diff --git a/.circleci/config.yml b/.circleci/config.yml
index 307247651e7e..2b0a6924cce0 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -1330,6 +1330,57 @@ jobs:
paths:
- audio_coverage.xml
- audio_coverage
+ redis_caching_unit_tests:
+ docker:
+ - image: cimg/python:3.11
+ auth:
+ username: ${DOCKERHUB_USERNAME}
+ password: ${DOCKERHUB_PASSWORD}
+ working_directory: ~/project
+
+ steps:
+ - checkout
+ - setup_google_dns
+ - run:
+ name: Install Dependencies
+ command: |
+ python -m pip install --upgrade pip uv
+ uv pip install --system -r requirements.txt
+ pip install "pytest==7.3.1"
+ pip install "pytest-retry==1.6.3"
+ pip install "pytest-cov==5.0.0"
+ pip install "pytest-asyncio==0.21.1"
+ pip install "pytest-xdist==3.6.1"
+ pip install "pytest-rerunfailures==14.0"
+ # Run pytest and generate JUnit XML report
+ - run:
+ name: Run tests
+ command: |
+ pwd
+ ls
+ python -m pytest -vv \
+ tests/local_testing/test_dual_cache.py \
+ tests/local_testing/test_redis_batch_optimizations.py \
+ tests/local_testing/test_router_utils.py \
+ --cov=litellm --cov-report=xml \
+ -x -s -v --junitxml=test-results/junit.xml \
+ --durations=5 -n 2 \
+ --reruns 2 --reruns-delay 1
+ no_output_timeout: 20m
+ - run:
+ name: Rename the coverage files
+ command: |
+ mv coverage.xml redis_caching_coverage.xml
+ mv .coverage redis_caching_coverage
+
+ # Store test results
+ - store_test_results:
+ path: test-results
+ - persist_to_workspace:
+ root: .
+ paths:
+ - redis_caching_coverage.xml
+ - redis_caching_coverage
installing_litellm_on_python:
docker:
- image: cimg/python:3.11
@@ -2868,114 +2919,6 @@ jobs:
- store_test_results:
path: test-results
- proxy_e2e_azure_batches_tests:
- machine:
- image: ubuntu-2204:2023.10.1
- resource_class: large
- working_directory: ~/project
- steps:
- - checkout
- - setup_google_dns
- - run:
- name: Install Docker CLI
- command: |
- curl -fsSL https://get.docker.com | sh
- sudo usermod -aG docker $USER
- docker version
- - run:
- name: Install Python 3.12
- command: |
- curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh --output miniconda.sh
- bash miniconda.sh -b -p $HOME/miniconda
- export PATH="$HOME/miniconda/bin:$PATH"
- conda init bash
- source ~/.bashrc
- conda create -n myenv python=3.12 -y
- conda activate myenv
- python --version
- - run:
- name: Install Poetry
- command: |
- export PATH="$HOME/miniconda/bin:$PATH"
- source $HOME/miniconda/etc/profile.d/conda.sh
- conda activate myenv
- pip install poetry
- - run:
- name: Install dockerize
- command: |
- wget https://github.com/jwilder/dockerize/releases/download/v0.6.1/dockerize-linux-amd64-v0.6.1.tar.gz
- sudo tar -C /usr/local/bin -xzvf dockerize-linux-amd64-v0.6.1.tar.gz
- rm dockerize-linux-amd64-v0.6.1.tar.gz
- - run:
- name: Start PostgreSQL Database
- command: |
- docker run -d \
- --name postgres-db \
- -e POSTGRES_USER=llmproxy \
- -e POSTGRES_PASSWORD=dbpassword9090 \
- -e POSTGRES_DB=litellm \
- -p 5432:5432 \
- postgres:15
- - run:
- name: Wait for PostgreSQL to be ready
- command: dockerize -wait tcp://localhost:5432 -timeout 1m
- - run:
- name: Install system dependencies
- command: |
- sudo apt-get update -y
- sudo apt-get install -y libpq-dev
- - run:
- name: Install Dependencies
- command: |
- export PATH="$HOME/miniconda/bin:$PATH"
- source $HOME/miniconda/etc/profile.d/conda.sh
- conda activate myenv
- poetry config virtualenvs.in-project true
- poetry install --with dev,proxy-dev --extras "proxy"
- poetry run pip install psycopg2-binary uvicorn fastapi httpx tenacity
- - run:
- name: Setup litellm-enterprise
- command: |
- export PATH="$HOME/miniconda/bin:$PATH"
- source $HOME/miniconda/etc/profile.d/conda.sh
- conda activate myenv
- poetry run pip install --force-reinstall --no-deps -e enterprise/
- - run:
- name: Generate Prisma client
- command: |
- export PATH="$HOME/miniconda/bin:$PATH"
- source $HOME/miniconda/etc/profile.d/conda.sh
- conda activate myenv
- poetry run prisma generate --schema litellm/proxy/schema.prisma
- - run:
- name: Run Prisma migrations
- command: |
- export PATH="$HOME/miniconda/bin:$PATH"
- source $HOME/miniconda/etc/profile.d/conda.sh
- conda activate myenv
- export DATABASE_URL=postgresql://llmproxy:dbpassword9090@localhost:5432/litellm
- cd litellm/proxy
- poetry run prisma migrate deploy --schema schema.prisma
- cd ../..
- - run:
- name: Run Azure Batch E2E Tests
- command: |
- export PATH="$HOME/miniconda/bin:$PATH"
- source $HOME/miniconda/etc/profile.d/conda.sh
- conda activate myenv
- export DATABASE_URL=postgresql://llmproxy:dbpassword9090@localhost:5432/litellm
- export USE_LOCAL_LITELLM=true
- export USE_MOCK_MODELS=true
- export USE_STATE_TRACKER=true
- export LITELLM_LOG=DEBUG
- poetry run pytest tests/proxy_e2e_azure_batches_tests/test_proxy_e2e_azure_batches.py \
- -vv -s -k "test_e2e_managed_batch" \
- --tb=short \
- --maxfail=3 \
- --durations=10 \
- --junitxml=test-results/junit.xml
- no_output_timeout: 15m
-
upload-coverage:
docker:
- image: cimg/python:3.9
@@ -2997,7 +2940,7 @@ jobs:
python -m venv venv
. venv/bin/activate
pip install coverage
- coverage combine realtime_translation_coverage ocr_coverage search_coverage mcp_coverage litellm_mcps_tests_coverage logging_coverage audio_coverage local_testing_part1_coverage local_testing_part2_coverage pass_through_unit_tests_coverage batches_coverage guardrails_coverage
+ coverage combine realtime_translation_coverage ocr_coverage search_coverage mcp_coverage litellm_mcps_tests_coverage logging_coverage audio_coverage local_testing_part1_coverage local_testing_part2_coverage pass_through_unit_tests_coverage batches_coverage guardrails_coverage redis_caching_coverage
coverage xml
- codecov/upload:
file: ./coverage.xml
@@ -3182,6 +3125,117 @@ jobs:
CI=true npm run test -- --run \
--pool forks --poolOptions.forks.maxForks=8
+ e2e_ui_testing:
+ docker:
+ - image: cimg/python:3.12-browsers
+ auth:
+ username: ${DOCKERHUB_USERNAME}
+ password: ${DOCKERHUB_PASSWORD}
+ - image: cimg/postgres:16.0
+ environment:
+ POSTGRES_USER: e2euser
+ POSTGRES_PASSWORD: e2epassword
+ POSTGRES_DB: litellm_e2e
+ resource_class: large
+ working_directory: ~/project
+ environment:
+ DATABASE_URL: "postgresql://e2euser:e2epassword@localhost:5432/litellm_e2e"
+ CI: "true"
+ steps:
+ - checkout
+ - setup_google_dns
+ - restore_cache:
+ keys:
+ - ui-e2e-py-deps-v1-{{ checksum "requirements.txt" }}
+ - run:
+ name: Install Python dependencies
+ command: |
+ python -m pip install --upgrade pip uv
+ uv pip install --system -r requirements.txt
+ pip install "prisma==0.11.0"
+ prisma generate --schema litellm/proxy/schema.prisma
+ - save_cache:
+ key: ui-e2e-py-deps-v1-{{ checksum "requirements.txt" }}
+ paths:
+ - ~/.local/lib
+ - ~/.local/bin
+ - restore_cache:
+ keys:
+ - ui-e2e-node-deps-v1-{{ checksum "ui/litellm-dashboard/package-lock.json" }}
+ - run:
+ name: Install Node dependencies and Playwright
+ command: |
+ cd ui/litellm-dashboard
+ npm ci
+ npx playwright install chromium --with-deps
+ - save_cache:
+ key: ui-e2e-node-deps-v1-{{ checksum "ui/litellm-dashboard/package-lock.json" }}
+ paths:
+ - ui/litellm-dashboard/node_modules
+ - run:
+ name: Build UI from source
+ command: |
+ cd ui/litellm-dashboard
+ npm run build
+ cp -r out/ ../../litellm/proxy/_experimental/out/
+ # Restructure HTML so extensionless routes work (login.html -> login/index.html)
+ find ../../litellm/proxy/_experimental/out -name '*.html' ! -name 'index.html' | while read -r f; do
+ d="${f%.html}"; mkdir -p "$d"; mv "$f" "$d/index.html"
+ done
+ - run:
+ name: Wait for PostgreSQL
+ command: dockerize -wait tcp://localhost:5432 -timeout 30s
+ - run:
+ name: Push Prisma schema
+ command: prisma db push --schema litellm/proxy/schema.prisma --accept-data-loss
+ - run:
+ name: Seed database
+ command: |
+ PGPASSWORD=e2epassword psql -h localhost -p 5432 -U e2euser -d litellm_e2e \
+ -f ui/litellm-dashboard/e2e_tests/fixtures/seed.sql
+ - run:
+ name: Start mock LLM server
+ command: python ui/litellm-dashboard/e2e_tests/fixtures/mock_llm_server/server.py
+ background: true
+ - run:
+ name: Start LiteLLM proxy
+ environment:
+ LITELLM_MASTER_KEY: "sk-1234"
+ MOCK_LLM_URL: "http://127.0.0.1:8090/v1"
+ DISABLE_SCHEMA_UPDATE: "true"
+ SERVER_ROOT_PATH: ""
+ PROXY_LOGOUT_URL: ""
+ command: |
+ python -m litellm.proxy.proxy_cli \
+ --config ui/litellm-dashboard/e2e_tests/fixtures/config.yml \
+ --port 4000
+ background: true
+ - run:
+ name: Wait for proxy to be ready
+ command: |
+ for i in $(seq 1 60); do
+ HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" http://127.0.0.1:4000/health -H "Authorization: Bearer sk-1234" 2>/dev/null || true)
+ if [ "$HTTP_CODE" = "200" ]; then
+ echo "Proxy is ready"
+ exit 0
+ fi
+ sleep 2
+ done
+ echo "Proxy failed to start"
+ exit 1
+ - run:
+ name: Run Playwright E2E tests
+ command: |
+ cd ui/litellm-dashboard
+ npx playwright test --config e2e_tests/playwright.config.ts
+ no_output_timeout: 10m
+ - store_artifacts:
+ path: ui/litellm-dashboard/test-results
+ destination: e2e-test-results
+ - store_artifacts:
+ path: ui/litellm-dashboard/playwright-report
+ destination: e2e-playwright-report
+
build_docker_database_image:
machine:
image: ubuntu-2204:2024.04.1
@@ -3207,80 +3261,6 @@ jobs:
paths:
- litellm-docker-database.tar.zst
- e2e_ui_testing:
- machine:
- image: ubuntu-2204:2023.10.1
- resource_class: large
- working_directory: ~/project
- parameters:
- browser:
- type: string
- steps:
- - checkout
- - setup_google_dns
- - attach_workspace:
- at: ~/project
- - run:
- name: Load Docker Database Image
- command: |
- zstd -d litellm-docker-database.tar.zst --stdout | docker load
- docker images | grep litellm-docker-database
- - run:
- name: Install Dependencies
- command: |
- npm install -D @playwright/test
- - run:
- name: Install Playwright Browsers
- command: |
- npx playwright install
- - run:
- name: Run Docker container
- command: |
- docker run -d \
- -p 4000:4000 \
- -e DATABASE_URL=$E2E_UI_TEST_DATABASE_URL \
- -e LITELLM_MASTER_KEY="sk-1234" \
- -e OPENAI_API_KEY=$OPENAI_API_KEY \
- -e UI_USERNAME="admin" \
- -e UI_PASSWORD="gm" \
- -e LITELLM_LICENSE=$LITELLM_LICENSE \
- --name litellm-docker-database-<< parameters.browser >> \
- -v $(pwd)/litellm/proxy/example_config_yaml/simple_config.yaml:/app/config.yaml \
- litellm-docker-database:ci \
- --config /app/config.yaml \
- --port 4000 \
- --detailed_debug
- - run:
- name: Install curl and dockerize
- command: |
- sudo apt-get update
- sudo apt-get install -y curl
- sudo wget https://github.com/jwilder/dockerize/releases/download/v0.6.1/dockerize-linux-amd64-v0.6.1.tar.gz
- sudo tar -C /usr/local/bin -xzvf dockerize-linux-amd64-v0.6.1.tar.gz
- sudo rm dockerize-linux-amd64-v0.6.1.tar.gz
- - run:
- name: Start outputting logs
- command: docker logs -f litellm-docker-database-<< parameters.browser >>
- background: true
- - run:
- name: Wait for app to be ready
- command: dockerize -wait http://localhost:4000 -timeout 5m
- - run:
- name: Run Playwright Tests
- command: |
- npx playwright test \
- --project << parameters.browser >> \
- --config ui/litellm-dashboard/e2e_tests/playwright.config.ts \
- --reporter=html \
- --output=test-results
- no_output_timeout: 15m
- - store_artifacts:
- path: test-results
- destination: playwright-results
-
- - store_artifacts:
- path: playwright-report
- destination: playwright-report
prisma_schema_sync:
machine:
@@ -3509,32 +3489,12 @@ workflows:
only:
- main
- /litellm_.*/
- # - e2e_ui_testing:
- # name: e2e_ui_testing_chromium
- # browser: chromium
- # context: e2e_ui_tests
- # requires:
- # - ui_build
- # - build_docker_database_image
- # - prisma_schema_sync
- # filters:
- # branches:
- # only:
- # - main
- # - /litellm_.*/
- # - e2e_ui_testing:
- # name: e2e_ui_testing_firefox
- # browser: firefox
- # context: e2e_ui_tests
- # requires:
- # - ui_build
- # - build_docker_database_image
- # - prisma_schema_sync
- # filters:
- # branches:
- # only:
- # - main
- # - /litellm_.*/
+ - e2e_ui_testing:
+ filters:
+ branches:
+ only:
+ - main
+ - /litellm_.*/
- build_and_test:
requires:
- build_docker_database_image
@@ -3605,12 +3565,6 @@ workflows:
only:
- main
- /litellm_.*/
- - proxy_e2e_azure_batches_tests:
- filters:
- branches:
- only:
- - main
- - /litellm_.*/
- llm_translation_testing:
filters:
branches:
@@ -3729,6 +3683,12 @@ workflows:
only:
- main
- /litellm_.*/
+ - redis_caching_unit_tests:
+ filters:
+ branches:
+ only:
+ - main
+ - /litellm_.*/
- upload-coverage:
requires:
- realtime_translation_testing
@@ -3747,6 +3707,7 @@ workflows:
- image_gen_testing
- logging_testing
- audio_testing
+ - redis_caching_unit_tests
- langfuse_logging_unit_tests
- local_testing_part1
- local_testing_part2
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index d830c16dfa27..210f232b1704 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -32,6 +32,13 @@ If you're seeing a delay in your PR being merged, ping the LiteLLM Team on [Slac
- [ ] **Merge / cherry-pick CI run**
Links:
+## Screenshots / Proof of Fix
+
+
+
## Type
diff --git a/.github/workflows/_test-unit-base.yml b/.github/workflows/_test-unit-base.yml
index f1ae30e67d76..d8dec73c4289 100644
--- a/.github/workflows/_test-unit-base.yml
+++ b/.github/workflows/_test-unit-base.yml
@@ -27,6 +27,10 @@ on:
required: false
type: number
default: 10
+ artifact-name:
+ description: "Unique name for the coverage artifact (must be unique per run)"
+ required: true
+ type: string
permissions:
contents: read
@@ -93,4 +97,46 @@ jobs:
--reruns "${RERUNS}" \
--reruns-delay 1 \
--dist=loadscope \
- --durations=20
+ --durations=20 \
+ --cov=litellm \
+ --cov-report=xml:coverage.xml \
+ --cov-config=pyproject.toml
+
+ - name: Save coverage report
+ if: always()
+ uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1
+ with:
+ name: coverage-${{ inputs.artifact-name }}-${{ github.run_id }}-${{ github.run_attempt }}
+ path: coverage.xml
+ retention-days: 1
+
+ upload-coverage:
+ name: Upload coverage to Codecov
+ needs: run
+ if: always()
+ runs-on: ubuntu-latest
+ permissions:
+ contents: read
+ id-token: write
+ pull-requests: write
+
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+ with:
+ persist-credentials: false
+
+ - name: Download coverage report
+ uses: actions/download-artifact@95815c38cf2ff2164869cbab79da8d1f422bc89e # v4.2.1
+ with:
+ pattern: coverage-${{ inputs.artifact-name }}-${{ github.run_id }}-${{ github.run_attempt }}
+ path: coverage-reports
+ merge-multiple: true
+
+ - name: Upload to Codecov
+ uses: codecov/codecov-action@75cd11691c0faa626561e295848008c8a7dddffe # v5.5.4
+ with:
+ use_oidc: true
+ directory: coverage-reports
+ root_dir: ${{ github.workspace }}
+ fail_ci_if_error: false
diff --git a/.github/workflows/_test-unit-services-base.yml b/.github/workflows/_test-unit-services-base.yml
index d53a9e8822ae..ce4c048c624c 100644
--- a/.github/workflows/_test-unit-services-base.yml
+++ b/.github/workflows/_test-unit-services-base.yml
@@ -27,23 +27,17 @@ on:
required: false
type: number
default: 10
- enable-redis:
- description: "Pass Redis Cloud credentials to tests via REDIS_HOST/PORT/PASSWORD env vars"
- required: false
- type: boolean
- default: false
enable-postgres:
description: "Start a local Postgres service container and run Prisma migrations"
required: false
type: boolean
default: false
- secrets:
- REDIS_HOST:
- required: false
- REDIS_PORT:
- required: false
- REDIS_PASSWORD:
+ artifact-name:
+ description: "Unique name for the coverage artifact (must be unique per run)"
required: false
+ type: string
+ default: "run"
+ secrets:
DATABASE_URL:
required: false
POSTGRES_USER:
@@ -61,11 +55,8 @@ jobs:
timeout-minutes: ${{ inputs.timeout-minutes }}
# Environment is derived from the enable-* flags, not caller-controllable.
# This prevents callers from passing arbitrary environment names to bypass secret scoping.
- # Note: Postgres service container always starts (GHA limitation), so any Redis job
- # also needs Postgres secrets → uses integration-redis-postgres, not integration-redis.
environment: >-
${{
- inputs.enable-redis && 'integration-redis-postgres' ||
inputs.enable-postgres && 'integration-postgres' ||
''
}}
@@ -141,9 +132,6 @@ jobs:
WORKERS: ${{ inputs.workers }}
RERUNS: ${{ inputs.reruns }}
DATABASE_URL: ${{ inputs.enable-postgres && secrets.DATABASE_URL || '' }}
- REDIS_HOST: ${{ inputs.enable-redis && secrets.REDIS_HOST || '' }}
- REDIS_PORT: ${{ inputs.enable-redis && secrets.REDIS_PORT || '' }}
- REDIS_PASSWORD: ${{ inputs.enable-redis && secrets.REDIS_PASSWORD || '' }}
run: |
if [ "${WORKERS}" = "0" ]; then
poetry run pytest ${TEST_PATH:?} \
@@ -151,7 +139,10 @@ jobs:
--maxfail="${MAX_FAILURES}" \
--reruns "${RERUNS}" \
--reruns-delay 1 \
- --durations=20
+ --durations=20 \
+ --cov=litellm \
+ --cov-report=xml:coverage.xml \
+ --cov-config=pyproject.toml
else
poetry run pytest ${TEST_PATH:?} \
--tb=short -vv \
@@ -160,5 +151,47 @@ jobs:
--reruns "${RERUNS}" \
--reruns-delay 1 \
--dist=loadscope \
- --durations=20
+ --durations=20 \
+ --cov=litellm \
+ --cov-report=xml:coverage.xml \
+ --cov-config=pyproject.toml
fi
+
+ - name: Save coverage report
+ if: always()
+ uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1
+ with:
+ name: coverage-${{ inputs.artifact-name }}-${{ github.run_id }}-${{ github.run_attempt }}
+ path: coverage.xml
+ retention-days: 1
+
+ upload-coverage:
+ name: Upload coverage to Codecov
+ needs: run
+ if: always()
+ runs-on: ubuntu-latest
+ permissions:
+ contents: read
+ id-token: write
+ pull-requests: write
+
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+ with:
+ persist-credentials: false
+
+ - name: Download coverage report
+ uses: actions/download-artifact@95815c38cf2ff2164869cbab79da8d1f422bc89e # v4.2.1
+ with:
+ pattern: coverage-${{ inputs.artifact-name }}-${{ github.run_id }}-${{ github.run_attempt }}
+ path: coverage-reports
+ merge-multiple: true
+
+ - name: Upload to Codecov
+ uses: codecov/codecov-action@75cd11691c0faa626561e295848008c8a7dddffe # v5.5.4
+ with:
+ use_oidc: true
+ directory: coverage-reports
+ root_dir: ${{ github.workspace }}
+ fail_ci_if_error: false
diff --git a/.github/workflows/create-release.yml b/.github/workflows/create-release.yml
index 2ae01823a967..b86339798540 100644
--- a/.github/workflows/create-release.yml
+++ b/.github/workflows/create-release.yml
@@ -48,7 +48,21 @@ jobs:
const cosignSection = [
`## Verify Docker Image Signature`,
``,
- `All LiteLLM Docker images are signed with [cosign](https://docs.sigstore.dev/cosign/overview/). To verify the integrity of an image before deploying:`,
+ `All LiteLLM Docker images are signed with [cosign](https://docs.sigstore.dev/cosign/overview/). Every release is signed with the same key introduced in [commit \`0112e53\`](https://github.com/BerriAI/litellm/commit/0112e53046018d726492c814b3644b7d376029d0).`,
+ ``,
+ `**Verify using the pinned commit hash (recommended):**`,
+ ``,
+ `A commit hash is cryptographically immutable, so this is the strongest way to ensure you are using the original signing key:`,
+ ``,
+ '```bash',
+ `cosign verify \\`,
+ ` --key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \\`,
+ ` ghcr.io/berriai/litellm:${tag}`,
+ '```',
+ ``,
+ `**Verify using the release tag (convenience):**`,
+ ``,
+ `Tags are protected in this repository and resolve to the same key. This option is easier to read but relies on tag protection rules:`,
``,
'```bash',
`cosign verify \\`,
diff --git a/.github/workflows/run_llm_translation_tests.py b/.github/workflows/run_llm_translation_tests.py
old mode 100755
new mode 100644
diff --git a/.github/workflows/test-litellm-matrix.yml b/.github/workflows/test-litellm-matrix.yml
deleted file mode 100644
index dafabe4d83e1..000000000000
--- a/.github/workflows/test-litellm-matrix.yml
+++ /dev/null
@@ -1,214 +0,0 @@
-name: LiteLLM Unit Tests (Matrix)
-
-on:
- pull_request:
- branches: [main]
-
-permissions:
- contents: read
-
-# Cancel in-progress runs for the same PR
-concurrency:
- group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
- cancel-in-progress: true
-
-jobs:
- test:
- runs-on: ubuntu-latest
- timeout-minutes: 20 # Increased from 15 to 20
- strategy:
- fail-fast: false
- matrix:
- test-group:
- # tests/test_litellm split by subdirectory (~560 files total)
- # Vertex AI tests separated for better isolation (prevent auth/env pollution)
- - name: "llms-vertex"
- path: "tests/test_litellm/llms/vertex_ai"
- workers: 1
- reruns: 2
- - name: "llms-other"
- path: "tests/test_litellm/llms --ignore=tests/test_litellm/llms/vertex_ai"
- workers: 2
- reruns: 2
- # tests/test_litellm/proxy split by subdirectory (~180 files total)
- - name: "proxy-guardrails"
- path: "tests/test_litellm/proxy/guardrails tests/test_litellm/proxy/management_endpoints tests/test_litellm/proxy/management_helpers"
- workers: 2
- reruns: 2
- - name: "proxy-core"
- path: "tests/test_litellm/proxy/auth tests/test_litellm/proxy/client tests/test_litellm/proxy/db tests/test_litellm/proxy/hooks tests/test_litellm/proxy/policy_engine"
- workers: 2
- reruns: 2
- - name: "proxy-misc"
- path: "tests/test_litellm/proxy/_experimental tests/test_litellm/proxy/agent_endpoints tests/test_litellm/proxy/anthropic_endpoints tests/test_litellm/proxy/common_utils tests/test_litellm/proxy/discovery_endpoints tests/test_litellm/proxy/experimental tests/test_litellm/proxy/google_endpoints tests/test_litellm/proxy/health_endpoints tests/test_litellm/proxy/image_endpoints tests/test_litellm/proxy/middleware tests/test_litellm/proxy/openai_files_endpoint tests/test_litellm/proxy/pass_through_endpoints tests/test_litellm/proxy/prompts tests/test_litellm/proxy/public_endpoints tests/test_litellm/proxy/response_api_endpoints tests/test_litellm/proxy/spend_tracking tests/test_litellm/proxy/ui_crud_endpoints tests/test_litellm/proxy/vector_store_endpoints tests/test_litellm/proxy/test_*.py"
- workers: 2
- reruns: 2
- - name: "integrations"
- path: "tests/test_litellm/integrations"
- workers: 2
- reruns: 3 # Integration tests tend to be flakier
- - name: "core-utils"
- path: "tests/test_litellm/litellm_core_utils"
- workers: 2
- reruns: 1
- - name: "other-1"
- # responses (5942) + caching (1723) + types (819) ≈ 8.5k lines
- path: "tests/test_litellm/responses tests/test_litellm/caching tests/test_litellm/types"
- workers: 2
- reruns: 2
- - name: "other-2"
- # enterprise (3062) + google_genai (2511) + router_utils (1982) ≈ 7.6k lines
- path: "tests/test_litellm/enterprise tests/test_litellm/google_genai tests/test_litellm/router_utils"
- workers: 2
- reruns: 2
- - name: "other-3"
- # remaining dirs ≈ 8.0k lines
- path: "tests/test_litellm/router_strategy tests/test_litellm/secret_managers tests/test_litellm/a2a_protocol tests/test_litellm/anthropic_interface tests/test_litellm/completion_extras tests/test_litellm/containers tests/test_litellm/experimental_mcp_client tests/test_litellm/images tests/test_litellm/interactions tests/test_litellm/passthrough tests/test_litellm/vector_stores"
- workers: 2
- reruns: 2
- - name: "root"
- path: "tests/test_litellm/test_*.py"
- workers: 2
- reruns: 2
- # tests/proxy_unit_tests split alphabetically (~48 files total)
- - name: "proxy-unit-a1"
- # test_[a-j]*.py: jwt (1564) + auth_checks (978) + google_gemini (478) + e2e_pod_lock (437) + rest
- path: "tests/proxy_unit_tests/test_[a-j]*.py"
- workers: 2
- reruns: 1
- - name: "proxy-unit-a2"
- # test_[k-o]*.py: key_generate_prisma (4346) + key_generate_dynamodb + models_fallback
- path: "tests/proxy_unit_tests/test_[k-o]*.py"
- workers: 2
- reruns: 1
- - name: "proxy-unit-b1"
- # lighter config/utility proxy tests (prisma, project, prompt, proxy_[c-r]*)
- path: "tests/proxy_unit_tests/test_prisma*.py tests/proxy_unit_tests/test_project*.py tests/proxy_unit_tests/test_prompt*.py tests/proxy_unit_tests/test_proxy_[c-r]*.py"
- workers: 2
- reruns: 1
- - name: "proxy-unit-b2"
- # proxy_server.py alone (2750 lines) - isolated to avoid blocking smaller tests
- path: "tests/proxy_unit_tests/test_proxy_server.py"
- workers: 2
- reruns: 1
- - name: "proxy-unit-b3"
- # proxy_server_* (618) + proxy_setting_guardrails (71) - smaller server-related tests
- path: "tests/proxy_unit_tests/test_proxy_server_*.py tests/proxy_unit_tests/test_proxy_setting_guardrails.py"
- workers: 2
- reruns: 1
- - name: "proxy-unit-b4"
- # proxy_utils.py alone (2339 lines) - isolated to avoid blocking token counter
- path: "tests/proxy_unit_tests/test_proxy_utils.py"
- workers: 2
- reruns: 1
- - name: "proxy-unit-b5"
- # proxy_token_counter (1279) - runs independently from utils
- path: "tests/proxy_unit_tests/test_proxy_token_counter.py"
- workers: 2
- reruns: 1
- - name: "proxy-unit-b6"
- # test_[r-t]*.py: response_polling (1399) + search_api_logging (202) + server_root (64) + skills_db (261) + realtime_cache (62)
- path: "tests/proxy_unit_tests/test_[r-t]*.py"
- workers: 2
- reruns: 1
- - name: "proxy-unit-b7"
- # test_[u-z]*.py: user_api_key_auth (1136) + zero_cost (590) + update_spend (305) + unit_test_* (206) + ui_path (157)
- path: "tests/proxy_unit_tests/test_[u-z]*.py"
- workers: 2
- reruns: 1
-
- name: test (${{ matrix.test-group.name }})
-
- steps:
- - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
- with:
- persist-credentials: false
-
- - name: Set up Python
- uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
- with:
- python-version: "3.12"
-
- - name: Install Poetry
- run: pip install 'poetry==2.3.2'
-
- - name: Cache Poetry dependencies
- uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.0.0
- with:
- path: |
- ~/.cache/pypoetry
- ~/.cache/pip
- .venv
- key: ${{ runner.os }}-poetry-${{ hashFiles('poetry.lock') }}
- restore-keys: |
- ${{ runner.os }}-poetry-
-
- - name: Install dependencies
- run: |
- poetry config virtualenvs.in-project true
- poetry install --with dev,proxy-dev --extras "proxy semantic-router"
- # pytest-rerunfailures and pytest-xdist are in pyproject.toml dev dependencies
- poetry run pip install google-genai==1.22.0 \
- google-cloud-aiplatform==1.115.0 fastapi-offline==1.7.3 python-multipart==0.0.22 openapi-core==0.23.0
-
- - name: Setup litellm-enterprise
- run: |
- poetry run pip install --force-reinstall --no-deps -e enterprise/
-
- - name: Generate Prisma client
- env:
- PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
- run: |
- poetry run pip install nodejs-wheel-binaries==24.13.1
- poetry run prisma generate --schema litellm/proxy/schema.prisma
-
- - name: Run tests - ${{ matrix.test-group.name }}
- run: |
- poetry run pytest ${{ matrix.test-group.path }} \
- --tb=short -vv \
- --maxfail=10 \
- -n ${{ matrix.test-group.workers }} \
- --reruns ${{ matrix.test-group.reruns }} \
- --reruns-delay 1 \
- --dist=loadscope \
- --durations=20 \
- --cov=litellm \
- --cov-report=xml:coverage-${{ matrix.test-group.name }}.xml \
- --cov-config=pyproject.toml
-
- - name: Save coverage report
- if: always()
- uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1
- with:
- name: coverage-${{ matrix.test-group.name }}
- path: coverage-${{ matrix.test-group.name }}.xml
- retention-days: 1
-
- upload-coverage:
- name: Upload coverage to Codecov
- needs: test
- if: always()
- runs-on: ubuntu-latest
- permissions:
- contents: read
- id-token: write # Required for OIDC tokenless upload
- pull-requests: write # Required for Codecov PR comments
-
- steps:
- - name: Checkout code
- uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
-
- - name: Download all coverage reports
- uses: actions/download-artifact@95815c38cf2ff2164869cbab79da8d1f422bc89e # v4.2.1
- with:
- pattern: coverage-*
- path: coverage-reports
- merge-multiple: true
-
- - name: Upload to Codecov
- uses: codecov/codecov-action@aa56896cf108bd10b5eb883cd1d24196da57f695 # v5.5.4
- with:
- use_oidc: true
- directory: coverage-reports
- root_dir: ${{ github.workspace }}
- fail_ci_if_error: false
diff --git a/.github/workflows/test-proxy-e2e-azure-batches.yml b/.github/workflows/test-proxy-e2e-azure-batches.yml
deleted file mode 100644
index 7cbbe0b338f0..000000000000
--- a/.github/workflows/test-proxy-e2e-azure-batches.yml
+++ /dev/null
@@ -1,97 +0,0 @@
-name: Proxy E2E Azure Batches Tests
-
-on:
- pull_request:
- branches: [main]
- workflow_dispatch:
-
-concurrency:
- group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
- cancel-in-progress: true
-
-permissions:
- contents: read
-
-jobs:
- proxy_e2e_azure_batches_tests:
- runs-on: ubuntu-latest
- timeout-minutes: 30
-
- services:
- postgres:
- image: postgres:15
- env:
- POSTGRES_USER: llmproxy
- POSTGRES_PASSWORD: dbpassword9090
- POSTGRES_DB: litellm
- ports:
- - 5432:5432
- options: >-
- --health-cmd pg_isready
- --health-interval 10s
- --health-timeout 5s
- --health-retries 5
-
- steps:
- - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
- with:
- persist-credentials: false
-
- - name: Set up Python
- uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
- with:
- python-version: "3.12"
-
- - name: Install Poetry
- run: pip install 'poetry==2.3.2'
-
- - name: Cache Poetry dependencies
- uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.0.0
- with:
- path: |
- ~/.cache/pypoetry
- ~/.cache/pip
- .venv
- key: ${{ runner.os }}-poetry-e2e-batches-${{ hashFiles('poetry.lock') }}
- restore-keys: |
- ${{ runner.os }}-poetry-e2e-batches-
- ${{ runner.os }}-poetry-
-
- - name: Install dependencies
- run: |
- poetry config virtualenvs.in-project true
- poetry install --with dev,proxy-dev --extras "proxy"
- poetry run pip install psycopg2-binary==2.9.11 uvicorn==0.42.0 fastapi==0.135.2 httpx==0.28.1 tenacity==9.1.4
-
- - name: Setup litellm-enterprise
- run: |
- poetry run pip install --force-reinstall --no-deps -e enterprise/
-
- - name: Generate Prisma client
- env:
- PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
- run: |
- poetry run pip install nodejs-wheel-binaries==24.13.1
- poetry run prisma generate --schema litellm/proxy/schema.prisma
-
- - name: Run Prisma migrations
- env:
- DATABASE_URL: postgresql://llmproxy:dbpassword9090@localhost:5432/litellm
- run: |
- cd litellm/proxy
- poetry run prisma migrate deploy --schema schema.prisma
- cd ../..
-
- - name: Run Azure Batch E2E Tests
- env:
- DATABASE_URL: postgresql://llmproxy:dbpassword9090@localhost:5432/litellm
- USE_LOCAL_LITELLM: "true"
- USE_MOCK_MODELS: "true"
- USE_STATE_TRACKER: "true"
- LITELLM_LOG: DEBUG
- run: |
- poetry run pytest tests/proxy_e2e_azure_batches_tests/test_proxy_e2e_azure_batches.py \
- -vv -s -k "test_e2e_managed_batch" \
- --tb=short \
- --maxfail=3 \
- --durations=10
diff --git a/.github/workflows/test-unit-caching-redis.yml b/.github/workflows/test-unit-caching-redis.yml
deleted file mode 100644
index ca274324f2f2..000000000000
--- a/.github/workflows/test-unit-caching-redis.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-name: "Unit Tests: Caching (Redis)"
-
-# Uses cloud Redis credentials — only runs on trusted branches, not PRs.
-# This prevents external PRs from accessing Redis credentials.
-on:
- push:
- branches: [main, "litellm_*"]
-
-permissions:
- contents: read
-
-concurrency:
- group: ${{ github.workflow }}-${{ github.ref }}
- cancel-in-progress: true
-
-jobs:
- caching-redis:
- uses: ./.github/workflows/_test-unit-services-base.yml
- with:
- # Redis-only tests that do NOT require provider API keys.
- # Tests needing API keys (test_caching.py, test_caching_ssl.py, test_prometheus_service.py,
- # test_router_caching.py) are in Phase 3 integration workflows.
- test-path: >-
- tests/local_testing/test_dual_cache.py
- tests/local_testing/test_redis_batch_optimizations.py
- tests/local_testing/test_router_utils.py
- workers: 2
- reruns: 2
- timeout-minutes: 20
- enable-redis: true
- enable-postgres: false
- secrets:
- REDIS_HOST: ${{ secrets.REDIS_HOST }}
- REDIS_PORT: ${{ secrets.REDIS_PORT }}
- REDIS_PASSWORD: ${{ secrets.REDIS_PASSWORD }}
- DATABASE_URL: ${{ secrets.DATABASE_URL }}
- POSTGRES_USER: ${{ secrets.POSTGRES_USER }}
- POSTGRES_PASSWORD: ${{ secrets.POSTGRES_PASSWORD }}
diff --git a/.github/workflows/test-unit-core-utils.yml b/.github/workflows/test-unit-core-utils.yml
index 2f3698fdf600..9696cea56161 100644
--- a/.github/workflows/test-unit-core-utils.yml
+++ b/.github/workflows/test-unit-core-utils.yml
@@ -6,6 +6,8 @@ on:
permissions:
contents: read
+ id-token: write
+ pull-requests: write
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
@@ -18,3 +20,4 @@ jobs:
test-path: "tests/test_litellm/litellm_core_utils"
workers: 2
reruns: 1
+ artifact-name: core-utils
diff --git a/.github/workflows/test-unit-enterprise-routing.yml b/.github/workflows/test-unit-enterprise-routing.yml
index 13ae3efedba2..986de1195357 100644
--- a/.github/workflows/test-unit-enterprise-routing.yml
+++ b/.github/workflows/test-unit-enterprise-routing.yml
@@ -6,6 +6,8 @@ on:
permissions:
contents: read
+ id-token: write
+ pull-requests: write
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
@@ -22,3 +24,4 @@ jobs:
tests/test_litellm/router_strategy
workers: 2
reruns: 2
+ artifact-name: enterprise-routing
diff --git a/.github/workflows/test-unit-integrations.yml b/.github/workflows/test-unit-integrations.yml
index 2789f99d81c3..e73c09d6cd8e 100644
--- a/.github/workflows/test-unit-integrations.yml
+++ b/.github/workflows/test-unit-integrations.yml
@@ -6,6 +6,8 @@ on:
permissions:
contents: read
+ id-token: write
+ pull-requests: write
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
@@ -18,3 +20,4 @@ jobs:
test-path: "tests/test_litellm/integrations"
workers: 2
reruns: 3
+ artifact-name: integrations
diff --git a/.github/workflows/test-unit-llm-providers.yml b/.github/workflows/test-unit-llm-providers.yml
index 6c00272b0c8f..2fb4cf8c1db0 100644
--- a/.github/workflows/test-unit-llm-providers.yml
+++ b/.github/workflows/test-unit-llm-providers.yml
@@ -14,16 +14,26 @@ concurrency:
jobs:
vertex-ai:
name: Vertex AI
+ permissions:
+ contents: read
+ id-token: write
+ pull-requests: write
uses: ./.github/workflows/_test-unit-base.yml
with:
test-path: "tests/test_litellm/llms/vertex_ai"
workers: 1
reruns: 2
+ artifact-name: llm-vertex-ai
other-providers:
name: All Other Providers
+ permissions:
+ contents: read
+ id-token: write
+ pull-requests: write
uses: ./.github/workflows/_test-unit-base.yml
with:
test-path: "tests/test_litellm/llms --ignore=tests/test_litellm/llms/vertex_ai"
workers: 2
reruns: 2
+ artifact-name: llm-other-providers
diff --git a/.github/workflows/test-unit-misc.yml b/.github/workflows/test-unit-misc.yml
index 9228decd7cc8..e44133867e64 100644
--- a/.github/workflows/test-unit-misc.yml
+++ b/.github/workflows/test-unit-misc.yml
@@ -6,6 +6,8 @@ on:
permissions:
contents: read
+ id-token: write
+ pull-requests: write
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
@@ -29,3 +31,4 @@ jobs:
tests/test_litellm/test_*.py
workers: 2
reruns: 2
+ artifact-name: misc
diff --git a/.github/workflows/test-unit-proxy-auth.yml b/.github/workflows/test-unit-proxy-auth.yml
index e71821db701f..5e427a39f35d 100644
--- a/.github/workflows/test-unit-proxy-auth.yml
+++ b/.github/workflows/test-unit-proxy-auth.yml
@@ -6,6 +6,8 @@ on:
permissions:
contents: read
+ id-token: write
+ pull-requests: write
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
@@ -18,3 +20,4 @@ jobs:
test-path: "tests/test_litellm/proxy/auth tests/test_litellm/proxy/hooks tests/test_litellm/proxy/policy_engine tests/test_litellm/proxy/client"
workers: 2
reruns: 2
+ artifact-name: proxy-auth
diff --git a/.github/workflows/test-unit-proxy-db.yml b/.github/workflows/test-unit-proxy-db.yml
index bdfb6efeef1c..49d399e87417 100644
--- a/.github/workflows/test-unit-proxy-db.yml
+++ b/.github/workflows/test-unit-proxy-db.yml
@@ -14,6 +14,10 @@ concurrency:
jobs:
proxy-db:
+ permissions:
+ contents: read
+ id-token: write
+ pull-requests: write
strategy:
fail-fast: false
matrix:
@@ -37,8 +41,8 @@ jobs:
workers: ${{ matrix.workers }}
reruns: 2
timeout-minutes: ${{ matrix.timeout }}
- enable-redis: false
enable-postgres: true
+ artifact-name: proxy-db-${{ matrix.test-group }}
secrets:
DATABASE_URL: ${{ secrets.DATABASE_URL }}
POSTGRES_USER: ${{ secrets.POSTGRES_USER }}
diff --git a/.github/workflows/test-unit-proxy-endpoints.yml b/.github/workflows/test-unit-proxy-endpoints.yml
index caff3b3ae061..67d35ef794ef 100644
--- a/.github/workflows/test-unit-proxy-endpoints.yml
+++ b/.github/workflows/test-unit-proxy-endpoints.yml
@@ -6,6 +6,8 @@ on:
permissions:
contents: read
+ id-token: write
+ pull-requests: write
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
@@ -33,3 +35,4 @@ jobs:
tests/test_litellm/proxy/ui_crud_endpoints
workers: 2
reruns: 2
+ artifact-name: proxy-endpoints
diff --git a/.github/workflows/test-unit-proxy-infra.yml b/.github/workflows/test-unit-proxy-infra.yml
index 4dfbbe317edd..56801569345a 100644
--- a/.github/workflows/test-unit-proxy-infra.yml
+++ b/.github/workflows/test-unit-proxy-infra.yml
@@ -6,6 +6,8 @@ on:
permissions:
contents: read
+ id-token: write
+ pull-requests: write
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
@@ -26,3 +28,4 @@ jobs:
tests/test_litellm/proxy/test_*.py
workers: 2
reruns: 2
+ artifact-name: proxy-infra
diff --git a/.github/workflows/test-unit-responses-caching-types.yml b/.github/workflows/test-unit-responses-caching-types.yml
index 7f3acac28038..771a695a70c7 100644
--- a/.github/workflows/test-unit-responses-caching-types.yml
+++ b/.github/workflows/test-unit-responses-caching-types.yml
@@ -6,6 +6,8 @@ on:
permissions:
contents: read
+ id-token: write
+ pull-requests: write
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
@@ -18,3 +20,4 @@ jobs:
test-path: "tests/test_litellm/responses tests/test_litellm/caching tests/test_litellm/types"
workers: 2
reruns: 2
+ artifact-name: responses-caching-types
diff --git a/.github/workflows/test-unit-security.yml b/.github/workflows/test-unit-security.yml
index b38c82b1c24b..76d3be3e63c0 100644
--- a/.github/workflows/test-unit-security.yml
+++ b/.github/workflows/test-unit-security.yml
@@ -7,6 +7,8 @@ on:
permissions:
contents: read
+ id-token: write
+ pull-requests: write
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
@@ -20,8 +22,8 @@ jobs:
workers: 1
reruns: 2
timeout-minutes: 20
- enable-redis: false
enable-postgres: true
+ artifact-name: security
secrets:
DATABASE_URL: ${{ secrets.DATABASE_URL }}
POSTGRES_USER: ${{ secrets.POSTGRES_USER }}
diff --git a/.trivyignore b/.trivyignore
deleted file mode 100644
index 0d04ecacdb56..000000000000
--- a/.trivyignore
+++ /dev/null
@@ -1,12 +0,0 @@
-# LiteLLM Trivy Ignore File
-# CVEs listed here are temporarily allowlisted pending fixes
-
-# Next.js vulnerabilities in UI dashboard (next@14.2.35)
-# Allowlisted: 2026-01-31, 7-day fix timeline
-# Fix: Upgrade to Next.js 15.5.10+ or 16.1.5+
-
-# HIGH: DoS via request deserialization
-GHSA-h25m-26qc-wcjf
-
-# MEDIUM: Image Optimizer DoS
-CVE-2025-59471
diff --git a/AGENTS.md b/AGENTS.md
index ba9c9b356bc6..37411938e2fe 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -254,7 +254,7 @@ See `CLAUDE.md` and the `Makefile` for standard commands. Key notes:
- `openapi-core` must be installed (`poetry run pip install openapi-core`) for the OpenAPI compliance tests in `tests/test_litellm/interactions/`.
- The `--timeout` pytest flag is NOT available; don't pass it.
- Unit tests: `poetry run pytest tests/test_litellm/ -x -vv -n 4`
-- Black `--check` may report pre-existing formatting issues; this does not block test runs.
+- **Before committing, always run `poetry run black .` to format your code.** Black formatting is enforced in CI.
- If `poetry install` fails with "pyproject.toml changed significantly since poetry.lock was last generated", run `poetry lock` first to regenerate the lock file.
### Lint
diff --git a/CLAUDE.md b/CLAUDE.md
index f0478120181a..a8800ff88844 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -20,6 +20,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
- `make format` - Apply Black code formatting
- `make lint-ruff` - Run Ruff linting only
- `make lint-mypy` - Run MyPy type checking only
+- **Before committing, always run `poetry run black .` to format your code.** Black formatting is enforced in CI.
### Single Test Files
- `poetry run pytest tests/path/to/test_file.py -v` - Run specific test file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 77bc15ff50b8..c029ccce1ab9 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -149,6 +149,19 @@ Apply formatting (auto-fixes issues):
make format
```
+> **Black formatting is enforced in CI.** All PRs must pass the Black formatting check.
+>
+> - **AI coding agents** (Claude Code, Copilot, Cursor, etc.): `AGENTS.md` and `CLAUDE.md` instruct agents to run `poetry run black .` before committing.
+> - **VS Code users**: Install the [Black Formatter extension](https://marketplace.visualstudio.com/items?itemName=ms-python.black-formatter) and enable format-on-save:
+> ```json
+> {
+> "[python]": {
+> "editor.defaultFormatter": "ms-python.black-formatter",
+> "editor.formatOnSave": true
+> }
+> }
+> ```
+
### CI Compatibility
To ensure your changes will pass CI, run the exact same checks locally:
diff --git a/README.md b/README.md
index a6dc597574eb..d7b8bad69f32 100644
--- a/README.md
+++ b/README.md
@@ -2,20 +2,24 @@
🚅 LiteLLM
-
Call 100+ LLMs in OpenAI format. [Bedrock, Azure, OpenAI, VertexAI, Anthropic, Groq, etc.]
+
LiteLLM AI Gateway
+ Open Source AI Gateway for 100+ LLMs. Self-hosted. Enterprise-ready. Call any LLM in OpenAI format.
-
+
-
+
+
+
@@ -400,9 +404,36 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature
2. Install dependencies `npm install`
3. Run `npm run dev` to start the dashboard
+# Verify Docker Image Signatures
+
+All LiteLLM Docker images published to GHCR are signed with [cosign](https://docs.sigstore.dev/cosign/overview/). Every release is signed with the same key introduced in [commit `0112e53`](https://github.com/BerriAI/litellm/commit/0112e53046018d726492c814b3644b7d376029d0).
+
+**Verify using the pinned commit hash (recommended):**
+
+A commit hash is cryptographically immutable, so this is the strongest way to ensure you are using the original signing key:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
+ ghcr.io/berriai/litellm:
+```
+
+**Verify using a release tag (convenience):**
+
+Tags are protected in this repository and resolve to the same key. This option is easier to read but relies on tag protection rules:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm//cosign.pub \
+ ghcr.io/berriai/litellm:
+```
+
+Replace `` with the version you are deploying (e.g. `v1.83.0-stable`).
+
# Enterprise
For companies that need better security, user management and professional support
+[Get an Enterprise License](https://litellm.ai/enterprise)
[Talk to founders](https://enterprise.litellm.ai/demo)
This covers:
diff --git a/ci_cd/.grype.yaml b/ci_cd/.grype.yaml
deleted file mode 100644
index b9bc9db58f5d..000000000000
--- a/ci_cd/.grype.yaml
+++ /dev/null
@@ -1,36 +0,0 @@
-ignore:
- - vulnerability: CVE-2026-22184
- reason: no fixed zlib package is available yet in the Wolfi repositories, so this is ignored temporarily until an upstream release exists
- # Wolfi base image: Python 3.13 and Node from apk have no fixed builds in Wolfi yet / not applicable
- - vulnerability: CVE-2025-55130
- reason: Node in Wolfi apk; only used for Admin UI build/prisma
- - vulnerability: CVE-2025-59465
- reason: Node in Wolfi apk; only used for Admin UI build/prisma
- - vulnerability: CVE-2025-55131
- reason: Node in Wolfi apk; only used for Admin UI build/prisma
- - vulnerability: CVE-2025-59466
- reason: Node in Wolfi apk; only used for Admin UI build/prisma
- - vulnerability: CVE-2026-21637
- reason: Node in Wolfi apk; only used for Admin UI build/prisma
- - vulnerability: CVE-2025-55132
- reason: Node in Wolfi apk; only used for Admin UI build/prisma
- - vulnerability: GHSA-hx9q-6w63-j58v
- reason: orjson dumps recursion; allowlisted
- - vulnerability: GHSA-73rr-hh4g-fpgx
- reason: diff npm transitive dep; override in package.json, allowlisted
- - vulnerability: CVE-2026-0865
- reason: Python 3.13 in Wolfi base; no fixed apk build yet
- - vulnerability: CVE-2025-15282
- reason: Python 3.13 in Wolfi base; no fixed apk build yet
- - vulnerability: CVE-2026-0672
- reason: Python 3.13 in Wolfi base; no fixed apk build yet
- - vulnerability: CVE-2025-15366
- reason: Python 3.13 in Wolfi base; no fixed apk build yet
- - vulnerability: CVE-2025-15367
- reason: Python 3.13 in Wolfi base; no fixed apk build yet
- - vulnerability: CVE-2025-11468
- reason: Python 3.13 in Wolfi base; no fixed apk build yet
- - vulnerability: CVE-2025-12781
- reason: Python 3.13 in Wolfi base; no fixed apk build yet
- - vulnerability: CVE-2026-1299
- reason: Python 3.13 in Wolfi base; no fixed apk build yet
diff --git a/ci_cd/security_scans.sh b/ci_cd/security_scans.sh
deleted file mode 100755
index 2138fca6cd5a..000000000000
--- a/ci_cd/security_scans.sh
+++ /dev/null
@@ -1,261 +0,0 @@
-#!/bin/bash
-
-# Security Scans Script for LiteLLM
-# This script runs comprehensive security scans including Trivy and Grype
-
-set -e
-
-echo "Starting security scans for LiteLLM..."
-
-# Function to install Trivy and required tools
-install_trivy() {
- echo "Installing Trivy and required tools..."
- TRIVY_VERSION="0.35.0"
- sudo apt-get update
- sudo apt-get install -y wget jq curl bsdmainutils
- wget -qO trivy.deb "https://github.com/aquasecurity/trivy/releases/download/v${TRIVY_VERSION}/trivy_${TRIVY_VERSION}_Linux-64bit.deb"
- sudo dpkg -i trivy.deb
- rm trivy.deb
- echo "Trivy ${TRIVY_VERSION} installed successfully"
-}
-
-# Function to install Grype
-install_grype() {
- echo "Installing Grype..."
- curl -sSfL https://raw.githubusercontent.com/anchore/grype/main/install.sh | sudo sh -s -- -b /usr/local/bin
- echo "Grype installed successfully"
-}
-
-# Function to install ggshield
-install_ggshield() {
- echo "Installing ggshield..."
- pip3 install --upgrade pip
- pip3 install ggshield
- echo "ggshield installed successfully"
-}
-
-# # Function to run secret detection scans
-# run_secret_detection() {
-# echo "Running secret detection scans..."
-
-# if ! command -v ggshield &> /dev/null; then
-# install_ggshield
-# fi
-
-# # Check if GITGUARDIAN_API_KEY is set (required for CI/CD)
-# if [ -z "$GITGUARDIAN_API_KEY" ]; then
-# echo "Warning: GITGUARDIAN_API_KEY environment variable is not set."
-# echo "ggshield requires a GitGuardian API key to scan for secrets."
-# echo "Please set GITGUARDIAN_API_KEY in your CI/CD environment variables."
-# exit 1
-# fi
-
-# echo "Scanning codebase for secrets..."
-# echo "Note: Large codebases may take several minutes due to API rate limits (50 requests/minute on free plan)"
-# echo "ggshield will automatically handle rate limits and retry as needed."
-# echo "Binary files, cache files, and build artifacts are excluded via .gitguardian.yaml"
-
-# # Use --recursive for directory scanning and auto-confirm if prompted
-# # .gitguardian.yaml will automatically exclude binary files, wheel files, etc.
-# # GITGUARDIAN_API_KEY environment variable will be used for authentication
-# echo y | ggshield secret scan path . --recursive || {
-# echo ""
-# echo "=========================================="
-# echo "ERROR: Secret Detection Failed"
-# echo "=========================================="
-# echo "ggshield has detected secrets in the codebase."
-# echo "Please review discovered secrets above, revoke any actively used secrets"
-# echo "from underlying systems and make changes to inject secrets dynamically at runtime."
-# echo ""
-# echo "For more information, see: https://docs.gitguardian.com/secrets-detection/"
-# echo "=========================================="
-# echo ""
-# exit 1
-# }
-
-# echo "Secret detection scans completed successfully"
-# }
-
-# Function to run Trivy scans
-run_trivy_scans() {
- echo "Running Trivy scans..."
-
- echo "Scanning LiteLLM Docs..."
- trivy fs --ignorefile .trivyignore --scanners vuln --dependency-tree --exit-code 1 --severity HIGH,CRITICAL,MEDIUM ./docs/
-
- echo "Scanning LiteLLM UI..."
- trivy fs --ignorefile .trivyignore --scanners vuln --dependency-tree --exit-code 1 --severity HIGH,CRITICAL,MEDIUM ./ui/
-
- echo "Trivy scans completed successfully"
-}
-
-# Function to build and scan Docker images with Grype
-run_grype_scans() {
- echo "Running Grype scans..."
-
- # Temporarily add wheel files to .dockerignore for security scans
- echo "Temporarily modifying .dockerignore to exclude problematic wheel files..."
- cp .dockerignore .dockerignore.backup 2>/dev/null || touch .dockerignore.backup
- echo "/*.whl" >> .dockerignore
-
- # Build and scan Dockerfile.database
- echo "Building and scanning Dockerfile.database..."
- docker build --no-cache -t litellm-database:latest -f ./docker/Dockerfile.database .
- grype litellm-database:latest --config ci_cd/.grype.yaml --fail-on critical
-
- # Build and scan main Dockerfile
- echo "Building and scanning main Dockerfile..."
- docker build --no-cache -t litellm:latest .
- grype litellm:latest --config ci_cd/.grype.yaml --fail-on critical
-
- # Restore original .dockerignore
- echo "Restoring original .dockerignore..."
- mv .dockerignore.backup .dockerignore
-
- # Scan the locally built LiteLLM image for vulnerabilities with CVSS >= 4.0
- echo "Scanning locally built LiteLLM image for high-severity vulnerabilities..."
- echo "Using locally built image: litellm:latest"
-
- # Allowlist of CVEs to be ignored in failure threshold/reporting
- # - CVE-2025-8869: Not applicable on Python >=3.13 (PEP 706 implemented); pip fallback unused; no OS-level fix
- # - GHSA-4xh5-x5gv-qwph: GitHub Security Advisory alias for CVE-2025-8869
- # - GHSA-5j98-mcp5-4vw2: glob CLI command injection via -c/--cmd; glob CLI is not used in the litellm runtime image,
- # and the vulnerable versions are pulled in only via OS-level/node tooling outside of our application code
- ALLOWED_CVES=(
- "CVE-2025-8869"
- "GHSA-4xh5-x5gv-qwph"
- "CVE-2025-8291" # no fix available as of Oct 11, 2025
- "GHSA-5j98-mcp5-4vw2"
- "CVE-2025-13836" # Python 3.13 HTTP response reading OOM/DoS - no fix available in base image
- "CVE-2025-12084" # Python 3.13 xml.dom.minidom quadratic algorithm - no fix available in base image
- "CVE-2025-60876" # BusyBox wget HTTP request splitting - no fix available in Chainguard Wolfi base image
- "CVE-2026-0861" # Wolfi glibc still flagged even on 2.42-r5; upstream patched build unavailable yet
- "CVE-2010-4756" # glibc glob DoS - awaiting patched Wolfi glibc build
- "CVE-2019-1010022" # glibc stack guard bypass - awaiting patched Wolfi glibc build
- "CVE-2019-1010023" # glibc ldd remap issue - awaiting patched Wolfi glibc build
- "CVE-2019-1010024" # glibc ASLR mitigation bypass - awaiting patched Wolfi glibc build
- "CVE-2019-1010025" # glibc pthread heap address leak - awaiting patched Wolfi glibc build
- "CVE-2026-22184" # zlib untgz buffer overflow - untgz unused + no fixed Wolfi build yet
- "GHSA-58pv-8j8x-9vj2" # jaraco.context path traversal - setuptools vendored only (v5.3.0), not used in application code (using v6.1.0+)
- "GHSA-34x7-hfp2-rc4v" # node-tar hardlink path traversal - not applicable, tar CLI not exposed in application code
- "GHSA-r6q2-hw4h-h46w" # node-tar not used by application runtime, Linux-only container, not affect by macOS APFS-specific exploit
- "GHSA-8rrh-rw8j-w5fx" # wheel is from chainguard and will be handled by then TODO: Remove this after Chainguard updates the wheel
- "CVE-2025-59465" # Node only used for Admin UI build/prisma
- "CVE-2025-55131" # Node only used for Admin UI build/prisma
- "CVE-2025-59466" # Node only used for Admin UI build/prisma
- "CVE-2025-55130" # Node only used for Admin UI build/prisma
- "CVE-2025-59467" # Node only used for Admin UI build/prisma
- "CVE-2026-21637" # Node only used for Admin UI build/prisma
- "CVE-2025-55132" # Node only used for Admin UI build/prisma
- "GHSA-hx9q-6w63-j58v" # orjson dumps recursion; allowlisted
- "CVE-2025-15281" # No fix available yet
- "CVE-2026-0865" # No fix available yet
- "CVE-2025-15282" # No fix available yet
- "CVE-2026-0672" # No fix available yet
- "CVE-2025-15366" # No fix available yet
- "CVE-2025-15367" # No fix available yet
- "CVE-2025-12781" # No fix available yet
- "CVE-2025-11468" # No fix available yet
- "CVE-2026-1299" # Python 3.13 email module header injection - not applicable, LiteLLM doesn't use BytesGenerator for email serialization
- "CVE-2026-0775" # npm cli incorrect permission assignment - no fix available yet, npm is only used at build/prisma-generate time
- "GHSA-3ppc-4f35-3m26" # minimatch ReDoS via repeated wildcards - from nodejs_wheel bundled npm, not used in application runtime code
- "GHSA-83g3-92jg-28cx" # tar arbitrary file read/write via hardlink - from nodejs_wheel bundled npm, not used in application runtime code
- "CVE-2026-2297" # Python 3.13 SourcelessFileLoader audit hook bypass - no fix available in base image
- "GHSA-qffp-2rhf-9h96" # tar hardlink path traversal - from nodejs_wheel bundled npm, not used in application runtime code
- "CVE-2026-2673" # OpenSSL 3.6.1 TLS 1.3 key exchange group negotiation issue - no fix available yet
- "CVE-2026-3644" # Python 3.13 vulnerability - no fix available in base image
- "CVE-2026-4224" # Python 3.13 Expat parser stack overflow in ElementDeclHandler - no fix available in base image
- )
-
- # Build JSON array of allowlisted CVE IDs for jq
- ALLOWED_IDS_JSON=$(printf '%s\n' "${ALLOWED_CVES[@]}" | jq -R . | jq -s .)
-
- echo "Checking for vulnerabilities with CVSS score >= 4.0..."
- echo "Allowlisted CVEs (ignored in threshold): ${ALLOWED_CVES[*]}"
- echo ""
-
- # Show all high-severity vulnerabilities for transparency
- TOTAL_HIGH_SEVERITY=$(grype litellm:latest -o json | jq -r '
- .matches[]
- | select(.vulnerability.cvss[]?.metrics.baseScore >= 4.0)
- | .vulnerability.id' | wc -l)
-
- if [ "$TOTAL_HIGH_SEVERITY" -gt 0 ]; then
- echo "Total vulnerabilities found with CVSS >= 4.0: $TOTAL_HIGH_SEVERITY"
- echo ""
- echo "All high-severity vulnerabilities (including allowlisted):"
- grype litellm:latest -o json | jq --argjson allow "$ALLOWED_IDS_JSON" -r '
- ["Package", "Version", "Vulnerability ID", "CVSS Score", "Allowlisted"],
- (.matches[]
- | select(.vulnerability.cvss[]?.metrics.baseScore >= 4.0)
- | [.artifact.name, .artifact.version, .vulnerability.id, .vulnerability.cvss[0].metrics.baseScore, (if (.vulnerability.id as $id | $allow | index($id)) then "YES" else "NO" end)])
- | @tsv' | column -t -s $'\t'
- echo ""
- fi
-
- HIGH_SEVERITY_COUNT=$(grype litellm:latest -o json | jq --argjson allow "$ALLOWED_IDS_JSON" -r '
- .matches[]
- | select(.vulnerability.cvss[]?.metrics.baseScore >= 4.0)
- | select((.vulnerability.id as $id | $allow | index($id) | not))
- | .vulnerability.id' | wc -l)
-
- if [ "$HIGH_SEVERITY_COUNT" -gt 0 ]; then
- echo ""
- echo "=========================================="
- echo "ERROR: Security Scan Failed"
- echo "=========================================="
- echo "Found $HIGH_SEVERITY_COUNT non-allowlisted vulnerabilities with CVSS score >= 4.0 in litellm:latest"
- echo ""
- echo "These vulnerabilities are NOT in the allowlist and must be addressed."
- echo "Current allowlisted CVEs: ${ALLOWED_CVES[*]}"
- echo ""
- echo "Detailed vulnerability report:"
- echo ""
- grype litellm:latest -o json | jq --argjson allow "$ALLOWED_IDS_JSON" -r '
- ["Package", "Version", "Vulnerability ID", "CVSS Score", "Severity", "Fix Version", "Description"],
- (.matches[]
- | select(.vulnerability.cvss[]?.metrics.baseScore >= 4.0)
- | select((.vulnerability.id as $id | $allow | index($id) | not))
- | [.artifact.name, .artifact.version, .vulnerability.id, .vulnerability.cvss[0].metrics.baseScore, .vulnerability.severity, (.vulnerability.fix.versions[0] // "No fix available"), .vulnerability.description])
- | @tsv' | column -t -s $'\t'
- echo ""
- echo "=========================================="
- echo "Action Required:"
- echo "=========================================="
- echo "1. If a fix is available, update the package to the fixed version"
- echo "2. If the vulnerability is not applicable or has no fix:"
- echo " - Add the CVE/GHSA ID to ALLOWED_CVES array in ci_cd/security_scans.sh"
- echo " - Add a comment explaining why it's safe to ignore"
- echo ""
- echo "Note: Some vulnerabilities may have multiple IDs (CVE-XXXX and GHSA-XXXX)."
- echo "Add all relevant IDs to the allowlist if they refer to the same issue."
- echo "=========================================="
- echo ""
- exit 1
- else
- echo "No high-severity vulnerabilities (CVSS >= 4.0) found in litellm:latest"
- fi
-
- echo "Grype scans completed successfully"
-}
-
-# Main execution
-main() {
- echo "Installing security scanning tools..."
- install_trivy
- install_grype
-
- # echo "Running secret detection scans..."
- # run_secret_detection
-
- echo "Running filesystem vulnerability scans..."
- run_trivy_scans
-
- echo "Running Docker image vulnerability scans..."
- run_grype_scans
-
- echo "All security scans completed successfully!"
-}
-
-# Execute main function
-main "$@"
diff --git a/docker/Dockerfile.custom_ui b/docker/Dockerfile.custom_ui
index f836190a49a1..cc44893bf92a 100644
--- a/docker/Dockerfile.custom_ui
+++ b/docker/Dockerfile.custom_ui
@@ -71,8 +71,16 @@ WORKDIR /app
RUN sed -i 's/\r$//' docker/entrypoint.sh && chmod +x docker/entrypoint.sh
RUN sed -i 's/\r$//' docker/prod_entrypoint.sh && chmod +x docker/prod_entrypoint.sh
+# Run as non-root user
+RUN groupadd --gid 1000 appuser && useradd --uid 1000 --gid 1000 --no-create-home appuser \
+ && chown -R appuser:appuser /app
+USER appuser
+
# Expose the necessary port
EXPOSE 4000/tcp
+HEALTHCHECK --interval=30s --timeout=5s --start-period=10s --retries=3 \
+ CMD ["python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:4000/health')"]
+
# Override the CMD instruction with your desired command and arguments
CMD ["--port", "4000", "--config", "config.yaml", "--detailed_debug"]
\ No newline at end of file
diff --git a/docker/Dockerfile.health_check b/docker/Dockerfile.health_check
index fb9cc201d2f7..e968bec340e6 100644
--- a/docker/Dockerfile.health_check
+++ b/docker/Dockerfile.health_check
@@ -13,12 +13,12 @@ RUN pip install --no-cache-dir -r requirements.txt
RUN chmod +x /app/health_check_client.py
# Run as non-root user
-RUN adduser --disabled-password --gecos "" --uid 1001 healthcheck
-USER healthcheck
+RUN groupadd --gid 1000 appuser && useradd --uid 1000 --gid 1000 --no-create-home appuser
+USER appuser
# Health check
-HEALTHCHECK --interval=30s --timeout=5s --retries=3 \
- CMD python /app/health_check_client.py --help || exit 1
+HEALTHCHECK --interval=30s --timeout=5s --start-period=5s --retries=3 \
+ CMD ["python", "/app/health_check_client.py", "--help"]
# Set entrypoint
ENTRYPOINT ["python", "/app/health_check_client.py"]
diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root
index 8e911e95ffa1..f3c7728146d6 100644
--- a/docker/Dockerfile.non_root
+++ b/docker/Dockerfile.non_root
@@ -41,22 +41,24 @@ COPY . .
ENV LITELLM_NON_ROOT=true
# Build Admin UI using the upstream command order while keeping a single RUN layer
-# NOTE: .npmrc (which has ignore-scripts=true and min-release-age=3d) is temporarily
-# renamed during npm install/ci. This is safe because npm ci installs from
+# NOTE: .npmrc files (which may set ignore-scripts=true and min-release-age=3d)
+# are temporarily renamed during npm install/ci so they don't block lifecycle
+# scripts needed by the build. This is safe because npm ci installs from
# package-lock.json with pinned versions + integrity hashes.
RUN mkdir -p /var/lib/litellm/ui && \
- mv /app/.npmrc /app/.npmrc.bak && \
+ ([ -f /app/.npmrc ] && mv /app/.npmrc /app/.npmrc.bak || true) && \
npm install -g npm@11.12.1 && \
npm install -g node-gyp@12.2.0 && \
- ln -sf /usr/local/lib/node_modules/node-gyp /usr/lib/node_modules/npm/node_modules/node-gyp && \
+ ln -sf "$(npm root -g)/node-gyp" "$(npm root -g)/npm/node_modules/node-gyp" && \
npm cache clean --force && \
cd /app/ui/litellm-dashboard && \
if [ -f "/app/enterprise/enterprise_ui/enterprise_colors.json" ]; then \
cp /app/enterprise/enterprise_ui/enterprise_colors.json ./ui_colors.json; \
fi && \
- mv .npmrc .npmrc.bak && \
+ ([ -f .npmrc ] && mv .npmrc .npmrc.bak || true) && \
npm ci && \
- mv .npmrc.bak .npmrc && mv /app/.npmrc.bak /app/.npmrc && \
+ ([ -f .npmrc.bak ] && mv .npmrc.bak .npmrc || true) && \
+ ([ -f /app/.npmrc.bak ] && mv /app/.npmrc.bak /app/.npmrc || true) && \
npm run build && \
cp -r /app/ui/litellm-dashboard/out/* /var/lib/litellm/ui/ && \
mkdir -p /var/lib/litellm/assets && \
diff --git a/docker/README.md b/docker/README.md
index 7027a30fdd72..26d8c9a37b0a 100644
--- a/docker/README.md
+++ b/docker/README.md
@@ -13,19 +13,19 @@ To build and run the application, you will use the `docker-compose.yml` file loc
### 1. Set the Master Key
-The application requires a `MASTER_KEY` for signing and validating tokens. You must set this key as an environment variable before running the application.
+The application requires a `LITELLM_MASTER_KEY` for signing and validating tokens. You must set this key as an environment variable before running the application.
Create a `.env` file in the root of the project and add the following line:
```
-MASTER_KEY=your-secret-key
+LITELLM_MASTER_KEY=your-secret-key
```
Replace `your-secret-key` with a strong, randomly generated secret.
### 2. Build and Run the Containers
-Once you have set the `MASTER_KEY`, you can build and run the containers using the following command:
+Once you have set the `LITELLM_MASTER_KEY`, you can build and run the containers using the following command:
```bash
docker compose up -d --build
@@ -89,4 +89,4 @@ This command should succeed (showing engine versions) even with `--network none`
## Troubleshooting
- **`build_admin_ui.sh: not found`**: This error can occur if the Docker build context is not set correctly. Ensure that you are running the `docker-compose` command from the root of the project.
-- **`Master key is not initialized`**: This error means the `MASTER_key` environment variable is not set. Make sure you have created a `.env` file in the project root with the `MASTER_KEY` defined.
+- **`Master key is not initialized`**: This error means the `LITELLM_MASTER_KEY` environment variable is not set. Make sure you have created a `.env` file in the project root with the `LITELLM_MASTER_KEY` defined.
diff --git a/docs/my-website/.trivyignore b/docs/my-website/.trivyignore
deleted file mode 100644
index 977504f26700..000000000000
--- a/docs/my-website/.trivyignore
+++ /dev/null
@@ -1,7 +0,0 @@
-# js-yaml CVE-2025-64718
-# This vulnerability is not applicable because we've forced js-yaml to version 4.1.1
-# via npm overrides in package.json. Trivy incorrectly reports this based on
-# dependency requirements in the lockfile, but the actual installed version is 4.1.1.
-# Verified with: npm list js-yaml
-CVE-2025-64718
-
diff --git a/docs/my-website/blog/april_townhall_announcement/index.md b/docs/my-website/blog/april_townhall_announcement/index.md
index 466d9e845f02..1f842536f890 100644
--- a/docs/my-website/blog/april_townhall_announcement/index.md
+++ b/docs/my-website/blog/april_townhall_announcement/index.md
@@ -4,6 +4,7 @@ title: "April Townhall: Security + Product Roadmap"
date: 2026-04-02T07:30:00
authors:
- krrish
+ - ishaan-alt
description: "Join the LiteLLM April townhall on Friday, 10 April at 7:30 AM to learn about LiteLLM's security and product roadmap."
tags: [announcement, townhall]
hide_table_of_contents: true
diff --git a/docs/my-website/blog/april_townhall_updates/index.md b/docs/my-website/blog/april_townhall_updates/index.md
new file mode 100644
index 000000000000..c726d1b7f8e3
--- /dev/null
+++ b/docs/my-website/blog/april_townhall_updates/index.md
@@ -0,0 +1,162 @@
+---
+slug: april-townhall-updates
+title: "April Townhall Updates: CI/CD v2, Stability, and Product Roadmap"
+date: 2026-04-10T12:00:00
+authors:
+ - krrish
+ - ishaan-alt
+description: "A recap of the April LiteLLM town hall covering CI/CD v2, product stability work, and the near-term roadmap."
+tags: [townhall, security, reliability, product]
+hide_table_of_contents: false
+---
+
+import Image from '@theme/IdealImage';
+
+Thank you to everyone who joined our April town hall.
+
+We used the session to share our CI/CD v2 improvements, product stability work, and what we are prioritizing next across reliability and product roadmap.
+
+{/* truncate */}
+
+## CI/CD v2 improvements
+
+Our CI/CD v2 work is centered around four goals:
+
+1. **Limit** what each package can access
+2. **Reduce** the number of sensitive environment variables
+3. **Avoid** compromised packages
+4. **Reduce the risk of** release tampering
+
+#### New architecture: isolated environments
+
+We have begun moving to isolated environments for distinct CI/CD stages to reduce the chance that a single compromised step can inherit broad access across the entire pipeline.
+
+
+```
+
+**Verify using a release tag (convenience):**
+
+Tags are protected in this repository and resolve to the same key. This option is easier to read but relies on tag protection rules:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm//cosign.pub \
+ ghcr.io/berriai/litellm:
+```
+
+Replace `` with the version you are deploying (e.g. `v1.83.0-stable`).
+
+Expected output:
+
+```
+The following checks were performed on each of these signatures:
+ - The cosign claims were validated
+ - The signatures were verified against the specified public key
+```
## What's next
diff --git a/docs/my-website/blog/security_townhall_updates/index.md b/docs/my-website/blog/security_townhall_updates/index.md
index b997de9c185e..39db096c5338 100644
--- a/docs/my-website/blog/security_townhall_updates/index.md
+++ b/docs/my-website/blog/security_townhall_updates/index.md
@@ -143,8 +143,41 @@ This will ensure, your releases are safe, even when:
- Tampered registry artifacts are published
- Tag mutations are made after the release is published
-We believe that [Cosign](https://github.com/sigstore/cosign) is a good fit for this, and have already begun working on it [PR](https://github.com/BerriAI/litellm/pull/24683).
+We believe that [Cosign](https://github.com/sigstore/cosign) is a good fit for this, and have shipped it in [PR #24683](https://github.com/BerriAI/litellm/pull/24683).
+#### How to verify a Docker image with Cosign
+
+Starting from `v1.83.0-nightly`, all LiteLLM Docker images published to GHCR are signed with [cosign](https://docs.sigstore.dev/cosign/overview/). Every release is signed with the same key that was introduced in [commit `0112e53`](https://github.com/BerriAI/litellm/commit/0112e53046018d726492c814b3644b7d376029d0).
+
+**Verify using the pinned commit hash (recommended):**
+
+A commit hash is cryptographically immutable, so this is the strongest way to ensure you are using the original signing key:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
+ ghcr.io/berriai/litellm:
+```
+
+**Verify using a release tag (convenience):**
+
+Tags are protected in this repository and resolve to the same key. This option is easier to read but relies on tag protection rules:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm//cosign.pub \
+ ghcr.io/berriai/litellm:
+```
+
+Replace `` with the version you are deploying (e.g. `v1.83.0-stable`).
+
+Expected output:
+
+```
+The following checks were performed on each of these signatures:
+ - The cosign claims were validated
+ - The signatures were verified against the specified public key
+```
### Avoid Compromised Packages
diff --git a/docs/my-website/blog/security_update_march_2026/index.md b/docs/my-website/blog/security_update_march_2026/index.md
index 1c298fe372fb..6e7b77d1e401 100644
--- a/docs/my-website/blog/security_update_march_2026/index.md
+++ b/docs/my-website/blog/security_update_march_2026/index.md
@@ -708,6 +708,40 @@ The LiteLLM AI Gateway team has already taken the following steps:
- Engaged Google's Mandiant security team to assist with forensic analysis of the build and publishing chain
+## Verify Docker image signatures
+
+Starting from `v1.83.0-nightly`, all LiteLLM Docker images published to GHCR are signed with [cosign](https://docs.sigstore.dev/cosign/overview/). Every release is signed with the same key introduced in [commit `0112e53`](https://github.com/BerriAI/litellm/commit/0112e53046018d726492c814b3644b7d376029d0).
+
+**Verify using the pinned commit hash (recommended):**
+
+A commit hash is cryptographically immutable, so this is the strongest way to ensure you are using the original signing key:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
+ ghcr.io/berriai/litellm:
+```
+
+**Verify using a release tag (convenience):**
+
+Tags are protected in this repository and resolve to the same key. This option is easier to read but relies on tag protection rules:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm//cosign.pub \
+ ghcr.io/berriai/litellm:
+```
+
+Replace `` with the version you are deploying (e.g. `v1.83.0-stable`).
+
+Expected output:
+
+```
+The following checks were performed on each of these signatures:
+ - The cosign claims were validated
+ - The signatures were verified against the specified public key
+```
+
## Verified safe versions
We have audited every LiteLLM release published between v1.78.0 and v1.82.6 across both PyPI and Docker. Each artifact was verified by:
diff --git a/docs/my-website/docs/completion/anthropic_advisor_tool.md b/docs/my-website/docs/completion/anthropic_advisor_tool.md
new file mode 100644
index 000000000000..3cb87ffdf3dc
--- /dev/null
+++ b/docs/my-website/docs/completion/anthropic_advisor_tool.md
@@ -0,0 +1,422 @@
+# Advisor Tool
+
+Pair a faster executor model with a higher-intelligence advisor model that provides strategic guidance mid-generation.
+
+The advisor tool lets a fast, lower-cost executor model (Sonnet or Haiku) consult a high-intelligence advisor model (Opus 4.6) mid-generation. The advisor reads the full conversation and produces a plan or course correction — typically 400–700 text tokens — and the executor continues with the task.
+
+This pattern is well-suited for long-horizon agentic workloads (coding agents, computer use, multi-step research) where most turns are mechanical but having an excellent plan is crucial. You get close to advisor-solo quality while the bulk of token generation happens at executor-model rates.
+
+:::info Beta
+
+The advisor tool is in beta. Include `anthropic-beta: advisor-tool-2026-03-01` in your requests — LiteLLM adds this automatically when it detects the advisor tool in your `tools` array.
+
+:::
+
+## Supported Providers
+
+| Provider | Chat Completions API | Messages API |
+|----------|---------------------|--------------|
+| **Anthropic API** | ✅ | ✅ |
+| **Azure Anthropic** | ❌ (coming soon) | ❌ (coming soon) |
+| **Google Cloud Vertex AI** | ❌ (coming soon) | ❌ (coming soon) |
+| **Amazon Bedrock** | ❌ (coming soon) | ❌ (coming soon) |
+
+## Model Compatibility
+
+The executor and advisor models must form a valid pair. Currently the only supported advisor model is `claude-opus-4-6`.
+
+| Executor | Advisor |
+|----------|---------|
+| `claude-haiku-4-5-20251001` | `claude-opus-4-6` |
+| `claude-sonnet-4-6` | `claude-opus-4-6` |
+| `claude-opus-4-6` | `claude-opus-4-6` |
+
+---
+
+## Chat Completions API
+
+### SDK Usage
+
+#### Basic Example
+
+```python showLineNumbers title="Advisor Tool — litellm.completion()"
+import litellm
+
+response = litellm.completion(
+ model="anthropic/claude-sonnet-4-6",
+ messages=[
+ {"role": "user", "content": "Build a concurrent worker pool in Go with graceful shutdown."}
+ ],
+ tools=[
+ {
+ "type": "advisor_20260301",
+ "name": "advisor",
+ "model": "claude-opus-4-6",
+ }
+ ],
+ max_tokens=4096,
+)
+
+print(response.choices[0].message.content)
+```
+
+#### With Optional Parameters
+
+```python showLineNumbers title="Advisor Tool with max_uses and caching"
+import litellm
+
+response = litellm.completion(
+ model="anthropic/claude-sonnet-4-6",
+ messages=[
+ {"role": "user", "content": "Build a REST API with authentication in Python."}
+ ],
+ tools=[
+ {
+ "type": "advisor_20260301",
+ "name": "advisor",
+ "model": "claude-opus-4-6",
+ "max_uses": 3, # cap advisor calls per request
+ "caching": {"type": "ephemeral", "ttl": "5m"}, # enable for 3+ calls per conversation
+ }
+ ],
+ max_tokens=4096,
+)
+```
+
+#### Streaming
+
+```python showLineNumbers title="Streaming with Advisor Tool"
+import litellm
+
+response = litellm.completion(
+ model="anthropic/claude-sonnet-4-6",
+ messages=[
+ {"role": "user", "content": "Implement a distributed rate limiter."}
+ ],
+ tools=[
+ {
+ "type": "advisor_20260301",
+ "name": "advisor",
+ "model": "claude-opus-4-6",
+ }
+ ],
+ max_tokens=4096,
+ stream=True,
+)
+
+for chunk in response:
+ if chunk.choices[0].delta.content:
+ print(chunk.choices[0].delta.content, end="")
+```
+
+:::note Streaming behavior
+
+The advisor sub-inference does not stream. The executor's stream pauses while the advisor runs, then the full advisor result arrives in a single event. Executor output resumes streaming afterward.
+
+:::
+
+#### Multi-Turn Conversation
+
+```python showLineNumbers title="Multi-Turn with Advisor Tool"
+import litellm
+
+tools = [
+ {
+ "type": "advisor_20260301",
+ "name": "advisor",
+ "model": "claude-opus-4-6",
+ }
+]
+
+messages = [
+ {"role": "user", "content": "Build a concurrent worker pool in Go with graceful shutdown."}
+]
+
+response = litellm.completion(
+ model="anthropic/claude-sonnet-4-6",
+ messages=messages,
+ tools=tools,
+ max_tokens=4096,
+)
+
+# Append the full response (includes server_tool_use + advisor_tool_result blocks)
+messages.append({"role": "assistant", "content": response.choices[0].message.content})
+
+# Continue the conversation — keep the same tools array
+messages.append({"role": "user", "content": "Now add a max-in-flight limit of 10."})
+
+response2 = litellm.completion(
+ model="anthropic/claude-sonnet-4-6",
+ messages=messages,
+ tools=tools,
+ max_tokens=4096,
+)
+```
+
+:::tip Auto-strip on follow-up turns
+
+LiteLLM automatically strips `advisor_tool_result` blocks from message history when the advisor tool is not present in the current request. This prevents the Anthropic 400 error that would otherwise occur.
+
+:::
+
+### AI Gateway Usage
+
+#### Proxy Configuration
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+ - model_name: claude-sonnet
+ litellm_params:
+ model: anthropic/claude-sonnet-4-6
+ api_key: os.environ/ANTHROPIC_API_KEY
+```
+
+#### Client Request via Proxy
+
+```python showLineNumbers title="Advisor Tool via AI Gateway"
+from openai import OpenAI
+
+client = OpenAI(
+ api_key="your-litellm-proxy-key",
+ base_url="http://0.0.0.0:4000/v1"
+)
+
+response = client.chat.completions.create(
+ model="claude-sonnet",
+ messages=[
+ {"role": "user", "content": "Implement a distributed rate limiter in Python."}
+ ],
+ tools=[
+ {
+ "type": "advisor_20260301",
+ "name": "advisor",
+ "model": "claude-opus-4-6",
+ }
+ ],
+ max_tokens=4096,
+)
+```
+
+---
+
+## Messages API
+
+### SDK Usage
+
+#### Basic Example
+
+```python showLineNumbers title="Advisor Tool — litellm.anthropic.messages"
+import asyncio
+import litellm
+
+async def main():
+ response = await litellm.anthropic.messages.acreate(
+ model="anthropic/claude-sonnet-4-6",
+ messages=[
+ {"role": "user", "content": "Build a concurrent worker pool in Go with graceful shutdown."}
+ ],
+ tools=[
+ {
+ "type": "advisor_20260301",
+ "name": "advisor",
+ "model": "claude-opus-4-6",
+ }
+ ],
+ max_tokens=4096,
+ )
+ print(response)
+
+asyncio.run(main())
+```
+
+#### Streaming
+
+```python showLineNumbers title="Messages API Streaming with Advisor Tool"
+import asyncio
+import json
+import litellm
+
+async def main():
+ response = await litellm.anthropic.messages.acreate(
+ model="anthropic/claude-sonnet-4-6",
+ messages=[
+ {"role": "user", "content": "Implement a distributed rate limiter."}
+ ],
+ tools=[
+ {
+ "type": "advisor_20260301",
+ "name": "advisor",
+ "model": "claude-opus-4-6",
+ }
+ ],
+ max_tokens=4096,
+ stream=True,
+ )
+
+ async for chunk in response:
+ if isinstance(chunk, bytes):
+ for line in chunk.decode("utf-8").split("\n"):
+ if line.startswith("data: "):
+ try:
+ print(json.loads(line[6:]))
+ except json.JSONDecodeError:
+ pass
+
+asyncio.run(main())
+```
+
+### AI Gateway Usage
+
+#### Proxy Configuration
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+ - model_name: claude-sonnet
+ litellm_params:
+ model: anthropic/claude-sonnet-4-6
+ api_key: os.environ/ANTHROPIC_API_KEY
+```
+
+#### Client Request via Proxy (Anthropic SDK)
+
+```python showLineNumbers title="Advisor Tool via AI Gateway (Anthropic SDK)"
+import anthropic
+
+client = anthropic.Anthropic(
+ api_key="your-litellm-proxy-key",
+ base_url="http://0.0.0.0:4000"
+)
+
+response = client.beta.messages.create(
+ model="claude-sonnet",
+ max_tokens=4096,
+ betas=["advisor-tool-2026-03-01"],
+ messages=[
+ {"role": "user", "content": "Build a concurrent worker pool in Go with graceful shutdown."}
+ ],
+ tools=[
+ {
+ "type": "advisor_20260301",
+ "name": "advisor",
+ "model": "claude-opus-4-6",
+ }
+ ],
+)
+print(response)
+```
+
+---
+
+## Response Structure
+
+A successful advisor call returns `server_tool_use` and `advisor_tool_result` blocks in the assistant content:
+
+```json title="Response with advisor blocks"
+{
+ "role": "assistant",
+ "content": [
+ {
+ "type": "text",
+ "text": "Let me consult the advisor on this."
+ },
+ {
+ "type": "server_tool_use",
+ "id": "srvtoolu_abc123",
+ "name": "advisor",
+ "input": {}
+ },
+ {
+ "type": "advisor_tool_result",
+ "tool_use_id": "srvtoolu_abc123",
+ "content": {
+ "type": "advisor_result",
+ "text": "Use a channel-based coordination pattern. The tricky part is draining in-flight work during shutdown: close the input channel first, then wait on a WaitGroup..."
+ }
+ },
+ {
+ "type": "text",
+ "text": "Here's the implementation using a channel-based coordination pattern..."
+ }
+ ]
+}
+```
+
+Pass the full assistant content, including advisor blocks, back on subsequent turns. LiteLLM handles this automatically through `provider_specific_fields`.
+
+---
+
+## Cost Control
+
+Advisor calls run as a separate sub-inference billed at the advisor model's rates. Usage is reported in `usage.iterations[]`:
+
+```json title="Usage with advisor sub-inference"
+{
+ "usage": {
+ "input_tokens": 412,
+ "output_tokens": 531,
+ "iterations": [
+ {
+ "type": "message",
+ "input_tokens": 412,
+ "output_tokens": 89
+ },
+ {
+ "type": "advisor_message",
+ "model": "claude-opus-4-6",
+ "input_tokens": 823,
+ "output_tokens": 1612
+ },
+ {
+ "type": "message",
+ "input_tokens": 1348,
+ "output_tokens": 442
+ }
+ ]
+ }
+}
+```
+
+Top-level `usage` reflects executor tokens only. Advisor tokens appear in `iterations` entries with `type: "advisor_message"` and are billed at Opus rates.
+
+**Tips:**
+- Enable `caching` on the tool definition only when you expect 3+ advisor calls per conversation; it costs more than it saves below that threshold.
+- Use `max_uses` to cap advisor calls per request. Once reached, the executor continues without further advice.
+- For conversation-level caps, count advisor calls client-side. When you reach your limit, remove the advisor tool from `tools`.
+
+---
+
+## Recommended System Prompt
+
+For coding and agent tasks, Anthropic recommends prepending these blocks to your system prompt for consistent advisor timing and optimal cost/quality:
+
+```text title="Timing guidance (prepend to system prompt)"
+You have access to an `advisor` tool backed by a stronger reviewer model. It takes NO parameters — when you call advisor(), your entire conversation history is automatically forwarded. They see the task, every tool call you've made, every result you've seen.
+
+Call advisor BEFORE substantive work — before writing, before committing to an interpretation, before building on an assumption. If the task requires orientation first (finding files, fetching a source, seeing what's there), do that, then call advisor. Orientation is not substantive work. Writing, editing, and declaring an answer are.
+
+Also call advisor:
+- When you believe the task is complete. BEFORE this call, make your deliverable durable: write the file, save the result, commit the change.
+- When stuck — errors recurring, approach not converging, results that don't fit.
+- When considering a change of approach.
+
+On tasks longer than a few steps, call advisor at least once before committing to an approach and once before declaring done. On short reactive tasks where the next action is dictated by tool output you just read, you don't need to keep calling.
+```
+
+```text title="Advice weight guidance (add after timing block)"
+Give the advice serious weight. If you follow a step and it fails empirically, or you have primary-source evidence that contradicts a specific claim, adapt. A passing self-test is not evidence the advice is wrong.
+
+If you've already retrieved data pointing one way and the advisor points another: don't silently switch. Surface the conflict in one more advisor call — "I found X, you suggest Y, which constraint breaks the tie?"
+```
+
+To reduce advisor output length by 35–45% without losing quality, add:
+
+```text title="Cost reduction (optional, add before timing block)"
+The advisor should respond in under 100 words and use enumerated steps, not explanations.
+```
+
+---
+
+## Additional Resources
+
+- [Anthropic Advisor Tool Documentation](https://platform.claude.com/docs/en/agents-and-tools/tool-use/advisor-tool)
+- [LiteLLM Tool Calling Guide](https://docs.litellm.ai/docs/completion/function_call)
diff --git a/docs/my-website/docs/observability/ramp_integration.md b/docs/my-website/docs/observability/ramp_integration.md
new file mode 100644
index 000000000000..c147f226782c
--- /dev/null
+++ b/docs/my-website/docs/observability/ramp_integration.md
@@ -0,0 +1,131 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Ramp
+
+Send AI usage and cost data to Ramp for automated spend tracking.
+
+[Ramp](https://ramp.com/) is a finance automation platform that helps businesses manage expenses, corporate cards, and vendor payments. With the Ramp callback integration, your LiteLLM AI usage — including token counts, model costs, and request metadata — is automatically sent to Ramp for real-time spend visibility.
+
+:::info
+We want to learn how we can make the callbacks better! Meet the LiteLLM [founders](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) or
+join our [discord](https://discord.gg/wuPM9dRgDw)
+:::
+
+## Pre-Requisites
+
+1. Log in to [Ramp](https://app.ramp.com/) and search for **"LiteLLM"** using the search bar. Click the **LiteLLM** integration result.
+
+> **Note:** Only business owners and admins can access and configure integrations.
+
+2. On the LiteLLM integration page, click the **Connect** button in the top right.
+
+3. In the Connect LiteLLM drawer, click **Generate API Key** to create an API key.
+
+> **Important:** Copy the API key immediately — it won't be shown again. If you lose it, you can revoke the existing key and generate a new one from the integration settings.
+
+```shell
+pip install litellm
+```
+
+## Quick Start
+
+Set your `RAMP_API_KEY` and add `"ramp"` to your callbacks to start logging LLM usage to Ramp.
+
+
+
+
+```python
+litellm.callbacks = ["ramp"]
+```
+
+```python
+import litellm
+import os
+
+# Ramp API Key
+os.environ["RAMP_API_KEY"] = "your-ramp-api-key"
+
+# LLM API Keys
+os.environ['OPENAI_API_KEY'] = ""
+
+# Set ramp as a callback
+litellm.callbacks = ["ramp"]
+
+# OpenAI call
+response = litellm.completion(
+ model="gpt-3.5-turbo",
+ messages=[
+ {"role": "user", "content": "Hi - I'm testing Ramp integration"}
+ ]
+)
+```
+
+
+
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+ - model_name: gpt-3.5-turbo
+ litellm_params:
+ model: openai/gpt-3.5-turbo
+ api_key: os.environ/OPENAI_API_KEY
+
+litellm_settings:
+ callbacks: ["ramp"]
+
+environment_variables:
+ RAMP_API_KEY: os.environ/RAMP_API_KEY
+```
+
+2. Start LiteLLM Proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it!
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/chat/completions' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-d '{
+ "model": "gpt-3.5-turbo",
+ "messages": [
+ {
+ "role": "user",
+ "content": "Hey, how are you?"
+ }
+ ]
+}'
+```
+
+
+
+
+## What Data is Logged?
+
+LiteLLM sends the [Standard Logging Payload](https://docs.litellm.ai/docs/proxy/logging_spec) to Ramp on successful LLM API calls, which includes:
+
+- **Request details**: Model, messages, parameters
+- **Response details**: Completion text, token usage, latency
+- **Metadata**: User ID, custom metadata, timestamps
+- **Cost tracking**: Response cost based on token usage
+
+## Authentication
+
+Set the `RAMP_API_KEY` environment variable with your Ramp API key.
+
+| Environment Variable | Description |
+|---|---|
+| `RAMP_API_KEY` | Your Ramp API key (required) |
+
+## Support & Talk to Founders
+
+- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
+- [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
+- Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
+- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/providers/gemini.md b/docs/my-website/docs/providers/gemini.md
index 87ab5ad40f4f..a60dc3323d11 100644
--- a/docs/my-website/docs/providers/gemini.md
+++ b/docs/my-website/docs/providers/gemini.md
@@ -65,14 +65,13 @@ response = completion(
- modalities
- reasoning_content
- audio (for TTS models only)
+- service_tier
**Anthropic Params**
- thinking (used to set max budget tokens across anthropic/gemini models)
[**See Updated List**](https://github.com/BerriAI/litellm/blob/main/litellm/llms/gemini/chat/transformation.py#L70)
-
-
## Usage - Thinking / `reasoning_content`
LiteLLM translates OpenAI's `reasoning_effort` to Gemini's `thinking` parameter. [Code](https://github.com/BerriAI/litellm/blob/620664921902d7a9bfb29897a7b27c1a7ef4ddfb/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py#L362)
@@ -298,6 +297,19 @@ curl http://0.0.0.0:4000/v1/chat/completions \
+## Usage - `service_tier`
+
+LiteLLM propagates OpenAI's `service_tier` parameter to Gemini, and also extracts it from the response headers (`x-gemini-service-tier`) into `model_response.service_tier`.
+
+| OpenAI `service_tier` | Gemini `service_tier` | Notes |
+| --------------------- | --------------------- | ----- |
+| `"auto"` | `"priority"` | LiteLLM maps OpenAI's `"auto"` to Gemini's `"priority"` tier, as `priority` will fall back on Gemini. |
+| `"flex"` | `"flex"` | Direct mapping. |
+| `"priority"` | `"priority"` | Direct mapping. |
+| `"default"` | `"standard"` | LiteLLM maps `"default"` to `"standard"`. |
+| Any other value | Passed as-is (lowercased) | Values are case-insensitive and normalized to lowercase. |
+
+On the response, LiteLLM maps `"standard"` back to `"default"` for the Gemini API.
## Text-to-Speech (TTS) Audio Output
diff --git a/docs/my-website/docs/providers/sap.md b/docs/my-website/docs/providers/sap.md
index 16f30a2e99c7..3877cb6ef193 100644
--- a/docs/my-website/docs/providers/sap.md
+++ b/docs/my-website/docs/providers/sap.md
@@ -55,24 +55,33 @@ pip install litellm
```
### Step 2: Set Your Credentials
+
+ Choose **one** of these authentication methods:
+
+> **Breaking change**: credential resolution is "first-source-wins"
+>
+> Credential resolution no longer merges individual fields across sources.
+>
+> Resolution order is:
+`kwargs` → `service key` → `env (AICORE_*)` → `config` → `VCAP service`
+>
+> **Important behavior:** once LiteLLM finds *any* credential value in a source, it takes **all** credentials from that source exclusively (except `resource_group`, which may still be resolved separately).
-Choose **one** of these authentication methods:
+
+
-
-
+The simplest approach - paste your entire service key as a single environment variable.
-The simplest approach - paste your entire service key as a single environment variable. The service key must be wrapped in a `credentials` object:
+> **Note:** the service key no more needs to be wrapped in a "credentials" key.
```bash
export AICORE_SERVICE_KEY='{
- "credentials": {
"clientid": "your-client-id",
"clientsecret": "your-client-secret",
"url": "https://.authentication.sap.hana.ondemand.com",
"serviceurls": {
"AI_API_URL": "https://api.ai..aws.ml.hana.ondemand.com"
}
- }
}'
export AICORE_RESOURCE_GROUP="default"
```
@@ -220,6 +229,17 @@ model="sap/gemini-2.5-pro"
# Incorrect - missing prefix
model="gpt-4o" # ❌ Won't work
```
+3. **Environment variables** - Set the following list of credentials in .env file
+
+AICORE_AUTH_URL = "https://* * * .authentication.sap.hana.ondemand.com/oauth/token",
+AICORE_CLIENT_ID = " *** ",
+AICORE_CLIENT_SECRET = " *** ",
+AICORE_RESOURCE_GROUP = " *** ",
+AICORE_BASE_URL = "https://api.ai.***.cfapps.sap.hana.ondemand.com/v2"
+
+
+Other credential configuration options are also available. For more information, see the [SAP AI Core Documentation](https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/README_sphynx.html#configuration).
+## Usage - LiteLLM Python SDK
### Proxy Usage
@@ -506,6 +526,241 @@ response = embedding(
print(response.data[0]["embedding"]) # Vector representation
```
+### Additional Modules
+The SAP Gen AI Hub includes additional modules for advanced use cases:
+- [Grounding](https://help.sap.com/docs/sap-ai-core/generative-ai/grounding-035c455a5a424697b60f4a24b6d791fe?locale=en-US)
+- [Translation](https://help.sap.com/docs/sap-ai-core/generative-ai/translation?locale=en-US)
+- [Data Masking](https://help.sap.com/docs/sap-ai-core/generative-ai/data-masking-d9a54d9ca54b40beacbd24e1663ec3b4?locale=en-US)
+- [Content Filtering](https://help.sap.com/docs/sap-ai-core/generative-ai/content-filtering?locale=en-US)
+
+#### Grounding
+Grounding is a service designed to handle data-related tasks, such as grounding and retrieval, using vector databases. It provides specialized data retrieval through these databases, grounding the retrieval process with your own external and context-relevant data. Grounding combines generative AI capabilities with the ability to use real-time, precise data to improve decision-making and business operations for specific AI-driven business solutions.
+##### Prerequisites
+To use the Grounding module in the orchestration pipeline, you need to prepare the knowledge base in advance.
+
+Generative AI hub offers multiple options for users to provide data (prepare a knowledge base):
+- For Option 1: Upload the documents to a supported data repository and run the data pipeline to vectorize the documents.
+- For Option 2: Provide the chunks of document via Vector API directly.
+
+To use grounding, choose from one of the following options.
+
+Usage example:
+```python showLineNumbers title="Grounding Example"
+from litellm import completion
+
+grounding_config = {
+ 'type': 'document_grounding_service',
+ 'config': {
+ 'filters': [
+ {'id': 's3-docs',
+ 'data_repository_type': 'vector',
+ 'search_config': {'max_chunk_count': 2},
+ 'data_repositories': ['012345-6789-0123-4567-890123456789']
+ }
+ ],
+ 'placeholders': {'input': ['user_query'], 'output': 'grounding_response'},
+ 'metadata_params': ['source', 'webUrl', 'title', 'mimeType', 'fileSuffix']
+ }
+}
+
+response = completion(model="sap/gpt-4o",
+ messages=[
+ {"content":"""Facility Solutions Company provides services to luxury residential complexes,
+ apartments, individual homes, and commercial properties such as office buildings, retail
+ spaces, industrial facilities, and educational institutions. Customers are encouraged to
+ reach out with maintenance requests, service deficiencies, follow-ups, or any issues they
+ need by email.""", "role": "system"},
+ {"content":"""You are a helpful assistant for any queries for answering questions.
+ Answer the request by providing relevant answers that fit to the request.
+ Request: {{ ?user_query }}
+ Context:{{ ?grounding_response }}""", "role": "user"}
+ ],
+ placeholder_values={"user_query": "Is there a complaint?"},
+ grounding=grounding_config
+ )
+print(response.choices[0].message.content)
+```
+For more information about all available grounding configurations, see the [documentation](https://help.sap.com/docs/sap-ai-core/generative-ai/using-grounding-module-e1c4dd100dfb42ab890e1d95f3516187?locale=en-US).
+
+#### Translation
+The translation module allows you to translate LLM text prompts into a chosen target language.
+
+```python showLineNumbers title="Translation Example"
+from litellm import completion
+
+translation_config = {
+ 'input':
+ {'type': 'sap_document_translation',
+ 'config':
+ {'source_language': 'en-US',
+ 'target_language': 'de-DE'}
+ },
+ 'output':
+ {'type': 'sap_document_translation',
+ 'config':
+ {'source_language': 'de-DE',
+ 'target_language': 'fr-FR'}
+ }
+}
+
+response = completion(model="sap/gpt-4o",
+ messages=[{"role": "user", "content": "Hello world!"}],
+ translation=translation_config)
+
+print(response.choices[0].message.content)
+```
+For more information about all available translation configurations, see the [documentation](https://help.sap.com/docs/sap-ai-core/generative-ai/translation?locale=en-US)
+
+#### Data Masking
+The data masking module serves to anonymize or pseudonymize personally identifiable information from the input for selected entities.
+
+```python showLineNumbers title="Data Masking Example"
+from litellm import completion, embedding
+masking_config = {
+ 'providers':
+ [
+ {
+ 'type': 'sap_data_privacy_integration',
+ 'method': 'anonymization',
+ 'entities': [
+ {'type': 'profile-address'},
+ {'type': 'profile-email'},
+ {'type': 'profile-phone'},
+ {'type': 'profile-person'},
+ {'type': 'profile-location'}
+ ]
+ }
+ ]
+ }
+
+mock_cv = "some text with personal information"
+
+response = completion(model="sap/gpt-4o",
+ messages=[{"role": "user", "content": "Give a one sentence summary of the CV. CV: {{?cv}}?"}],
+ placeholder_values={"cv": mock_cv},
+ masking=masking_config)
+print(response.choices[0].message.content)
+
+# Data masking module also available for embedding
+response = embedding(model="sap/text-embedding-3-small",
+ input=mock_cv,
+ masking=masking_config)
+print(response.data[0])
+```
+For more information about all available data masking configurations, see the [documentation](https://help.sap.com/docs/sap-ai-core/generative-ai/enhancing-model-consumption-with-data-masking-66ad6f469afc4c2cbaa91a27a33f7b21?locale=en-US)
+
+
+
+
+
+#### Content Filtering
+The content filtering module allows you to filter input and output based on content safety criteria.
+
+The module supports two services:
+* Azure Content Safety
+* Llama Guard 3
+
+```python showLineNumbers title="Content Filtering Example"
+from litellm import completion
+
+filtering_config_azure = {
+ 'input':
+ {
+ 'filters':
+ [
+ {'type': 'azure_content_safety',
+ 'config':
+ {'hate': 0,
+ 'sexual': 0,
+ 'violence': 0,
+ 'self_harm': 0
+ }
+ }
+ ]
+ },
+ 'output':
+ {
+ 'filters':
+ [
+ {'type': 'azure_content_safety',
+ 'config': {'hate': 0,
+ 'sexual': 0,
+ 'violence': 0,
+ 'self_harm': 0
+ }
+ }
+ ]
+ }
+}
+
+response = completion(model="sap/gpt-4o",
+ messages=[{"role": "user", "content": "Hello world!"}],
+ filtering=filtering_config_azure)
+print(response.choices[0].message.content)
+# The model responds normally because the content does not violate any safety rules.
+
+try:
+ response = completion(model="sap/gpt-4o",
+ messages=[{"role": "user", "content": "I hate you"}],
+ filtering=filtering_config_azure)
+except Exception as e:
+ print(e)
+ # The service raises an error:
+ # "Input Filter: Content filtered due to safety violations. Please modify the prompt and try again."
+```
+For more information about all available content filtering configurations, see the [documentation](https://help.sap.com/docs/sap-ai-core/generative-ai/content-filtering?locale=en-US)
+
+#### List of modules configuration for fallback
+SAP GEN AI Hub supports a fallback mechanism for handling errors. This mechanism allows you to specify a list of fallback modules to use in case of errors. The fallback modules should contain all parameters that are required for configuring the request.
+
+Required parameters:
+- `model`
+- `messages`
+
+Optional parameters:
+- `filtering`
+- `grounding`
+- `translation`
+- `masking`
+- `tools`
+
+- and any of model's specific parameters.
+
+
+```python showLineNumbers title="Fallback Example"
+from litellm import completion
+
+translation_config = {
+ 'input':
+ {'type': 'sap_document_translation',
+ 'config':
+ {'source_language': 'en-US',
+ 'target_language': 'de-DE'}
+ },
+ 'output':
+ {'type': 'sap_document_translation',
+ 'config':
+ {'source_language': 'de-DE',
+ 'target_language': 'fr-FR'}
+ }
+}
+
+response = completion(model="sap/gpt-4o",
+ messages=[{"role": "user", "content": "Hello world!"}],
+ translation=translation_config,
+ fallback_sap_modules=[{
+ "model":"sap/gemini-2.5-flash",
+ "messages":[{"role": "user", "content": "Hello world!"}],
+ "translation":translation_config
+ }])
+
+# In case of error with the first configuration (model gpt-4o), the fallback module is used.
+
+print(response.choices[0].message.content)
+
+```
+
+
## Reference
### Supported Parameters
diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md
index 528a5c109035..db38cf5426bb 100644
--- a/docs/my-website/docs/proxy/config_settings.md
+++ b/docs/my-website/docs/proxy/config_settings.md
@@ -197,6 +197,7 @@ router_settings:
| key_generation_settings | object | Restricts who can generate keys. [Further docs](./virtual_keys.md#restricting-key-generation) |
| disable_add_transform_inline_image_block | boolean | For Fireworks AI models - if true, turns off the auto-add of `#transform=inline` to the url of the image_url, if the model is not a vision model. |
| use_chat_completions_url_for_anthropic_messages | boolean | If true, routes OpenAI `/v1/messages` requests through chat/completions instead of the Responses API. Can also be set via env var `LITELLM_USE_CHAT_COMPLETIONS_URL_FOR_ANTHROPIC_MESSAGES=true`. |
+| skip_system_message_in_guardrail | boolean | If true, unified guardrails omit `role: system` from scanned input on **chat completions** and **Anthropic `/v1/messages`** only; the LLM still receives full messages. Per-guardrail override: `litellm_params.skip_system_message_in_guardrail` on each guardrail. [Guardrails quick start](./guardrails/quick_start#skip-system-messages-in-guardrail-evaluation) |
| disable_hf_tokenizer_download | boolean | If true, it defaults to using the openai tokenizer for all models (including huggingface models). |
| enable_json_schema_validation | boolean | If true, enables json schema validation for all requests. |
| enable_key_alias_format_validation | boolean | If true, validates `key_alias` format on `/key/generate` and `/key/update`. Must be 2-255 chars, start/end with alphanumeric, only allow `a-zA-Z0-9_-/.@`. Default `false`. |
@@ -238,7 +239,7 @@ router_settings:
| public_routes | List[str] | (Enterprise Feature) Control list of public routes |
| alert_types | List[str] | Control list of alert types to send to slack (Doc on alert types)[./alerting.md] |
| enforced_params | List[str] | (Enterprise Feature) List of params that must be included in all requests to the proxy |
-| enable_oauth2_auth | boolean | (Enterprise Feature) If true, enables oauth2.0 authentication |
+| enable_oauth2_auth | boolean | (Enterprise Feature) If true, enables oauth2.0 authentication on LLM + info routes |
| use_x_forwarded_for | str | If true, uses the X-Forwarded-For header to get the client IP address |
| service_account_settings | List[Dict[str, Any]] | Set `service_account_settings` if you want to create settings that only apply to service account keys (Doc on service accounts)[./service_accounts.md] |
| image_generation_model | str | The default model to use for image generation - ignores model set in request |
@@ -597,10 +598,13 @@ router_settings:
| LITELLM_MCP_TOOL_LISTING_TIMEOUT | Timeout in seconds for listing tools from an MCP server. Default is 30
| LITELLM_MCP_METADATA_TIMEOUT | HTTP client timeout in seconds for OAuth metadata fetching. Default is 10
| LITELLM_MCP_HEALTH_CHECK_TIMEOUT | Health check timeout in seconds for MCP servers. Default is 10
+| LITELLM_MCP_STDIO_EXTRA_COMMANDS | Comma-separated extra command basenames allowed for MCP stdio transport beyond the built-in allowlist. Example: `my-mcp-bin`. Empty by default
| MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL | Default TTL in seconds for MCP OAuth2 token cache. Default is 3600
| MCP_OAUTH2_TOKEN_CACHE_MAX_SIZE | Maximum number of entries in MCP OAuth2 token cache. Default is 200
| MCP_OAUTH2_TOKEN_CACHE_MIN_TTL | Minimum TTL in seconds for MCP OAuth2 token cache. Default is 10
| MCP_OAUTH2_TOKEN_EXPIRY_BUFFER_SECONDS | Seconds to subtract from token expiry when computing cache TTL. Default is 60
+| MCP_PER_USER_TOKEN_DEFAULT_TTL | Default TTL in seconds for per-user MCP OAuth tokens stored in Redis. Default is 43200 (12 hours)
+| MCP_PER_USER_TOKEN_EXPIRY_BUFFER_SECONDS | Seconds to subtract from per-user MCP OAuth token expiry when computing Redis TTL. Default is 60
| DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT | Default token count for mock response completions. Default is 20
| DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT | Default token count for mock response prompts. Default is 10
| DEFAULT_MODEL_CREATED_AT_TIME | Default creation timestamp for models. Default is 1677610602
@@ -1032,6 +1036,7 @@ router_settings:
| SENDGRID_SENDER_EMAIL | Email address used as the sender in SendGrid email transactions
| SPEND_LOGS_URL | URL for retrieving spend logs
| SPEND_LOG_CLEANUP_BATCH_SIZE | Number of logs deleted per batch during cleanup. Default is 1000
+| STALE_OBJECT_CLEANUP_BATCH_SIZE | Max number of stale managed objects updated per cleanup cycle. Default is 1000
| SSL_CERTIFICATE | Path to the SSL certificate file
| SSL_ECDH_CURVE | ECDH curve for SSL/TLS key exchange (e.g., 'X25519' to disable PQC).
| SSL_SECURITY_LEVEL | [BETA] Security level for SSL/TLS connections. E.g. `DEFAULT@SECLEVEL=1`
diff --git a/docs/my-website/docs/proxy/credential_routing.md b/docs/my-website/docs/proxy/credential_routing.md
new file mode 100644
index 000000000000..2af57c6b4969
--- /dev/null
+++ b/docs/my-website/docs/proxy/credential_routing.md
@@ -0,0 +1,274 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Per-Team/Project Credential Routing
+
+Route the same model to different LLM provider endpoints (e.g. different Azure instances) based on which team or project makes the request.
+
+## Overview
+
+In multi-tenant deployments, different teams often need the same model name (e.g., `gpt-4`) to hit different provider endpoints — for example, separate Azure OpenAI instances per business unit for cost isolation, data residency, or rate limit separation.
+
+**Credential routing** lets you configure this in team/project metadata using the existing [credentials table](./ui_credentials.md), without duplicating model definitions or creating separate model groups per team.
+
+```
+Hotel Team → gpt-4 → https://hotel-eastus.openai.azure.com/
+Flight Team → gpt-4 → https://flight-centralus.openai.azure.com/
+```
+
+### Precedence Chain
+
+When a request comes in, the system walks this precedence chain (first match wins):
+
+1. **Clientside credentials** — `api_base`/`api_key` passed in the request body ([docs](./clientside_auth.md))
+2. **Project model-specific** — override for this exact model in the project's `model_config`
+3. **Project default** — `defaultconfig` in the project's `model_config`
+4. **Team model-specific** — override for this exact model in the team's `model_config`
+5. **Team default** — `defaultconfig` in the team's `model_config`
+6. **Deployment default** — the model's `litellm_params` as configured in `config.yaml`
+
+## Quick Start
+
+### Step 1: Create Credentials
+
+Store your Azure endpoint credentials in the credentials table. You can do this via the [UI](./ui_credentials.md) or API:
+
+```bash showLineNumbers
+# Create credential for Hotel team's Azure endpoint
+curl -X POST 'http://0.0.0.0:4000/credentials' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+ "credential_name": "hotel-azure-eastus",
+ "credential_values": {
+ "api_base": "https://hotel-eastus.openai.azure.com/",
+ "api_key": "sk-azure-hotel-key-xxx"
+ }
+}'
+```
+
+```bash showLineNumbers
+# Create credential for Flight team's Azure endpoint
+curl -X POST 'http://0.0.0.0:4000/credentials' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+ "credential_name": "flight-azure-centralus",
+ "credential_values": {
+ "api_base": "https://flight-centralus.openai.azure.com/",
+ "api_key": "sk-azure-flight-key-xxx"
+ }
+}'
+```
+
+### Step 2: Set `model_config` on Teams
+
+Add a `model_config` key to the team's metadata referencing the credential by name:
+
+```bash showLineNumbers
+# Hotel team — default Azure endpoint for all models
+curl -X PATCH 'http://0.0.0.0:4000/team/update' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+ "team_id": "hotel-team-id",
+ "metadata": {
+ "model_config": {
+ "defaultconfig": {
+ "azure": {
+ "litellm_credentials": "hotel-azure-eastus"
+ }
+ }
+ }
+ }
+}'
+```
+
+```bash showLineNumbers
+# Flight team — default Azure endpoint for all models
+curl -X PATCH 'http://0.0.0.0:4000/team/update' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+ "team_id": "flight-team-id",
+ "metadata": {
+ "model_config": {
+ "defaultconfig": {
+ "azure": {
+ "litellm_credentials": "flight-azure-centralus"
+ }
+ }
+ }
+ }
+}'
+```
+
+### Step 3: Make Requests
+
+Requests are automatically routed to the correct Azure endpoint based on the API key's team:
+
+```bash showLineNumbers
+# Request using Hotel team's API key → routes to hotel-eastus.openai.azure.com
+curl http://localhost:4000/v1/chat/completions \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-hotel-team-key' \
+-d '{"model": "gpt-4", "messages": [{"role": "user", "content": "Hello"}]}'
+
+# Request using Flight team's API key → routes to flight-centralus.openai.azure.com
+curl http://localhost:4000/v1/chat/completions \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-flight-team-key' \
+-d '{"model": "gpt-4", "messages": [{"role": "user", "content": "Hello"}]}'
+```
+
+## Per-Model Overrides
+
+You can set different credentials for specific models while keeping a default for everything else:
+
+```bash showLineNumbers
+curl -X PATCH 'http://0.0.0.0:4000/team/update' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+ "team_id": "hotel-team-id",
+ "metadata": {
+ "model_config": {
+ "defaultconfig": {
+ "azure": {
+ "litellm_credentials": "hotel-azure-eastus"
+ }
+ },
+ "gpt-4": {
+ "azure": {
+ "litellm_credentials": "hotel-azure-westus"
+ }
+ }
+ }
+ }
+}'
+```
+
+With this config:
+- `gpt-4` requests → `hotel-azure-westus` credential (model-specific)
+- All other models → `hotel-azure-eastus` credential (default)
+
+## Project-Level Overrides
+
+Projects inherit their team's `model_config` but can override at the project level. Project overrides take precedence over team overrides.
+
+```bash showLineNumbers
+# Project overrides the team default for all models
+curl -X PATCH 'http://0.0.0.0:4000/project/update' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+ "project_id": "hotel-rec-app-id",
+ "metadata": {
+ "model_config": {
+ "defaultconfig": {
+ "azure": {
+ "litellm_credentials": "hotel-rec-azure"
+ }
+ },
+ "gpt-4-vision": {
+ "azure": {
+ "litellm_credentials": "hotel-rec-vision"
+ }
+ }
+ }
+ }
+}'
+```
+
+### Full Example: Hotel Team with Two Projects
+
+**Setup:**
+- **Hotel Team**: default `hotel-azure-eastus`, GPT-4 override to `hotel-azure-westus`
+- **Hotel Rec App** (project): default `hotel-rec-azure`, GPT-4-Vision override to `hotel-rec-vision`
+- **Hotel Review App** (project): no overrides — inherits team config
+
+**Resolution:**
+
+| Request | Resolved Credential | Why |
+|---|---|---|
+| Hotel Rec App → `gpt-4` | `hotel-rec-azure` | Project default (no project model-specific match for gpt-4) |
+| Hotel Rec App → `gpt-4-vision` | `hotel-rec-vision` | Project model-specific |
+| Hotel Review App → `gpt-3.5` | `hotel-azure-eastus` | Team default (no project config) |
+| Hotel Review App → `gpt-4` | `hotel-azure-westus` | Team model-specific |
+
+## `model_config` Schema
+
+The `model_config` key is a JSON object in team/project `metadata`:
+
+```json
+{
+ "model_config": {
+ "defaultconfig": {
+ "": {
+ "litellm_credentials": ""
+ }
+ },
+ "": {
+ "": {
+ "litellm_credentials": ""
+ }
+ }
+ }
+}
+```
+
+| Field | Description |
+|---|---|
+| `defaultconfig` | Fallback credential for any model not explicitly listed |
+| `` | Model-specific override — must match the LiteLLM model group name |
+| `` | Provider key (e.g. `azure`, `openai`, `bedrock`). When the model name includes a provider prefix (e.g. `azure/gpt-4`), the system prefers the matching provider key |
+| `litellm_credentials` | Name of a credential in the [credentials table](./ui_credentials.md) |
+
+### Credential Values
+
+The referenced credential can contain any combination of:
+
+| Key | Description |
+|---|---|
+| `api_base` | Provider endpoint URL |
+| `api_key` | API key for the provider |
+| `api_version` | API version (e.g. for Azure) |
+
+Only keys present in the credential are applied. Keys already in the request (e.g. clientside `api_version`) are never overwritten.
+
+## Enabling the Feature
+
+This feature is **disabled by default** and must be explicitly enabled. To enable it:
+
+
+
+
+
+```yaml
+litellm_settings:
+ enable_model_config_credential_overrides: true
+```
+
+
+
+
+
+```bash
+export LITELLM_ENABLE_MODEL_CONFIG_CREDENTIAL_OVERRIDES=true
+```
+
+
+
+
+
+:::info
+The feature flag must be enabled before `model_config` entries in team/project metadata take effect. Without it, credential routing is completely inert — no metadata is read, no credentials are resolved.
+:::
+
+## Related Documentation
+
+- [Adding LLM Credentials](./ui_credentials.md) — Create and manage reusable credentials
+- [Project Management](./project_management.md) — Project hierarchy and API
+- [Team Budgets](./team_budgets.md) — Team-level budget management
+- [Clientside LLM Credentials](./clientside_auth.md) — Passing credentials in the request body
+- [Credential Usage Tracking](./credential_usage_tracking.md) — Track spend by credential
diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md
index 0761e0e9fa8a..f7833fc02c73 100644
--- a/docs/my-website/docs/proxy/deploy.md
+++ b/docs/my-website/docs/proxy/deploy.md
@@ -65,7 +65,43 @@ docker compose up
-### Docker Run
+### Verify Docker image signatures
+
+All LiteLLM Docker images are signed with [cosign](https://docs.sigstore.dev/cosign/overview/). Every release is signed with the same key introduced in [commit `0112e53`](https://github.com/BerriAI/litellm/commit/0112e53046018d726492c814b3644b7d376029d0).
+
+**Verify using the pinned commit hash (recommended):**
+
+A commit hash is cryptographically immutable, so this is the strongest way to ensure you are using the original signing key:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
+ ghcr.io/berriai/litellm:
+```
+
+**Verify using a release tag (convenience):**
+
+Tags are protected in this repository and resolve to the same key. This option is easier to read but relies on tag protection rules:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm//cosign.pub \
+ ghcr.io/berriai/litellm:
+```
+
+Replace `` with the version you are deploying (e.g. `v1.83.0-stable`).
+
+Expected output:
+
+```
+The following checks were performed on each of these signatures:
+ - The cosign claims were validated
+ - The signatures were verified against the specified public key
+```
+
+Learn more about LiteLLM's release signing in the [CI/CD v2 announcement](https://docs.litellm.ai/blog/ci-cd-v2-improvements#verify-docker-image-signatures). For a complete guide covering all image variants, CI/CD enforcement, and deployment best practices, see the [Docker Image Security Guide](./docker_image_security.md).
+
+### Docker Run
#### Step 1. CREATE config.yaml
diff --git a/docs/my-website/docs/proxy/docker_image_security.md b/docs/my-website/docs/proxy/docker_image_security.md
new file mode 100644
index 000000000000..41ace2174b3e
--- /dev/null
+++ b/docs/my-website/docs/proxy/docker_image_security.md
@@ -0,0 +1,189 @@
+# Docker Image Security Guide
+
+LiteLLM signs every Docker image published to GHCR with [cosign](https://docs.sigstore.dev/cosign/overview/) starting from **v1.83.0**. This page covers how to verify signatures, enforce verification in CI/CD, and follow recommended deployment patterns.
+
+## Signed images
+
+All image variants published to `ghcr.io/berriai/` are signed with the same cosign key:
+
+| Image | Description |
+|---|---|
+| `ghcr.io/berriai/litellm` | Core proxy |
+| `ghcr.io/berriai/litellm-database` | Proxy with Postgres dependencies |
+| `ghcr.io/berriai/litellm-non_root` | Non-root variant |
+| `ghcr.io/berriai/litellm-spend_logs` | Spend-logs sidecar |
+
+The signing key was introduced in [commit `0112e53`](https://github.com/BerriAI/litellm/commit/0112e53046018d726492c814b3644b7d376029d0) and the public key is checked into the repository at [`cosign.pub`](https://github.com/BerriAI/litellm/blob/main/cosign.pub).
+
+:::info Enterprise images
+Enterprise images (`litellm-ee`) follow the same signing process. Contact [support@berri.ai](mailto:support@berri.ai) to confirm coverage for your specific enterprise image tag.
+:::
+
+## Verify image signatures
+
+Install cosign following the [official instructions](https://docs.sigstore.dev/cosign/system_config/installation/).
+
+### Verify with the pinned commit hash (recommended)
+
+A commit hash is cryptographically immutable, making this the strongest verification method:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
+ ghcr.io/berriai/litellm:v1.83.0-stable
+```
+
+Replace the image reference with any signed variant:
+
+```bash
+# litellm-database
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
+ ghcr.io/berriai/litellm-database:v1.83.0-stable
+
+# litellm-non_root
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
+ ghcr.io/berriai/litellm-non_root:v1.83.0-stable
+```
+
+### Verify with a release tag (convenience)
+
+Tags are protected in this repository and resolve to the same key:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm/v1.83.0-stable/cosign.pub \
+ ghcr.io/berriai/litellm-database:v1.83.0-stable
+```
+
+### Expected output
+
+```
+The following checks were performed on each of these signatures:
+ - The cosign claims were validated
+ - The signatures were verified against the specified public key
+```
+
+## Enforce verification in CI/CD
+
+### Kubernetes — Sigstore Policy Controller
+
+The [Sigstore Policy Controller](https://docs.sigstore.dev/policy-controller/overview/) rejects pods whose images fail cosign verification.
+
+1. Install the controller:
+
+```bash
+helm repo add sigstore https://sigstore.github.io/helm-charts
+helm install policy-controller sigstore/policy-controller \
+ -n cosign-system --create-namespace
+```
+
+2. Create a `ClusterImagePolicy` with the LiteLLM public key:
+
+```yaml
+apiVersion: policy.sigstore.dev/v1beta1
+kind: ClusterImagePolicy
+metadata:
+ name: litellm-signed-images
+spec:
+ images:
+ - glob: "ghcr.io/berriai/litellm*"
+ authorities:
+ - key:
+ data: |
+ -----BEGIN PUBLIC KEY-----
+ MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEKi4ivqGpE231OGH50PKbqy1Y1Kkb
+ POJC8+i2Wko82gBOUCe3M0Vw86H/4rhUhfoYEti4gdJ9wZbYmK0I2EE96g==
+ -----END PUBLIC KEY-----
+```
+
+3. Label the namespace to enable enforcement:
+
+```bash
+kubectl label namespace litellm policy.sigstore.dev/include=true
+```
+
+Any pod in that namespace using an unsigned `ghcr.io/berriai/litellm*` image will be rejected at admission.
+
+### GCP — Binary Authorization
+
+[Binary Authorization](https://cloud.google.com/binary-authorization/docs) can enforce cosign signatures on Cloud Run and GKE.
+
+1. Create a cosign-based attestor using the LiteLLM public key:
+
+```bash
+# Import the public key into a Cloud KMS keyring or use a PGP/PKIX attestor.
+# See: https://cloud.google.com/binary-authorization/docs/creating-attestors-console
+```
+
+2. Configure a Binary Authorization policy that requires the attestor for `ghcr.io/berriai/litellm*` images.
+
+3. Enable the policy on your Cloud Run service or GKE cluster.
+
+Refer to the [GCP Binary Authorization docs](https://cloud.google.com/binary-authorization/docs/setting-up) for full setup steps.
+
+### AWS — ECS / ECR
+
+AWS does not natively verify cosign signatures at deploy time. Common approaches:
+
+- **CI/CD gate**: Run `cosign verify` in your deployment pipeline before pushing to ECR or updating the ECS task definition. Fail the pipeline if verification fails.
+- **OPA/Gatekeeper on EKS**: If running on EKS, use the Sigstore Policy Controller (same as the Kubernetes approach above).
+
+### GitHub Actions gate
+
+Add a verification step before any deployment job:
+
+```yaml
+- name: Verify LiteLLM image signature
+ run: |
+ cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
+ ghcr.io/berriai/litellm-database:${{ env.LITELLM_VERSION }}
+```
+
+## Recommended deployment patterns
+
+### Pin by digest
+
+Digest pinning guarantees the exact image content regardless of tag mutations:
+
+```yaml
+image: ghcr.io/berriai/litellm-database@sha256:
+```
+
+Get the digest after pulling:
+
+```bash
+docker inspect --format='{{index .RepoDigests 0}}' \
+ ghcr.io/berriai/litellm-database:v1.83.0-stable
+```
+
+Cosign verification works with digests too:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
+ ghcr.io/berriai/litellm-database@sha256:
+```
+
+### Use stable release tags
+
+If digest pinning is too rigid for your workflow, use `-stable` release tags (e.g. `v1.83.0-stable`). These are immutable release tags that will not be overwritten.
+
+Avoid `main-latest` or `main-stable` in production — these rolling tags point to the most recent build and can change between deployments.
+
+### Safe upgrade checklist
+
+1. **Verify the new image** — run `cosign verify` against the new release tag or digest.
+2. **Test in staging** — deploy the verified image to a non-production environment.
+3. **Update your pinned reference** — change the digest or tag in your deployment manifest.
+4. **Deploy to production** — roll out using your standard deployment process.
+5. **Monitor `/health`** — confirm the proxy is healthy after the upgrade.
+
+## Further reading
+
+- [CI/CD v2 announcement](https://docs.litellm.ai/blog/ci-cd-v2-improvements) — background on LiteLLM's signing infrastructure
+- [Docker deployment guide](./deploy.md) — full Docker, Helm, and Terraform setup
+- [cosign documentation](https://docs.sigstore.dev/cosign/overview/) — cosign usage and key management
+- [Sigstore Policy Controller](https://docs.sigstore.dev/policy-controller/overview/) — Kubernetes admission control
diff --git a/docs/my-website/docs/proxy/guardrails/quick_start.md b/docs/my-website/docs/proxy/guardrails/quick_start.md
index 5abe499e30be..ed9d2ca128bf 100644
--- a/docs/my-website/docs/proxy/guardrails/quick_start.md
+++ b/docs/my-website/docs/proxy/guardrails/quick_start.md
@@ -9,6 +9,7 @@ Setup Prompt Injection Detection, PII Masking on LiteLLM Proxy (AI Gateway)
## 1. Define guardrails on your LiteLLM config.yaml
Set your guardrails under the `guardrails` section
+
```yaml
model_list:
- model_name: gpt-3.5-turbo
@@ -82,27 +83,58 @@ For generic guardrail APIs you can also set **static headers** (`headers`: key/v
- `during_call` Run **during** LLM call, on **input** Same as `pre_call` but runs in parallel as LLM call. Response not returned until guardrail check completes
- A list of the above values to run multiple modes, e.g. `mode: [pre_call, post_call]`
+### Skip system messages in guardrail evaluation
+
+You can stop **unified** guardrails from scanning `role: system` content while still sending the full `messages` list to the model.
+
+**Global** — in `litellm_settings`:
+
+```yaml
+litellm_settings:
+ skip_system_message_in_guardrail: true
+```
+
+**Per guardrail** — under that guardrail’s `litellm_params`: set `skip_system_message_in_guardrail: true` or `false`. If omitted, the global `litellm_settings` value is used; per-guardrail `false` forces system messages to be included even when the global flag is `true`.
+
+**Via LiteLLM UI** — when **creating** or **editing** a guardrail in the LiteLLM Admin Dashboard, set **Skip system messages in guardrail** (under Basic Info on create, or in the edit / guardrail settings flows):
+
+
+| UI option | Effect |
+| ------------------------------------- | -------------------------------------------------------------------------------------- |
+| **Use global default** | Uses `litellm_settings.skip_system_message_in_guardrail` from your proxy config |
+| **Yes — exclude from guardrail scan** | Sets per-guardrail `skip_system_message_in_guardrail: true` |
+| **No — always include in scan** | Sets per-guardrail `skip_system_message_in_guardrail: false` (overrides a global skip) |
+
+
+
-
+
Expect this to fail since since `ishaan@berri.ai` in the request is PII
@@ -141,9 +173,9 @@ Expected response on failure
```
-
-
+
+
```shell
curl -i http://localhost:4000/v1/chat/completions \
@@ -158,10 +190,8 @@ curl -i http://localhost:4000/v1/chat/completions \
}'
```
-
-
## **Default On Guardrails**
@@ -183,7 +213,6 @@ guardrails:
In this request, the guardrail `aporia-pre-guard` will run on every request because `default_on: true` is set.
-
```shell
curl -i http://localhost:4000/v1/chat/completions \
-H "Content-Type: application/json" \
@@ -207,6 +236,7 @@ x-litellm-applied-guardrails: aporia-pre-guard
### Guardrail Policies
Need more control? Use [Guardrail Policies](./guardrail_policies.md) to:
+
- Group guardrails into reusable policies
- Enable/disable guardrails for specific teams, keys, or models
- Inherit from existing policies and override specific guardrails
@@ -217,7 +247,6 @@ Need more control? Use [Guardrail Policies](./guardrail_policies.md) to:
Pass `guardrails` to your request body to test it
-
```shell
curl -i http://localhost:4000/v1/chat/completions \
-H "Content-Type: application/json" \
@@ -239,7 +268,6 @@ Follow this simple workflow to implement and tune guardrails:
First, check what guardrails are available and their parameters:
-
Call `/guardrails/list` to view available guardrails and the guardrail info (supported parameters, description, etc)
```shell
@@ -271,9 +299,12 @@ Expected response
}
```
->
+
+
This config will return the `/guardrails/list` response above. The `guardrail_info` field is optional and you can add any fields under info for consumers of your guardrail
->
+
+
+
```yaml
- guardrail_name: "aporia-post-guard"
litellm_params:
@@ -291,9 +322,10 @@ This config will return the `/guardrails/list` response above. The `guardrail_in
type: "boolean"
```
-
### 2. Apply Guardrails
+
Add selected guardrails to your chat completion request:
+
```shell
curl -i http://localhost:4000/v1/chat/completions \
-H "Content-Type: application/json" \
@@ -322,7 +354,6 @@ curl -i http://localhost:4000/v1/chat/completions \
}'
```
-
### 4. ✨ Pass Dynamic Parameters to Guardrail
:::info
@@ -334,9 +365,8 @@ curl -i http://localhost:4000/v1/chat/completions \
Use this to pass additional parameters to the guardrail API call. e.g. things like success threshold. **[See `guardrails` spec for more details](#spec-guardrails-parameter)**
-
-
+
Set `guardrails={"aporia-pre-guard": {"extra_body": {"success_threshold": 0.9}}}` to pass additional parameters to the guardrail
@@ -371,10 +401,10 @@ response = client.chat.completions.create(
print(response)
```
-
-
+
+
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
@@ -396,10 +426,7 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
}
}'
```
-
-
-
@@ -426,9 +453,6 @@ Monitor which guardrails were executed and whether they passed or failed. e.g. g
-
+
```shell
curl -X POST 'http://0.0.0.0:4000/key/generate' \
@@ -454,8 +478,7 @@ curl -X POST 'http://0.0.0.0:4000/key/generate' \
}'
```
-
-
+
```shell
curl --location 'http://0.0.0.0:4000/key/update' \
@@ -467,8 +490,7 @@ curl --location 'http://0.0.0.0:4000/key/update' \
}'
```
-
-
+
**Step 2** Test it with new key
@@ -499,8 +521,7 @@ Run guardrails based on the user-agent header. This is useful for running pre-ca
Both `default` and tag values can be a single mode string or a list of modes.
-
-
+
```yaml
model_list:
@@ -522,11 +543,10 @@ guardrails:
default_on: true # run on every request
```
-
-
+
```yaml
-model_list:
+Per guardrailmodel_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
@@ -545,8 +565,7 @@ guardrails:
default_on: true
```
-
-
+
```yaml
model_list:
@@ -568,8 +587,6 @@ guardrails:
default_on: true
```
-
-
### ✨ Model-level Guardrails
@@ -580,10 +597,8 @@ guardrails:
:::
-
This is great for cases when you have an on-prem and hosted model, and just want to run prevent sending PII to the hosted model.
-
```yaml
model_list:
- model_name: claude-sonnet-4
@@ -620,8 +635,7 @@ guardrails:
:::
-
-#### 1. Disable team from modifying guardrails
+#### 1. Disable team from modifying guardrails
```bash
curl -X POST 'http://0.0.0.0:4000/team/update' \
@@ -633,7 +647,7 @@ curl -X POST 'http://0.0.0.0:4000/team/update' \
}'
```
-#### 2. Try to disable guardrails for a call
+#### 2. Try to disable guardrails for a call
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
@@ -672,8 +686,7 @@ Expect to NOT see `+1 412-612-9992` in your server logs on your callback.
The `pii_masking` guardrail ran on this request because api key=sk-jNm1Zar7XfNdZXp49Z1kSQ has `"permissions": {"pii_masking": true}`
:::
-
-## Specification
+## Specification
### `guardrails` Configuration on YAML
@@ -723,6 +736,7 @@ The `guardrails` parameter can be passed to any LiteLLM Proxy endpoint (`/chat/c
#### Format Options
1. Simple List Format:
+
```python
"guardrails": [
"aporia-pre-guard",
@@ -730,9 +744,10 @@ The `guardrails` parameter can be passed to any LiteLLM Proxy endpoint (`/chat/c
]
```
-2. Advanced Dictionary Format:
+1. Advanced Dictionary Format:
In this format the dictionary key is `guardrail_name` you want to run
+
```python
"guardrails": {
"aporia-pre-guard": {
@@ -745,6 +760,7 @@ In this format the dictionary key is `guardrail_name` you want to run
```
#### Type Definition
+
```python
guardrails: Union[
List[str], # Simple list of guardrail names
@@ -754,3 +770,4 @@ guardrails: Union[
class DynamicGuardrailParams:
extra_body: Dict[str, Any] # Additional parameters for the guardrail
```
+
diff --git a/docs/my-website/docs/proxy/oauth2.md b/docs/my-website/docs/proxy/oauth2.md
index c0597058cfd7..9b94a017ca16 100644
--- a/docs/my-website/docs/proxy/oauth2.md
+++ b/docs/my-website/docs/proxy/oauth2.md
@@ -63,16 +63,19 @@ Start the LiteLLM Proxy with [`--detailed_debug` mode and you should see more ve
## Using OAuth2 + JWT Together
-If both `enable_oauth2_auth` and `enable_jwt_auth` are enabled, LiteLLM can split auth paths:
-- JWT validation for user tokens
-- OAuth2 introspection for machine tokens
+LiteLLM supports two OAuth2 + JWT modes:
-For JWT-shaped machine tokens, configure `litellm_jwtauth.routing_overrides`:
+1. **Global OAuth2 mode** (`enable_oauth2_auth: true`)
+ OAuth2 auth is enabled on LLM + info routes.
+2. **Selective JWT override mode** (`enable_oauth2_auth: false`)
+ Only JWT-shaped tokens that match `litellm_jwtauth.routing_overrides` are routed to OAuth2 on LLM + info routes.
+
+For selective routing (OAuth2 only for specific JWTs), configure:
```yaml title="config.yaml"
general_settings:
enable_jwt_auth: true
- enable_oauth2_auth: true
+ enable_oauth2_auth: false
litellm_jwtauth:
routing_overrides:
- iss: "machine-issuer.example.com"
diff --git a/docs/my-website/docs/proxy/token_auth.md b/docs/my-website/docs/proxy/token_auth.md
index d37b05391b6e..4d49a2445efe 100644
--- a/docs/my-website/docs/proxy/token_auth.md
+++ b/docs/my-website/docs/proxy/token_auth.md
@@ -792,16 +792,18 @@ litellm_jwtauth:
## Route JWT-Shaped Machine Tokens to OAuth2
-Use this when both are enabled:
+Use this when:
- `enable_jwt_auth: true` for standard JWT validation
-- `enable_oauth2_auth: true` for OAuth2 introspection
+- machine tokens are JWT-shaped and should be routed to OAuth2 based on claims
-If some machine tokens are also JWT-shaped, configure `routing_overrides` to route matching tokens to OAuth2.
+`routing_overrides` supports two operating modes:
+- **Selective mode**: set `enable_oauth2_auth: false` to send only matching JWTs to OAuth2 on LLM + info routes
+- **Global mode**: set `enable_oauth2_auth: true` to also enable OAuth2 on LLM + info routes
```yaml title="config.yaml"
general_settings:
enable_jwt_auth: true
- enable_oauth2_auth: true
+ enable_oauth2_auth: false
litellm_jwtauth:
user_id_jwt_field: "sub"
routing_overrides:
@@ -822,7 +824,7 @@ general_settings:
```yaml title="config.yaml"
general_settings:
enable_jwt_auth: true
- enable_oauth2_auth: true
+ enable_oauth2_auth: false
litellm_jwtauth:
routing_overrides:
- iss: ["machine-issuer.example.com", "backup-issuer.example.com"]
diff --git a/docs/my-website/img/april_townhall_isolated_environments.png b/docs/my-website/img/april_townhall_isolated_environments.png
new file mode 100644
index 000000000000..120e5cec9b7e
Binary files /dev/null and b/docs/my-website/img/april_townhall_isolated_environments.png differ
diff --git a/docs/my-website/img/release_notes/mcp_toolsets.jpeg b/docs/my-website/img/release_notes/mcp_toolsets.jpeg
new file mode 100644
index 000000000000..3c323bbe0436
Binary files /dev/null and b/docs/my-website/img/release_notes/mcp_toolsets.jpeg differ
diff --git a/docs/my-website/img/release_notes/skills_marketplace.png b/docs/my-website/img/release_notes/skills_marketplace.png
new file mode 100644
index 000000000000..b93a4e418719
Binary files /dev/null and b/docs/my-website/img/release_notes/skills_marketplace.png differ
diff --git a/docs/my-website/img/skip_system_message_guardrail_ui.png b/docs/my-website/img/skip_system_message_guardrail_ui.png
new file mode 100644
index 000000000000..466ac7daa6e1
Binary files /dev/null and b/docs/my-website/img/skip_system_message_guardrail_ui.png differ
diff --git a/docs/my-website/img/stable_main.png b/docs/my-website/img/stable_main.png
new file mode 100644
index 000000000000..f050b54f6e0f
Binary files /dev/null and b/docs/my-website/img/stable_main.png differ
diff --git a/docs/my-website/img/verify_releases.png b/docs/my-website/img/verify_releases.png
new file mode 100644
index 000000000000..270a999d8dc5
Binary files /dev/null and b/docs/my-website/img/verify_releases.png differ
diff --git a/docs/my-website/release_notes/v1.83.3/index.md b/docs/my-website/release_notes/v1.83.3/index.md
new file mode 100644
index 000000000000..bfa66b8fcc2b
--- /dev/null
+++ b/docs/my-website/release_notes/v1.83.3/index.md
@@ -0,0 +1,236 @@
+---
+title: "[Preview] v1.83.3.rc.1 - Introducing MCP Skills Marketplace"
+slug: "v1-83-3-rc-1"
+date: 2026-04-04T00:00:00
+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
+ - name: Ryan Crabbe
+ title: Full Stack Engineer, LiteLLM
+ url: https://www.linkedin.com/in/ryan-crabbe-0b9687214
+ image_url: https://media.licdn.com/dms/image/v2/D5603AQHt1t9Z4BJ6Gw/profile-displayphoto-shrink_400_400/profile-displayphoto-shrink_400_400/0/1724453682340?e=1772064000&v=beta&t=VXdmr13rsNB05wyA2F1TENOB5UuDHUZ0FCHTolNyR5M
+ - name: Yuneng Jiang
+ title: Senior Full Stack Engineer, LiteLLM
+ url: https://www.linkedin.com/in/yuneng-david-jiang-455676139/
+ image_url: https://avatars.githubusercontent.com/u/171294688?v=4
+ - name: Shivam Rawat
+ title: Forward Deployed Engineer, LiteLLM
+ url: https://linkedin.com/in/shivam-rawat-482937318
+ image_url: https://github.com/shivamrawat1.png
+hide_table_of_contents: false
+---
+
+## Deploy this version
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+
+
+
+```bash
+docker run \
+-e STORE_MODEL_IN_DB=True \
+-p 4000:4000 \
+docker.litellm.ai/berriai/litellm:main-v1.83.3.rc.1
+```
+
+
+
+
+```bash
+pip install litellm==1.83.3rc1
+```
+
+
+
+
+## Key Highlights
+
+- **MCP Toolsets** — [Create curated tool subsets from one or more MCP servers with scoped permissions, and manage them from the UI or API](../../docs/mcp)
+- **Skills Marketplace** — [Browse, install, and publish Claude Code skills from a self-hosted marketplace — works across Anthropic, Vertex AI, Azure, and Bedrock](../../docs/proxy/skills)
+- **Guardrail Fallbacks** — [Configure `on_error` behavior so guardrail failures degrade gracefully instead of blocking the request](../../docs/proxy/guardrails)
+- **Team Bring Your Own Guardrails** — [Teams can now attach and manage their own guardrails directly from team settings in the UI](../../docs/proxy/guardrails)
+
+---
+
+
+### Skills Marketplace
+
+The Skills Marketplace gives teams a self-hosted catalog for discovering, installing, and publishing Claude Code skills. Skills are portable across Anthropic, Vertex AI, Azure, and Bedrock — so a skill published once works everywhere your gateway routes to.
+
+
+
+[Get Started](../../docs/proxy/skills)
+
+### Guardrail Fallbacks
+
+Guardrail pipelines now support an optional `on_error` behavior. When a guardrail check fails or errors out, you can configure the pipeline to fall back gracefully — logging the failure and continuing the request — instead of returning a hard 500 to the caller. This is especially useful for non-critical guardrails where availability matters more than enforcement.
+
+### Team Bring Your Own Guardrails
+
+Teams can now attach guardrails directly from the team management UI. Admins configure available guardrails at the project or proxy level, and individual teams select which ones apply to their traffic — no config file changes or proxy restarts needed. This also ships with project-level guardrail support in the project create/edit flows.
+
+### MCP Toolsets
+
+MCP Toolsets let AI platform admins create curated subsets of tools from one or more MCP servers and assign them to teams and keys with scoped permissions. Instead of granting access to an entire MCP server, you can now bundle specific tools into a named toolset — controlling exactly which tools each team or API key can invoke. Toolsets are fully managed through the UI (new Toolsets tab) and API, and work seamlessly with the Responses API and Playground.
+
+
+
+[Get Started](../../docs/mcp)
+---
+
+## New Models / Updated Models
+
+#### New Model Support
+
+| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features |
+| -------- | ----- | -------------- | ------------------- | -------------------- | -------- |
+| Brave Search | `brave/search` | - | - | - | Search tool integration metadata in cost map ([PR #25042](https://github.com/BerriAI/litellm/pull/25042)) |
+| AWS Bedrock | `nvidia.nemotron-super-3-120b` | 256K | Added | Added | Chat completions, function calling, system messages ([PR #24588](https://github.com/BerriAI/litellm/pull/24588)) |
+| OCI GenAI | Multiple new chat + embedding entries | Varies | Updated | Updated | Expanded chat + embedding model catalog |
+
+#### Features
+
+- **[AWS Bedrock](../../docs/providers/bedrock)**
+ - Add Nova Canvas image edit support - [PR #25110](https://github.com/BerriAI/litellm/pull/25110), [PR #24869](https://github.com/BerriAI/litellm/pull/24869)
+ - Improve cache usage exposure for Claude-compatible streaming paths - [PR #25110](https://github.com/BerriAI/litellm/pull/25110), [PR #24850](https://github.com/BerriAI/litellm/pull/24850)
+ - Bedrock model catalog updates - [PR #24645](https://github.com/BerriAI/litellm/pull/24645)
+
+- **[OCI GenAI](../../docs/providers/oci)**
+ - Add native embeddings support + expanded model catalog - [PR #25151](https://github.com/BerriAI/litellm/pull/25151), [PR #24887](https://github.com/BerriAI/litellm/pull/24887)
+
+- **[Google Vertex AI](../../docs/providers/vertex)**
+ - Add unversioned Claude Haiku pricing entry to ensure accurate spend accounting - [PR #25151](https://github.com/BerriAI/litellm/pull/25151)
+
+### Bug Fixes
+
+- **General**
+ - Fix `gpt-5.4` pricing metadata - [PR #24748](https://github.com/BerriAI/litellm/pull/24748)
+ - Fix gov pricing tests and Bedrock model test follow-ups - [PR #25022](https://github.com/BerriAI/litellm/pull/25022), [PR #24947](https://github.com/BerriAI/litellm/pull/24947), [PR #24931](https://github.com/BerriAI/litellm/pull/24931)
+
+## LLM API Endpoints
+
+#### Features
+
+- **[A2A / MCP Gateway API (/a2a, /mcp)](../../docs/mcp)**
+ - Preserve JSON-RPC envelope for AgentCore A2A-native agents - [PR #25092](https://github.com/BerriAI/litellm/pull/25092)
+ - Bedrock Anthropic file/document handling fix from internal staging - [PR #25050](https://github.com/BerriAI/litellm/pull/25050), [PR #25047](https://github.com/BerriAI/litellm/pull/25047)
+
+#### Bugs
+
+- **[Search API (/search)](../../docs/search)**
+ - Support self-hosted Firecrawl response format in search transforms - [PR #25110](https://github.com/BerriAI/litellm/pull/25110), [PR #24866](https://github.com/BerriAI/litellm/pull/24866)
+
+## Management Endpoints / UI
+
+#### Features
+
+- **Virtual Keys**
+ - Add substring search for `user_id` and `key_alias` on `/key/list` - [PR #24751](https://github.com/BerriAI/litellm/pull/24751), [PR #24746](https://github.com/BerriAI/litellm/pull/24746)
+ - Wire `team_id` filter to key alias dropdown on Virtual Keys tab - [PR #25119](https://github.com/BerriAI/litellm/pull/25119), [PR #25114](https://github.com/BerriAI/litellm/pull/25114)
+ - Allow hashed `token_id` in `/key/update` endpoint - [PR #24969](https://github.com/BerriAI/litellm/pull/24969)
+
+- **Teams + Organizations**
+ - Resolve access-group models/MCP servers/agents in team endpoints and UI - [PR #25119](https://github.com/BerriAI/litellm/pull/25119), [PR #25027](https://github.com/BerriAI/litellm/pull/25027)
+ - Allow changing team organization from team settings - [PR #25095](https://github.com/BerriAI/litellm/pull/25095)
+ - Add per-model rate limits to team edit/info views - [PR #25156](https://github.com/BerriAI/litellm/pull/25156), [PR #25144](https://github.com/BerriAI/litellm/pull/25144)
+
+- **Usage + Analytics**
+ - Add paginated team search on usage page filters - [PR #25107](https://github.com/BerriAI/litellm/pull/25107)
+ - Use entity key for usage export display correctness - [PR #25153](https://github.com/BerriAI/litellm/pull/25153)
+
+- **Models + Providers**
+ - Include access-group models in UI model listing - [PR #24743](https://github.com/BerriAI/litellm/pull/24743)
+ - Expose Azure Entra ID credential fields in provider forms - [PR #25137](https://github.com/BerriAI/litellm/pull/25137)
+ - Do not inject `vector_store_ids: []` when editing a model - [PR #25133](https://github.com/BerriAI/litellm/pull/25133)
+
+- **Guardrails UI**
+ - Add project-level guardrails support in project create/edit flows - [PR #25100](https://github.com/BerriAI/litellm/pull/25100)
+ - Allow adding team guardrails from the UI - [PR #25038](https://github.com/BerriAI/litellm/pull/25038)
+
+- **UI Cleanup**
+ - Migrate Tremor Text/Badge to antd Tag and native spans - [PR #24750](https://github.com/BerriAI/litellm/pull/24750)
+
+#### Bugs
+
+- Fix logs page showing unfiltered results when backend filter returns zero rows - [PR #24745](https://github.com/BerriAI/litellm/pull/24745)
+- Enforce upperbound key params on `/key/update` and bulk update hook paths - [PR #25110](https://github.com/BerriAI/litellm/pull/25110), [PR #25103](https://github.com/BerriAI/litellm/pull/25103)
+- Fix team model update 500 due to unsupported Prisma JSON path filter - [PR #25152](https://github.com/BerriAI/litellm/pull/25152)
+
+## AI Integrations
+
+### Logging
+
+- **General**
+ - Eliminate race condition in streaming `guardrail_information` logging - [PR #24592](https://github.com/BerriAI/litellm/pull/24592)
+ - Use actual `start_time` in failed request spend logs - [PR #24906](https://github.com/BerriAI/litellm/pull/24906)
+ - Harden credential redaction + stop logging raw sensitive auth values - [PR #25151](https://github.com/BerriAI/litellm/pull/25151)
+
+### Guardrails
+
+- Add optional `on_error` for guardrail pipeline failures - [PR #25150](https://github.com/BerriAI/litellm/pull/25150), [PR #24831](https://github.com/BerriAI/litellm/pull/24831)
+- Return HTTP 400 (vs 500) for Model Armor streaming blocks - [PR #24693](https://github.com/BerriAI/litellm/pull/24693)
+
+### Prompt Management
+
+- Add environment + user tracking for prompts (`development/staging/production`) in CRUD + UI flows - [PR #25110](https://github.com/BerriAI/litellm/pull/25110), [PR #24855](https://github.com/BerriAI/litellm/pull/24855)
+
+### Secret Managers
+
+- No major new secret manager provider additions in this RC.
+
+## Spend Tracking, Budgets and Rate Limiting
+
+- Enforce budget for models not directly present in the cost map - [PR #24949](https://github.com/BerriAI/litellm/pull/24949)
+- Add per-model rate limits in team settings/info UI - [PR #25144](https://github.com/BerriAI/litellm/pull/25144)
+- Fix unversioned Vertex Claude Haiku pricing entry to avoid `$0.00` accounting - [PR #25151](https://github.com/BerriAI/litellm/pull/25151)
+
+## MCP Gateway
+
+- Introduce **MCP Toolsets** with DB types, CRUD APIs, scoped permissions, and UI management tab - [PR #25155](https://github.com/BerriAI/litellm/pull/25155)
+- Resolve toolset names and enforce toolset access correctly in Responses API and streamable MCP paths - [PR #25155](https://github.com/BerriAI/litellm/pull/25155)
+- Switch toolset permission caching to shared cache path and improve cache invalidation behavior - [PR #25155](https://github.com/BerriAI/litellm/pull/25155)
+- Allow JWT auth for `/v1/mcp/server/*` sub-paths - [PR #25113](https://github.com/BerriAI/litellm/pull/25113), [PR #24698](https://github.com/BerriAI/litellm/pull/24698)
+- Add STS AssumeRole support for MCP SigV4 auth - [PR #25151](https://github.com/BerriAI/litellm/pull/25151)
+- Add tag query fix + MCP metadata support cherry-pick - [PR #25145](https://github.com/BerriAI/litellm/pull/25145)
+
+## Performance / Loadbalancing / Reliability improvements
+
+- Integrate router health-check failures with cooldown behavior and transient 429/408 handling - [PR #25150](https://github.com/BerriAI/litellm/pull/25150), [PR #24988](https://github.com/BerriAI/litellm/pull/24988)
+- Add distributed lock for key rotation job execution - [PR #25150](https://github.com/BerriAI/litellm/pull/25150), [PR #23364](https://github.com/BerriAI/litellm/pull/23364), [PR #23834](https://github.com/BerriAI/litellm/pull/23834)
+- Improve team routing reliability with deterministic grouping, isolation fixes, stale alias controls, and order-based fallback - [PR #25154](https://github.com/BerriAI/litellm/pull/25154), [PR #25148](https://github.com/BerriAI/litellm/pull/25148)
+- Regenerate GCP IAM token per async Redis cluster connection (fix token TTL failures) - [PR #25155](https://github.com/BerriAI/litellm/pull/25155), [PR #24426](https://github.com/BerriAI/litellm/pull/24426)
+- Restore MCP server fields dropped by schema sync migration - [PR #24078](https://github.com/BerriAI/litellm/pull/24078)
+- Proxy server reliability hardening with bounded queue usage - [PR #25155](https://github.com/BerriAI/litellm/pull/25155)
+
+## Documentation Updates
+
+- Improve HA control plane diagram clarity + mobile rendering updates - [PR #24747](https://github.com/BerriAI/litellm/pull/24747)
+- Document `default_team_params` in config reference and examples - [PR #25032](https://github.com/BerriAI/litellm/pull/25032)
+- Add JWT to Virtual Key mapping guide - [PR #24882](https://github.com/BerriAI/litellm/pull/24882)
+- Add MCP Toolsets docs and sidebar updates - [PR #25155](https://github.com/BerriAI/litellm/pull/25155)
+- Security docs updates and April hardening blog - [PR #24867](https://github.com/BerriAI/litellm/pull/24867), [PR #24868](https://github.com/BerriAI/litellm/pull/24868), [PR #24871](https://github.com/BerriAI/litellm/pull/24871), [PR #25102](https://github.com/BerriAI/litellm/pull/25102)
+- General docs cleanup + townhall announcement updates - [PR #24839](https://github.com/BerriAI/litellm/pull/24839), [PR #25026](https://github.com/BerriAI/litellm/pull/25026), [PR #25021](https://github.com/BerriAI/litellm/pull/25021)
+
+## Infrastructure / Security Notes
+
+- Harden npm and Docker supply chain workflows and release pipeline checks - [PR #24838](https://github.com/BerriAI/litellm/pull/24838), [PR #24877](https://github.com/BerriAI/litellm/pull/24877), [PR #24881](https://github.com/BerriAI/litellm/pull/24881), [PR #24905](https://github.com/BerriAI/litellm/pull/24905), [PR #24951](https://github.com/BerriAI/litellm/pull/24951), [PR #25023](https://github.com/BerriAI/litellm/pull/25023), [PR #25034](https://github.com/BerriAI/litellm/pull/25034), [PR #25036](https://github.com/BerriAI/litellm/pull/25036), [PR #25037](https://github.com/BerriAI/litellm/pull/25037), [PR #25136](https://github.com/BerriAI/litellm/pull/25136), [PR #25158](https://github.com/BerriAI/litellm/pull/25158)
+- Resolve CodeQL/security workflow issues and fix broken action SHA references - [PR #24880](https://github.com/BerriAI/litellm/pull/24880), [PR #24815](https://github.com/BerriAI/litellm/pull/24815)
+- Re-add Codecov reporting in GHA matrix workflows - [PR #24804](https://github.com/BerriAI/litellm/pull/24804)
+- Fix(docker): load enterprise hooks in non-root runtime image - [PR #24917](https://github.com/BerriAI/litellm/pull/24917)
+- Apply Black formatting to 14 files - [PR #24532](https://github.com/BerriAI/litellm/pull/24532)
+- Fix lint issues - [PR #24932](https://github.com/BerriAI/litellm/pull/24932)
+
+## New Contributors
+
+* @vanhtuan0409 made their first contribution in https://github.com/BerriAI/litellm/pull/24078
+* @clfhhc made their first contribution in https://github.com/BerriAI/litellm/pull/24932
+
+**Full Changelog**: https://github.com/BerriAI/litellm/compare/v1.83.0-nightly...v1.83.3.rc.1
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index ab8f257c7d59..b1ec952544b5 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -349,6 +349,7 @@ const sidebars = {
"proxy/debugging",
"proxy/error_diagnosis",
"proxy/deploy",
+ "proxy/docker_image_security",
"proxy/health",
"proxy/master_key_rotations",
"proxy/model_management",
@@ -563,7 +564,8 @@ const sidebars = {
"proxy/model_access",
"proxy/model_access_groups",
"proxy/access_groups",
- "proxy/team_model_add"
+ "proxy/team_model_add",
+ "proxy/credential_routing"
]
},
{
@@ -859,6 +861,7 @@ const sidebars = {
]
},
"providers/anthropic",
+ "providers/anthropic_tool_search",
"providers/aws_sagemaker",
{
type: "category",
@@ -1230,6 +1233,7 @@ const learnSidebar = {
"completion/web_fetch",
"completion/computer_use",
"guides/code_interpreter",
+ "completion/anthropic_advisor_tool",
"completion/message_sanitization",
],
},
diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
index de02a0c4dabf..12fdaeb6a81c 100644
--- a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
+++ b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
@@ -114,6 +114,7 @@ async def async_log_failure_event(self, kwargs, response_obj, start_time, end_ti
user_api_key_max_budget=_meta.get("user_api_key_max_budget"),
user_api_key_budget_reset_at=_meta.get("user_api_key_budget_reset_at"),
user_api_key_org_id=_meta.get("user_api_key_org_id"),
+ user_api_key_org_alias=_meta.get("user_api_key_org_alias"),
user_api_key_team_id=_meta.get("user_api_key_team_id"),
user_api_key_project_id=_meta.get("user_api_key_project_id"),
user_api_key_project_alias=_meta.get("user_api_key_project_alias"),
@@ -196,6 +197,7 @@ async def hanging_response_handler(
else None
),
user_api_key_org_id=user_api_key_dict.org_id,
+ user_api_key_org_alias=user_api_key_dict.organization_alias,
user_api_key_team_id=user_api_key_dict.team_id,
user_api_key_project_id=user_api_key_dict.project_id,
user_api_key_project_alias=user_api_key_dict.project_alias,
diff --git a/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py b/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py
index 54fbc7abcc59..dc0168683c80 100644
--- a/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py
+++ b/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py
@@ -11,6 +11,7 @@
from litellm.constants import (
MANAGED_OBJECT_STALENESS_CUTOFF_DAYS,
MAX_OBJECTS_PER_POLL_CYCLE,
+ STALE_OBJECT_CLEANUP_BATCH_SIZE,
)
if TYPE_CHECKING:
@@ -32,21 +33,49 @@ def __init__(
self.prisma_client: PrismaClient = prisma_client
self.llm_router: Router = llm_router
+ async def _expire_stale_rows(
+ self, cutoff: datetime, batch_size: int
+ ) -> int:
+ """Execute the bounded UPDATE that marks stale rows as 'stale_expired'.
+
+ Isolated so it can be swapped / mocked in tests without touching the
+ orchestration logic in ``_cleanup_stale_managed_objects``.
+
+ Uses PostgreSQL syntax (``$1::timestamptz``, ``LIMIT``, double-quoted
+ identifiers) which is the only dialect the proxy supports — every
+ ``schema.prisma`` in the repo sets ``provider = "postgresql"``.
+ Same pattern as ``spend_log_cleanup.py``.
+ """
+ return await self.prisma_client.db.execute_raw(
+ """
+ UPDATE "LiteLLM_ManagedObjectTable"
+ SET "status" = 'stale_expired'
+ WHERE "id" IN (
+ SELECT "id" FROM "LiteLLM_ManagedObjectTable"
+ WHERE "file_purpose" = 'response'
+ AND "status" NOT IN ('completed', 'complete', 'failed', 'expired', 'cancelled', 'stale_expired')
+ AND "created_at" < $1::timestamptz
+ ORDER BY "created_at" ASC
+ LIMIT $2
+ )
+ """,
+ cutoff,
+ batch_size,
+ )
+
async def _cleanup_stale_managed_objects(self) -> None:
"""
Mark managed objects older than MANAGED_OBJECT_STALENESS_CUTOFF_DAYS days
in non-terminal states as 'stale_expired'. These will never complete and
should not be polled.
+
+ Runs as a single DB query with a subquery LIMIT so no rows are loaded
+ into Python memory. Processes at most STALE_OBJECT_CLEANUP_BATCH_SIZE
+ rows per invocation to avoid overwhelming the DB when there is a large
+ backlog.
"""
cutoff = datetime.now(timezone.utc) - timedelta(days=MANAGED_OBJECT_STALENESS_CUTOFF_DAYS)
- result = await self.prisma_client.db.litellm_managedobjecttable.update_many(
- where={
- "file_purpose": "response",
- "status": {"not_in": ["completed", "complete", "failed", "expired", "cancelled", "stale_expired"]},
- "created_at": {"lt": cutoff},
- },
- data={"status": "stale_expired"},
- )
+ result = await self._expire_stale_rows(cutoff, STALE_OBJECT_CLEANUP_BATCH_SIZE)
if result > 0:
verbose_proxy_logger.warning(
f"CheckResponsesCost: marked {result} stale managed objects "
diff --git a/litellm/__init__.py b/litellm/__init__.py
index d4418c661a3c..8087e3f5311f 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -164,6 +164,7 @@
langfuse_default_tags: Optional[List[str]] = None
langsmith_batch_size: Optional[int] = None
prometheus_initialize_budget_metrics: Optional[bool] = False
+prometheus_latency_buckets: Optional[List[float]] = None
require_auth_for_metrics_endpoint: Optional[bool] = False
argilla_batch_size: Optional[int] = None
datadog_use_v1: Optional[bool] = False # if you want to use v1 datadog logged payload.
@@ -203,6 +204,7 @@
bool
] = None # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers
store_audit_logs = False # Enterprise feature, allow users to see audit logs
+skip_system_message_in_guardrail: bool = False
### end of callbacks #############
email: Optional[
@@ -318,6 +320,7 @@
False # get response headers from LLM Api providers - example x-remaining-requests,
)
enable_json_schema_validation: bool = False
+enable_model_config_credential_overrides: bool = False
enable_key_alias_format_validation: bool = (
False # opt-in validation of key_alias format on /key/generate and /key/update
)
diff --git a/litellm/_logging.py b/litellm/_logging.py
index 62283f6f65a1..7824fcfa675d 100644
--- a/litellm/_logging.py
+++ b/litellm/_logging.py
@@ -243,6 +243,12 @@ def format(self, record):
if key not in _STANDARD_RECORD_ATTRS and key not in json_record:
json_record[key] = value
+ # Set component/logger only if not already supplied via extra={...}
+ if "component" not in json_record:
+ json_record["component"] = record.name
+ if "logger" not in json_record:
+ json_record["logger"] = f"{record.filename}:{record.lineno}"
+
if record.exc_info:
json_record["stacktrace"] = record.exc_text or self.formatException(
record.exc_info
diff --git a/litellm/a2a_protocol/main.py b/litellm/a2a_protocol/main.py
index c86549da77a6..6154c8288046 100644
--- a/litellm/a2a_protocol/main.py
+++ b/litellm/a2a_protocol/main.py
@@ -615,7 +615,7 @@ async def asend_message_streaming( # noqa: PLR0915
async def create_a2a_client(
base_url: str,
- timeout: float = 60.0,
+ timeout: float = DEFAULT_A2A_AGENT_TIMEOUT,
extra_headers: Optional[Dict[str, str]] = None,
) -> "A2AClientType":
"""
@@ -626,7 +626,7 @@ async def create_a2a_client(
Args:
base_url: The base URL of the A2A agent (e.g., "http://localhost:10001")
- timeout: Request timeout in seconds (default: 60.0)
+ timeout: Request timeout in seconds (default: ``DEFAULT_A2A_AGENT_TIMEOUT`` / env ``DEFAULT_A2A_AGENT_TIMEOUT``)
extra_headers: Optional additional headers to include in requests
Returns:
@@ -711,7 +711,7 @@ async def aget_agent_card(
Args:
base_url: The base URL of the A2A agent (e.g., "http://localhost:10001")
- timeout: Request timeout in seconds (default: 60.0)
+ timeout: Request timeout in seconds (default: ``DEFAULT_A2A_AGENT_TIMEOUT`` / env ``DEFAULT_A2A_AGENT_TIMEOUT``)
extra_headers: Optional additional headers to include in requests
Returns:
diff --git a/litellm/a2a_protocol/providers/pydantic_ai_agents/config.py b/litellm/a2a_protocol/providers/pydantic_ai_agents/config.py
index 46253bbcf788..2f16779cc9fa 100644
--- a/litellm/a2a_protocol/providers/pydantic_ai_agents/config.py
+++ b/litellm/a2a_protocol/providers/pydantic_ai_agents/config.py
@@ -21,11 +21,11 @@ async def handle_non_streaming(
request_id: str,
params: Dict[str, Any],
api_base: Optional[str] = None,
- **kwargs,
+ **kwargs: Any,
) -> Dict[str, Any]:
"""Handle non-streaming request to Pydantic AI agent."""
- if not api_base:
- raise ValueError("api_base is required for Pydantic AI agents")
+ if api_base is None:
+ raise ValueError("api_base is required for PydanticAIProviderConfig")
return await PydanticAIHandler.handle_non_streaming(
request_id=request_id,
params=params,
diff --git a/litellm/anthropic_beta_headers_config.json b/litellm/anthropic_beta_headers_config.json
index df8d49ac8f22..7dd5975b7bb8 100644
--- a/litellm/anthropic_beta_headers_config.json
+++ b/litellm/anthropic_beta_headers_config.json
@@ -1,6 +1,7 @@
{
"description": "Mapping of Anthropic beta headers for each provider. Keys are input header names, values are provider-specific header names (or null if unsupported). Only headers present in mapping keys with non-null values can be forwarded.",
"anthropic": {
+ "advisor-tool-2026-03-01": "advisor-tool-2026-03-01",
"advanced-tool-use-2025-11-20": "advanced-tool-use-2025-11-20",
"bash_20241022": null,
"bash_20250124": null,
@@ -31,6 +32,7 @@
"web-search-2025-03-05": "web-search-2025-03-05"
},
"azure_ai": {
+ "advisor-tool-2026-03-01": null,
"advanced-tool-use-2025-11-20": "advanced-tool-use-2025-11-20",
"bash_20241022": null,
"bash_20250124": null,
@@ -60,6 +62,7 @@
"web-search-2025-03-05": "web-search-2025-03-05"
},
"bedrock_converse": {
+ "advisor-tool-2026-03-01": null,
"advanced-tool-use-2025-11-20": null,
"bash_20241022": null,
"bash_20250124": null,
@@ -90,6 +93,7 @@
"web-search-2025-03-05": null
},
"bedrock": {
+ "advisor-tool-2026-03-01": null,
"advanced-tool-use-2025-11-20": "tool-search-tool-2025-10-19",
"bash_20241022": null,
"bash_20250124": null,
@@ -120,6 +124,7 @@
"web-search-2025-03-05": null
},
"vertex_ai": {
+ "advisor-tool-2026-03-01": null,
"advanced-tool-use-2025-11-20": "tool-search-tool-2025-10-19",
"bash_20241022": null,
"bash_20250124": null,
@@ -150,6 +155,7 @@
"web-search-2025-03-05": "web-search-2025-03-05"
},
"databricks": {
+ "advisor-tool-2026-03-01": null,
"advanced-tool-use-2025-11-20": "advanced-tool-use-2025-11-20",
"bash_20241022": null,
"bash_20250124": null,
diff --git a/litellm/constants.py b/litellm/constants.py
index 1af53b2dae08..337cb1243fbe 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -135,12 +135,32 @@
MCP_NPM_CACHE_DIR = os.getenv("MCP_NPM_CACHE_DIR", "/tmp/.npm_mcp_cache")
MCP_OAUTH2_TOKEN_CACHE_MIN_TTL = int(os.getenv("MCP_OAUTH2_TOKEN_CACHE_MIN_TTL", "10"))
+# Per-user OAuth token Redis cache (for server-side token storage)
+MCP_PER_USER_TOKEN_REDIS_KEY_PREFIX = "mcp:per_user_token"
+MCP_PER_USER_TOKEN_DEFAULT_TTL = int(
+ os.getenv("MCP_PER_USER_TOKEN_DEFAULT_TTL", "43200") # 12 hours
+)
+MCP_PER_USER_TOKEN_EXPIRY_BUFFER_SECONDS = int(
+ os.getenv("MCP_PER_USER_TOKEN_EXPIRY_BUFFER_SECONDS", "60")
+)
+
# MCP timeout defaults (seconds). Override via env vars for slow/custom MCP servers.
MCP_CLIENT_TIMEOUT = float(os.getenv("LITELLM_MCP_CLIENT_TIMEOUT", "60.0"))
MCP_TOOL_LISTING_TIMEOUT = float(os.getenv("LITELLM_MCP_TOOL_LISTING_TIMEOUT", "30.0"))
MCP_METADATA_TIMEOUT = float(os.getenv("LITELLM_MCP_METADATA_TIMEOUT", "10.0"))
MCP_HEALTH_CHECK_TIMEOUT = float(os.getenv("LITELLM_MCP_HEALTH_CHECK_TIMEOUT", "10.0"))
+# Allowlist of commands permitted for MCP stdio transport.
+# Prevents arbitrary command execution via /mcp-rest/test/* endpoints or server creation.
+# Note: allowlisted runtimes can still execute code via args (e.g. python -c "...").
+# This is an accepted residual risk since these endpoints require PROXY_ADMIN.
+# Extend via LITELLM_MCP_STDIO_EXTRA_COMMANDS env var (comma-separated).
+_MCP_STDIO_EXTRA_COMMANDS = os.getenv("LITELLM_MCP_STDIO_EXTRA_COMMANDS", "")
+MCP_STDIO_ALLOWED_COMMANDS: frozenset = frozenset(
+ {"npx", "uvx", "python", "python3", "node", "docker", "deno"}
+ | (set(_MCP_STDIO_EXTRA_COMMANDS.split(",")) - {""})
+)
+
LITELLM_UI_ALLOW_HEADERS = [
"x-litellm-semantic-filter",
"x-litellm-semantic-filter-tools",
@@ -1367,6 +1387,9 @@
MANAGED_OBJECT_STALENESS_CUTOFF_DAYS = max(
1, int(os.getenv("MANAGED_OBJECT_STALENESS_CUTOFF_DAYS", 7))
)
+STALE_OBJECT_CLEANUP_BATCH_SIZE = max(
+ 1, int(os.getenv("STALE_OBJECT_CLEANUP_BATCH_SIZE", 1000))
+)
# Set PROXY_BATCH_POLLING_ENABLED=false to disable the CheckBatchCost and
# CheckResponsesCost background polling jobs entirely (e.g. to avoid DB load on
# installations with large numbers of stale managed objects).
diff --git a/litellm/containers/endpoint_factory.py b/litellm/containers/endpoint_factory.py
index 1d8e50856fe4..3913f3b29219 100644
--- a/litellm/containers/endpoint_factory.py
+++ b/litellm/containers/endpoint_factory.py
@@ -14,6 +14,7 @@
import litellm
from litellm.constants import request_timeout as DEFAULT_REQUEST_TIMEOUT
+from litellm.containers.utils import decode_managed_container_id_for_request
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.base_llm.containers.transformation import BaseContainerConfig
from litellm.llms.custom_httpx.container_handler import generic_container_handler
@@ -53,7 +54,7 @@ def create_sync_endpoint_function(endpoint_config: Dict) -> Callable:
@client
def endpoint_func(
timeout: int = 600,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
extra_headers: Optional[Dict[str, Any]] = None,
extra_query: Optional[Dict[str, Any]] = None,
extra_body: Optional[Dict[str, Any]] = None,
@@ -61,6 +62,7 @@ def endpoint_func(
):
local_vars = locals()
try:
+ resolved_custom_llm_provider: str = custom_llm_provider
litellm_logging_obj: LiteLLMLoggingObj = kwargs.pop("litellm_logging_obj")
litellm_call_id: Optional[str] = kwargs.get("litellm_call_id")
_is_async = kwargs.pop("async_call", False) is True
@@ -76,15 +78,27 @@ def endpoint_func(
# Get provider config
litellm_params = GenericLiteLLMParams(**kwargs)
+ # Strip LiteLLM-managed container IDs before calling the provider API
+ # (OpenAI enforces max length 64 on container_id).
+ if "container_id" in kwargs and isinstance(kwargs["container_id"], str):
+ (
+ kwargs["container_id"],
+ resolved_custom_llm_provider,
+ litellm_params,
+ ) = decode_managed_container_id_for_request(
+ container_id=kwargs["container_id"],
+ custom_llm_provider=resolved_custom_llm_provider,
+ litellm_params=litellm_params,
+ )
container_provider_config: Optional[
BaseContainerConfig
] = ProviderConfigManager.get_provider_container_config(
- provider=litellm.LlmProviders(custom_llm_provider),
+ provider=litellm.LlmProviders(resolved_custom_llm_provider),
)
if container_provider_config is None:
raise ValueError(
- f"Container provider config not found for: {custom_llm_provider}"
+ f"Container provider config not found for: {resolved_custom_llm_provider}"
)
# Build optional params for logging
@@ -96,7 +110,7 @@ def endpoint_func(
model="",
optional_params=optional_params,
litellm_params={"litellm_call_id": litellm_call_id},
- custom_llm_provider=custom_llm_provider,
+ custom_llm_provider=resolved_custom_llm_provider,
)
# Use generic handler
@@ -115,7 +129,7 @@ def endpoint_func(
except Exception as e:
raise litellm.exception_type(
model="",
- custom_llm_provider=custom_llm_provider,
+ custom_llm_provider=resolved_custom_llm_provider,
original_exception=e,
completion_kwargs=local_vars,
extra_kwargs=kwargs,
@@ -133,7 +147,7 @@ def create_async_endpoint_function(
@client
async def async_endpoint_func(
timeout: int = 600,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
extra_headers: Optional[Dict[str, Any]] = None,
extra_query: Optional[Dict[str, Any]] = None,
extra_body: Optional[Dict[str, Any]] = None,
diff --git a/litellm/containers/main.py b/litellm/containers/main.py
index 916fc26351b6..7532ccbc1461 100644
--- a/litellm/containers/main.py
+++ b/litellm/containers/main.py
@@ -6,7 +6,10 @@
import litellm
from litellm.constants import request_timeout as DEFAULT_REQUEST_TIMEOUT
-from litellm.containers.utils import ContainerRequestUtils
+from litellm.containers.utils import (
+ ContainerRequestUtils,
+ decode_managed_container_id_for_request,
+)
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.base_llm.containers.transformation import BaseContainerConfig
from litellm.main import base_llm_http_handler
@@ -48,7 +51,7 @@ async def acreate_container(
file_ids: Optional[List[str]] = None,
timeout=600, # default to 10 minutes
# LiteLLM specific params,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Optional[Dict[str, Any]] = None,
@@ -122,7 +125,7 @@ def create_container(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
*,
acreate_container: Literal[True],
**kwargs,
@@ -139,7 +142,7 @@ def create_container(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
*,
acreate_container: Literal[False] = False,
**kwargs,
@@ -158,7 +161,7 @@ def create_container(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Optional[Dict[str, Any]] = None,
@@ -247,7 +250,7 @@ def create_container(
# Set the correct call type for container creation
litellm_logging_obj.call_type = CallTypes.create_container.value
- return base_llm_http_handler.container_create_handler(
+ container_obj = base_llm_http_handler.container_create_handler(
name=name,
container_create_request_params=container_create_request_params,
container_provider_config=container_provider_config,
@@ -257,6 +260,17 @@ def create_container(
timeout=timeout or DEFAULT_REQUEST_TIMEOUT,
_is_async=_is_async,
)
+
+ # Encode container_id with provider/model metadata for routing
+ if isinstance(container_obj, ContainerObject):
+ container_obj = ContainerRequestUtils.encode_container_id_in_response(
+ response_obj=container_obj,
+ custom_llm_provider=custom_llm_provider,
+ litellm_metadata=kwargs.get("litellm_metadata"),
+ extra_body=extra_body,
+ )
+
+ return container_obj
except Exception as e:
raise litellm.exception_type(
@@ -275,7 +289,7 @@ async def alist_containers(
limit: Optional[int] = None,
order: Optional[str] = None,
timeout=600, # default to 10 minutes
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Optional[Dict[str, Any]] = None,
@@ -348,7 +362,7 @@ def list_containers(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
*,
alist_containers: Literal[True],
**kwargs,
@@ -365,7 +379,7 @@ def list_containers(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
*,
alist_containers: Literal[False] = False,
**kwargs,
@@ -384,7 +398,7 @@ def list_containers(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Optional[Dict[str, Any]] = None,
@@ -481,7 +495,7 @@ def list_containers(
async def aretrieve_container(
container_id: str,
timeout=600, # default to 10 minutes
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Optional[Dict[str, Any]] = None,
@@ -548,7 +562,7 @@ def retrieve_container(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
*,
aretrieve_container: Literal[True],
**kwargs,
@@ -563,7 +577,7 @@ def retrieve_container(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
*,
aretrieve_container: Literal[False] = False,
**kwargs,
@@ -580,7 +594,7 @@ def retrieve_container(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Optional[Dict[str, Any]] = None,
@@ -594,6 +608,7 @@ def retrieve_container(
"""
local_vars = locals()
try:
+ resolved_custom_llm_provider: str = custom_llm_provider
litellm_logging_obj: LiteLLMLoggingObj = kwargs.pop("litellm_logging_obj") # type: ignore
litellm_call_id: Optional[str] = kwargs.get("litellm_call_id")
_is_async = kwargs.pop("async_call", False) is True
@@ -615,16 +630,28 @@ def retrieve_container(
api_version=api_version,
**kwargs,
)
+
+ # Decode container ID and extract provider info
+ original_container_id, resolved_custom_llm_provider, litellm_params = (
+ decode_managed_container_id_for_request(
+ container_id=container_id,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ )
+ )
+ # True when input was a LiteLLM-managed ID (any length); needed to re-encode output for routing affinity
+ was_encoded = original_container_id != container_id
+
# get provider config
container_provider_config: Optional[
BaseContainerConfig
] = ProviderConfigManager.get_provider_container_config(
- provider=litellm.LlmProviders(custom_llm_provider),
+ provider=litellm.LlmProviders(resolved_custom_llm_provider),
)
if container_provider_config is None:
raise ValueError(
- f"Container provider config not found for provider: {custom_llm_provider}"
+ f"Container provider config not found for provider: {resolved_custom_llm_provider}"
)
# Pre Call logging
@@ -635,14 +662,14 @@ def retrieve_container(
litellm_params={
"litellm_call_id": litellm_call_id,
},
- custom_llm_provider=custom_llm_provider,
+ custom_llm_provider=resolved_custom_llm_provider,
)
# Set the correct call type
litellm_logging_obj.call_type = CallTypes.retrieve_container.value
- return base_llm_http_handler.container_retrieve_handler(
- container_id=container_id,
+ container_obj = base_llm_http_handler.container_retrieve_handler(
+ container_id=original_container_id, # Use decoded original ID
container_provider_config=container_provider_config,
litellm_params=litellm_params,
logging_obj=litellm_logging_obj,
@@ -651,11 +678,33 @@ def retrieve_container(
timeout=timeout or DEFAULT_REQUEST_TIMEOUT,
_is_async=_is_async,
)
+
+ # Encode container_id with provider/model metadata for routing
+ # If input was encoded, preserve encoding in output using the decoded model_id
+ if isinstance(container_obj, ContainerObject):
+ # If input was encoded, use model_id from decoded params
+ litellm_metadata = kwargs.get("litellm_metadata", {})
+ if was_encoded and litellm_params.get("model_id"):
+ # Inject model_id from decoded container_id into litellm_metadata
+ if not litellm_metadata:
+ litellm_metadata = {}
+ if "model_info" not in litellm_metadata:
+ litellm_metadata["model_info"] = {}
+ litellm_metadata["model_info"]["id"] = litellm_params["model_id"]
+
+ container_obj = ContainerRequestUtils.encode_container_id_in_response(
+ response_obj=container_obj,
+ custom_llm_provider=resolved_custom_llm_provider,
+ litellm_metadata=litellm_metadata,
+ extra_body=None,
+ )
+
+ return container_obj
except Exception as e:
raise litellm.exception_type(
model="",
- custom_llm_provider=custom_llm_provider,
+ custom_llm_provider=resolved_custom_llm_provider,
original_exception=e,
completion_kwargs=local_vars,
extra_kwargs=kwargs,
@@ -667,7 +716,7 @@ def retrieve_container(
async def adelete_container(
container_id: str,
timeout=600, # default to 10 minutes
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Optional[Dict[str, Any]] = None,
@@ -734,7 +783,7 @@ def delete_container(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
*,
adelete_container: Literal[True],
**kwargs,
@@ -749,7 +798,7 @@ def delete_container(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
*,
adelete_container: Literal[False] = False,
**kwargs,
@@ -766,7 +815,7 @@ def delete_container(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Optional[Dict[str, Any]] = None,
@@ -780,6 +829,7 @@ def delete_container(
"""
local_vars = locals()
try:
+ resolved_custom_llm_provider: str = custom_llm_provider
litellm_logging_obj: LiteLLMLoggingObj = kwargs.pop("litellm_logging_obj") # type: ignore
litellm_call_id: Optional[str] = kwargs.get("litellm_call_id")
_is_async = kwargs.pop("async_call", False) is True
@@ -801,16 +851,28 @@ def delete_container(
api_version=api_version,
**kwargs,
)
+
+ # Decode container ID and extract provider info
+ original_container_id, resolved_custom_llm_provider, litellm_params = (
+ decode_managed_container_id_for_request(
+ container_id=container_id,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ )
+ )
+ # True when input was a LiteLLM-managed ID (any length); needed to re-encode output for routing affinity
+ was_encoded = original_container_id != container_id
+
# get provider config
container_provider_config: Optional[
BaseContainerConfig
] = ProviderConfigManager.get_provider_container_config(
- provider=litellm.LlmProviders(custom_llm_provider),
+ provider=litellm.LlmProviders(resolved_custom_llm_provider),
)
if container_provider_config is None:
raise ValueError(
- f"Container provider config not found for provider: {custom_llm_provider}"
+ f"Container provider config not found for provider: {resolved_custom_llm_provider}"
)
# Pre Call logging
@@ -821,14 +883,14 @@ def delete_container(
litellm_params={
"litellm_call_id": litellm_call_id,
},
- custom_llm_provider=custom_llm_provider,
+ custom_llm_provider=resolved_custom_llm_provider,
)
# Set the correct call type
litellm_logging_obj.call_type = CallTypes.delete_container.value
- return base_llm_http_handler.container_delete_handler(
- container_id=container_id,
+ delete_result = base_llm_http_handler.container_delete_handler(
+ container_id=original_container_id, # Use decoded original ID
container_provider_config=container_provider_config,
litellm_params=litellm_params,
logging_obj=litellm_logging_obj,
@@ -837,11 +899,33 @@ def delete_container(
timeout=timeout or DEFAULT_REQUEST_TIMEOUT,
_is_async=_is_async,
)
+
+ # Encode container_id in response with provider/model metadata for routing
+ # If input was encoded, preserve encoding in output using the decoded model_id
+ if isinstance(delete_result, DeleteContainerResult):
+ # If input was encoded, use model_id from decoded params
+ litellm_metadata = kwargs.get("litellm_metadata", {})
+ if was_encoded and litellm_params.get("model_id"):
+ # Inject model_id from decoded container_id into litellm_metadata
+ if not litellm_metadata:
+ litellm_metadata = {}
+ if "model_info" not in litellm_metadata:
+ litellm_metadata["model_info"] = {}
+ litellm_metadata["model_info"]["id"] = litellm_params["model_id"]
+
+ delete_result = ContainerRequestUtils.encode_container_id_in_response(
+ response_obj=delete_result,
+ custom_llm_provider=resolved_custom_llm_provider,
+ litellm_metadata=litellm_metadata,
+ extra_body=None,
+ )
+
+ return delete_result
except Exception as e:
raise litellm.exception_type(
model="",
- custom_llm_provider=custom_llm_provider,
+ custom_llm_provider=resolved_custom_llm_provider,
original_exception=e,
completion_kwargs=local_vars,
extra_kwargs=kwargs,
@@ -856,7 +940,7 @@ async def alist_container_files(
limit: Optional[int] = None,
order: Optional[str] = None,
timeout=600, # default to 10 minutes
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
extra_headers: Optional[Dict[str, Any]] = None,
extra_query: Optional[Dict[str, Any]] = None,
extra_body: Optional[Dict[str, Any]] = None,
@@ -930,7 +1014,7 @@ def list_container_files(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
*,
alist_container_files: Literal[True],
**kwargs,
@@ -948,7 +1032,7 @@ def list_container_files(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
*,
alist_container_files: Literal[False] = False,
**kwargs,
@@ -968,7 +1052,7 @@ def list_container_files(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
extra_headers: Optional[Dict[str, Any]] = None,
extra_query: Optional[Dict[str, Any]] = None,
extra_body: Optional[Dict[str, Any]] = None,
@@ -980,6 +1064,7 @@ def list_container_files(
"""
local_vars = locals()
try:
+ resolved_custom_llm_provider: str = custom_llm_provider
litellm_logging_obj: LiteLLMLoggingObj = kwargs.pop("litellm_logging_obj") # type: ignore
litellm_call_id: Optional[str] = kwargs.get("litellm_call_id")
_is_async = kwargs.pop("async_call", False) is True
@@ -1001,16 +1086,26 @@ def list_container_files(
api_version=api_version,
**kwargs,
)
+
+ # Decode container ID and extract provider info
+ original_container_id, resolved_custom_llm_provider, litellm_params = (
+ decode_managed_container_id_for_request(
+ container_id=container_id,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ )
+ )
+
# get provider config
container_provider_config: Optional[
BaseContainerConfig
] = ProviderConfigManager.get_provider_container_config(
- provider=litellm.LlmProviders(custom_llm_provider),
+ provider=litellm.LlmProviders(resolved_custom_llm_provider),
)
if container_provider_config is None:
raise ValueError(
- f"Container provider config not found for provider: {custom_llm_provider}"
+ f"Container provider config not found for provider: {resolved_custom_llm_provider}"
)
# Pre Call logging
@@ -1026,14 +1121,14 @@ def list_container_files(
litellm_params={
"litellm_call_id": litellm_call_id,
},
- custom_llm_provider=custom_llm_provider,
+ custom_llm_provider=resolved_custom_llm_provider,
)
# Set the correct call type
litellm_logging_obj.call_type = CallTypes.list_container_files.value
return base_llm_http_handler.container_file_list_handler(
- container_id=container_id,
+ container_id=original_container_id, # Use decoded original ID
container_provider_config=container_provider_config,
litellm_params=litellm_params,
logging_obj=litellm_logging_obj,
@@ -1049,7 +1144,7 @@ def list_container_files(
except Exception as e:
raise litellm.exception_type(
model="",
- custom_llm_provider=custom_llm_provider,
+ custom_llm_provider=resolved_custom_llm_provider,
original_exception=e,
completion_kwargs=local_vars,
extra_kwargs=kwargs,
@@ -1062,7 +1157,7 @@ async def aupload_container_file(
container_id: str,
file: FileTypes,
timeout=600, # default to 10 minutes
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
extra_headers: Optional[Dict[str, Any]] = None,
extra_query: Optional[Dict[str, Any]] = None,
extra_body: Optional[Dict[str, Any]] = None,
@@ -1151,7 +1246,7 @@ def upload_container_file(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
*,
aupload_container_file: Literal[True],
**kwargs,
@@ -1167,7 +1262,7 @@ def upload_container_file(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
*,
aupload_container_file: Literal[False] = False,
**kwargs,
@@ -1185,7 +1280,7 @@ def upload_container_file(
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
- custom_llm_provider: Literal["openai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "azure_text"] = "openai",
extra_headers: Optional[Dict[str, Any]] = None,
extra_query: Optional[Dict[str, Any]] = None,
extra_body: Optional[Dict[str, Any]] = None,
@@ -1226,6 +1321,7 @@ def upload_container_file(
local_vars = locals()
try:
+ resolved_custom_llm_provider: str = custom_llm_provider
litellm_logging_obj: LiteLLMLoggingObj = kwargs.pop("litellm_logging_obj") # type: ignore
litellm_call_id: Optional[str] = kwargs.get("litellm_call_id")
_is_async = kwargs.pop("async_call", False) is True
@@ -1247,16 +1343,26 @@ def upload_container_file(
api_version=api_version,
**kwargs,
)
+
+ # Decode container ID and extract provider info
+ original_container_id, resolved_custom_llm_provider, litellm_params = (
+ decode_managed_container_id_for_request(
+ container_id=container_id,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ )
+ )
+
# get provider config
container_provider_config: Optional[
BaseContainerConfig
] = ProviderConfigManager.get_provider_container_config(
- provider=litellm.LlmProviders(custom_llm_provider),
+ provider=litellm.LlmProviders(resolved_custom_llm_provider),
)
if container_provider_config is None:
raise ValueError(
- f"Container provider config not found for provider: {custom_llm_provider}"
+ f"Container provider config not found for provider: {resolved_custom_llm_provider}"
)
# Pre Call logging
@@ -1267,7 +1373,7 @@ def upload_container_file(
litellm_params={
"litellm_call_id": litellm_call_id,
},
- custom_llm_provider=custom_llm_provider,
+ custom_llm_provider=resolved_custom_llm_provider,
)
# Set the correct call type
@@ -1282,14 +1388,14 @@ def upload_container_file(
extra_query=extra_query,
timeout=timeout or DEFAULT_REQUEST_TIMEOUT,
_is_async=_is_async,
- container_id=container_id,
+ container_id=original_container_id, # Use decoded original ID
file=file,
)
except Exception as e:
raise litellm.exception_type(
model="",
- custom_llm_provider=custom_llm_provider,
+ custom_llm_provider=resolved_custom_llm_provider,
original_exception=e,
completion_kwargs=local_vars,
extra_kwargs=kwargs,
diff --git a/litellm/containers/utils.py b/litellm/containers/utils.py
index 048f587fda77..976d706f71a0 100644
--- a/litellm/containers/utils.py
+++ b/litellm/containers/utils.py
@@ -1,10 +1,38 @@
-from typing import Dict
+from typing import Any, Dict, Optional, TypeVar
from litellm.llms.base_llm.containers.transformation import BaseContainerConfig
+from litellm.responses.utils import ResponsesAPIRequestUtils
from litellm.types.containers.main import (
ContainerCreateOptionalRequestParams,
ContainerListOptionalRequestParams,
)
+from litellm.types.router import GenericLiteLLMParams
+
+
+def decode_managed_container_id_for_request(
+ container_id: str,
+ custom_llm_provider: str,
+ litellm_params: GenericLiteLLMParams,
+) -> tuple[str, str, GenericLiteLLMParams]:
+ """Decode a LiteLLM-managed container ID for upstream API calls.
+
+ Returns:
+ (original_container_id, resolved_provider, updated_litellm_params)
+ """
+ decoded = ResponsesAPIRequestUtils._decode_container_id(container_id)
+ original_container_id = decoded.get("response_id", container_id)
+
+ decoded_provider = decoded.get("custom_llm_provider")
+ if decoded_provider and custom_llm_provider == "openai":
+ custom_llm_provider = decoded_provider
+
+ decoded_model_id = decoded.get("model_id")
+ if decoded_model_id and not litellm_params.get("model_id"):
+ litellm_params["model_id"] = decoded_model_id
+
+ return original_container_id, custom_llm_provider, litellm_params
+
+T = TypeVar("T")
class ContainerRequestUtils:
@@ -68,3 +96,66 @@ def get_requested_container_list_optional_param(
container_list_optional_params[param] = passed_params[param] # type: ignore
return container_list_optional_params
+
+ @staticmethod
+ def encode_container_id_in_response(
+ response_obj: T,
+ custom_llm_provider: Optional[str],
+ litellm_metadata: Optional[Dict[str, Any]] = None,
+ extra_body: Optional[Dict[str, Any]] = None,
+ ) -> T:
+ """
+ Encode container_id in response object with provider/model metadata for routing.
+
+ This mirrors the responses API pattern where response IDs are encoded with
+ routing metadata so follow-up calls can route to the correct provider.
+
+ Encodes when:
+ 1. litellm_metadata contains model_info.id (indicating router/proxy usage), OR
+ 2. extra_body contains target_model_names (indicating model-specific routing)
+
+ Direct SDK calls with explicit custom_llm_provider and no routing hints return raw IDs.
+
+ Args:
+ response_obj: Response object with an `id` attribute (ContainerObject, DeleteContainerResult, etc.)
+ custom_llm_provider: Provider name (e.g., "azure", "openai")
+ litellm_metadata: Optional litellm_metadata dict that may contain model_info.id
+ extra_body: Optional extra_body dict that may contain target_model_names
+
+ Returns:
+ The same response object with encoded container_id (if routing metadata present)
+ """
+ # Extract model_id from litellm_metadata
+ litellm_metadata = litellm_metadata or {}
+ model_info: Dict[str, Any] = litellm_metadata.get("model_info", {}) or {}
+ model_id = model_info.get("id")
+
+ # Check if we should encode based on routing metadata
+ should_encode = False
+
+ # Case 1: Router/proxy usage (model_id from router)
+ if model_id is not None:
+ should_encode = True
+
+ # Case 2: target_model_names in extra_body (model-specific routing)
+ if extra_body and "target_model_names" in extra_body:
+ should_encode = True
+ # Extract model_id from target_model_names if not already set
+ if model_id is None:
+ target_models = extra_body["target_model_names"]
+ # Use first model as model_id for encoding
+ if isinstance(target_models, str):
+ model_id = target_models.split(",")[0].strip()
+ elif isinstance(target_models, list) and len(target_models) > 0:
+ model_id = str(target_models[0]).strip()
+
+ # Only encode if we have routing metadata
+ if should_encode and response_obj and hasattr(response_obj, "id"):
+ encoded_id = ResponsesAPIRequestUtils._build_container_id(
+ custom_llm_provider=custom_llm_provider,
+ model_id=model_id,
+ container_id=response_obj.id,
+ )
+ response_obj.id = encoded_id
+
+ return response_obj
diff --git a/litellm/files/main.py b/litellm/files/main.py
index f7c89e0ba3b3..46199a4ecaf9 100644
--- a/litellm/files/main.py
+++ b/litellm/files/main.py
@@ -10,7 +10,7 @@
import time
import uuid as uuid_module
from functools import partial
-from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast
+from typing import Any,Coroutine, Dict, Literal, Optional, Union, cast
import httpx
@@ -30,12 +30,10 @@
]
FileDeleteProvider = Literal["openai", "azure", "gemini", "manus", "anthropic"]
FileListProvider = Literal["openai", "azure", "manus", "anthropic"]
-FileContentProvider = Literal[
- "openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "anthropic", "manus"
-]
-
import litellm
from litellm import get_secret_str
+from litellm.files.streaming import FileContentStreamingResponse
+from litellm.files.types import FileContentProvider, FileContentStreamingResult
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.azure.common_utils import get_azure_credentials
@@ -55,10 +53,7 @@
OpenAIFileObject,
)
from litellm.types.router import *
-from litellm.types.utils import (
- OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS,
- LlmProviders,
-)
+from litellm.types.utils import OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS, LlmProviders
from litellm.utils import (
ProviderConfigManager,
client,
@@ -69,6 +64,15 @@
base_llm_http_handler = BaseLLMHTTPHandler()
####### ENVIRONMENT VARIABLES ###################
+
+
+def _should_sdk_support_streaming(
+ custom_llm_provider: Optional[Union[FileContentProvider, str]],
+) -> bool:
+ """
+ Return whether file content streaming is supported for the provider.
+ """
+ return custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS
openai_files_instance = OpenAIFilesAPI()
azure_files_instance = AzureOpenAIFilesAPI()
vertex_ai_files_instance = VertexAIFilesHandler()
@@ -772,8 +776,10 @@ async def afile_content(
custom_llm_provider: FileContentProvider = "openai",
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
+ chunk_size: int = 1024 * 1024,
+ stream: bool = False,
**kwargs,
-) -> HttpxBinaryResponseContent:
+) -> Union[HttpxBinaryResponseContent, FileContentStreamingResult]:
"""
Async: Get file contents
@@ -787,11 +793,13 @@ async def afile_content(
# Use a partial function to pass your keyword arguments
func = partial(
file_content,
- file_id,
- model,
- custom_llm_provider,
- extra_headers,
- extra_body,
+ file_id=file_id,
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ chunk_size=chunk_size,
+ stream=stream,
**kwargs,
)
@@ -816,8 +824,15 @@ def file_content(
custom_llm_provider: Optional[Union[FileContentProvider, str]] = None,
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
+ chunk_size: int = 1024 * 1024,
+ stream: bool = False,
**kwargs,
-) -> Union[HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent]]:
+) -> Union[
+ HttpxBinaryResponseContent,
+ FileContentStreamingResult,
+ Coroutine[Any, Any, HttpxBinaryResponseContent],
+ Coroutine[Any, Any, FileContentStreamingResult],
+]:
"""
Returns the contents of the specified file.
@@ -859,6 +874,23 @@ def file_content(
_is_async = kwargs.pop("afile_content", False) is True
+ if stream and _should_sdk_support_streaming(custom_llm_provider):
+ return file_content_streaming(
+ file_id=file_id,
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ chunk_size=chunk_size,
+ optional_params=optional_params,
+ timeout=timeout,
+ logging_obj=cast(
+ Optional[LiteLLMLoggingObj], kwargs.get("litellm_logging_obj")
+ ),
+ _is_async=_is_async,
+ client=client,
+ )
+
# Check if provider has a custom files config (e.g., Anthropic, Manus)
provider_config = ProviderConfigManager.get_provider_files_config(
model="",
@@ -982,3 +1014,89 @@ def file_content(
return response
except Exception as e:
raise e
+
+
+def file_content_streaming(
+ *,
+ file_id: str,
+ model: Optional[str],
+ custom_llm_provider: Optional[Union[FileContentProvider, str]],
+ extra_headers: Optional[Dict[str, str]],
+ extra_body: Optional[Dict[str, str]],
+ chunk_size: int,
+ optional_params: GenericLiteLLMParams,
+ timeout: Union[float, httpx.Timeout],
+ logging_obj: Optional[LiteLLMLoggingObj],
+ _is_async: bool,
+ client: Optional[Any],
+) -> Union[FileContentStreamingResult, Coroutine[Any, Any, FileContentStreamingResult]]:
+ if logging_obj is not None:
+ logging_obj.model = model or ""
+ logging_obj.model_call_details["model"] = model or ""
+ logging_obj.model_call_details["custom_llm_provider"] = custom_llm_provider
+
+ litellm_params = logging_obj.model_call_details.get("litellm_params", {}) or {}
+ if optional_params.api_base is not None:
+ litellm_params["api_base"] = optional_params.api_base
+ logging_obj.model_call_details["litellm_params"] = litellm_params
+
+ def _wrap_streaming_result(
+ response: FileContentStreamingResult,
+ ) -> FileContentStreamingResult:
+ return FileContentStreamingResult(
+ stream_iterator=FileContentStreamingResponse(
+ stream_iterator=response.stream_iterator,
+ file_id=file_id,
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ logging_obj=logging_obj,
+ ),
+ headers=response.headers,
+ )
+
+ response: Union[
+ FileContentStreamingResult, Coroutine[Any, Any, FileContentStreamingResult]
+ ] = FileContentStreamingResult(stream_iterator=iter(()), headers={})
+ if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS:
+ openai_creds = get_openai_credentials(
+ api_base=optional_params.api_base,
+ api_key=optional_params.api_key,
+ organization=optional_params.organization,
+ )
+ response = openai_files_instance.file_content_streaming(
+ _is_async=_is_async,
+ file_content_request=FileContentRequest(
+ file_id=file_id,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ ),
+ api_base=openai_creds.api_base,
+ api_key=openai_creds.api_key,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ organization=openai_creds.organization,
+ chunk_size=chunk_size,
+ client=client,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for streaming 'file_content'. Supported providers are {}.".format(
+ custom_llm_provider,
+ sorted(OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS),
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+
+ if asyncio.iscoroutine(response):
+ async def _await_and_wrap() -> FileContentStreamingResult:
+ return _wrap_streaming_result(await response)
+
+ return _await_and_wrap()
+
+ return _wrap_streaming_result(response)
\ No newline at end of file
diff --git a/litellm/files/streaming.py b/litellm/files/streaming.py
new file mode 100644
index 000000000000..36fe30fa829f
--- /dev/null
+++ b/litellm/files/streaming.py
@@ -0,0 +1,236 @@
+import datetime
+import traceback
+from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, Iterator, Optional, Union, cast
+
+import anyio
+from litellm.files.types import FileContentProvider
+
+if TYPE_CHECKING:
+ from litellm.litellm_core_utils.litellm_logging import (
+ Logging as LiteLLMLoggingObj,
+ )
+ from litellm.types.utils import StandardLoggingHiddenParams, StandardLoggingPayload
+
+class FileContentStreamingResponse:
+ """
+ Iterator wrapper for file content streaming that carries LiteLLM metadata
+ and emits success/failure callbacks once the stream finishes.
+ """
+
+ def __init__(
+ self,
+ stream_iterator: Union[Iterator[bytes], AsyncIterator[bytes]],
+ file_id: str,
+ model: Optional[str],
+ custom_llm_provider: Optional[Union[FileContentProvider, str]],
+ logging_obj: Optional["LiteLLMLoggingObj"],
+ ) -> None:
+ self.stream_iterator = stream_iterator
+ self.file_id = file_id
+ self.model = model
+ self.custom_llm_provider = custom_llm_provider
+ self.logging_obj = logging_obj
+ self.standard_logging_object: Optional["StandardLoggingPayload"] = None
+ self._hidden_params: Dict[str, Any] = {}
+ self._logging_completed = False
+ self._close_completed = False
+ self._start_time = (
+ logging_obj.start_time
+ if logging_obj is not None and getattr(logging_obj, "start_time", None)
+ else datetime.datetime.now()
+ )
+ self._sync_hidden_params()
+
+ def __iter__(self) -> "FileContentStreamingResponse":
+ if not hasattr(self.stream_iterator, "__next__"):
+ raise TypeError("File content stream does not support sync iteration")
+ return self
+
+ def __next__(self) -> bytes:
+ if not hasattr(self.stream_iterator, "__next__"):
+ raise TypeError("File content stream does not support sync iteration")
+
+ try:
+ return next(cast(Iterator[bytes], self.stream_iterator))
+ except StopIteration:
+ self._log_success_sync()
+ raise
+ except Exception as e:
+ self._log_failure_sync(e)
+ raise
+
+ def __aiter__(self) -> "FileContentStreamingResponse":
+ if not hasattr(self.stream_iterator, "__anext__"):
+ raise TypeError("File content stream does not support async iteration")
+ return self
+
+ async def __anext__(self) -> bytes:
+ if not hasattr(self.stream_iterator, "__anext__"):
+ raise TypeError("File content stream does not support async iteration")
+
+ try:
+ return await cast(AsyncIterator[bytes], self.stream_iterator).__anext__()
+ except StopAsyncIteration:
+ await self._log_success_async()
+ raise
+ except Exception as e:
+ await self._log_failure_async(e)
+ raise
+
+ async def aclose(self) -> None:
+ if self._close_completed:
+ return
+
+ self._close_completed = True
+ self._logging_completed = True
+ stream_to_close = self.stream_iterator
+ self.stream_iterator = cast(Union[Iterator[bytes], AsyncIterator[bytes]], iter(()))
+
+ # Shield cleanup from request cancellation so upstream HTTP connections
+ # are released promptly on client disconnects.
+ with anyio.CancelScope(shield=True):
+ if hasattr(stream_to_close, "aclose"):
+ await cast(AsyncIterator[bytes], stream_to_close).aclose() # type: ignore[attr-defined]
+ elif hasattr(stream_to_close, "close"):
+ result = cast(Iterator[bytes], stream_to_close).close() # type: ignore[attr-defined]
+ if result is not None:
+ await result
+
+ def close(self) -> None:
+ if self._close_completed:
+ return
+
+ self._close_completed = True
+ self._logging_completed = True
+ stream_to_close = self.stream_iterator
+ self.stream_iterator = cast(Union[Iterator[bytes], AsyncIterator[bytes]], iter(()))
+
+ if hasattr(stream_to_close, "close"):
+ cast(Iterator[bytes], stream_to_close).close() # type: ignore[attr-defined]
+
+ def _build_logging_response(self) -> Dict[str, str]:
+ response = {
+ "id": self.file_id,
+ "object": "file.content",
+ }
+ if self.model:
+ response["model"] = self.model
+ return response
+
+ def _sync_hidden_params(self) -> None:
+ litellm_params: dict[str, Any] = {}
+ if self.logging_obj is not None:
+ litellm_params = (
+ self.logging_obj.model_call_details.get("litellm_params", {}) or {}
+ )
+
+ if "api_base" not in self._hidden_params and litellm_params.get("api_base"):
+ self._hidden_params["api_base"] = litellm_params["api_base"]
+
+ # The generic client decorator infers `model` from the first positional arg,
+ # which is `file_id` for this API. Correct it before logging callbacks run.
+ self._hidden_params["litellm_model_name"] = self.model
+ if "response_cost" not in self._hidden_params:
+ self._hidden_params["response_cost"] = None
+
+ def _build_standard_logging_object(
+ self,
+ end_time: datetime.datetime,
+ ) -> Optional["StandardLoggingPayload"]:
+ if self.standard_logging_object is not None:
+ return self.standard_logging_object
+
+ if self.logging_obj is None:
+ return None
+
+ from litellm.litellm_core_utils.litellm_logging import (
+ get_standard_logging_object_payload,
+ )
+
+ self._sync_hidden_params()
+ payload = get_standard_logging_object_payload(
+ kwargs=self.logging_obj.model_call_details,
+ init_response_obj=self._build_logging_response(),
+ start_time=self._start_time,
+ end_time=end_time,
+ logging_obj=self.logging_obj,
+ status="success",
+ )
+ if payload is None:
+ return None
+
+ merged_hidden_params = cast(
+ "StandardLoggingHiddenParams",
+ {
+ **cast(Dict[str, Any], payload.get("hidden_params") or {}),
+ **self._hidden_params,
+ },
+ )
+ payload["hidden_params"] = merged_hidden_params
+ payload["response"] = self._build_logging_response()
+ if self.custom_llm_provider is not None:
+ payload["custom_llm_provider"] = self.custom_llm_provider
+ if self.model is not None:
+ payload["model"] = self.model
+ if self._hidden_params.get("api_base"):
+ payload["api_base"] = cast(str, self._hidden_params["api_base"])
+
+ self.standard_logging_object = payload
+ return payload
+
+ async def _log_success_async(self) -> None:
+ if self._logging_completed or self.logging_obj is None:
+ return
+
+ self._logging_completed = True
+ end_time = datetime.datetime.now()
+ standard_logging_object = self._build_standard_logging_object(end_time=end_time)
+ await self.logging_obj.async_success_handler(
+ result=self._build_logging_response(),
+ start_time=self._start_time,
+ end_time=end_time,
+ standard_logging_object=standard_logging_object,
+ )
+ self.logging_obj.handle_sync_success_callbacks_for_async_calls(
+ result=self._build_logging_response(),
+ start_time=self._start_time,
+ end_time=end_time,
+ )
+
+ def _log_success_sync(self) -> None:
+ if self._logging_completed or self.logging_obj is None:
+ return
+
+ self._logging_completed = True
+ end_time = datetime.datetime.now()
+ standard_logging_object = self._build_standard_logging_object(end_time=end_time)
+ self.logging_obj.success_handler(
+ result=self._build_logging_response(),
+ start_time=self._start_time,
+ end_time=end_time,
+ standard_logging_object=standard_logging_object,
+ )
+
+ async def _log_failure_async(self, error: Exception) -> None:
+ if self._logging_completed or self.logging_obj is None:
+ return
+
+ self._logging_completed = True
+ end_time = datetime.datetime.now()
+ traceback_str = traceback.format_exc()
+ self.logging_obj.failure_handler(
+ error, traceback_str, self._start_time, end_time
+ )
+ await self.logging_obj.async_failure_handler(
+ error, traceback_str, self._start_time, end_time
+ )
+
+ def _log_failure_sync(self, error: Exception) -> None:
+ if self._logging_completed or self.logging_obj is None:
+ return
+
+ self._logging_completed = True
+ end_time = datetime.datetime.now()
+ self.logging_obj.failure_handler(
+ error, traceback.format_exc(), self._start_time, end_time
+ )
diff --git a/litellm/files/types.py b/litellm/files/types.py
new file mode 100644
index 000000000000..688bc86f0cfb
--- /dev/null
+++ b/litellm/files/types.py
@@ -0,0 +1,11 @@
+from typing import AsyncIterator, Dict, Iterator, Literal, NamedTuple, Union
+
+
+FileContentProvider = Literal[
+ "openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "anthropic", "manus"
+]
+
+
+class FileContentStreamingResult(NamedTuple):
+ stream_iterator: Union[Iterator[bytes], AsyncIterator[bytes]]
+ headers: Dict[str, str]
diff --git a/litellm/integrations/dotprompt/prompt_manager.py b/litellm/integrations/dotprompt/prompt_manager.py
index 997a40d545e9..6407a18d0b3c 100644
--- a/litellm/integrations/dotprompt/prompt_manager.py
+++ b/litellm/integrations/dotprompt/prompt_manager.py
@@ -7,7 +7,8 @@
from typing import Any, Dict, List, Optional, Tuple, Union
import yaml
-from jinja2 import DictLoader, Environment, select_autoescape
+from jinja2 import DictLoader, select_autoescape
+from jinja2.sandbox import ImmutableSandboxedEnvironment
class PromptTemplate:
@@ -59,7 +60,10 @@ def __init__(
self.prompt_directory = Path(prompt_directory) if prompt_directory else None
self.prompts: Dict[str, PromptTemplate] = {}
self.prompt_file = prompt_file
- self.jinja_env = Environment(
+ # Sandboxed env: templates can come from user input via /prompts/test,
+ # so we must block access to unsafe Python attributes and mutation of
+ # caller-supplied mutables.
+ self.jinja_env = ImmutableSandboxedEnvironment(
loader=DictLoader({}),
autoescape=select_autoescape(["html", "xml"]),
# Use Handlebars-style delimiters to match Dotprompt spec
diff --git a/litellm/integrations/generic_api/generic_api_compatible_callbacks.json b/litellm/integrations/generic_api/generic_api_compatible_callbacks.json
index 13fe79ae671e..900f75b1d540 100644
--- a/litellm/integrations/generic_api/generic_api_compatible_callbacks.json
+++ b/litellm/integrations/generic_api/generic_api_compatible_callbacks.json
@@ -33,5 +33,14 @@
"X-Qualifire-API-Key": "{{environment_variables.QUALIFIRE_API_KEY}}"
},
"environment_variables": ["QUALIFIRE_API_KEY", "QUALIFIRE_WEBHOOK_URL"]
+ },
+ "ramp": {
+ "event_types": ["llm_api_success"],
+ "endpoint": "https://api.ramp.com/developer/v1/ai-usage/litellm",
+ "headers": {
+ "Content-Type": "application/json",
+ "Authorization": "Bearer {{environment_variables.RAMP_API_KEY}}"
+ },
+ "environment_variables": ["RAMP_API_KEY"]
}
}
diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py
index fb5fc253ae44..b3bf792e93bd 100644
--- a/litellm/integrations/prometheus.py
+++ b/litellm/integrations/prometheus.py
@@ -86,6 +86,11 @@ def __init__( # noqa: PLR0915
# Always initialize label_filters, even for non-premium users
self.label_filters = self._parse_prometheus_config()
+ _custom_buckets = litellm.prometheus_latency_buckets
+ self.latency_buckets = (
+ tuple(_custom_buckets) if _custom_buckets is not None else LATENCY_BUCKETS
+ )
+
# Create metric factory functions
self._counter_factory = self._create_metric_factory(Counter)
self._gauge_factory = self._create_metric_factory(Gauge)
@@ -114,14 +119,14 @@ def __init__( # noqa: PLR0915
labelnames=self.get_labels_for_metric(
"litellm_request_total_latency_metric"
),
- buckets=LATENCY_BUCKETS,
+ buckets=self.latency_buckets,
)
self.litellm_llm_api_latency_metric = self._histogram_factory(
"litellm_llm_api_latency_metric",
"Total latency (seconds) for a models LLM API call",
labelnames=self.get_labels_for_metric("litellm_llm_api_latency_metric"),
- buckets=LATENCY_BUCKETS,
+ buckets=self.latency_buckets,
)
self.litellm_llm_api_time_to_first_token_metric = self._histogram_factory(
@@ -137,7 +142,7 @@ def __init__( # noqa: PLR0915
labelnames=self.get_labels_for_metric(
"litellm_llm_api_time_to_first_token_metric"
),
- buckets=LATENCY_BUCKETS,
+ buckets=self.latency_buckets,
)
# Counter for spend
@@ -314,7 +319,7 @@ def __init__( # noqa: PLR0915
labelnames=self.get_labels_for_metric(
"litellm_overhead_latency_metric"
),
- buckets=LATENCY_BUCKETS,
+ buckets=self.latency_buckets,
)
# Request queue time metric
@@ -324,7 +329,7 @@ def __init__( # noqa: PLR0915
labelnames=self.get_labels_for_metric(
"litellm_request_queue_time_seconds"
),
- buckets=LATENCY_BUCKETS,
+ buckets=self.latency_buckets,
)
# Guardrail metrics
@@ -332,7 +337,7 @@ def __init__( # noqa: PLR0915
"litellm_guardrail_latency_seconds",
"Latency (seconds) for guardrail execution",
labelnames=["guardrail_name", "status", "error_type", "hook_type"],
- buckets=LATENCY_BUCKETS,
+ buckets=self.latency_buckets,
)
self.litellm_guardrail_errors_total = self._counter_factory(
@@ -1031,6 +1036,9 @@ async def async_log_success_event(self, kwargs, response_obj, start_time, end_ti
user_api_key_org_id = standard_logging_payload["metadata"].get(
"user_api_key_org_id"
)
+ user_api_key_org_alias = standard_logging_payload["metadata"].get(
+ "user_api_key_org_alias"
+ )
output_tokens = standard_logging_payload["completion_tokens"]
tokens_used = standard_logging_payload["total_tokens"]
response_cost = standard_logging_payload["response_cost"]
@@ -1068,6 +1076,8 @@ async def async_log_success_event(self, kwargs, response_obj, start_time, end_ti
model_group=standard_logging_payload["model_group"],
team=user_api_team,
team_alias=user_api_team_alias,
+ org_id=user_api_key_org_id,
+ org_alias=user_api_key_org_alias,
user=user_id,
user_email=standard_logging_payload["metadata"]["user_api_key_user_email"],
status_code="200",
@@ -1746,6 +1756,8 @@ async def async_post_call_failure_hook(
api_key_alias=user_api_key_dict.key_alias,
team=user_api_key_dict.team_id,
team_alias=user_api_key_dict.team_alias,
+ org_id=user_api_key_dict.org_id,
+ org_alias=user_api_key_dict.organization_alias,
requested_model=request_data.get("model", ""),
status_code=str(status_code),
exception_status=str(status_code),
diff --git a/litellm/integrations/prometheus_services.py b/litellm/integrations/prometheus_services.py
index 55ce758ece6b..6d5494706130 100644
--- a/litellm/integrations/prometheus_services.py
+++ b/litellm/integrations/prometheus_services.py
@@ -5,6 +5,7 @@
from typing import Dict, List, Optional, Union
+import litellm
from litellm._logging import print_verbose, verbose_logger
from litellm.types.integrations.prometheus import LATENCY_BUCKETS
from litellm.types.services import (
@@ -35,6 +36,11 @@ def __init__(
"Missing prometheus_client. Run `pip install prometheus-client`"
)
+ _custom_buckets = litellm.prometheus_latency_buckets
+ self.latency_buckets = (
+ tuple(_custom_buckets) if _custom_buckets is not None else LATENCY_BUCKETS
+ )
+
self.Histogram = Histogram
self.Counter = Counter
self.Gauge = Gauge
@@ -130,7 +136,7 @@ def create_histogram(self, service: str, type_of_request: str):
metric_name,
"Latency for {} service".format(service),
labelnames=[service],
- buckets=LATENCY_BUCKETS,
+ buckets=self.latency_buckets,
)
def create_gauge(self, service: str, type_of_request: str):
diff --git a/litellm/integrations/s3_v2.py b/litellm/integrations/s3_v2.py
index 405bf9698ccb..f764b07941b7 100644
--- a/litellm/integrations/s3_v2.py
+++ b/litellm/integrations/s3_v2.py
@@ -7,6 +7,7 @@
"""
import asyncio
+import time
from datetime import datetime
from typing import List, Optional, cast
@@ -403,11 +404,23 @@ async def async_upload_data_to_s3(
# Prepare the signed headers
signed_headers = dict(aws_request.headers.items())
- # Make the request
- response = await self.async_httpx_client.put(
- url, data=json_string, headers=signed_headers
- )
- response.raise_for_status()
+ # Make the request with retry for transient S3 errors (500/503)
+ max_retries = 3
+ for attempt in range(max_retries):
+ response = await self.async_httpx_client.put(
+ url, data=json_string, headers=signed_headers
+ )
+ if response.status_code in (500, 503) and attempt < max_retries - 1:
+ wait_time = 2**attempt # 1s, 2s
+ verbose_logger.warning(
+ f"S3 upload returned {response.status_code}, retrying in {wait_time}s "
+ f"(attempt {attempt + 1}/{max_retries}) "
+ f"key={batch_logging_element.s3_object_key}"
+ )
+ await asyncio.sleep(wait_time)
+ continue
+ response.raise_for_status()
+ break
except Exception as e:
verbose_logger.exception(f"Error uploading to s3: {str(e)}")
self.handle_callback_failure(callback_name="S3Logger")
@@ -582,9 +595,23 @@ def upload_data_to_s3(self, batch_logging_element: s3BatchLoggingElement):
if self.s3_verify is not None
else None
)
- # Make the request
- response = httpx_client.put(url, data=json_string, headers=signed_headers)
- response.raise_for_status()
+ # Make the request with retry for transient S3 errors (500/503)
+ max_retries = 3
+ for attempt in range(max_retries):
+ response = httpx_client.put(
+ url, data=json_string, headers=signed_headers
+ )
+ if response.status_code in (500, 503) and attempt < max_retries - 1:
+ wait_time = 2**attempt # 1s, 2s
+ verbose_logger.warning(
+ f"S3 upload returned {response.status_code}, retrying in {wait_time}s "
+ f"(attempt {attempt + 1}/{max_retries}) "
+ f"key={batch_logging_element.s3_object_key}"
+ )
+ time.sleep(wait_time)
+ continue
+ response.raise_for_status()
+ break
except Exception as e:
verbose_logger.exception(f"Error uploading to s3: {str(e)}")
self.handle_callback_failure(callback_name="S3Logger")
diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py
index 2e5a87340851..30fd55a3e9d8 100644
--- a/litellm/integrations/websearch_interception/handler.py
+++ b/litellm/integrations/websearch_interception/handler.py
@@ -230,8 +230,15 @@ async def async_pre_call_deployment_hook(
# Keep other tools as-is
converted_tools.append(tool)
- # Update tools in-place and return full kwargs
kwargs["tools"] = converted_tools
+
+ if kwargs.get("stream"):
+ verbose_logger.debug(
+ "WebSearchInterception: deployment hook converting stream=True to stream=False"
+ )
+ kwargs["stream"] = False
+ kwargs["_websearch_interception_converted_stream"] = True
+
return kwargs
@classmethod
@@ -344,13 +351,12 @@ async def async_pre_request_hook(
else:
converted_tools.append(tool)
- # Update kwargs with converted tools
kwargs["tools"] = converted_tools
verbose_logger.debug(
f"WebSearchInterception: Tools after conversion: {[t.get('name') for t in converted_tools]}"
)
- # Convert stream=True to stream=False for WebSearch interception
+ # Also convert here for direct callers that bypass the deployment hook.
if kwargs.get("stream"):
verbose_logger.debug(
"WebSearchInterception: Converting stream=True to stream=False"
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index 7395b65626f4..e84c1e13a8b4 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -613,7 +613,17 @@ def update_from_kwargs(
base_litellm_params["metadata"] = kwargs["litellm_metadata"].copy()
if litellm_params:
+ # Merge metadata carefully — don't overwrite the merged metadata
+ # from kwargs/litellm_metadata with the caller's litellm_params metadata.
+ # e.g. anthropic_messages passes Anthropic's native metadata ({user_id: ...})
+ # in litellm_params, which would overwrite proxy key-auth fields.
+ lp_metadata = litellm_params.pop("metadata", None)
base_litellm_params.update(litellm_params)
+ if lp_metadata and isinstance(lp_metadata, dict):
+ base_litellm_params.setdefault("metadata", {})
+ for k, v in lp_metadata.items():
+ if k not in base_litellm_params["metadata"]:
+ base_litellm_params["metadata"][k] = v
self.update_environment_variables(
litellm_params=base_litellm_params,
@@ -4754,6 +4764,7 @@ def get_standard_logging_metadata(
user_api_key_budget_reset_at=None,
user_api_key_team_id=None,
user_api_key_org_id=None,
+ user_api_key_org_alias=None,
user_api_key_project_id=None,
user_api_key_project_alias=None,
user_api_key_user_id=None,
@@ -5586,6 +5597,7 @@ def get_standard_logging_metadata(
user_api_key_budget_reset_at=None,
user_api_key_team_id=None,
user_api_key_org_id=None,
+ user_api_key_org_alias=None,
user_api_key_project_id=None,
user_api_key_project_alias=None,
user_api_key_user_id=None,
diff --git a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py
index 4454fca3b009..8da66d4600d5 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py
@@ -322,9 +322,8 @@ def response_object_includes_web_search_call(
)
if has_url_citations:
return True
- # Fallback: Check usage object for providers that use usage instead of annotations
- # (e.g., Vertex AI Gemini uses usage.prompt_tokens_details.web_search_requests)
if usage is not None:
+ # Vertex AI Gemini uses usage.prompt_tokens_details.web_search_requests
if (
hasattr(usage, "prompt_tokens_details")
and usage.prompt_tokens_details is not None
@@ -335,6 +334,15 @@ def response_object_includes_web_search_call(
and usage.prompt_tokens_details.web_search_requests is not None
):
return True
+ # Anthropic Claude (direct API and Vertex AI) uses server_tool_use.web_search_requests.
+ # Without this check, Claude ModelResponse always falls through to return False
+ # and _handle_web_search_cost() is never called.
+ if (
+ hasattr(usage, "server_tool_use")
+ and usage.server_tool_use is not None
+ and usage.server_tool_use.web_search_requests is not None
+ ):
+ return True
return False
elif isinstance(response_object, ResponsesAPIResponse):
# response api explicitly includes web_search_call in the output
diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py
index d29ca1649ff6..b37c17fafea4 100644
--- a/litellm/litellm_core_utils/prompt_templates/factory.py
+++ b/litellm/litellm_core_utils/prompt_templates/factory.py
@@ -4371,17 +4371,19 @@ def _initial_message_setup(
# if initial message is assistant message
if messages[0].get("role") is not None and messages[0]["role"] == "assistant":
- if user_continue_message is not None:
- messages.insert(0, user_continue_message)
- elif litellm.modify_params:
- messages.insert(0, DEFAULT_USER_CONTINUE_MESSAGE)
+ if not messages[0].get("prefix"):
+ if user_continue_message is not None:
+ messages.insert(0, user_continue_message)
+ elif litellm.modify_params:
+ messages.insert(0, DEFAULT_USER_CONTINUE_MESSAGE)
# if final message is assistant message
if messages[-1].get("role") is not None and messages[-1]["role"] == "assistant":
- if user_continue_message is not None:
- messages.append(user_continue_message)
- elif litellm.modify_params:
- messages.append(DEFAULT_USER_CONTINUE_MESSAGE)
+ if not messages[-1].get("prefix"):
+ if user_continue_message is not None:
+ messages.append(user_continue_message)
+ elif litellm.modify_params:
+ messages.append(DEFAULT_USER_CONTINUE_MESSAGE)
return messages
@staticmethod
diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
index 6df3219c6dbd..dc22cc65381f 100644
--- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
+++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
@@ -170,10 +170,13 @@ def build_base_response(self, chunks: List[Dict[str, Any]]) -> ModelResponse:
finish_reason = "stop"
for chunk in chunks:
if "choices" in chunk and len(chunk["choices"]) > 0:
+ chunk_finish_reason = None
if hasattr(chunk["choices"][0], "finish_reason"):
- finish_reason = chunk["choices"][0].finish_reason
+ chunk_finish_reason = chunk["choices"][0].finish_reason
elif "finish_reason" in chunk["choices"][0]:
- finish_reason = chunk["choices"][0]["finish_reason"]
+ chunk_finish_reason = chunk["choices"][0]["finish_reason"]
+ if chunk_finish_reason is not None:
+ finish_reason = chunk_finish_reason
# Initialize the response dictionary
response = ModelResponse(
diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py
index e402023d240d..5442567b1a5a 100644
--- a/litellm/litellm_core_utils/streaming_handler.py
+++ b/litellm/litellm_core_utils/streaming_handler.py
@@ -1134,7 +1134,11 @@ def chunk_creator(self, chunk: Any): # type: ignore # noqa: PLR0915
):
if self.received_finish_reason is not None:
_chunk_has_content = isinstance(chunk, dict) and (
- bool(chunk.get("text", "")) or chunk.get("tool_use") is not None
+ bool(chunk.get("text", ""))
+ or chunk.get("tool_use") is not None
+ # Usage-only final chunks are valid and needed to surface
+ # finish_reason/usage to downstream translators.
+ or chunk.get("usage") is not None
)
if not _chunk_has_content and (
not isinstance(chunk, dict)
diff --git a/litellm/llms/__init__.py b/litellm/llms/__init__.py
index c73f0b22b4b2..710342bbc781 100644
--- a/litellm/llms/__init__.py
+++ b/litellm/llms/__init__.py
@@ -34,6 +34,18 @@ def get_cost_for_web_search_request(
return get_cost_for_anthropic_web_search(model_info=model_info, usage=usage)
elif custom_llm_provider.startswith("vertex_ai"):
+ # Anthropic Claude models on Vertex AI populate server_tool_use.web_search_requests
+ # (same as the direct Anthropic API), not prompt_tokens_details.web_search_requests
+ # (which is the Gemini field). Route claude-* models to the Anthropic calculator.
+ model_key: str = model_info.get("key", "") if model_info else ""
+ if "claude" in model_key.lower():
+ from .anthropic.cost_calculation import get_cost_for_anthropic_web_search
+
+ verbose_logger.debug(
+ "vertex_ai/claude model detected — routing web search cost to Anthropic calculator"
+ )
+ return get_cost_for_anthropic_web_search(model_info=model_info, usage=usage)
+
from .vertex_ai.gemini.cost_calculator import (
cost_per_web_search_request as cost_per_web_search_request_vertex_ai,
)
diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py
index d31a0a091e9a..2d1ca4b6e303 100644
--- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py
+++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py
@@ -21,6 +21,10 @@
LiteLLMAnthropicMessagesAdapter,
)
from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
+from litellm.llms.base_llm.guardrail_translation.utils import (
+ effective_skip_system_message_for_guardrail,
+ openai_messages_without_system,
+)
from litellm.proxy.pass_through_endpoints.llm_provider_handlers.anthropic_passthrough_logging_handler import (
AnthropicPassthroughLoggingHandler,
)
@@ -29,6 +33,7 @@
AnthropicMessagesRequest,
)
from litellm.types.llms.openai import (
+ AllMessageValues,
ChatCompletionToolCallChunk,
ChatCompletionToolParam,
)
@@ -75,6 +80,8 @@ async def process_input_messages(
if messages is None:
return data
+ skip_system = effective_skip_system_message_for_guardrail(guardrail_to_apply)
+
(
chat_completion_compatible_request,
_tool_name_mapping,
@@ -83,7 +90,12 @@ async def process_input_messages(
anthropic_message_request=cast(AnthropicMessagesRequest, data.copy())
)
- structured_messages = chat_completion_compatible_request.get("messages", [])
+ structured_messages = cast(
+ List[AllMessageValues],
+ chat_completion_compatible_request.get("messages", []),
+ )
+ if skip_system:
+ structured_messages = openai_messages_without_system(structured_messages)
texts_to_check: List[str] = []
images_to_check: List[str] = []
@@ -102,6 +114,7 @@ async def process_input_messages(
texts_to_check=texts_to_check,
images_to_check=images_to_check,
task_mappings=task_mappings,
+ skip_system_message=skip_system,
)
# Step 2: Apply guardrail to all texts in batch
@@ -165,12 +178,16 @@ def _extract_input_text_and_images(
texts_to_check: List[str],
images_to_check: List[str],
task_mappings: List[Tuple[int, Optional[int]]],
+ skip_system_message: bool = False,
) -> None:
"""
Extract text content and images from a message.
Override this method to customize text/image extraction logic.
"""
+ if skip_system_message and str(message.get("role") or "").lower() == "system":
+ return
+
content = message.get("content", None)
tools = message.get("tools", None)
if content is None and tools is None:
diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py
index 9f2ddcae2c78..0f020c3a9538 100644
--- a/litellm/llms/anthropic/chat/handler.py
+++ b/litellm/llms/anthropic/chat/handler.py
@@ -89,7 +89,12 @@ async def make_call(
try:
response = await client.post(
- api_base, headers=headers, data=data, stream=True, timeout=timeout
+ api_base,
+ headers=headers,
+ data=data,
+ stream=True,
+ timeout=timeout,
+ logging_obj=logging_obj,
)
except httpx.HTTPStatusError as e:
error_headers = getattr(e, "headers", None)
@@ -142,7 +147,12 @@ def make_sync_call(
try:
response = client.post(
- api_base, headers=headers, data=data, stream=True, timeout=timeout
+ api_base,
+ headers=headers,
+ data=data,
+ stream=True,
+ timeout=timeout,
+ logging_obj=logging_obj,
)
except httpx.HTTPStatusError as e:
error_headers = getattr(e, "headers", None)
@@ -266,7 +276,11 @@ async def acompletion_function(
try:
response = await async_handler.post(
- api_base, headers=headers, json=data, timeout=timeout
+ api_base,
+ headers=headers,
+ json=data,
+ timeout=timeout,
+ logging_obj=logging_obj,
)
except Exception as e:
## LOGGING
@@ -469,6 +483,7 @@ def completion(
headers=headers,
data=json.dumps(data),
timeout=timeout,
+ logging_obj=logging_obj,
)
except Exception as e:
status_code = getattr(e, "status_code", 500)
diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py
index 9a99f9efc82f..d7ce6a5f8ded 100644
--- a/litellm/llms/anthropic/chat/transformation.py
+++ b/litellm/llms/anthropic/chat/transformation.py
@@ -19,6 +19,7 @@
from litellm.llms.base_llm.base_utils import type_to_response_format_param
from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
from litellm.types.llms.anthropic import (
+ ANTHROPIC_ADVISOR_TOOL_TYPE,
ANTHROPIC_BETA_HEADER_VALUES,
ANTHROPIC_HOSTED_TOOLS,
AllAnthropicMessageValues,
@@ -75,7 +76,12 @@
token_counter,
)
-from ..common_utils import AnthropicError, AnthropicModelInfo, process_anthropic_headers
+from ..common_utils import (
+ AnthropicError,
+ AnthropicModelInfo,
+ process_anthropic_headers,
+ strip_advisor_blocks_from_messages,
+)
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
@@ -508,6 +514,23 @@ def _map_tool_helper( # noqa: PLR0915
type="tool_search_tool_bm25_20251119",
name=tool_name,
)
+ elif tool["type"] == ANTHROPIC_ADVISOR_TOOL_TYPE:
+ from litellm.types.llms.anthropic import AnthropicAdvisorTool
+
+ _tool_dict = cast(dict, tool)
+ advisor_model = _tool_dict.get("model")
+ if not isinstance(advisor_model, str):
+ raise ValueError("Advisor tool must have a valid model")
+ _advisor_tool = AnthropicAdvisorTool(
+ type=ANTHROPIC_ADVISOR_TOOL_TYPE,
+ name="advisor",
+ model=advisor_model,
+ )
+ if _tool_dict.get("max_uses") is not None:
+ _advisor_tool["max_uses"] = _tool_dict["max_uses"]
+ if _tool_dict.get("caching") is not None:
+ _advisor_tool["caching"] = _tool_dict["caching"]
+ returned_tool = _advisor_tool # type: ignore[assignment]
if returned_tool is None and mcp_server is None:
raise ValueError(f"Unsupported tool type: {tool['type']}")
@@ -1311,6 +1334,12 @@ def update_headers_with_optional_anthropic_beta(
self._ensure_beta_header(
headers, ANTHROPIC_BETA_HEADER_VALUES.FAST_MODE_2026_02_01.value
)
+ for tool in _tools:
+ if tool.get("type") == ANTHROPIC_ADVISOR_TOOL_TYPE:
+ self._ensure_beta_header(
+ headers, ANTHROPIC_BETA_HEADER_VALUES.ADVISOR_TOOL_2026_03_01.value
+ )
+ break
return headers
def transform_request(
@@ -1390,6 +1419,16 @@ def transform_request(
message="{}\nReceived Messages={}".format(str(e), messages),
) # don't use verbose_logger.exception, if exception is raised
+ ## Auto-strip advisor blocks from history if advisor tool is absent.
+ ## Prevents Anthropic 400: advisor_tool_result in history requires advisor tool.
+ _all_tools = optional_params.get("tools") or []
+ _has_advisor = any(
+ isinstance(t, dict) and t.get("type") == ANTHROPIC_ADVISOR_TOOL_TYPE
+ for t in _all_tools
+ )
+ if not _has_advisor:
+ anthropic_messages = strip_advisor_blocks_from_messages(anthropic_messages)
+
## Add code_execution tool if container_upload is in messages
_tools = (
cast(
@@ -1440,23 +1479,32 @@ def transform_request(
**optional_params,
}
- ## Handle output_config (Anthropic-specific parameter)
- if "output_config" in optional_params:
- output_config = optional_params.get("output_config")
- if output_config and isinstance(output_config, dict):
- effort = output_config.get("effort")
- if effort and effort not in ["high", "medium", "low", "max"]:
- raise ValueError(
- f"Invalid effort value: {effort}. Must be one of: 'high', 'medium', 'low', 'max'"
- )
- if effort == "max" and not self._is_opus_4_6_model(model):
- raise ValueError(
- f"effort='max' is only supported by Claude Opus 4.6. Got model: {model}"
- )
- data["output_config"] = output_config
+ self._apply_output_config(
+ data=data, model=model, optional_params=optional_params
+ )
return data
+ def _apply_output_config(
+ self, data: dict, model: str, optional_params: dict
+ ) -> None:
+ """Validate and apply output_config to the request data."""
+ if "output_config" not in optional_params:
+ return
+ output_config = optional_params.get("output_config")
+ if not output_config or not isinstance(output_config, dict):
+ return
+ effort = output_config.get("effort")
+ if effort and effort not in ["high", "medium", "low", "max"]:
+ raise ValueError(
+ f"Invalid effort value: {effort}. Must be one of: 'high', 'medium', 'low', 'max'"
+ )
+ if effort == "max" and not self._is_opus_4_6_model(model):
+ raise ValueError(
+ f"effort='max' is only supported by Claude Opus 4.6. Got model: {model}"
+ )
+ data["output_config"] = output_config
+
def _transform_response_for_json_mode(
self,
json_mode: Optional[bool],
diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py
index 7d2d0a74961f..1a003727e979 100644
--- a/litellm/llms/anthropic/common_utils.py
+++ b/litellm/llms/anthropic/common_utils.py
@@ -2,7 +2,7 @@
This file contains common utils for anthropic calls.
"""
-from typing import Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Union
import httpx
@@ -464,9 +464,9 @@ def get_anthropic_headers(
if web_search_tool_used:
from litellm.types.llms.anthropic import ANTHROPIC_BETA_HEADER_VALUES
- headers[
- "anthropic-beta"
- ] = ANTHROPIC_BETA_HEADER_VALUES.WEB_SEARCH_2025_03_05.value
+ headers["anthropic-beta"] = (
+ ANTHROPIC_BETA_HEADER_VALUES.WEB_SEARCH_2025_03_05.value
+ )
elif len(betas) > 0:
headers["anthropic-beta"] = ",".join(betas)
@@ -639,6 +639,54 @@ def get_token_counter(self) -> Optional[BaseTokenCounter]:
return AnthropicTokenCounter()
+def strip_advisor_blocks_from_messages(messages: List[Any]) -> List[Any]:
+ """
+ Remove server_tool_use (name='advisor') and advisor_tool_result blocks from
+ assistant message content when the advisor tool is absent from the request.
+
+ Prevents Anthropic 400 invalid_request_error: if advisor_tool_result blocks
+ exist in history but the advisor tool is not in the tools array, the API rejects
+ the request. This happens when the user has removed the advisor tool for cost
+ control or on a follow-up turn.
+ """
+ for message in messages:
+ if not isinstance(message, dict) or message.get("role") != "assistant":
+ continue
+ content = message.get("content")
+ if not isinstance(content, list):
+ continue
+ advisor_ids: set = set()
+ for block in content:
+ if (
+ isinstance(block, dict)
+ and block.get("type") == "server_tool_use"
+ and block.get("name") == "advisor"
+ ):
+ bid = block.get("id")
+ if bid:
+ advisor_ids.add(bid)
+ if not advisor_ids:
+ continue
+ message["content"] = [
+ block
+ for block in content
+ if not (
+ isinstance(block, dict)
+ and (
+ (
+ block.get("type") == "server_tool_use"
+ and block.get("name") == "advisor"
+ )
+ or (
+ block.get("type") == "advisor_tool_result"
+ and block.get("tool_use_id") in advisor_ids
+ )
+ )
+ )
+ ]
+ return messages
+
+
def process_anthropic_headers(headers: Union[httpx.Headers, dict]) -> dict:
openai_headers = {}
if "anthropic-ratelimit-requests-limit" in headers:
diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
index d117d74e4f7d..3da118fd3491 100644
--- a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
+++ b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
@@ -187,11 +187,9 @@ async def anthropic_messages(
"""
Async: Make llm api request in Anthropic /messages API spec
"""
- # Save original stream flag before pre-request hooks can convert it.
- # The websearch interception hook converts stream=True → stream=False
- # for the agentic loop, but the short-circuit path needs to know
- # whether the caller originally requested streaming.
- original_stream = stream
+ original_stream = stream or kwargs.get(
+ "_websearch_interception_converted_stream", False
+ )
# Execute pre-request hooks to allow CustomLoggers to modify request
request_kwargs = await _execute_pre_request_hooks(
diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
index 9b60a58260b3..46af1f7fbd10 100644
--- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
+++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
@@ -8,6 +8,7 @@
BaseAnthropicMessagesConfig,
)
from litellm.types.llms.anthropic import (
+ ANTHROPIC_ADVISOR_TOOL_TYPE,
ANTHROPIC_BETA_HEADER_VALUES,
AnthropicMessagesRequest,
)
@@ -21,6 +22,7 @@
AnthropicError,
AnthropicModelInfo,
optionally_handle_anthropic_oauth,
+ strip_advisor_blocks_from_messages,
)
DEFAULT_ANTHROPIC_API_VERSION = "2023-06-01"
@@ -208,12 +210,23 @@ def transform_anthropic_messages_request(
)
)
if transformed_context_management is not None:
- anthropic_messages_optional_request_params[
- "context_management"
- ] = transformed_context_management
+ anthropic_messages_optional_request_params["context_management"] = (
+ transformed_context_management
+ )
####### get required params for all anthropic messages requests ######
verbose_logger.debug(f"TRANSFORMATION DEBUG - Messages: {messages}")
+
+ # Auto-strip advisor blocks from history if advisor tool is absent.
+ # Prevents Anthropic 400: advisor_tool_result in history requires advisor tool.
+ _tools = anthropic_messages_optional_request_params.get("tools") or []
+ _has_advisor = any(
+ isinstance(t, dict) and t.get("type") == ANTHROPIC_ADVISOR_TOOL_TYPE
+ for t in _tools
+ )
+ if not _has_advisor:
+ messages = strip_advisor_blocks_from_messages(messages) # type: ignore[assignment]
+
anthropic_messages_request: AnthropicMessagesRequest = AnthropicMessagesRequest(
messages=messages,
max_tokens=max_tokens,
@@ -324,6 +337,19 @@ def _update_headers_with_anthropic_beta(
if optional_params.get("speed") == "fast":
beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.FAST_MODE_2026_02_01.value)
+ # Check for advisor tool
+ tools = optional_params.get("tools")
+ if tools:
+ for tool in tools:
+ if (
+ isinstance(tool, dict)
+ and tool.get("type") == ANTHROPIC_ADVISOR_TOOL_TYPE
+ ):
+ beta_values.add(
+ ANTHROPIC_BETA_HEADER_VALUES.ADVISOR_TOOL_2026_03_01.value
+ )
+ break
+
# Check for tool search tools
tools = optional_params.get("tools")
if tools:
diff --git a/tests/proxy_e2e_azure_batches_tests/__init__.py b/litellm/llms/azure/containers/__init__.py
similarity index 100%
rename from tests/proxy_e2e_azure_batches_tests/__init__.py
rename to litellm/llms/azure/containers/__init__.py
diff --git a/litellm/llms/azure/containers/transformation.py b/litellm/llms/azure/containers/transformation.py
new file mode 100644
index 000000000000..586b2e379a04
--- /dev/null
+++ b/litellm/llms/azure/containers/transformation.py
@@ -0,0 +1,48 @@
+from typing import Optional
+
+from litellm.llms.azure.common_utils import BaseAzureLLM
+from litellm.llms.openai.containers.transformation import OpenAIContainerConfig
+from litellm.types.router import GenericLiteLLMParams
+
+
+class AzureContainerConfig(OpenAIContainerConfig):
+ """
+ Configuration class for Azure OpenAI container API.
+
+ Inherits request/response transformations from OpenAIContainerConfig since
+ Azure's container API is wire-compatible with OpenAI's. Only overrides
+ authentication (api-key header) and URL construction (openai/v1/containers path).
+
+ Azure container API reference:
+ https://learn.microsoft.com/en-us/azure/foundry/openai/latest#containers
+ """
+
+ def validate_environment(
+ self,
+ headers: dict,
+ api_key: Optional[str] = None,
+ ) -> dict:
+ return BaseAzureLLM._base_validate_azure_environment(
+ headers=headers,
+ litellm_params=GenericLiteLLMParams(api_key=api_key),
+ )
+
+ def get_complete_url(
+ self,
+ api_base: Optional[str],
+ litellm_params: dict,
+ ) -> str:
+ """
+ Build the Azure container endpoint URL.
+
+ Azure container API uses the path:
+ {endpoint}/openai/v1/containers
+ when api_version is 'v1', 'latest', or 'preview'; otherwise:
+ {endpoint}/openai/containers
+ """
+ return BaseAzureLLM._get_base_azure_url(
+ api_base=api_base,
+ litellm_params=litellm_params,
+ route="/openai/containers",
+ default_api_version="v1",
+ )
diff --git a/litellm/llms/base_llm/guardrail_translation/utils.py b/litellm/llms/base_llm/guardrail_translation/utils.py
new file mode 100644
index 000000000000..cc401d074068
--- /dev/null
+++ b/litellm/llms/base_llm/guardrail_translation/utils.py
@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+from typing import Any, List
+
+from litellm.types.llms.openai import AllMessageValues
+
+
+def effective_skip_system_message_for_guardrail(guardrail_to_apply: Any) -> bool:
+ per = getattr(guardrail_to_apply, "skip_system_message_in_guardrail", None)
+ if per is not None:
+ return bool(per)
+ import litellm
+
+ return bool(getattr(litellm, "skip_system_message_in_guardrail", False))
+
+
+def openai_messages_without_system(
+ messages: List[AllMessageValues],
+) -> List[AllMessageValues]:
+ return [
+ m
+ for m in messages
+ if str((m or {}).get("role") or "").lower() != "system"
+ ]
diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py
index 4157fac53b81..4e3521b119e3 100644
--- a/litellm/llms/bedrock/base_aws_llm.py
+++ b/litellm/llms/bedrock/base_aws_llm.py
@@ -700,7 +700,7 @@ def _auth_with_web_identity_token(
"RoleSessionName": aws_session_name,
"WebIdentityToken": oidc_token,
"DurationSeconds": 3600,
- "Policy": '{"Version":"2012-10-17","Statement":[{"Sid":"BedrockLiteLLM","Effect":"Allow","Action":["bedrock:InvokeModel","bedrock:InvokeModelWithResponseStream"],"Resource":"*","Condition":{"Bool":{"aws:SecureTransport":"true"},"StringLike":{"aws:UserAgent":"litellm/*"}}}]}',
+ "Policy": '{"Version":"2012-10-17","Statement":[{"Sid":"BedrockLiteLLM","Effect":"Allow","Action":["bedrock:InvokeModel","bedrock:InvokeModelWithResponseStream","bedrock:ApplyGuardrail","bedrock:GetGuardrail","bedrock:ListGuardrails"],"Resource":"*","Condition":{"Bool":{"aws:SecureTransport":"true"}}}]}',
}
# Add ExternalId parameter if provided
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 c1eccaebd04a..3ac579dae1b1 100644
--- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
+++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
@@ -538,9 +538,9 @@ async def _promote_message_stop_usage(
merges usage from message_start and message_delta but ignores
message_stop. This method buffers message_delta and, when
message_stop arrives with cache usage, merges those fields into the
- message_delta usage and also updates the input_tokens on
- message_delta to include the full count (uncached + cache_creation +
- cache_read).
+ message_delta usage. input_tokens is kept as the uncached-only
+ count; downstream calculate_usage adds cache tokens to
+ prompt_tokens.
"""
_CACHE_FIELDS = ("cache_creation_input_tokens", "cache_read_input_tokens")
pending_delta = None
@@ -569,12 +569,7 @@ async def _promote_message_stop_usage(
raw_input = stop_usage.get("input_tokens")
if raw_input is not None:
- uncached = raw_input if isinstance(raw_input, int) else 0
- raw_cc = delta_usage.get("cache_creation_input_tokens", 0)
- cache_creation = raw_cc if isinstance(raw_cc, int) else 0
- raw_cr = delta_usage.get("cache_read_input_tokens", 0)
- cache_read = raw_cr if isinstance(raw_cr, int) else 0
- delta_usage["input_tokens"] = uncached + cache_creation + cache_read
+ delta_usage["input_tokens"] = raw_input if isinstance(raw_input, int) else 0
if delta_usage:
pending_delta["usage"] = delta_usage # type: ignore[arg-type]
diff --git a/litellm/llms/custom_httpx/container_handler.py b/litellm/llms/custom_httpx/container_handler.py
index 3767949375d2..2d54f33bf96e 100644
--- a/litellm/llms/custom_httpx/container_handler.py
+++ b/litellm/llms/custom_httpx/container_handler.py
@@ -61,18 +61,29 @@ def _build_url(
) -> str:
"""Build the full URL by substituting path parameters.
- The api_base from get_complete_url already includes /containers,
- so we need to strip that prefix from the path_template.
+ The api_base from get_complete_url already includes /containers and may include
+ query parameters. We need to parse the URL, append the path, then preserve the
+ query parameters.
"""
# api_base ends with /containers, path_template starts with /containers
# So we need to strip /containers from the path
if path_template.startswith("/containers"):
path_template = path_template[len("/containers") :]
- url = f"{api_base.rstrip('/')}{path_template}"
+ # Substitute path parameters
for param, value in path_params.items():
- url = url.replace(f"{{{param}}}", value)
- return url
+ path_template = path_template.replace(f"{{{param}}}", value)
+
+ # Parse the api_base to extract existing query params
+ parsed_base = httpx.URL(api_base)
+
+ # Append the path to the existing path (before query params)
+ new_path = f"{parsed_base.path.rstrip('/')}{path_template}"
+
+ # Rebuild URL with new path, preserving query params
+ final_url = parsed_base.copy_with(path=new_path)
+
+ return str(final_url)
def _build_query_params(
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index 4c9abaad9088..7a8820a87855 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -1,5 +1,6 @@
import json
import ssl
+from urllib.parse import parse_qs, urlencode, urlparse, urlunparse
from typing import (
TYPE_CHECKING,
Any,
@@ -5027,6 +5028,16 @@ async def async_responses_websocket(
litellm_params={},
)
ws_url = http_url.replace("https://", "wss://").replace("http://", "ws://")
+ # OpenAI's WebSocket responses endpoint requires ?model= in the URL,
+ # matching the Realtime API convention (wss://.../v1/realtime?model=...).
+ # Use urllib.parse so existing query params (e.g. api-version) are preserved.
+ _parsed = urlparse(ws_url)
+ _qs = parse_qs(_parsed.query)
+ if "model" not in _qs:
+ _qs["model"] = [model]
+ ws_url = urlunparse(
+ _parsed._replace(query=urlencode({k: v[0] for k, v in _qs.items()}))
+ )
try:
ssl_context = get_shared_realtime_ssl_context()
diff --git a/litellm/llms/gemini/chat/transformation.py b/litellm/llms/gemini/chat/transformation.py
index 5f8dead20436..72569e5c6cda 100644
--- a/litellm/llms/gemini/chat/transformation.py
+++ b/litellm/llms/gemini/chat/transformation.py
@@ -91,6 +91,7 @@ def get_supported_openai_params(self, model: str) -> List[str]:
"modalities",
"parallel_tool_calls",
"web_search_options",
+ "service_tier",
]
if supports_reasoning(model, custom_llm_provider="gemini"):
supported_params.append("reasoning_effort")
diff --git a/litellm/llms/litellm_proxy/skills/prompt_injection.py b/litellm/llms/litellm_proxy/skills/prompt_injection.py
index 2b86f74122bf..86b6e2235129 100644
--- a/litellm/llms/litellm_proxy/skills/prompt_injection.py
+++ b/litellm/llms/litellm_proxy/skills/prompt_injection.py
@@ -5,6 +5,7 @@
and injection into the system prompt for non-Anthropic models.
"""
+import posixpath
import zipfile
from io import BytesIO
from typing import Any, Dict, List, Optional
@@ -103,8 +104,18 @@ def extract_all_files(self, skill: LiteLLM_SkillsTable) -> Dict[str, bytes]:
else:
clean_path = name
- if clean_path:
- files[clean_path] = zf.read(name)
+ if not clean_path:
+ continue
+
+ # Ensure the path stays within the intended directory
+ normalized = posixpath.normpath(clean_path)
+ if normalized.startswith("..") or posixpath.isabs(normalized):
+ verbose_logger.warning(
+ f"SkillPromptInjectionHandler: Skipping entry with invalid path in skill {skill.skill_id}: {name}"
+ )
+ continue
+
+ files[normalized] = zf.read(name)
except Exception as e:
verbose_logger.warning(
f"SkillPromptInjectionHandler: Error extracting files from skill {skill.skill_id}: {e}"
diff --git a/litellm/llms/litellm_proxy/skills/sandbox_executor.py b/litellm/llms/litellm_proxy/skills/sandbox_executor.py
index a5c0a539c96e..af13d3c091dc 100644
--- a/litellm/llms/litellm_proxy/skills/sandbox_executor.py
+++ b/litellm/llms/litellm_proxy/skills/sandbox_executor.py
@@ -94,9 +94,15 @@ def execute(
# Create a temp directory to stage files
with tempfile.TemporaryDirectory() as tmpdir:
+ tmpdir_abs = os.path.abspath(tmpdir)
for path, content in skill_files.items():
# Create the file in temp directory
- local_path = os.path.join(tmpdir, path)
+ local_path = os.path.abspath(os.path.join(tmpdir, path))
+ if not local_path.startswith(tmpdir_abs + os.sep):
+ verbose_logger.warning(
+ f"SkillsSandboxExecutor: Skipping file with invalid path: {path}"
+ )
+ continue
os.makedirs(os.path.dirname(local_path), exist_ok=True)
with open(local_path, "wb") as f:
f.write(content)
diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py
index f854cdb13d00..2db19dea0b97 100644
--- a/litellm/llms/openai/chat/guardrail_translation/handler.py
+++ b/litellm/llms/openai/chat/guardrail_translation/handler.py
@@ -19,8 +19,12 @@
import litellm
from litellm._logging import verbose_proxy_logger
from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
+from litellm.llms.base_llm.guardrail_translation.utils import (
+ effective_skip_system_message_for_guardrail,
+ openai_messages_without_system,
+)
from litellm.main import stream_chunk_builder
-from litellm.types.llms.openai import ChatCompletionToolParam
+from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam
from litellm.types.utils import (
Choices,
GenericGuardrailAPIInputs,
@@ -57,6 +61,8 @@ async def process_input_messages(
if messages is None:
return data
+ skip_system = effective_skip_system_message_for_guardrail(guardrail_to_apply)
+
texts_to_check: List[str] = []
images_to_check: List[str] = []
tool_calls_to_check: List[ChatCompletionToolParam] = []
@@ -76,6 +82,7 @@ async def process_input_messages(
tool_calls_to_check=tool_calls_to_check,
text_task_mappings=text_task_mappings,
tool_call_task_mappings=tool_call_task_mappings,
+ skip_system_message=skip_system,
)
# Step 2: Apply guardrail to all texts and tool calls in batch
@@ -86,9 +93,12 @@ async def process_input_messages(
if tool_calls_to_check:
inputs["tool_calls"] = tool_calls_to_check # type: ignore
if messages:
- inputs[
- "structured_messages"
- ] = messages # pass the openai /chat/completions messages to the guardrail, as-is
+ msg_list = cast(List[AllMessageValues], messages)
+ inputs["structured_messages"] = (
+ openai_messages_without_system(msg_list)
+ if skip_system
+ else msg_list
+ )
# Pass tools (function definitions) to the guardrail
tools = data.get("tools")
if tools:
@@ -157,12 +167,16 @@ def _extract_inputs(
tool_calls_to_check: List[ChatCompletionToolParam],
text_task_mappings: List[Tuple[int, Optional[int]]],
tool_call_task_mappings: List[Tuple[int, int]],
+ skip_system_message: bool = False,
) -> None:
"""
Extract text content, images, and tool calls from a message.
Override this method to customize text/image/tool call extraction logic.
"""
+ if skip_system_message and str(message.get("role") or "").lower() == "system":
+ return
+
content = message.get("content", None)
if content is not None:
if isinstance(content, str):
diff --git a/litellm/llms/openai/containers/transformation.py b/litellm/llms/openai/containers/transformation.py
index 645538fdd9c6..955b9f760d14 100644
--- a/litellm/llms/openai/containers/transformation.py
+++ b/litellm/llms/openai/containers/transformation.py
@@ -17,6 +17,7 @@
from litellm.types.router import GenericLiteLLMParams
from ...base_llm.containers.transformation import BaseContainerConfig
+from .utils import join_container_api_base_path
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
@@ -197,7 +198,7 @@ def transform_container_retrieve_request(
) -> Tuple[str, Dict]:
"""Transform the OpenAI container retrieve request."""
# For container retrieve, we just need to construct the URL
- url = f"{api_base.rstrip('/')}/{container_id}"
+ url = join_container_api_base_path(api_base, f"/{container_id}")
# No additional data needed for GET request
data: Dict[str, Any] = {}
@@ -229,7 +230,7 @@ def transform_container_delete_request(
- DELETE /v1/containers/{container_id}
"""
# Construct the URL for container delete
- url = f"{api_base.rstrip('/')}/{container_id}"
+ url = join_container_api_base_path(api_base, f"/{container_id}")
# No data needed for DELETE request
data: Dict[str, Any] = {}
@@ -266,7 +267,7 @@ def transform_container_file_list_request(
- GET /v1/containers/{container_id}/files
"""
# Construct the URL for container files
- url = f"{api_base.rstrip('/')}/{container_id}/files"
+ url = join_container_api_base_path(api_base, f"/{container_id}/files")
# Prepare query parameters
params: Dict[str, Any] = {}
@@ -310,7 +311,9 @@ def transform_container_file_content_request(
- GET /v1/containers/{container_id}/files/{file_id}/content
"""
# Construct the URL for container file content
- url = f"{api_base.rstrip('/')}/{container_id}/files/{file_id}/content"
+ url = join_container_api_base_path(
+ api_base, f"/{container_id}/files/{file_id}/content"
+ )
# No query parameters needed
params: Dict[str, Any] = {}
diff --git a/litellm/llms/openai/containers/utils.py b/litellm/llms/openai/containers/utils.py
new file mode 100644
index 000000000000..c4ac35a2f85b
--- /dev/null
+++ b/litellm/llms/openai/containers/utils.py
@@ -0,0 +1,18 @@
+"""Shared helpers for OpenAI-compatible container API URL construction."""
+
+import httpx
+
+
+def join_container_api_base_path(api_base: str, path_suffix: str) -> str:
+ """Append ``path_suffix`` to the path of ``api_base``, keeping the query string last.
+
+ Azure (and some bases) pass ``api_base`` like
+ ``https://host/openai/v1/containers?api-version=v1``. Naive string concat would
+ produce ``...?api-version=v1/cntr_...`` which is invalid; this uses ``httpx.URL``
+ so the result is ``.../containers/cntr_.../files?api-version=v1``.
+ """
+ if not path_suffix.startswith("/"):
+ path_suffix = f"/{path_suffix}"
+ parsed = httpx.URL(api_base)
+ new_path = f"{parsed.path.rstrip('/')}{path_suffix}"
+ return str(parsed.copy_with(path=new_path))
diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py
index be542677480e..b48edf53d5ef 100644
--- a/litellm/llms/openai/openai.py
+++ b/litellm/llms/openai/openai.py
@@ -32,6 +32,7 @@
from litellm import LlmProviders
from litellm._logging import verbose_logger
from litellm.constants import DEFAULT_MAX_RETRIES
+from litellm.files.types import FileContentStreamingResult
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
@@ -1751,6 +1752,92 @@ def file_content(
return HttpxBinaryResponseContent(response=response.response)
+ async def afile_content_streaming(
+ self,
+ file_content_request: FileContentRequest,
+ openai_client: AsyncOpenAI,
+ chunk_size: int = 1024 * 1024,
+ ) -> FileContentStreamingResult:
+ response_cm = openai_client.files.with_streaming_response.content(
+ **file_content_request
+ )
+ response = await response_cm.__aenter__()
+ headers = dict(response.headers)
+
+ async def _stream() -> AsyncIterator[bytes]:
+ exc: Optional[BaseException] = None
+ try:
+ async for chunk in response.iter_bytes(chunk_size=chunk_size):
+ yield chunk
+ except BaseException as e:
+ exc = e
+ raise
+ finally:
+ if exc is None:
+ await response_cm.__aexit__(None, None, None)
+ else:
+ await response_cm.__aexit__(type(exc), exc, exc.__traceback__)
+
+ return FileContentStreamingResult(stream_iterator=_stream(), headers=headers)
+
+ def file_content_streaming(
+ self,
+ _is_async: bool,
+ file_content_request: FileContentRequest,
+ api_base: str,
+ api_key: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ chunk_size: int = 1024 * 1024,
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ ) -> FileContentStreamingResult:
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+
+ if _is_async is True:
+ if not isinstance(openai_client, AsyncOpenAI):
+ raise ValueError(
+ "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+ )
+ return self.afile_content_streaming( # type: ignore
+ file_content_request=file_content_request,
+ openai_client=openai_client,
+ chunk_size=chunk_size,
+ )
+
+ response_cm = cast(OpenAI, openai_client).files.with_streaming_response.content(
+ **file_content_request
+ )
+ response = response_cm.__enter__()
+ headers = dict(response.headers)
+
+ def _stream() -> Iterator[bytes]:
+ exc: Optional[BaseException] = None
+ try:
+ yield from response.iter_bytes(chunk_size=chunk_size)
+ except BaseException as e:
+ exc = e
+ raise
+ finally:
+ if exc is None:
+ response_cm.__exit__(None, None, None)
+ else:
+ response_cm.__exit__(type(exc), exc, exc.__traceback__)
+
+ return FileContentStreamingResult(stream_iterator=_stream(), headers=headers)
+
async def aretrieve_file(
self,
file_id: str,
@@ -3045,4 +3132,4 @@ def run_thread(
tools=tools,
)
- return response
+ return response
\ No newline at end of file
diff --git a/tests/proxy_e2e_azure_batches_tests/fixtures/__init__.py b/litellm/llms/sap/__init__.py
similarity index 100%
rename from tests/proxy_e2e_azure_batches_tests/fixtures/__init__.py
rename to litellm/llms/sap/__init__.py
diff --git a/litellm/llms/sap/chat/models.py b/litellm/llms/sap/chat/models.py
index 8ca2aa7a690e..d685d50277a7 100644
--- a/litellm/llms/sap/chat/models.py
+++ b/litellm/llms/sap/chat/models.py
@@ -1,6 +1,8 @@
-from typing import Union, Literal
+from typing import Union, Literal, Optional
+from enum import Enum
+import warnings
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field, field_validator, model_validator
def validate_different_content(v: Union[str, dict, list]) -> str:
@@ -20,7 +22,7 @@ def validate_different_content(v: Union[str, dict, list]) -> str:
elif isinstance(v, str):
return v
raise ValueError("Content must be a string")
- return v
+
class TextContent(BaseModel):
@@ -49,6 +51,10 @@ class FunctionTool(BaseModel):
parameters: dict = {"type": "object", "properties": {}}
strict: bool = False
+ def model_dump(self, **kwargs) -> dict:
+ kwargs["exclude_unset"] = False
+ return super().model_dump(**kwargs)
+
@field_validator("parameters", mode="before")
@classmethod
def ensure_object_type(cls, v: dict) -> dict:
@@ -66,6 +72,10 @@ class ChatCompletionTool(BaseModel):
type_: Literal["function"] = Field(default="function", alias="type")
function: FunctionTool
+ def model_dump(self, **kwargs) -> dict:
+ kwargs["exclude_unset"] = False
+ return super().model_dump(**kwargs)
+
class MessageToolCall(BaseModel):
id: str
@@ -114,6 +124,9 @@ class SAPToolChatMessage(BaseModel):
)
+ChatMessage = Union[SAPMessage, SAPUserMessage, SAPAssistantMessage, SAPToolChatMessage]
+
+
class ResponseFormat(BaseModel):
type_: Literal["text", "json_object"] = Field(default="text", alias="type")
@@ -128,3 +141,607 @@ class JSONResponseSchema(BaseModel):
class ResponseFormatJSONSchema(BaseModel):
type_: Literal["json_schema"] = Field(default="json_schema", alias="type")
json_schema: JSONResponseSchema
+
+
+class KeyValueListPair(BaseModel):
+ key: str
+ value: list[str]
+
+
+class DocumentMetadataKeyValueListPairs(KeyValueListPair):
+ select_mode: Optional[list[Literal["ignoreIfKeyAbsent"]]] = None
+
+
+class GroundingSearchConfig(BaseModel):
+ max_chunk_count: Optional[int] = Field(default=None, ge=0)
+ max_document_count: Optional[int] = Field(default=None, ge=0)
+
+ @model_validator(mode="after")
+ def validate_max_chunk_count_and_max_document_count(self):
+ if self.max_chunk_count is not None and self.max_document_count is not None:
+ raise ValueError("Cannot specify both maxChunkCount and maxDocumentCount.")
+ return self
+
+
+class DocumentGroundingFilter(BaseModel):
+ id_: Optional[str] = Field(default=None, alias="id")
+ data_repository_type: Literal["vector", "help.sap.com"]
+ search_config: Optional[GroundingSearchConfig] = None
+ data_repositories: Optional[list[str]] = None
+ data_repository_metadata: Optional[list[KeyValueListPair]] = None
+ document_metadata: Optional[list[DocumentMetadataKeyValueListPairs]] = None
+ chunk_metadata: Optional[list[KeyValueListPair]] = None
+
+
+class DocumentGroundingPlaceholders(BaseModel):
+ input: list[str] = Field(min_length=1)
+ output: str
+
+
+class DocumentGroundingConfig(BaseModel):
+ filters: Optional[list[DocumentGroundingFilter]] = None
+ placeholders: DocumentGroundingPlaceholders
+ metadata_params: Optional[list[str]] = None
+
+
+class GroundingModuleConfig(BaseModel):
+ type_: Literal["document_grounding_service"] = Field(
+ default="document_grounding_service", alias="type"
+ )
+ config: DocumentGroundingConfig
+
+
+class Template(BaseModel):
+ template: list[ChatMessage]
+ defaults: Optional[dict[str, str]] = None
+ response_format: Optional[Union[ResponseFormat, ResponseFormatJSONSchema]] = None
+ tools: Optional[list[ChatCompletionTool]] = None
+
+
+class LLMModelDetails(BaseModel):
+ name: str
+ version: str = "latest"
+ params: Optional[dict] = None
+
+
+class PromptTemplatingModuleConfig(BaseModel):
+ prompt: Template
+ model: LLMModelDetails
+
+
+class SAPMaskingProfileEntity(str, Enum):
+ """
+ Enumerates the entity categories that can be masked by the SAP Data Privacy Integration service.
+
+ This enum lists different types of personal or sensitive information (PII) that can be detected and masked
+ by the data masking module, such as personal details, organizational data, contact information, and identifiers.
+
+ Values:
+ PERSON: Represents personal names.
+
+ ORG: Represents organizational names.
+
+ UNIVERSITY: Represents educational institutions.
+
+ LOCATION: Represents geographical locations.
+
+ EMAIL: Represents email addresses.
+
+ PHONE: Represents phone numbers.
+
+ ADDRESS: Represents physical addresses.
+
+ SAP_IDS_INTERNAL: Represents internal SAP identifiers.
+
+ SAP_IDS_PUBLIC: Represents public SAP identifiers.
+
+ URL: Represents URLs.
+
+ USERNAME_PASSWORD: Represents usernames and passwords.
+
+ NATIONAL_ID: Represents national identification numbers.
+
+ IBAN: Represents International Bank Account Numbers.
+
+ SSN: Represents Social Security Numbers.
+
+ CREDIT_CARD_NUMBER: Represents credit card numbers.
+
+ PASSPORT: Represents passport numbers.
+
+ DRIVING_LICENSE: Represents driving license numbers.
+
+ NATIONALITY: Represents nationality information.
+
+ RELIGIOUS_GROUP: Represents religious group affiliation.
+
+ POLITICAL_GROUP: Represents political group affiliation.
+
+ PRONOUNS_GENDER: Represents pronouns and gender identity.
+
+ GENDER: Represents gender information.
+
+ SEXUAL_ORIENTATION: Represents sexual orientation.
+
+ TRADE_UNION: Represents trade union membership.
+
+ SENSITIVE_DATA: Represents any other sensitive information.
+ """
+
+ PERSON = "profile-person"
+ ORG = "profile-org"
+ UNIVERSITY = "profile-university"
+ LOCATION = "profile-location"
+ EMAIL = "profile-email"
+ PHONE = "profile-phone"
+ ADDRESS = "profile-address"
+ SAP_IDS_INTERNAL = "profile-sapids-internal"
+ SAP_IDS_PUBLIC = "profile-sapids-public"
+ URL = "profile-url"
+ USERNAME_PASSWORD = "profile-username-password"
+ NATIONAL_ID = "profile-nationalid"
+ IBAN = "profile-iban"
+ SSN = "profile-ssn"
+ CREDIT_CARD_NUMBER = "profile-credit-card-number"
+ PASSPORT = "profile-passport"
+ DRIVING_LICENSE = "profile-driverlicense"
+ NATIONALITY = "profile-nationality"
+ RELIGIOUS_GROUP = "profile-religious-group"
+ POLITICAL_GROUP = "profile-political-group"
+ PRONOUNS_GENDER = "profile-pronouns-gender"
+ GENDER = "profile-gender"
+ SEXUAL_ORIENTATION = "profile-sexual-orientation"
+ TRADE_UNION = "profile-trade-union"
+ SENSITIVE_DATA = "profile-sensitive-data"
+ ETHNICITY = "profile-ethnicity"
+
+
+class DPIMethodConstant(BaseModel):
+ """
+ Replaces the entity with the specified value followed by an incrementing number
+ """
+
+ method: Literal["constant"] = "constant"
+ value: str
+
+
+class DPIMethodFabricatedData(BaseModel):
+ """
+ Replaces the entity with a randomly generated value appropriate to its type.
+ """
+
+ method: Literal["fabricated_data"] = "fabricated_data"
+
+
+class DPICustomEntity(BaseModel):
+ """
+ regex: Regular expression to match the entity
+ replacement_strategy: Replacement strategy to be used for the entity
+ """
+
+ regex: str
+ replacement_strategy: DPIMethodConstant
+
+
+class DPIStandardEntity(BaseModel):
+ """
+ type: Standard entity type to be masked
+ replacement_strategy: Replacement strategy to be used for the entity
+ """
+
+ type_: SAPMaskingProfileEntity = Field(..., alias="type")
+ replacement_strategy: Optional[
+ Union[DPIMethodConstant, DPIMethodFabricatedData]
+ ] = None
+
+
+class MaskGroundingInput(BaseModel):
+ """
+ Controls whether the input to the grounding module will be masked with the configuration
+ supplied in the masking module
+ """
+
+ enabled: bool = False
+
+
+class MaskingProviderConfig(BaseModel):
+ """
+ SAP Data Privacy Integration provider for data masking.
+
+ This class implements the SAP Data Privacy Integration service, which can anonymize or pseudonymize
+ specified entity categories in the input data. It supports masking sensitive information like personal names,
+ contact details, and identifiers.
+
+ Args:
+ method: The method of masking to apply (anonymization or pseudonymization).
+
+ entities: A list of entity categories to be masked, such as names, locations, or emails.
+
+ allowlist: A list of strings that should not be masked.
+
+ mask_grounding_input: A flag indicating whether to mask input to the grounding module.
+ """
+
+ type_: Literal["sap_data_privacy_integration"] = Field(
+ default="sap_data_privacy_integration", alias="type"
+ )
+ method: Literal["anonymization", "pseudonymization"]
+ entities: list[Union[DPIStandardEntity, DPICustomEntity]]
+ allowlist: Optional[list[str]] = None
+ mask_grounding_input: Optional[MaskGroundingInput] = None
+
+
+class MaskingModuleConfig(BaseModel):
+ """
+ Configuration for the data masking module.
+
+ Args:
+ providers: list of masking service provider configurations
+ masking_providers: list of masking provider configurations
+ IMPORTANT: use exactly one of the parameters to set the list of masking provider configurations.
+ DEPRECATED: parameter 'masking_providers' will be removed Sept 15, 2026. Use 'providers' instead.
+ """
+
+ providers: Optional[list[MaskingProviderConfig]] = Field(min_length=1, default=None)
+ masking_providers: Optional[list[MaskingProviderConfig]] = Field(
+ min_length=1, default=None
+ )
+
+ @model_validator(mode="after")
+ def enforce_exactly_one_provider_list(self):
+ has_providers = self.providers is not None
+ has_masking_providers = self.masking_providers is not None
+
+ if not has_providers and not has_masking_providers:
+ raise ValueError(
+ "For SAP Masking Module Config you must provide 'providers'."
+ )
+ if has_providers and has_masking_providers:
+ raise ValueError(
+ "For SAP Masking Module Config you must set exactly one of: 'providers' or 'masking_providers', not both."
+ )
+
+ if has_masking_providers:
+ warnings.warn(
+ "The 'masking_providers' parameter is deprecated and will be removed on Sept 15, 2026. "
+ "Use 'providers' instead.",
+ DeprecationWarning,
+ stacklevel=5,
+ )
+
+ return self
+
+
+class AzureThreshold(int, Enum):
+ """
+ Enumerates the threshold levels for the Azure Content Safety service.
+
+ This enum defines the various threshold levels that can be used to filter
+ content based on its safety score. Each threshold value represents a specific
+ level of content moderation.
+
+ Values:
+ ALLOW_SAFE: Allows only Safe content.
+
+ ALLOW_SAFE_LOW: Allows Safe and Low content.
+
+ ALLOW_SAFE_LOW_MEDIUM: Allows Safe, Low, and Medium content.
+
+ ALLOW_ALL: Allows all content (Safe, Low, Medium, and High).
+ """
+
+ ALLOW_SAFE = 0
+ ALLOW_SAFE_LOW = 2
+ ALLOW_SAFE_LOW_MEDIUM = 4
+ ALLOW_ALL = 6
+
+
+class AzureContentFilter(BaseModel):
+ """
+ Specific filter configuration for Azure Content Safety.
+
+ This class configures content filtering based on Azure's categories and
+ severity levels. It allows setting thresholds for hate speech, sexual content,
+ violence, and self-harm content.
+
+ Values:
+ hate: Threshold for hate speech content.
+
+ sexual: Threshold for sexual content.
+
+ violence: Threshold for violent content.
+
+ self_harm: Threshold for self-harm content.
+ """
+
+ hate: Optional[Union[AzureThreshold, Literal[0, 2, 4, 6]]] = None
+ sexual: Optional[Union[AzureThreshold, Literal[0, 2, 4, 6]]] = None
+ violence: Optional[Union[AzureThreshold, Literal[0, 2, 4, 6]]] = None
+ self_harm: Optional[Union[AzureThreshold, Literal[0, 2, 4, 6]]] = None
+
+
+class AzureContentSafetyInput(AzureContentFilter):
+ """
+ Filter configuration for Azure Content Safety Input
+
+ Values:
+ hate: Threshold for hate speech content.
+
+ sexual: Threshold for sexual content.
+
+ violence: Threshold for violent content.
+
+ self_harm: Threshold for self-harm content.
+
+ prompt_shield: A flag to use prompt shield
+ """
+
+ prompt_shield: Optional[bool] = False
+
+
+class AzureContentSafetyOutput(AzureContentFilter):
+ """
+ Filter configuration for Azure Content Safety Output
+
+ Values:
+ hate: Threshold for hate speech content.
+
+ sexual: Threshold for sexual content.
+
+ violence: Threshold for violent content.
+
+ self_harm: Threshold for self-harm content.
+
+ protected_material_code: Detect protected code content from known GitHub repositories.
+ The scan includes software libraries, source code, algorithms,
+ and other proprietary programming content.
+ """
+
+ protected_material_code: Optional[bool] = False
+
+
+class LlamaGuard38bFilter(BaseModel):
+ """
+ Specific implementation of ContentFilter for Llama Guard 3. Llama Guard 3 is a
+ Llama-3.1-8B pretrained model, fine-tuned for content safety classification.
+
+ Args:
+ violent_crimes: Responses that enable, encourage, or endorse the commission of violent crimes.
+
+ non_violent_crimes: Responses that enable, encourage, or endorse the commission of non-violent crimes.
+
+ sex_crimes: Responses that enable, encourage, or endorse the commission of sex-related crimes.
+
+ child_exploitation: Responses that contain, describe, enable or endorse the sexual abuse of children.
+
+ defamation: Responses that are both verifiably false and likely to injure a living person's reputation.
+
+ specialized_advice: Responses that contain specialized financial, medical or legal advice.
+
+ privacy: Responses that contain sensitive or nonpublic personal information.
+
+ intellectual_property: Responses that may violate the intellectual property rights of any third party.
+
+ indiscriminate_weapons: Responses that enable, encourage, or endorse the creation of indiscriminate weapons.
+
+ hate: Responses that demean or dehumanize people on the basis of their sensitive, personal characteristics.
+
+ self_harm: Responses that enable, encourage, or endorse acts of intentional self-harm.
+
+ sexual_content: Responses that contain erotica.
+
+ elections: Responses that contain factually incorrect information about electoral systems and processes.
+
+ code_interpreter_abuse: Responses that seek to abuse code interpreters.
+ """
+
+ violent_crimes: bool = Field(default=False)
+ non_violent_crimes: bool = Field(default=False)
+ sex_crimes: bool = Field(default=False)
+ child_exploitation: bool = Field(default=False)
+ defamation: bool = Field(default=False)
+ specialized_advice: bool = Field(default=False)
+ privacy: bool = Field(default=False)
+ intellectual_property: bool = Field(default=False)
+ indiscriminate_weapons: bool = Field(default=False)
+ hate: bool = Field(default=False)
+ self_harm: bool = Field(default=False)
+ sexual_content: bool = Field(default=False)
+ elections: bool = Field(default=False)
+ code_interpreter_abuse: bool = Field(default=False)
+
+
+class LlamaGuard38bFilterConfig(BaseModel):
+ type_: Literal["llama_guard_3_8b"] = Field(default="llama_guard_3_8b", alias="type")
+ config: LlamaGuard38bFilter
+
+
+class AzureContentSafetyInputFilterConfig(BaseModel):
+ type_: Literal["azure_content_safety"] = Field(
+ default="azure_content_safety", alias="type"
+ )
+ config: Optional[AzureContentSafetyInput] = None
+
+
+class AzureContentSafetyOutputFilterConfig(BaseModel):
+ type_: Literal["azure_content_safety"] = Field(
+ default="azure_content_safety", alias="type"
+ )
+ config: Optional[AzureContentSafetyOutput] = None
+
+
+class FilteringStreamOptions(BaseModel):
+ """
+ overlap: Number of characters that should be additionally sent to content filtering services
+ from previous chunks as additional context.
+ """
+
+ overlap: Optional[int] = Field(default=0, ge=0, le=10000)
+
+
+class InputFiltering(BaseModel):
+ """Module for managing and applying input content filters.
+
+ Args:
+ filters: List of ContentFilter objects to be applied to input content.
+ """
+
+ filters: list[
+ Union[AzureContentSafetyInputFilterConfig, LlamaGuard38bFilterConfig]
+ ] = Field(min_length=1)
+
+
+class OutputFiltering(BaseModel):
+ """Module for managing and applying output content filters.
+
+ Args:
+ filters: List of ContentFilter objects to be applied to output content.
+
+ stream_options: Module-specific streaming options.
+ """
+
+ filters: list[
+ Union[AzureContentSafetyOutputFilterConfig, LlamaGuard38bFilterConfig]
+ ] = Field(min_length=1)
+ stream_options: Optional[FilteringStreamOptions] = None
+
+
+class FilteringModuleConfig(BaseModel):
+ """Module for managing and applying content filters.
+
+ Args:
+ input: Module for filtering and validating input content before processing.
+
+ output: Module for filtering and validating output content after generation.
+ """
+
+ input: Optional[InputFiltering] = None
+ output: Optional[OutputFiltering] = None
+
+ @model_validator(mode="after")
+ def enforce_min_properties(self) -> "FilteringModuleConfig":
+ """
+ Ensure at least one of input or output filtering is provided.
+ """
+ if self.input is None and self.output is None:
+ raise ValueError(
+ "For using SAP Filtering Module you must provide at least one property: input or output filters."
+ )
+ return self
+
+
+class SAPDocumentTranslationApplyToSelector(BaseModel):
+ """
+ This selector allows you to define the scope of translation, such as specific placeholders or
+ messages with specific roles.
+ For example, {"category": "placeholders",
+ "items": ["user_input"],
+ "source_language": "de-DE"}
+ targets the value of "user_input" in placeholder_values specified in the request payload;
+ and considers the value to be in German.
+ """
+
+ category: Literal["placeholders", "template_roles"]
+ items: list[str]
+ source_language: str
+
+
+class InputTranslationConfig(BaseModel):
+ """
+ Configuration for input translation.
+
+ Args:
+ source_language: Language of the text to be translated. Example: de-DE
+ target_language: Language to which the text should be translated. Example: en-US
+ apply_to: List of selectors that define the scope of translation.
+ """
+
+ source_language: Optional[str] = None
+ target_language: str
+ apply_to: Optional[list[SAPDocumentTranslationApplyToSelector]] = None
+
+
+class OutputTranslationConfig(BaseModel):
+ source_language: Optional[str] = None
+ target_language: Union[str, SAPDocumentTranslationApplyToSelector]
+
+
+class SAPDocumentTranslationInput(BaseModel):
+ """
+ Configuration for input translation
+
+ Args:
+ type: The type of translation module (e.g., 'sap_document_translation').
+
+ translate_messages_history: If true, the messages history will be translated as well.
+
+ config: Configuration object for the translation module.
+ """
+
+ type_: Literal["sap_document_translation"] = Field(
+ default="sap_document_translation", alias="type"
+ )
+ translate_messages_history: Optional[bool] = None
+ config: InputTranslationConfig
+
+
+class SAPDocumentTranslationOutput(BaseModel):
+ """
+ Configuration for output translation
+
+ Args:
+ type: The type of translation module (e.g., 'sap_document_translation').
+
+ config: Configuration object for the translation module.
+ """
+
+ type_: Literal["sap_document_translation"] = Field(
+ default="sap_document_translation", alias="type"
+ )
+ config: OutputTranslationConfig
+
+
+class TranslationModuleConfig(BaseModel):
+ """
+ Configuration for translation module
+
+ Args:
+ input: Configuration for input translation
+
+ output: Configuration for output translation
+ """
+
+ input: Optional[SAPDocumentTranslationInput] = None
+ output: Optional[SAPDocumentTranslationOutput] = None
+
+ @model_validator(mode="after")
+ def enforce_min_properties(self) -> "TranslationModuleConfig":
+ if self.input is None and self.output is None:
+ raise ValueError(
+ "TranslationModuleConfig requires at least one of 'input' or 'output'."
+ )
+ return self
+
+
+class ModuleConfig(BaseModel):
+ prompt_templating: PromptTemplatingModuleConfig
+ filtering: Optional[FilteringModuleConfig] = None
+ masking: Optional[MaskingModuleConfig] = None
+ grounding: Optional[GroundingModuleConfig] = None
+ translation: Optional[TranslationModuleConfig] = None
+
+
+class GlobalStreamOptions(BaseModel):
+ enabled: bool = False
+ chunk_size: Optional[int] = Field(default=None, ge=1)
+ delimiters: Optional[list[str]] = None
+
+
+class OrchestrationConfig(BaseModel):
+ modules: Union[ModuleConfig, list[ModuleConfig]]
+ stream: Optional[GlobalStreamOptions] = None
+
+
+class OrchestrationRequest(BaseModel):
+ config: OrchestrationConfig
+ placeholder_values: Optional[dict[str, str]] = None
diff --git a/litellm/llms/sap/chat/transformation.py b/litellm/llms/sap/chat/transformation.py
index 7f6bab4a1d55..a55ec7463502 100755
--- a/litellm/llms/sap/chat/transformation.py
+++ b/litellm/llms/sap/chat/transformation.py
@@ -11,6 +11,7 @@
TYPE_CHECKING,
Iterator,
AsyncIterator,
+ FrozenSet,
)
from functools import cached_property
import litellm
@@ -31,12 +32,13 @@
from ..credentials import get_token_creator
from .models import (
- SAPMessage,
- SAPAssistantMessage,
- SAPToolChatMessage,
ChatCompletionTool,
- ResponseFormatJSONSchema,
+ OrchestrationRequest,
ResponseFormat,
+ ResponseFormatJSONSchema,
+ SAPAssistantMessage,
+ SAPMessage,
+ SAPToolChatMessage,
SAPUserMessage,
)
from .handler import (
@@ -45,9 +47,65 @@
SAPStreamIterator,
)
+# Keys routed outside SAP orchestration `model.params` (prompt, stream, fallbacks, etc.)
+_SAP_MODEL_PARAMS_EXCLUDED_KEYS: FrozenSet[str] = frozenset(
+ {
+ "tools",
+ "tool_choice",
+ "stream_options",
+ "fallback_sap_modules",
+ "placeholder_values",
+ "model_version",
+ }
+)
+
def validate_dict(data: dict, model) -> dict:
- return model(**data).model_dump(by_alias=True)
+ return model(**data).model_dump(by_alias=True, exclude_unset=True)
+
+
+def _messages_to_sap_template(messages: List[Dict[str, str]]) -> list: # type: ignore[type-arg]
+ template = []
+ for message in messages:
+ if message["role"] == "user":
+ template.append(validate_dict(message, SAPUserMessage))
+ elif message["role"] == "assistant":
+ template.append(validate_dict(message, SAPAssistantMessage))
+ elif message["role"] == "tool":
+ template.append(validate_dict(message, SAPToolChatMessage))
+ else:
+ template.append(validate_dict(message, SAPMessage))
+ return template
+
+
+def _tools_response_format_and_stream(
+ optional_params: dict, model_params: dict
+) -> Tuple[dict, dict, dict]:
+ tools_ = optional_params.pop("tools", [])
+ tools_ = [validate_dict(tool, ChatCompletionTool) for tool in tools_]
+ tools: dict = {"tools": tools_} if tools_ else {}
+
+ response_format = model_params.pop("response_format", {})
+ resp_type = response_format.get("type", None)
+ if resp_type:
+ if resp_type == "json_schema":
+ response_format = validate_dict(
+ response_format, ResponseFormatJSONSchema
+ )
+ else:
+ response_format = validate_dict(response_format, ResponseFormat)
+ response_format = {"response_format": response_format}
+
+ model_params.pop("stream", False)
+ stream_config: dict = {}
+ if "stream_options" in optional_params:
+ stream_options = optional_params.pop("stream_options", {})
+ if "chunk_size" in stream_options:
+ stream_config["chunk_size"] = stream_options.get("chunk_size")
+ if "delimiters" in stream_options:
+ stream_config["delimiters"] = stream_options.get("delimiters")
+
+ return tools, response_format, stream_config
class GenAIHubOrchestrationConfig(OpenAIGPTConfig):
@@ -208,48 +266,25 @@ def get_complete_url(
api_base_ = f"{self.deployment_url}/v2/completion"
return api_base_
- def transform_request(
+ def _build_prompt_module(
self,
- model: str,
- messages: List[Dict[str, str]], # type: ignore
- optional_params: dict,
- litellm_params: dict,
- headers: dict,
+ model_name: str,
+ template_messages: List[Dict[str, str]],
+ params: dict,
) -> dict:
- # Filter out parameters that are not valid model params for SAP Orchestration API
- # - tools, model_version, deployment_url: handled separately
- excluded_params = {"tools", "model_version", "deployment_url"}
-
# Filter strict for GPT models only - SAP AI Core doesn't accept it as a model param
# LangChain agents pass strict=true at top level, which fails for GPT models
# Anthropic models accept strict, so preserve it for them
- if model.startswith("gpt"):
- excluded_params.add("strict")
+ if model_name.startswith("gpt") and "strict" in params:
+ params.pop("strict")
- model_params = {
- k: v for k, v in optional_params.items() if k not in excluded_params
- }
-
- model_version = optional_params.pop("model_version", "latest")
- template = []
- for message in messages:
- if message["role"] == "user":
- template.append(validate_dict(message, SAPUserMessage))
- elif message["role"] == "assistant":
- template.append(validate_dict(message, SAPAssistantMessage))
- elif message["role"] == "tool":
- template.append(validate_dict(message, SAPToolChatMessage))
- else:
- template.append(validate_dict(message, SAPMessage))
+ model_version = params.pop("model_version", "latest")
- tools_ = optional_params.pop("tools", [])
+ tools_ = params.pop("tools", [])
tools_ = [validate_dict(tool, ChatCompletionTool) for tool in tools_]
- if tools_ != []:
- tools = {"tools": tools_}
- else:
- tools = {}
+ tools = {"tools": tools_} if tools_ else {}
- response_format = model_params.pop("response_format", {})
+ response_format = params.pop("response_format", {})
resp_type = response_format.get("type", None)
if resp_type:
if resp_type == "json_schema":
@@ -259,33 +294,104 @@ def transform_request(
else:
response_format = validate_dict(response_format, ResponseFormat)
response_format = {"response_format": response_format}
- model_params.pop("stream", False)
- stream_config = {}
- if "stream_options" in model_params:
- # stream_config["enabled"] = True
- stream_options = model_params.pop("stream_options", {})
- stream_config["chunk_size"] = stream_options.get("chunk_size", 100)
- if "delimiters" in stream_options:
- stream_config["delimiters"] = stream_options.get("delimiters")
- # else:
- # stream_config["enabled"] = False
- config = {
- "config": {
- "modules": {
- "prompt_templating": {
- "prompt": {"template": template, **tools, **response_format},
- "model": {
- "name": model,
- "params": model_params,
- "version": model_version,
- },
- },
+ else:
+ response_format = {}
+
+ placeholder_defaults = params.pop("placeholder_defaults", {})
+ placeholder_defaults = (
+ {"defaults": placeholder_defaults} if placeholder_defaults else {}
+ )
+
+ optional_modules = {}
+ optional_modules_lst = ["grounding", "masking", "filtering", "translation"]
+ for module in optional_modules_lst:
+ if params.get(module, None) is not None:
+ optional_modules[module] = params.pop(module)
+
+ return {
+ "prompt_templating": {
+ "prompt": {
+ "template": template_messages,
+ **placeholder_defaults,
+ **tools,
+ **response_format,
},
- "stream": stream_config,
- }
+ "model": {
+ "name": model_name,
+ "params": params,
+ "version": model_version,
+ },
+ },
+ **optional_modules,
+ }
+
+ def transform_request(
+ self,
+ model: str,
+ messages: List[Dict[str, str]], # type: ignore
+ optional_params: dict,
+ litellm_params: dict,
+ headers: dict,
+ ) -> dict:
+ optional_params = dict(optional_params)
+ optional_params.pop("deployment_url", None)
+
+ template = _messages_to_sap_template(messages)
+
+ placeholder_values = optional_params.pop("placeholder_values", None)
+ fallback_modules = optional_params.pop("fallback_sap_modules", [])
+
+ optional_params.pop("stream", None)
+ stream_config: dict = {}
+ if "stream_options" in optional_params:
+ stream_options = optional_params.pop("stream_options", {})
+ if "chunk_size" in stream_options:
+ stream_config["chunk_size"] = stream_options["chunk_size"]
+ if "delimiters" in stream_options:
+ stream_config["delimiters"] = stream_options["delimiters"]
+
+ optional_params.pop("tool_choice", None)
+
+ modules = [
+ self._build_prompt_module(
+ model_name=model,
+ template_messages=template,
+ params=dict(optional_params),
+ )
+ ]
+
+ for modules_dict in fallback_modules:
+ modules_dict = dict(modules_dict)
+ fallback_model = modules_dict.pop("model", None)
+ if fallback_model is None:
+ raise ValueError(
+ "Each entry in `fallback_sap_modules` must include a 'model' key."
+ )
+ if fallback_model.startswith("sap/"):
+ fallback_model = fallback_model[4:]
+ fallback_template = modules_dict.pop("messages", [])
+
+ modules.append(
+ self._build_prompt_module(
+ model_name=fallback_model,
+ template_messages=fallback_template,
+ params=modules_dict,
+ )
+ )
+
+ config_payload: Dict[str, Any] = {
+ "modules": modules if len(modules) > 1 else modules[0],
}
+ if stream_config:
+ config_payload["stream"] = stream_config
+
+ request_body: Dict[str, Any] = {"config": config_payload}
+ if placeholder_values is not None:
+ request_body["placeholder_values"] = placeholder_values
+
+ body = validate_dict(request_body, OrchestrationRequest)
- return config
+ return body
def transform_response(
self,
diff --git a/litellm/llms/sap/credentials.py b/litellm/llms/sap/credentials.py
index aeae51bf0bb6..0ae351783e87 100644
--- a/litellm/llms/sap/credentials.py
+++ b/litellm/llms/sap/credentials.py
@@ -1,5 +1,5 @@
from __future__ import annotations
-from typing import Any, Callable, Dict, Final, List, Optional, Sequence, Tuple
+from typing import Any, Callable, Dict, Final, List, Optional, Sequence, Tuple, Union
from datetime import datetime, timedelta, timezone
from threading import Lock
from pathlib import Path
@@ -7,9 +7,11 @@
import json
import os
import tempfile
+import httpx
-from litellm import sap_service_key
-from litellm.llms.custom_httpx.http_handler import _get_httpx_client
+from litellm.llms.custom_httpx.http_handler import _get_httpx_client, HTTPHandler
+from litellm._logging import verbose_logger
+import litellm
AUTH_ENDPOINT_SUFFIX = "/oauth/token"
@@ -28,11 +30,25 @@ def _get_home() -> str:
return os.getenv(HOME_PATH_ENV_VAR, DEFAULT_HOME_PATH)
-def _get_nested(d: Dict[str, Any], path: Sequence[str]) -> Any:
+def _get_nested(d: Union[Dict[str, Any], str], path: Sequence[str]) -> Any:
cur: Any = d
+ if isinstance(cur, str):
+ # This shouldn't happen if service keys are pre-parsed correctly
+ try:
+ cur = json.loads(cur)
+ except json.JSONDecodeError:
+ verbose_logger.warning(
+ "SAP service key or VCAP service is a string but not valid JSON."
+ )
+ return None
for k in path:
- if not isinstance(cur, dict) or k not in cur:
- raise KeyError(".".join(path))
+ if not isinstance(cur, dict):
+ verbose_logger.warning(
+ f"SAP service key or VCAP service traversal hit non-dict type '{type(cur).__name__}' at key '{k}'."
+ )
+ return None
+ if k not in cur:
+ return None
cur = cur[k]
return cur
@@ -47,6 +63,13 @@ def _load_json_env(var_name: str) -> Optional[Dict[str, Any]]:
return None
+def _str_or_none(value) -> Optional[str]:
+ try:
+ return str(value) if value is not None else None
+ except Exception:
+ return None
+
+
def _load_vcap() -> Dict[str, Any]:
return _load_json_env(VCAP_SERVICES_ENV_VAR) or {}
@@ -59,6 +82,12 @@ def _get_vcap_service(label: str) -> Optional[Dict[str, Any]]:
return None
+@dataclass
+class Source:
+ name: str
+ get: Callable[[CredentialsValue], Optional[str]]
+
+
@dataclass(frozen=True)
class CredentialsValue:
name: str
@@ -82,7 +111,6 @@ class CredentialsValue:
transform_fn=lambda url: url.rstrip("/")
+ ("" if url.endswith("/v2") else "/v2"),
),
- CredentialsValue("resource_group", default="default"),
CredentialsValue(
"cert_url",
("certurl",),
@@ -145,81 +173,239 @@ def _env_name(name: str) -> str:
return f"AICORE_{name.upper()}"
-def _resolve_value(
- cred: CredentialsValue,
- *,
- kwargs: Dict[str, Any],
- env: Dict[str, str],
- config: Dict[str, Any],
- service_like: Optional[Dict[str, Any]],
-) -> Optional[str]:
- # 1) explicit kwargs
- if cred.name in kwargs and kwargs[cred.name] is not None:
- return kwargs[cred.name]
-
- # 2) environment variables (primary name)
- env_key = _env_name(cred.name)
- if env_key in env and env[env_key] is not None:
- return env[env_key]
-
- # 3) config file (accept both prefixed and plain keys)
- for key in (env_key, cred.name):
- if key in config and config[key] is not None:
- return config[key]
-
- # 4) service-like source (AICORE_SERVICE_KEY first, else VCAP)
- if service_like and cred.vcap_key:
+def extract_credentials(source: Source) -> Dict[str, str]:
+ """Extract all credentials from a source."""
+ credentials = {}
+ for cv in CREDENTIAL_VALUES:
+ value = source.get(cv)
+ if value is not None:
+ credentials[cv.name] = cv.transform_fn(value) if cv.transform_fn else value
+ return credentials
+
+
+def resolve_credentials(sources: List[Source]) -> Dict[str, str]:
+ """Extract credentials from the first source that has any defined."""
+ for source in sources:
+ credentials = extract_credentials(source)
+ if credentials:
+ verbose_logger.debug(f"Resolved SAP credentials from source {source.name}")
+ return credentials
+ raise ValueError("No credentials found in any source")
+
+
+def resolve_resource_group(sources: List[Source]) -> Optional[str]:
+ """Find resource_group from the first source that defines it."""
+ rg_cred = CredentialsValue("resource_group", default="default")
+ for source in sources:
+ value = source.get(rg_cred)
+ if value is not None:
+ verbose_logger.debug(
+ f"Resolved GEN AI Hub resource_group from source {source.name}"
+ )
+ return value
+ return rg_cred.default
+
+
+def _parse_service_key_once(
+ service_key: Optional[Union[str, dict]]
+) -> Optional[Dict[str, Any]]:
+ """
+ Pre-parse service_key if it's a string to avoid repeated JSON parsing.
+
+ Returns None if parsing fails (other credential sources may still work).
+ """
+ if service_key is None:
+ return None
+ if isinstance(service_key, dict):
+ return service_key
+ if isinstance(service_key, str):
try:
- val = _get_nested(service_like, ("credentials",) + cred.vcap_key)
- if val is not None:
- return val
- except KeyError:
- pass
+ return json.loads(service_key)
+ except json.JSONDecodeError:
+ verbose_logger.warning(
+ "SAP service key is a string but not valid JSON. Skipping this source."
+ )
+ return None
+ verbose_logger.warning(
+ f"SAP service key has unexpected type '{type(service_key).__name__}'. Expected str or dict. Ignoring."
+ )
+ return None
+
- # 5) default
- return cred.default
+def _resolve_credential_from_service_key(
+ service_key: Optional[Union[str, dict]], cv: CredentialsValue
+) -> Optional[str]:
+ if service_key is None:
+ return None
+ val = _str_or_none(
+ _get_nested(
+ service_key, (("credentials",) + cv.vcap_key) if cv.vcap_key else (cv.name,)
+ )
+ )
+ if val is None:
+ return _str_or_none(
+ _get_nested(service_key, cv.vcap_key if cv.vcap_key else (cv.name,))
+ )
+ return val
def fetch_credentials(
- service_key: Optional[str] = None, profile: Optional[str] = None, **kwargs
+ service_key: Optional[Union[str, dict]] = None,
+ profile: Optional[str] = None,
+ **kwargs,
) -> Dict[str, str]:
"""
- Resolution order per key:
+ Resolution order (first-source-wins):
+
+ Sources are checked in this order:
kwargs
+ > service key
> env (AICORE_)
> config (AICORE_ or plain )
- > service-like source from JSON in $AICORE_SERVICE_KEY (same structure as a VCAP service object)
- falling back to service entry in $VCAP_SERVICES with label 'aicore'
+ > vcap service key
> default
+
+ Important:
+ - Credentials are extracted from the FIRST source that provides any credential value.
+ - Values are NOT merged per key across sources. Except resource_group, which is merged.
+
+ Warning:
+ - This function does NOT validate the returned credentials just parsed it from the sources.
+ - Callers MUST explicitly call validate_credentials() on the returned dict
"""
config = init_conf(profile)
- env = os.environ # snapshot for testability
- service_like = None
-
- if not config:
- # Prefer AICORE_SERVICE_KEY if present; otherwise fall back to the VCAP service.
- service_like = (
- service_key
- or sap_service_key
- or _load_json_env(SERVICE_KEY_ENV_VAR)
- or _get_vcap_service(VCAP_AICORE_SERVICE_NAME)
+
+ service_key = _parse_service_key_once(
+ service_key or litellm.sap_service_key or os.environ.get(SERVICE_KEY_ENV_VAR)
+ )
+ vcap_service = _get_vcap_service(VCAP_AICORE_SERVICE_NAME)
+
+ sources = [
+ Source("kwargs", lambda cv: _str_or_none(kwargs.get(cv.name))),
+ Source(
+ "service key",
+ lambda cv: _resolve_credential_from_service_key(service_key, cv),
+ ),
+ Source(
+ "environment variables",
+ lambda cv: _str_or_none(os.environ.get(f"AICORE_{cv.name.upper()}")),
+ ),
+ Source(
+ "config file",
+ lambda cv: _str_or_none(
+ config.get(f"AICORE_{cv.name.upper()}")
+ if config.get(f"AICORE_{cv.name.upper()}") is not None
+ else config.get(cv.name)
+ ),
+ ),
+ Source(
+ "VCAP service",
+ lambda cv: (
+ _str_or_none(
+ _get_nested(
+ vcap_service,
+ (("credentials",) + cv.vcap_key) if cv.vcap_key else (cv.name,),
+ )
+ )
+ if vcap_service
+ else None
+ ),
+ ), # type: ignore[arg-type]
+ ]
+
+ credentials = resolve_credentials(sources)
+
+ resource_group = resolve_resource_group(sources)
+ if resource_group is not None:
+ credentials["resource_group"] = resource_group
+
+ if "cert_url" in credentials:
+ credentials["auth_url"] = credentials.pop("cert_url")
+ return credentials
+
+
+def validate_credentials(
+ auth_url: Optional[str] = None,
+ base_url: Optional[str] = None,
+ client_id: Optional[str] = None,
+ client_secret: Optional[str] = None,
+ cert_str: Optional[str] = None,
+ key_str: Optional[str] = None,
+ cert_file_path: Optional[str] = None,
+ key_file_path: Optional[str] = None,
+) -> None:
+ """
+ Validate SAP AI Core credentials for completeness and consistency.
+
+ Args:
+ auth_url: OAuth2 token endpoint URL (required)
+ base_url: SAP AI Core API base URL (required)
+ client_id: OAuth2 client ID (required)
+ client_secret: OAuth2 client secret (for secret-based auth)
+ cert_str: PEM-encoded certificate string (for cert-based auth)
+ key_str: PEM-encoded private key string (for cert-based auth)
+ cert_file_path: Path to certificate file (for file-based cert auth)
+ key_file_path: Path to private key file (for file-based cert auth)
+
+ Raises:
+ ValueError: If required fields are missing or authentication mode is ambiguous.
+
+ Note:
+ - This function does NOT validate resource_group (resolved separately).
+ - Exactly one authentication method must be provided:
+ * client_secret, OR
+ * (cert_str AND key_str), OR
+ * (cert_file_path AND key_file_path)
+ """
+ if not auth_url or not client_id or not base_url:
+ raise ValueError(
+ "SAP AI Core credentials not found. "
+ "Please provide credentials by setting appropriate environment variables "
+ "(e.g. AICORE_CLIENT_ID, AICORE_CLIENT_SECRET, etc.)"
+ )
+
+ modes = [
+ bool(client_secret),
+ bool(cert_str) and bool(key_str),
+ bool(cert_file_path) and bool(key_file_path),
+ ]
+ if sum(bool(m) for m in modes) != 1:
+ raise ValueError(
+ "SAP AI Core credentials are incomplete. "
+ "Invalid credentials: provide exactly one of client_secret, "
+ "(cert_str & key_str), or (cert_file_path & key_file_path)."
)
- out: Dict[str, str] = {}
- for cred in CREDENTIAL_VALUES:
- value = _resolve_value(cred, kwargs=kwargs, env=env, config=config, service_like=service_like) # type: ignore
- if value is None:
- continue
- if cred.transform_fn:
- value = cred.transform_fn(value)
- out[cred.name] = value
- if "cert_url" in out.keys():
- out["auth_url"] = out.pop("cert_url")
- return out
+
+def _request_token(
+ client_id: str, auth_url: str, timeout: float, cert_pair=None, client_secret=None
+) -> tuple[str, datetime]:
+ data = {"grant_type": "client_credentials", "client_id": client_id}
+ if client_secret:
+ data["client_secret"] = client_secret
+
+ resp: Optional[httpx.Response] = None
+ try:
+ if cert_pair:
+ with httpx.Client(cert=cert_pair) as raw_client:
+ handler = HTTPHandler(client=raw_client)
+ resp = handler.post(auth_url, data=data, timeout=timeout) # type: ignore[arg-type]
+ payload = resp.json()
+ else:
+ handler = _get_httpx_client()
+ resp = handler.post(auth_url, data=data, timeout=timeout) # type: ignore[arg-type]
+ payload = resp.json()
+ access_token = payload["access_token"]
+ expires_in = int(payload.get("expires_in", 3600))
+ expiry_date = datetime.now(timezone.utc) + timedelta(seconds=expires_in)
+ return f"Bearer {access_token}", expiry_date
+ except Exception as e:
+ msg = resp.text if resp is not None else getattr(e, "text", str(e))
+ raise RuntimeError(f"Token request failed: {msg}") from e
def get_token_creator(
- service_key: Optional[str] = None,
+ service_key: Optional[Union[str, dict]] = None,
profile: Optional[str] = None,
*,
timeout: float = 30.0,
@@ -237,7 +423,7 @@ def get_token_creator(
Args:
profile: Optional AICore profile name
- timeout: HTTP request timeout in seconds (default 30s)
+ timeout: Timeout for HTTP requests
expiry_buffer_minutes: Refresh the token this many minutes before expiry
overrides: Any explicit credential overrides (client_id, client_secret, etc.)
@@ -251,6 +437,7 @@ def get_token_creator(
)
auth_url = credentials.get("auth_url")
+ base_url = credentials.get("base_url")
client_id = credentials.get("client_id")
client_secret = credentials.get("client_secret")
cert_str = credentials.get("cert_str")
@@ -259,49 +446,30 @@ def get_token_creator(
key_file_path = credentials.get("key_file_path")
# Sanity check
- if not auth_url or not client_id:
- raise ValueError(
- "fetch_credentials did not return valid 'auth_url' or 'client_id'"
- )
-
- modes = [
- client_secret is not None,
- (cert_str is not None and key_str is not None),
- (cert_file_path is not None and key_file_path is not None),
- ]
- if sum(bool(m) for m in modes) != 1:
- raise ValueError(
- "Invalid credentials: provide exactly one of client_secret, "
- "(cert_str & key_str), or (cert_file_path & key_file_path)."
- )
+ validate_credentials(
+ auth_url,
+ base_url,
+ client_id,
+ client_secret,
+ cert_str,
+ key_str,
+ cert_file_path,
+ key_file_path,
+ )
lock = Lock()
token: Optional[str] = None
token_expiry: Optional[datetime] = None
- def _request_token(cert_pair=None) -> tuple[str, datetime]:
- data = {"grant_type": "client_credentials", "client_id": client_id}
- if client_secret:
- data["client_secret"] = client_secret
-
- client = _get_httpx_client()
- # with httpx.Client(cert=cert_pair, timeout=timeout) as client:
- resp = client.post(auth_url, data=data)
- try:
- resp.raise_for_status()
- payload = resp.json()
- access_token = payload["access_token"]
- expires_in = int(payload.get("expires_in", 3600))
- expiry_date = datetime.now(timezone.utc) + timedelta(seconds=expires_in)
- return f"Bearer {access_token}", expiry_date
- except Exception as e:
- msg = getattr(resp, "text", str(e))
- raise RuntimeError(f"Token request failed: {msg}") from e
-
def _fetch_token() -> tuple[str, datetime]:
# Case 1: secret-based auth
if client_secret:
- return _request_token()
+ return _request_token(
+ auth_url=auth_url, # type: ignore[arg-type]
+ client_id=client_id, # type: ignore[arg-type]
+ timeout=timeout,
+ client_secret=client_secret,
+ )
# Case 2: cert/key strings
if cert_str and key_str:
cert_str_fixed = cert_str.replace("\\n", "\n")
@@ -313,9 +481,24 @@ def _fetch_token() -> tuple[str, datetime]:
f.write(cert_str_fixed)
with open(key_path, "w") as f:
f.write(key_str_fixed)
- return _request_token(cert_pair=(cert_path, key_path))
+ return _request_token(
+ auth_url=auth_url, # type: ignore[arg-type]
+ client_id=client_id, # type: ignore[arg-type]
+ timeout=timeout,
+ cert_pair=(cert_path, key_path),
+ )
# Case 3: file-based cert/key
- return _request_token(cert_pair=(cert_file_path, key_file_path))
+ if cert_file_path is not None and key_file_path is not None:
+ return _request_token(
+ auth_url=auth_url, # type: ignore[arg-type]
+ client_id=client_id, # type: ignore[arg-type]
+ timeout=timeout,
+ cert_pair=(cert_file_path, key_file_path),
+ )
+ # Defensive guard: should never reach here due to validate_credentials()
+ raise ValueError(
+ "Invalid authentication configuration: no valid credentials found. "
+ )
def get_token() -> str:
nonlocal token, token_expiry
diff --git a/litellm/llms/sap/embed/transformation.py b/litellm/llms/sap/embed/transformation.py
index 0bbf4f259f7e..c74f21c36859 100644
--- a/litellm/llms/sap/embed/transformation.py
+++ b/litellm/llms/sap/embed/transformation.py
@@ -5,6 +5,7 @@
from typing import Optional, List, Dict, Literal, Union
from pydantic import BaseModel, Field
from functools import cached_property
+from litellm.llms.sap.chat.models import MaskingModuleConfig
import httpx
@@ -47,25 +48,36 @@ class EmbeddingsResponse(BaseModel):
class EmbeddingModel(BaseModel):
name: str
version: str = "latest"
- params: dict = Field(default_factory=dict, validation_alias="parameters")
+ params: dict = Field(default_factory=dict)
+ timeout: Optional[int] = Field(default=None, ge=1, le=600)
+ max_retries: Optional[int] = Field(default=None, ge=0, le=5)
+
+
+class EmbeddingsModelConfig(BaseModel):
+ model: EmbeddingModel
class EmbeddingsModules(BaseModel):
- embeddings: EmbeddingModel
+ embeddings: EmbeddingsModelConfig
+ masking: Optional[MaskingModuleConfig] = None
class EmbeddingInput(BaseModel):
text: Union[str, List[str]]
- type: Literal["text", "document", "query"] = "text"
+ type: Optional[Literal["text", "document", "query"]] = None
+
+
+class EmbeddingConfig(BaseModel):
+ modules: EmbeddingsModules
class EmbeddingRequest(BaseModel):
- config: EmbeddingsModules
+ config: EmbeddingConfig
input: EmbeddingInput
def validate_dict(data: dict, model) -> dict:
- return model(**data).model_dump()
+ return model(**data).model_dump(exclude_unset=True, by_alias=True)
class GenAIHubEmbeddingConfig(BaseEmbeddingConfig):
@@ -152,15 +164,23 @@ def transform_embedding_request(
model_dict["name"] = model
model_dict["version"] = optional_params.get("version", "latest")
model_dict["params"] = optional_params.get("parameters", {})
+ timeout = optional_params.get("timeout", None)
+ if timeout is not None:
+ model_dict["timeout"] = timeout
+ max_retries = optional_params.get("max_retries", None)
+ if max_retries is not None:
+ model_dict["max_retries"] = max_retries
input_dict = {"text": input}
+ input_type = optional_params.get("type")
+ if input_type is not None:
+ input_dict["type"] = input_type
+ masking = optional_params.get("masking")
+ masking = {"masking": masking} if masking is not None else {}
body = {
- "config": {
- "modules": {
- "embeddings": {"model": validate_dict(model_dict, EmbeddingModel)}
- }
- },
- "input": validate_dict(input_dict, EmbeddingInput),
+ "config": {"modules": {"embeddings": {"model": model_dict}, **masking}},
+ "input": input_dict,
}
+ body = validate_dict(body, EmbeddingRequest)
return body
def transform_embedding_response(
diff --git a/litellm/llms/triton/embedding/transformation.py b/litellm/llms/triton/embedding/transformation.py
index 8ab0277e3696..93d1c25f1696 100644
--- a/litellm/llms/triton/embedding/transformation.py
+++ b/litellm/llms/triton/embedding/transformation.py
@@ -8,7 +8,8 @@
LiteLLMLoggingObj,
)
from litellm.types.llms.openai import AllEmbeddingInputValues
-from litellm.types.utils import EmbeddingResponse
+from litellm.types.utils import EmbeddingResponse, Usage
+from litellm.utils import token_counter
from ..common_utils import TritonError
@@ -103,8 +104,36 @@ def transform_embedding_response(
model_response.model = raw_response_json.get("model_name", "None")
model_response.data = _embedding_output
+ model_response.usage = self._build_embedding_usage(
+ model=model, request_data=request_data
+ )
return model_response
+ def _build_embedding_usage(self, model: str, request_data: dict) -> Usage:
+ input_data = request_data.get("inputs", [])
+ input_text_values: List[str] = []
+ for item in input_data:
+ if isinstance(item, dict) and item.get("name") == "input_text":
+ data_values = item.get("data", [])
+ if isinstance(data_values, list):
+ input_text_values = [str(value) for value in data_values]
+ break
+
+ prompt_tokens = 0
+ for text in input_text_values:
+ if not text:
+ continue
+ try:
+ prompt_tokens += token_counter(model=model, text=text)
+ except Exception:
+ prompt_tokens += len(text.split())
+
+ return Usage(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=0,
+ total_tokens=prompt_tokens,
+ )
+
def get_error_class(
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
) -> BaseLLMException:
diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py
index 7945c44d44cd..6157a384dc08 100644
--- a/litellm/llms/vertex_ai/gemini/transformation.py
+++ b/litellm/llms/vertex_ai/gemini/transformation.py
@@ -763,6 +763,16 @@ def _transform_request_body( # noqa: PLR0915
data["generationConfig"] = generation_config
if cached_content is not None:
data["cachedContent"] = cached_content
+
+ if service_tier := optional_params.pop("service_tier", None):
+ if isinstance(service_tier, str):
+ if service_tier.lower() == "default":
+ data["serviceTier"] = "standard"
+ else:
+ data["serviceTier"] = service_tier.lower()
+ else:
+ data["serviceTier"] = service_tier
+
# Only add labels for Vertex AI endpoints (not Google GenAI/AI Studio) and only if non-empty
if labels and custom_llm_provider != LlmProviders.GEMINI:
data["labels"] = labels
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 36f51c5b2f59..e6e548ab98a6 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
@@ -318,6 +318,7 @@ def get_supported_openai_params(self, model: str) -> List[str]:
"parallel_tool_calls",
"web_search_options",
"include_server_side_tool_invocations",
+ "service_tier",
]
# Add penalty parameters only for non-preview models
@@ -362,6 +363,17 @@ def _map_web_search_options(self, value: dict) -> Tools:
"""
return Tools(googleSearch={})
+ def _map_service_tier_param(self, value: str, optional_params: dict) -> None:
+ """
+ Map OpenAI service_tier (string) to Gemini serviceTier.
+ 'auto' maps to 'priority'.
+ Other values are passed lowercased.
+ """
+ if value.lower() == "auto":
+ optional_params["service_tier"] = "priority"
+ else:
+ optional_params["service_tier"] = value.lower()
+
def _transform_computer_use_config(self, computer_use_config: dict) -> dict:
"""
Transform Computer Use configuration to Gemini API format.
@@ -1121,6 +1133,8 @@ def map_openai_params( # noqa: PLR0915
optional_params = self._add_tools_to_optional_params(
optional_params, [_tools]
)
+ elif param == "service_tier" and isinstance(value, str):
+ self._map_service_tier_param(value, optional_params)
elif param == "include_server_side_tool_invocations" and value is True:
optional_params["include_server_side_tool_invocations"] = True
if litellm.vertex_ai_safety_settings is not None:
@@ -2415,6 +2429,14 @@ def _transform_google_generate_content_to_openai_model_response(
"provider_specific_fields", {}
)["traffic_type"] = traffic_type
+ ## ADD SERVICE TIER ##
+ if getattr(raw_response, "headers", None):
+ if service_tier := raw_response.headers.get("x-gemini-service-tier"):
+ if service_tier.lower() == "standard":
+ setattr(model_response, "service_tier", "default")
+ else:
+ setattr(model_response, "service_tier", service_tier.lower())
+
except Exception as e:
raise VertexAIError(
message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format(
@@ -2513,6 +2535,7 @@ async def make_call(
streaming_response=response.aiter_lines(),
sync_stream=False,
logging_obj=logging_obj,
+ response_headers=response.headers,
)
# LOGGING
logging_obj.post_call(
@@ -2555,6 +2578,7 @@ def make_sync_call(
streaming_response=response.iter_lines(),
sync_stream=True,
logging_obj=logging_obj,
+ response_headers=response.headers,
)
# LOGGING
@@ -3011,7 +3035,11 @@ def completion(
class ModelResponseIterator:
def __init__(
- self, streaming_response, sync_stream: bool, logging_obj: LoggingClass
+ self,
+ streaming_response,
+ sync_stream: bool,
+ logging_obj: LoggingClass,
+ response_headers: Optional[Dict[str, str]] = None,
):
from litellm.litellm_core_utils.prompt_templates.common_utils import (
check_is_function_call,
@@ -3022,10 +3050,120 @@ def __init__(
self.accumulated_json = ""
self.sent_first_chunk = False
self.logging_obj = logging_obj
+ self.response_headers = response_headers or {}
self.is_function_call = check_is_function_call(logging_obj)
self.cumulative_tool_call_index: int = 0
self.has_seen_tool_calls: bool = False
+ def _apply_stream_candidates(
+ self,
+ _candidates: List[Candidates],
+ model_response: Any,
+ ) -> Tuple[List[dict], List[dict], List[dict], List[dict]]:
+ (
+ grounding_metadata,
+ url_context_metadata,
+ safety_ratings,
+ citation_metadata,
+ self.cumulative_tool_call_index,
+ ) = VertexGeminiConfig._process_candidates(
+ _candidates,
+ model_response,
+ self.logging_obj.optional_params,
+ cumulative_tool_call_index=self.cumulative_tool_call_index,
+ )
+
+ # Track whether tool_calls have been seen across streaming chunks.
+ # Gemini sends tool_calls and finishReason in separate chunks,
+ # so we need to remember if earlier chunks contained tool_calls
+ # to correctly set finish_reason="tool_calls" per the OpenAI spec.
+ if not self.has_seen_tool_calls:
+ for choice in model_response.choices:
+ if (
+ hasattr(choice, "delta")
+ and choice.delta
+ and choice.delta.tool_calls
+ ):
+ self.has_seen_tool_calls = True
+ break
+
+ # Handle final chunk with finishReason but no content.
+ # _process_candidates skips candidates without "content",
+ # so the finish_reason from the final chunk is lost.
+ if not model_response.choices and _candidates:
+ from litellm.types.utils import Delta, StreamingChoices
+
+ for candidate in _candidates:
+ finish_reason_str = candidate.get("finishReason")
+ if finish_reason_str is not None:
+ if self.has_seen_tool_calls:
+ mapped_finish_reason = "tool_calls"
+ else:
+ mapped_finish_reason = VertexGeminiConfig._check_finish_reason(
+ None, finish_reason_str
+ )
+ choice = StreamingChoices(
+ finish_reason=mapped_finish_reason,
+ index=candidate.get("index", 0),
+ delta=Delta(content=None, role=None),
+ logprobs=None,
+ enhancements=None,
+ )
+ model_response.choices.append(choice)
+
+ # Also handle the case where the final chunk has empty
+ # content (e.g. text:"") WITH finishReason. In this case
+ # _process_candidates DOES create a choice, but maps
+ # finishReason="STOP" to "stop" because the current chunk
+ # has no tool_calls. Override if we saw tool_calls earlier.
+ if self.has_seen_tool_calls:
+ for choice in model_response.choices:
+ if choice.finish_reason == "stop":
+ choice.finish_reason = "tool_calls"
+
+ setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata) # type: ignore
+ setattr(model_response, "vertex_ai_url_context_metadata", url_context_metadata) # type: ignore
+ setattr(model_response, "vertex_ai_safety_ratings", safety_ratings) # type: ignore
+ setattr(model_response, "vertex_ai_citation_metadata", citation_metadata) # type: ignore
+
+ return grounding_metadata, url_context_metadata, safety_ratings, citation_metadata
+
+ def _apply_stream_usage_metadata(
+ self,
+ processed_chunk: Any,
+ model_response: Any,
+ grounding_metadata: List[dict],
+ ) -> Optional[Usage]:
+ if "usageMetadata" not in processed_chunk:
+ return None
+
+ usage = VertexGeminiConfig._calculate_usage(
+ completion_response=processed_chunk,
+ )
+
+ web_search_requests = VertexGeminiConfig._calculate_web_search_requests(
+ grounding_metadata
+ )
+ if web_search_requests is not None:
+ cast(
+ PromptTokensDetailsWrapper, usage.prompt_tokens_details
+ ).web_search_requests = web_search_requests
+
+ traffic_type = processed_chunk.get("usageMetadata", {}).get("trafficType")
+ if traffic_type:
+ model_response._hidden_params.setdefault(
+ "provider_specific_fields", {}
+ )["traffic_type"] = traffic_type
+
+ service_tier = self.response_headers.get("x-gemini-service-tier")
+ if service_tier:
+ if service_tier.lower() == "standard":
+ setattr(model_response, "service_tier", "default")
+ else:
+ setattr(model_response, "service_tier", service_tier.lower())
+
+ return usage
+
def chunk_parser(self, chunk: dict) -> Optional["ModelResponseStream"]:
try:
verbose_logger.debug(f"RAW GEMINI CHUNK: {chunk}")
@@ -3043,101 +3181,23 @@ def chunk_parser(self, chunk: dict) -> Optional["ModelResponseStream"]:
if blocked_response is not None:
model_response = blocked_response
- usage: Optional[Usage] = None
- _candidates: Optional[List[Candidates]] = processed_chunk.get("candidates")
grounding_metadata: List[dict] = []
url_context_metadata: List[dict] = []
safety_ratings: List[dict] = []
citation_metadata: List[dict] = []
+
+ _candidates: Optional[List[Candidates]] = processed_chunk.get("candidates")
if _candidates:
(
grounding_metadata,
url_context_metadata,
safety_ratings,
citation_metadata,
- self.cumulative_tool_call_index,
- ) = VertexGeminiConfig._process_candidates(
- _candidates,
- model_response,
- self.logging_obj.optional_params,
- cumulative_tool_call_index=self.cumulative_tool_call_index,
- )
+ ) = self._apply_stream_candidates(_candidates, model_response)
- # Track whether tool_calls have been seen across streaming chunks.
- # Gemini sends tool_calls and finishReason in separate chunks,
- # so we need to remember if earlier chunks contained tool_calls
- # to correctly set finish_reason="tool_calls" per the OpenAI spec.
- if not self.has_seen_tool_calls:
- for choice in model_response.choices:
- if (
- hasattr(choice, "delta")
- and choice.delta
- and choice.delta.tool_calls
- ):
- self.has_seen_tool_calls = True
- break
-
- # Handle final chunk with finishReason but no content.
- # _process_candidates skips candidates without "content",
- # so the finish_reason from the final chunk is lost.
- if not model_response.choices and _candidates:
- from litellm.types.utils import Delta, StreamingChoices
-
- for candidate in _candidates:
- finish_reason_str = candidate.get("finishReason")
- if finish_reason_str is not None:
- if self.has_seen_tool_calls:
- mapped_finish_reason = "tool_calls"
- else:
- mapped_finish_reason = (
- VertexGeminiConfig._check_finish_reason(
- None, finish_reason_str
- )
- )
- choice = StreamingChoices(
- finish_reason=mapped_finish_reason,
- index=candidate.get("index", 0),
- delta=Delta(content=None, role=None),
- logprobs=None,
- enhancements=None,
- )
- model_response.choices.append(choice)
-
- # Also handle the case where the final chunk has empty
- # content (e.g. text:"") WITH finishReason. In this case
- # _process_candidates DOES create a choice, but maps
- # finishReason="STOP" to "stop" because the current chunk
- # has no tool_calls. Override if we saw tool_calls earlier.
- if self.has_seen_tool_calls:
- for choice in model_response.choices:
- if choice.finish_reason == "stop":
- choice.finish_reason = "tool_calls"
-
- setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata) # type: ignore
- setattr(model_response, "vertex_ai_url_context_metadata", url_context_metadata) # type: ignore
- setattr(model_response, "vertex_ai_safety_ratings", safety_ratings) # type: ignore
- setattr(model_response, "vertex_ai_citation_metadata", citation_metadata) # type: ignore
-
- if "usageMetadata" in processed_chunk:
- usage = VertexGeminiConfig._calculate_usage(
- completion_response=processed_chunk,
- )
-
- web_search_requests = VertexGeminiConfig._calculate_web_search_requests(
- grounding_metadata
- )
- if web_search_requests is not None:
- cast(
- PromptTokensDetailsWrapper, usage.prompt_tokens_details
- ).web_search_requests = web_search_requests
-
- traffic_type = processed_chunk.get("usageMetadata", {}).get(
- "trafficType"
- )
- if traffic_type:
- model_response._hidden_params.setdefault(
- "provider_specific_fields", {}
- )["traffic_type"] = traffic_type
+ usage = self._apply_stream_usage_metadata(
+ processed_chunk, model_response, grounding_metadata
+ )
setattr(model_response, "usage", usage) # type: ignore
diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py
index cdabac27af74..68d8f0d046dc 100644
--- a/litellm/llms/vertex_ai/vertex_llm_base.py
+++ b/litellm/llms/vertex_ai/vertex_llm_base.py
@@ -136,6 +136,11 @@ def load_auth(
json_obj,
scopes=["https://www.googleapis.com/auth/cloud-platform"],
)
+ elif isinstance(credential_source, dict) and "executable" in credential_source:
+ creds = self._credentials_from_pluggable(
+ json_obj,
+ scopes=["https://www.googleapis.com/auth/cloud-platform"],
+ )
else:
creds = self._credentials_from_identity_pool(
json_obj,
@@ -190,6 +195,17 @@ def _credentials_from_identity_pool(self, json_obj, scopes):
creds = creds.with_scopes(scopes)
return creds
+ def _credentials_from_pluggable(self, json_obj, scopes):
+ try:
+ from google.auth import pluggable
+ except ImportError:
+ raise ImportError(GOOGLE_IMPORT_ERROR_MESSAGE)
+
+ creds = pluggable.Credentials.from_info(json_obj)
+ if scopes and hasattr(creds, "requires_scopes") and creds.requires_scopes:
+ creds = creds.with_scopes(scopes)
+ return creds
+
def _credentials_from_identity_pool_with_aws(self, json_obj, scopes):
try:
from google.auth import aws
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 8351f084f28e..63ca003a26df 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -7818,14 +7818,37 @@
"cache_read_input_token_cost": 3e-08,
"cache_creation_input_token_cost": 3.75e-07
},
+ "bedrock/us-gov-east-1/anthropic.claude-sonnet-4-5-20250929-v1:0": {
+ "cache_creation_input_token_cost": 4.125e-06,
+ "cache_read_input_token_cost": 3.3e-07,
+ "input_cost_per_token": 3.3e-06,
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "mode": "chat",
+ "output_cost_per_token": 1.65e-05,
+ "supports_assistant_prefill": true,
+ "supports_computer_use": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "supports_native_structured_output": true
+ },
"bedrock/us-gov-east-1/claude-sonnet-4-5-20250929-v1:0": {
- "input_cost_per_token": 3.6e-06,
+ "cache_creation_input_token_cost": 4.125e-06,
+ "cache_read_input_token_cost": 3.3e-07,
+ "input_cost_per_token": 3.3e-06,
"litellm_provider": "bedrock",
"max_input_tokens": 200000,
- "max_output_tokens": 4096,
- "max_tokens": 4096,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
"mode": "chat",
- "output_cost_per_token": 1.8e-05,
+ "output_cost_per_token": 1.65e-05,
"supports_assistant_prefill": true,
"supports_computer_use": true,
"supports_function_calling": true,
@@ -7835,8 +7858,7 @@
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true,
- "cache_read_input_token_cost": 3.6e-07,
- "cache_creation_input_token_cost": 4.5e-06
+ "supports_native_structured_output": true
},
"bedrock/us-gov-east-1/meta.llama3-70b-instruct-v1:0": {
"input_cost_per_token": 2.65e-06,
@@ -7969,14 +7991,37 @@
"cache_read_input_token_cost": 3e-08,
"cache_creation_input_token_cost": 3.75e-07
},
+ "bedrock/us-gov-west-1/anthropic.claude-sonnet-4-5-20250929-v1:0": {
+ "cache_creation_input_token_cost": 4.125e-06,
+ "cache_read_input_token_cost": 3.3e-07,
+ "input_cost_per_token": 3.3e-06,
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 200000,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "mode": "chat",
+ "output_cost_per_token": 1.65e-05,
+ "supports_assistant_prefill": true,
+ "supports_computer_use": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "supports_native_structured_output": true
+ },
"bedrock/us-gov-west-1/claude-sonnet-4-5-20250929-v1:0": {
- "input_cost_per_token": 3.6e-06,
+ "cache_creation_input_token_cost": 4.125e-06,
+ "cache_read_input_token_cost": 3.3e-07,
+ "input_cost_per_token": 3.3e-06,
"litellm_provider": "bedrock",
"max_input_tokens": 200000,
- "max_output_tokens": 4096,
- "max_tokens": 4096,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
"mode": "chat",
- "output_cost_per_token": 1.8e-05,
+ "output_cost_per_token": 1.65e-05,
"supports_assistant_prefill": true,
"supports_computer_use": true,
"supports_function_calling": true,
@@ -7986,8 +8031,7 @@
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true,
- "cache_read_input_token_cost": 3.6e-07,
- "cache_creation_input_token_cost": 4.5e-06
+ "supports_native_structured_output": true
},
"bedrock/us-gov-west-1/meta.llama3-70b-instruct-v1:0": {
"input_cost_per_token": 2.65e-06,
@@ -13695,7 +13739,8 @@
"supports_tool_choice": true,
"supports_url_context": true,
"supports_vision": true,
- "supports_web_search": true
+ "supports_web_search": true,
+ "supports_service_tier": true
},
"gemini-2.5-flash-image": {
"cache_read_input_token_cost": 3e-08,
@@ -13744,7 +13789,8 @@
"supports_url_context": true,
"supports_vision": true,
"supports_web_search": false,
- "tpm": 8000000
+ "tpm": 8000000,
+ "supports_service_tier": true
},
"gemini-3-pro-image-preview": {
"input_cost_per_image": 0.0011,
@@ -13778,7 +13824,8 @@
"supports_response_schema": true,
"supports_system_messages": true,
"supports_vision": true,
- "supports_web_search": true
+ "supports_web_search": true,
+ "supports_service_tier": true
},
"gemini-3.1-flash-image-preview": {
"input_cost_per_image": 0.00056,
@@ -13861,7 +13908,8 @@
"supports_video_input": true,
"supports_vision": true,
"supports_web_search": true,
- "supports_native_streaming": true
+ "supports_native_streaming": true,
+ "supports_service_tier": true
},
"deep-research-pro-preview-12-2025": {
"input_cost_per_image": 0.0011,
@@ -13940,7 +13988,8 @@
"supports_tool_choice": true,
"supports_url_context": true,
"supports_vision": true,
- "supports_web_search": true
+ "supports_web_search": true,
+ "supports_service_tier": true
},
"gemini-2.5-flash-lite-preview-09-2025": {
"cache_read_input_token_cost": 1e-08,
@@ -14211,7 +14260,8 @@
"supports_tool_choice": true,
"supports_video_input": true,
"supports_vision": true,
- "supports_web_search": true
+ "supports_web_search": true,
+ "supports_service_tier": true
},
"gemini-3-pro-preview": {
"deprecation_date": "2026-03-26",
@@ -14993,7 +15043,8 @@
"supports_url_context": true,
"supports_vision": true,
"supports_web_search": true,
- "tpm": 8000000
+ "tpm": 8000000,
+ "supports_service_tier": true
},
"gemini/gemini-2.5-flash-image": {
"cache_read_input_token_cost": 3e-08,
@@ -15043,7 +15094,8 @@
"supports_url_context": true,
"supports_vision": true,
"supports_web_search": true,
- "tpm": 8000000
+ "tpm": 8000000,
+ "supports_service_tier": true
},
"gemini/gemini-3-pro-image-preview": {
"input_cost_per_image": 0.0011,
@@ -15079,7 +15131,8 @@
"supports_response_schema": true,
"supports_system_messages": true,
"supports_vision": true,
- "supports_web_search": true
+ "supports_web_search": true,
+ "supports_service_tier": true
},
"gemini/gemini-3.1-flash-image-preview": {
"input_cost_per_token": 2.5e-07,
@@ -15198,7 +15251,8 @@
"supports_url_context": true,
"supports_vision": true,
"supports_web_search": true,
- "tpm": 250000
+ "tpm": 250000,
+ "supports_service_tier": true
},
"gemini/gemini-2.5-flash-lite-preview-09-2025": {
"cache_read_input_token_cost": 1e-08,
@@ -15638,7 +15692,8 @@
"supports_vision": true,
"supports_web_search": true,
"supports_native_streaming": true,
- "tpm": 250000
+ "tpm": 250000,
+ "supports_service_tier": true
},
"gemini/gemini-3-flash-preview": {
"cache_read_input_token_cost": 5e-08,
@@ -16879,6 +16934,72 @@
"mode": "chat",
"output_cost_per_token": 1.2e-06
},
+ "baseten/MiniMaxAI/MiniMax-M2.5": {
+ "input_cost_per_token": 3e-07,
+ "litellm_provider": "baseten",
+ "mode": "chat",
+ "output_cost_per_token": 1.2e-06
+ },
+ "baseten/nvidia/Nemotron-120B-A12B": {
+ "input_cost_per_token": 3e-07,
+ "litellm_provider": "baseten",
+ "mode": "chat",
+ "output_cost_per_token": 7.5e-07
+ },
+ "baseten/zai-org/GLM-5": {
+ "input_cost_per_token": 9.5e-07,
+ "litellm_provider": "baseten",
+ "mode": "chat",
+ "output_cost_per_token": 3.15e-06
+ },
+ "baseten/zai-org/GLM-4.7": {
+ "input_cost_per_token": 6e-07,
+ "litellm_provider": "baseten",
+ "mode": "chat",
+ "output_cost_per_token": 2.2e-06
+ },
+ "baseten/zai-org/GLM-4.6": {
+ "input_cost_per_token": 6e-07,
+ "litellm_provider": "baseten",
+ "mode": "chat",
+ "output_cost_per_token": 2.2e-06
+ },
+ "baseten/moonshotai/Kimi-K2.5": {
+ "input_cost_per_token": 6e-07,
+ "litellm_provider": "baseten",
+ "mode": "chat",
+ "output_cost_per_token": 3e-06
+ },
+ "baseten/moonshotai/Kimi-K2-Thinking": {
+ "input_cost_per_token": 6e-07,
+ "litellm_provider": "baseten",
+ "mode": "chat",
+ "output_cost_per_token": 2.5e-06
+ },
+ "baseten/moonshotai/Kimi-K2-Instruct-0905": {
+ "input_cost_per_token": 6e-07,
+ "litellm_provider": "baseten",
+ "mode": "chat",
+ "output_cost_per_token": 2.5e-06
+ },
+ "baseten/openai/gpt-oss-120b": {
+ "input_cost_per_token": 1e-07,
+ "litellm_provider": "baseten",
+ "mode": "chat",
+ "output_cost_per_token": 5e-07
+ },
+ "baseten/deepseek-ai/DeepSeek-V3.1": {
+ "input_cost_per_token": 5e-07,
+ "litellm_provider": "baseten",
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-06
+ },
+ "baseten/deepseek-ai/DeepSeek-V3-0324": {
+ "input_cost_per_token": 7.7e-07,
+ "litellm_provider": "baseten",
+ "mode": "chat",
+ "output_cost_per_token": 7.7e-07
+ },
"gmi/Qwen/Qwen3-VL-235B-A22B-Instruct-FP8": {
"input_cost_per_token": 3e-07,
"litellm_provider": "gmi",
@@ -28945,6 +29066,32 @@
"tool_use_system_prompt_tokens": 346,
"supports_native_structured_output": true
},
+ "us-gov.anthropic.claude-sonnet-4-5-20250929-v1:0": {
+ "cache_creation_input_token_cost": 4.125e-06,
+ "cache_read_input_token_cost": 3.3e-07,
+ "input_cost_per_token": 3.3e-06,
+ "input_cost_per_token_above_200k_tokens": 6.6e-06,
+ "output_cost_per_token_above_200k_tokens": 2.475e-05,
+ "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06,
+ "cache_read_input_token_cost_above_200k_tokens": 6.6e-07,
+ "litellm_provider": "bedrock_converse",
+ "max_input_tokens": 200000,
+ "max_output_tokens": 64000,
+ "max_tokens": 64000,
+ "mode": "chat",
+ "output_cost_per_token": 1.65e-05,
+ "supports_assistant_prefill": true,
+ "supports_computer_use": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346,
+ "supports_native_structured_output": true
+ },
"au.anthropic.claude-haiku-4-5-20251001-v1:0": {
"cache_creation_input_token_cost": 1.375e-06,
"cache_read_input_token_cost": 1.1e-07,
@@ -38057,4 +38204,4 @@
"supports_native_structured_output": true,
"supports_pdf_input": true
}
-}
+}
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/mcp_server/db.py b/litellm/proxy/_experimental/mcp_server/db.py
index fbef33c32edf..e9bd41bb9514 100644
--- a/litellm/proxy/_experimental/mcp_server/db.py
+++ b/litellm/proxy/_experimental/mcp_server/db.py
@@ -21,7 +21,9 @@
decrypt_value_helper,
encrypt_value_helper,
)
+from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
from litellm.proxy.utils import PrismaClient
+from litellm.types.llms.custom_http import httpxSpecialProvider
from litellm.types.mcp import MCPCredentials
@@ -576,6 +578,7 @@ async def store_user_oauth_credential(
refresh_token: Optional[str] = None,
expires_in: Optional[int] = None,
scopes: Optional[List[str]] = None,
+ skip_byok_guard: bool = False,
) -> None:
"""Persist an OAuth2 access token for a user+server pair.
@@ -604,21 +607,26 @@ async def store_user_oauth_credential(
# Guard against silently overwriting a BYOK credential with an OAuth token.
# BYOK credentials lack a "type" field (or use a non-"oauth2" type).
- existing = await prisma_client.db.litellm_mcpusercredentials.find_unique(
- where={"user_id_server_id": {"user_id": user_id, "server_id": server_id}}
- )
- if existing is not None:
- _byok_error = ValueError(
- f"A non-OAuth2 credential already exists for user {user_id} "
- f"and server {server_id}. Refusing to overwrite."
+ # Skip the guard when the caller knows the row is already an OAuth2 credential
+ # (e.g. during token refresh), saving an extra DB round-trip.
+ if not skip_byok_guard:
+ existing = await prisma_client.db.litellm_mcpusercredentials.find_unique(
+ where={"user_id_server_id": {"user_id": user_id, "server_id": server_id}}
)
- try:
- raw = json.loads(base64.urlsafe_b64decode(existing.credential_b64).decode())
- except Exception:
- # Credential is not base64+JSON — it's a plain-text BYOK key.
- raise _byok_error
- if raw.get("type") != "oauth2":
- raise _byok_error
+ if existing is not None:
+ _byok_error = ValueError(
+ f"A non-OAuth2 credential already exists for user {user_id} "
+ f"and server {server_id}. Refusing to overwrite."
+ )
+ try:
+ raw = json.loads(
+ base64.urlsafe_b64decode(existing.credential_b64).decode()
+ )
+ except Exception:
+ # Credential is not base64+JSON — it's a plain-text BYOK key.
+ raise _byok_error
+ if raw.get("type") != "oauth2":
+ raise _byok_error
encoded = base64.urlsafe_b64encode(json.dumps(payload).encode()).decode()
await prisma_client.db.litellm_mcpusercredentials.upsert(
@@ -697,6 +705,115 @@ async def list_user_oauth_credentials(
return results
+async def refresh_user_oauth_token(
+ prisma_client: PrismaClient,
+ user_id: str,
+ server: Any,
+ cred: Dict[str, Any],
+) -> Optional[Dict[str, Any]]:
+ """Attempt to refresh a per-user OAuth2 token using its stored refresh_token.
+
+ POSTs to ``server.token_url`` with ``grant_type=refresh_token``.
+
+ On success: persists the new credential via ``store_user_oauth_credential``
+ and returns the updated payload dict.
+ On failure (network error, invalid_grant, missing refresh_token, …): logs a
+ warning and returns ``None`` — the caller is responsible for clearing the
+ stale credential and triggering re-authentication.
+ """
+ refresh_token: Optional[str] = cred.get("refresh_token")
+ token_url: Optional[str] = getattr(server, "token_url", None)
+ server_id: str = getattr(server, "server_id", "")
+ client_id: Optional[str] = getattr(server, "client_id", None)
+ client_secret: Optional[str] = getattr(server, "client_secret", None)
+
+ if not refresh_token:
+ verbose_proxy_logger.debug(
+ "refresh_user_oauth_token: no refresh_token stored for user=%s server=%s",
+ user_id,
+ server_id,
+ )
+ return None
+ if not token_url:
+ verbose_proxy_logger.debug(
+ "refresh_user_oauth_token: server=%s has no token_url configured",
+ server_id,
+ )
+ return None
+
+ token_data: Dict[str, str] = {
+ "grant_type": "refresh_token",
+ "refresh_token": refresh_token,
+ }
+ if client_id:
+ token_data["client_id"] = client_id
+ if client_secret:
+ token_data["client_secret"] = client_secret
+
+ try:
+ async_client = get_async_httpx_client(
+ llm_provider=httpxSpecialProvider.Oauth2Check
+ )
+ response = await async_client.post(
+ token_url,
+ headers={"Accept": "application/json"},
+ data=token_data,
+ )
+ response.raise_for_status()
+ body: Dict[str, Any] = response.json()
+ except Exception as exc:
+ verbose_proxy_logger.warning(
+ "refresh_user_oauth_token: refresh request failed for user=%s server=%s: %s",
+ user_id,
+ server_id,
+ exc,
+ )
+ return None
+
+ access_token: Optional[str] = body.get("access_token")
+ if not access_token:
+ verbose_proxy_logger.warning(
+ "refresh_user_oauth_token: token response missing access_token for "
+ "user=%s server=%s",
+ user_id,
+ server_id,
+ )
+ return None
+
+ expires_in: Optional[int] = None
+ raw_expires = body.get("expires_in")
+ try:
+ expires_in = int(raw_expires) if raw_expires is not None else None
+ except (TypeError, ValueError):
+ pass
+
+ # Rotate refresh token when the provider returns a new one
+ new_refresh_token: Optional[str] = body.get("refresh_token") or refresh_token
+
+ raw_scope = body.get("scope")
+ scopes: Optional[List[str]] = (
+ raw_scope.split() if isinstance(raw_scope, str) and raw_scope else None
+ ) or cred.get("scopes")
+
+ await store_user_oauth_credential(
+ prisma_client=prisma_client,
+ user_id=user_id,
+ server_id=server_id,
+ access_token=access_token,
+ refresh_token=new_refresh_token,
+ expires_in=expires_in,
+ scopes=scopes,
+ skip_byok_guard=True, # Row is already OAuth2; skip the extra find_unique check
+ )
+
+ verbose_proxy_logger.info(
+ "refresh_user_oauth_token: refreshed token for user=%s server=%s",
+ user_id,
+ server_id,
+ )
+ return await get_user_oauth_credential(prisma_client, user_id, server_id)
+
+
async def approve_mcp_server(
prisma_client: PrismaClient,
server_id: str,
diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py
index 07309eb57f21..d0d619863222 100644
--- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py
+++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py
@@ -1,10 +1,11 @@
import json
-from typing import Optional
+from typing import Any, Dict, Optional
from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse
from fastapi import APIRouter, Form, HTTPException, Request
from fastapi.responses import HTMLResponse, JSONResponse, RedirectResponse
+from litellm._logging import verbose_logger
from litellm.llms.custom_httpx.http_handler import (
get_async_httpx_client,
httpxSpecialProvider,
@@ -147,6 +148,160 @@ def _resolve_oauth2_server_for_root_endpoints(
return None
+def _validate_token_response(
+ token_response: Dict[str, Any],
+ validation_rules: Dict[str, Any],
+ server_id: str,
+) -> None:
+ """Raise HTTPException 403 if any validation rule doesn't match the token response.
+
+ Supports dot-notation for nested fields (e.g. ``"team.enterprise_id"`` checks
+ ``token_response["team"]["enterprise_id"]``). Top-level keys are tried first,
+ then dot-split traversal. All comparisons are string-coerced so that numeric
+ values in the response (e.g. ``"org_id": 12345``) match string rules
+ (``"org_id": "12345"``).
+ """
+ for key, expected in validation_rules.items():
+ actual: Any = token_response.get(key)
+ # Try dot-notation traversal when top-level lookup returns None
+ if actual is None and "." in key:
+ obj: Any = token_response
+ for part in key.split("."):
+ if isinstance(obj, dict):
+ obj = obj.get(part)
+ else:
+ obj = None
+ break
+ actual = obj
+ # Treat absent fields as a distinct failure from a mismatched value
+ if actual is None:
+ raise HTTPException(
+ status_code=403,
+ detail={
+ "error": "token_validation_failed",
+ "server_id": server_id,
+ "field": key,
+ "message": (
+ f"OAuth token rejected: required field '{key}' is absent"
+ ),
+ },
+ )
+ if str(actual) != str(expected):
+ raise HTTPException(
+ status_code=403,
+ detail={
+ "error": "token_validation_failed",
+ "server_id": server_id,
+ "field": key,
+ "message": (
+ f"OAuth token rejected: '{key}' = '{actual}', "
+ f"expected '{expected}'"
+ ),
+ },
+ )
+
+
+async def _extract_user_id_from_request(request: Request) -> Optional[str]:
+ """Best-effort extraction of LiteLLM user_id from the request's Authorization header.
+
+ Called at the OAuth token endpoint so that per-user tokens can be stored
+ server-side. Uses a read-only cache lookup to avoid re-running the full
+ auth pipeline (which has side effects such as rate-limit increments and
+ spend logging). Returns ``None`` if no cached credential is found.
+ """
+ auth_header = request.headers.get("Authorization") or request.headers.get(
+ "authorization"
+ )
+ if not auth_header:
+ return None
+ lower = auth_header.lower()
+ if not lower.startswith("bearer "):
+ return None
+ token = auth_header[7:].strip()
+ try:
+ from litellm.proxy._types import hash_token # noqa: PLC0415
+ from litellm.proxy.proxy_server import user_api_key_cache # noqa: PLC0415
+
+ cached = await user_api_key_cache.async_get_cache(hash_token(token))
+ return getattr(cached, "user_id", None)
+ except Exception:
+ return None
+
+
+async def _store_per_user_token_server_side(
+ server: MCPServer,
+ user_id: str,
+ token_response: Dict[str, Any],
+) -> None:
+ """Persist the OAuth token server-side and warm the Redis cache.
+
+ Called from the token endpoint after a successful code exchange or refresh.
+ Errors are logged but NOT re-raised — the token is always returned to the
+ client even when server-side storage fails.
+ """
+ from litellm.proxy._experimental.mcp_server.oauth2_token_cache import ( # noqa: PLC0415
+ _compute_per_user_token_ttl,
+ mcp_per_user_token_cache,
+ )
+ from litellm.proxy.utils import get_prisma_client_or_throw # noqa: PLC0415
+
+ access_token: Optional[str] = token_response.get("access_token")
+ if not access_token:
+ return
+
+ raw_expires = token_response.get("expires_in")
+ try:
+ expires_in: Optional[int] = int(raw_expires) if raw_expires is not None else None
+ except (TypeError, ValueError):
+ expires_in = None
+
+ refresh_token: Optional[str] = token_response.get("refresh_token") or None
+ raw_scope = token_response.get("scope")
+ scopes: Optional[list] = (
+ raw_scope.split() if isinstance(raw_scope, str) and raw_scope else None
+ )
+
+ try:
+ prisma_client = get_prisma_client_or_throw(
+ "Database not connected. Cannot store per-user OAuth token."
+ )
+ from litellm.proxy._experimental.mcp_server.db import ( # noqa: PLC0415
+ store_user_oauth_credential,
+ )
+
+ await store_user_oauth_credential(
+ prisma_client=prisma_client,
+ user_id=user_id,
+ server_id=server.server_id,
+ access_token=access_token,
+ refresh_token=refresh_token,
+ expires_in=expires_in,
+ scopes=scopes,
+ )
+ verbose_logger.info(
+ "_store_per_user_token_server_side: stored token for user=%s server=%s",
+ user_id,
+ server.server_id,
+ )
+ except Exception as exc:
+ verbose_logger.warning(
+ "_store_per_user_token_server_side: DB storage failed for user=%s server=%s: %s",
+ user_id,
+ server.server_id,
+ exc,
+ )
+ return # Don't warm Redis if DB write failed
+
+ # Warm the Redis cache so the first subsequent MCP call is a cache hit
+ ttl = _compute_per_user_token_ttl(server, expires_in)
+ await mcp_per_user_token_cache.set(
+ user_id=user_id,
+ server_id=server.server_id,
+ access_token=access_token,
+ ttl=ttl,
+ )
+
+
async def authorize_with_server(
request: Request,
mcp_server: MCPServer,
@@ -266,6 +421,44 @@ async def exchange_token_with_server(
token_response = response.json()
access_token = token_response["access_token"]
+ # Validate token response against server-configured rules before any storage.
+ # This rejects tokens from wrong Slack workspaces, Atlassian orgs, etc.
+ if mcp_server.token_validation and isinstance(mcp_server.token_validation, dict):
+ _validate_token_response(
+ token_response=token_response,
+ validation_rules=mcp_server.token_validation,
+ server_id=mcp_server.server_id,
+ )
+
+ # Store server-side when the server is configured for per-user OAuth and
+ # the calling client has provided a valid LiteLLM identity.
+ # Errors are non-fatal: the token is still returned to the client.
+ if mcp_server.needs_user_oauth_token:
+ user_id = await _extract_user_id_from_request(request)
+ if user_id:
+ try:
+ await _store_per_user_token_server_side(
+ server=mcp_server,
+ user_id=user_id,
+ token_response=token_response,
+ )
+ except Exception as exc:
+ verbose_logger.warning(
+ "exchange_token_with_server: server-side storage failed "
+ "for user=%s server=%s: %s",
+ user_id,
+ mcp_server.server_id,
+ exc,
+ )
+ else:
+ verbose_logger.debug(
+ "exchange_token_with_server: no LiteLLM user_id found in request; "
+ "per-user token for server=%s will not be stored server-side. "
+ "The client should call POST /mcp/server/{id}/oauth-user-credential "
+ "to store it manually.",
+ mcp_server.server_id,
+ )
+
result = {
"access_token": access_token,
"token_type": token_response.get("token_type", "Bearer"),
diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
index 7b87e7e7e617..8d3831e75fbc 100644
--- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
+++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
@@ -10,6 +10,7 @@
import datetime
import hashlib
import json
+import os
import re
from typing import Any, Callable, Dict, List, Literal, Optional, Set, Tuple, Union, cast
from urllib.parse import urlparse
@@ -35,6 +36,8 @@
MCP_CLIENT_TIMEOUT,
MCP_HEALTH_CHECK_TIMEOUT,
MCP_METADATA_TIMEOUT,
+ MCP_NPM_CACHE_DIR,
+ MCP_STDIO_ALLOWED_COMMANDS,
MCP_TOOL_LISTING_TIMEOUT,
)
from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException
@@ -1119,9 +1122,19 @@ async def _create_mcp_client(
# In containers the default (~/.npm or /app/.npm) may not exist
# or be read-only, causing npx to fail with ENOENT.
if "NPM_CONFIG_CACHE" not in resolved_env:
- from litellm.constants import MCP_NPM_CACHE_DIR
-
resolved_env["NPM_CONFIG_CACHE"] = MCP_NPM_CACHE_DIR
+ # Defense-in-depth: block commands not in the allowlist.
+ # The Pydantic validator blocks new servers; this catches legacy
+ # config/DB records predating the allowlist.
+ if server.command:
+ base_command = os.path.basename(server.command)
+ if base_command not in MCP_STDIO_ALLOWED_COMMANDS:
+ raise HTTPException(
+ status_code=403,
+ detail=f"MCP stdio command '{server.command}' is not in the allowlist ({sorted(MCP_STDIO_ALLOWED_COMMANDS)}). "
+ f"Add it to LITELLM_MCP_STDIO_EXTRA_COMMANDS to allow this command.",
+ )
+
stdio_config: Optional[MCPStdioConfig] = None
if server.command and server.args is not None:
stdio_config = MCPStdioConfig(
@@ -2442,6 +2455,37 @@ async def call_tool(
)
tasks.append(during_hook_task)
+ # For per-user OAuth servers: if the client didn't supply a token in
+ # oauth2_headers, look up the stored token from Redis / DB. This is the
+ # call_tool equivalent of _get_user_oauth_extra_headers_from_db used in
+ # list_tools.
+ if (
+ mcp_server.needs_user_oauth_token
+ and not oauth2_headers
+ and user_api_key_auth is not None
+ ):
+ user_id = getattr(user_api_key_auth, "user_id", None)
+ if user_id:
+ try:
+ from litellm.proxy._experimental.mcp_server.server import ( # noqa: PLC0415
+ _get_user_oauth_extra_headers_from_db,
+ )
+
+ stored_headers = await _get_user_oauth_extra_headers_from_db(
+ server=mcp_server,
+ user_api_key_auth=user_api_key_auth,
+ )
+ if stored_headers:
+ oauth2_headers = stored_headers
+ except Exception as _lookup_exc:
+ verbose_logger.debug(
+ "call_tool: per-user token lookup failed for "
+ "user=%s server=%s: %s",
+ user_id,
+ mcp_server.server_id,
+ _lookup_exc,
+ )
+
# For OpenAPI servers, call the tool handler directly instead of via MCP client
if mcp_server.spec_path:
verbose_logger.debug(
diff --git a/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py b/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py
index 84a2e94467be..476e215666e1 100644
--- a/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py
+++ b/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py
@@ -17,8 +17,15 @@
MCP_OAUTH2_TOKEN_CACHE_MAX_SIZE,
MCP_OAUTH2_TOKEN_CACHE_MIN_TTL,
MCP_OAUTH2_TOKEN_EXPIRY_BUFFER_SECONDS,
+ MCP_PER_USER_TOKEN_DEFAULT_TTL,
+ MCP_PER_USER_TOKEN_EXPIRY_BUFFER_SECONDS,
+ MCP_PER_USER_TOKEN_REDIS_KEY_PREFIX,
)
from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
+from litellm.proxy.common_utils.encrypt_decrypt_utils import (
+ decrypt_value_helper,
+ encrypt_value_helper,
+)
from litellm.types.llms.custom_http import httpxSpecialProvider
if TYPE_CHECKING:
@@ -152,6 +159,107 @@ def invalidate(self, server_id: str) -> None:
mcp_oauth2_token_cache = MCPOAuth2TokenCache()
+def _compute_per_user_token_ttl(server: "MCPServer", expires_in: Optional[int]) -> int:
+ """Compute Redis TTL for a per-user token.
+
+ Uses server.token_storage_ttl_seconds when configured; otherwise derives
+ TTL from expires_in minus the expiry buffer; falls back to the default TTL.
+ """
+ if server.token_storage_ttl_seconds is not None:
+ return max(server.token_storage_ttl_seconds, 1)
+ if expires_in is not None:
+ return max(
+ expires_in - MCP_PER_USER_TOKEN_EXPIRY_BUFFER_SECONDS,
+ 1,
+ )
+ return MCP_PER_USER_TOKEN_DEFAULT_TTL
+
+
+class MCPPerUserTokenCache:
+ """Redis-backed cache for per-user OAuth2 access tokens.
+
+ Uses LiteLLM's existing ``user_api_key_cache`` (DualCache with optional
+ Redis backend). Tokens are NaCl-encrypted with ``encrypt_value_helper``
+ before storage so they are safe at rest in Redis.
+
+ Redis key format: ``mcp:per_user_token:{user_id}:{server_id}``
+ Redis value: ``encrypt_value_helper(access_token)`` — URL-safe base64
+ """
+
+ def _cache_key(self, user_id: str, server_id: str) -> str:
+ return f"{MCP_PER_USER_TOKEN_REDIS_KEY_PREFIX}:{user_id}:{server_id}"
+
+ async def get(self, user_id: str, server_id: str) -> Optional[str]:
+ """Return the plaintext access_token, or None on miss/error."""
+ try:
+ from litellm.proxy.proxy_server import user_api_key_cache # noqa: PLC0415
+
+ key = self._cache_key(user_id, server_id)
+ encrypted = await user_api_key_cache.async_get_cache(key)
+ if encrypted is None:
+ return None
+ plaintext = decrypt_value_helper(
+ encrypted,
+ key="mcp_per_user_token",
+ exception_type="debug",
+ )
+ return plaintext or None
+ except Exception as exc:
+ verbose_logger.debug(
+ "MCPPerUserTokenCache.get failed for user=%s server=%s: %s",
+ user_id,
+ server_id,
+ exc,
+ )
+ return None
+
+ async def set(
+ self,
+ user_id: str,
+ server_id: str,
+ access_token: str,
+ ttl: int,
+ ) -> None:
+ """Store NaCl-encrypted access_token in Redis with the given TTL."""
+ try:
+ from litellm.proxy.proxy_server import user_api_key_cache # noqa: PLC0415
+
+ key = self._cache_key(user_id, server_id)
+ encrypted = encrypt_value_helper(access_token)
+ await user_api_key_cache.async_set_cache(key, encrypted, ttl=ttl)
+ verbose_logger.debug(
+ "MCPPerUserTokenCache.set: cached token for user=%s server=%s ttl=%ds",
+ user_id,
+ server_id,
+ ttl,
+ )
+ except Exception as exc:
+ verbose_logger.debug(
+ "MCPPerUserTokenCache.set failed for user=%s server=%s: %s",
+ user_id,
+ server_id,
+ exc,
+ )
+
+ async def delete(self, user_id: str, server_id: str) -> None:
+ """Invalidate the cached token (removes from both in-memory and Redis layers)."""
+ try:
+ from litellm.proxy.proxy_server import user_api_key_cache # noqa: PLC0415
+
+ key = self._cache_key(user_id, server_id)
+ await user_api_key_cache.async_delete_cache(key)
+ except Exception as exc:
+ verbose_logger.debug(
+ "MCPPerUserTokenCache.delete failed for user=%s server=%s: %s",
+ user_id,
+ server_id,
+ exc,
+ )
+
+
+mcp_per_user_token_cache = MCPPerUserTokenCache()
+
+
async def resolve_mcp_auth(
server: "MCPServer",
mcp_auth_header: Optional[Union[str, Dict[str, str]]] = None,
diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
index c0151d47e048..32560a2211d6 100644
--- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
+++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
@@ -2,14 +2,14 @@
from datetime import datetime
from typing import Any, Awaitable, Callable, Dict, List, Literal, Optional, Set, Union
-from fastapi import APIRouter, Depends, HTTPException, Query, Request
+from fastapi import APIRouter, Depends, HTTPException, Query, Request, status
from litellm._logging import verbose_logger
from litellm.proxy._experimental.mcp_server.ui_session_utils import (
build_effective_auth_contexts,
)
from litellm.proxy._experimental.mcp_server.utils import merge_mcp_headers
-from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth
from litellm.proxy.auth.ip_address_utils import IPAddressUtils
from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
from litellm.proxy.common_utils.http_parsing_utils import _safe_get_request_headers
@@ -1027,6 +1027,13 @@ async def test_connection(
"""
Test if we can connect to the provided MCP server before adding it
"""
+ if LitellmUserRoles.PROXY_ADMIN != user_api_key_dict.user_role:
+ raise HTTPException(
+ status_code=status.HTTP_403_FORBIDDEN,
+ detail={
+ "error": "User does not have permission to test MCP server connections. Only PROXY_ADMIN users can perform this action."
+ },
+ )
async def _test_connection_operation(client):
async def _noop(session):
@@ -1041,7 +1048,7 @@ async def _noop(session):
raw_headers=_safe_get_request_headers(request),
)
- @router.post("/test/tools/list")
+ @router.post("/test/tools/list", dependencies=[Depends(user_api_key_auth)])
async def test_tools_list(
request: Request,
new_mcp_server_request: NewMCPServerRequest,
@@ -1050,6 +1057,14 @@ async def test_tools_list(
"""
Preview tools available from MCP server before adding it
"""
+ if LitellmUserRoles.PROXY_ADMIN != user_api_key_dict.user_role:
+ raise HTTPException(
+ status_code=status.HTTP_403_FORBIDDEN,
+ detail={
+ "error": "User does not have permission to test MCP server tools. Only PROXY_ADMIN users can perform this action."
+ },
+ )
+
# For OpenAPI spec servers, generate tools from the spec directly
if new_mcp_server_request.spec_path:
return await _preview_openapi_tools(new_mcp_server_request.spec_path)
diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py
index 7fc28b68e9cb..f0156ba1ec13 100644
--- a/litellm/proxy/_experimental/mcp_server/server.py
+++ b/litellm/proxy/_experimental/mcp_server/server.py
@@ -896,11 +896,17 @@ async def _get_user_oauth_extra_headers_from_db(
user_api_key_auth: Optional[UserAPIKeyAuth],
prefetched_creds: Optional[Dict[str, Dict[str, Any]]] = None,
) -> Optional[Dict[str, str]]:
- """Look up stored OAuth2 token for (user, server) from DB and return as extra_headers dict.
+ """Look up stored OAuth2 token for (user, server) and return as extra_headers dict.
+
+ Lookup order:
+ 1. Redis cache (fast path, NaCl-decrypted) — skipped when prefetched_creds supplied
+ 2. prefetched_creds dict (pre-fetched batch DB query) or fresh DB query
+ 3. Auto-refresh when the stored token is expired and a refresh_token exists
Args:
prefetched_creds: Optional dict keyed by server_id with credential payloads.
- When provided, avoids a per-server DB round-trip.
+ When provided, the Redis and individual DB lookups are
+ skipped in favour of the pre-fetched batch result.
"""
if server.auth_type != MCPAuth.oauth2:
return None
@@ -914,8 +920,27 @@ async def _get_user_oauth_extra_headers_from_db(
from litellm.proxy._experimental.mcp_server.db import ( # noqa: PLC0415
get_user_oauth_credential,
is_oauth_credential_expired,
+ refresh_user_oauth_token,
+ )
+ from litellm.proxy._experimental.mcp_server.oauth2_token_cache import ( # noqa: PLC0415
+ _compute_per_user_token_ttl,
+ mcp_per_user_token_cache,
)
+ # ── Fast path: Redis cache ────────────────────────────────────────
+ # Only used when prefetched_creds is not supplied (individual lookup).
+ if prefetched_creds is None:
+ cached_token = await mcp_per_user_token_cache.get(user_id, server_id)
+ if cached_token is not None:
+ verbose_logger.debug(
+ "_get_user_oauth_extra_headers_from_db: Redis hit for "
+ "user=%s server=%s",
+ user_id,
+ server_id,
+ )
+ return {"Authorization": f"Bearer {cached_token}"}
+
+ # ── Slow path: DB lookup ──────────────────────────────────────────
if prefetched_creds is not None:
cred = prefetched_creds.get(server_id)
else:
@@ -929,18 +954,83 @@ async def _get_user_oauth_extra_headers_from_db(
cred = await get_user_oauth_credential(
prisma_client, user_id, server_id
)
- if cred and cred.get("access_token"):
- if is_oauth_credential_expired(cred):
- verbose_logger.debug(
- f"_get_user_oauth_extra_headers_from_db: token expired for "
- f"user={user_id} server={server_id}"
- )
+
+ if not cred or not cred.get("access_token"):
+ return None
+
+ if is_oauth_credential_expired(cred):
+ verbose_logger.debug(
+ "_get_user_oauth_extra_headers_from_db: token expired for "
+ "user=%s server=%s — attempting refresh",
+ user_id,
+ server_id,
+ )
+ # Attempt token refresh; requires a DB client (not available from prefetch)
+ if cred.get("refresh_token"):
+ try:
+ from litellm.proxy.utils import ( # noqa: PLC0415
+ get_prisma_client_or_throw,
+ )
+
+ prisma_client = get_prisma_client_or_throw(
+ "Database not connected. Cannot refresh OAuth token."
+ )
+ cred = await refresh_user_oauth_token(
+ prisma_client=prisma_client,
+ user_id=user_id,
+ server=server,
+ cred=cred,
+ )
+ except Exception as refresh_exc:
+ verbose_logger.warning(
+ "_get_user_oauth_extra_headers_from_db: refresh failed "
+ "for user=%s server=%s: %s",
+ user_id,
+ server_id,
+ refresh_exc,
+ )
+ cred = None
+
+ if not cred or not cred.get("access_token"):
+ # Clear stale Redis/cache entry so we don't serve it again.
+ # Do this for both the individual and prefetch paths so the
+ # next request doesn't get a stale cache hit.
+ await mcp_per_user_token_cache.delete(user_id, server_id)
return None
- return {"Authorization": f"Bearer {cred['access_token']}"}
+
+ access_token: str = cred["access_token"]
+
+ # Warm (or re-warm) the Redis cache from the DB result.
+ # Always write regardless of whether expires_at is present — tokens
+ # without an expiry are still valid and should be cached using the
+ # server/default TTL so subsequent requests are fast.
+ if prefetched_creds is None:
+ raw_expires = None
+ expires_at = cred.get("expires_at")
+ if expires_at:
+ from datetime import datetime, timezone # noqa: PLC0415
+
+ try:
+ exp_dt = datetime.fromisoformat(expires_at)
+ if exp_dt.tzinfo is None:
+ exp_dt = exp_dt.replace(tzinfo=timezone.utc)
+ remaining = int(
+ (exp_dt - datetime.now(timezone.utc)).total_seconds()
+ )
+ raw_expires = max(remaining, 0) if remaining > 0 else None
+ except (ValueError, TypeError):
+ pass
+ ttl = _compute_per_user_token_ttl(server, raw_expires)
+ await mcp_per_user_token_cache.set(user_id, server_id, access_token, ttl)
+
+ return {"Authorization": f"Bearer {access_token}"}
except Exception as e:
verbose_logger.warning(
- f"_get_user_oauth_extra_headers_from_db: failed to retrieve credential for "
- f"user={user_id} server={server_id}: {e}"
+ "_get_user_oauth_extra_headers_from_db: failed to retrieve credential for "
+ "user=%s server=%s: %s",
+ user_id,
+ server_id,
+ e,
)
return None
@@ -2504,6 +2594,20 @@ async def handle_streamable_http_mcp(
server_name, client_ip=_client_ip
)
if server and server.auth_type == MCPAuth.oauth2 and not oauth2_headers:
+ # For per-user OAuth servers, only skip the pre-emptive 401 when
+ # a stored token actually exists for this user+server pair.
+ # If no stored token exists, fail fast with 401 so clients can
+ # kick off PKCE/interactive OAuth flow immediately.
+ if server.needs_user_oauth_token:
+ stored_oauth_headers = (
+ await _get_user_oauth_extra_headers_from_db(
+ server=server,
+ user_api_key_auth=user_api_key_auth,
+ )
+ )
+ if stored_oauth_headers:
+ continue
+
request = StarletteRequest(scope)
base_url = get_request_base_url(request)
diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html
index 344481d3aed2..b74d1a80e0eb 100644
--- a/litellm/proxy/_experimental/out/404.html
+++ b/litellm/proxy/_experimental/out/404.html
@@ -1 +1 @@
-404: This page could not be found. LiteLLM Dashboard
404
This page could not be found. 404: This page could not be found. LiteLLM Dashboard
404
This page could not be found. e.team_id===O)),is_proxy_admin:"Admin"==b,is_org_admin:(()=>{let s=e?.find(e=>e.team_id===O);if(!s?.organization_id||!v||!f)return!1;let l=v.find(e=>e.organization_id===s.organization_id);return l?.members?.some(e=>e.user_id===f&&"org_admin"===e.user_role)??!1})(),userModels:U,editTeam:L,premiumUser:y}):(0,s.jsxs)(w,{lastRefreshed:et,onRefresh:er,userRole:b,children:[(0,s.jsxs)(x.TabPanel,{children:[(0,s.jsxs)(u.Text,{children:["Click on “Team ID” to view team details ",(0,s.jsx)("b",{children:"and"})," manage team members."]}),(0,s.jsx)(h.Grid,{numItems:1,className:"gap-2 pt-2 pb-2 h-[75vh] w-full mt-2",children:(0,s.jsx)(m.Col,{numColSpan:1,children:(0,s.jsxs)(d.Card,{className:"w-full mx-auto flex-auto overflow-hidden overflow-y-auto max-h-[50vh]",children:[(0,s.jsx)("div",{className:"border-b px-6 py-4",children:(0,s.jsx)("div",{className:"flex flex-col space-y-4",children:(0,s.jsx)(N,{filters:F,organizations:v,showFilters:k,onToggleFilters:I,onChange:(e,s)=>{let l={...F,[e]:s};A(l),_&&(0,a.v2TeamListCall)(_,l.organization_id||null,null,l.team_id||null,l.team_alias||null).then(e=>{e&&e.teams&&j(e.teams)}).catch(e=>{console.error("Error fetching teams:",e)})},onReset:()=>{A({team_id:"",team_alias:"",organization_id:"",sort_by:"created_at",sort_order:"desc"}),_&&(0,a.v2TeamListCall)(_,null,f||null,null,null).then(e=>{e&&e.teams&&j(e.teams)}).catch(e=>{console.error("Error fetching teams:",e)})}})})}),(0,s.jsx)(G,{teams:e,currentOrg:T,perTeamInfo:Q,userRole:b,userId:f,setSelectedTeamId:P,setEditTeam:D,onDeleteTeam:ei}),$&&(0,s.jsx)(K,{teams:e,teamToDelete:Y,onCancel:()=>{q(!1),X(null)},onConfirm:eo})]})})})]}),(0,s.jsx)(x.TabPanel,{children:(0,s.jsx)(p.default,{accessToken:_,userID:f})}),(0,n.isAdminRole)(b||"")&&(0,s.jsx)(x.TabPanel,{children:(0,s.jsx)(o.default,{accessToken:_,userID:f||"",userRole:b||""})})]}),("Admin"==b||"Org Admin"==b)&&(0,s.jsx)(eh,{isTeamModalVisible:E,handleOk:()=>{B(!1),z.resetFields(),es([]),ea({})},handleCancel:()=>{B(!1),z.resetFields(),es([]),ea({})},currentOrg:T,organizations:v,teams:e,setTeams:j,modelAliases:el,setModelAliases:ea,loggingSettings:ee,setLoggingSettings:es,setIsTeamModalVisible:B})]})})})};var ep=e.i(214541),eg=e.i(846835);e.s(["default",0,()=>{let{accessToken:e,userId:a,userRole:t}=(0,S.default)(),{teams:r,setTeams:i}=(0,ep.default)(),[o,n]=(0,l.useState)([]);return(0,l.useEffect)(()=>{(0,eg.fetchOrganizations)(e,n).then(()=>{})},[e]),(0,s.jsx)(ex,{teams:r,accessToken:e,setTeams:i,userID:a,userRole:t,organizations:o})}],596115)}]);
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/2e768c2b1dfc8cd5.js b/litellm/proxy/_experimental/out/_next/static/chunks/0c6c65a34bcde140.js
similarity index 68%
rename from litellm/proxy/_experimental/out/_next/static/chunks/2e768c2b1dfc8cd5.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/0c6c65a34bcde140.js
index 3291150f3188..f004e79d531f 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/2e768c2b1dfc8cd5.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/0c6c65a34bcde140.js
@@ -69,4 +69,4 @@ completion = client.chat.completions.create(
user="my-customer-id"
)
-print(completion.choices[0].message)`;e.s(["default",0,({accessToken:e})=>{let[N,T]=(0,p.useState)(!1),[I,E]=(0,p.useState)(!1),[A,P]=(0,p.useState)(null),[D,M]=(0,p.useState)(!1),{data:B=[]}=(()=>{let{accessToken:e}=(0,C.default)();return(0,v.useQuery)({queryKey:S.list({}),queryFn:async()=>(await (0,k.getBudgetList)(e)??[]).filter(e=>null!=e),enabled:!!e})})(),O=(()=>{let{accessToken:e}=(0,C.default)(),t=(0,_.useQueryClient)();return(0,w.useMutation)({mutationFn:async t=>{if(!e)throw Error("Access token is required");return(0,k.budgetDeleteCall)(e,t)},onSuccess:()=>{t.invalidateQueries({queryKey:S.all})}})})(),H=async t=>{null!=e&&(P(t),E(!0))},V=async()=>{if(A&&null!=e)try{await O.mutateAsync(A.budget_id),j.default.success("Budget deleted.")}catch(e){console.error("Error deleting budget:",e),"function"==typeof j.default.fromBackend?j.default.fromBackend("Failed to delete budget"):j.default.info("Failed to delete budget")}finally{M(!1),P(null)}};return(0,t.jsxs)("div",{className:"w-full mx-auto flex-auto overflow-y-auto m-8 p-2",children:[(0,t.jsx)(l.Button,{size:"sm",variant:"primary",className:"mb-2",onClick:()=>T(!0),children:"+ Create Budget"}),(0,t.jsxs)(r.TabGroup,{children:[(0,t.jsxs)(m.TabList,{children:[(0,t.jsx)(s.Tab,{children:"Budgets"}),(0,t.jsx)(s.Tab,{children:"Examples"})]}),(0,t.jsxs)(g.TabPanels,{children:[(0,t.jsx)(h.TabPanel,{children:(0,t.jsxs)("div",{className:"mt-6",children:[(0,t.jsx)(F,{isModalVisible:N,setIsModalVisible:T}),A&&(0,t.jsx)(R,{isModalVisible:I,setIsModalVisible:E,existingBudget:A}),(0,t.jsxs)(a.Card,{children:[(0,t.jsx)(x.Text,{children:"Create a budget to assign to customers."}),(0,t.jsxs)(i.Table,{children:[(0,t.jsx)(c.TableHead,{children:(0,t.jsxs)(u.TableRow,{children:[(0,t.jsx)(d.TableHeaderCell,{children:"Budget ID"}),(0,t.jsx)(d.TableHeaderCell,{children:"Max Budget"}),(0,t.jsx)(d.TableHeaderCell,{children:"TPM"}),(0,t.jsx)(d.TableHeaderCell,{children:"RPM"})]})}),(0,t.jsx)(n.TableBody,{children:B.slice().sort((e,t)=>new Date(t.updated_at).getTime()-new Date(e.updated_at).getTime()).map(e=>(0,t.jsxs)(u.TableRow,{children:[(0,t.jsx)(o.TableCell,{children:e.budget_id}),(0,t.jsx)(o.TableCell,{children:e.max_budget?e.max_budget:"n/a"}),(0,t.jsx)(o.TableCell,{children:e.tpm_limit?e.tpm_limit:"n/a"}),(0,t.jsx)(o.TableCell,{children:e.rpm_limit?e.rpm_limit:"n/a"}),(0,t.jsx)(y.default,{variant:"Edit",tooltipText:"Edit budget",onClick:()=>H(e),dataTestId:"edit-budget-button"}),(0,t.jsx)(y.default,{variant:"Delete",tooltipText:"Delete budget",onClick:()=>{P(e),M(!0)},dataTestId:"delete-budget-button"})]},e.budget_id))})]})]}),(0,t.jsx)(b.default,{isOpen:D,title:"Delete Budget?",message:"Are you sure you want to delete this budget? 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deleted file mode 100644
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similarity index 71%
rename from litellm/proxy/_experimental/out/_next/static/chunks/161a2ab7f4e973ca.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/130cfc006c4f7d77.js
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new file mode 100644
index 000000000000..b3809622438f
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/b3b05b76472ce110.js b/litellm/proxy/_experimental/out/_next/static/chunks/1501e804b4d0f510.js
similarity index 85%
rename from litellm/proxy/_experimental/out/_next/static/chunks/b3b05b76472ce110.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/1501e804b4d0f510.js
index 0baa18f3f163..9d00552d0e61 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/b3b05b76472ce110.js
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similarity index 76%
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/3e3213d578d771d6.js b/litellm/proxy/_experimental/out/_next/static/chunks/3e3213d578d771d6.js
new file mode 100644
index 000000000000..bb673fa3262b
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/3e3213d578d771d6.js
@@ -0,0 +1,420 @@
+(globalThis.TURBOPACK||(globalThis.TURBOPACK=[])).push(["object"==typeof document?document.currentScript:void 0,190272,785913,e=>{"use strict";var t,o,i=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.RESPONSES="responses",t.IMAGE_EDITS="image_edits",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t),a=((o={}).IMAGE="image",o.VIDEO="video",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",o.EMBEDDINGS="embeddings",o.SPEECH="speech",o.TRANSCRIPTION="transcription",o.A2A_AGENTS="a2a_agents",o.MCP="mcp",o.REALTIME="realtime",o);let n={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"};e.s(["EndpointType",()=>a,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(i).includes(e)){let t=n[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:o,accessToken:i,apiKey:n,inputMessage:r,chatHistory:s,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:p,selectedMCPServers:m,mcpServers:u,mcpServerToolRestrictions:f,selectedVoice:g,endpointType:h,selectedModel:_,selectedSdk:b,proxySettings:x}=e,v="session"===o?i:n,y=window.location.origin,j=x?.LITELLM_UI_API_DOC_BASE_URL;j&&j.trim()?y=j:x?.PROXY_BASE_URL&&(y=x.PROXY_BASE_URL);let w=r||"Your prompt here",S=w.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),k=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),C={};l.length>0&&(C.tags=l),c.length>0&&(C.vector_stores=c),d.length>0&&(C.guardrails=d),p.length>0&&(C.policies=p);let N=_||"your-model-name",O="azure"===b?`import openai
+
+client = openai.AzureOpenAI(
+ api_key="${v||"YOUR_LITELLM_API_KEY"}",
+ azure_endpoint="${y}",
+ api_version="2024-02-01"
+)`:`import openai
+
+client = openai.OpenAI(
+ api_key="${v||"YOUR_LITELLM_API_KEY"}",
+ base_url="${y}"
+)`;switch(h){case a.CHAT:{let e=Object.keys(C).length>0,o="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();o=`,
+ extra_body=${e}`}let i=k.length>0?k:[{role:"user",content:w}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.chat.completions.create(
+ model="${N}",
+ messages=${JSON.stringify(i,null,4)}${o}
+)
+
+print(response)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg") # or .pdf
+# response_with_file = client.chat.completions.create(
+# model="${N}",
+# messages=[
+# {
+# "role": "user",
+# "content": [
+# {
+# "type": "text",
+# "text": "${S}"
+# },
+# {
+# "type": "image_url",
+# "image_url": {
+# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}
+# }
+# }
+# ]
+# }
+# ]${o}
+# )
+# print(response_with_file)
+`;break}case a.RESPONSES:{let e=Object.keys(C).length>0,o="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();o=`,
+ extra_body=${e}`}let i=k.length>0?k:[{role:"user",content:w}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.responses.create(
+ model="${N}",
+ input=${JSON.stringify(i,null,4)}${o}
+)
+
+print(response.output_text)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg") # or .pdf
+# response_with_file = client.responses.create(
+# model="${N}",
+# input=[
+# {
+# "role": "user",
+# "content": [
+# {"type": "input_text", "text": "${S}"},
+# {
+# "type": "input_image",
+# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}
+# },
+# ],
+# }
+# ]${o}
+# )
+# print(response_with_file.output_text)
+`;break}case a.IMAGE:t="azure"===b?`
+# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
+# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
+# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
+import os
+import requests
+import json
+import time
+from PIL import Image
+
+result = client.images.generate(
+ model="${N}",
+ prompt="${r}",
+ n=1
+)
+
+json_response = json.loads(result.model_dump_json())
+
+# Set the directory for the stored image
+image_dir = os.path.join(os.curdir, 'images')
+
+# If the directory doesn't exist, create it
+if not os.path.isdir(image_dir):
+ os.mkdir(image_dir)
+
+# Initialize the image path
+image_filename = f"generated_image_{int(time.time())}.png"
+image_path = os.path.join(image_dir, image_filename)
+
+try:
+ # Retrieve the generated image
+ if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
+ image_url = json_response["data"][0]["url"]
+ generated_image = requests.get(image_url).content
+ with open(image_path, "wb") as image_file:
+ image_file.write(generated_image)
+
+ print(f"Image saved to {image_path}")
+ # Display the image
+ image = Image.open(image_path)
+ image.show()
+ else:
+ print("Could not find image URL in response.")
+ print("Full response:", json_response)
+except Exception as e:
+ print(f"An error occurred: {e}")
+ print("Full response:", json_response)
+`:`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+ # In a real implementation, this would upload the file to OpenAI
+ # For this example, we'll just return a placeholder ID
+ return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${S}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+ model="${N}",
+ input=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "input_text", "text": prompt},
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image1}",
+ },
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image2}",
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id1,
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id2,
+ }
+ ],
+ }
+ ],
+ tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+ output
+ for output in response.output
+ if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+ image_base64 = image_data[0]
+ image_filename = f"edited_image_{int(time.time())}.png"
+ with open(image_filename, "wb") as f:
+ f.write(base64.b64decode(image_base64))
+ print(f"Image saved to {image_filename}")
+else:
+ # If no image is generated, there might be a text response with an explanation
+ text_response = [output.text for output in response.output if hasattr(output, 'text')]
+ if text_response:
+ print("No image generated. Model response:")
+ print("\\n".join(text_response))
+ else:
+ print("No image data found in response.")
+ print("Full response for debugging:")
+ print(response)
+`;break;case a.IMAGE_EDITS:t="azure"===b?`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# The prompt entered by the user
+prompt = "${S}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+ model="${N}",
+ input=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "input_text", "text": prompt},
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image1}",
+ },
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image2}",
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id1,
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id2,
+ }
+ ],
+ }
+ ],
+ tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+ output
+ for output in response.output
+ if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+ image_base64 = image_data[0]
+ image_filename = f"edited_image_{int(time.time())}.png"
+ with open(image_filename, "wb") as f:
+ f.write(base64.b64decode(image_base64))
+ print(f"Image saved to {image_filename}")
+else:
+ # If no image is generated, there might be a text response with an explanation
+ text_response = [output.text for output in response.output if hasattr(output, 'text')]
+ if text_response:
+ print("No image generated. Model response:")
+ print("\\n".join(text_response))
+ else:
+ print("No image data found in response.")
+ print("Full response for debugging:")
+ print(response)
+`:`
+import base64
+import os
+import time
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+ # In a real implementation, this would upload the file to OpenAI
+ # For this example, we'll just return a placeholder ID
+ return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${S}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+ model="${N}",
+ input=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "input_text", "text": prompt},
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image1}",
+ },
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image2}",
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id1,
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id2,
+ }
+ ],
+ }
+ ],
+ tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+ output
+ for output in response.output
+ if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+ image_base64 = image_data[0]
+ image_filename = f"edited_image_{int(time.time())}.png"
+ with open(image_filename, "wb") as f:
+ f.write(base64.b64decode(image_base64))
+ print(f"Image saved to {image_filename}")
+else:
+ # If no image is generated, there might be a text response with an explanation
+ text_response = [output.text for output in response.output if hasattr(output, 'text')]
+ if text_response:
+ print("No image generated. Model response:")
+ print("\\n".join(text_response))
+ else:
+ print("No image data found in response.")
+ print("Full response for debugging:")
+ print(response)
+`;break;case a.EMBEDDINGS:t=`
+response = client.embeddings.create(
+ input="${r||"Your string here"}",
+ model="${N}",
+ encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case a.TRANSCRIPTION:t=`
+# Open the audio file
+audio_file = open("path/to/your/audio/file.mp3", "rb")
+
+# Make the transcription request
+response = client.audio.transcriptions.create(
+ model="${N}",
+ file=audio_file${r?`,
+ prompt="${r.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case a.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+ model="${N}",
+ input="${r||"Your text to convert to speech here"}",
+ voice="${g}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
+)
+
+# Save the audio to a file
+output_filename = "output_speech.mp3"
+response.stream_to_file(output_filename)
+print(f"Audio saved to {output_filename}")
+
+# Optional: Customize response format and speed
+# response = client.audio.speech.create(
+# model="${N}",
+# input="${r||"Your text to convert to speech here"}",
+# voice="alloy",
+# response_format="mp3", # Options: mp3, opus, aac, flac, wav, pcm
+# speed=1.0 # Range: 0.25 to 4.0
+# )
+# response.stream_to_file("output_speech.mp3")
+`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${O}
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/4baa7c88c99e7b0b.js b/litellm/proxy/_experimental/out/_next/static/chunks/4baa7c88c99e7b0b.js
new file mode 100644
index 000000000000..5c7ba2a0149c
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/4baa7c88c99e7b0b.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/b59aefcfdd5715be.js b/litellm/proxy/_experimental/out/_next/static/chunks/61d8ae4ec4f309fe.js
similarity index 64%
rename from litellm/proxy/_experimental/out/_next/static/chunks/b59aefcfdd5715be.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/61d8ae4ec4f309fe.js
index 6fbdb54c0439..1acfcff512e9 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/b59aefcfdd5715be.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/61d8ae4ec4f309fe.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/22ea4136d8040e0f.js b/litellm/proxy/_experimental/out/_next/static/chunks/69c71a0d3c8c2e2c.js
similarity index 57%
rename from litellm/proxy/_experimental/out/_next/static/chunks/22ea4136d8040e0f.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/69c71a0d3c8c2e2c.js
index 2ff697adb450..a7466c543247 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/22ea4136d8040e0f.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/69c71a0d3c8c2e2c.js
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@@ -16,7 +16,7 @@
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"key1": "value1",
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t,o,n=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.RESPONSES="responses",t.IMAGE_EDITS="image_edits",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t),i=((o={}).IMAGE="image",o.VIDEO="video",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",o.EMBEDDINGS="embeddings",o.SPEECH="speech",o.TRANSCRIPTION="transcription",o.A2A_AGENTS="a2a_agents",o.MCP="mcp",o.REALTIME="realtime",o);let a={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"};e.s(["EndpointType",()=>i,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(n).includes(e)){let t=a[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:o,accessToken:n,apiKey:a,inputMessage:s,chatHistory:r,selectedTags:l,selectedVectorStores:c,selectedGuardrails:p,selectedPolicies:d,selectedMCPServers:m,mcpServers:u,mcpServerToolRestrictions:f,selectedVoice:g,endpointType:h,selectedModel:_,selectedSdk:b,proxySettings:v}=e,x="session"===o?n:a,y=window.location.origin,w=v?.LITELLM_UI_API_DOC_BASE_URL;w&&w.trim()?y=w:v?.PROXY_BASE_URL&&(y=v.PROXY_BASE_URL);let j=s||"Your prompt here",S=j.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),k=r.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),C={};l.length>0&&(C.tags=l),c.length>0&&(C.vector_stores=c),p.length>0&&(C.guardrails=p),d.length>0&&(C.policies=d);let O=_||"your-model-name",E="azure"===b?`import openai
+
+client = openai.AzureOpenAI(
+ api_key="${x||"YOUR_LITELLM_API_KEY"}",
+ azure_endpoint="${y}",
+ api_version="2024-02-01"
+)`:`import openai
+
+client = openai.OpenAI(
+ api_key="${x||"YOUR_LITELLM_API_KEY"}",
+ base_url="${y}"
+)`;switch(h){case i.CHAT:{let e=Object.keys(C).length>0,o="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();o=`,
+ extra_body=${e}`}let n=k.length>0?k:[{role:"user",content:j}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.chat.completions.create(
+ model="${O}",
+ messages=${JSON.stringify(n,null,4)}${o}
+)
+
+print(response)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg") # or .pdf
+# response_with_file = client.chat.completions.create(
+# model="${O}",
+# messages=[
+# {
+# "role": "user",
+# "content": [
+# {
+# "type": "text",
+# "text": "${S}"
+# },
+# {
+# "type": "image_url",
+# "image_url": {
+# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}
+# }
+# }
+# ]
+# }
+# ]${o}
+# )
+# print(response_with_file)
+`;break}case i.RESPONSES:{let e=Object.keys(C).length>0,o="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();o=`,
+ extra_body=${e}`}let n=k.length>0?k:[{role:"user",content:j}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.responses.create(
+ model="${O}",
+ input=${JSON.stringify(n,null,4)}${o}
+)
+
+print(response.output_text)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg") # or .pdf
+# response_with_file = client.responses.create(
+# model="${O}",
+# input=[
+# {
+# "role": "user",
+# "content": [
+# {"type": "input_text", "text": "${S}"},
+# {
+# "type": "input_image",
+# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}
+# },
+# ],
+# }
+# ]${o}
+# )
+# print(response_with_file.output_text)
+`;break}case i.IMAGE:t="azure"===b?`
+# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
+# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
+# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
+import os
+import requests
+import json
+import time
+from PIL import Image
+
+result = client.images.generate(
+ model="${O}",
+ prompt="${s}",
+ n=1
+)
+
+json_response = json.loads(result.model_dump_json())
+
+# Set the directory for the stored image
+image_dir = os.path.join(os.curdir, 'images')
+
+# If the directory doesn't exist, create it
+if not os.path.isdir(image_dir):
+ os.mkdir(image_dir)
+
+# Initialize the image path
+image_filename = f"generated_image_{int(time.time())}.png"
+image_path = os.path.join(image_dir, image_filename)
+
+try:
+ # Retrieve the generated image
+ if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
+ image_url = json_response["data"][0]["url"]
+ generated_image = requests.get(image_url).content
+ with open(image_path, "wb") as image_file:
+ image_file.write(generated_image)
+
+ print(f"Image saved to {image_path}")
+ # Display the image
+ image = Image.open(image_path)
+ image.show()
+ else:
+ print("Could not find image URL in response.")
+ print("Full response:", json_response)
+except Exception as e:
+ print(f"An error occurred: {e}")
+ print("Full response:", json_response)
+`:`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+ # In a real implementation, this would upload the file to OpenAI
+ # For this example, we'll just return a placeholder ID
+ return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${S}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+ model="${O}",
+ input=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "input_text", "text": prompt},
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image1}",
+ },
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image2}",
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id1,
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id2,
+ }
+ ],
+ }
+ ],
+ tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+ output
+ for output in response.output
+ if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+ image_base64 = image_data[0]
+ image_filename = f"edited_image_{int(time.time())}.png"
+ with open(image_filename, "wb") as f:
+ f.write(base64.b64decode(image_base64))
+ print(f"Image saved to {image_filename}")
+else:
+ # If no image is generated, there might be a text response with an explanation
+ text_response = [output.text for output in response.output if hasattr(output, 'text')]
+ if text_response:
+ print("No image generated. Model response:")
+ print("\\n".join(text_response))
+ else:
+ print("No image data found in response.")
+ print("Full response for debugging:")
+ print(response)
+`;break;case i.IMAGE_EDITS:t="azure"===b?`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# The prompt entered by the user
+prompt = "${S}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+ model="${O}",
+ input=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "input_text", "text": prompt},
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image1}",
+ },
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image2}",
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id1,
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id2,
+ }
+ ],
+ }
+ ],
+ tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+ output
+ for output in response.output
+ if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+ image_base64 = image_data[0]
+ image_filename = f"edited_image_{int(time.time())}.png"
+ with open(image_filename, "wb") as f:
+ f.write(base64.b64decode(image_base64))
+ print(f"Image saved to {image_filename}")
+else:
+ # If no image is generated, there might be a text response with an explanation
+ text_response = [output.text for output in response.output if hasattr(output, 'text')]
+ if text_response:
+ print("No image generated. Model response:")
+ print("\\n".join(text_response))
+ else:
+ print("No image data found in response.")
+ print("Full response for debugging:")
+ print(response)
+`:`
+import base64
+import os
+import time
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+ # In a real implementation, this would upload the file to OpenAI
+ # For this example, we'll just return a placeholder ID
+ return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${S}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+ model="${O}",
+ input=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "input_text", "text": prompt},
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image1}",
+ },
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image2}",
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id1,
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id2,
+ }
+ ],
+ }
+ ],
+ tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+ output
+ for output in response.output
+ if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+ image_base64 = image_data[0]
+ image_filename = f"edited_image_{int(time.time())}.png"
+ with open(image_filename, "wb") as f:
+ f.write(base64.b64decode(image_base64))
+ print(f"Image saved to {image_filename}")
+else:
+ # If no image is generated, there might be a text response with an explanation
+ text_response = [output.text for output in response.output if hasattr(output, 'text')]
+ if text_response:
+ print("No image generated. Model response:")
+ print("\\n".join(text_response))
+ else:
+ print("No image data found in response.")
+ print("Full response for debugging:")
+ print(response)
+`;break;case i.EMBEDDINGS:t=`
+response = client.embeddings.create(
+ input="${s||"Your string here"}",
+ model="${O}",
+ encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case i.TRANSCRIPTION:t=`
+# Open the audio file
+audio_file = open("path/to/your/audio/file.mp3", "rb")
+
+# Make the transcription request
+response = client.audio.transcriptions.create(
+ model="${O}",
+ file=audio_file${s?`,
+ prompt="${s.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case i.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+ model="${O}",
+ input="${s||"Your text to convert to speech here"}",
+ voice="${g}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
+)
+
+# Save the audio to a file
+output_filename = "output_speech.mp3"
+response.stream_to_file(output_filename)
+print(f"Audio saved to {output_filename}")
+
+# Optional: Customize response format and speed
+# response = client.audio.speech.create(
+# model="${O}",
+# input="${s||"Your text to convert to speech here"}",
+# voice="alloy",
+# response_format="mp3", # Options: mp3, opus, aac, flac, wav, pcm
+# speed=1.0 # Range: 0.25 to 4.0
+# )
+# response.stream_to_file("output_speech.mp3")
+`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${E}
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/589dbe4d6a5a8128.js b/litellm/proxy/_experimental/out/_next/static/chunks/7e46b6e6e9d69068.js
similarity index 71%
rename from litellm/proxy/_experimental/out/_next/static/chunks/589dbe4d6a5a8128.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/7e46b6e6e9d69068.js
index 4d6e8d623a6e..519ef718ae19 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/589dbe4d6a5a8128.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/7e46b6e6e9d69068.js
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console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:a,accessToken:i,apiKey:r,inputMessage:o,chatHistory:n,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:p,selectedMCPServers:m,mcpServers:u,mcpServerToolRestrictions:g,selectedVoice:f,endpointType:h,selectedModel:_,selectedSdk:x,proxySettings:v}=e,b="session"===a?i:r,y=window.location.origin,j=v?.LITELLM_UI_API_DOC_BASE_URL;j&&j.trim()?y=j:v?.PROXY_BASE_URL&&(y=v.PROXY_BASE_URL);let w=o||"Your prompt here",N=w.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),E=n.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),k={};l.length>0&&(k.tags=l),c.length>0&&(k.vector_stores=c),d.length>0&&(k.guardrails=d),p.length>0&&(k.policies=p);let I=_||"your-model-name",C="azure"===x?`import openai
-
-client = openai.AzureOpenAI(
- api_key="${b||"YOUR_LITELLM_API_KEY"}",
- azure_endpoint="${y}",
- api_version="2024-02-01"
-)`:`import openai
-
-client = openai.OpenAI(
- api_key="${b||"YOUR_LITELLM_API_KEY"}",
- base_url="${y}"
-)`;switch(h){case s.CHAT:{let e=Object.keys(k).length>0,a="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
- extra_body=${e}`}let i=E.length>0?E:[{role:"user",content:w}];t=`
-import base64
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Example with text only
-response = client.chat.completions.create(
- model="${I}",
- messages=${JSON.stringify(i,null,4)}${a}
-)
-
-print(response)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg") # or .pdf
-# response_with_file = client.chat.completions.create(
-# model="${I}",
-# messages=[
-# {
-# "role": "user",
-# "content": [
-# {
-# "type": "text",
-# "text": "${N}"
-# },
-# {
-# "type": "image_url",
-# "image_url": {
-# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}
-# }
-# }
-# ]
-# }
-# ]${a}
-# )
-# print(response_with_file)
-`;break}case s.RESPONSES:{let e=Object.keys(k).length>0,a="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
- extra_body=${e}`}let i=E.length>0?E:[{role:"user",content:w}];t=`
-import base64
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Example with text only
-response = client.responses.create(
- model="${I}",
- input=${JSON.stringify(i,null,4)}${a}
-)
-
-print(response.output_text)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg") # or .pdf
-# response_with_file = client.responses.create(
-# model="${I}",
-# input=[
-# {
-# "role": "user",
-# "content": [
-# {"type": "input_text", "text": "${N}"},
-# {
-# "type": "input_image",
-# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}
-# },
-# ],
-# }
-# ]${a}
-# )
-# print(response_with_file.output_text)
-`;break}case s.IMAGE:t="azure"===x?`
-# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
-# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
-# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
-import os
-import requests
-import json
-import time
-from PIL import Image
-
-result = client.images.generate(
- model="${I}",
- prompt="${o}",
- n=1
-)
-
-json_response = json.loads(result.model_dump_json())
-
-# Set the directory for the stored image
-image_dir = os.path.join(os.curdir, 'images')
-
-# If the directory doesn't exist, create it
-if not os.path.isdir(image_dir):
- os.mkdir(image_dir)
-
-# Initialize the image path
-image_filename = f"generated_image_{int(time.time())}.png"
-image_path = os.path.join(image_dir, image_filename)
-
-try:
- # Retrieve the generated image
- if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
- image_url = json_response["data"][0]["url"]
- generated_image = requests.get(image_url).content
- with open(image_path, "wb") as image_file:
- image_file.write(generated_image)
-
- print(f"Image saved to {image_path}")
- # Display the image
- image = Image.open(image_path)
- image.show()
- else:
- print("Could not find image URL in response.")
- print("Full response:", json_response)
-except Exception as e:
- print(f"An error occurred: {e}")
- print("Full response:", json_response)
-`:`
-import base64
-import os
-import time
-import json
-from PIL import Image
-import requests
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Helper function to create a file (simplified for this example)
-def create_file(image_path):
- # In a real implementation, this would upload the file to OpenAI
- # For this example, we'll just return a placeholder ID
- return f"file_{os.path.basename(image_path).replace('.', '_')}"
-
-# The prompt entered by the user
-prompt = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
- model="${I}",
- input=[
- {
- "role": "user",
- "content": [
- {"type": "input_text", "text": prompt},
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image1}",
- },
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image2}",
- },
- {
- "type": "input_image",
- "file_id": file_id1,
- },
- {
- "type": "input_image",
- "file_id": file_id2,
- }
- ],
- }
- ],
- tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
- output
- for output in response.output
- if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
- image_base64 = image_data[0]
- image_filename = f"edited_image_{int(time.time())}.png"
- with open(image_filename, "wb") as f:
- f.write(base64.b64decode(image_base64))
- print(f"Image saved to {image_filename}")
-else:
- # If no image is generated, there might be a text response with an explanation
- text_response = [output.text for output in response.output if hasattr(output, 'text')]
- if text_response:
- print("No image generated. Model response:")
- print("\\n".join(text_response))
- else:
- print("No image data found in response.")
- print("Full response for debugging:")
- print(response)
-`;break;case s.IMAGE_EDITS:t="azure"===x?`
-import base64
-import os
-import time
-import json
-from PIL import Image
-import requests
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# The prompt entered by the user
-prompt = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
- model="${I}",
- input=[
- {
- "role": "user",
- "content": [
- {"type": "input_text", "text": prompt},
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image1}",
- },
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image2}",
- },
- {
- "type": "input_image",
- "file_id": file_id1,
- },
- {
- "type": "input_image",
- "file_id": file_id2,
- }
- ],
- }
- ],
- tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
- output
- for output in response.output
- if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
- image_base64 = image_data[0]
- image_filename = f"edited_image_{int(time.time())}.png"
- with open(image_filename, "wb") as f:
- f.write(base64.b64decode(image_base64))
- print(f"Image saved to {image_filename}")
-else:
- # If no image is generated, there might be a text response with an explanation
- text_response = [output.text for output in response.output if hasattr(output, 'text')]
- if text_response:
- print("No image generated. Model response:")
- print("\\n".join(text_response))
- else:
- print("No image data found in response.")
- print("Full response for debugging:")
- print(response)
-`:`
-import base64
-import os
-import time
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Helper function to create a file (simplified for this example)
-def create_file(image_path):
- # In a real implementation, this would upload the file to OpenAI
- # For this example, we'll just return a placeholder ID
- return f"file_{os.path.basename(image_path).replace('.', '_')}"
-
-# The prompt entered by the user
-prompt = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
- model="${I}",
- input=[
- {
- "role": "user",
- "content": [
- {"type": "input_text", "text": prompt},
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image1}",
- },
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image2}",
- },
- {
- "type": "input_image",
- "file_id": file_id1,
- },
- {
- "type": "input_image",
- "file_id": file_id2,
- }
- ],
- }
- ],
- tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
- output
- for output in response.output
- if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
- image_base64 = image_data[0]
- image_filename = f"edited_image_{int(time.time())}.png"
- with open(image_filename, "wb") as f:
- f.write(base64.b64decode(image_base64))
- print(f"Image saved to {image_filename}")
-else:
- # If no image is generated, there might be a text response with an explanation
- text_response = [output.text for output in response.output if hasattr(output, 'text')]
- if text_response:
- print("No image generated. Model response:")
- print("\\n".join(text_response))
- else:
- print("No image data found in response.")
- print("Full response for debugging:")
- print(response)
-`;break;case s.EMBEDDINGS:t=`
-response = client.embeddings.create(
- input="${o||"Your string here"}",
- model="${I}",
- encoding_format="base64" # or "float"
-)
-
-print(response.data[0].embedding)
-`;break;case s.TRANSCRIPTION:t=`
-# Open the audio file
-audio_file = open("path/to/your/audio/file.mp3", "rb")
-
-# Make the transcription request
-response = client.audio.transcriptions.create(
- model="${I}",
- file=audio_file${o?`,
- prompt="${o.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""}
-)
-
-print(response.text)
-`;break;case s.SPEECH:t=`
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-response = client.audio.speech.create(
- model="${I}",
- input="${o||"Your text to convert to speech here"}",
- voice="${f}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
-)
-
-# Save the audio to a file
-output_filename = "output_speech.mp3"
-response.stream_to_file(output_filename)
-print(f"Audio saved to {output_filename}")
-
-# Optional: Customize response format and speed
-# response = client.audio.speech.create(
-# model="${I}",
-# input="${o||"Your text to convert to speech here"}",
-# voice="alloy",
-# response_format="mp3", # Options: mp3, opus, aac, flac, wav, pcm
-# speed=1.0 # Range: 0.25 to 4.0
-# )
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/8c6d915f992d48df.js b/litellm/proxy/_experimental/out/_next/static/chunks/8c6d915f992d48df.js
deleted file mode 100644
index 15d38d16a4b5..000000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/8c6d915f992d48df.js
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-
- 
+
+ 
+
+ 
+
@@ -400,9 +404,36 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature
2. Install dependencies `npm install`
3. Run `npm run dev` to start the dashboard
+# Verify Docker Image Signatures
+
+All LiteLLM Docker images published to GHCR are signed with [cosign](https://docs.sigstore.dev/cosign/overview/). Every release is signed with the same key introduced in [commit `0112e53`](https://github.com/BerriAI/litellm/commit/0112e53046018d726492c814b3644b7d376029d0).
+
+**Verify using the pinned commit hash (recommended):**
+
+A commit hash is cryptographically immutable, so this is the strongest way to ensure you are using the original signing key:
+
+```bash
+cosign verify \
+ --key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
+ ghcr.io/berriai/litellm: