextUI,
       "gpt-4",
       "test-token",
-      undefined, // tags
-      undefined, // signal
-      undefined, // onReasoningContent
-      undefined, // onTimingData
-      undefined, // onUsageData
-      undefined, // traceId
-      undefined, // vector_store_ids
-      undefined, // guardrails
-      undefined, // policies
+      undefined,
+      undefined,
+      undefined,
+      undefined,
+      undefined,
+      undefined,
+      undefined,
+      undefined,
       selectedMCPServers,
-      undefined, // previousResponseId
-      undefined, // onResponseId
-      undefined, // onMCPEvent
-      undefined, // codeInterpreterEnabled
-      undefined, // onCodeInterpreterResult
-      undefined, // customBaseUrl
+      undefined,
+      undefined,
+      undefined,
+      undefined,
+      undefined,
+      undefined,
       mcpServers,
       mcpServerToolRestrictions,
     );
diff --git a/ui/litellm-dashboard/src/components/policies/add_attachment_form.tsx b/ui/litellm-dashboard/src/components/policies/add_attachment_form.tsx
index 9198eda8a94..e00f20fa163 100644
--- a/ui/litellm-dashboard/src/components/policies/add_attachment_form.tsx
+++ b/ui/litellm-dashboard/src/components/policies/add_attachment_form.tsx
@@ -2,9 +2,8 @@ import React, { useState, useEffect } from "react";
 import { Modal, Form, Select, Radio, Divider, Typography } from "antd";
 import { Button } from "@tremor/react";
 import { Policy, PolicyAttachmentCreateRequest } from "./types";
-import { teamListCall, keyInfoCall, modelAvailableCall } from "../networking";
+import { createPolicyAttachmentCall, teamListCall, keyInfoCall } from "../networking";
 import NotificationsManager from "../molecules/notifications_manager";
-import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
 
 const { Text } = Typography;
 
@@ -14,7 +13,6 @@ interface AddAttachmentFormProps {
   onSuccess: () => void;
   accessToken: string | null;
   policies: Policy[];
-  createAttachment: (accessToken: string, attachmentData: any) => Promise<any>;
 }
 
 const AddAttachmentForm: React.FC<AddAttachmentFormProps> = ({
@@ -23,34 +21,29 @@ const AddAttachmentForm: React.FC<AddAttachmentFormProps> = ({
   onSuccess,
   accessToken,
   policies,
-  createAttachment,
 }) => {
   const [form] = Form.useForm();
   const [isSubmitting, setIsSubmitting] = useState(false);
   const [scopeType, setScopeType] = useState<"global" | "specific">("global");
   const [availableTeams, setAvailableTeams] = useState<string[]>([]);
   const [availableKeys, setAvailableKeys] = useState<string[]>([]);
-  const [availableModels, setAvailableModels] = useState<string[]>([]);
   const [isLoadingTeams, setIsLoadingTeams] = useState(false);
   const [isLoadingKeys, setIsLoadingKeys] = useState(false);
-  const [isLoadingModels, setIsLoadingModels] = useState(false);
-  const { userId, userRole } = useAuthorized();
 
   useEffect(() => {
     if (visible && accessToken) {
-      loadTeamsKeysAndModels();
+      loadTeamsAndKeys();
     }
     // eslint-disable-next-line react-hooks/exhaustive-deps
   }, [visible, accessToken]);
 
-  const loadTeamsKeysAndModels = async () => {
+  const loadTeamsAndKeys = async () => {
     if (!accessToken) return;
 
     // Load teams
     setIsLoadingTeams(true);
     try {
-      // Pass null for organizationID since we're loading all teams the user has access to
-      const teamsResponse = await teamListCall(accessToken, null, userId);
+      const teamsResponse = await teamListCall(accessToken);
       if (teamsResponse?.data) {
         const teamAliases = teamsResponse.data
           .map((t: any) => t.team_alias)
@@ -66,7 +59,7 @@ const AddAttachmentForm: React.FC<AddAttachmentFormProps> = ({
     // Load keys
     setIsLoadingKeys(true);
     try {
-      const keysResponse = await keyInfoCall(accessToken, []);
+      const keysResponse = await keyInfoCall(accessToken, null, null);
       if (keysResponse?.data) {
         const keyAliases = keysResponse.data
           .map((k: any) => k.key_alias)
@@ -78,22 +71,6 @@ const AddAttachmentForm: React.FC<AddAttachmentFormProps> = ({
     } finally {
       setIsLoadingKeys(false);
     }
-
-    // Load models
-    setIsLoadingModels(true);
-    try {
-      const modelsResponse = await modelAvailableCall(accessToken, userId || "", userRole || "");
-      if (modelsResponse?.data) {
-        const modelIds = modelsResponse.data
-          .map((m: any) => m.id || m.model_name)
-          .filter(Boolean);
-        setAvailableModels(modelIds);
-      }
-    } catch (error) {
-      console.error("Failed to load models:", error);
-    } finally {
-      setIsLoadingModels(false);
-    }
   };
 
   const resetForm = () => {
@@ -134,7 +111,7 @@ const AddAttachmentForm: React.FC<AddAttachmentFormProps> = ({
         }
       }
 
-      await createAttachment(accessToken, data);
+      await createPolicyAttachmentCall(accessToken, data);
       NotificationsManager.success("Attachment created successfully");
 
       resetForm();
@@ -249,21 +226,12 @@ const AddAttachmentForm: React.FC<AddAttachmentFormProps> = ({
             <Form.Item
               name="models"
               label="Models"
-              tooltip="Model names this attachment applies to. Supports wildcards (e.g., gpt-4*). Leave empty to apply to all models."
+              tooltip="Model names this attachment applies to. Supports wildcards (e.g., gpt-4*)"
             >
               <Select
                 mode="tags"
-                placeholder={isLoadingModels ? "Loading models..." : "Select or enter model names (e.g., gpt-4, bedrock/*)"}
-                loading={isLoadingModels}
-                options={availableModels.map((model) => ({
-                  label: model,
-                  value: model,
-                }))}
+                placeholder="Enter model names (e.g., gpt-4, bedrock/*)"
                 tokenSeparators={[","]}
-                showSearch
-                filterOption={(input, option) =>
-                  (option?.label ?? "").toLowerCase().includes(input.toLowerCase())
-                }
                 style={{ width: "100%" }}
               />
             </Form.Item>
diff --git a/ui/litellm-dashboard/src/components/policies/add_policy_form.tsx b/ui/litellm-dashboard/src/components/policies/add_policy_form.tsx
index 383e9c45dfb..945c29931f1 100644
--- a/ui/litellm-dashboard/src/components/policies/add_policy_form.tsx
+++ b/ui/litellm-dashboard/src/components/policies/add_policy_form.tsx
@@ -3,9 +3,8 @@ import { Form, Select, Modal, Divider, Typography, Tag, Alert, Radio } from "ant
 import { Button, TextInput, Textarea } from "@tremor/react";
 import { Policy, PolicyCreateRequest, PolicyUpdateRequest } from "./types";
 import { Guardrail } from "../guardrails/types";
-import { getResolvedGuardrails, modelAvailableCall } from "../networking";
+import { createPolicyCall, updatePolicyCall, getResolvedGuardrails, modelAvailableCall } from "../networking";
 import NotificationsManager from "../molecules/notifications_manager";
-import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
 
 const { Text } = Typography;
 const { Option } = Select;
@@ -18,8 +17,6 @@ interface AddPolicyFormProps {
   editingPolicy?: Policy | null;
   existingPolicies: Policy[];
   availableGuardrails: Guardrail[];
-  createPolicy: (accessToken: string, policyData: any) => Promise<any>;
-  updatePolicy: (accessToken: string, policyId: string, policyData: any) => Promise<any>;
 }
 
 const AddPolicyForm: React.FC<AddPolicyFormProps> = ({
@@ -30,8 +27,6 @@ const AddPolicyForm: React.FC<AddPolicyFormProps> = ({
   editingPolicy,
   existingPolicies,
   availableGuardrails,
-  createPolicy,
-  updatePolicy,
 }) => {
   const [form] = Form.useForm();
   const [isSubmitting, setIsSubmitting] = useState(false);
@@ -39,7 +34,6 @@ const AddPolicyForm: React.FC<AddPolicyFormProps> = ({
   const [isLoadingResolved, setIsLoadingResolved] = useState(false);
   const [modelConditionType, setModelConditionType] = useState<"model" | "regex">("model");
   const [availableModels, setAvailableModels] = useState<string[]>([]);
-  const { userId, userRole } = useAuthorized();
 
   const isEditing = !!editingPolicy;
 
@@ -49,7 +43,7 @@ const AddPolicyForm: React.FC<AddPolicyFormProps> = ({
       // Detect if it's a regex pattern (contains *, ., [, ], etc.)
       const isRegex = modelCondition && /[.*+?^${}()|[\]\\]/.test(modelCondition);
       setModelConditionType(isRegex ? "regex" : "model");
-
+      
       form.setFieldsValue({
         policy_name: editingPolicy.policy_name,
         description: editingPolicy.description,
@@ -79,9 +73,9 @@ const AddPolicyForm: React.FC<AddPolicyFormProps> = ({
 
   const loadAvailableModels = async () => {
     if (!accessToken) return;
-
+    
     try {
-      const response = await modelAvailableCall(accessToken, userId, userRole);
+      const response = await modelAvailableCall(accessToken, null, null, null);
       if (response?.data) {
         const models = response.data.map((m: any) => m.id || m.model_name).filter(Boolean);
         setAvailableModels(models);
@@ -93,7 +87,7 @@ const AddPolicyForm: React.FC<AddPolicyFormProps> = ({
 
   const loadResolvedGuardrails = async (policyId: string) => {
     if (!accessToken) return;
-
+    
     setIsLoadingResolved(true);
     try {
       const data = await getResolvedGuardrails(accessToken, policyId);
@@ -134,7 +128,7 @@ const AddPolicyForm: React.FC<AddPolicyFormProps> = ({
 
   const resolveParentGuardrails = (policy: Policy): string[] => {
     let resolved = new Set<string>();
-
+    
     // If parent inherits, resolve recursively
     if (policy.inherit) {
       const grandparent = existingPolicies.find(p => p.policy_name === policy.inherit);
@@ -194,10 +188,10 @@ const AddPolicyForm: React.FC<AddPolicyFormProps> = ({
       };
 
       if (isEditing && editingPolicy) {
-        await updatePolicy(accessToken, editingPolicy.policy_id, data as PolicyUpdateRequest);
+        await updatePolicyCall(accessToken, editingPolicy.policy_id, data as PolicyUpdateRequest);
         NotificationsManager.success("Policy updated successfully");
       } else {
-        await createPolicy(accessToken, data as PolicyCreateRequest);
+        await createPolicyCall(accessToken, data as PolicyCreateRequest);
         NotificationsManager.success("Policy created successfully");
       }
 
@@ -344,14 +338,6 @@ const AddPolicyForm: React.FC<AddPolicyFormProps> = ({
           <Text strong>Conditions (Optional)</Text>
         </Divider>
 
-        <Alert
-          message="Model Scope"
-          description="By default, this policy will run on all models. You can optionally restrict it to specific models below."
-          type="info"
-          showIcon
-          style={{ marginBottom: 16 }}
-        />
-
         <Form.Item label="Model Condition Type">
           <Radio.Group
             value={modelConditionType}
@@ -367,18 +353,18 @@ const AddPolicyForm: React.FC<AddPolicyFormProps> = ({
 
         <Form.Item
           name="model_condition"
-          label={modelConditionType === "model" ? "Model (Optional)" : "Regex Pattern (Optional)"}
+          label={modelConditionType === "model" ? "Model" : "Regex Pattern"}
           tooltip={
             modelConditionType === "model"
-              ? "Select a specific model to apply this policy to. Leave empty to apply to all models."
-              : "Enter a regex pattern to match models (e.g., gpt-4.* or bedrock/.*). Leave empty to apply to all models."
+              ? "Select a specific model to apply this policy to"
+              : "Enter a regex pattern to match models (e.g., gpt-4.* or bedrock/.*)"
           }
         >
           {modelConditionType === "model" ? (
             <Select
               showSearch
               allowClear
-              placeholder="Leave empty to apply to all models"
+              placeholder="Select a model"
               options={availableModels.map((model) => ({
                 label: model,
                 value: model,
@@ -389,7 +375,7 @@ const AddPolicyForm: React.FC<AddPolicyFormProps> = ({
               style={{ width: "100%" }}
             />
           ) : (
-            <TextInput placeholder="Leave empty to apply to all models (e.g., gpt-4.* or bedrock/claude-.*)" />
+            <TextInput placeholder="e.g., gpt-4.* or bedrock/claude-.*" />
           )}
         </Form.Item>
 
diff --git a/ui/litellm-dashboard/src/components/policies/index.tsx b/ui/litellm-dashboard/src/components/policies/index.tsx
index 717a6ff612f..5a137d704ad 100644
--- a/ui/litellm-dashboard/src/components/policies/index.tsx
+++ b/ui/litellm-dashboard/src/components/policies/index.tsx
@@ -1,7 +1,7 @@
 import React, { useState, useEffect, useCallback } from "react";
 import { Button, TabGroup, TabList, Tab, TabPanels, TabPanel } from "@tremor/react";
-import { Modal, message, Alert } from "antd";
-import { ExclamationCircleOutlined, InfoCircleOutlined } from "@ant-design/icons";
+import { Modal, message } from "antd";
+import { ExclamationCircleOutlined } from "@ant-design/icons";
 import { isAdminRole } from "@/utils/roles";
 import PolicyTable from "./policy_table";
 import PolicyInfoView from "./policy_info";
@@ -14,10 +14,6 @@ import {
   getPolicyAttachmentsList,
   deletePolicyAttachmentCall,
   getGuardrailsList,
-  getPolicyInfo,
-  createPolicyCall,
-  updatePolicyCall,
-  createPolicyAttachmentCall,
 } from "../networking";
 import {
   Policy,
@@ -181,36 +177,6 @@ const PoliciesPanel: React.FC<PoliciesPanelProps> = ({
 
         <TabPanels>
           <TabPanel>
-            <Alert
-              message="About Policies"
-              description={
-                <div>
-                  <p className="mb-3">
-                    Use policies to group guardrails and control which ones run for specific teams, keys, or models.
-                  </p>
-                  <p className="mb-2 font-semibold">Why use policies?</p>
-                  <ul className="list-disc list-inside mb-3 space-y-1 ml-2">
-                    <li>Enable/disable specific guardrails for teams, keys, or models</li>
-                    <li>Group guardrails into a single policy</li>
-                    <li>Inherit from existing policies and override what you need</li>
-                  </ul>
-                  <a
-                    href="https://docs.litellm.ai/docs/proxy/guardrails/guardrail_policies"
-                    target="_blank"
-                    rel="noopener noreferrer"
-                    className="text-blue-600 hover:text-blue-800 underline inline-block mt-1"
-                  >
-                    Learn more in the documentation →
-                  </a>
-                </div>
-              }
-              type="info"
-              icon={<InfoCircleOutlined />}
-              showIcon
-              closable
-              className="mb-6"
-            />
-
             <div className="flex justify-between items-center mb-4">
               <Button onClick={handleAddPolicy} disabled={!accessToken}>
                 + Add New Policy
@@ -228,7 +194,6 @@ const PoliciesPanel: React.FC<PoliciesPanelProps> = ({
                 }}
                 accessToken={accessToken}
                 isAdmin={isAdmin}
-                getPolicy={getPolicyInfo}
               />
             ) : (
               <PolicyTable
@@ -252,8 +217,6 @@ const PoliciesPanel: React.FC<PoliciesPanelProps> = ({
               editingPolicy={editingPolicy}
               existingPolicies={policiesList}
               availableGuardrails={guardrailsList}
-              createPolicy={createPolicyCall}
-              updatePolicy={updatePolicyCall}
             />
 
             <DeleteResourceModal
@@ -274,37 +237,6 @@ const PoliciesPanel: React.FC<PoliciesPanelProps> = ({
           </TabPanel>
 
           <TabPanel>
-            <Alert
-              message="About Policy Attachments"
-              description={
-                <div>
-                  <p className="mb-3">
-                    Policy attachments control where your policies apply. Policies don&apos;t do anything until you attach them to specific teams, keys, models, or globally.
-                  </p>
-                  <p className="mb-2 font-semibold">Attachment Scopes:</p>
-                  <ul className="list-disc list-inside mb-3 space-y-1 ml-2">
-                    <li><strong>Global (*)</strong> - Applies to all requests</li>
-                    <li><strong>Teams</strong> - Applies only to specific teams</li>
-                    <li><strong>Keys</strong> - Applies only to specific API keys (supports wildcards like dev-*)</li>
-                    <li><strong>Models</strong> - Applies only when specific models are used</li>
-                  </ul>
-                  <a
-                    href="https://docs.litellm.ai/docs/proxy/guardrails/guardrail_policies#attachments"
-                    target="_blank"
-                    rel="noopener noreferrer"
-                    className="text-blue-600 hover:text-blue-800 underline inline-block mt-1"
-                  >
-                    Learn more about attachments →
-                  </a>
-                </div>
-              }
-              type="info"
-              icon={<InfoCircleOutlined />}
-              showIcon
-              closable
-              className="mb-6"
-            />
-
             <div className="flex justify-between items-center mb-4">
               <Button
                 onClick={() => setIsAddAttachmentModalVisible(true)}
@@ -327,7 +259,6 @@ const PoliciesPanel: React.FC<PoliciesPanelProps> = ({
               onSuccess={handleAttachmentSuccess}
               accessToken={accessToken}
               policies={policiesList}
-              createAttachment={createPolicyAttachmentCall}
             />
           </TabPanel>
         </TabPanels>
diff --git a/ui/litellm-dashboard/src/components/policies/policy_info.tsx b/ui/litellm-dashboard/src/components/policies/policy_info.tsx
index 1ef8aa0530d..a7ea4703ee5 100644
--- a/ui/litellm-dashboard/src/components/policies/policy_info.tsx
+++ b/ui/litellm-dashboard/src/components/policies/policy_info.tsx
@@ -3,7 +3,7 @@ import { Card, Badge, Button } from "@tremor/react";
 import { ArrowLeftIcon, PencilIcon } from "@heroicons/react/outline";
 import { Descriptions, Tag, Spin, Divider, Typography, Alert } from "antd";
 import { Policy } from "./types";
-import { getResolvedGuardrails } from "../networking";
+import { getPolicyInfo, getResolvedGuardrails } from "../networking";
 
 const { Title, Text } = Typography;
 
@@ -13,7 +13,6 @@ interface PolicyInfoViewProps {
   onEdit: (policy: Policy) => void;
   accessToken: string | null;
   isAdmin: boolean;
-  getPolicy: (accessToken: string, policyId: string) => Promise<any>;
 }
 
 const PolicyInfoView: React.FC<PolicyInfoViewProps> = ({
@@ -22,7 +21,6 @@ const PolicyInfoView: React.FC<PolicyInfoViewProps> = ({
   onEdit,
   accessToken,
   isAdmin,
-  getPolicy,
 }) => {
   const [policy, setPolicy] = useState<Policy | null>(null);
   const [isLoading, setIsLoading] = useState(true);
@@ -34,7 +32,7 @@ const PolicyInfoView: React.FC<PolicyInfoViewProps> = ({
 
     setIsLoading(true);
     try {
-      const data = await getPolicy(accessToken, policyId);
+      const data = await getPolicyInfo(accessToken, policyId);
       setPolicy(data);
       
       // Also fetch resolved guardrails
@@ -52,7 +50,7 @@ const PolicyInfoView: React.FC<PolicyInfoViewProps> = ({
     } finally {
       setIsLoading(false);
     }
-  }, [policyId, accessToken, getPolicy]);
+  }, [policyId, accessToken]);
 
   useEffect(() => {
     fetchPolicy();
diff --git a/ui/litellm-dashboard/src/components/public_model_hub.tsx b/ui/litellm-dashboard/src/components/public_model_hub.tsx
index db316d44106..6f95904e9f8 100644
--- a/ui/litellm-dashboard/src/components/public_model_hub.tsx
+++ b/ui/litellm-dashboard/src/components/public_model_hub.tsx
@@ -962,8 +962,6 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
             proxySettings={proxySettings}
             accessToken={accessToken || null}
             isPublicPage={true}
-            isDarkMode={false}
-            toggleDarkMode={() => { }}
           />
         )}
 
@@ -1472,7 +1470,6 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
                         selectedTags: [],
                         selectedVectorStores: [],
                         selectedGuardrails: [],
-                        selectedPolicies: [],
                         selectedMCPServers: [],
                         endpointType: getEndpointType(selectedModel.mode || "chat"),
                         selectedModel: selectedModel.model_group,
@@ -1494,7 +1491,6 @@ const PublicModelHub: React.FC<PublicModelHubProps> = ({ accessToken, isEmbedded
                         selectedTags: [],
                         selectedVectorStores: [],
                         selectedGuardrails: [],
-                        selectedPolicies: [],
                         selectedMCPServers: [],
                         endpointType: getEndpointType(selectedModel.mode || "chat"),
                         selectedModel: selectedModel.model_group,
diff --git a/ui/litellm-dashboard/src/components/router_settings/ReliabilityRetriesSection.tsx b/ui/litellm-dashboard/src/components/router_settings/ReliabilityRetriesSection.tsx
index d217a45358b..6601950fdea 100644
--- a/ui/litellm-dashboard/src/components/router_settings/ReliabilityRetriesSection.tsx
+++ b/ui/litellm-dashboard/src/components/router_settings/ReliabilityRetriesSection.tsx
@@ -16,7 +16,7 @@ const ReliabilityRetriesSection: React.FC<ReliabilityRetriesSectionProps> = ({
         <h3 className="text-sm font-medium text-gray-900">Reliability & Retries</h3>
         <p className="text-xs text-gray-500 mt-1">Configure retry logic and failure handling</p>
       </div>
-
+      
       <div className="grid grid-cols-1 gap-6 lg:grid-cols-2 xl:grid-cols-3">
         {Object.entries(routerSettings)
           .filter(
@@ -39,11 +39,7 @@ const ReliabilityRetriesSection: React.FC<ReliabilityRetriesSectionProps> = ({
                 <TextInput
                   name={param}
                   defaultValue={
-                    value === null || value === undefined || value === "null"
-                      ? ""
-                      : typeof value === "object"
-                        ? JSON.stringify(value, null, 2)
-                        : value?.toString() || ""
+                    typeof value === "object" ? JSON.stringify(value, null, 2) : value?.toString() || ""
                   }
                   placeholder="—"
                   className="font-mono text-sm w-full"
diff --git a/ui/litellm-dashboard/src/components/router_settings/RouterSettingsForm.tsx b/ui/litellm-dashboard/src/components/router_settings/RouterSettingsForm.tsx
deleted file mode 100644
index 7a2dfcbffeb..00000000000
--- a/ui/litellm-dashboard/src/components/router_settings/RouterSettingsForm.tsx
+++ /dev/null
@@ -1,84 +0,0 @@
-import React from "react";
-import LatencyBasedConfiguration from "./LatencyBasedConfiguration";
-import ReliabilityRetriesSection from "./ReliabilityRetriesSection";
-import RoutingStrategySelector from "./RoutingStrategySelector";
-import TagFilteringToggle from "./TagFilteringToggle";
-
-export interface RouterSettingsFormValue {
-  routerSettings: { [key: string]: any };
-  selectedStrategy: string | null;
-  enableTagFiltering: boolean;
-}
-
-interface RouterSettingsFormProps {
-  value: RouterSettingsFormValue;
-  onChange: (value: RouterSettingsFormValue) => void;
-  routerFieldsMetadata: { [key: string]: any };
-  availableRoutingStrategies: string[];
-  routingStrategyDescriptions: { [key: string]: string };
-}
-
-const RouterSettingsForm: React.FC<RouterSettingsFormProps> = ({
-  value,
-  onChange,
-  routerFieldsMetadata,
-  availableRoutingStrategies,
-  routingStrategyDescriptions,
-}) => {
-  const handleStrategyChange = (strategy: string) => {
-    onChange({
-      ...value,
-      selectedStrategy: strategy,
-    });
-  };
-
-  const handleTagFilteringToggle = (enabled: boolean) => {
-    onChange({
-      ...value,
-      enableTagFiltering: enabled,
-    });
-  };
-
-  return (
-    <div className="w-full space-y-8 py-2">
-      {/* Routing Settings Section */}
-      <div className="space-y-6">
-        <div className="max-w-3xl">
-          <h3 className="text-sm font-medium text-gray-900">Routing Settings</h3>
-          <p className="text-xs text-gray-500 mt-1">Configure how requests are routed to deployments</p>
-        </div>
-
-        {/* Routing Strategy */}
-        {availableRoutingStrategies.length > 0 && (
-          <RoutingStrategySelector
-            selectedStrategy={value.selectedStrategy || value.routerSettings.routing_strategy || null}
-            availableStrategies={availableRoutingStrategies}
-            routingStrategyDescriptions={routingStrategyDescriptions}
-            routerFieldsMetadata={routerFieldsMetadata}
-            onStrategyChange={handleStrategyChange}
-          />
-        )}
-
-        {/* Tag Filtering */}
-        <TagFilteringToggle
-          enabled={value.enableTagFiltering}
-          routerFieldsMetadata={routerFieldsMetadata}
-          onToggle={handleTagFilteringToggle}
-        />
-      </div>
-
-      {/* Divider */}
-      <div className="border-t border-gray-200" />
-
-      {/* Strategy-Specific Args - Show immediately after strategy if latency-based */}
-      {value.selectedStrategy === "latency-based-routing" && (
-        <LatencyBasedConfiguration routingStrategyArgs={value.routerSettings["routing_strategy_args"]} />
-      )}
-
-      {/* Other Settings */}
-      <ReliabilityRetriesSection routerSettings={value.routerSettings} routerFieldsMetadata={routerFieldsMetadata} />
-    </div>
-  );
-};
-
-export default RouterSettingsForm;
diff --git a/ui/litellm-dashboard/src/components/router_settings/index.tsx b/ui/litellm-dashboard/src/components/router_settings/index.tsx
index aaad5306832..a94860eacd1 100644
--- a/ui/litellm-dashboard/src/components/router_settings/index.tsx
+++ b/ui/litellm-dashboard/src/components/router_settings/index.tsx
@@ -2,7 +2,10 @@ import { Button } from "@tremor/react";
 import React, { useEffect, useState } from "react";
 import NotificationsManager from "../molecules/notifications_manager";
 import { getCallbacksCall, getRouterSettingsCall, setCallbacksCall } from "../networking";
-import RouterSettingsForm, { RouterSettingsFormValue } from "./RouterSettingsForm";
+import LatencyBasedConfiguration from "./LatencyBasedConfiguration";
+import ReliabilityRetriesSection from "./ReliabilityRetriesSection";
+import RoutingStrategySelector from "./RoutingStrategySelector";
+import TagFilteringToggle from "./TagFilteringToggle";
 
 interface RouterSettingsProps {
   accessToken: string | null;
@@ -17,14 +20,12 @@ interface routingStrategyArgs {
 }
 
 const RouterSettings: React.FC<RouterSettingsProps> = ({ accessToken, userRole, userID, modelData }) => {
-  const [formValue, setFormValue] = useState<RouterSettingsFormValue>({
-    routerSettings: {},
-    selectedStrategy: null,
-    enableTagFiltering: false,
-  });
+  const [routerSettings, setRouterSettings] = useState<{ [key: string]: any }>({});
+  const [selectedStrategy, setSelectedStrategy] = useState<string | null>(null);
   const [availableRoutingStrategies, setAvailableRoutingStrategies] = useState<string[]>([]);
   const [routerFieldsMetadata, setRouterFieldsMetadata] = useState<{ [key: string]: any }>({});
   const [routingStrategyDescriptions, setRoutingStrategyDescriptions] = useState<{ [key: string]: string }>({});
+  const [enableTagFiltering, setEnableTagFiltering] = useState<boolean>(false);
 
   useEffect(() => {
     if (!accessToken || !userRole || !userID) {
@@ -36,13 +37,11 @@ const RouterSettings: React.FC<RouterSettingsProps> = ({ accessToken, userRole,
       if ("model_group_retry_policy" in router_settings) {
         delete router_settings["model_group_retry_policy"];
       }
+      setRouterSettings(router_settings);
       // Set initial selected strategy
-      const initialStrategy = router_settings.routing_strategy || null;
-      setFormValue((prev) => ({
-        ...prev,
-        routerSettings: router_settings,
-        selectedStrategy: initialStrategy,
-      }));
+      if (router_settings.routing_strategy) {
+        setSelectedStrategy(router_settings.routing_strategy);
+      }
     });
     getRouterSettingsCall(accessToken).then((data) => {
       console.log("router settings from API", data);
@@ -73,21 +72,17 @@ const RouterSettings: React.FC<RouterSettingsProps> = ({ accessToken, userRole,
         // Set enable_tag_filtering value
         const tagFilteringField = data.fields.find((field: any) => field.field_name === "enable_tag_filtering");
         if (tagFilteringField?.field_value !== null && tagFilteringField?.field_value !== undefined) {
-          setFormValue((prev) => ({
-            ...prev,
-            enableTagFiltering: tagFilteringField.field_value,
-          }));
+          setEnableTagFiltering(tagFilteringField.field_value);
         }
       }
     });
   }, [accessToken, userRole, userID]);
 
-  const handleSaveChanges = () => {
+  const handleSaveChanges = (router_settings: any) => {
     if (!accessToken) {
       return;
     }
 
-    const router_settings = formValue.routerSettings;
     console.log("router_settings", router_settings);
 
     const numberKeys = new Set(["allowed_fails", "cooldown_time", "num_retries", "timeout", "retry_after"]);
@@ -123,7 +118,7 @@ const RouterSettings: React.FC<RouterSettingsProps> = ({ accessToken, userRole,
     // Add enable_tag_filtering to router_settings before processing
     const settingsToUpdate = {
       ...router_settings,
-      enable_tag_filtering: formValue.enableTagFiltering,
+      enable_tag_filtering: enableTagFiltering,
     };
 
     const updatedVariables = Object.fromEntries(
@@ -134,10 +129,10 @@ const RouterSettings: React.FC<RouterSettingsProps> = ({ accessToken, userRole,
             const parsed = parseInputValue(key, inputEl?.value, value);
             return [key, parsed];
           } else if (key === "routing_strategy") {
-            return [key, formValue.selectedStrategy];
+            return [key, selectedStrategy];
           } else if (key === "enable_tag_filtering") {
-            return [key, formValue.enableTagFiltering];
-          } else if (key === "routing_strategy_args" && formValue.selectedStrategy === "latency-based-routing") {
+            return [key, enableTagFiltering];
+          } else if (key === "routing_strategy_args" && selectedStrategy === "latency-based-routing") {
             let setRoutingStrategyArgs: routingStrategyArgs = {};
 
             const lowestLatencyBufferElement = document.querySelector(
@@ -180,21 +175,50 @@ const RouterSettings: React.FC<RouterSettingsProps> = ({ accessToken, userRole,
   }
 
   return (
-    <div className="w-full">
-      <RouterSettingsForm
-        value={formValue}
-        onChange={setFormValue}
-        routerFieldsMetadata={routerFieldsMetadata}
-        availableRoutingStrategies={availableRoutingStrategies}
-        routingStrategyDescriptions={routingStrategyDescriptions}
-      />
+    <div className="w-full space-y-8 py-2">
+      {/* Routing Settings Section */}
+      <div className="space-y-6">
+        <div className="max-w-3xl">
+          <h3 className="text-sm font-medium text-gray-900">Routing Settings</h3>
+          <p className="text-xs text-gray-500 mt-1">Configure how requests are routed to deployments</p>
+        </div>
+
+        {/* Routing Strategy */}
+        {routerSettings.routing_strategy && (
+          <RoutingStrategySelector
+            selectedStrategy={selectedStrategy || routerSettings.routing_strategy}
+            availableStrategies={availableRoutingStrategies}
+            routingStrategyDescriptions={routingStrategyDescriptions}
+            routerFieldsMetadata={routerFieldsMetadata}
+            onStrategyChange={setSelectedStrategy}
+          />
+        )}
+
+        {/* Tag Filtering */}
+        <TagFilteringToggle
+          enabled={enableTagFiltering}
+          routerFieldsMetadata={routerFieldsMetadata}
+          onToggle={setEnableTagFiltering}
+        />
+      </div>
+
+      {/* Divider */}
+      <div className="border-t border-gray-200" />
+
+      {/* Strategy-Specific Args - Show immediately after strategy if latency-based */}
+      {selectedStrategy === "latency-based-routing" && (
+        <LatencyBasedConfiguration routingStrategyArgs={routerSettings["routing_strategy_args"]} />
+      )}
+
+      {/* Other Settings */}
+      <ReliabilityRetriesSection routerSettings={routerSettings} routerFieldsMetadata={routerFieldsMetadata} />
 
       {/* Actions - Sticky at bottom */}
       <div className="border-t border-gray-200 pt-6 flex justify-end gap-3">
         <Button variant="secondary" size="sm" onClick={() => window.location.reload()} className="text-sm">
           Reset
         </Button>
-        <Button size="sm" onClick={handleSaveChanges} className="text-sm font-medium">
+        <Button size="sm" onClick={() => handleSaveChanges(routerSettings)} className="text-sm font-medium">
           Save Changes
         </Button>
       </div>
@@ -203,5 +227,3 @@ const RouterSettings: React.FC<RouterSettingsProps> = ({ accessToken, userRole,
 };
 
 export default RouterSettings;
-export { RouterSettingsForm };
-export type { RouterSettingsFormValue };
diff --git a/ui/litellm-dashboard/src/components/survey/NudgePrompt.test.tsx b/ui/litellm-dashboard/src/components/survey/NudgePrompt.test.tsx
deleted file mode 100644
index 26db8a680c5..00000000000
--- a/ui/litellm-dashboard/src/components/survey/NudgePrompt.test.tsx
+++ /dev/null
@@ -1,101 +0,0 @@
-import { render, screen } from "@testing-library/react";
-import { MessageSquare } from "lucide-react";
-import { afterEach, beforeEach, describe, expect, it, vi } from "vitest";
-import { NudgePrompt } from "./NudgePrompt";
-
-vi.mock("@/app/(dashboard)/hooks/useDisableShowPrompts", () => ({
-  useDisableShowPrompts: vi.fn(),
-}));
-
-vi.mock("@/utils/localStorageUtils", () => ({
-  setLocalStorageItem: vi.fn(),
-  emitLocalStorageChange: vi.fn(),
-  LOCAL_STORAGE_EVENT: "local-storage-change",
-}));
-
-import { useDisableShowPrompts } from "@/app/(dashboard)/hooks/useDisableShowPrompts";
-import { emitLocalStorageChange, setLocalStorageItem } from "@/utils/localStorageUtils";
-
-const mockUseDisableShowPrompts = vi.mocked(useDisableShowPrompts);
-const mockSetLocalStorageItem = vi.mocked(setLocalStorageItem);
-const mockEmitLocalStorageChange = vi.mocked(emitLocalStorageChange);
-
-const defaultProps = {
-  onOpen: vi.fn(),
-  onDismiss: vi.fn(),
-  isVisible: true,
-  title: "Test Title",
-  description: "Test Description",
-  buttonText: "Open Modal",
-  icon: MessageSquare,
-  accentColor: "#3b82f6",
-};
-
-describe("NudgePrompt", () => {
-  beforeEach(() => {
-    vi.clearAllMocks();
-    mockUseDisableShowPrompts.mockReturnValue(false);
-    vi.useFakeTimers();
-  });
-
-  afterEach(() => {
-    vi.useRealTimers();
-  });
-
-  it("should render", () => {
-    render(<NudgePrompt {...defaultProps} />);
-
-    expect(screen.getByText("Test Title")).toBeInTheDocument();
-  });
-
-  it("should render with all provided props", () => {
-    const { container } = render(<NudgePrompt {...defaultProps} />);
-
-    expect(screen.getByText("Test Title")).toBeInTheDocument();
-    expect(screen.getByText("Test Description")).toBeInTheDocument();
-    expect(screen.getByRole("button", { name: "Open Modal" })).toBeInTheDocument();
-    expect(container.querySelector("svg")).toBeInTheDocument();
-  });
-
-  it("should not render when isVisible is false", () => {
-    render(<NudgePrompt {...defaultProps} isVisible={false} />);
-
-    expect(screen.queryByText("Test Title")).not.toBeInTheDocument();
-  });
-
-  it("should not render when disableShowPrompts is true", () => {
-    mockUseDisableShowPrompts.mockReturnValue(true);
-
-    render(<NudgePrompt {...defaultProps} />);
-
-    expect(screen.queryByText("Test Title")).not.toBeInTheDocument();
-  });
-
-  it("should display progress bar with correct accent color", () => {
-    const { container } = render(<NudgePrompt {...defaultProps} accentColor="#ff0000" />);
-
-    const progressBar = container.querySelector("div[style*='width']");
-    expect(progressBar).toHaveStyle({ backgroundColor: "#ff0000" });
-  });
-
-  it("should reset progress when isVisible becomes false", () => {
-    const { rerender, container } = render(<NudgePrompt {...defaultProps} />);
-
-    vi.advanceTimersByTime(5000);
-
-    rerender(<NudgePrompt {...defaultProps} isVisible={false} />);
-
-    rerender(<NudgePrompt {...defaultProps} isVisible={true} />);
-
-    const progressBar = container.querySelector("div[style*='width']");
-    expect(progressBar?.getAttribute("style")).toContain("width: 100%");
-  });
-
-  it("should apply custom button style when provided", () => {
-    const buttonStyle = { backgroundColor: "#custom-color" };
-    render(<NudgePrompt {...defaultProps} buttonStyle={buttonStyle} />);
-
-    const openButton = screen.getByRole("button", { name: "Open Modal" });
-    expect(openButton).toHaveStyle(buttonStyle);
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/survey/NudgePrompt.tsx b/ui/litellm-dashboard/src/components/survey/NudgePrompt.tsx
index 3c7f10af74c..9095c6c21c5 100644
--- a/ui/litellm-dashboard/src/components/survey/NudgePrompt.tsx
+++ b/ui/litellm-dashboard/src/components/survey/NudgePrompt.tsx
@@ -1,8 +1,6 @@
 import React, { useEffect, useState } from "react";
-import { X, LucideIcon, Check } from "lucide-react";
+import { X, LucideIcon } from "lucide-react";
 import { Button } from "antd";
-import { useDisableShowPrompts } from "@/app/(dashboard)/hooks/useDisableShowPrompts";
-import { setLocalStorageItem, emitLocalStorageChange } from "@/utils/localStorageUtils";
 
 interface NudgePromptProps {
   onOpen: () => void;
@@ -17,7 +15,6 @@ interface NudgePromptProps {
 }
 
 const DISMISS_DURATION = 15000; // 15 seconds
-const CONFIRMATION_DURATION = 5000; // 5 seconds
 
 export function NudgePrompt({
   onOpen,
@@ -30,14 +27,11 @@ export function NudgePrompt({
   accentColor,
   buttonStyle,
 }: NudgePromptProps) {
-  const disableShowPrompts = useDisableShowPrompts();
   const [progress, setProgress] = useState(100);
-  const [showConfirmation, setShowConfirmation] = useState(false);
 
   useEffect(() => {
     if (!isVisible) {
       setProgress(100);
-      setShowConfirmation(false);
       return;
     }
 
@@ -55,53 +49,13 @@ export function NudgePrompt({
     return () => clearInterval(interval);
   }, [isVisible]);
 
-  useEffect(() => {
-    if (showConfirmation) {
-      const timer = setTimeout(() => {
-        setShowConfirmation(false);
-        onDismiss();
-      }, CONFIRMATION_DURATION);
-
-      return () => clearTimeout(timer);
-    }
-  }, [showConfirmation, onDismiss]);
-
-  const handleDontAskAgain = () => {
-    setLocalStorageItem("disableShowPrompts", "true");
-    emitLocalStorageChange("disableShowPrompts");
-    setShowConfirmation(true);
-  };
-
-  // Show confirmation even if disableShowPrompts is true (since we just set it)
-  if (showConfirmation) {
-    return (
-      <div
-        className={`fixed bottom-6 right-6 z-40 w-80 bg-white rounded-lg shadow-xl border border-gray-200 overflow-hidden transform transition-all duration-300 ease-out ${isVisible ? "translate-y-0 opacity-100 scale-100" : "translate-y-4 opacity-0 scale-95"
-          }`}
-      >
-        <div className="p-4">
-          <div className="flex items-center gap-3">
-            <div className="flex-shrink-0 w-8 h-8 rounded-full bg-green-100 flex items-center justify-center">
-              <Check className="h-5 w-5 text-green-600" />
-            </div>
-            <div className="flex-1">
-              <p className="text-sm text-gray-700 font-medium">
-                Got it, we will not ask again. Reactivate this at any time in the User Menu.
-              </p>
-            </div>
-          </div>
-        </div>
-      </div>
-    );
-  }
-
-  // Don't show the prompt if disabled (unless we're showing confirmation)
-  if (!isVisible || disableShowPrompts) return null;
+  if (!isVisible) return null;
 
   return (
     <div
-      className={`fixed bottom-6 right-6 z-40 w-80 bg-white rounded-lg shadow-xl border border-gray-200 overflow-hidden transform transition-all duration-300 ease-out ${isVisible ? "translate-y-0 opacity-100 scale-100" : "translate-y-4 opacity-0 scale-95"
-        }`}
+      className={`fixed bottom-6 right-6 z-40 w-80 bg-white rounded-lg shadow-xl border border-gray-200 overflow-hidden transform transition-all duration-300 ease-out ${
+        isVisible ? "translate-y-0 opacity-100 scale-100" : "translate-y-4 opacity-0 scale-95"
+      }`}
     >
       {/* Progress bar at top showing time remaining */}
       <div className="h-1 bg-gray-100 w-full">
@@ -127,20 +81,9 @@ export function NudgePrompt({
 
         <p className="text-sm text-gray-600 mb-3">{description}</p>
 
-        <div className="space-y-2">
-          <Button type="primary" block onClick={onOpen} style={buttonStyle}>
-            {buttonText}
-          </Button>
-          <Button
-            variant="outlined"
-            danger
-            block
-            onClick={handleDontAskAgain}
-            className="text-xs"
-          >
-            Don&apos;t ask me again
-          </Button>
-        </div>
+        <Button type="primary" block onClick={onOpen} style={buttonStyle}>
+          {buttonText}
+        </Button>
       </div>
     </div>
   );
diff --git a/ui/litellm-dashboard/src/components/team/team_info.tsx b/ui/litellm-dashboard/src/components/team/team_info.tsx
index 193d056fdd4..819c9f0f9b2 100644
--- a/ui/litellm-dashboard/src/components/team/team_info.tsx
+++ b/ui/litellm-dashboard/src/components/team/team_info.tsx
@@ -2,8 +2,6 @@ import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
 import UserSearchModal from "@/components/common_components/user_search_modal";
 import {
   getGuardrailsList,
-  getPoliciesList,
-  getPolicyInfoWithGuardrails,
   Member,
   Organization,
   organizationInfoCall,
@@ -96,8 +94,6 @@ export interface TeamData {
       model_aliases: Record<string, string>;
     } | null;
     created_at: string;
-    guardrails?: string[];
-    policies?: string[];
     object_permission?: {
       object_permission_id: string;
       mcp_servers: string[];
@@ -175,9 +171,6 @@ const TeamInfoView: React.FC<TeamInfoProps> = ({
   const [mcpAccessGroupsLoaded, setMcpAccessGroupsLoaded] = useState(false);
   const [copiedStates, setCopiedStates] = useState<Record<string, boolean>>({});
   const [guardrailsList, setGuardrailsList] = useState<string[]>([]);
-  const [policiesList, setPoliciesList] = useState<string[]>([]);
-  const [policyGuardrails, setPolicyGuardrails] = useState<Record<string, string[]>>({});
-  const [loadingPolicies, setLoadingPolicies] = useState(false);
   const [memberToDelete, setMemberToDelete] = useState<Member | null>(null);
   const [isDeleteModalOpen, setIsDeleteModalOpen] = useState(false);
   const [isDeleting, setIsDeleting] = useState(false);
@@ -254,54 +247,9 @@ const TeamInfoView: React.FC<TeamInfoProps> = ({
       }
     };
 
-    const fetchPolicies = async () => {
-      try {
-        if (!accessToken) return;
-        const response = await getPoliciesList(accessToken);
-        const policyNames = response.policies.map((p: { policy_name: string }) => p.policy_name);
-        setPoliciesList(policyNames);
-      } catch (error) {
-        console.error("Failed to fetch policies:", error);
-      }
-    };
-
     fetchGuardrails();
-    fetchPolicies();
   }, [accessToken]);
 
-  // Fetch resolved guardrails for all policies
-  useEffect(() => {
-    const fetchPolicyGuardrails = async () => {
-      if (!accessToken || !teamData?.team_info?.policies || teamData.team_info.policies.length === 0) {
-        return;
-      }
-
-      setLoadingPolicies(true);
-      const guardrailsMap: Record<string, string[]> = {};
-
-      try {
-        await Promise.all(
-          teamData.team_info.policies.map(async (policyName: string) => {
-            try {
-              const policyInfo = await getPolicyInfoWithGuardrails(accessToken, policyName);
-              guardrailsMap[policyName] = policyInfo.resolved_guardrails || [];
-            } catch (error) {
-              console.error(`Failed to fetch guardrails for policy ${policyName}:`, error);
-              guardrailsMap[policyName] = [];
-            }
-          })
-        );
-        setPolicyGuardrails(guardrailsMap);
-      } catch (error) {
-        console.error("Failed to fetch policy guardrails:", error);
-      } finally {
-        setLoadingPolicies(false);
-      }
-    };
-
-    fetchPolicyGuardrails();
-  }, [accessToken, teamData?.team_info?.policies]);
-
   const handleMemberCreate = async (values: any) => {
     try {
       if (accessToken == null) return;
@@ -464,7 +412,6 @@ const TeamInfoView: React.FC<TeamInfoProps> = ({
           logging: values.logging_settings || [],
           ...(secretManagerSettings !== undefined ? { secret_manager_settings: secretManagerSettings } : {}),
         },
-        policies: values.policies || [],
         organization_id: values.organization_id,
       };
 
@@ -656,56 +603,6 @@ const TeamInfoView: React.FC<TeamInfoProps> = ({
                 accessToken={accessToken}
               />
 
-              <Card>
-                <Text className="font-semibold text-gray-900 mb-3">Guardrails</Text>
-                {info.guardrails && info.guardrails.length > 0 ? (
-                  <div className="flex flex-wrap gap-2">
-                    {info.guardrails.map((guardrail: string, index: number) => (
-                      <Badge key={index} color="blue">
-                        {guardrail}
-                      </Badge>
-                    ))}
-                  </div>
-                ) : (
-                  <Text className="text-gray-500">No guardrails configured</Text>
-                )}
-                {info.metadata?.disable_global_guardrails && (
-                  <div className="mt-3 pt-3 border-t border-gray-200">
-                    <Badge color="yellow">Global Guardrails Disabled</Badge>
-                  </div>
-                )}
-              </Card>
-
-              <Card>
-                <Text className="font-semibold text-gray-900 mb-3">Policies</Text>
-                {info.policies && info.policies.length > 0 ? (
-                  <div className="space-y-4">
-                    {info.policies.map((policy: string, index: number) => (
-                      <div key={index} className="space-y-2">
-                        <div className="flex items-center gap-2">
-                          <Badge color="purple">{policy}</Badge>
-                          {loadingPolicies && <Text className="text-xs text-gray-400">Loading guardrails...</Text>}
-                        </div>
-                        {!loadingPolicies && policyGuardrails[policy] && policyGuardrails[policy].length > 0 && (
-                          <div className="ml-4 pl-3 border-l-2 border-gray-200">
-                            <Text className="text-xs text-gray-500 mb-1">Resolved Guardrails:</Text>
-                            <div className="flex flex-wrap gap-1">
-                              {policyGuardrails[policy].map((guardrail: string, gIndex: number) => (
-                                <Badge key={gIndex} color="blue" size="xs">
-                                  {guardrail}
-                                </Badge>
-                              ))}
-                            </div>
-                          </div>
-                        )}
-                      </div>
-                    ))}
-                  </div>
-                ) : (
-                  <Text className="text-gray-500">No policies configured</Text>
-                )}
-              </Card>
-
               <LoggingSettingsView
                 loggingConfigs={info.metadata?.logging || []}
                 disabledCallbacks={[]}
@@ -760,7 +657,6 @@ const TeamInfoView: React.FC<TeamInfoProps> = ({
                     team_member_budget: info.team_member_budget_table?.max_budget,
                     team_member_budget_duration: info.team_member_budget_table?.budget_duration,
                     guardrails: info.metadata?.guardrails || [],
-                    policies: info.policies || [],
                     disable_global_guardrails: info.metadata?.disable_global_guardrails || false,
                     metadata: info.metadata
                       ? JSON.stringify(
@@ -918,32 +814,6 @@ const TeamInfoView: React.FC<TeamInfoProps> = ({
                     <Switch checkedChildren="Yes" unCheckedChildren="No" />
                   </Form.Item>
 
-                  <Form.Item
-                    label={
-                      <span>
-                        Policies{" "}
-                        <Tooltip title="Apply policies to this team to control guardrails and other settings">
-                          <a
-                            href="https://docs.litellm.ai/docs/proxy/guardrails/guardrail_policies"
-                            target="_blank"
-                            rel="noopener noreferrer"
-                            onClick={(e) => e.stopPropagation()}
-                          >
-                            <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-                          </a>
-                        </Tooltip>
-                      </span>
-                    }
-                    name="policies"
-                    help="Select existing policies or enter new ones"
-                  >
-                    <Select
-                      mode="tags"
-                      placeholder="Select or enter policies"
-                      options={policiesList.map((name) => ({ value: name, label: name }))}
-                    />
-                  </Form.Item>
-
                   <Form.Item label="Vector Stores" name="vector_stores" aria-label="Vector Stores">
                     <VectorStoreSelector
                       onChange={(values: string[]) => form.setFieldValue("vector_stores", values)}
diff --git a/ui/litellm-dashboard/src/components/templates/key_edit_view.tsx b/ui/litellm-dashboard/src/components/templates/key_edit_view.tsx
index 2d3eaf2bb7f..836ebb385f1 100644
--- a/ui/litellm-dashboard/src/components/templates/key_edit_view.tsx
+++ b/ui/litellm-dashboard/src/components/templates/key_edit_view.tsx
@@ -1,5 +1,4 @@
 import GuardrailSelector from "@/components/guardrails/GuardrailSelector";
-import PolicySelector from "@/components/policies/PolicySelector";
 import { InfoCircleOutlined } from "@ant-design/icons";
 import { TextInput, Button as TremorButton } from "@tremor/react";
 import { Form, Input, Select, Switch, Tooltip } from "antd";
@@ -407,28 +406,6 @@ export function KeyEditView({
         <Switch disabled={!premiumUser} checkedChildren="Yes" unCheckedChildren="No" />
       </Form.Item>
 
-      <Form.Item
-        label={
-          <span>
-            Policies{" "}
-            <Tooltip title="Apply policies to this key to control guardrails and other settings">
-              <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-            </Tooltip>
-          </span>
-        }
-        name="policies"
-      >
-        {accessToken && (
-          <PolicySelector
-            onChange={(v) => {
-              form.setFieldValue("policies", v);
-            }}
-            accessToken={accessToken}
-            disabled={!premiumUser}
-          />
-        )}
-      </Form.Item>
-
       <Form.Item label="Tags" name="tags">
         <Select
           mode="tags"
diff --git a/ui/litellm-dashboard/src/components/templates/key_info_view.tsx b/ui/litellm-dashboard/src/components/templates/key_info_view.tsx
index d9d4bf53384..e22ec226fc9 100644
--- a/ui/litellm-dashboard/src/components/templates/key_info_view.tsx
+++ b/ui/litellm-dashboard/src/components/templates/key_info_view.tsx
@@ -14,7 +14,7 @@ import { extractLoggingSettings, formatMetadataForDisplay, stripTagsFromMetadata
 import { KeyResponse } from "../key_team_helpers/key_list";
 import LoggingSettingsView from "../logging_settings_view";
 import NotificationManager from "../molecules/notifications_manager";
-import { keyDeleteCall, keyUpdateCall, getPolicyInfoWithGuardrails } from "../networking";
+import { keyDeleteCall, keyUpdateCall } from "../networking";
 import ObjectPermissionsView from "../object_permissions_view";
 import { RegenerateKeyModal } from "../organisms/regenerate_key_modal";
 import { parseErrorMessage } from "../shared/errorUtils";
@@ -60,8 +60,6 @@ export default function KeyInfoView({
   const [currentKeyData, setCurrentKeyData] = useState<KeyResponse | undefined>(keyData);
   const [lastRegeneratedAt, setLastRegeneratedAt] = useState<Date | null>(null);
   const [isRecentlyRegenerated, setIsRecentlyRegenerated] = useState(false);
-  const [policyGuardrails, setPolicyGuardrails] = useState<Record<string, string[]>>({});
-  const [loadingPolicies, setLoadingPolicies] = useState(false);
 
   // Update local state when keyData prop changes (but don't reset to undefined)
   useEffect(() => {
@@ -70,40 +68,6 @@ export default function KeyInfoView({
     }
   }, [keyData]);
 
-  // Fetch resolved guardrails for all policies
-  useEffect(() => {
-    const fetchPolicyGuardrails = async () => {
-      const policies = currentKeyData?.metadata?.policies;
-      if (!accessToken || !policies || !Array.isArray(policies) || policies.length === 0) {
-        return;
-      }
-
-      setLoadingPolicies(true);
-      const guardrailsMap: Record<string, string[]> = {};
-
-      try {
-        await Promise.all(
-          policies.map(async (policyName: string) => {
-            try {
-              const policyInfo = await getPolicyInfoWithGuardrails(accessToken, policyName);
-              guardrailsMap[policyName] = policyInfo.resolved_guardrails || [];
-            } catch (error) {
-              console.error(`Failed to fetch guardrails for policy ${policyName}:`, error);
-              guardrailsMap[policyName] = [];
-            }
-          })
-        );
-        setPolicyGuardrails(guardrailsMap);
-      } catch (error) {
-        console.error("Failed to fetch policy guardrails:", error);
-      } finally {
-        setLoadingPolicies(false);
-      }
-    };
-
-    fetchPolicyGuardrails();
-  }, [accessToken, currentKeyData?.metadata?.policies]);
-
   // Reset recent regeneration indicator after 5 seconds
   useEffect(() => {
     if (isRecentlyRegenerated) {
@@ -520,57 +484,6 @@ export default function KeyInfoView({
                 />
               </Card>
 
-              <Card>
-                <Text className="font-medium mb-3">Guardrails</Text>
-                {Array.isArray(currentKeyData.metadata?.guardrails) && currentKeyData.metadata.guardrails.length > 0 ? (
-                  <div className="flex flex-wrap gap-2">
-                    {currentKeyData.metadata.guardrails.map((guardrail: string, index: number) => (
-                      <Badge key={index} color="blue">
-                        {guardrail}
-                      </Badge>
-                    ))}
-                  </div>
-                ) : (
-                  <Text className="text-gray-500">No guardrails configured</Text>
-                )}
-                {typeof currentKeyData.metadata?.disable_global_guardrails === "boolean" &&
-                  currentKeyData.metadata.disable_global_guardrails === true && (
-                    <div className="mt-3 pt-3 border-t border-gray-200">
-                      <Badge color="yellow">Global Guardrails Disabled</Badge>
-                    </div>
-                  )}
-              </Card>
-
-              <Card>
-                <Text className="font-medium mb-3">Policies</Text>
-                {Array.isArray(currentKeyData.metadata?.policies) && currentKeyData.metadata.policies.length > 0 ? (
-                  <div className="space-y-4">
-                    {currentKeyData.metadata.policies.map((policy: string, index: number) => (
-                      <div key={index} className="space-y-2">
-                        <div className="flex items-center gap-2">
-                          <Badge color="purple">{policy}</Badge>
-                          {loadingPolicies && <Text className="text-xs text-gray-400">Loading guardrails...</Text>}
-                        </div>
-                        {!loadingPolicies && policyGuardrails[policy] && policyGuardrails[policy].length > 0 && (
-                          <div className="ml-4 pl-3 border-l-2 border-gray-200">
-                            <Text className="text-xs text-gray-500 mb-1">Resolved Guardrails:</Text>
-                            <div className="flex flex-wrap gap-1">
-                              {policyGuardrails[policy].map((guardrail: string, gIndex: number) => (
-                                <Badge key={gIndex} color="blue" size="xs">
-                                  {guardrail}
-                                </Badge>
-                              ))}
-                            </div>
-                          </div>
-                        )}
-                      </div>
-                    ))}
-                  </div>
-                ) : (
-                  <Text className="text-gray-500">No policies configured</Text>
-                )}
-              </Card>
-
               <LoggingSettingsView
                 loggingConfigs={extractLoggingSettings(currentKeyData.metadata)}
                 disabledCallbacks={
diff --git a/ui/litellm-dashboard/src/components/templates/model_dashboard.tsx b/ui/litellm-dashboard/src/components/templates/model_dashboard.tsx
index e4dc896f55d..e3a27dc11fe 100644
--- a/ui/litellm-dashboard/src/components/templates/model_dashboard.tsx
+++ b/ui/litellm-dashboard/src/components/templates/model_dashboard.tsx
@@ -1025,6 +1025,7 @@ const OldModelDashboard: React.FC<ModelDashboardProps> = ({
                 setSelectedModelId(null);
                 setEditModel(false);
               }}
+              modelData={modelData.data.find((model: any) => model.model_info.id === selectedModelId)}
               accessToken={accessToken}
               userID={userID}
               userRole={userRole}
@@ -1269,9 +1270,9 @@ const OldModelDashboard: React.FC<ModelDashboardProps> = ({
                               <span className="text-sm text-gray-700">
                                 {filteredData.length > 0
                                   ? `Showing ${pagination.pageIndex * pagination.pageSize + 1} - ${Math.min(
-                                    (pagination.pageIndex + 1) * pagination.pageSize,
-                                    filteredData.length,
-                                  )} of ${filteredData.length} results`
+                                      (pagination.pageIndex + 1) * pagination.pageSize,
+                                      filteredData.length,
+                                    )} of ${filteredData.length} results`
                                   : "Showing 0 results"}
                               </span>
 
@@ -1283,10 +1284,11 @@ const OldModelDashboard: React.FC<ModelDashboardProps> = ({
                                       setPagination((prev) => ({ ...prev, pageIndex: prev.pageIndex - 1 }))
                                     }
                                     disabled={pagination.pageIndex === 0}
-                                    className={`px-3 py-1 text-sm border rounded-md ${pagination.pageIndex === 0
-                                      ? "bg-gray-100 text-gray-400 cursor-not-allowed"
-                                      : "hover:bg-gray-50"
-                                      }`}
+                                    className={`px-3 py-1 text-sm border rounded-md ${
+                                      pagination.pageIndex === 0
+                                        ? "bg-gray-100 text-gray-400 cursor-not-allowed"
+                                        : "hover:bg-gray-50"
+                                    }`}
                                   >
                                     Previous
                                   </button>
@@ -1298,10 +1300,11 @@ const OldModelDashboard: React.FC<ModelDashboardProps> = ({
                                     disabled={
                                       pagination.pageIndex >= Math.ceil(filteredData.length / pagination.pageSize) - 1
                                     }
-                                    className={`px-3 py-1 text-sm border rounded-md ${pagination.pageIndex >= Math.ceil(filteredData.length / pagination.pageSize) - 1
-                                      ? "bg-gray-100 text-gray-400 cursor-not-allowed"
-                                      : "hover:bg-gray-50"
-                                      }`}
+                                    className={`px-3 py-1 text-sm border rounded-md ${
+                                      pagination.pageIndex >= Math.ceil(filteredData.length / pagination.pageSize) - 1
+                                        ? "bg-gray-100 text-gray-400 cursor-not-allowed"
+                                        : "hover:bg-gray-50"
+                                    }`}
                                   >
                                     Next
                                   </button>
diff --git a/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.test.tsx
deleted file mode 100644
index a8ba55b91f6..00000000000
--- a/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.test.tsx
+++ /dev/null
@@ -1,276 +0,0 @@
-import { render, screen, fireEvent, waitFor, act } from "@testing-library/react";
-import { describe, it, expect, vi, beforeEach } from "vitest";
-import CreateVectorStore from "./CreateVectorStore";
-import * as networking from "../networking";
-
-// Mock the networking module
-vi.mock("../networking", () => ({
-  ragIngestCall: vi.fn(),
-}));
-
-// Mock NotificationsManager
-vi.mock("../molecules/notifications_manager", () => ({
-  default: {
-    success: vi.fn(),
-    fromBackend: vi.fn(),
-  },
-}));
-
-// Mock vector_store_providers
-vi.mock("../vector_store_providers", () => ({
-  VectorStoreProviders: {
-    BEDROCK: "Amazon Bedrock",
-    OPENAI: "OpenAI",
-    AZURE_OPENAI: "Azure OpenAI",
-    S3Vectors: "AWS S3 Vectors",
-  },
-  vectorStoreProviderMap: {
-    BEDROCK: "bedrock",
-    OPENAI: "openai",
-    AZURE_OPENAI: "azure_openai",
-    S3Vectors: "s3_vectors",
-  },
-  vectorStoreProviderLogoMap: {
-    "Amazon Bedrock": "https://example.com/bedrock.png",
-    "OpenAI": "https://example.com/openai.png",
-    "Azure OpenAI": "https://example.com/azure.png",
-    "AWS S3 Vectors": "https://example.com/aws.png",
-  },
-  getProviderSpecificFields: vi.fn((provider: string) => {
-    if (provider === "s3_vectors") {
-      return [
-        {
-          name: "vector_bucket_name",
-          label: "Vector Bucket Name",
-          tooltip: "S3 bucket name for vector storage",
-          placeholder: "my-vector-bucket",
-          required: true,
-          type: "text",
-        },
-        {
-          name: "aws_region_name",
-          label: "AWS Region",
-          tooltip: "AWS region",
-          placeholder: "us-west-2",
-          required: true,
-          type: "text",
-        },
-        {
-          name: "embedding_model",
-          label: "Embedding Model",
-          tooltip: "Embedding model to use",
-          placeholder: "text-embedding-3-small",
-          required: true,
-          type: "select",
-        },
-      ];
-    }
-    return [];
-  }),
-}));
-
-describe("CreateVectorStore", () => {
-  beforeEach(() => {
-    vi.clearAllMocks();
-  });
-
-  it("should render the component successfully", () => {
-    render(<CreateVectorStore accessToken="test-token" />);
-
-    expect(screen.getAllByText("Create Vector Store").length).toBeGreaterThan(0);
-    expect(screen.getByText("Step 1: Upload Documents")).toBeInTheDocument();
-    expect(screen.getByText("Step 2: Configure Vector Store")).toBeInTheDocument();
-  });
-
-  it("should display upload area with correct text", () => {
-    render(<CreateVectorStore accessToken="test-token" />);
-
-    expect(screen.getByText("Click or drag files to this area to upload")).toBeInTheDocument();
-    expect(screen.getByText(/Support for single or bulk upload/)).toBeInTheDocument();
-  });
-
-  it("should have provider selection dropdown", () => {
-    render(<CreateVectorStore accessToken="test-token" />);
-
-    expect(screen.getByText("Provider")).toBeInTheDocument();
-  });
-
-  it("should have create button disabled initially when no documents", () => {
-    render(<CreateVectorStore accessToken="test-token" />);
-
-    const createButton = screen.getByRole("button", { name: /Create Vector Store/i });
-    expect(createButton).toBeDisabled();
-  });
-
-  it("should show uploaded documents table when files are added", async () => {
-    render(<CreateVectorStore accessToken="test-token" />);
-
-    // Create a mock file
-    const file = new File(["test content"], "test.pdf", { type: "application/pdf" });
-
-    // Find the upload input (it's hidden but accessible)
-    const uploadInput = document.querySelector('input[type="file"]') as HTMLInputElement;
-
-    await act(async () => {
-      if (uploadInput) {
-        fireEvent.change(uploadInput, { target: { files: [file] } });
-      }
-    });
-
-    await waitFor(() => {
-      expect(screen.getByText("Uploaded Documents (1)")).toBeInTheDocument();
-    });
-  });
-
-  it("should call ragIngestCall when create button is clicked", async () => {
-    const mockRagIngestCall = vi.spyOn(networking, "ragIngestCall");
-    mockRagIngestCall.mockResolvedValue({
-      id: "test-id",
-      status: "completed",
-      vector_store_id: "vs_123",
-      file_id: "file_123",
-    });
-
-    const onSuccess = vi.fn();
-    render(<CreateVectorStore accessToken="test-token" onSuccess={onSuccess} />);
-
-    // Create a mock file
-    const file = new File(["test content"], "test.pdf", { type: "application/pdf" });
-    const uploadInput = document.querySelector('input[type="file"]') as HTMLInputElement;
-
-    await act(async () => {
-      if (uploadInput) {
-        fireEvent.change(uploadInput, { target: { files: [file] } });
-      }
-    });
-
-    // Wait for file to be added
-    await waitFor(() => {
-      expect(screen.getByText("Uploaded Documents (1)")).toBeInTheDocument();
-    });
-
-    // Click create button
-    const createButton = screen.getByRole("button", { name: /Create Vector Store/i });
-
-    await act(async () => {
-      fireEvent.click(createButton);
-    });
-
-    await waitFor(() => {
-      expect(mockRagIngestCall).toHaveBeenCalledWith(
-        "test-token",
-        expect.any(File),
-        "bedrock",
-        undefined,
-        undefined,
-        undefined,
-        {}
-      );
-    });
-  });
-
-  it("should display success message after successful creation", async () => {
-    const mockRagIngestCall = vi.spyOn(networking, "ragIngestCall");
-    mockRagIngestCall.mockResolvedValue({
-      id: "test-id",
-      status: "completed",
-      vector_store_id: "vs_123",
-      file_id: "file_123",
-    });
-
-    render(<CreateVectorStore accessToken="test-token" />);
-
-    // Create and upload a mock file
-    const file = new File(["test content"], "test.pdf", { type: "application/pdf" });
-    const uploadInput = document.querySelector('input[type="file"]') as HTMLInputElement;
-
-    await act(async () => {
-      if (uploadInput) {
-        fireEvent.change(uploadInput, { target: { files: [file] } });
-      }
-    });
-
-    await waitFor(() => {
-      expect(screen.getByText("Uploaded Documents (1)")).toBeInTheDocument();
-    });
-
-    // Click create button
-    const createButton = screen.getByRole("button", { name: /Create Vector Store/i });
-
-    await act(async () => {
-      fireEvent.click(createButton);
-    });
-
-    await waitFor(() => {
-      expect(screen.getByText("Vector Store Created Successfully")).toBeInTheDocument();
-    });
-  });
-
-  it("should display S3 Vectors provider-specific fields when selected", async () => {
-    render(<CreateVectorStore accessToken="test-token" />);
-
-    // Find and click the provider dropdown
-    const providerSelect = screen.getByRole("combobox");
-
-    await act(async () => {
-      fireEvent.mouseDown(providerSelect);
-    });
-
-    // Wait for dropdown options to appear
-    await waitFor(() => {
-      const s3Option = screen.queryByText("AWS S3 Vectors");
-      if (s3Option) {
-        fireEvent.click(s3Option);
-      }
-    });
-
-    // Check if S3-specific fields are displayed
-    await waitFor(() => {
-      expect(screen.queryByText("Vector Bucket Name")).toBeInTheDocument();
-      expect(screen.queryByText("AWS Region")).toBeInTheDocument();
-      expect(screen.queryByText("Embedding Model")).toBeInTheDocument();
-    });
-  });
-
-  it("should validate S3 Vectors required fields before submission", async () => {
-    render(<CreateVectorStore accessToken="test-token" />);
-
-    // Upload a file first
-    const file = new File(["test content"], "test.pdf", { type: "application/pdf" });
-    const uploadInput = document.querySelector('input[type="file"]') as HTMLInputElement;
-
-    await act(async () => {
-      if (uploadInput) {
-        fireEvent.change(uploadInput, { target: { files: [file] } });
-      }
-    });
-
-    await waitFor(() => {
-      expect(screen.getByText("Uploaded Documents (1)")).toBeInTheDocument();
-    });
-
-    // Select S3 Vectors provider
-    const providerSelect = screen.getByRole("combobox");
-
-    await act(async () => {
-      fireEvent.mouseDown(providerSelect);
-    });
-
-    await waitFor(() => {
-      const s3Option = screen.queryByText("AWS S3 Vectors");
-      if (s3Option) {
-        fireEvent.click(s3Option);
-      }
-    });
-
-    // Try to create without filling required fields
-    const createButton = screen.getByRole("button", { name: /Create Vector Store/i });
-
-    await act(async () => {
-      fireEvent.click(createButton);
-    });
-
-    // Should show validation warning (mocked message.warning would be called)
-    // The actual validation happens in the component
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.tsx b/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.tsx
deleted file mode 100644
index 97162aa132d..00000000000
--- a/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.tsx
+++ /dev/null
@@ -1,434 +0,0 @@
-import React, { useState } from "react";
-import { Card, Title, Text } from "@tremor/react";
-import { Upload, Button, Select, Form, message, Alert, Tooltip, Input } from "antd";
-import { InboxOutlined, InfoCircleOutlined } from "@ant-design/icons";
-import type { UploadProps } from "antd";
-import { ragIngestCall } from "../networking";
-import { DocumentUpload, RAGIngestResponse } from "./types";
-import DocumentsTable from "./DocumentsTable";
-import {
-  VectorStoreProviders,
-  vectorStoreProviderLogoMap,
-  vectorStoreProviderMap,
-  getProviderSpecificFields,
-  VectorStoreFieldConfig,
-} from "../vector_store_providers";
-import NotificationsManager from "../molecules/notifications_manager";
-import S3VectorsConfig from "./S3VectorsConfig";
-
-const { Dragger } = Upload;
-
-interface CreateVectorStoreProps {
-  accessToken: string | null;
-  onSuccess?: (vectorStoreId: string) => void;
-}
-
-const CreateVectorStore: React.FC<CreateVectorStoreProps> = ({ accessToken, onSuccess }) => {
-  const [form] = Form.useForm();
-  const [documents, setDocuments] = useState<DocumentUpload[]>([]);
-  const [isCreating, setIsCreating] = useState(false);
-  const [selectedProvider, setSelectedProvider] = useState<string>("bedrock");
-  const [vectorStoreName, setVectorStoreName] = useState<string>("");
-  const [vectorStoreDescription, setVectorStoreDescription] = useState<string>("");
-  const [ingestResults, setIngestResults] = useState<RAGIngestResponse[]>([]);
-  const [providerParams, setProviderParams] = useState<Record<string, any>>({});
-
-  const uploadProps: UploadProps = {
-    name: "file",
-    multiple: true,
-    accept: ".pdf,.txt,.docx,.md,.doc",
-    beforeUpload: (file) => {
-      const isValidType = [
-        "application/pdf",
-        "text/plain",
-        "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-        "application/msword",
-        "text/markdown",
-      ].includes(file.type);
-
-      if (!isValidType) {
-        message.error(`${file.name} is not a supported file type. Please upload PDF, TXT, DOCX, or MD files.`);
-        return Upload.LIST_IGNORE;
-      }
-
-      const isLt50M = file.size / 1024 / 1024 < 50;
-      if (!isLt50M) {
-        message.error(`${file.name} must be smaller than 50MB!`);
-        return Upload.LIST_IGNORE;
-      }
-
-      const newDoc: DocumentUpload = {
-        uid: file.uid,
-        name: file.name,
-        status: "done",
-        size: file.size,
-        type: file.type,
-        originFileObj: file,
-      };
-
-      setDocuments((prev) => [...prev, newDoc]);
-      return false; // Prevent auto upload
-    },
-    onRemove: (file) => {
-      setDocuments((prev) => prev.filter((doc) => doc.uid !== file.uid));
-    },
-    fileList: documents.map((doc) => ({
-      uid: doc.uid,
-      name: doc.name,
-      status: doc.status,
-      size: doc.size,
-    })),
-    showUploadList: false, // We'll use our custom table
-  };
-
-  const handleRemoveDocument = (uid: string) => {
-    setDocuments((prev) => prev.filter((doc) => doc.uid !== uid));
-  };
-
-  const handleCreateVectorStore = async () => {
-    if (documents.length === 0) {
-      message.warning("Please upload at least one document");
-      return;
-    }
-
-    if (!selectedProvider) {
-      message.warning("Please select a provider");
-      return;
-    }
-
-    // Validate provider-specific required fields
-    const requiredFields = getProviderSpecificFields(selectedProvider).filter((field) => field.required);
-    for (const field of requiredFields) {
-      if (!providerParams[field.name]) {
-        message.warning(`Please provide ${field.label}`);
-        return;
-      }
-    }
-
-    // S3 Vectors specific validation
-    if (selectedProvider === "s3_vectors") {
-      if (providerParams.vector_bucket_name && providerParams.vector_bucket_name.length < 3) {
-        message.warning("Vector bucket name must be at least 3 characters");
-        return;
-      }
-      if (providerParams.index_name && providerParams.index_name.length > 0 && providerParams.index_name.length < 3) {
-        message.warning("Index name must be at least 3 characters if provided");
-        return;
-      }
-    }
-
-    if (!accessToken) {
-      message.error("No access token available");
-      return;
-    }
-
-    setIsCreating(true);
-    const results: RAGIngestResponse[] = [];
-    let vectorStoreId: string | undefined;
-
-    try {
-      // Ingest each document
-      for (const doc of documents) {
-        if (!doc.originFileObj) continue;
-
-        // Update document status to uploading
-        setDocuments((prev) =>
-          prev.map((d) => (d.uid === doc.uid ? { ...d, status: "uploading" as const } : d))
-        );
-
-        try {
-          const result = await ragIngestCall(
-            accessToken,
-            doc.originFileObj,
-            selectedProvider,
-            vectorStoreId, // Use the same vector store ID for subsequent uploads
-            vectorStoreName || undefined,
-            vectorStoreDescription || undefined,
-            providerParams
-          );
-
-          // Store the vector store ID from the first successful ingest
-          if (!vectorStoreId && result.vector_store_id) {
-            vectorStoreId = result.vector_store_id;
-          }
-
-          results.push(result);
-
-          // Update document status to done
-          setDocuments((prev) =>
-            prev.map((d) => (d.uid === doc.uid ? { ...d, status: "done" as const } : d))
-          );
-        } catch (error) {
-          console.error(`Error ingesting ${doc.name}:`, error);
-          // Update document status to error
-          setDocuments((prev) =>
-            prev.map((d) => (d.uid === doc.uid ? { ...d, status: "error" as const } : d))
-          );
-          throw error; // Stop processing on first error
-        }
-      }
-
-      setIngestResults(results);
-      NotificationsManager.success(
-        `Successfully created vector store with ${results.length} document(s). Vector Store ID: ${vectorStoreId}`
-      );
-
-      if (onSuccess && vectorStoreId) {
-        onSuccess(vectorStoreId);
-      }
-
-      // Clear documents after successful creation
-      setTimeout(() => {
-        setDocuments([]);
-        setIngestResults([]);
-      }, 3000);
-    } catch (error) {
-      console.error("Error creating vector store:", error);
-      NotificationsManager.fromBackend(`Failed to create vector store: ${error}`);
-    } finally {
-      setIsCreating(false);
-    }
-  };
-
-  return (
-    <div className="space-y-6">
-      <div>
-        <Title>Create Vector Store</Title>
-        <Text className="text-gray-500">
-          Upload documents and select a provider to create a new vector store with embedded content.
-        </Text>
-      </div>
-
-      {/* Upload Area */}
-      <Card>
-        <div className="mb-4">
-          <Text className="font-medium">Step 1: Upload Documents</Text>
-          <Text className="text-sm text-gray-500 block mt-1">
-            Upload one or more documents (PDF, TXT, DOCX, MD). Maximum file size: 50MB per file.
-          </Text>
-        </div>
-        <Dragger {...uploadProps}>
-          <p className="ant-upload-drag-icon">
-            <InboxOutlined style={{ fontSize: "48px", color: "#1890ff" }} />
-          </p>
-          <p className="ant-upload-text">Click or drag files to this area to upload</p>
-          <p className="ant-upload-hint">
-            Support for single or bulk upload. Supported formats: PDF, TXT, DOCX, MD
-          </p>
-        </Dragger>
-      </Card>
-
-      {/* Documents Table */}
-      {documents.length > 0 && (
-        <Card>
-          <div className="mb-4">
-            <Text className="font-medium">Uploaded Documents ({documents.length})</Text>
-          </div>
-          <DocumentsTable documents={documents} onRemove={handleRemoveDocument} />
-        </Card>
-      )}
-
-      {/* Provider Selection and Vector Store Details */}
-      <Card>
-        <div className="space-y-4">
-          <div>
-            <Text className="font-medium">Step 2: Configure Vector Store</Text>
-            <Text className="text-sm text-gray-500 block mt-1">
-              Choose the provider and optionally provide a name and description for your vector store.
-            </Text>
-          </div>
-
-          <Form form={form} layout="vertical">
-            <Form.Item
-              label={
-                <span>
-                  Vector Store Name{" "}
-                  <Tooltip title="Optional: Give your vector store a meaningful name">
-                    <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-                  </Tooltip>
-                </span>
-              }
-            >
-              <Input
-                value={vectorStoreName}
-                onChange={(e) => setVectorStoreName(e.target.value)}
-                placeholder="e.g., Product Documentation, Customer Support KB"
-                size="large"
-                className="rounded-md"
-              />
-            </Form.Item>
-
-            <Form.Item
-              label={
-                <span>
-                  Description{" "}
-                  <Tooltip title="Optional: Describe what this vector store contains">
-                    <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-                  </Tooltip>
-                </span>
-              }
-            >
-              <Input.TextArea
-                value={vectorStoreDescription}
-                onChange={(e) => setVectorStoreDescription(e.target.value)}
-                placeholder="e.g., Contains all product documentation and user guides"
-                rows={2}
-                size="large"
-                className="rounded-md"
-              />
-            </Form.Item>
-
-            <Form.Item
-              label={
-                <span>
-                  Provider{" "}
-                  <Tooltip title="Select the provider for embedding and vector store operations">
-                    <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-                  </Tooltip>
-                </span>
-              }
-              required
-            >
-              <Select
-                value={selectedProvider}
-                onChange={setSelectedProvider}
-                placeholder="Select a provider"
-                size="large"
-                style={{ width: "100%" }}
-              >
-                {Object.entries(VectorStoreProviders).map(([providerEnum, providerDisplayName]) => {
-                  return (
-                    <Select.Option key={providerEnum} value={vectorStoreProviderMap[providerEnum]}>
-                      <div className="flex items-center space-x-2">
-                        <img
-                          src={vectorStoreProviderLogoMap[providerDisplayName]}
-                          alt={`${providerEnum} logo`}
-                          className="w-5 h-5"
-                          onError={(e) => {
-                            // Create a div with provider initial as fallback
-                            const target = e.target as HTMLImageElement;
-                            const parent = target.parentElement;
-                            if (parent) {
-                              const fallbackDiv = document.createElement("div");
-                              fallbackDiv.className =
-                                "w-5 h-5 rounded-full bg-gray-200 flex items-center justify-center text-xs";
-                              fallbackDiv.textContent = providerDisplayName.charAt(0);
-                              parent.replaceChild(fallbackDiv, target);
-                            }
-                          }}
-                        />
-                        <span>{providerDisplayName}</span>
-                      </div>
-                    </Select.Option>
-                  );
-                })}
-              </Select>
-            </Form.Item>
-
-            {/* S3 Vectors Configuration */}
-            {selectedProvider === "s3_vectors" && (
-              <S3VectorsConfig
-                accessToken={accessToken}
-                providerParams={providerParams}
-                onParamsChange={setProviderParams}
-              />
-            )}
-
-            {/* Other Provider-specific fields */}
-            {selectedProvider !== "s3_vectors" &&
-              getProviderSpecificFields(selectedProvider).map((field: VectorStoreFieldConfig) => {
-                if (field.type === "select") {
-                  // For embedding model selection, we'd need to fetch available models
-                  // For now, provide a text input as fallback
-                  return (
-                    <Form.Item
-                      key={field.name}
-                      label={
-                        <span>
-                          {field.label}{" "}
-                          <Tooltip title={field.tooltip}>
-                            <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-                          </Tooltip>
-                        </span>
-                      }
-                      required={field.required}
-                    >
-                      <Input
-                        value={providerParams[field.name] || ""}
-                        onChange={(e) =>
-                          setProviderParams((prev) => ({ ...prev, [field.name]: e.target.value }))
-                        }
-                        placeholder={field.placeholder}
-                        size="large"
-                        className="rounded-md"
-                      />
-                    </Form.Item>
-                  );
-                }
-
-                return (
-                  <Form.Item
-                    key={field.name}
-                    label={
-                      <span>
-                        {field.label}{" "}
-                        <Tooltip title={field.tooltip}>
-                          <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-                        </Tooltip>
-                      </span>
-                    }
-                    required={field.required}
-                  >
-                    <Input
-                      type={field.type === "password" ? "password" : "text"}
-                      value={providerParams[field.name] || ""}
-                      onChange={(e) =>
-                        setProviderParams((prev) => ({ ...prev, [field.name]: e.target.value }))
-                      }
-                      placeholder={field.placeholder}
-                      size="large"
-                      className="rounded-md"
-                    />
-                  </Form.Item>
-                );
-              })}
-          </Form>
-
-          <div className="flex justify-end">
-            <Button
-              type="primary"
-              size="large"
-              onClick={handleCreateVectorStore}
-              loading={isCreating}
-              disabled={documents.length === 0 || !selectedProvider}
-            >
-              {isCreating ? "Creating Vector Store..." : "Create Vector Store"}
-            </Button>
-          </div>
-        </div>
-      </Card>
-
-      {/* Success Message */}
-      {ingestResults.length > 0 && (
-        <Alert
-          message="Vector Store Created Successfully"
-          description={
-            <div>
-              <p>
-                <strong>Vector Store ID:</strong> {ingestResults[0]?.vector_store_id}
-              </p>
-              <p>
-                <strong>Documents Ingested:</strong> {ingestResults.length}
-              </p>
-            </div>
-          }
-          type="success"
-          showIcon
-          closable
-        />
-      )}
-    </div>
-  );
-};
-
-export default CreateVectorStore;
diff --git a/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.test.tsx
deleted file mode 100644
index 507ad0537d6..00000000000
--- a/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.test.tsx
+++ /dev/null
@@ -1,102 +0,0 @@
-import { render, screen, fireEvent, act } from "@testing-library/react";
-import { describe, it, expect, vi } from "vitest";
-import DocumentsTable from "./DocumentsTable";
-import { DocumentUpload } from "./types";
-
-// Mock antd message
-vi.mock("antd", async () => {
-  const actual = await vi.importActual("antd");
-  return {
-    ...actual,
-    message: {
-      success: vi.fn(),
-    },
-  };
-});
-
-describe("DocumentsTable", () => {
-  const mockDocuments: DocumentUpload[] = [
-    {
-      uid: "1",
-      name: "test1.pdf",
-      status: "done",
-      size: 1024000,
-      type: "application/pdf",
-    },
-    {
-      uid: "2",
-      name: "test2.txt",
-      status: "uploading",
-      size: 2048000,
-      type: "text/plain",
-    },
-    {
-      uid: "3",
-      name: "test3.docx",
-      status: "error",
-      size: 512000,
-      type: "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-    },
-  ];
-
-  it("should render the table successfully", () => {
-    const onRemove = vi.fn();
-    render(<DocumentsTable documents={mockDocuments} onRemove={onRemove} />);
-
-    expect(screen.getByText("test1.pdf")).toBeInTheDocument();
-    expect(screen.getByText("test2.txt")).toBeInTheDocument();
-    expect(screen.getByText("test3.docx")).toBeInTheDocument();
-  });
-
-  it("should display correct status badges", () => {
-    const onRemove = vi.fn();
-    render(<DocumentsTable documents={mockDocuments} onRemove={onRemove} />);
-
-    expect(screen.getByText("Ready")).toBeInTheDocument();
-    expect(screen.getByText("Uploading")).toBeInTheDocument();
-    expect(screen.getByText("Error")).toBeInTheDocument();
-  });
-
-  it("should display file sizes", () => {
-    const onRemove = vi.fn();
-    render(<DocumentsTable documents={mockDocuments} onRemove={onRemove} />);
-
-    expect(screen.getByText(/1000.00 KB/)).toBeInTheDocument();
-    expect(screen.getByText(/1.95 MB/)).toBeInTheDocument();
-    expect(screen.getByText(/500.00 KB/)).toBeInTheDocument();
-  });
-
-  it("should call onRemove when delete button is clicked", () => {
-    const onRemove = vi.fn();
-    render(<DocumentsTable documents={mockDocuments} onRemove={onRemove} />);
-
-    const deleteButtons = screen.getAllByLabelText(/delete/i);
-
-    act(() => {
-      fireEvent.click(deleteButtons[0]);
-    });
-
-    expect(onRemove).toHaveBeenCalledWith("1");
-  });
-
-  it("should show empty state when no documents", () => {
-    const onRemove = vi.fn();
-    render(<DocumentsTable documents={[]} onRemove={onRemove} />);
-
-    expect(screen.getByText(/No documents uploaded yet/)).toBeInTheDocument();
-  });
-
-  it("should have action buttons for each document", () => {
-    const onRemove = vi.fn();
-    render(<DocumentsTable documents={mockDocuments} onRemove={onRemove} />);
-
-    // Each document should have 3 action buttons (view, copy, delete)
-    const viewButtons = screen.getAllByLabelText(/eye/i);
-    const copyButtons = screen.getAllByLabelText(/copy/i);
-    const deleteButtons = screen.getAllByLabelText(/delete/i);
-
-    expect(viewButtons).toHaveLength(3);
-    expect(copyButtons).toHaveLength(3);
-    expect(deleteButtons).toHaveLength(3);
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.tsx b/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.tsx
deleted file mode 100644
index aeb4240d366..00000000000
--- a/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.tsx
+++ /dev/null
@@ -1,98 +0,0 @@
-import React from "react";
-import { Table, Badge, Tooltip, message } from "antd";
-import { EyeOutlined, CopyOutlined, DeleteOutlined } from "@ant-design/icons";
-import { DocumentUpload } from "./types";
-
-interface DocumentsTableProps {
-  documents: DocumentUpload[];
-  onRemove: (uid: string) => void;
-}
-
-const DocumentsTable: React.FC<DocumentsTableProps> = ({ documents, onRemove }) => {
-  const handleCopyId = (uid: string) => {
-    navigator.clipboard.writeText(uid);
-    message.success("Document ID copied to clipboard");
-  };
-
-  const getStatusBadge = (status: DocumentUpload["status"]) => {
-    const statusConfig = {
-      uploading: { color: "blue", text: "Uploading" },
-      done: { color: "green", text: "Ready" },
-      error: { color: "red", text: "Error" },
-      removed: { color: "default", text: "Removed" },
-    };
-
-    const config = statusConfig[status];
-    return <Badge color={config.color} text={config.text} />;
-  };
-
-  const formatFileSize = (bytes?: number) => {
-    if (!bytes) return "-";
-    const kb = bytes / 1024;
-    if (kb < 1024) return `${kb.toFixed(2)} KB`;
-    return `${(kb / 1024).toFixed(2)} MB`;
-  };
-
-  const columns = [
-    {
-      title: "Name",
-      dataIndex: "name",
-      key: "name",
-      render: (name: string, record: DocumentUpload) => (
-        <div className="flex items-center space-x-2">
-          <span className="text-sm">{name}</span>
-          {record.size && <span className="text-xs text-gray-400">({formatFileSize(record.size)})</span>}
-        </div>
-      ),
-    },
-    {
-      title: "Status",
-      dataIndex: "status",
-      key: "status",
-      width: 150,
-      render: (status: DocumentUpload["status"]) => getStatusBadge(status),
-    },
-    {
-      title: "Actions",
-      key: "actions",
-      width: 120,
-      render: (_: any, record: DocumentUpload) => (
-        <div className="flex items-center space-x-2">
-          <Tooltip title="View details">
-            <EyeOutlined
-              className="cursor-pointer text-gray-600 hover:text-blue-500"
-              onClick={() => console.log("View", record)}
-            />
-          </Tooltip>
-          <Tooltip title="Copy ID">
-            <CopyOutlined
-              className="cursor-pointer text-gray-600 hover:text-blue-500"
-              onClick={() => handleCopyId(record.uid)}
-            />
-          </Tooltip>
-          <Tooltip title="Remove">
-            <DeleteOutlined
-              className="cursor-pointer text-gray-600 hover:text-red-500"
-              onClick={() => onRemove(record.uid)}
-            />
-          </Tooltip>
-        </div>
-      ),
-    },
-  ];
-
-  return (
-    <Table
-      dataSource={documents}
-      columns={columns}
-      rowKey="uid"
-      pagination={false}
-      locale={{
-        emptyText: "No documents uploaded yet. Upload documents above to get started.",
-      }}
-      size="small"
-    />
-  );
-};
-
-export default DocumentsTable;
diff --git a/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.test.tsx
deleted file mode 100644
index 28f885a9dd6..00000000000
--- a/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.test.tsx
+++ /dev/null
@@ -1,203 +0,0 @@
-import { render, screen, fireEvent, waitFor, act } from "@testing-library/react";
-import { describe, it, expect, vi, beforeEach } from "vitest";
-import S3VectorsConfig from "./S3VectorsConfig";
-import * as fetchModels from "../playground/llm_calls/fetch_models";
-
-// Mock fetchAvailableModels
-vi.mock("../playground/llm_calls/fetch_models", () => ({
-  fetchAvailableModels: vi.fn(),
-}));
-
-describe("S3VectorsConfig", () => {
-  const mockOnParamsChange = vi.fn();
-  const defaultProps = {
-    accessToken: "test-token",
-    providerParams: {},
-    onParamsChange: mockOnParamsChange,
-  };
-
-  beforeEach(() => {
-    vi.clearAllMocks();
-  });
-
-  it("should render the component successfully", () => {
-    vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue([]);
-
-    render(<S3VectorsConfig {...defaultProps} />);
-
-    expect(screen.getByText("AWS S3 Vectors Setup")).toBeInTheDocument();
-    expect(screen.getByText("Vector Bucket Name")).toBeInTheDocument();
-    expect(screen.getByText("Index Name")).toBeInTheDocument();
-    expect(screen.getByText("AWS Region")).toBeInTheDocument();
-    expect(screen.getByText("Embedding Model")).toBeInTheDocument();
-  });
-
-  it("should display setup instructions", () => {
-    vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue([]);
-
-    render(<S3VectorsConfig {...defaultProps} />);
-
-    expect(
-      screen.getByText(/AWS S3 Vectors allows you to store and query vector embeddings directly in S3/)
-    ).toBeInTheDocument();
-    expect(screen.getByText(/Vector buckets and indexes will be automatically created/)).toBeInTheDocument();
-    expect(screen.getByText(/Vector dimensions are auto-detected/)).toBeInTheDocument();
-  });
-
-  it("should fetch embedding models on mount", async () => {
-    const mockModels = [
-      { model_group: "text-embedding-3-small", mode: "embedding" },
-      { model_group: "text-embedding-3-large", mode: "embedding" },
-      { model_group: "gpt-4", mode: "chat" },
-    ];
-
-    const fetchSpy = vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue(mockModels);
-
-    render(<S3VectorsConfig {...defaultProps} />);
-
-    await waitFor(() => {
-      expect(fetchSpy).toHaveBeenCalledWith("test-token");
-    });
-  });
-
-  it("should filter and display only embedding models", async () => {
-    const mockModels = [
-      { model_group: "text-embedding-3-small", mode: "embedding" },
-      { model_group: "text-embedding-3-large", mode: "embedding" },
-      { model_group: "gpt-4", mode: "chat" },
-      { model_group: "gpt-3.5-turbo", mode: "chat" },
-    ];
-
-    vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue(mockModels);
-
-    render(<S3VectorsConfig {...defaultProps} />);
-
-    // Wait for models to load
-    await waitFor(() => {
-      expect(fetchModels.fetchAvailableModels).toHaveBeenCalled();
-    });
-
-    // The component should filter to only embedding models internally
-    // We can verify this by checking the component loaded successfully
-    expect(screen.getByText("Embedding Model")).toBeInTheDocument();
-  });
-
-  it("should call onParamsChange when vector bucket name changes", async () => {
-    vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue([]);
-
-    render(<S3VectorsConfig {...defaultProps} />);
-
-    const bucketInput = screen.getByPlaceholderText("my-vector-bucket (min 3 chars)");
-
-    await act(async () => {
-      fireEvent.change(bucketInput, { target: { value: "test-bucket" } });
-    });
-
-    expect(mockOnParamsChange).toHaveBeenCalledWith({
-      vector_bucket_name: "test-bucket",
-    });
-  });
-
-  it("should call onParamsChange when AWS region changes", async () => {
-    vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue([]);
-
-    render(<S3VectorsConfig {...defaultProps} />);
-
-    const regionInput = screen.getByPlaceholderText("us-west-2");
-
-    await act(async () => {
-      fireEvent.change(regionInput, { target: { value: "us-east-1" } });
-    });
-
-    expect(mockOnParamsChange).toHaveBeenCalledWith({
-      aws_region_name: "us-east-1",
-    });
-  });
-
-  it("should call onParamsChange when embedding model is selected", async () => {
-    const mockModels = [
-      { model_group: "text-embedding-3-small", mode: "embedding" },
-      { model_group: "text-embedding-3-large", mode: "embedding" },
-    ];
-
-    vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue(mockModels);
-
-    render(<S3VectorsConfig {...defaultProps} />);
-
-    await waitFor(() => {
-      expect(fetchModels.fetchAvailableModels).toHaveBeenCalled();
-    });
-
-    // Find the Select component and trigger change directly
-    const selectElement = screen.getByRole("combobox");
-
-    await act(async () => {
-      // Simulate selecting a value by firing the change event
-      fireEvent.change(selectElement, { target: { value: "text-embedding-3-small" } });
-    });
-
-    // The component should handle the selection
-    expect(screen.getByText("Embedding Model")).toBeInTheDocument();
-  });
-
-  it("should preserve existing params when updating a field", async () => {
-    vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue([]);
-
-    const existingParams = {
-      vector_bucket_name: "existing-bucket",
-      aws_region_name: "us-west-2",
-    };
-
-    render(<S3VectorsConfig {...defaultProps} providerParams={existingParams} />);
-
-    const indexInput = screen.getByPlaceholderText("my-vector-index (optional, min 3 chars)");
-
-    await act(async () => {
-      fireEvent.change(indexInput, { target: { value: "my-index" } });
-    });
-
-    expect(mockOnParamsChange).toHaveBeenCalledWith({
-      vector_bucket_name: "existing-bucket",
-      aws_region_name: "us-west-2",
-      index_name: "my-index",
-    });
-  });
-
-  it("should display existing param values", () => {
-    vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue([]);
-
-    const existingParams = {
-      vector_bucket_name: "my-bucket",
-      index_name: "my-index",
-      aws_region_name: "eu-west-1",
-      embedding_model: "text-embedding-3-small",
-    };
-
-    render(<S3VectorsConfig {...defaultProps} providerParams={existingParams} />);
-
-    expect(screen.getByDisplayValue("my-bucket")).toBeInTheDocument();
-    expect(screen.getByDisplayValue("my-index")).toBeInTheDocument();
-    expect(screen.getByDisplayValue("eu-west-1")).toBeInTheDocument();
-  });
-
-  it("should handle model fetch error gracefully", async () => {
-    const consoleErrorSpy = vi.spyOn(console, "error").mockImplementation(() => {});
-    vi.spyOn(fetchModels, "fetchAvailableModels").mockRejectedValue(new Error("Failed to fetch models"));
-
-    render(<S3VectorsConfig {...defaultProps} />);
-
-    await waitFor(() => {
-      expect(consoleErrorSpy).toHaveBeenCalledWith("Error fetching embedding models:", expect.any(Error));
-    });
-
-    consoleErrorSpy.mockRestore();
-  });
-
-  it("should not fetch models if accessToken is null", () => {
-    const fetchSpy = vi.spyOn(fetchModels, "fetchAvailableModels");
-
-    render(<S3VectorsConfig {...defaultProps} accessToken={null} />);
-
-    expect(fetchSpy).not.toHaveBeenCalled();
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.tsx b/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.tsx
deleted file mode 100644
index bf714923120..00000000000
--- a/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.tsx
+++ /dev/null
@@ -1,192 +0,0 @@
-import React, { useState, useEffect } from "react";
-import { Alert, Form, Input, Select, Tooltip } from "antd";
-import { InfoCircleOutlined } from "@ant-design/icons";
-import { fetchAvailableModels, ModelGroup } from "../playground/llm_calls/fetch_models";
-
-interface S3VectorsConfigProps {
-  accessToken: string | null;
-  providerParams: Record<string, any>;
-  onParamsChange: (params: Record<string, any>) => void;
-}
-
-const S3VectorsConfig: React.FC<S3VectorsConfigProps> = ({
-  accessToken,
-  providerParams,
-  onParamsChange,
-}) => {
-  const [embeddingModels, setEmbeddingModels] = useState<ModelGroup[]>([]);
-  const [isLoadingModels, setIsLoadingModels] = useState(false);
-
-  useEffect(() => {
-    if (!accessToken) return;
-
-    const loadModels = async () => {
-      setIsLoadingModels(true);
-      try {
-        const models = await fetchAvailableModels(accessToken);
-        // Filter for embedding models only
-        const embeddingOnly = models.filter((model) => model.mode === "embedding");
-        setEmbeddingModels(embeddingOnly);
-      } catch (error) {
-        console.error("Error fetching embedding models:", error);
-      } finally {
-        setIsLoadingModels(false);
-      }
-    };
-
-    loadModels();
-  }, [accessToken]);
-
-  const handleFieldChange = (fieldName: string, value: string) => {
-    onParamsChange({
-      ...providerParams,
-      [fieldName]: value,
-    });
-  };
-
-  return (
-    <>
-      {/* S3 Vectors Setup Instructions */}
-      <Alert
-        message="AWS S3 Vectors Setup"
-        description={
-          <div>
-            <p>AWS S3 Vectors allows you to store and query vector embeddings directly in S3:</p>
-            <ul style={{ marginLeft: "16px", marginTop: "8px" }}>
-              <li>Vector buckets and indexes will be automatically created if they don&apos;t exist</li>
-              <li>Vector dimensions are auto-detected from your selected embedding model</li>
-              <li>Ensure your AWS credentials have permissions for S3 Vectors operations</li>
-              <li>
-                Learn more:{" "}
-                <a
-                  href="https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vector-buckets.html"
-                  target="_blank"
-                  rel="noopener noreferrer"
-                >
-                  AWS S3 Vectors Documentation
-                </a>
-              </li>
-            </ul>
-          </div>
-        }
-        type="info"
-        showIcon
-        style={{ marginBottom: "16px" }}
-      />
-
-      {/* Vector Bucket Name */}
-      <Form.Item
-        label={
-          <span>
-            Vector Bucket Name{" "}
-            <Tooltip title="S3 bucket name for vector storage (must be at least 3 characters, lowercase letters, numbers, hyphens, and periods only)">
-              <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-            </Tooltip>
-          </span>
-        }
-        required
-        validateStatus={
-          providerParams.vector_bucket_name && providerParams.vector_bucket_name.length < 3
-            ? "error"
-            : undefined
-        }
-        help={
-          providerParams.vector_bucket_name && providerParams.vector_bucket_name.length < 3
-            ? "Bucket name must be at least 3 characters"
-            : undefined
-        }
-      >
-        <Input
-          value={providerParams.vector_bucket_name || ""}
-          onChange={(e) => handleFieldChange("vector_bucket_name", e.target.value)}
-          placeholder="my-vector-bucket (min 3 chars)"
-          size="large"
-          className="rounded-md"
-        />
-      </Form.Item>
-
-      {/* Index Name (Optional) */}
-      <Form.Item
-        label={
-          <span>
-            Index Name{" "}
-            <Tooltip title="Name for the vector index (optional, will be auto-generated if not provided). If provided, must be at least 3 characters.">
-              <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-            </Tooltip>
-          </span>
-        }
-        validateStatus={
-          providerParams.index_name && providerParams.index_name.length > 0 && providerParams.index_name.length < 3
-            ? "error"
-            : undefined
-        }
-        help={
-          providerParams.index_name && providerParams.index_name.length > 0 && providerParams.index_name.length < 3
-            ? "Index name must be at least 3 characters if provided"
-            : undefined
-        }
-      >
-        <Input
-          value={providerParams.index_name || ""}
-          onChange={(e) => handleFieldChange("index_name", e.target.value)}
-          placeholder="my-vector-index (optional, min 3 chars)"
-          size="large"
-          className="rounded-md"
-        />
-      </Form.Item>
-
-      {/* AWS Region */}
-      <Form.Item
-        label={
-          <span>
-            AWS Region{" "}
-            <Tooltip title="AWS region where the S3 bucket is located (e.g., us-west-2)">
-              <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-            </Tooltip>
-          </span>
-        }
-        required
-      >
-        <Input
-          value={providerParams.aws_region_name || ""}
-          onChange={(e) => handleFieldChange("aws_region_name", e.target.value)}
-          placeholder="us-west-2"
-          size="large"
-          className="rounded-md"
-        />
-      </Form.Item>
-
-      {/* Embedding Model */}
-      <Form.Item
-        label={
-          <span>
-            Embedding Model{" "}
-            <Tooltip title="Select the embedding model to use for vector generation">
-              <InfoCircleOutlined style={{ marginLeft: "4px" }} />
-            </Tooltip>
-          </span>
-        }
-        required
-      >
-        <Select
-          value={providerParams.embedding_model || undefined}
-          onChange={(value) => handleFieldChange("embedding_model", value)}
-          placeholder="Select an embedding model"
-          size="large"
-          showSearch
-          loading={isLoadingModels}
-          filterOption={(input, option) =>
-            (option?.label ?? "").toLowerCase().includes(input.toLowerCase())
-          }
-          options={embeddingModels.map((model) => ({
-            value: model.model_group,
-            label: model.model_group,
-          }))}
-          style={{ width: "100%" }}
-        />
-      </Form.Item>
-    </>
-  );
-};
-
-export default S3VectorsConfig;
diff --git a/ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.test.tsx
deleted file mode 100644
index ad6ef02e87b..00000000000
--- a/ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.test.tsx
+++ /dev/null
@@ -1,90 +0,0 @@
-import { render, screen, fireEvent } from "@testing-library/react";
-import { describe, it, expect, vi } from "vitest";
-import TestVectorStoreTab from "./TestVectorStoreTab";
-import { VectorStore } from "./types";
-
-// Mock VectorStoreTester component
-vi.mock("./VectorStoreTester", () => ({
-  VectorStoreTester: ({ vectorStoreId, accessToken }: { vectorStoreId: string; accessToken: string }) => (
-    <div data-testid="vector-store-tester">
-      <div data-testid="tester-vector-store-id">{vectorStoreId}</div>
-      <div data-testid="tester-access-token">{accessToken}</div>
-    </div>
-  ),
-}));
-
-const mockVectorStores: VectorStore[] = [
-  {
-    vector_store_id: "vs_123",
-    custom_llm_provider: "openai",
-    vector_store_name: "Test Store 1",
-    vector_store_description: "Description 1",
-    created_at: "2024-01-01T00:00:00Z",
-    updated_at: "2024-01-01T00:00:00Z",
-  },
-  {
-    vector_store_id: "vs_456",
-    custom_llm_provider: "bedrock",
-    vector_store_name: "Test Store 2",
-    vector_store_description: "Description 2",
-    created_at: "2024-01-02T00:00:00Z",
-    updated_at: "2024-01-02T00:00:00Z",
-  },
-];
-
-describe("TestVectorStoreTab", () => {
-  it("should render the component successfully", () => {
-    render(<TestVectorStoreTab accessToken="test-token" vectorStores={mockVectorStores} />);
-
-    expect(screen.getByText("Select Vector Store")).toBeInTheDocument();
-    expect(screen.getByText("Choose a vector store to test search queries against")).toBeInTheDocument();
-  });
-
-  it("should show message when no access token", () => {
-    render(<TestVectorStoreTab accessToken={null} vectorStores={mockVectorStores} />);
-
-    expect(screen.getByText("Access token is required to test vector stores.")).toBeInTheDocument();
-  });
-
-  it("should show message when no vector stores available", () => {
-    render(<TestVectorStoreTab accessToken="test-token" vectorStores={[]} />);
-
-    expect(screen.getByText("No vector stores available. Create one first to test it.")).toBeInTheDocument();
-  });
-
-  it("should render VectorStoreTester with first vector store by default", () => {
-    render(<TestVectorStoreTab accessToken="test-token" vectorStores={mockVectorStores} />);
-
-    expect(screen.getByTestId("vector-store-tester")).toBeInTheDocument();
-    expect(screen.getByTestId("tester-vector-store-id")).toHaveTextContent("vs_123");
-    expect(screen.getByTestId("tester-access-token")).toHaveTextContent("test-token");
-  });
-
-  it("should update VectorStoreTester when selecting different vector store", () => {
-    render(<TestVectorStoreTab accessToken="test-token" vectorStores={mockVectorStores} />);
-
-    // Find the select component
-    const selectElement = screen.getByRole("combobox");
-
-    // Change selection
-    fireEvent.mouseDown(selectElement);
-
-    // Wait for options to appear and click the second one
-    const option2 = screen.getByText("Test Store 2");
-    fireEvent.click(option2);
-
-    // Verify the tester component updated
-    expect(screen.getByTestId("tester-vector-store-id")).toHaveTextContent("vs_456");
-  });
-
-  it("should display vector store names in select options", () => {
-    render(<TestVectorStoreTab accessToken="test-token" vectorStores={mockVectorStores} />);
-
-    const selectElement = screen.getByRole("combobox");
-    fireEvent.mouseDown(selectElement);
-
-    // Use getAllByText since the selected value also shows the name
-    expect(screen.getAllByText("Test Store 1").length).toBeGreaterThan(0);
-    expect(screen.getByText("Test Store 2")).toBeInTheDocument();
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.tsx b/ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.tsx
deleted file mode 100644
index da005491ca6..00000000000
--- a/ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.tsx
+++ /dev/null
@@ -1,75 +0,0 @@
-import React, { useState } from "react";
-import { Card, Select, Typography } from "antd";
-import { VectorStoreTester } from "./VectorStoreTester";
-import { VectorStore } from "./types";
-
-const { Text, Title } = Typography;
-
-interface TestVectorStoreTabProps {
-  accessToken: string | null;
-  vectorStores: VectorStore[];
-}
-
-const TestVectorStoreTab: React.FC<TestVectorStoreTabProps> = ({ accessToken, vectorStores }) => {
-  const [selectedVectorStoreId, setSelectedVectorStoreId] = useState<string | undefined>(
-    vectorStores.length > 0 ? vectorStores[0].vector_store_id : undefined
-  );
-
-  if (!accessToken) {
-    return (
-      <Card>
-        <Text type="secondary">Access token is required to test vector stores.</Text>
-      </Card>
-    );
-  }
-
-  if (vectorStores.length === 0) {
-    return (
-      <Card>
-        <div className="text-center py-8">
-          <Text type="secondary">No vector stores available. Create one first to test it.</Text>
-        </div>
-      </Card>
-    );
-  }
-
-  return (
-    <div className="space-y-4">
-      <Card>
-        <div className="space-y-4">
-          <div>
-            <Title level={5}>Select Vector Store</Title>
-            <Text type="secondary">Choose a vector store to test search queries against</Text>
-          </div>
-
-          <Select
-            value={selectedVectorStoreId}
-            onChange={setSelectedVectorStoreId}
-            placeholder="Select a vector store"
-            size="large"
-            style={{ width: "100%" }}
-            showSearch
-            optionFilterProp="children"
-          >
-            {vectorStores.map((vs) => (
-              <Select.Option key={vs.vector_store_id} value={vs.vector_store_id}>
-                <div className="flex flex-col">
-                  <span className="font-medium">{vs.vector_store_name || vs.vector_store_id}</span>
-                  {vs.vector_store_name && (
-                    <span className="text-xs text-gray-500 font-mono">{vs.vector_store_id}</span>
-                  )}
-                </div>
-              </Select.Option>
-            ))}
-          </Select>
-        </div>
-      </Card>
-
-      {selectedVectorStoreId && (
-        <VectorStoreTester vectorStoreId={selectedVectorStoreId} accessToken={accessToken} />
-      )}
-    </div>
-  );
-};
-
-export default TestVectorStoreTab;
diff --git a/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.test.tsx
index ac790122507..65d15260c4c 100644
--- a/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.test.tsx
+++ b/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.test.tsx
@@ -130,9 +130,9 @@ describe("VectorStoreTable", () => {
       expect(screen.getByText("Provider")).toBeInTheDocument();
       expect(screen.getByText("Created At")).toBeInTheDocument();
       expect(screen.getByText("Updated At")).toBeInTheDocument();
-      // Check that we have the expected number of header cells (7 data + 1 actions)
+      // Check that we have the expected number of header cells (6 data + 1 actions)
       const headers = screen.getAllByRole("columnheader");
-      expect(headers).toHaveLength(8);
+      expect(headers).toHaveLength(7);
     });
 
     it("should render all vector store rows", () => {
@@ -183,7 +183,7 @@ describe("VectorStoreTable", () => {
     it("should render fallback for missing name", () => {
       renderComponent();
       const fallbackElements = screen.getAllByText("-");
-      expect(fallbackElements.length).toBe(5); // One for missing name, one for missing description, three for missing files (one per store)
+      expect(fallbackElements.length).toBe(2); // One for missing name, one for missing description
     });
 
     it("should wrap name in tooltip", () => {
@@ -203,7 +203,7 @@ describe("VectorStoreTable", () => {
     it("should render fallback for missing description", () => {
       renderComponent();
       const fallbackElements = screen.getAllByText("-");
-      expect(fallbackElements.length).toBe(5); // One for missing name, one for missing description, three for missing files (one per store)
+      expect(fallbackElements.length).toBe(2); // One for missing name, one for missing description
     });
 
     it("should wrap description in tooltip", () => {
@@ -386,7 +386,7 @@ describe("VectorStoreTable", () => {
     it("should span all columns in empty state", () => {
       renderComponent({ data: [] });
       const emptyCell = screen.getByText("No vector stores found").closest("td");
-      expect(emptyCell).toHaveAttribute("colSpan", "8"); // 7 data columns + 1 actions column
+      expect(emptyCell).toHaveAttribute("colSpan", "7"); // 6 data columns + 1 actions column
     });
   });
 
@@ -403,7 +403,7 @@ describe("VectorStoreTable", () => {
 
       renderComponent({ data: minimalData });
       expect(screen.getByText("minimal")).toBeInTheDocument();
-      expect(screen.getAllByText("-")).toHaveLength(3); // Name, description, and files fallbacks
+      expect(screen.getAllByText("-")).toHaveLength(2); // Name and description fallbacks
     });
 
     it("should handle single vector store", () => {
diff --git a/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.tsx b/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.tsx
index 41e2b63112e..52462c02e98 100644
--- a/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.tsx
+++ b/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.tsx
@@ -66,32 +66,6 @@ const VectorStoreTable: React.FC<VectorStoreTableProps> = ({ data, onView, onEdi
         );
       },
     },
-    {
-      header: "Files",
-      accessorKey: "vector_store_metadata",
-      cell: ({ row }) => {
-        const vectorStore = row.original;
-        const ingestedFiles = vectorStore.vector_store_metadata?.ingested_files || [];
-        
-        if (ingestedFiles.length === 0) {
-          return <span className="text-xs text-gray-400">-</span>;
-        }
-        
-        const filenames = ingestedFiles
-          .map((file) => file.filename || file.file_url || "Unknown")
-          .join(", ");
-        
-        const displayText = ingestedFiles.length === 1 
-          ? ingestedFiles[0].filename || ingestedFiles[0].file_url || "1 file"
-          : `${ingestedFiles.length} files`;
-        
-        return (
-          <Tooltip title={filenames}>
-            <span className="text-xs text-blue-600">{displayText}</span>
-          </Tooltip>
-        );
-      },
-    },
     {
       header: "Provider",
       accessorKey: "custom_llm_provider",
diff --git a/ui/litellm-dashboard/src/components/vector_store_management/index.tsx b/ui/litellm-dashboard/src/components/vector_store_management/index.tsx
index 9cb57b8b9f6..6d21e861d4a 100644
--- a/ui/litellm-dashboard/src/components/vector_store_management/index.tsx
+++ b/ui/litellm-dashboard/src/components/vector_store_management/index.tsx
@@ -1,5 +1,5 @@
 import React, { useState, useEffect } from "react";
-import { Icon, Button as TremorButton, Col, Text, Grid, TabGroup, TabList, Tab, TabPanels, TabPanel } from "@tremor/react";
+import { Icon, Button as TremorButton, Col, Text, Grid } from "@tremor/react";
 import { RefreshIcon } from "@heroicons/react/outline";
 import { vectorStoreListCall, vectorStoreDeleteCall, credentialListCall, CredentialItem } from "../networking";
 import { VectorStore } from "./types";
@@ -7,8 +7,6 @@ import VectorStoreTable from "./VectorStoreTable";
 import VectorStoreForm from "./VectorStoreForm";
 import DeleteResourceModal from "../common_components/DeleteResourceModal";
 import VectorStoreInfoView from "./vector_store_info";
-import CreateVectorStore from "./CreateVectorStore";
-import TestVectorStoreTab from "./TestVectorStoreTab";
 import { isAdminRole } from "@/utils/roles";
 import NotificationsManager from "../molecules/notifications_manager";
 
@@ -103,12 +101,6 @@ const VectorStoreManagement: React.FC<VectorStoreProps> = ({ accessToken, userID
     fetchVectorStores();
   };
 
-  const handleVectorStoreCreated = (vectorStoreId: string) => {
-    console.log("Vector store created:", vectorStoreId);
-    fetchVectorStores();
-    // Optionally switch to the manage tab
-  };
-
   useEffect(() => {
     fetchVectorStores();
     fetchCredentials();
@@ -142,46 +134,18 @@ const VectorStoreManagement: React.FC<VectorStoreProps> = ({ accessToken, userID
         </div>
 
         <Text className="mb-4">
-          <p>You can use vector stores to store and retrieve LLM embeddings.</p>
+          <p>You can use vector stores to store and retrieve LLM embeddings..</p>
         </Text>
 
-        <TabGroup>
-          <TabList className="mb-6">
-            <Tab>Create Vector Store</Tab>
-            <Tab>Manage Vector Stores</Tab>
-            <Tab>Test Vector Store</Tab>
-          </TabList>
-
-          <TabPanels>
-            {/* Tab 1: Create Vector Store */}
-            <TabPanel>
-              <CreateVectorStore accessToken={accessToken} onSuccess={handleVectorStoreCreated} />
-            </TabPanel>
-
-            {/* Tab 2: Manage Vector Stores */}
-            <TabPanel>
-              <TremorButton className="mb-4" onClick={() => setIsCreateModalVisible(true)}>
-                + Add Vector Store
-              </TremorButton>
-
-              <Grid numItems={1} className="gap-2 pt-2 pb-2 w-full mt-2">
-                <Col numColSpan={1}>
-                  <VectorStoreTable
-                    data={vectorStores}
-                    onView={handleView}
-                    onEdit={handleEdit}
-                    onDelete={handleDelete}
-                  />
-                </Col>
-              </Grid>
-            </TabPanel>
-
-            {/* Tab 3: Test Vector Store */}
-            <TabPanel>
-              <TestVectorStoreTab accessToken={accessToken} vectorStores={vectorStores} />
-            </TabPanel>
-          </TabPanels>
-        </TabGroup>
+        <TremorButton className="mb-4" onClick={() => setIsCreateModalVisible(true)}>
+          + Add Vector Store
+        </TremorButton>
+
+        <Grid numItems={1} className="gap-2 pt-2 pb-2 h-[75vh] w-full mt-2">
+          <Col numColSpan={1}>
+            <VectorStoreTable data={vectorStores} onView={handleView} onEdit={handleEdit} onDelete={handleDelete} />
+          </Col>
+        </Grid>
 
         {/* Create Vector Store Modal */}
         <VectorStoreForm
diff --git a/ui/litellm-dashboard/src/components/vector_store_management/types.tsx b/ui/litellm-dashboard/src/components/vector_store_management/types.tsx
index 5be7825e9a1..c08ab741654 100644
--- a/ui/litellm-dashboard/src/components/vector_store_management/types.tsx
+++ b/ui/litellm-dashboard/src/components/vector_store_management/types.tsx
@@ -1,23 +1,9 @@
-export interface IngestedFile {
-  file_id?: string;
-  filename?: string;
-  file_url?: string;
-  ingested_at: string;
-  file_size?: number;
-  content_type?: string;
-}
-
-export interface VectorStoreMetadata {
-  ingested_files?: IngestedFile[];
-  [key: string]: any;
-}
-
 export interface VectorStore {
   vector_store_id: string;
   custom_llm_provider: string;
   vector_store_name?: string;
   vector_store_description?: string;
-  vector_store_metadata?: VectorStoreMetadata;
+  vector_store_metadata?: Record<string, any>;
   created_at: string;
   updated_at: string;
   created_by?: string;
@@ -55,32 +41,3 @@ export interface VectorStoreListResponse {
   current_page: number;
   total_pages: number;
 }
-
-// Document ingestion types
-export interface DocumentUpload {
-  uid: string;
-  name: string;
-  status: "uploading" | "done" | "error" | "removed";
-  size?: number;
-  type?: string;
-  originFileObj?: File;
-}
-
-export interface RAGIngestRequest {
-  file_url?: string;
-  file_id?: string;
-  ingest_options: {
-    vector_store: {
-      custom_llm_provider: string;
-      vector_store_id?: string;
-    };
-  };
-}
-
-export interface RAGIngestResponse {
-  id: string;
-  status: "completed" | "processing" | "failed";
-  vector_store_id: string;
-  file_id: string;
-  error?: string;
-}
diff --git a/ui/litellm-dashboard/src/components/vector_store_providers.tsx b/ui/litellm-dashboard/src/components/vector_store_providers.tsx
index 9efda3a5a71..3dcf8eed87a 100644
--- a/ui/litellm-dashboard/src/components/vector_store_providers.tsx
+++ b/ui/litellm-dashboard/src/components/vector_store_providers.tsx
@@ -1,6 +1,5 @@
 export enum VectorStoreProviders {
   Bedrock = "Amazon Bedrock",
-  S3Vectors = "Amazon S3 Vectors",
   PgVector = "PostgreSQL pgvector (LiteLLM Connector)",
   VertexRagEngine = "Vertex AI RAG Engine",
   OpenAI = "OpenAI",
@@ -15,7 +14,6 @@ export const vectorStoreProviderMap: Record<string, string> = {
   OpenAI: "openai",
   Azure: "azure",
   Milvus: "milvus",
-  S3Vectors: "s3_vectors",
 };
 
 const asset_logos_folder = "../ui/assets/logos/";
@@ -27,7 +25,6 @@ export const vectorStoreProviderLogoMap: Record<string, string> = {
   [VectorStoreProviders.OpenAI]: `${asset_logos_folder}openai_small.svg`,
   [VectorStoreProviders.Azure]: `${asset_logos_folder}microsoft_azure.svg`,
   [VectorStoreProviders.Milvus]: `${asset_logos_folder}milvus.svg`,
-  [VectorStoreProviders.S3Vectors]: `${asset_logos_folder}s3_vector.png`,
 };
 
 // Define field types for provider-specific configurations
@@ -117,40 +114,6 @@ export const vectorStoreProviderFields: Record<string, VectorStoreFieldConfig[]>
       type: "select",
     },
   ],
-  s3_vectors: [
-    {
-      name: "vector_bucket_name",
-      label: "Vector Bucket Name",
-      tooltip: "S3 bucket name for vector storage (will be auto-created if it doesn't exist)",
-      placeholder: "my-vector-bucket",
-      required: true,
-      type: "text",
-    },
-    {
-      name: "index_name",
-      label: "Index Name",
-      tooltip: "Name for the vector index (optional, will be auto-generated if not provided)",
-      placeholder: "my-vector-index",
-      required: false,
-      type: "text",
-    },
-    {
-      name: "aws_region_name",
-      label: "AWS Region",
-      tooltip: "AWS region where the S3 bucket is located (e.g., us-west-2)",
-      placeholder: "us-west-2",
-      required: true,
-      type: "text",
-    },
-    {
-      name: "embedding_model",
-      label: "Embedding Model",
-      tooltip: "Select the embedding model to use for vector generation",
-      placeholder: "text-embedding-3-small",
-      required: true,
-      type: "select",
-    },
-  ],
 };
 
 export const getVectorStoreProviderLogoAndName = (providerValue: string): { logo: string; displayName: string } => {
diff --git a/ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.test.tsx b/ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.test.tsx
deleted file mode 100644
index e955f42872b..00000000000
--- a/ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.test.tsx
+++ /dev/null
@@ -1,348 +0,0 @@
-import { useStoreRequestInSpendLogs } from "@/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs";
-import NotificationsManager from "@/components/molecules/notifications_manager";
-import { parseErrorMessage } from "@/components/shared/errorUtils";
-import { screen, waitFor } from "@testing-library/react";
-import userEvent from "@testing-library/user-event";
-import { beforeEach, describe, expect, it, vi } from "vitest";
-import { renderWithProviders } from "../../../../tests/test-utils";
-import SpendLogsSettingsModal from "./SpendLogsSettingsModal";
-
-vi.mock("@/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs");
-vi.mock("@/components/molecules/notifications_manager", () => ({
-  default: {
-    success: vi.fn(),
-    fromBackend: vi.fn(),
-  },
-}));
-vi.mock("@/components/shared/errorUtils", () => ({
-  parseErrorMessage: vi.fn(),
-}));
-
-const mockUseStoreRequestInSpendLogs = vi.mocked(useStoreRequestInSpendLogs);
-const mockNotificationsManager = vi.mocked(NotificationsManager);
-const mockParseErrorMessage = vi.mocked(parseErrorMessage);
-
-describe("SpendLogsSettingsModal", () => {
-  const mockOnCancel = vi.fn();
-  const mockOnSuccess = vi.fn();
-  const mockMutateAsync = vi.fn();
-
-  const defaultProps = {
-    isVisible: true,
-    onCancel: mockOnCancel,
-    onSuccess: mockOnSuccess,
-  };
-
-  beforeEach(() => {
-    vi.clearAllMocks();
-    mockUseStoreRequestInSpendLogs.mockReturnValue({
-      mutateAsync: mockMutateAsync,
-      isPending: false,
-    } as any);
-    mockParseErrorMessage.mockImplementation((error: any) => error?.message || String(error));
-  });
-
-  it("should render the modal", () => {
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-    expect(screen.getByRole("dialog")).toBeInTheDocument();
-    expect(screen.getByText("Spend Logs Settings")).toBeInTheDocument();
-  });
-
-  it("should render form fields with initial values", () => {
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    expect(screen.getByText("Store Prompts in Spend Logs")).toBeInTheDocument();
-    expect(screen.getByLabelText("Maximum Spend Logs Retention Period (Optional)")).toBeInTheDocument();
-    expect(screen.getByPlaceholderText("e.g., 7d, 30d")).toBeInTheDocument();
-  });
-
-  it("should render cancel and save buttons", () => {
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    expect(screen.getByRole("button", { name: "Cancel" })).toBeInTheDocument();
-    expect(screen.getByRole("button", { name: "Save Settings" })).toBeInTheDocument();
-  });
-
-  it("should call onCancel when cancel button is clicked", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const cancelButton = screen.getByRole("button", { name: "Cancel" });
-    await user.click(cancelButton);
-
-    expect(mockOnCancel).toHaveBeenCalledTimes(1);
-  });
-
-  it("should call onCancel when modal close button is clicked", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const closeButton = screen.getByRole("button", { name: /close/i });
-    await user.click(closeButton);
-
-    expect(mockOnCancel).toHaveBeenCalledTimes(1);
-  });
-
-  it("should toggle store prompts switch", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const switchElement = screen.getByRole("switch");
-    expect(switchElement).not.toBeChecked();
-
-    await user.click(switchElement);
-
-    await waitFor(() => {
-      expect(switchElement).toBeChecked();
-    });
-  });
-
-  it("should update retention period input", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const retentionInput = screen.getByPlaceholderText("e.g., 7d, 30d");
-    await user.type(retentionInput, "30d");
-
-    expect(retentionInput).toHaveValue("30d");
-  });
-
-  it("should submit form with store prompts enabled and retention period", async () => {
-    const user = userEvent.setup();
-    mockMutateAsync.mockImplementation(async (params, options) => {
-      await Promise.resolve();
-      options?.onSuccess?.();
-      return { message: "Success" };
-    });
-
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const switchElement = screen.getByRole("switch");
-    await user.click(switchElement);
-
-    const retentionInput = screen.getByPlaceholderText("e.g., 7d, 30d");
-    await user.type(retentionInput, "30d");
-
-    const saveButton = screen.getByRole("button", { name: "Save Settings" });
-    await user.click(saveButton);
-
-    await waitFor(() => {
-      expect(mockMutateAsync).toHaveBeenCalledWith(
-        {
-          store_prompts_in_spend_logs: true,
-          maximum_spend_logs_retention_period: "30d",
-        },
-        expect.any(Object)
-      );
-    });
-  });
-
-  it("should submit form with store prompts disabled and no retention period", async () => {
-    const user = userEvent.setup();
-    mockMutateAsync.mockImplementation(async (params, options) => {
-      await Promise.resolve();
-      options?.onSuccess?.();
-      return { message: "Success" };
-    });
-
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const saveButton = screen.getByRole("button", { name: "Save Settings" });
-    await user.click(saveButton);
-
-    await waitFor(() => {
-      expect(mockMutateAsync).toHaveBeenCalledWith(
-        {
-          store_prompts_in_spend_logs: false,
-          maximum_spend_logs_retention_period: undefined,
-        },
-        expect.any(Object)
-      );
-    });
-  });
-
-  it("should show success notification and call onSuccess on successful submission", async () => {
-    const user = userEvent.setup();
-    mockMutateAsync.mockImplementation(async (params, options) => {
-      await Promise.resolve();
-      options?.onSuccess?.();
-      return { message: "Success" };
-    });
-
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const saveButton = screen.getByRole("button", { name: "Save Settings" });
-    await user.click(saveButton);
-
-    await waitFor(() => {
-      expect(mockNotificationsManager.success).toHaveBeenCalledWith("Spend logs settings updated successfully");
-      expect(mockOnSuccess).toHaveBeenCalledTimes(1);
-    });
-  });
-
-  it("should show error notification when submission fails", async () => {
-    const user = userEvent.setup();
-    const error = new Error("Network error");
-    mockMutateAsync.mockRejectedValue(error);
-    mockParseErrorMessage.mockReturnValue("Network error");
-
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const saveButton = screen.getByRole("button", { name: "Save Settings" });
-    await user.click(saveButton);
-
-    await waitFor(() => {
-      expect(mockNotificationsManager.fromBackend).toHaveBeenCalledWith("Failed to save spend logs settings: Network error");
-    });
-  });
-
-  it("should show error notification from onError callback", async () => {
-    const user = userEvent.setup();
-    const error = new Error("Backend error");
-    mockMutateAsync.mockImplementation((params, options) => {
-      options?.onError?.(error);
-      return Promise.reject(error);
-    });
-    mockParseErrorMessage.mockReturnValue("Backend error");
-
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const saveButton = screen.getByRole("button", { name: "Save Settings" });
-    await user.click(saveButton);
-
-    await waitFor(() => {
-      expect(mockNotificationsManager.fromBackend).toHaveBeenCalledWith("Failed to save spend logs settings: Backend error");
-    });
-  });
-
-  it("should disable cancel button when pending", () => {
-    mockUseStoreRequestInSpendLogs.mockReturnValue({
-      mutateAsync: mockMutateAsync,
-      isPending: true,
-    } as any);
-
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const cancelButton = screen.getByRole("button", { name: "Cancel" });
-    expect(cancelButton).toBeDisabled();
-  });
-
-  it("should show loading state on save button when pending", () => {
-    mockUseStoreRequestInSpendLogs.mockReturnValue({
-      mutateAsync: mockMutateAsync,
-      isPending: true,
-    } as any);
-
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const saveButton = screen.getByRole("button", { name: /Saving/i });
-    expect(saveButton).toBeInTheDocument();
-    expect(saveButton.className).toContain("ant-btn-loading");
-  });
-
-  it("should call onCancel when cancel button is clicked after modifying form", async () => {
-    const user = userEvent.setup();
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const switchElement = screen.getByRole("switch");
-    await user.click(switchElement);
-
-    const retentionInput = screen.getByPlaceholderText("e.g., 7d, 30d");
-    await user.type(retentionInput, "30d");
-
-    expect(switchElement).toBeChecked();
-    expect(retentionInput).toHaveValue("30d");
-
-    const cancelButton = screen.getByRole("button", { name: "Cancel" });
-    await user.click(cancelButton);
-
-    expect(mockOnCancel).toHaveBeenCalledTimes(1);
-  });
-
-  it("should reset form fields after successful submission", async () => {
-    const user = userEvent.setup();
-    mockMutateAsync.mockImplementation(async (params, options) => {
-      await Promise.resolve();
-      options?.onSuccess?.();
-      return { message: "Success" };
-    });
-
-    const { rerender } = renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const switchElement = screen.getByRole("switch");
-    await user.click(switchElement);
-
-    const retentionInput = screen.getByPlaceholderText("e.g., 7d, 30d");
-    await user.type(retentionInput, "30d");
-
-    expect(switchElement).toBeChecked();
-    expect(retentionInput).toHaveValue("30d");
-
-    const saveButton = screen.getByRole("button", { name: "Save Settings" });
-    await user.click(saveButton);
-
-    await waitFor(() => {
-      expect(mockNotificationsManager.success).toHaveBeenCalled();
-    });
-
-    rerender(<SpendLogsSettingsModal {...defaultProps} />);
-
-    await waitFor(() => {
-      const updatedSwitchElement = screen.getByRole("switch");
-      const updatedRetentionInput = screen.getByPlaceholderText("e.g., 7d, 30d");
-      expect(updatedSwitchElement).not.toBeChecked();
-      expect(updatedRetentionInput).toHaveValue("");
-    });
-  });
-
-  it("should not call onSuccess when it is not provided", async () => {
-    const user = userEvent.setup();
-    mockMutateAsync.mockImplementation(async (params, options) => {
-      await Promise.resolve();
-      options?.onSuccess?.();
-      return { message: "Success" };
-    });
-
-    renderWithProviders(<SpendLogsSettingsModal isVisible={true} onCancel={mockOnCancel} />);
-
-    const saveButton = screen.getByRole("button", { name: "Save Settings" });
-    await user.click(saveButton);
-
-    await waitFor(() => {
-      expect(mockNotificationsManager.success).toHaveBeenCalled();
-    });
-  });
-
-  it("should not render modal when isVisible is false", () => {
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} isVisible={false} />);
-
-    expect(screen.queryByRole("dialog")).not.toBeInTheDocument();
-  });
-
-  it("should submit form with only store prompts enabled and no retention period", async () => {
-    const user = userEvent.setup();
-    mockMutateAsync.mockImplementation(async (params, options) => {
-      await Promise.resolve();
-      options?.onSuccess?.();
-      return { message: "Success" };
-    });
-
-    renderWithProviders(<SpendLogsSettingsModal {...defaultProps} />);
-
-    const switchElement = screen.getByRole("switch");
-    await user.click(switchElement);
-
-    const saveButton = screen.getByRole("button", { name: "Save Settings" });
-    await user.click(saveButton);
-
-    await waitFor(() => {
-      expect(mockMutateAsync).toHaveBeenCalledWith(
-        {
-          store_prompts_in_spend_logs: true,
-          maximum_spend_logs_retention_period: undefined,
-        },
-        expect.any(Object)
-      );
-    });
-  });
-});
diff --git a/ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.tsx b/ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.tsx
deleted file mode 100644
index cf8e51be94b..00000000000
--- a/ui/litellm-dashboard/src/components/view_logs/SpendLogsSettingsModal/SpendLogsSettingsModal.tsx
+++ /dev/null
@@ -1,100 +0,0 @@
-"use client";
-
-import { StoreRequestInSpendLogsParams, useStoreRequestInSpendLogs } from "@/app/(dashboard)/hooks/storeRequestInSpendLogs/useStoreRequestInSpendLogs";
-import NotificationsManager from "@/components/molecules/notifications_manager";
-import { parseErrorMessage } from "@/components/shared/errorUtils";
-import { ClockCircleOutlined } from "@ant-design/icons";
-import { Button, Form, Input, Modal, Space, Switch } from "antd";
-import React from "react";
-
-interface SpendLogsSettingsModalProps {
-  isVisible: boolean;
-  onCancel: () => void;
-  onSuccess?: () => void;
-}
-
-const SpendLogsSettingsModal: React.FC<SpendLogsSettingsModalProps> = ({ isVisible, onCancel, onSuccess }) => {
-  const [form] = Form.useForm();
-  const { mutateAsync, isPending } = useStoreRequestInSpendLogs();
-  const storePromptsValue = Form.useWatch('store_prompts_in_spend_logs', form);
-
-  const handleFormSubmit = async (formValues: StoreRequestInSpendLogsParams) => {
-    try {
-      await mutateAsync(formValues, {
-        onSuccess: () => {
-          NotificationsManager.success("Spend logs settings updated successfully");
-          form.resetFields();
-          onSuccess?.();
-        },
-        onError: (error) => {
-          NotificationsManager.fromBackend("Failed to save spend logs settings: " + parseErrorMessage(error));
-        },
-      });
-    } catch (error) {
-      NotificationsManager.fromBackend("Failed to save spend logs settings: " + parseErrorMessage(error));
-    }
-  };
-
-  const handleCancel = () => {
-    form.resetFields();
-    onCancel();
-  };
-
-  return (
-    <Modal
-      title="Spend Logs Settings"
-      open={isVisible}
-      width={600}
-      footer={
-        <Space>
-          <Button onClick={handleCancel} disabled={isPending}>
-            Cancel
-          </Button>
-          <Button type="primary" loading={isPending} onClick={() => form.submit()}>
-            {isPending ? "Saving..." : "Save Settings"}
-          </Button>
-        </Space>
-      }
-      onCancel={handleCancel}
-    >
-      <Form
-        form={form}
-        layout="horizontal"
-        labelCol={{ flex: "auto", style: { textAlign: "left" } }}
-        wrapperCol={{ flex: "auto", style: { textAlign: "right" } }}
-        onFinish={handleFormSubmit}
-        initialValues={{
-          store_prompts_in_spend_logs: false,
-          maximum_spend_logs_retention_period: undefined,
-        }}
-      >
-        <Form.Item
-          name="store_prompts_in_spend_logs"
-          tooltip="When enabled, prompts will be stored in spend logs for tracking and analysis purposes."
-          valuePropName="checked"
-        >
-          <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
-            <span>Store Prompts in Spend Logs</span>
-            <Switch checked={storePromptsValue ?? false} onChange={(checked) => form.setFieldValue('store_prompts_in_spend_logs', checked)} />
-          </div>
-        </Form.Item>
-
-
-        <Form.Item
-          label="Maximum Spend Logs Retention Period (Optional)"
-          name="maximum_spend_logs_retention_period"
-          tooltip="Set the maximum retention period for spend logs (e.g., '7d' for 7 days, '30d' for 30 days). Leave empty for no limit."
-          labelCol={{ flex: "auto", style: { textAlign: "left" } }}
-          wrapperCol={{ flex: "0 0 25%", style: { textAlign: "right" } }}
-        >
-          <Input
-            placeholder="e.g., 7d, 30d"
-            prefix={<ClockCircleOutlined />}
-          />
-        </Form.Item>
-      </Form>
-    </Modal>
-  );
-};
-
-export default SpendLogsSettingsModal;
diff --git a/ui/litellm-dashboard/src/components/view_logs/index.tsx b/ui/litellm-dashboard/src/components/view_logs/index.tsx
index 22af41f6d08..904d052073a 100644
--- a/ui/litellm-dashboard/src/components/view_logs/index.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/index.tsx
@@ -12,7 +12,7 @@ import { RequestResponsePanel } from "./RequestResponsePanel";
 import { ErrorViewer } from "./ErrorViewer";
 import { internalUserRoles } from "../../utils/roles";
 import { ConfigInfoMessage } from "./ConfigInfoMessage";
-import { Button, Tooltip } from "antd";
+import { Tooltip } from "antd";
 import { KeyResponse, Team } from "../key_team_helpers/key_list";
 import KeyInfoView from "../templates/key_info_view";
 import { SessionView } from "./SessionView";
@@ -31,8 +31,6 @@ import { truncateString } from "@/utils/textUtils";
 import DeletedKeysPage from "../DeletedKeysPage/DeletedKeysPage";
 import DeletedTeamsPage from "../DeletedTeamsPage/DeletedTeamsPage";
 import NewBadge from "../common_components/NewBadge";
-import SpendLogsSettingsModal from "./SpendLogsSettingsModal/SpendLogsSettingsModal";
-import { SettingOutlined } from "@ant-design/icons";
 
 interface SpendLogsTableProps {
   accessToken: string | null;
@@ -93,7 +91,6 @@ export default function SpendLogsTable({
 
   const [expandedRequestId, setExpandedRequestId] = useState<string | null>(null);
   const [selectedSessionId, setSelectedSessionId] = useState<string | null>(null);
-  const [isSpendLogsSettingsModalVisible, setIsSpendLogsSettingsModalVisible] = useState(false);
 
   const queryClient = useQueryClient();
 
@@ -464,11 +461,6 @@ export default function SpendLogsTable({
       label: "Key Hash",
       isSearchable: false,
     },
-    {
-      name: "Error Message",
-      label: "Error Message",
-      isSearchable: false,
-    },
   ];
 
   // When a session is selected, render the SessionView component
@@ -534,13 +526,6 @@ export default function SpendLogsTable({
                   "Request Logs"
                 )}
               </h1>
-              {!selectedSessionId && (
-                <Button
-                  icon={<SettingOutlined />}
-                  onClick={() => setIsSpendLogsSettingsModalVisible(true)}
-                  title="Spend Logs Settings"
-                />
-              )}
             </div>
             {selectedKeyInfo && selectedKeyIdInfoView && selectedKeyInfo.api_key === selectedKeyIdInfoView ? (
               <KeyInfoView
@@ -567,11 +552,6 @@ export default function SpendLogsTable({
                   onApplyFilters={handleFilterChange}
                   onResetFilters={handleFilterReset}
                 />
-                <SpendLogsSettingsModal
-                  isVisible={isSpendLogsSettingsModalVisible}
-                  onCancel={() => setIsSpendLogsSettingsModalVisible(false)}
-                  onSuccess={() => setIsSpendLogsSettingsModalVisible(false)}
-                />
                 <div className="bg-white rounded-lg shadow w-full max-w-full box-border">
                   <div className="border-b px-6 py-4 w-full max-w-full box-border">
                     <div className="flex flex-col md:flex-row items-start md:items-center justify-between space-y-4 md:space-y-0 w-full max-w-full box-border">
diff --git a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx
index 0e875bc4fd1..f56e72df496 100644
--- a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx
@@ -18,7 +18,6 @@ export const FILTER_KEYS = {
   STATUS: "Status",
   KEY_ALIAS: "Key Alias",
   ERROR_CODE: "Error Code",
-  ERROR_MESSAGE: "Error Message",
 } as const;
 
 export type FilterKey = keyof typeof FILTER_KEYS;
@@ -56,7 +55,6 @@ export function useLogFilterLogic({
       [FILTER_KEYS.STATUS]: "",
       [FILTER_KEYS.KEY_ALIAS]: "",
       [FILTER_KEYS.ERROR_CODE]: "",
-      [FILTER_KEYS.ERROR_MESSAGE]: "",
     }),
     [],
   );
@@ -99,7 +97,6 @@ export function useLogFilterLogic({
           filters[FILTER_KEYS.MODEL] || undefined,
           filters[FILTER_KEYS.KEY_ALIAS] || undefined,
           filters[FILTER_KEYS.ERROR_CODE] || undefined,
-          filters[FILTER_KEYS.ERROR_MESSAGE] || undefined,
         );
 
         if (currentTimestamp === lastSearchTimestamp.current && response.data) {
@@ -140,8 +137,7 @@ export function useLogFilterLogic({
         filters[FILTER_KEYS.REQUEST_ID] ||
         filters[FILTER_KEYS.USER_ID] ||
         filters[FILTER_KEYS.END_USER] ||
-        filters[FILTER_KEYS.ERROR_CODE] ||
-        filters[FILTER_KEYS.ERROR_MESSAGE]
+        filters[FILTER_KEYS.ERROR_CODE]
       ),
     [filters],
   );
diff --git a/ui/litellm-dashboard/src/components/view_users.tsx b/ui/litellm-dashboard/src/components/view_users.tsx
index 8a09c6d1f97..f8cf8302a5e 100644
--- a/ui/litellm-dashboard/src/components/view_users.tsx
+++ b/ui/litellm-dashboard/src/components/view_users.tsx
@@ -2,7 +2,7 @@ import { Tab, TabGroup, TabList, TabPanel, TabPanels } from "@tremor/react";
 import React, { useEffect, useState } from "react";
 
 import { Button } from "@tremor/react";
-import BulkEditUserModal from "./BulkEditUsers";
+import BulkEditUserModal from "./bulk_edit_user";
 import CreateUser from "./create_user_button";
 import EditUserModal from "./edit_user";
 import {
@@ -286,7 +286,7 @@ const ViewUserDashboard: React.FC<ViewUserDashboardProps> = ({ accessToken, toke
     },
     handleDelete,
     handleResetPassword,
-    () => { }, // placeholder function, will be overridden in UserDataTable
+    () => {}, // placeholder function, will be overridden in UserDataTable
   );
 
   return (
@@ -415,7 +415,7 @@ const ViewUserDashboard: React.FC<ViewUserDashboardProps> = ({ accessToken, toke
       />
 
       <BulkEditUserModal
-        open={isBulkEditModalVisible}
+        visible={isBulkEditModalVisible}
         onCancel={() => setIsBulkEditModalVisible(false)}
         selectedUsers={selectedUsers}
         possibleUIRoles={possibleUIRoles}
diff --git a/ui/litellm-dashboard/vitest.config.ts b/ui/litellm-dashboard/vitest.config.ts
index 7c52b88d3b6..108f0715704 100644
--- a/ui/litellm-dashboard/vitest.config.ts
+++ b/ui/litellm-dashboard/vitest.config.ts
@@ -7,7 +7,6 @@ export default defineConfig({
     setupFiles: ["tests/setupTests.ts"],
     globals: true,
     css: true, // lets you import CSS/modules without extra mocks
-    testTimeout: 10000,
     coverage: {
       provider: "v8",
       reporter: ["text", "lcov"],