chore(ci): promote internal staging to main - #33868
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…single-server REST statuses
The aggregate MCP tools/list absorbed every per-server failure (upstream 401/403/5xx, timeouts,
network errors) into that server contributing zero tools, making a broken upstream indistinguishable
from a healthy server with no tools; the single-server REST list masked the same failures as
{"tools": [], "error": null, "message": "Successfully retrieved tools"}
Phase 2 of the MCP error-handling framework (LIT-4419): the manager fetch hops now raise a
classified MCPServerListError (faults/list_outcomes.py: total classifier, frozen outcome values)
instead of returning [], and each boundary applies the relay-vs-absorb policy matrix. The aggregate
keeps serving the healthy subset but records each server's outcome, surfaced on the tools/list
result _meta under litellm.ai/server_outcomes (the SDK passes a ListToolsResult through unwrapped)
and in spend logs as per_server_list_outcomes. Single-server REST requests relay truthful statuses
(unreachable/upstream_error 502, timeout 504, internal 500) and access denials now surface as real
403s instead of 200 unexpected_error bodies; upstream 403s surface through MCPUpstreamAuthError
like 401s. Outcome wire values carry category and status code only, never upstream prose
Resolves LIT-4421
…a healthy empty server A cancelled fetch absorbed to [] made that server contribute ServerListOk(tool_count=0), the exact healthy-but-empty impostor this change removes. Cancellation stays suppressed (the pre-existing choice); it now carries an internal fault so outcomes stay truthful
…ding the upstream response
…m listing failures Both review findings shared one root cause: two exception-tree walkers with drifted semantics. _extract_upstream_auth_failure walked the incidental __context__ chain before explicit causes, so a 403 raised while handling the causal 401 could shadow it; and the generic _get_tools_from_server arm classified without extracting the challenge, so a nested 401 at client-build time surfaced without the WWW-Authenticate the client needs. upstream_auth_challenge and raise_classified_list_failure in faults/list_outcomes.py are now the single traversal and the single choice-point; both fetch arms and _extract_upstream_auth_failure (also serving tool calls and the connect-time probe) delegate to them, with dcr_bridge challenge suppression as a parameter so it holds on every path. The stale _fetch_tools_with_timeout docstring describing the pre-change 403 absorb is rewritten to the actual contract: 403 relays with its own status, an upstream-sent challenge relays verbatim per RFC 6750 insufficient_scope, and a challenge is only ever fabricated for a challenge-less 401
…che_control injection Anthropic only caches a prompt when the request carries explicit cache_control breakpoints, unlike OpenAI where prompt caching is automatic and needs no configuration. Today litellm can inject those breakpoints server-side, but only when an admin hand-writes cache_control_injection_points into a model's litellm_params (or router_settings.default_litellm_params). Clients such as Claude Code and Claude Desktop never set cache_control themselves, and the admin recipe is easy to miss, so Anthropic traffic through the proxy silently pays full price on every repeated prefix. This adds an opt-in litellm_settings flag, enable_anthropic_prompt_caching. When it is on and the request has no injection points configured and no client-supplied cache_control, litellm synthesizes a default pair of breakpoints (the system prompt and the trailing turn) so the stable prefix is cached while the breakpoint advances with the conversation. It is wired into both surfaces: /chat/completions seeds the points before the existing prompt-management gate, and /v1/messages resolves them in maybe_inject_cache_control, so the existing AnthropicCacheControlHook applies them unchanged and keeps its four-block cap and its refusal to overwrite client breakpoints. The default is off, so no existing deployment changes behavior. Injection is gated to providers that actually consume cache_control markers (anthropic and bedrock) and to models the cost map flags as supporting prompt caching; note that supports_prompt_caching alone is not a sufficient gate, since OpenAI, Azure and Gemini models report it as well but never take cache_control markers. The default ttl is Anthropic's 5 minute ephemeral cache, with an optional anthropic_prompt_caching_ttl of "5m" or "1h"; ttl is also added to ChatCompletionCachedContent, which the bedrock and anthropic transforms already read at runtime but the type never declared Resolves LIT-4478
Both enable_anthropic_prompt_caching and anthropic_prompt_caching_ttl are now read from LITELLM_ENABLE_ANTHROPIC_PROMPT_CACHING and LITELLM_ANTHROPIC_PROMPT_CACHING_TTL at import, so the flag can be turned on without a config file. An unsupported ttl falls back to the provider default rather than reaching the provider verbatim
…ols/call honors them
_request_has_cache_control only looked at messages and system, so a client that marks cache_control on tools alone did not suppress auto-injection. Tool breakpoints count toward the provider's four-block limit, so three of them plus the two injected here is five, which Anthropic rejects. Thread tools through both entry points and treat a client-marked tool as the stand-down signal it already is for messages and system.
… 1024 MINIMUM_PROMPT_CACHE_TOKEN_COUNT was a flat 1024 described as "minimum number of tokens to cache a prompt by Anthropic". Anthropic's minimum cacheable prefix is per-model and ranges from 512 to 4096, and it can differ per platform for the same model, so one constant is wrong in both directions is_prompt_caching_valid_prompt gates PromptCachingDeploymentCheck, which is what optional_pre_call_checks: ["prompt_caching"] turns on. When it believes a prompt is cacheable, async_filter_deployments pins routing to whichever deployment previously served that prefix. For a prompt between 1024 and 4096 tokens on Opus 4.6, Opus 4.5 or Haiku 4.5, litellm judged it cacheable and constrained routing while the provider never cached it, so the pin cost load balancing for nothing. In the other direction Fable 5 caches from 512 tokens, so a 512 to 1024 token prefix was refused a pin it had earned The minimum now resolves from prompt_cache_min_tokens in the model cost map, which keeps it current with new models and lets the Bedrock override for Fable 5 fall out of the existing per-entry keys with no special casing. MINIMUM_PROMPT_CACHE_TOKEN_COUNT stays as a global escape hatch when explicitly set, and as the fallback for models the cost map has no entry for async_filter_deployments only ever receives the model group alias, never a model name, so it resolves the threshold from healthy_deployments instead. A group may mix models with different minimums, so it takes the max: a prompt is only treated as cacheable when it clears every member's minimum, because an unnecessary pin is the defect being fixed while a missed pin only forfeits an optimization Gemini context caching shares this gate and has the same defect; its entries are left unset so they keep today's behavior, tracked separately in LIT-4525
get_model_info is lru_cached, so swapping litellm.model_cost is not enough on its own. An earlier test that resolved these models against the remote map, which does not carry prompt_cache_min_tokens yet, leaves cached entries without it, and the stale hit resolves to the default. The assertions would then pass for the wrong reason or fail depending on execution order Clear on teardown as well, so entries these tests warm against the local map do not leak into later tests, matching the fixture already used in test_utils.py Also pin that a wildcard route resolves the underlying model's minimum. That works only because pattern_match_deployments substitutes the real model name into litellm_params before the deployment reaches the check; without the assertion that claim is unpinned and the threshold would silently fall back to the default
…sage (#33533) Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
…ghest The read gate cannot cause a wrong pin. A deployment is only pinned when the cache already holds an entry for the prefix, and async_log_success_event writes entries against the deployment's real model rather than the group alias, so a model that will not cache a prefix never records one and there is nothing to pin it to That makes this gate purely a cheap short-circuit deciding whether the cache lookup is worth doing, so the threshold must be the lowest minimum in the group. Taking the highest skipped the lookup for a prefix a lower-minimum member had genuinely cached, losing a hit it earned, and protected against nothing. It also broke the Fable 5 direction this ticket is meant to fix: its real minimum is 512, so a group gate stuck at a higher value would skip the lookup for a prefix Fable 5 had actually cached
…t) (#33634) * test(e2e): harness fixes for long_context, complexity router, UI, and unit coverage Point long_context_1m at 1M-capable models, harden complexity-smart-router registration and spend-log assertions, fix key models dropdown selectors, and add gateway/lifecycle/transport and claude_code unit tests * test(e2e): harden remaining stage failures in harness Register complexity-smart-router via create_model + callable probe, fix create-key UI navigation race, retry management writes and budget ALB 502s, mark Vertex count_tokens N/A when unsupported, and tighten tool_search model lists for Azure/Bedrock capability gaps * test(e2e): drop claude_code and harness unit tests from this PR Keep management, router, budget, and shared conftest harness fixes only * test(e2e): restore E2E_RESULT pytest_runtest_makereport hook Accidentally dropped in an earlier harness commit; Grafana status history depends on these structured log lines * test(e2e): drop management control-plane write retries Transient 500 retries do not fix the underlying control plane failures * test(e2e): skip stage-red claude_code cells; fix multi-window budget latency Mark the twelve failing claude_code matrix cells skip until product/config lands. Multi-window budget polls gpt-5.5 with max_tokens=1 instead of Claude so the reset wait stays under ALB target idle timeout rather than masking awselb 502s * test(e2e): require exactly one LLM-tier spend row for complexity router Keep alias membership for compose vs stage model names, but assert len(served) == 1 so a leaked classifier sub-call cannot pass. Also pin LIT-4521 skip and align LIT-4522/23/24 skip reasons * test(e2e): harden router callable probe and multi-window budget exhaustion _router_is_callable treated any non-success chat whose body lacked "Invalid model name" as callable, so an unpropagated probe key (401), a generic 502, or a connection reset let the session proceed and hit real "Invalid model name" failures inside the tests. Require a Success outcome instead; the reload-race 400 and every infra/auth error now correctly read as not-callable. The multi-window budget test capped the tight window at 3e-6, which gpt-5.5 exhausts on the first call but a cheaper CHEAP_OPENAI_MODEL might not within the 20-call loop, turning a reset test into a spurious "window never enforced" failure. Drop the tight cap to 1e-9 so the first billed call exhausts it regardless of model price; the roomy 1m window stays at 1.0 and never blocks. * test(e2e): use a tradeoff-decision prompt for the complexity router classifier "Is P equal to NP?" reads to the LLM classifier as a short yes/no question, so gpt-5.5 classified it SIMPLE and the request routed to the openai backend, which made the test fail even though the classifier was running. The tier definitions key on what the request demands, not how hard the answer is, and a short direct question maps to SIMPLE regardless of subject. Swap in "Should I pay off my mortgage early or invest the extra money instead?". It carries none of the heuristic scorer's reasoning/technical/code keywords and stays short, so heuristic scoring still lands SIMPLE (openai), but the LLM reads it as a decision that has to weigh tradeoffs and lands it above SIMPLE, which the config routes to anthropic. Any non-SIMPLE tier serves anthropic, so the classifier only has to avoid SIMPLE for the test to distinguish a real classifier run from the heuristic fallback.
fix(router): resolve prompt cache minimum per model instead of a flat 1024
Team-scoped wildcard deployments like openai/* are indexed separately from global router models, so vector store file requests failed with api_key=None when a team also had other yaml/db models. Pass team_id into credential lookup and consult team model indexes and pattern routers. Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The consolidation regressed the pre-existing walker semantics: _extract_upstream_auth_failure used to keep scanning until it found a 401/403, while the consolidated helper took the first response of any status and then tested it, so a causal 401 sitting behind an unrelated 5xx (retry attempts, multi-stream task groups) was misclassified as upstream_error and its challenge lost on the listing, tool-call, and probe paths. The traversal is now an iterator in deliberate order and each consumer applies its predicate over the stream: the auth scan takes the first 401/403 even behind non-auth responses, generic classification takes the first response, and classify_list_exception derives its auth arm from the same scan so the carrier choice and the classification can never disagree
…l_name (#33691) * fix(router): tag-aware pre-routing strategy selection for shared model_name Complexity/auto/adaptive/quality router registries were keyed by model_name alone, so a second deployment sharing a model_name but carrying different tags was rejected and every request used the first config. This made tag-based routing to distinct provider configs behind one alias impossible, surfacing as 401 'Not allowed to access model due to tags configuration' for the second tag. Each registry now holds a list of tag-scoped strategies and async_pre_routing_hook selects the entry whose tags match the request before classification, falling back to a default-tagged then first-registered entry. A repeat of the same (model_name, tags) pair is still rejected. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(router): cover tag-scoped pre-routing strategy registry helpers Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * chore: re-trigger CI Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
…auge (#32441) * fix(proxy): enforce max_parallel_requests as a per-slot concurrency gauge The v3 rate limiter tracked max_parallel_requests with the same sliding-window machinery as RPM/TPM. A concurrency gauge cannot live on a windowed counter: every window roll reset the counter to 1 while requests were still in flight, the completion decrements for those forgotten requests then drove the counter negative, and rejected requests left stranded increments that nothing released. Under sustained load a key with max_parallel_requests=5 let backend concurrency climb to the full client concurrency (observed 60 on a live proxy) while the proxy kept returning 429s for everyone else Replace the windowed counter with a per-slot registry (Redis sorted set of slot ids scored by acquire time, with an asyncio-locked in-memory fallback): admission atomically prunes expired slots and registers a new slot id only when in_flight + 1 <= limit, so rejected requests never occupy a slot; success, failure, and client-disconnect paths release exactly the slot id this request acquired (stashed in the request metadata channels), so a release without a matching acquire or a double-fired callback can never free another request's slot; and a slot leaked by a crashed worker is pruned individually after its TTL even under continuous traffic Resolves LIT-4259 Fixes #16011 * fix(proxy): release every acquired gauge and respect mirrored counts in the in-memory fallback Address review findings on the slot-registry gauge: the acquisition stash now carries the gauge counter keys alongside the slot id, so the release paths free the slot from every gauge it was registered under instead of hardcoding the api_key scope, and the disconnect release keys off the stashed acquisition instead of the key object's current max_parallel_requests configuration (which can change mid-request). The in-memory fallback now treats a cached integer (the count mirrored from the last successful Redis script call) as real occupancy, carrying it forward as a floored counter during a Redis outage instead of restarting from an empty registry * fix(proxy): release the parallel slot on proxy-level rejections async_post_call_failure_hook is the only callback that fires when a downstream hook (guardrail, budget check) rejects a request after the rate limiter's pre-call hook acquired a slot; async_log_failure_event is a completion-level callback and never runs for proxy-side rejections. Release the stashed acquisition at the top of the hook, before the TPM reservation guard, so those slots do not linger for the full slot TTL and wedge the key at its limit under moderate rejection rates. Clearing the acquisition marker keeps the release idempotent when a later failure callback runs in the same flow * test(proxy): cover success release, read-only count, Redis release mirror, and TPM rejection release Four behaviors of the slot-registry gauge had no direct test: a successful completion releasing exactly its acquired slot, read_only callers counting in-flight slots through the count script (and degrading to the local mirror when the script fails) without acquiring, the Redis release script mirroring returned counts into the local cache, and the TPM reservation rejection releasing the already-acquired slot before raising * style(proxy): use builtin generics and union syntax in new rate limiter annotations The slot-gauge code added Tuple/List/Dict and Optional[...] annotations, pushing the UP006 and UP045 strict-rule totals past their ceilings in ruff-strict-budget.json. Convert only the annotations this branch introduces to builtin generics and PEP 604 unions, leaving the rest of the module untouched.
…l on auth-enforced pass-through routes (#33710) * fix(proxy): stop treating upstream model body field as a LiteLLM model on auth-enforced pass-through routes An auth: true user-defined pass-through endpoint runs full virtual-key auth, and get_model_from_request unconditionally extracted the request body model field, so key/team/user/project model allowlist checks rejected requests whose model only exists upstream (key_model_access_denied), even when the key was explicitly granted the route via allowed_passthrough_routes. The pass-through route registry moves to a leaf module (route_registry.py) that the auth layer can import without re-entering the pass_through_endpoints -> user_api_key_auth -> auth_utils import cycle. get_model_from_request now returns None for routes registered as user-defined pass-through endpoints (exact and subpath), which skips model allowlist and per-model budget enforcement on those routes while key auth, allowed_passthrough_routes, and spend/budget checks stay intact. Built-in provider passthrough routes (/vertex_ai, /gemini, ...) keep model enforcement. Resolves LIT-4299 * fix(proxy): key pass-through model-access skip on the dispatched endpoint, not the request path Addresses a model-authorization bypass: the first version decided whether to skip model-allowlist extraction by matching the request path against the pass-through route registry. That ignored the HTTP method and, more importantly, whether the request was actually dispatched to a pass-through handler. A custom pass-through whose path collides with a built-in route (e.g. /v1/chat/completions, or an include_subpath prefix of one) still writes a registry entry even though FastAPI serves the built-in handler, so a normal request to that route had its model checks skipped and could reach a model outside the key/team/user/project allowlist. The skip is now keyed off the FastAPI-resolved endpoint. create_pass_through_route tags its handler with LITELLM_PASS_THROUGH_ENDPOINT_MARKER, and get_model_from_request returns None only when request.scope["endpoint"] carries that marker. Because routing runs before auth dependencies, this reflects the handler that actually serves the request: on a collision the built-in handler is dispatched and carries no marker, so model enforcement stays on. This also removes the need for the separate route_registry module, so that extraction is reverted. Regression tests cover a pass-through-dispatched request (model suppressed), a built-in-dispatched request on the same path (model still enforced), and the no-request budget path. Resolves LIT-4299
…rants fix(mcp): expand toolset grants in shared permission primitives so tools/call honors them
…omes Append-append conflict at the end of test_mcp_server.py between this branch's aggregate-outcome tests and the mode-aware preemptive-401 tests from staging; both kept
* feat(complexity-router): user-triggered escalation keywords Add an escalation_keywords config option to the complexity router so a user can force a bump to the next-higher complexity tier by including a phrase in their message (a stronger model, but not one they get to choose). Defaults to ['LITELLM ESCALATE'] when unset, case-sensitive so it only fires on the deliberate shouted form; admins can override the list or set [] to disable. Escalation applies across every routing path: heuristic/LLM classification, literal and semantic keyword_tier_rules overrides, adaptive routing, and session affinity (where it bumps relative to the pinned model and persists the higher tier for the rest of the session). Capped at the highest configured tier and skips unconfigured intermediate tiers. Expose it in the Auto-Router v2 UI as an Escalation Keywords field wired into the complexity_router_config payload. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(complexity-router): validate escalation keywords and pin at tier ceiling Strip blank/whitespace escalation keywords so an empty phrase can't match every message and escalate all traffic. Keep the exact pinned model when a session escalates at the highest configured tier instead of randomly hopping to a peer in a multi-model pool. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
…coped fixture (#33750) The shared proxy wrapper in tests/e2e/e2e_gateway.py was misnamed: Gateway is not a gateway server, it is the client every suite uses to talk to the proxy (keys, models, chat/embed/ocr, spend read-backs, poll helpers). Rename the module to proxy_client.py and the class to ProxyClient, with build_gateway becoming build_proxy_client and the GatewayProvider protocol becoming ProxyClientProvider. The .gateway attribute suites held is now .proxy. Only identifiers changed; prose and string literals that use the word gateway for the proxy-server concept were left alone. Each suite previously built its own instance through a per-suite build_client() that called build_gateway() inside, duplicating the proxy wiring across suites. There is now one session-scoped proxy fixture in tests/e2e/conftest.py; every suite's client fixture depends on it and injects it, so the wiring lives in one place. claude_code keeps building its own client directly since it has its own harness and does not use the shared fixtures. Behavior is unchanged: shared transport, data-plane/control-plane split routing, poll budget, typed request/response models, and resource cleanup all go through the same object.
…ust:true (#33616) * feat(messages): route Azure Anthropic /messages through Rust behind rust:true Adds an opt-in Rust path for non-streaming Azure Anthropic Messages. A deployment sets rust: true in litellm_params to route litellm.messages() and the proxy /v1/messages endpoint through the native Rust bridge; a missing flag or rust: false keeps the existing Python path, and non-Azure providers, streaming, an unavailable bridge, or a None result all fall back to Python. Rust-backed responses carry an x-litellm-rust: true response header so callers can see which path served the request. Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * test(docs): exclude LITELLM_USE_RUST_MESSAGES rollout flag from env-doc check Mirrors the existing LITELLM_USE_RUST_OCR entry; the flag is an internal rollout toggle that is intentionally not in the public environment settings docs yet. Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * fix(rust_bridge): isolate OCR enable flag and drop dead messages global toggle use_litellm_rust only mutates the OCR enabled flag when configuring OCR (or called with no bridge kwargs, preserving the legacy contract), so configuring only the messages bridge no longer flips OCR state. Remove the vestigial global enabled/env state from the messages bridge. Routing is controlled per deployment by rust:true in the shared handler gate, so the messages module never consulted the global toggle; drop it rather than leave a no-op switch. Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * refactor(rust/messages): split Anthropic config into its own provider file and type the request/response contract Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * feat(messages): route eligible Azure Anthropic streaming through Rust via buffered fake-stream Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * fix(messages): fold system-role messages for Azure Anthropic and fall back to Python on Rust bridge errors Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * fix(rust_bridge): use Python::attach for amessages after pyo3 bump Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * test(proxy): mock get_configured_token_limits in model_info tests Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * ci: run rust_bridge unit tests in misc shard Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * Revert "ci: run rust_bridge unit tests in misc shard" This reverts commit c86d861. * test(anthropic): move rust messages bridge tests into misc-shard dir Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
CodSpeed benchmarks the SDK with no IO, so it can't catch regressions that only appear under real concurrent load through the full proxy stack (auth, routing, logging, spend, Postgres, Redis). This adds a Locust load test under tests/e2e/load that drives concurrent POST /chat/completions traffic against a mock deployment (litellm_params.mock_response), so the measured throughput reflects proxy overhead rather than a provider's latency, and asserts an aggregate RPS SLO with a failure-ratio guard. The test is marked load and the parent conftest sorts load-marked items last so it never perturbs latency-sensitive suites. Covers reliability.perf.throughput.under_slo.
fix(proxy): resolve team wildcard credentials for vector store files
…ds (#33760) * refactor(e2e): fold claude_code HTTP probes onto shared Gateway methods Migrate tests/e2e/claude_code/http_probe.py off its own httpx client onto the shared transport, and promote count_tokens and native anthropic messages to first-class Gateway methods (Gateway.count_tokens / Gateway.messages) with typed request/response models in the shared models.py so other suites reuse them. The probes now take an injected Gateway and issue their request through the shared count_tokens/messages methods, reusing the split control/data-plane routing, timeout, and typed Result handling the rest of tests/e2e uses. The wire shape is preserved: the pydantic bodies serialize byte-for-byte to what the old httpx probes sent, and the anthropic-version header is carried by a small AnthropicHeaders model. httpx is gone from the module. * test(e2e): drop unit-level probe harness test Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(e2e): harden stage flakes for batches, UI, and MCP Unique batch model names avoid load-balancing onto stale azure-batch deployments that still pointed at the retired gpt-4.1-mini-batch, which only the managed/unified path was hitting. Retry batch retrieve on 500 and /ui/api-keys navigation on ERR_ABORTED. Skip the MCP key-access suite when the compose-only mcp-upstream is unreachable on stage k8s * test(e2e): cover Datadog remote MCP via search_datadog_logs Register the regional Datadog MCP endpoint with DD-API-KEY / DD-APPLICATION-KEY static headers (CI-safe header auth; browser OAuth is not headless-automatable). Seed a chat completion marked e2e-datadog-mcp-*, assert the proxy shipped it, list tools, call search_datadog_logs for the marker, and delete the server on teardown. Math-upstream key-access tests only skip when that compose service is unreachable * test(e2e): drop compose math MCP upstream; use Datadog only Key-access denial and happy-path MCP e2e both register the real regional Datadog remote MCP server with DD-API-KEY / DD-APPLICATION-KEY headers. Remove the mcp-upstream compose service and FastMCP add/multiply fixture * docs(e2e): require real Datadog MCP for all mcp suite tests Document that tests/e2e/mcp must register via datadog_mcp helpers against mcp.<site>/v1/mcp and must not introduce compose or fake MCP upstreams * chore: restore mcp_e2e_upstream_server.py Keep the FastMCP fixture file; e2e no longer wires it in compose, but the module itself is not part of the Datadog-only cleanup * fix(e2e): load tests/e2e/.env and fix datadog_reader importlib load pytest on the host never inherited compose env_file keys, so DD_API_KEY stayed empty. load_dotenv tests/e2e/.env in e2e_config. Register the dynamically loaded datadog_reader module in sys.modules so dataclasses do not crash under Python 3.12 * test(e2e/batches): harden azure/vertex unified lifecycle flakes Put the provider deployment name in every JSONL body so Azure does not depend on a perfect model rewrite. Retry create/retrieve/cancel on transient statuses with backoff. Drop cancel assertions for azure and vertex (registry only has a shared basic cell; create+retrieve prove routing, cancel stays best-effort cleanup) * test(e2e/ui): treat api-keys shell as success after SPA ERR_ABORTED Post-login client redirects abort the first /ui/api-keys/ goto on stage. Wait off /ui/login after cookie set, then accept the page once Create New Key is visible even if goto raised ERR_ABORTED * test(e2e): drop flaky key models dropdown Playwright suite API management e2e already covers key generate/update persistence. The UI Models-dropdown sentinel cases only added SPA ERR_ABORTED noise and no unique product signal. Remove the suite and unused browser fixtures
* test(e2e): harden stage flakes for batches, UI, and MCP Unique batch model names avoid load-balancing onto stale azure-batch deployments that still pointed at the retired gpt-4.1-mini-batch, which only the managed/unified path was hitting. Retry batch retrieve on 500 and /ui/api-keys navigation on ERR_ABORTED. Skip the MCP key-access suite when the compose-only mcp-upstream is unreachable on stage k8s * test(e2e): cover Datadog remote MCP via search_datadog_logs Register the regional Datadog MCP endpoint with DD-API-KEY / DD-APPLICATION-KEY static headers (CI-safe header auth; browser OAuth is not headless-automatable). Seed a chat completion marked e2e-datadog-mcp-*, assert the proxy shipped it, list tools, call search_datadog_logs for the marker, and delete the server on teardown. Math-upstream key-access tests only skip when that compose service is unreachable * test(e2e): drop compose math MCP upstream; use Datadog only Key-access denial and happy-path MCP e2e both register the real regional Datadog remote MCP server with DD-API-KEY / DD-APPLICATION-KEY headers. Remove the mcp-upstream compose service and FastMCP add/multiply fixture * docs(e2e): require real Datadog MCP for all mcp suite tests Document that tests/e2e/mcp must register via datadog_mcp helpers against mcp.<site>/v1/mcp and must not introduce compose or fake MCP upstreams * chore: restore mcp_e2e_upstream_server.py Keep the FastMCP fixture file; e2e no longer wires it in compose, but the module itself is not part of the Datadog-only cleanup * fix(e2e): load tests/e2e/.env and fix datadog_reader importlib load pytest on the host never inherited compose env_file keys, so DD_API_KEY stayed empty. load_dotenv tests/e2e/.env in e2e_config. Register the dynamically loaded datadog_reader module in sys.modules so dataclasses do not crash under Python 3.12 * test(e2e/batches): harden azure/vertex unified lifecycle flakes Put the provider deployment name in every JSONL body so Azure does not depend on a perfect model rewrite. Retry create/retrieve/cancel on transient statuses with backoff. Drop cancel assertions for azure and vertex (registry only has a shared basic cell; create+retrieve prove routing, cancel stays best-effort cleanup) * test(e2e/ui): treat api-keys shell as success after SPA ERR_ABORTED Post-login client redirects abort the first /ui/api-keys/ goto on stage. Wait off /ui/login after cookie set, then accept the page once Create New Key is visible even if goto raised ERR_ABORTED * test(e2e): drop flaky key models dropdown Playwright suite API management e2e already covers key generate/update persistence. The UI Models-dropdown sentinel cases only added SPA ERR_ABORTED noise and no unique product signal. Remove the suite and unused browser fixtures * test(e2e/batches): fail clearly when OPENAI/AZURE provider is missing Replace bare next() over PROVIDERS with _model_for that raises ValueError naming the missing provider and the known list, instead of StopIteration * fix(e2e): migrate load suite from e2e_gateway to ProxyClient Stage collection failed with ModuleNotFoundError: e2e_gateway after the Gateway rename. Wire load/conftest and LoadClient to the shared ProxyClient fixture like every other suite * fix(e2e): drop duplicate datadog_mcp_url and CLAUDE section after merge
Co-authored-by: yassin <yassin@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
) * test(e2e): cover /v1/responses openai basic nonstream and stream Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * test(e2e): assert responses stream ends on final raw completed event Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * test(e2e): centralize responses stream event models Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* test(e2e): cover /v1/responses openai basic nonstream and stream Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * test(e2e): assert responses stream ends on final raw completed event Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * test(e2e): cover /v1/responses openai cost_logged and tool_use Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * test(e2e): centralize responses stream event models Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
…dge (#33753) Add a live spend-tracking e2e that drives a streaming anthropic-format /v1/messages request through litellm's anthropic-messages -> OpenAI Responses adapter and asserts the consumed stream writes exactly one SpendLogs row with nonzero cost and token counts, attributed to the calling key under custom_llm_provider openai and the /v1/messages call_type. The deployment is a Responses-only OpenAI model (gpt-5.3-codex), so a served, costed row proves the Responses path was taken; the chat-completions bridge would have failed at OpenAI on an endpoint the model does not expose. Adds a streaming /v1/messages method to the shared Gateway and the suite client, the model to the inline compose config and driver-model registration, a coverage registry row (quota_management.spend_tracking.messages_bridge.logs_cost), and the matching variant vocab entry. The _summarize spend-row detail also gains call_type and custom_llm_provider so a failed assertion prints the fields it asserts on. Resolves LIT-4546
…migrations work for any uid offline (#33853) * fix(docker): bake prisma CLI and engines at a fixed path so fresh-DB migrations work for any uid offline The runtime image shipped the prisma CLI and engines under /root/.cache, the default HOME-derived prisma-python cache location. Any deployment whose runtime HOME is not /root (kubernetes runAsUser, docker --user, HOME overrides) missed that cache on a fresh database, fell back to a nodeenv Node download that crashes on Wolfi (libatomic.so.1), and started the proxy with zero tables while every DB-backed endpoint returned 500 The bake now lives at /opt/prisma, a path no HOME resolution or cache volume mount can shadow. The builder records the engine paths there at generate time, and the runtime stage pins PRISMA_BINARY_CACHE_DIR, PRISMA_CLI_PATH, PRISMA_CLI_QUERY_ENGINE_TYPE=binary and PRISMA_OFFLINE_MODE so both litellm-proxy-extras and prisma-python resolve the baked CLI and engines directly. prisma migrate deploy on a fresh database now needs no npm and no network access for any runtime uid, including readOnlyRootFilesystem deployments Verified against live containers: fresh and existing databases as root, uid 12345, HOME overridden, on an internal-only docker network, and with a read-only root filesystem all migrate and serve /team/new successfully Fixes #33650, #24554 * chore(docker): fail the image build if the baked prisma CLI layout drifts Asserts the baked CLI shim is executable and its entrypoint exists in the runtime stage after the COPY and chmod, so a layout change in a future prisma-python release breaks the image build loudly instead of silently degrading the migration path at container startup
) * feat(chat-ui): add personal Logs view scoped to the current user Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(chat-ui): show request payload from proxy_server_request in logs detail Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(chat-ui): address logs panel review feedback (stable detail key, error state) Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
…les (#33867) Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
…/models (#33864) A deployment whose model_info carried a non-numeric max_input_tokens or max_output_tokens (for example "128,000" or an empty string) made the bare int() in get_configured_token_limits raise inside the per-model /v1/models loop, so one misconfigured deployment turned the entire listing into a 500. Coerce each configured limit safely and treat malformed values as absent, matching the graceful degradation the listing had before the cost-map switch
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
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t,{apiKeySource:i,accessToken:o,apiKey:n,inputMessage:a,chatHistory:l,selectedTags:s,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:p,selectedMCPServers:m,mcpServers:u,mcpServerToolRestrictions:g,selectedVoice:f,endpointType:h,selectedModel:b,selectedSdk:v,proxySettings:$}=e,_="session"===i?o:n,x=window.location.origin,I=$?.LITELLM_UI_API_DOC_BASE_URL;I&&I.trim()?x=I:$?.PROXY_BASE_URL&&(x=$.PROXY_BASE_URL);let A=a||"Your prompt here",y=A.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),C=l.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),w={};s.length>0&&(w.tags=s),c.length>0&&(w.vector_stores=c),d.length>0&&(w.guardrails=d),p.length>0&&(w.policies=p);let S=b||"your-model-name",k="azure"===v?`import openai | |||
| onChange={(checked) => onChange(setting.field_name, checked)} | ||
| /> | ||
| ); | ||
| } |
Codecov Report❌ Patch coverage is 📢 Thoughts on this report? Let us know! |
Contributor
ryan-crabbe-berri
approved these changes
Jul 18, 2026
tin-berri
approved these changes
Jul 18, 2026
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Relevant issues
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Screenshots / Proof of Fix
Type
🆕 New Feature
🐛 Bug Fix
🧹 Refactoring
📖 Documentation
🚄 Infrastructure
✅ Test
Changes
QA runbook
Final Attestation