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39772 commits
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Krrish Dholakia
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53593f697d
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feat(sandbox): e2b code execution primitive (#30898)
* feat(sandbox): add e2b code execution primitive Add a provider-agnostic code execution primitive that runs model-generated code in an isolated sandbox and returns the output, with e2b as the first backend over raw httpx (no SDK dependency). Public API: litellm.acode_interpreter_tool (ephemeral create -> run -> delete) plus the low-level lifecycle litellm.acreate_sandbox / arun_code / adelete_sandbox. Each is @client-decorated so operations are logged like litellm.asearch. Backends implement BaseSandboxConfig; resolved via ProviderConfigManager.get_provider_sandbox_config. * fix(sandbox): address review feedback and CI gates - document e2b provider in provider_endpoints_support.json and add a sandbox endpoint definition - regenerate dashboard CallTypes after the sandbox call-type additions - guard explicit timeout=0 instead of coercing it to the default - require a ContainerHandle access token before running code; reject bare-id runs - return False on a 404 delete now that the shared http handler raises for status - skip non-JSON NDJSON lines and cap streamed output to bound memory - move the real-network integration tests out of tests/test_litellm into tests/integration/sandbox * fix(sandbox): satisfy strict ruff gate and scope star-exports - modernize annotations in the new sandbox modules to PEP 585/604 (list/dict, X | None) and drop the now-unnecessary quoted forward refs so the strict-rule budget delta for UP006/UP037/UP045 returns to zero - add __all__ to litellm/sandbox/main.py so 'import *' only re-exports the four public entrypoints instead of leaking module-level imports * fix(sandbox): drop quotes on sandbox config return annotation utils.py uses 'from __future__ import annotations', so the quoted forward ref tripped UP037; the unquoted union is lazily evaluated and keeps the strict-rule delta at zero * chore(sandbox): re-trigger automated review after addressing feedback |
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Mateo Wang
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b16cfd7de9
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test: point router/completion/triton tests at the local fake OpenAI endpoint (#30900)
* test: point router/completion/triton tests at the local fake OpenAI endpoint The shared Railway-hosted mock (exampleopenaiendpoint-production.up.railway.app) takes down unrelated CI jobs whenever it is unreachable. #30695 moved the mounted proxy configs onto a job-local fake server but left these in-Python api_base literals pointing at the dead host, so litellm_router_testing, local_testing_part1, local_testing_part2 and llm_translation_testing still fail with a 404 "Application not found" when Railway is down Resolve the api_base from FAKE_OPENAI_API_BASE (default http://127.0.0.1:8190) through a shared helper, auto-start the canned server from the local_testing and llm_translation conftests when nothing is already serving, and extend the server with a Triton embeddings route and a slow-endpoint delay so the triton and latency-timeout tests run fully offline. The deliberately broken fallback URL is left as-is so fallback handling still has a failing upstream * fix: ignore non-loopback FAKE_OPENAI_API_BASE so the local mock is used in CI * fix: drop 0.0.0.0 from loopback hosts, an unreliable client connect target * fix(tests): keep fake OpenAI mock alive across xdist workers ensure_fake_openai_endpoint registered atexit on the worker that spawned the subprocess, so under -n 4 the first worker to drain its queue would terminate the shared mock while siblings were still hitting it. Detach the child via start_new_session and drop the per-worker teardown; reuse on /health handles re-runs and CI containers clean up themselves |
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Shivam Rawat
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c7efa77de3
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fix(watsonx): wrap string embedding input in array for WatsonX API (#30897)
* fix(watsonx): wrap string embedding input in array for WatsonX API WatsonX text/embeddings expects inputs as []string; OpenAI clients often send a single string. Co-authored-by: Cursor <cursoragent@cursor.com> * style(watsonx): format watsonx embed transformation for black Co-authored-by: Cursor <cursoragent@cursor.com> * fix(watsonx): avoid UP006 in embed transformation strict lint gate Use list[str] and branch-based input normalization instead of List and cast so the watsonx embedding change does not add strict ruff UP006 violations. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Shivam Rawat <shivamrawat@Shivams-MacBook-Pro.local> Co-authored-by: Cursor <cursoragent@cursor.com> |
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yuneng-jiang
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46d2861001
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chore(ci): bump deps (#30899)
* bump: version 0.1.42 → 0.1.43 * bump: version 1.89.0 → 1.90.0 * adding uv lock |
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yuneng-jiang
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140ca3012a
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chore: update Next.js build artifacts (2026-06-20 21:24 UTC, node v20.20.2) (#30894) | ||
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Mateo Wang
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a7b0b0ba09
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feat: add lint-gate target and truncation-proof summary to the strict ruff gate (#30877)
* feat: add CI-parity mode and truncation-proof summary to strict ruff gate * refactor: tolerant worktree cleanup and concrete GateInputs types * fix: clean up temp dir when git worktree add fails * fix: align lint-gate with CI by dropping unused --ci-parity path The lint-gate Makefile target invoked ruff_strict_gate.py with --ci-parity, which counted violations on a throwaway merge of base into HEAD against base counts at the base tip. CI in test-linting.yml runs the same script without --ci-parity on a PR-head checkout, taking the gather_fast path that counts on the live tree against base counts at the merge-base. A local pass could therefore disagree with CI. Drop --ci-parity from the Makefile and remove the now-unused gather_ci_parity branch and flag so there is one code path that both local and CI exercise. The docstring claim that CI runs against the synthetic merge ref was also wrong; the workflow checks out github.event.pull_request.head.sha. --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> |
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Mateo Wang
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15aa40b36e
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test(ui): isolate OldTeams delete-warning tests from leaked mock (#30871)
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The deprecated OldTeams component takes only accessToken, userID, userRole and premiumUser; it ignores the teams prop these tests passed and instead populates its table from the mocked teamListCall. The delete-warning block never set teamListCall, and vi.clearAllMocks clears call history but not implementations, so the table rendered the "Legacy Team" (keys.length 2) left behind by the previous block's last test. Both delete tests therefore ran against that leaked team: the keys-present case passed only because the leaked count happened to be 2, and the no-keys case rendered the same warning it asserted should be absent, so it failed. Seed the team through the channel the component actually reads (teamListCall) and drop the props it never consumes, so each test renders exactly the team it declares. The keys-present case now uses a distinctive count so it can no longer pass on a coincidental leak |
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Yassin Kortam
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9c3ad1b094
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feat(caching): add valkey-semantic cache backend and fix semantic cache scope keys (#30675)
Adds a "valkey-semantic" cache type so semantic prompt caching can run against Valkey clusters (for example AWS ElastiCache for Valkey) using the valkey-search module. The existing "redis-semantic" backend cannot drive valkey-search. RedisVL gates the connection on a RediSearch module version that valkey-search does not report, and its SemanticCache index declares the prompt as a TEXT field, which valkey-search does not implement. ValkeySemanticCache therefore talks to valkey-search directly over redis-py: it builds a vector index from the field types valkey-search supports (TAG for caller scope, VECTOR for the prompt embedding) and runs KNN queries for retrieval. Prompt extraction, embedding generation, and cached-response parsing are reused from RedisSemanticCache since those are backend agnostic. The redis dependency is imported lazily in the cache dispatch so importing litellm without redis installed still works. It also fixes semantic-cache scope keys so similarity matching works across reworded prompts. get_cache_key() hashed messages / prompt / input into the litellm_cache_key that every semantic backend filters its KNN search on, so a paraphrase landed in a different bucket and never matched, even far above the similarity threshold. For semantic cache types the prompt-bearing params are now excluded from the scope key and the server-set tenant identity (user_api_key, team, org) is appended instead, restoring embedding matching within a tenant while keeping cache entries scoped to the authenticated key / team / org. The three semantic backends share this key, so the same change fixes redis-semantic and qdrant-semantic. Connections resolve from VALKEY_HOST / VALKEY_PORT / VALKEY_PASSWORD, falling back to REDIS_* for drop-in compatibility, and passwordless clusters (IAM or no-auth) are supported. Resolves #29121 Fixes #29086 |
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yuneng-jiang
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ea17236a1e
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fix(ui): warn that team models are deleted in the delete-team modal (#29990)
The delete-team confirmation modal warned that a team's keys would be deleted but said nothing about models. #29977 made team deletion also delete the team's BYOK models, so the modal copy was understating what gets removed. The warning banner now mentions models alongside keys, and the always-shown confirmation message does too so a team that has models but no keys (the banner only renders when keys exist) still gets warned. |
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yuneng-jiang
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60dc8420ed
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fix(ui): repoint dead usage guide link to cost tracking docs (#30859)
The "View Usage Guide" button on the legacy Usage page (shown when DISABLE_EXPENSIVE_DB_QUERIES is set, i.e. SpendLogs has 1M+ rows) linked to docs/proxy/spending_monitoring, which was removed from the docs and now returns 404. Point it at docs/proxy/cost_tracking, which is live. Fixes LIT-2724 |
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Yassin Kortam
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4847fa5dd5
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fix(proxy): record partial spend on the failure row for interrupted streams (#30788)
A streaming request that breaks mid-flight, for example on a mid-stream read timeout, still bills the provider for the chunks already delivered, yet the proxy recorded that interrupted request as a zero-spend failure. An earlier revision logged the recovered partial usage through the success path, which mislabeled a failed request as a success and produced a misleading spend row This recovers the partial usage where the failure is actually logged. The streaming handler assembles the usage from the chunks seen so far and stashes it, with its cost, on the logging object before firing the failure handlers. The proxy failure hook lifts that usage and cost onto request_data before the non-serialisable logging object is popped, and the spend-log writer records the real partial spend on the failure row instead of a hardcoded zero; get_logging_payload honors the recovered usage for the token columns and _failure_handler_helper_fn preserves the recovered cost so the non-DB failure loggers stay consistent A request that recovers via a successful fallback is unaffected: the failure hook only fires when the whole request fails, so the fallback's combined-usage success row stays the single source of truth and there is no double counting Resolves LIT-3825 Co-authored-by: veria-ai[bot] <224490171+veria-ai[bot]@users.noreply.github.com> |
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Yassin Kortam
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bd74c62ff1
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fix(passthrough): recover output tokens for interrupted anthropic streams (#30787) | ||
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yucheng-berri
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1f9323792c
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fix(otel): one v2 logger owns the global provider; scope tenant OTLP creds per exporter (#30590)
* fix(otel): one v2 logger owns the global provider; scope tenant creds per exporter The proxy published the OTel global TracerProvider before callbacks were initialized, so no preset logger existed yet and a second generic logger was built that won the global provider. Server spans then exported through a different provider than the preset's gen-ai spans, orphaning the LLM span on the preset backend. Publish after callback init and reuse the already-built logger instead. Separately, per-request tenant OTLP credentials were stamped onto every OTLP exporter, leaking one backend's key onto a co-configured backend. Tag each exporter with the preset that contributed it and apply dynamic credentials only to the matching owner. * fix(otel): satisfy Any-discipline on changed lines Type the logger-selection parameter as Sequence[object] (isinstance narrows it), cast the list[Any] global at the single call site, and pass model_copy a typed dict[str, str] update so no changed line carries an Any value. * fix(otel): annotate the untyped-global boundary with any-ok select_global_otel_v2_logger consumes litellm._in_memory_loggers, a shared List[Any] global this change does not own. A cast doesn't satisfy the Any-discipline checker (it inspects the inner expression), and re-annotating the global is out of scope, so mark the single boundary line any-ok. * test(otel): cover the startup global-provider publish via injectable helper The publish step lived inline in proxy_startup_event (a FastAPI lifespan unit tests do not execute), so its lines were uncovered though the selection logic was tested. Extract publish_global_otel_v2_provider, which selects the single v2 logger and publishes its provider through an injected setter, and unit-test that the published provider is the selected logger's. proxy_server delegates to it. * refactor(otel): select global provider from the registered owner, not a list scan The startup publish picked the global TracerProvider by scanning _in_memory_loggers for the first OpenTelemetryV2, re-deriving an answer the factory already settled: the first logger built registers itself as proxy_server.open_telemetry_logger, and every other v2 path (guardrail, identity seeding, phase spans) routes through that owner via _registered_v2_logger. Pass that owner into select_global_otel_v2_logger so the global provider reuses the same logger instead of an independent, order-dependent guess; the list scan remains the SDK-path fallback. The owner is injected at the proxy call site to keep the helper free of hidden global reads. * refactor(otel): type ExporterSpec.owner as an ExporterOwner enum The owner field carried free-form strings that had to match preset callback names. Introduce a str-based ExporterOwner enum (values equal to the callback names, so per-request credential routing's owner==callback_name comparison still holds) and have each preset tag its exporter with the enum member. * refactor(otel): rename ExporterOwner.ARIZE to ARIZE_AX Distinguish the hosted Arize AX backend from Arize Phoenix at the member level while keeping the value 'arize' (the public callback name routing compares against). Add a comment noting AX and Phoenix are separate backends. |
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Mateo Wang
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1bd603d1ac
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chore(typing): add boto3/botocore stubs so basedpyright resolves the AWS SDK (#30815)
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Mateo Wang
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31eca17007
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chore: make pr template linear portion clearer (#30766) | ||
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Mateo Wang
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f9b8b9700c
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fix(proxy): use e.request_data for logging_obj in ModifyResponseException streaming passthrough (#30800)
* fix(proxy): use e.request_data for logging_obj in ModifyResponseException streaming passthrough
When a guardrail blocks a streaming request pre-call by raising
ModifyResponseException (or RejectedRequestError), chat_completion streams the
violation message back as a 200 by building a CustomStreamWrapper. It read the
logging object from the outer request body (`data.get("litellm_logging_obj")`),
but that dict never carries litellm_logging_obj -- it diverges from the
processor's data at function_setup, and only the processor copy (exposed as
e.request_data, already bound to `_data` here) gets the logging object
attached. CustomStreamWrapper.__init__ then dereferences
`logging_obj.model_call_details` on None and 500s the request with
"AttributeError: 'NoneType' object has no attribute 'model_call_details'".
Read logging_obj from `_data` (= e.request_data) in both streaming
passthrough handlers so the refusal streams correctly. The non-streaming and
the anthropic/responses passthrough paths were unaffected.
Adds a regression test asserting the wrapper receives the logging object from
e.request_data rather than None.
* test(proxy): cover RejectedRequestError streaming passthrough
The streaming logging_obj fix was applied to both the ModifyResponseException
and RejectedRequestError handlers, but only the former had a regression test.
Extract a shared helper and add a parallel test for the RejectedRequestError
streaming path so both handlers stay guarded against the None-logging_obj crash.
---------
Co-authored-by: Joseph Barker <joseph.barker@rubrik.com>
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yuneng-jiang
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7e5699c7ab
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ci(zizmor): gate PRs on medium+ findings and clear existing ones (#30797)
Switch the zizmor check to fail on any finding at medium severity or above (advanced-security off, min-severity medium, annotations on) so it can be promoted to a required check, and pin the engine to zizmor 1.24.1 through zizmor-action v0.5.6 for deterministic runs. Clear the findings that were outstanding so the check passes: correct mismatched action pin version comments, scope the proxy endpoint workflow's id-token and pull-requests permissions to the jobs that use them, and mark the server-root-path docker build as non-publishing while dropping its shared gha build cache. |
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yucheng-berri
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5637b3212e
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feat(proxy): configurable response headers and login-page hint (#30792)
* feat(proxy): add configurable response headers middleware Adds a small ASGI middleware that sets standard response headers (X-Frame-Options, Content-Security-Policy frame-ancestors, X-Content-Type-Options) on proxy and UI responses. Strict-Transport-Security is optional and gated behind LITELLM_ENABLE_HSTS for HTTPS deployments. Values use setdefault so a route that sets its own header is preserved. * feat(proxy/ui): make login page credentials hint configurable build_ui_login_form accepts a hide_default_credentials_hint parameter and google_login reads LITELLM_HIDE_DEFAULT_CREDENTIALS_HINT (or general_settings) so the legacy login page behaves consistently with the new UI. Also collapses a duplicated branch and removes an unused variable and module-level constant. * fix(proxy/ui): apply credentials hint flag on /fallback/login The /fallback/login handler still rendered the default-credentials hint regardless of LITELLM_HIDE_DEFAULT_CREDENTIALS_HINT. Collapse its duplicate branch and forward the flag, matching google_login, so all login surfaces behave consistently. Adds regression tests for /fallback/login and makes the ui_sso test helper restore os.environ so env vars do not leak across tests. |
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ryan-crabbe-berri
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32bdd004bd
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feat(ui): migrate api-keys landing to App Router path route (#30699)
Cut the default "Virtual Keys" landing (?page=api-keys) over to a path route at (dashboard)/api-keys. The dashboard is extracted into a shared ApiKeysDashboard component used by both the new route and the index's inline render, so there's no duplication. Adding the MIGRATED_PAGES entry repoints the sidebar item and redirects ?page=api-keys to /ui/api-keys. The index is the post-login landing and still hosts the legacy switch for the not-yet-migrated pages (models, pass-through, usage) plus the invitation flow, so it stays. The auto-redirect now fires only for an explicit ?page= param, leaving the bare /ui/ landing to render inline; this keeps the return-URL handling and the invitation_id flow (both of which run at the bare landing) intact, where a blanket redirect would have dropped them. The new route uses useAuthorized for the login gate, matching every other migrated route. |
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ryan-crabbe-berri
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e4a53f50de
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chore: remove in-product survey and Claude Code feedback nudges (#30773)
Delete the in-product survey and Claude Code feedback prompts end to end. Frontend: remove the src/components/survey/ module, the index page's nudge state/effects/handlers, the getInProductNudgesCall helper, and the orphaned "Disable UI nudges" toggle in the admin UI Settings page; prune the stale eslint-suppressions entries. Backend: remove the now-dead /in_product_nudges route, the InProductNudgeResponse type, and the disable_ui_nudges UI setting (Field + allowlist). Nothing read it for logic and the UISettings model is extra="allow", so existing stored configs are unaffected (the value is just no longer surfaced). schema.d.ts is regenerated and the two tests covering the removed route/setting are dropped. |
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ryan-crabbe-berri
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ba0233c4ce
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fix(test): drop references to removed Agent Shin workflows (#30791)
PR #30784 deleted .github/workflows/review_gate.yml and triage_pr_with_llm.yml, but test_github_triage_workflows.py still listed both in its parametrize tables, so _load_workflow raised FileNotFoundError for every case naming them. Remove the two stale entries from DESTRUCTIVE_GATE_ENV and LLM_CLIENT_INSTALLER_WORKFLOWS; the remaining four workflows that still exist keep their guardrail coverage. |
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yuneng-jiang
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e7532b72df
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ci(zizmor): also run on litellm_internal_staging (#30789)
* chore(ci): remove Agent Shin pull_request_target workflows Drop the two Agent Shin workflows that ran on the pull_request_target trigger: the PR triage workflow and the review gate. Both were dry-run and gated behind AGENT_SHIN_ENABLED, so no live automation changes. The shared scripts under .github/scripts stay in place; four other Agent Shin workflows still depend on them and run on schedule, dispatch, and issue events rather than pull_request_target * ci(zizmor): also run on litellm_internal_staging |
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Sameer Kankute
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4c25b7a13d
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chore: litellm oss staging (#30745)
* fix(proxy): bump health-check max_tokens default to 16 for GPT-5 compatibility (#30708) OpenAI GPT-5 models require max_completion_tokens >= 16. Health checks were using 5 (proxy/health_check.py) and 10 (health_check_helpers.py), causing failures on GPT-5 models. Fixes #23836 * fix: increase health check max_tokens from 5 to 16 (#23836) (#26610) GPT-5 models enforce a minimum of 16 for max_output_tokens. The current default of 5 still causes health checks to fail for these models. Bump the non-wildcard default to 16 — the smallest value that satisfies all known provider minimums while keeping health checks lightweight. Also tightens the wildcard test assertion from a weak disjunctive check to strict key-absence. Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix: ensure checks show gemini-3-flash-preview supports responseJsonS… (#30696) * fix: ensure checks show gemini-3-flash-preview supports responseJsonSchema. * fix: remove async keyword from test. * fix: make Bedrock Mantle Responses routing data-driven per model (#30700) * Make Bedrock Mantle Responses routing data-driven per model Route Bedrock Mantle models to the native Responses API based on each model's price-map capability signal instead of a hardcoded model-name heuristic, and derive the OpenAI-compatible base path segment per model. Responses dispatch now selects the native config when the model advertises responses support (/v1/responses in supported_endpoints, or mode=responses), both overridable via register_model and proxy model_info. This enables native Responses for gpt-oss-120b/20b and the gemma-4 family while keeping chat-only models (gpt-oss safeguard, nvidia, mistral, ...) on the existing chat-completions emulation. Capability is per-model, so gpt-oss-120b routes natively while gpt-oss-safeguard-120b does not despite sharing the gpt-oss substring. The wire path is a separate concern, driven by the existing use_openai_responses_path flag rather than a model-name match: gpt-5.x and gemma-4-* on /openai/v1, everything else (incl. gpt-oss) on /v1. The chat config now derives its base from the same flag, fixing gemma-4 chat-completions requests that previously went to /v1 instead of /openai/v1. Cost maps: add supported_endpoints to the gpt-oss entries (responses for the non-safeguard variants, chat-only for safeguard) and supported_endpoints + use_openai_responses_path to all three gemma-4 entries. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Address review: move capability helper into bedrock_mantle package Move the Responses capability check out of utils.py into litellm/llms/bedrock_mantle/common_utils.py as mantle_supports_responses, alongside its companion wire-path helper mantle_base_segment. Both are now pure functions of (model, model_cost): the price-map mode/supported_endpoints read replaces the get_model_info call, so the rules are unit-testable without patching global state and the Bedrock Mantle package is self-contained. Use str | None instead of Optional[str] on the new signatures to satisfy the ruff UP045 strict-rule gate. Add direct unit tests for both helpers. Fix test_register_model_restore_undoes_existing_key_overwrite: gpt-oss-120b now legitimately supports Responses, so it can no longer be the "None after restore" vehicle; use the chat-only safeguard variant, which isolates the register/restore effect from the model's own capability. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup (#30366) * fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup LiteLLM's Prisma datasource is pinned to provider = 'postgresql', so a sqlite:// or mysql:// DATABASE_URL can never connect. Today that surfaces as an opaque startup stall where the port never binds, and a separate 'DB not connected' 500 on /key/generate when no DATABASE_URL is set at all leaves operators guessing what to configure. Validate the DATABASE_URL / DIRECT_URL scheme in run_server before any Prisma call and exit with an actionable message naming the unsupported scheme. Also reword CommonProxyErrors.db_not_connected_error to tell the operator to set DATABASE_URL to a postgresql:// connection string. Add regression tests covering postgres acceptance and sqlite/mysql/mssql rejection. * fix: resolve CI failures and proxy DB URL typing issue * fix(dashscope): treat an explicit 0.0 tier cost as a real price, not missing (#30653) The tiered cost calculator resolved a tier's per-token cost with `tier.get(cost_key) or tier.get(fallback_cost_key, 0)`. Because `or` short-circuits on any falsy value, a tier that legitimately prices a component at 0.0 (e.g. a free-cache-read tier with cache_read_input_token_cost: 0.0, or a free-reasoning tier) is treated as missing and silently billed at the full fallback rate (input_cost_per_token / output_cost_per_token). The flat-pricing path in the same module already handles this correctly with an `is None` guard. Resolve tier costs through a small helper that mirrors it, so 0.0 is honored at both the in-range and overflow sites. No shipped model currently has a 0.0 tier cost, so this is a latent defect; the fix makes the tiered path consistent with the flat path and prevents over-charging the first time such a tier appears. Adds unit tests covering the in-range and overflow paths, and drops an unused import flagged by ruff in the touched test file. * feat(proxy): show session-aggregate cost and duration in request logs (#25708) (#30507) * fix(anthropic): don't leak tool 'type' into OpenAI function parameters schema (#30618) In the messages->chat/completions bridge, translate_anthropic_tools_to_openai merged every non-mapped tool key into the function parameters dict. The Anthropic tool 'type' (e.g. 'custom') thus overwrote parameters.type ('object' -> 'custom'), and providers reject it ('custom' is not a valid JSON-Schema type). Exclude 'type' from the passthrough. Fixes #30557. * fix(proxy): stop IAM-refresh engine restart from cascading reconnects (#29176) (#30183) An RDS IAM token refresh recreates the Prisma client, which SIGKILLs the running query-engine and spawns a new one. That planned kill was indistinguishable from a crash, and three reconnect paths used two uncoordinated locks, so a single refresh triggered a cascade of engine kill/respawn cycles: 1. `_safe_refresh_token` (holds `_reconnection_lock`) -> recreate -> kill old engine, spawn new one. 2. The engine-death watcher sees that kill, assumes a crash, and calls `attempt_db_reconnect(force=True)` (a different lock, `_db_reconnect_lock`) -> recreate again -> kills the fresh engine. 3. In-flight queries failing during the swap are classified as transport errors and trigger their own `attempt_db_reconnect` -> recreate again. Fix coordinates planned restarts across the wrapper and the watcher: - PrismaWrapper records the old engine PID in `_expected_engine_deaths` before killing it; all four watcher death-detectors (waitpid thread, pidfd, already-dead probe, os.kill poll) consume that PID and skip the reconnect instead of treating it as a crash. - `recreate_prisma_client` now serializes through `_reconnection_lock` and bumps a monotonic `_engine_generation`. Callers pass `expected_generation` as an optimistic-lock token, so racing/cascading recreates collapse into a single restart (losers no-op). This closes the two-lock gap. - The direct reconnect path probes the writer with SELECT 1 before recreating; a healthy connection (e.g. engine already replaced by a refresh) skips the recreate entirely. - `_safe_refresh_token` coalesces: it skips when the current token still has more than the refresh buffer of runway, so stacked triggers (proactive loop + __getattr__ fallback) don't each restart the engine. An `on_engine_replaced` hook re-arms the watcher on the new PID. RoutingPrismaWrapper forwards `expected_generation` and skips recreating the reader when the writer recreate was skipped. * feat(bedrock): support file content retrieval for batch output files (#30595) Implements transform_file_content_request and transform_file_content_response in BedrockFilesConfig so GET /v1/files/{id}/content works for Bedrock batch files. The request transform resolves the file id (direct s3:// URI or base64 unified id) to its S3 object, validates bucket and key prefix against the server-configured bucket, and SigV4-signs an S3 GetObject using the same credential and region resolution as the existing upload path. The credential and region params are validated into a typed model at the boundary, so the only untyped values left are the botocore signing primitives. Also fixes the proxy managed-files path: CredentialLiteLLMParams now carries s3_bucket_name (previously dropped when building deployment credentials) and the managed-files hook passes the deployment credential snapshot when routing afile_content, so unified-id content retrieval works with per-model bucket config instead of only the AWS_S3_BUCKET_NAME env var. Preserves managed-file access control: the proxy file-content endpoint now rejects raw cloud-storage ids (s3://, gs://), which would otherwise skip the owner/team check that only runs for unified ids and let a caller read another tenant's batch output by its object key. Managed outputs are reachable only through their unified file id. The afile_content "not found" error now reports the caller's unified id rather than the resolved internal S3 URI. Fixes #16186, #15563 * fix(oci): make Cohere {{trace}} judges work (tool param types + agentic tool-calling continuation) (#30646) * fix(oci): map Cohere tool array/object params to lowercase builtins OCI's Cohere backend returns HTTP 500 on a tool parameter typed as a bare "List", which is what OCI_JSON_TO_PYTHON_TYPES produced for JSON-schema arrays. MLflow {{trace}} judges trip this: their tools (get_root_span, get_span) take an attributes_to_fetch array. The lowercase builtins list/dict are accepted; only the bare "List" 500s ("Dict" happens to be tolerated, but both are lowercased for consistency). Verified live against us-chicago-1 (cohere.command-a-03-2025 and command-latest). Adds a unit regression on the transformed parameterDefinitions plus a gated integration test exercising an array-param tool end to end. * fix(oci): make Cohere agentic tool-calling continuation work Two bugs broke the OCI Cohere tool-calling loop that MLflow {{trace}} judges drive once a tool has been executed and its result is fed back. Request side: litellm pulled the last user message into the top-level `message` and emitted the tool result as a TOOL entry in chatHistory. OCI rejects that ("cannot specify message if the last entry in chat history contains tool results"), and an empty message alone is rejected too ("message must be at least 1 token long or tool results must be specified"). OCI carries the current turn's results in a dedicated top-level `toolResults` field. The Cohere transform now sends an empty message, keeps the user turn in chatHistory, and puts the results in `toolResults`, matching the langchain-oracle reference. Tool results are no longer represented as chatHistory entries. Response side: tool-grounded answers come back with citations carrying `documentIds` (camelCase) and no `document_ids`, which made the required `CohereCitation.document_ids` field fail validation and sink the whole response parse. Those citations are never surfaced, so the field (and CohereSearchQuery's generation_id) is now optional. Verified live against us-chicago-1 (cohere.command-a-03-2025 and command-latest), single and multi-round tool loops. Adds unit regressions on the transformed request shape and on citation parsing, plus gated integration tests for the continuation. * feat: integrate Repelloai Argus guardrail (#30673) * feat(guardrails): add RepelloAI Argus guardrail integration (#1) * feat(guardrails): add RepelloAI Argus guardrail integration Add a new guardrail hook backed by RepelloAI Argus, with dashboard-managed asset policies enforced via an asset_id and X-API-Key auth. * fix(guardrails): harden RepelloAI Argus guardrail - scan streaming responses on output (was bypassing the guardrail) - log blocked verdicts as guardrail_intervened instead of success - treat auth/config errors (401/403/404/422) as misconfiguration that always blocks, not a fail-open-able unreachable error - default unreachable_fallback to fail_closed and read it directly; block on unknown/malformed verdicts so an API change can't silently disable enforcement - type unreachable_fallback as a Literal, drop the duplicate config model, expose unreachable_fallback in the config schema, and stop leaking the raw provider response / exception strings to the client * fix(guardrails): address RepelloAI Argus review feedback - support ARGUS_API_KEY (with REPELLOAI_API_KEY fallback) - make asset_id required in the config model - normalize unreachable_fallback so only fail_open opens; block on 400 misconfig - correct the shared unreachable_fallback field description * docs(guardrails): add RepelloAI Argus docs page and dashboard listing - add docs page covering config, env vars, modes, verdicts, failure semantics - list RepelloAI Argus in the Guardrail Garden with provider/logo mappings - add a regression test for the provider logo and display-name resolution * fix(guardrails): keep RepelloAI asset_id optional in config model A required asset_id leaked onto the shared LitellmParams (which inherits RepelloAIGuardrailConfigModel), breaking validation for every other guardrail. Keep it optional like sibling models; the guardrail __init__ still raises when asset_id is missing, which is the real enforcement. * Add comment for last user turn scanning * feat(guardrails): harden repelloai scanning * feat(guardrails): expand repelloai scanning to include tool definitions Add extraction of tool definitions and tool call arguments to the RepelloAI guardrail scanning. Improves detection coverage by including function schemas and parameters in the prompt sent to the guardrail service. Also captures detailed error responses in logs and adds guardrail header to streaming responses. * refactor(guardrails): fix and harden repelloai schema text extraction - Fix duplicate text in _iter_schema_text: previously all dict values were re-queued onto the stack even after scalar/list keys were already extracted explicitly, causing names/descriptions to appear twice in the scanned prompt - Extract schema key frozensets to module-level constants so they are not reconstructed on every call - Change _iter_schema_text from @classmethod to @staticmethod (cls unused) - Narrow _call_analyze stage param from str to Literal["prompt", "response"] - Add HttpxResponse type annotation to _raise_for_config_error - Add LLMResponseTypes annotation to async_post_call_success_hook response param * fix(guardrails): resolve pyright type errors in repelloai guardrail - Narrow async_handler.post return from Response|None to Response with explicit None guard before calling raise_for_status/json - Fix list comprehension returning str|None by switching to explicit loop with isinstance guard so pyright tracks the narrowing - Cast model_dump() result to Dict since hasattr does not narrow object type in pyright * fix(guardrails/repello): include Responses API instructions field in prompt scan The /v1/responses top-level `instructions` field was not included in _extract_prompt_text, allowing a caller to bypass guardrail policy checks by putting blocked content in `instructions` while keeping `input` benign. * feat: add api_key to config model and read prompt from data dict * fix(guardrails/repello): plug input_text and tool-call response bypass gaps Responses API input content parts with type 'input_text' were silently dropped by build_inspection_messages (which only handles type='text'), allowing callers to send blocked content via that path without triggering the pre-call scan. Fix: add _extract_input_text_parts to RepelloAIGuardrail and call it when walking the Responses API input messages. Post-call scanning skipped responses whose choices contained only tool_calls or function_call (message.content=None), letting models put blocked output in function arguments undetected. Fix: _extract_chat_completion_text now calls _extract_tool_call_args_from_message on each choice message. Also replace typing.Dict/List with builtin dict/list to clear TID251 strict ruff violations introduced by this file. * fix(guardrails/repello): scan Responses API function_call output arguments Output items with type 'function_call' in a /v1/responses response were skipped by _extract_responses_api_text; only 'message' items were walked. A model could return blocked content in function_call.arguments undetected. Now extract arguments from function_call output items before scanning. * refactor(guardrails/repello): clean up typing and remove lint-any workarounds - Replace Optional[X]/Union[X,Y] with X|None/X|Y union syntax throughout - Use dict[str, object] instead of bare dict in all signatures - Remove **kwargs from __init__; declare guardrail_name, event_hook, default_on explicitly - Replace getattr(litellm_params, ...) with direct attribute access now that LitellmParams inherits RepelloAIGuardrailConfigModel - Add _event_hook_from_mode() to convert str|list[str]|Mode to typed GuardrailEventHooks - Use TypeAdapter.validate_json() instead of response.json() + manual dict construction - Add _is_object_dict/_is_object_list TypeGuard helpers to narrow object types without Any - Remove cast() workarounds and typed intermediate variables that existed only for the now-removed lint-any CI check - Drop _AddLiteLLMCallback Protocol; budget has sufficient slack for the one reportUnknownMemberType - Fix GuardrailConfigModel missing type arg: GuardrailConfigModel[BaseModel] * fix(guardrails/repello): suppress LIT007 on TypeGuard helpers and add streaming scan-skip warning - Add guard-ok suppressions to _is_object_dict and _is_object_list to satisfy the LIT007 hard-zero budget gate - Emit verbose_proxy_logger.warning when the streaming hook finds no inspectable text after assembly, matching observability of pre/post hooks * refactor: modifications for lint check * feat: add Pinstripes as an OpenAI-compatible provider (#30567) * feat: add Pinstripes as an OpenAI-compatible provider Pinstripes (https://pinstripes.io) is an OpenAI-compatible inference provider serving open-source models (GLM-4.5-Air, Qwen3, DeepSeek, etc.) with per-token pricing and no subscriptions. Changes: - `litellm/llms/openai_like/providers.json`: register pinstripes with base_url, api_key_env, and max_completion_tokens→max_tokens mapping - `litellm/types/utils.py`: add `PINSTRIPES = "pinstripes"` to LlmProviders - `litellm/constants.py`: add to openai_compatible_providers and openai_compatible_endpoints lists - `litellm/litellm_core_utils/get_llm_provider_logic.py`: auto-detect provider when api_base is "https://pinstripes.io/v1" - `provider_endpoints_support.json`: document supported endpoints - `tests/`: 7 unit tests covering provider registration, resolution, URL auto-detection, api_base override, and Router config Usage: import litellm response = litellm.completion( model="pinstripes/ps/glm-4.5-air", messages=[{"role": "user", "content": "Hello"}], api_key=os.environ["PINSTRIPES_API_KEY"], ) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(pinstripes): resolve Greptile P1 review comments - Add api_base_env: PINSTRIPES_API_BASE to providers.json so env var override works - Set responses: false in provider_endpoints_support.json — not actually wired up - Remove docs/my-website/docs/providers/pinstripes.md — belongs in litellm-docs repo Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(pinstripes): add api_base_env and correct responses capability - Add api_base_env: PINSTRIPES_API_BASE to providers.json - Set responses: false in provider_endpoints_support.json Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(pinstripes): wire up Responses API — add supported_endpoints Adds supported_endpoints: ["/v1/chat/completions", "/v1/responses"] so JSONProviderRegistry.supports_responses_api returns true correctly, matching what provider_endpoints_support.json advertises. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * feat(pinstripes): enable embeddings endpoint Pinstripes serves nomic-embed-text-v1.5 and bge-m3 via /v1/embeddings. Add /v1/embeddings to supported_endpoints and set embeddings: true. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(pinstripes): use 4-space indentation in model_prices_and_context_window.json Matches the file's existing convention. Flagged by Greptile review. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(pinstripes): set a2a: false — A2A protocol not implemented All comparable JSON-configured providers (tensormesh, parasail, empiriolabs, libertai, neosantara) have a2a: false. Pinstripes does not implement the Google A2A protocol, so this should be false to match. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> --------- Co-authored-by: inference_provider <max@redactedlab.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(rag): attach existing OpenAI file ids (#30628) * fix(rag): attach existing OpenAI file ids * chore: use modern typing in rag ingest fix * chore: retrigger ci * fix(anthropic-messages): apply cache_control_injection_points on /v1/messages path (#30341) cache_control_injection_points was only consumed by the chat/completions prompt-management hook; on the native Anthropic /v1/messages path it was forwarded unused, so deployment-level cache injection was silently dropped (cache_creation_input_tokens stayed 0 for Anthropic-native clients). Add AnthropicCacheControlHook.apply_to_anthropic_messages_request to inject cache_control at block level for system / tools / message locations (the only forms /v1/messages accepts), wire it into the native anthropic_messages handler, and pop the param so it does not leak upstream as an unknown field. A {location: message, role: system} config is redirected to the top-level system prompt so the same YAML works on both endpoints. Injection respects Anthropic's 4-block cache_control limit shared across system, tools, and messages: client-supplied markers count toward the cap and are never overwritten, a slot is reserved per Bedrock tool_config point, and injection stops once the budget is exhausted. Locations this path cannot represent (tool_config) are forwarded downstream instead of being silently consumed, mirroring get_chat_completion_prompt's remaining_points pass-through. Built on litellm_internal_staging. Refs BerriAI/litellm#30293 * fix(proxy): release budget reservation when a request is cancelled mid-flight (#30522) * fix(proxy): release budget reservation on cancel when no chunk was delivered The pre-call budget reservation increments the cross-pod spend counter by a request's worst-case cost, then reconciles it on success (cost callback) or error (failure hook). A client disconnect or timeout cancels the request and surfaces as CancelledError / GeneratorExit, which neither path catches, so the reservation leaks. Under a retry storm the leaked holds accumulate, pin the counter above real spend, and return spurious 429 "Budget has been exceeded" to keys whose spend is far below budget; the counter only recovers when its TTL lapses, so the failure is intermittent and self-healing. Release the reservation in async_streaming_data_generator (which the Anthropic and Google SSE generators delegate to) on the (CancelledError, GeneratorExit) path, alongside the existing max_parallel_requests release. release_budget_ reservation_on_cancel runs under asyncio.shield so it completes despite the in-progress cancellation, is guarded by the reservation's finalized flag, and swallows a failing release so it cannot replace the in-flight cancellation. The refund is gated on whether a chunk reached the client. The flag is set immediately before the yield, after the slow-path hook await: an async generator suspends at the yield, so a GeneratorExit on disconnect after a delivered chunk sees it True (keep the hold), while a cancellation during the slow-path await leaves it False (refund, nothing sent). A non-streaming cancellation delivers nothing and a completed non-streaming response is reconciled by the success callback, so neither needs a release here. Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix(proxy): reconcile a cancelled reservation to input cost, not zero A streaming request cancelled before the first chunk previously reconciled its reservation to zero and finalized it. But by the time the generator is consuming the response the provider call was already dispatched, so the input tokens were billed even though no chunk reached the client, and the success/failure cost callbacks are skipped on cancellation. Refunding to zero let a caller send an expensive request and abort pre-token to dodge the input charge. Compute the request's input-token cost at reservation time and reconcile the cancelled reservation to it instead of zero. The worst-case output portion of the reservation is still released (so a legitimate mid-flight cancellation no longer pins the counter and 429s the key), while the input the provider already processed is charged. --------- Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix(caching): encode object name in GCS cache GET path (#30378) GCS cache reads always missed when gcs_path was set. The GET methods interpolated the object name directly into the URL path, while the GCS JSON API requires it to be URL-encoded (a "/" must be sent as %2F). With gcs_path configured the object name is "<prefix>/<sha256>", so the raw slash produced a malformed object path and GCS returned 404. httpx does not raise on 4xx, so the status_code == 200 check fell through and get/async_get returned None, silently missing on every read. Without gcs_path the key has no slash, which is why this went unnoticed. Wrap the object name with urllib.parse.quote(..., safe="") in get_cache and async_get_cache. Apply the same encoding to the name= query parameter in set_cache and async_set_cache so the key written matches the key read back. Adds regression tests asserting the GET path and SET query are encoded (%2F) when gcs_path is set, for both sync and async paths; these fail on the unpatched code. Fixes #30377 * chore: add soniox stt-async-v5 model (#30672) * fix(proxy): include model group aliases in v1 model info (#30626) * Include model group aliases in v1 model info * Fix model info alias implementation * removed extra blank line * chore: rerun CI * fix(lint): remove redundant noqa directive in proxy_cli.py * fix: address greptile review - restore bedrock_mantle auth symbols, guard OCI empty message list, validate DIRECT_URL scheme * Revert "fix: address greptile review - restore bedrock_mantle auth symbols, guard OCI empty message list, validate DIRECT_URL scheme" This reverts commit |
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yuneng-jiang
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c0352c5aa8
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chore(ci): remove Agent Shin pull_request_target workflows (#30784)
Drop the two Agent Shin workflows that ran on the pull_request_target trigger: the PR triage workflow and the review gate. Both were dry-run and gated behind AGENT_SHIN_ENABLED, so no live automation changes. The shared scripts under .github/scripts stay in place; four other Agent Shin workflows still depend on them and run on schedule, dispatch, and issue events rather than pull_request_target |
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Yassin Kortam
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a8b94b9a87
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fix(proxy): enforce budgets against authoritative DB spend when the cross-pod counter is unreliable (#30684)
Some checks failed
Agent Shin — rollout heads-up (one-shot) / heads-up (push) Has been cancelled
Budget enforcement reads spend from the cross-pod Redis counter via get_current_spend, which trusted the counter whenever Redis returned a value. A Redis instance that restarts and reloads an older RDB snapshot (the customer's logs repeat "Redis is loading the dataset in memory") comes back with a stale-low counter; that read is a hit, not a clean miss, so the existing DB reseed never ran and a key kept getting admitted even though its recorded spend was already over max_budget. The symptom was recorded spend sitting above the limit while requests kept succeeding. Read-time enforcement: get_current_spend takes an optional max_budget and, when the counter would admit the request but reads below this caller's last-known recorded spend, re-reads the authoritative spend and enforces against the higher value. The authoritative source depends on the counter: key/team/user/org/team-member read the DB row, per-window budgets aggregate spend logs, and end-user/tag have no DB row so the caller's freshly-loaded recorded spend is used. Healthy primary counters and freshly reset keys stay off the DB path, and the value is cached in-process for a few seconds, so a persistently stale counter drives at most one read per counter per window. When the DB value is higher, the counter is repaired with a monotonic, atomic set-max (RedisCache.async_set_max) so every worker reads the corrected total and a concurrent increment is never clobbered. Reconcile no longer fails open: when the post-call reservation reconcile found the counter missing or an adjustment that would drive it negative, it deleted the counter and continued (the deletion is what left counters nil/unenforced after a Redis reload). It now reseeds from the DB's lagging authoritative floor instead of deleting; the monotonic set-max can only raise a stale-low counter, and the read-time floor converges to the true total as the spend buffer flushes. The pre-call admission resize path keeps its original fail-closed behavior. Opt-in strict enforcement: general_settings.fail_closed_budget_enforcement (default False) makes the authoritative re-check run for every budgeted entity (closing the gap where a stale-low counter and a stale-low cached fallback would otherwise both pass the cheap guard), and rejects a request with 503 when the spend backing an admit decision can be verified against neither Redis nor the database. Default behavior is unchanged; the re-check stays bounded by the in-process cache. Resolves LIT-3772 |
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ryan-crabbe-berri
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382d78ec16
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feat(ui): migrate old usage report to App Router path route (#30694)
Cut the legacy "Old Usage" report (?page=usage) over from the switch in
(dashboard)/page.tsx to a path route at (dashboard)/old-usage. The segment is
old-usage rather than usage because the modern usage dashboard (new_usage)
already owns /usage. Adding the MIGRATED_PAGES entry repoints the sidebar item
and redirects existing ?page=usage links to /ui/old-usage.
The report was the switch's catch-all else, so removing it means choosing a new
fallback: collapse the now-redundant explicit api-keys arm into the else so the
main dashboard (UserDashboard) is the default. Unknown ?page= values now land on
the dashboard instead of the Old Usage report, which is the sensible default.
The new route sources identity from useAuthorized() and passes keys={null}: the
key-filter dropdown read the parent's keys state, which was already empty on
direct navigation to ?page=usage, so this preserves that rather than wiring a
paginated key fetch into a deprecated report.
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Simantak Dabhade
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fb34c184b4
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feat(search): add TinyFish as search provider (#30634)
* feat(search): add TinyFish as search provider Adds TinyFish web search (GET https://api.search.tinyfish.ai) as the 16th search provider in LiteLLM. Follows the BaseSearchConfig pattern used by other GET-based providers like Brave. Includes unit tests in tests/test_litellm/ for full patch coverage. * fix(search/tinyfish): use concrete types to pass any-discipline and ruff UP006/UP045 Replace typing.Dict/List/Optional/Union with modern syntax (dict, list, X | None) and use concrete type parameters (dict[str, str] for headers, dict[str, object] for params) to eliminate LIT009 Any-discipline violations. Move _append_domain_filters to module level to avoid leaking Any through self. * fix(search/tinyfish): eliminate Any-typed values for any-discipline gate Use Pydantic BaseModel and TypeAdapter at httpx/base-class boundaries to validate untyped inputs (json(), params.get(), bare set). Three genuine external boundaries annotated with any-ok. * style: fix black formatting for long line * fix(search/tinyfish): move any-ok comment to violation line for any-discipline gate The any-discipline checker matches `# any-ok` comments by line number. The comment was on the closing-paren line (127) but the violation was on the call-expression line (126), so the suppression did not apply. * fix(search/tinyfish): align with approved PR #30158 Drop explicit AND from domain filter query to match the approved implementation. Set pricing to zero. Rename test to match behavior. |
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Mateo Wang
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a9e651d994
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fix(bedrock_mantle): add SigV4 fallback to chat completions auth (#30714) | ||
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Sameer Kankute
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e33e2917c6
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chore: litellm oss 170626 (#30637)
* fix(proxy): allow non-admin virtual keys to call GA Realtime WebRTC HTTP routes (#30089) * fix(proxy): allow non-admin virtual keys to call GA Realtime WebRTC HTTP routes Add the realtime WebRTC HTTP sub-routes (/realtime/client_secrets, /realtime/calls and their /v1 + /openai/v1 variants) to LiteLLMRoutes.openai_routes so is_llm_api_route() classifies them as LLM API routes. Without this, non-admin virtual keys received 401 'Only proxy admin can be used to generate, delete, update info for new keys/users/teams' when calling these endpoints. Fixes #29923 * fix(proxy): validate session.model for realtime routes in model-access check The GA Realtime WebRTC HTTP routes resolve the effective model from the nested session.model (falling back to the top-level model), but the auth layer's get_model_from_request() only extracted the top-level model. A model-restricted virtual key could therefore place a disallowed model in session.model, leave the top-level model unset, and skip can_key_call_model() entirely - obtaining an ephemeral token for a model it is not allowed to use. Extract session.model for the realtime client_secrets/calls routes so the model-access check runs against the model the request will actually use. Legitimate callers are unaffected; their permitted model still validates. Relates to https://github.com/BerriAI/litellm/issues/29923 * fix(proxy): classify realtime transcription_sessions routes as LLM API routes Add the GA Realtime WebRTC transcription_sessions HTTP routes to openai_routes so is_llm_api_route() returns True for them, matching the client_secrets and calls routes already fixed. These endpoints are registered with user_api_key_auth in realtime_endpoints/endpoints.py, so without this a non-admin virtual key calling POST /v1/realtime/transcription_sessions would hit the admin-only 401 branch. Extends the regression test parametrization accordingly. --------- Co-authored-by: habonlaci <4699494+habonlaci@users.noreply.github.com> * feat(proxy): surface max_input_tokens/max_output_tokens on /v1/models (#30272) * feat(proxy): surface max_input_tokens/max_output_tokens on /v1/models * fix(proxy): degrade /v1/models gracefully when model-group lookup fails --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix: sort tiered token-cost thresholds numerically (#30375) * fix: sort tiered token-cost thresholds numerically _get_token_base_cost iterated input_cost_per_token_above_<N>_tokens keys with a lexicographic sort, so for tiers whose thresholds have different digit lengths (e.g. 90k vs 128k) a request crossing both was billed at the lower tier that sorted first. Sort by the parsed numeric threshold instead, so the highest tier the request actually crosses is applied. * refactor: reuse _parse_above_token_threshold for inline threshold parse --------- Co-authored-by: Eric (GabiDevFamily) <271972409+santino18727-debug@users.noreply.github.com> * fix(openai): preserve cache_control for openai-compatible custom endpoints (#30387) * fix(openai): preserve cache_control for openai-compatible custom endpoints * fix(openai): use parsed hostname to detect real OpenAI for cache_control preservation * fix(proxy): drain all daily-spend batches per flush cycle (#30281) (#30505) * fix(types): prevent internal parallel_request_limiter fields from leaking to upstream providers (#30545) * fix(types): add internal parallel_request_limiter fields to all_litellm_params to prevent forwarding to upstream providers * test(types): add regression test for internal rate-limit fields in all_litellm_params * fix(init): add bool type annotation to suppress_debug_info (#30531) Module-level `suppress_debug_info = False` had no annotation, so strict type checkers (e.g. ty) infer it as `Literal[False]`. Reassigning it to `True` (as done in proxy_server.py and router.py) then fails with an invalid-assignment error. Annotate it as `bool` to match every other flag in this module. * fix: coalesce null aggregates in update_metrics for no-spend keys (#29945) * feat(team_endpoints): add query parameter `key_limit` to `/team/info` endpoint (#30006) * feat(team_endpoints): Add query parameter key_limit to /team/info * feat(team_endpoints): update schema.d.ts to include the new query parameter * feat(team_endpoints): add tests for limitting key count in /team/info response * feat(team_endpoints): Apply suggestions from greptile * Set greater-than constraint on key-limit * Fix type * fix(router): release aiohttp connection when stream iteration ends abnormally (#30271) * fix(router): release aiohttp connection when stream iteration ends abnormally A streaming response that terminates with a mid-stream read timeout, a task cancellation (client disconnect), or GeneratorExit never closed the underlying aiohttp ClientResponse. aiohttp only auto-releases the connector slot at body EOF, so each abnormally terminated stream permanently leaked one slot from the shared TCPConnector pool. During a backend traffic spike the pool drains; once exhausted every subsequent request to that host waits for a slot, times out and surfaces as a 408, indefinitely, even after the backend recovers. Only a proxy restart cleared the in-memory sessions, which matched the reported symptom of a router stuck returning 408 for a healthy vLLM backend. Close the response in a finally clause when iteration ends. On a fully read response the connection was already released at EOF and close() is a no-op, so keep-alive reuse for normal requests is unchanged. Fixes #30192 * test(aiohttp): cover GeneratorExit path with a mock instead of a live socket The previous slot-release test started a real aiohttp TCP server, which can flake in offline CI and does not exercise this fix's code path directly. Replace it with a dependency-injected mock that closes the stream generator (GeneratorExit) and asserts the response is closed, covering the third abnormal-exit path the finally block handles * feat(proxy): serve Anthropic-native /v1/models for Claude Code gateway discovery (#30273) * feat(proxy): serve Anthropic-native /v1/models for Claude Code gateway discovery * refactor(proxy): move Anthropic model-list formatter into llms/anthropic/common_utils * fix(proxy): make model_list request param optional for direct callers * feat(dashscope): add Responses API support (#30286) * feat(dashscope): add Responses API support DashScope's OpenAI-compatible endpoint serves /responses, so register a DashScopeResponsesAPIConfig that routes dashscope/* responses calls to {api_base}/responses without rewriting the upstream model id, instead of falling back to the chat-completions -> responses emulation pipeline. Closes #29780 * feat(dashscope): mark responses API as not supporting native websocket Matches the hosted_vllm/perplexity/openrouter responses configs, which all override supports_native_websocket() to False since the OpenAI-compatible endpoint has no native wss:// responses transport. --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(spend-logs): preserve error_message on ProxyException failures (#30381) * fix(spend-logs): preserve error_message on ProxyException failures `StandardLoggingPayloadSetup.get_error_information` used `str(original_exception)` to populate the human-readable error message stored in `spend_logs.metadata.error_information.error_message`. `ProxyException` (litellm/proxy/_types.py:3453) sets `self.message` in its constructor but does NOT call `super().__init__(message)` and does NOT define `__str__`. As a result, `str(ProxyException(...))` returns the empty string, and every auth/budget/quota rejection was landing in spend_logs with `error_message=""` despite a fully populated traceback. Operator impact: dashboard "LLM Failure" rows became untriageable — the only way to tell a 401 from a 429 was to manually unpack the traceback JSON via psql. Burst failure patterns (e.g. a UI session polling with a stale token) produced 20-30 indistinguishable `error_code=401` rows per second. Fix: prefer the `.message` attribute (set by ProxyException and every litellm.exceptions.* class) over `str(exc)`. The `str(exc)` fallback is retained for non-litellm exception types, preserving prior behavior. Test plan: - 2 new unit tests in tests/test_litellm/litellm_core_utils/ test_litellm_logging.py: * test_get_error_information_prefers_message_attribute_over_str * test_get_error_information_falls_back_to_str_when_no_message_attr - Existing test_get_error_information_error_code_priority still passes - End-to-end verified: bad-key 401 now stores full "Authentication Error, Invalid proxy server token passed..." message in spend_logs.metadata.error_information.error_message * fix(spend-logs): preserve explicit empty .message + drop dead reference Greptile P2 on #30381. The truthiness check `if message_attr:` silently skipped an explicit empty-string `.message` and fell through to `str(original_exception)`. For ProxyException-shaped objects both produce empty, so the bug was latent; for other exception types it would inject a different string into error_information.error_message and corrupt the signal. Use `is not None` so an empty string survives verbatim. Also drop the stale `See e2e/cases/11.` comment reference — that path does not exist anywhere in the repo and confuses future readers. Regression test added: an exception with `.message=""` and a non-empty `super().__init__()` arg must yield error_message == "". * ci: retrigger workflows after base branch change to litellm_internal_staging * fix(anthropic): strip LiteLLM-injected total_tokens from /v1/messages response (#30382) * fix(anthropic): strip LiteLLM-injected total_tokens from /v1/messages response The non-streaming /v1/messages response carries a LiteLLM-injected usage.total_tokens = input_tokens + output_tokens that is not part of the Anthropic API spec. This caused three problems: 1. Shape divergence with streaming on the same endpoint. message_delta.usage in the SSE path never carries total_tokens. Clients parsing both paths get two different schemas from one endpoint. 2. Shape divergence with upstream. Direct calls to https://api.anthropic.com/v1/messages return no total_tokens field, so clients using the official Anthropic SDK couldn't rely on it, and clients that did rely on the LiteLLM-injected one broke when bypassing the proxy. 3. Numerical misuse. total = input + output undercounts when cache_read_input_tokens and cache_creation_input_tokens are non-zero, because cache tokens are reported in their own fields. A 100k-token cached prompt with 1 non-cache input token + 200 output tokens reports total_tokens = 201, off by ~99.8% from any reasonable definition of "total." Fix: add _strip_total_tokens_from_anthropic_response in litellm/proxy/anthropic_endpoints/endpoints.py and invoke it in the success path of anthropic_response right before returning. Only mutates dict-shaped responses; streaming (which already lacks the field) is left untouched. spend_logs / Prometheus continue to compute total_tokens internally for billing — this fix only strips the field from the wire response. Scope: only the Anthropic passthrough endpoint /v1/messages. The OpenAI-shape /v1/chat/completions is unaffected. * fix(anthropic): gate total_tokens strip behind flag + handle Pydantic .usage Two P1 greptile threads on #30382: P1 — **Backwards-incompatible removal without a feature flag** Stripping `usage.total_tokens` unconditionally breaks any client currently reading the LiteLLM-shaped non-streaming /v1/messages response. Per the codebase's policy (mirrors #30418), gate behind a new flag. - `litellm.strip_anthropic_total_tokens: bool = False` (default — backward-compat: clients keep seeing total_tokens). - Env override: `LITELLM_STRIP_ANTHROPIC_TOTAL_TOKENS=true`. - Docstring: planned to flip to True in a future major release; opt in early. P1 — **Silent no-op if `result` is a Pydantic model** `base_process_llm_request` may return a Pydantic-style object whose `.usage` is a plain dict (the most common shape — e.g. objects wrapping raw upstream JSON). The original `isinstance(response, dict)` guard skipped strip on those, so `total_tokens` would still hit the wire. Helper now also reads `getattr(response, "usage", None)` and strips when that's a dict. Strongly-typed Pydantic `Usage` sub-models with required `total_tokens` fields are still skipped — those impose type constraints the helper doesn't try to subvert. Tests: - `test_strips_total_tokens_on_pydantic_model_with_dict_usage` - `test_flag_defaults_off` 8/8 pass locally. * fix(anthropic): drop env var for strip flag (docs CI) Mirrors #30418's pattern (`expose_router_debug_in_errors: bool = True`, no `os.getenv`). The `LITELLM_STRIP_ANTHROPIC_TOTAL_TOKENS` env var introduced in the prior commit was flagged by `tests/documentation_tests/test_env_keys.py` because the documentation file `docs/my-website/docs/proxy/config_settings.md` lives in `BerriAI/litellm-docs` (separate repo) and registering a new env key requires a parallel docs PR — a friction we avoid here by exposing the flag only as a Python attribute + `litellm_settings` config key, both of which load through the existing proxy config plumbing without needing the env-var registry to be updated. No semantic change: default still False, behavior identical when set via `litellm.strip_anthropic_total_tokens = True` or `litellm_settings.strip_anthropic_total_tokens: true` in config.yaml. Verified locally: env scan no longer surfaces the key; 8/8 tests pass. * ci: retrigger workflows after base branch change to litellm_internal_staging * fix(pricing): correct swapped input/output token costs for command-r7b-12-2024 (#30413) * fix(pricing): correct swapped input/output token costs for command-r7b-12-2024 * test: resolve model prices JSON relative to test file for pip installs * fix(exception-mapping): map Gemini upstream-error body code 429 to RateLimitError (#30417) * fix(exception-mapping): map Gemini upstream-error body code 429 to RateLimitError Some Gemini-compatible gateways (e.g. new-api) wrap a 429 rate-limit signal from upstream inside an HTTP 500/503 envelope, with the real code only surfaced in the JSON body: {"error":{"message":"...high demand...","type":"upstream_error", "param":"","code":429}} Previously LiteLLM only looked at the HTTP status and mapped this to InternalServerError, which Router treats as non-retryable for many configs — so users got hard 500s instead of fallback/retry. Now the Gemini/Vertex exception mapper parses error.code from the body and routes code 429 to RateLimitError before falling through to the HTTP-status branches. Other body codes fall through unchanged. Tests cover: - new-api gateway's `code:429` payload now maps to RateLimitError - Genuine 500-body responses stay InternalServerError - Non-JSON body strings fall through to status-code mapping unchanged * fix(exception-mapping): scope body-code 429 promotion to 5xx envelopes Addresses greptile P1/P2 + @Sameerlite's review on #30417. The new elif branch was firing for any HTTP status, so a gateway response of HTTP 400 with body {"error":{"code":429,...}} would be incorrectly promoted to RateLimitError (retryable) instead of falling through to BadRequestError. Same trap for 401 -> AuthenticationError. Scoped the body-code 429 check to `500 <= status_code < 600` — covers 500/502/503/504 (gateways wrapping upstream 429 in any 5xx envelope) without inviting the 4xx misclassification. Tests: parametrized table now covers 5xx (500/502/503), 4xx (400/401), and the existing fall-through cases, asserting each maps to the exception type that matches the HTTP status code. 50/50 pass locally. * ci: retrigger workflows after base branch change to litellm_internal_staging * feat(router): add expose_router_debug_in_errors flag (default True) to redact internal model_group/fallback names (#30418) * feat(router)!: redact internal model_group/fallback names from exception messages The Router was unconditionally appending internal config names onto exception.message: - "Received Model Group=..." - "Available Model Group Fallbacks=..." - "No fallback model group found... Fallbacks={...}" - "context_window_fallbacks={...}" - Deployment-timeout messages including model_group - Fallback failure detail listing fallback chain ProxyException forwards .message verbatim to clients, so gateways were leaking their model_name / fallback wiring in every failed call. Fix: gate all five mutation sites on a new `litellm.expose_router_debug_in_errors` flag (default False). Set to True to restore upstream debug behavior for local debugging. Why: matches the redaction posture this codebase already has for upstream model identifiers (cf. _litellm_returned_model_name) and removes the last common error-path leak of internal model_group names. Breaking change marker (!): if anything parses "Received Model Group=" out of client error messages, flip the flag on or migrate to the x-litellm-* response headers instead. Tests: 7 cases covering each of the 5 redaction sites + the flag-on inverse path, plus a "default off" sanity check. * test(router): cover sites 1 + 3 of expose_router_debug_in_errors gate Addresses Greptile / codecov feedback on #30418: patch coverage was 55.6% with 4 lines uncovered in litellm/router.py. The existing tests exercised sites 2 (ContextWindowExceededError), 4 (no-fallback-found), and 5 (Received Model Group) — both default and flag-on. Sites 1 and 3 were declared in the PR description as covered by "site 5 also fires" but the gate body lines for each (the `e.message +=` inside the `if litellm.expose_router_debug_in_errors:` branch) only execute when the flag is on AND the specific exception path is taken, which neither existing test triggered. Added 4 new tests (default + flag-on × 2 sites): - test_default_does_not_leak_deployment_timeout_debug - test_flag_on_leaks_deployment_timeout_debug - test_default_does_not_leak_content_policy_fallback_hint - test_flag_on_leaks_content_policy_fallback_hint Trigger details: - Site 1 (litellm.Timeout in _acompletion) is reached via the Router-supported `mock_timeout=True` + `timeout=0.001` kwargs on `acompletion(...)`. Cannot embed a Timeout instance in model_list because Router.__init__ deep-copies it and Timeout.__reduce__ does not preserve the required positional args. - Site 3 (ContentPolicyViolationError without content_policy_fallbacks set, in async_function_with_fallbacks_common_utils) is reached by passing a `mock_response=litellm.ContentPolicyViolationError(...)` instance via the call-site kwarg — same deepcopy-avoidance reason. 11/11 tests pass locally. Patch coverage on litellm/router.py for this PR's diff should now be 100%. * chore(router): flip expose_router_debug_in_errors default to True Addresses @Sameerlite's review on #30418 — maintain backward compat on the wire. Redact becomes opt-in via setting the flag to False; the historical behavior (leak internal model_group / fallback wiring through exception messages) is preserved as the default. - litellm/__init__.py: default flipped to True, docstring rewritten with deprecation note pointing at a future flip to False (redact by default) in a major release. - tests/test_litellm/test_router_exception_redaction.py: fixture resets to True (was False); the "off" tests now explicitly set False; the "default_leaks_*" tests rely on the fixture default. test_flag_defaults_off -> test_flag_defaults_on. - No router.py change needed; the gate keys off the same flag, only the default changes. - PR title no longer needs the breaking-change `!` marker — no client sees a behavior change at default settings. 11/11 pass locally. * ci: retrigger workflows after base branch change to litellm_internal_staging * feat(guardrails): integrate Repelloai Argus guardrail (#30465) * feat(guardrails): add RepelloAI Argus guardrail integration (#1) * feat(guardrails): add RepelloAI Argus guardrail integration Add a new guardrail hook backed by RepelloAI Argus, with dashboard-managed asset policies enforced via an asset_id and X-API-Key auth. * fix(guardrails): harden RepelloAI Argus guardrail - scan streaming responses on output (was bypassing the guardrail) - log blocked verdicts as guardrail_intervened instead of success - treat auth/config errors (401/403/404/422) as misconfiguration that always blocks, not a fail-open-able unreachable error - default unreachable_fallback to fail_closed and read it directly; block on unknown/malformed verdicts so an API change can't silently disable enforcement - type unreachable_fallback as a Literal, drop the duplicate config model, expose unreachable_fallback in the config schema, and stop leaking the raw provider response / exception strings to the client * fix(guardrails): address RepelloAI Argus review feedback - support ARGUS_API_KEY (with REPELLOAI_API_KEY fallback) - make asset_id required in the config model - normalize unreachable_fallback so only fail_open opens; block on 400 misconfig - correct the shared unreachable_fallback field description * docs(guardrails): add RepelloAI Argus docs page and dashboard listing - add docs page covering config, env vars, modes, verdicts, failure semantics - list RepelloAI Argus in the Guardrail Garden with provider/logo mappings - add a regression test for the provider logo and display-name resolution * fix(guardrails): keep RepelloAI asset_id optional in config model A required asset_id leaked onto the shared LitellmParams (which inherits RepelloAIGuardrailConfigModel), breaking validation for every other guardrail. Keep it optional like sibling models; the guardrail __init__ still raises when asset_id is missing, which is the real enforcement. * Add comment for last user turn scanning * feat(guardrails): harden repelloai scanning * feat(guardrails): expand repelloai scanning to include tool definitions Add extraction of tool definitions and tool call arguments to the RepelloAI guardrail scanning. Improves detection coverage by including function schemas and parameters in the prompt sent to the guardrail service. Also captures detailed error responses in logs and adds guardrail header to streaming responses. * refactor(guardrails): fix and harden repelloai schema text extraction - Fix duplicate text in _iter_schema_text: previously all dict values were re-queued onto the stack even after scalar/list keys were already extracted explicitly, causing names/descriptions to appear twice in the scanned prompt - Extract schema key frozensets to module-level constants so they are not reconstructed on every call - Change _iter_schema_text from @classmethod to @staticmethod (cls unused) - Narrow _call_analyze stage param from str to Literal["prompt", "response"] - Add HttpxResponse type annotation to _raise_for_config_error - Add LLMResponseTypes annotation to async_post_call_success_hook response param * fix(guardrails): resolve pyright type errors in repelloai guardrail - Narrow async_handler.post return from Response|None to Response with explicit None guard before calling raise_for_status/json - Fix list comprehension returning str|None by switching to explicit loop with isinstance guard so pyright tracks the narrowing - Cast model_dump() result to Dict since hasattr does not narrow object type in pyright * fix(guardrails/repello): include Responses API instructions field in prompt scan The /v1/responses top-level `instructions` field was not included in _extract_prompt_text, allowing a caller to bypass guardrail policy checks by putting blocked content in `instructions` while keeping `input` benign. * feat: add api_key to config model and read prompt from data dict * fix(guardrails/repello): plug input_text and tool-call response bypass gaps Responses API input content parts with type 'input_text' were silently dropped by build_inspection_messages (which only handles type='text'), allowing callers to send blocked content via that path without triggering the pre-call scan. Fix: add _extract_input_text_parts to RepelloAIGuardrail and call it when walking the Responses API input messages. Post-call scanning skipped responses whose choices contained only tool_calls or function_call (message.content=None), letting models put blocked output in function arguments undetected. Fix: _extract_chat_completion_text now calls _extract_tool_call_args_from_message on each choice message. Also replace typing.Dict/List with builtin dict/list to clear TID251 strict ruff violations introduced by this file. * fix(guardrails/repello): scan Responses API function_call output arguments Output items with type 'function_call' in a /v1/responses response were skipped by _extract_responses_api_text; only 'message' items were walked. A model could return blocked content in function_call.arguments undetected. Now extract arguments from function_call output items before scanning. * fix(anthropic): drop orphaned server_tool_use on multi-turn replay from generic OpenAI clients (#30486) * fix(anthropic): drop orphaned server_tool_use on multi-turn replay from generic OpenAI clients When an Anthropic server-side tool (web_search, id `srvtoolu_...`) is used, its result is carried in `provider_specific_fields.web_search_results` — PRs #17746 / #17798 restore it for callers that round-trip provider_specific_fields. A generic OpenAI client that does NOT preserve provider_specific_fields (e.g. Open WebUI talking to a Vertex/Anthropic model over /chat/completions) drops it on replay and instead sends back an assistant `tool_call` + a `tool` message both keyed to the `srvtoolu_` id. The transform then produced a bare `server_tool_use` (with no following *_tool_result) plus a user `tool_result` for the same id — both invalid, so the next turn 400s: messages.N.content.0: unexpected `tool_use_id` found in `tool_result` blocks: srvtoolu_... Each `tool_result` block must have a corresponding `tool_use` block in the previous message. This is the commonly-reported vertex_ai symptom where Gemini works but Claude 400s on the 2nd turn of a web-search chat. Fix (litellm/litellm_core_utils/prompt_templates/factory.py): - convert_to_anthropic_tool_invoke: only emit a server_tool_use when its matching *_tool_result is available to pair with it; otherwise skip it (a bare server_tool_use is itself rejected). - anthropic_messages_pt: drop a replayed `tool`/`function` message whose tool_call_id starts with `srvtoolu_` (a server-executed tool produces no client result; a user tool_result for it is invalid). The existing reconstruction path (provider_specific_fields present, e.g. the litellm SDK) is unchanged, as is regular client tool_use/tool_result. Tests (tests/llm_translation/test_prompt_factory.py): - update test_convert_to_anthropic_tool_invoke_server_tool -> test_convert_to_anthropic_tool_invoke_server_tool_without_result_is_dropped - add test_anthropic_messages_pt_generic_client_drops_orphan_server_tool Follow-up to #17746 / #17798; addresses the generic-client (no provider_specific_fields) case of #17737. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(anthropic): cover the srvtoolu_ round-trip fix in the test_litellm unit suite The regression tests added in tests/llm_translation/test_prompt_factory.py aren't run by the coverage CI job (it runs tests/test_litellm), so the new factory.py branches showed as uncovered (codecov patch coverage). Add equivalent focused tests in the unit suite so both new branches are exercised there: - convert_to_anthropic_tool_invoke drops a srvtoolu_ server_tool_use when no matching *_tool_result is available. - anthropic_messages_pt drops the orphaned srvtoolu_ tool message a generic OpenAI client replays. Refs #17737 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(anthropic): cover the server_tool_use + result valid-pair path in unit suite Covers the remaining patch-coverage lines codecov flagged: convert_to_anthropic_tool_invoke emitting server_tool_use followed by its web_search_tool_result when the matching result is present (the litellm-SDK round-trip path). Refs #17737 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * style(anthropic): flatten srvtoolu_ tool-message guard to a negated if Addresses the Greptile style nit: replace the if-pass/else with a single negated `if not (...)` guard around the tool_result append. Behavior unchanged. Refs #17737 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(proxy): require premium only when enabling premium metadata fields (#30285) (#30506) Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(perplexity): stop double-billing reasoning tokens in manual cost fallback (#30488) * fix(perplexity): stop double-billing reasoning tokens in manual cost fallback When perplexity_cost_per_token cannot use the API-provided usage.cost.total_cost short-circuit and falls back to manual calculation, it multiplies the full usage.completion_tokens by output_cost_per_token and then adds reasoning_tokens * output_cost_per_reasoning_token on top. Per the OpenAI/Perplexity usage convention codified for the central path in PR #18607, completion_tokens already INCLUDES reasoning_tokens, so the manual fallback double-bills reasoning at both the output and reasoning rate. Concrete impact on perplexity/sonar-deep-research (input 2e-6, output 8e-6, reasoning 3e-6): for the exact usage shape exercised by the live response fixture in tests/llm_translation/test_perplexity_reasoning.py (prompt_tokens=9, completion_tokens=20, reasoning_tokens=15) the current code charges 0.000223 vs the convention-correct 0.000103, a 2.165x overcharge. The bug is reachable whenever Perplexity omits the cost object (streaming chunks, fixture-driven paths, older API versions). Subtracts reasoning_tokens (clamped at zero) from completion_tokens before applying the output rate, mirroring how dashscope/cost_calculator.py and the central generic_cost_per_token already handle it. Preserves the existing fallback behaviour when output_cost_per_reasoning_token is unset (all completion_tokens stay at the output rate). Existing tests in tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py asserted the buggy math and are updated to the convention-correct math. Adds a focused regression test using the exact usage shape from the live response fixture so this class of bug cannot be silently reintroduced. * style(perplexity): drop redundant type annotation on else branch to satisfy mypy mypy [no-redef] flagged 'completion_cost' as declared in both if and else arms; keeping the annotation only on the first declaration matches existing patterns in this file. * fix(perplexity): update integration test expected costs for non-double-billed math Three tests in test_perplexity_integration.py asserted the old buggy expectation that reasoning_tokens are billed in addition to the full completion_tokens count. After the fix in cost_per_token, reasoning_tokens are billed at the reasoning rate and the remaining (completion_tokens - reasoning_tokens) at the standard output rate, matching OpenAI/Perplexity convention (PR #18607). Updates: test_end_to_end_cost_calculation_with_transformation, test_main_cost_calculator_integration, test_high_volume_cost_calculation. The high-volume sanity threshold drops to 0.25 to reflect the corrected total. * fix(ui): use dynamic proxy base URL in MCP usage examples (#30487) Replace hardcoded http://localhost:4000 with getProxyBaseUrl() in the MCP server usage example and copy-to-clipboard snippet so the generated configuration works for non-local deployments. Fixes #30466 * feat: add missing UK PII entity types to Presidio guardrail (#30537) * feat: add missing UK PII entity types to Presidio guardrail Add UK_PASSPORT, UK_POSTCODE, and UK_VEHICLE_REGISTRATION to PiiEntityType enum and PII_ENTITY_CATEGORIES_MAP. These entity types are supported by Microsoft Presidio but were missing from litellm's type definitions, preventing users from configuring UK-specific PII detection. * test: remove fragile hardcoded entity count test Remove test_uk_category_entity_count which hardcodes len() == 5. The test_uk_entities_match_presidio_recognizers test already verifies exact set equality, making the count test redundant and fragile to future Presidio additions. * style: apply Black formatting to match CI requirements * fix: route volcengine (Doubao) tiered-pricing models to the tiered cost handler (#30357) Volcengine (Doubao) models define `tiered_pricing` but no flat per-token cost, so cost_per_token fell through to generic_cost_per_token (which only reads flat costs) and tracked them at $0 Route custom_llm_provider == "volcengine" to the shared tiered-pricing handler in litellm/llms/dashscope/cost_calculator.py, which already computes graduated tier costs. Make that handler provider-agnostic by adding a custom_llm_provider argument (default "dashscope" preserves existing behavior) so get_model_info resolves the correct model map entry Fixes #30346 * feat(mcp): make MCP gateway name and description configurable via env vars (#30473) * feat(mcp): make MCP gateway name and description configurable via env vars * Rename function _restore_env to _apply_env * docs(mcp): document import-time capture of env-backed identity constants Address Greptile review feedback: clarify that LITELLM_MCP_SERVER_NAME and LITELLM_MCP_SERVER_DESCRIPTION are read once at import and require a module reload to observe env changes after import. Generated with AI assistance Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Yevhen Luhovtsov <yevhen.luhovtsov@intapp.com> Co-authored-by: Claude <noreply@anthropic.com> * fix(mcp): preserve native tools in semantic filter hook (#26650) * fix(mcp): preserve native tools in semantic filter hook The SemanticToolFilterHook.async_pre_call_hook passed ALL tools (MCP + native) to filter_tools(), which only knows MCP-registered tool names. Native tools silently failed the name match in _get_tools_by_names() and were dropped from the request. Fix: partition tools into native and MCP-registered before filtering. Run the semantic filter only on MCP tools, then merge native tools back unconditionally. Changes: - Robust _is_mcp_tool() using shape-based detection for OpenAI-format dicts, safe regardless of future _extract_tool_info changes - Single-pass partition loop (no double _is_mcp_tool calls) - Preserve native tools in MCP expansion path (mixed requests) - Track MCP expansion to prevent expanded tools bypassing filtering - filter_stats reports MCP-only counts for accurate metrics - Extracted _emit_filter_metadata() helper - Skip spurious filter headers for all-native tool requests Closes #26212 * remove stale docstring note referencing tools_expanded_from_mcp * fix: handle Responses API name collision and preserve tool ordering - Classify Responses API tools ({type: 'function', name: '...'}) as native to prevent name collisions with MCP canonical names - Preserve original request tool ordering using id()-based merge instead of naive native+mcp concatenation - Add 2 regression tests: name collision and ordering preservation * style: apply black formatting * fix(mcp): harden semantic filter — preserve all native tool formats, safe metadata access, graceful expansion failure, name-based merge * lint: suppress PLR0915 on async_pre_call_hook (matches codebase convention) * ci: retrigger checks after rebase onto litellm_internal_staging * feat(fireworks): sync Fireworks AI model registry with current platform catalog (#30616) Adds 12 new Fireworks serverless models and updates 3 existing entries in model_prices_and_context_window.json and its bundled backup to match the current Fireworks platform model list. New direct models: glm-5p2, qwen3p7-plus, minimax-m3, minimax-m2p7, kimi-k2p7-code, kimi-k2p6, deepseek-v4-pro, deepseek-v4-flash. New router endpoints: glm-5p1-fast, kimi-k2p6-fast, kimi-k2p7-code-fast. Updated: glm-5p1, gpt-oss-120b, and gpt-oss-20b now carry correct output token caps, cache-read pricing, and explicit capability flags max_tokens is set equal to max_output_tokens (not the full context window) for models whose generation cap is below their context window. This avoids the shared input+output budget path in get_modified_max_tokens, which would otherwise let callers request output sizes the model cannot produce. The same fix corrects the pre-existing glm-5p1, gpt-oss-120b, and gpt-oss-20b entries that had max_tokens equal to the full context window Short-form aliases (fireworks_ai/<model>) are added for every direct accounts/fireworks/models/ entry so cost attribution works for callers using bare model names. Router endpoints get short-form aliases too, and transform_request now routes bare names ending in -fast to the accounts/fireworks/routers/ path instead of defaulting every bare name to models/. This keeps the kimi-k2p6-fast router from being misrouted to the nonexistent models/kimi-k2p6-fast endpoint kimi-k2p6-turbo is intentionally excluded; kimi-k2p6-fast is its replacement. Context windows for deepseek-v4 and kimi models use the power-of-two values (1048576 and 262144) published on the Fireworks model pages, matching the convention already used by existing entries Two regression tests in test_utils.py assert the exact per-token costs, token limits, capability flags, and short-form-to-long-form equality for all 15 models against both the main and backup cost maps. Two routing tests in test_fireworks_ai_chat_transformation.py verify bare -fast names route to routers/ and bare direct-model names route to models/ * fix(bedrock): handle role:"system" inside the messages array on /v1/messages (#29698) (#30443) * feat(anthropic): hoist leading in-array system to top-level (helper) * test(anthropic): cover _system_content_to_blocks edge cases; deepcopy cache_control * test(anthropic): mid-conversation system normalization cases * feat: add supports_mid_conversation_system flag to Claude Opus 4.8 Add supports_mid_conversation_system: true to all 9 claude-opus-4-8 cost-map entries (Anthropic-native, Bedrock, Vertex, Azure AI) in both the root cost map and the bundled package backup, since the runtime helper and tests read the backup in local/offline mode. Pin the mid-system passthrough regression test to the local cost map via the existing local_model_cost_map fixture so it reads the branch-local flag rather than the network-fetched main copy. * fix(bedrock): normalize in-array system in /v1/messages handler (#29698) Wire normalize_system_messages_for_anthropic into anthropic_messages_handler so all Bedrock /v1/messages paths (Invoke / Mantle / ClaudePlatform / Converse-bridge) hoist leading in-array system entries (and demote mid-conversation ones on models lacking supports_mid_conversation_system) into the top-level system field. The normalized messages/system are written back into the local_vars snapshot the base_llm branch reads from, otherwise the Invoke/Mantle fix would silently no-op. Also fix the helper to resolve supports_mid_conversation_system through the prefix-aware AnthropicModelInfo._supports_model_capability resolver. The raw _supports_factory could not see the flag once get_llm_provider left the invoke/ prefix on the model id, which would have wrongly demoted mid-conversation system on a Bedrock invoke opus-4-8 path. * fix(bedrock): resolve mid-conversation-system flag through mantle/invoke/converse route prefixes; drop unused param * fix(types): widen system param to Union[str, List] for hoisted system blocks * refactor(bedrock): drop dead local_vars messages writeback * fix(bedrock/converse): translate in-array system in anthropic->openai adapter (#29698) * fix(bedrock/converse): preserve cache_control on in-array system; test drop-empty * fix(bedrock/converse): rename colliding local to satisfy mypy; test handler system-merge branches * fix(types): register supports_mid_conversation_system in model-info schema The cost-map JSON-schema validation test (test_aaamodel_prices_and_context_window_json_is_valid) rejects unknown properties, so adding supports_mid_conversation_system to the opus-4-8 cost-map entries failed CI with 'Additional properties are not allowed'. Register the flag in the INTENDED_SCHEMA allow-list and in the ProviderSpecificModelInfo TypedDict so it is a typed, first-class capability flag alongside its peers (supports_output_config, etc.). --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(bedrock/agentcore): optionally forward multimodal content blocks in InvokeAgentRuntime payload (#28885) * fix(bedrock/agentcore): optionally forward multimodal content blocks in InvokeAgentRuntime payload By default the agentcore provider flattens the last message to a text-only {"prompt": "..."} payload via convert_content_list_to_str, silently dropping OpenAI multimodal blocks (image_url, file, input_audio, ...). This adds an opt-in `forward_multimodal_content` litellm param. When truthy and the last message's content is a list containing a non-text block, the original OpenAI content list is forwarded verbatim under a new "content" field so an attachment-aware AgentCore agent can read it. Default off keeps the payload byte-identical to the legacy {"prompt": "..."} shape — existing agents are unaffected. The flag is read from optional_params (where other AgentCore params land) with a litellm_params fallback, and accepts a bool or a config/env string ('true', '1', ...). AgentCore Runtime is schemaless on the agent side — the agent's @app.entrypoint parses arbitrary JSON up to 100 MB (per https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/runtime-invoke-agent.html), so this is a purely upstream change; no AgentCore-side schema is asserted. * fix(bedrock/agentcore): shallow-copy forwarded multimodal content list Address review feedback (Sameerlite): payload["content"] = last_content aliased the caller's mutable messages[-1]["content"] list. Harmless today because the payload is JSON-serialized immediately, but a latent footgun if a future caller mutates the returned payload before serialization. Forward list(last_content) so the payload owns its own list. Block dicts stay shared on purpose — a deep copy would clone potentially large base64 media on the request hot path, and the flagged risk was the shared list, not the blocks. Update the passthrough tests to assert equality + distinct identity, and add a regression test that mutating the payload list can't leak back into the original message content. * Revert "fix(mcp): preserve native tools in semantic filter hook (#26650)" This reverts commit |
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Mateo Wang
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feat(agent-shin): automated PR/issue triage, low-quality auto-close, and review-gate label lifecycle (#30433)
* feat(triage): auto-close stale PRs with Greptile score <4/5
Adds .github/scripts/close_low_quality_prs.py and a daily workflow that
closes PRs which:
- are open for at least 7 days, and
- carry a most-recent greptile-apps review with Confidence Score <4/5,
- and are not drafts or opt-out-labeled ('do not close', 'wip', etc.).
Each closure posts an explanatory comment telling the contributor how to
bring the PR back (rebase, re-request greptile, reopen at 4+/5). The
4/5 bar is already documented in the PR template
(.github/pull_request_template.md), so this just enforces it.
Tested with a dry run against the live BerriAI/litellm backlog of 1000
open PRs: 100 candidates identified, 598 PRs pass the bar (4+/5), 186
are too young, 97 are drafts, 19 lack any Greptile review and are left
alone.
Workflow defaults to closing 25 PRs/run as a safety net and supports
workflow_dispatch with overrides (close=false for a dry run, custom
min_age_days/min_score/limit).
18 unit tests cover score extraction (HTML/markdown/plain text, login
variants, multi-review picks latest) and per-PR evaluation (drafts,
opt-out labels, age, missing/passing/failing scores).
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* docs(templates): require expected/actual + QA proof for external contributions
PR template:
- Make the rubric explicit at the top: link an issue, OR provide a clear
problem description + expected vs. actual + visual QA proof.
- Add dedicated sections for each piece so the bot has a deterministic
shape to read.
- Keep the existing 'Linear ticket' section for internal contributors
(they're exempt from the auto-triage rubric).
Bug report template:
- Split 'What happened?' into 'Actual behavior' + 'Expected behavior'.
- Make logs/screenshot a required textarea.
- Warning banner at the top tells external contributors that incomplete
reports will be auto-closed (with re-evaluation on reopen).
Feature request template:
- Require a concrete use case + example in the motivation field, not just
a one-liner pitch.
- Same auto-triage warning banner.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* feat(triage): Agent Shin LLM-as-judge for external PRs and issues
Adds a new triage flow that evaluates external pull requests and issues
against the project's contribution rubric and, when configured to do so,
auto-closes non-conforming ones with an explanatory comment. Contributors
can update + reopen to be re-evaluated.
Scope:
- Internal BerriAI contributors (author_association OWNER/MEMBER/COLLABORATOR)
and bot accounts are skipped entirely.
- 'Fixes #1234' / 'Resolves https://github.com/.../issues/N' in the PR body
short-circuits to PASS without burning LLM tokens.
- LLM judge returns structured JSON (verdict, missing[], explanation);
parser tolerates markdown fences and embedded JSON.
- LLM errors NEVER close PRs/issues — failure surfaces as 'skip-llm-error'.
Safety:
- pull_request_target / issues triggers are FORCED dry-run in the workflow;
only manual workflow_dispatch with close=true (and AGENT_SHIN_ENABLED=true)
takes destructive action.
- Default mode writes verdicts to GITHUB_STEP_SUMMARY only — no public
comments until the team flips the AGENT_SHIN_ENABLED repo variable.
- LLM uses an OpenAI-compatible endpoint (model and base URL configurable
via repo variables; key via OPENAI_API_KEY secret).
Files:
- .github/scripts/triage_with_llm.py - judge orchestrator + CLI
- .github/workflows/triage_pr_with_llm.yml
- .github/workflows/triage_issue_with_llm.yml
- tests/test_litellm/test_github_triage_with_llm.py - 33 unit tests
End-to-end validated against four real PRs (#28117 internal collaborator,
#28108 bot, #28129 'Fixes #28128', #28116 no linked issue) and issue
#28132 with a stubbed LLM judge: each path produces the expected action.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* feat(triage): scope Greptile auto-closer to external contributors + dry-run by default
- close_low_quality_prs.py now filters by GitHub author_association via
the REST API: PRs from OWNER / MEMBER / COLLABORATOR (and bot accounts)
are skipped with a new 'skip-internal' summary bucket.
- close_low_quality_prs.yml now defaults workflow_dispatch close=false,
and ignores 'close=true' unless the new repo variable
AGENT_SHIN_ENABLED is set to 'true'. Scheduled runs are dry-run only
until the team flips that switch.
- Updated unit tests: one new test asserting internal authors are
skipped, and an autouse fixture treats unspecified test PRs as
external so the rest of the suite still exercises the close path.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* fix(workflows): scheduled cron closes PRs; safe --close strip in triage
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(triage): scheduled cron stays dry-run; dedent prompts before interpolation
- close_low_quality_prs.yml: only workflow_dispatch with close=true (and
AGENT_SHIN_ENABLED=true) actually closes PRs. Scheduled runs are always
dry-run, matching the safety invariant documented for triage_pr/issue.
- triage_with_llm.py: textwrap.dedent on an f-string with multi-line
interpolated bodies fails because the body's 2nd+ lines start at column 0,
making the common-indent zero. Dedent the static template first, then
.format() the title/body in.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* Fix bugs in auto-close PR triage scripts
- close_low_quality_prs.py: Treat author_association API lookup failures
as internal (fail-safe) so transient errors don't cause internal
contributors' PRs to be auto-closed.
- triage_with_llm.py: Update summary heading from 'Would post comment:'
to 'Posted comment:' since this branch only runs after the comment
has already been posted.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* feat(triage): default Agent Shin to gpt-5.4-mini with reasoning_effort=none
- Bump DEFAULT_MODEL from gpt-4o-mini to gpt-5.4-mini (more modern;
4M total context window per OpenAI catalog, JSON-schema response
format, function calling all supported).
- For gpt-5.x family models, pass reasoning_effort="none" via
extra_body. gpt-5.x rejects temperature != 1 unless reasoning_effort
is explicitly "none"; setting it lets us keep temperature=0 for
deterministic JSON rubric judgments. extra_body works across openai
SDK versions regardless of whether they natively type the kwarg.
- For non-gpt5 overrides (TRIAGE_MODEL=gpt-4o-mini etc.), reasoning_effort
is not sent.
- 4 new unit tests cover: gpt-5.4-mini -> reasoning_effort=none,
capitalized/dated gpt-5 variants -> reasoning_effort=none,
gpt-4o-mini -> no extra_body, base_url passthrough.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* fix(triage): bugbot — drop dead gh_json and fix --optout-label append-with-default
- Removed the unused gh_json helper (bugbot low-severity dead code).
- Replaced argparse `action="append", default=[...]` with default=None
+ DEFAULT_OPTOUT_LABELS fallback. The mutable-default + append combo
silently APPENDS to the canonical defaults instead of replacing them,
so --optout-label could not actually scope the opt-out list.
- Added tests covering both the canonical default and the
flag-replaces-defaults behavior.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* fix(triage): bugbot — tighten linked-issue regex, fail-safe author_association, fix empty TRIAGE_MODEL
Three independent bugbot findings against triage_with_llm.py:
1. LINKED_ISSUE_PATTERN included weak keywords (`see`, `ref`,
`addresses`) so casual mentions like "See #1234 for context" were
short-circuited to pass-linked-issue without ever calling the LLM —
contradicting the prompt's own "a bare issue number without a closing
keyword counts only if it's clearly the related issue (not a passing
mention)" rubric. Limit the regex to GitHub's documented PR-closing
keywords (fixes/fix/fixed/closes/close/closed/resolves/resolve/resolved).
2. is_internal_contributor() treated an empty/missing author_association
as external (eligible for the destructive close path), while the sibling
is_external_pr_author() in close_low_quality_prs.py fail-safes the same
case as internal. Align the two so a partial/unknown GitHub response can
never make a PR eligible for auto-close.
3. argparse `default=os.environ.get("TRIAGE_MODEL", DEFAULT_MODEL)` returns
the empty string when GitHub Actions exposes an unset repo variable as
an empty-string env var (the optional vars.TRIAGE_MODEL case in the
workflow). Use `os.environ.get(...) or DEFAULT_MODEL` so empty -> default,
matching the existing OPENAI_BASE_URL pattern.
Tests:
- Casual mentions now must fall through to the LLM (parametrized);
added an orchestration test ensuring "See #1234" reaches the judge.
- Empty/missing author_association now fails safe (parametrized).
- Empty TRIAGE_MODEL env var falls back to DEFAULT_MODEL; explicit
TRIAGE_MODEL is still honored.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* fix(workflows): bugbot — gate Agent Shin --close on '= true' not '!= false'
The PR and issue Agent Shin workflows gated the destructive --close
flag with [ "${DISPATCH_CLOSE:-false}" != "false" ]. That pattern
treats anything other than the literal string "false" as enabling
closure — "True", "yes", "1", typos, accidental whitespace, etc.
The workflow_dispatch input UI is a 'true'/'false' choice dropdown so
the form is constrained, but the API (`gh workflow run -f close=...`)
accepts any string, and a CI cron / external invoker passing a
non-canonical truthy value would have silently enabled real
contributor PR closures.
Mirror the sibling Greptile closer's [ "${CLOSE_FLAG}" = "true" ]
pattern: only the EXACT string "true" enables --close; every other
value (including the unset/empty default) resolves to dry-run. This is
the fail-safe philosophy applied everywhere else in this PR.
Added tests/test_litellm/test_github_triage_workflows.py with two
parametrized invariants:
1. The destructive gate uses '= "true"' for its env-var
comparison (either bare '${ENV}' or '${ENV:-false}' form
accepted), and never the fail-open '!= "false"' pattern.
2. Every destructive gate is also gated on AGENT_SHIN_ENABLED being
"true" — either by entering the close branch on '=' or by
bailing out early on '!=' — so flipping the repo variable off is
a true kill switch regardless of per-run inputs.
Manually verified the test fails on the buggy '!= "false"' pattern and
passes on the fix, so it would have caught the regression at PR time.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* feat(triage): close any PR (incl. drafts, any age); add @agent-shin reconsider flow
Follow-up to PR #28117. Three behavior changes + one new workflow,
addressing the team's concerns on the original review:
1) Apply auto-close to ALL open PRs, not just those over a week old.
- close_low_quality_prs.py: --min-age-days default flipped from 7 to
0. The flag is preserved as an opt-in safety net for one-off
backfill runs that want to spare very-young PRs, but the daily
scheduled sweep now closes external-author PRs as soon as Greptile
scores them <4/5.
- close_low_quality_prs.yml: workflow_dispatch input default also
flipped to 0; doc comments updated.
2) Apply auto-close to draft PRs too.
- close_low_quality_prs.py: removed the skip-draft branch in
evaluate_pr. Drafts are NOT a free pass — the team's intent is
'open PR count == PRs internal collaborators need to action on',
so a draft Greptile scored 2/5 still belongs in the closed bucket.
Authors who genuinely need a long-lived draft can attach the 'wip'
opt-out label, which is unchanged.
- The 'skip-draft' action is gone; the 'wip' label still skips.
3) Address the 'OSS contributors cannot reopen a bot-closed PR' wrinkle.
GitHub does NOT let an external (non-write-access) contributor
reopen a PR that was closed by a bot or maintainer (long-standing
limitation). The original PR's close-comments told contributors to
'Reopen the PR — I'll re-evaluate automatically', which is broken
for the very audience this triage targets. Two changes:
a) Reword every close-comment (Greptile sweep + Agent Shin PR
close + Agent Shin issue close + PR template) to recommend:
- Open a new PR with the updated branch (primary path).
- Or comment '@agent-shin reconsider' on the closed PR for a
re-evaluation that, on pass, reopens the PR via the bot's
GH_TOKEN write access.
b) Add the @agent-shin reconsider workflow:
- .github/workflows/triage_reconsider.yml: new
'issue_comment'-triggered workflow. Authorizes only the
PR/issue author or an internal collaborator
(OWNER/MEMBER/COLLABORATOR), gated via a step output so
unauthorized commenters never reach the destructive steps.
Globally gated on AGENT_SHIN_ENABLED='true' (positive form,
matching the test_github_triage_workflows guardrail
patterns).
- triage_with_llm.py: --reconsider mode. On a closed PR/issue,
re-runs the LLM judge (or linked-issue regex short-circuit)
and:
- on pass: reopens via reopen_pr/reopen_issue + posts a
'Re-evaluated and reopened' comment.
- on fail: leaves closed and posts a 'still missing X'
comment so the contributor can iterate again.
Reconsider-on-open is a no-op ('skip-not-closed').
Internal-author + bot-account skips still take priority over
reconsider.
4) Greptile-on-closed-PRs question: the team asked whether Greptile can
re-review a closed PR. Greptile's docs don't address this and we
shouldn't promise behavior we can't verify, so the new close-comment
wording does NOT instruct contributors to 're-request greptile on
the closed PR'. Instead it points them at the new-PR path (which
Greptile definitely reviews) or the @agent-shin reconsider trigger
(which re-runs the LiteLLM-side rubric judge, not Greptile).
Tests: 93 passing (was 59).
- test_github_close_low_quality_prs.py: replaced 'skip drafts' test
with 'closes drafts when score is low' + 'closes brand-new PR when
min_age=0' + 'no skip when min_age=0'. The 'skip too young'
assertion is preserved as opt-in.
- test_github_triage_with_llm.py: 6 new TestTriageOrchestration cases
for reconsider mode (skip-not-closed on open, reopen on pass,
still-failing comment on fail, linked-issue short-circuit reopen,
skip internal author in reconsider, reopen-issue on pass) + a new
TestCloseCommentText class that pins the user-facing 'open a new
PR' + '@agent-shin reconsider' wording.
- test_github_triage_workflows.py: added triage_reconsider.yml to
the destructive-gate guardrail table; AGENT_SHIN_ENABLED is its
own destructive gate (no separate per-run flag needed).
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* test(triage): pin safe behavior for curly braces in PR/issue title+body
Adds regression tests covering the bugbot high-severity finding that
str.format() would crash on user-supplied content containing { or }.
Empirically str.format() does NOT re-parse interpolated values — only
the template literal is scanned for replacement fields — so the bug
does not exist in the current code, but pinning the safe behavior
prevents a future templating change from silently reintroducing it.
Also pins the dedented prompt shape (no leading 8-space indentation on
template lines) so a future change to the build_*_prompt functions can't
silently regress the LLM judge prompt format on multi-line bodies.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* fix(triage): bugbot — reconsider dry-run + bot-closed guard + rate limit
Address three Greptile/veria-ai concerns on the @agent-shin reconsider
flow:
1. **Reconsider had no dry-run path.** The previous reconsider mode
ignored `--close` and always posted comments + reopened on a pass.
A local operator running
`python triage_with_llm.py --reconsider --pr N` would silently
take destructive GitHub actions with no way to preview. Reconsider
now honors `close=False` the same way regular triage does and
returns `would-reopen` / `would-reconsider-still-failing` for
step-summary rendering.
2. **Reconsider could reopen maintainer-closed PRs/issues** (Medium
security finding from veria-ai). The workflow only checked that the
commenter was authorized — it did NOT check that the most recent
close was performed by Agent Shin. A contributor could comment
`@agent-shin reconsider` on a PR a maintainer closed for non-rubric
reasons (duplicate, security report, design rejection) and have the
bot reopen it. Add `was_closed_by_agent_shin()` which inspects the
issue events API for the most recent `closed` actor and only
permits reopen when that actor matches the configured bot login
(default `github-actions[bot]`, overridable via env). Fail-closed
on missing events.
3. **No rate-limiting on the reconsider trigger.** Every
`@agent-shin reconsider` comment burns CI minutes + an OpenAI API
call. Add a 10-minute cooldown via
`seconds_since_last_reconsider_verdict()` which greps the issue's
comment list for the bot's own verdict marker
(`<!-- agent-shin:reconsider-verdict -->`). Inside the window the
triage returns `skip-rate-limited` and the LLM never runs.
Workflow update:
- `triage_reconsider.yml` now passes `--close` only when
`AGENT_SHIN_ENABLED=true`, matching the pattern of
`triage_pr_with_llm.yml`. The script runs in both states so the
verdict still appears in the step summary for QA.
Tests:
- Add 5 reconsider safety tests: dry-run for pass / fail / linked-issue
short-circuit, bot-closed-guard refusal on maintainer close,
rate-limit refusal inside the cooldown window, and cooldown-elapsed
acceptance.
- Add unit tests for `was_closed_by_agent_shin` (bot / maintainer /
missing actor / env-override) and
`seconds_since_last_reconsider_verdict` (no marker / multiple
markers / non-bot comment with marker / bot comment without marker).
- Pin the `<!-- agent-shin:reconsider-verdict -->` marker in both
reopen and still-failing comments — dropping it would silently
break the cooldown.
Existing reconsider tests updated to pass `close=True` (the
production path now) + stub the new guards via
`_stub_reconsider_guards`. 112 tests pass (was 93).
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* feat(triage): 1-day grace period before close + SwiftWinds immediate-close bypass
- Add a 24-hour grace window between the first low-quality detection
and the actual auto-close. The first detection posts a warning
comment that explicitly says "You have 1 day to address this before
this PR is auto-closed" and points the contributor at:
* `@agent-shin reconsider` to request another look (and re-open)
* `@greptileai` to request a fresh Greptile review — works
even after the PR is closed
- Both `triage_with_llm.py` (LLM judge) and `close_low_quality_prs.py`
(Greptile-score closer) share the same `<!-- agent-shin:grace-warning -->`
HTML marker so a warning posted by either path is recognized by both.
- Add IMMEDIATE_CLOSE_LOGINS = {swiftwinds} to bypass BOTH the grace
period AND the dry-run / AGENT_SHIN_ENABLED gating. SwiftWinds is the
user's personal account (no push permissions to litellm) used to
dogfood the bot; user explicitly asked: "For SwiftWinds, just close
immediately. Faster iteration that way."
- Update the standard close comments to mention that `@greptileai`
works even after the PR is closed.
- Add 23 new tests covering: warn-grace on first detection, skip during
grace window, close after grace expires, SwiftWinds bypass (case
insensitive, with close=False, no random-login false positives), the
grace-warning text invariants, and the SwiftWinds entry in the
IMMEDIATE_CLOSE_LOGINS constant.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* fix: skip grace-period text in close comment for IMMEDIATE_CLOSE_LOGINS
For PRs from IMMEDIATE_CLOSE_LOGINS (e.g. swiftwinds), evaluate_pr
returns 'close' immediately without ever posting a grace warning, so
the close comment should not reference a 1-day grace period.
Make close_pr take a grace_period_elapsed flag, default True, and
pass False from the main loop when the close path was the
immediate-close branch.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(close-low-quality-prs): report actual closes in dry-run summary
IMMEDIATE_CLOSE_LOGINS PRs are closed even when the global --close flag is
not set, but the summary used the global dry-run flag to choose between
'would close' and 'closed'. Split the count so operators can see both
actual closures and dry-run would-be closures.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* chore(triage): vendor Agent Shin (#28117) onto demo branch
Brings the Agent Shin OSS-triage scripts, workflows, issue/PR templates, and
tests from PR #28117 onto this branch so the new review-gate feature and its
end-to-end demo are self-contained and runnable in CI.
https://claude.ai/code/session_01XyyWa8t2VYmoGd6mKMEqkZ
* feat(triage): add "ready for review" label lifecycle to Agent Shin
Adds review_gate(), a state machine that keeps a `ready for review` label in
sync with whether an external PR clears BOTH gates — the LLM rubric and
Greptile's most recent confidence score:
- pass (untagged) -> add label + "ready for review" / "all clear" comment
- pass (already tagged) -> no-op (idempotent across re-runs)
- regress (Greptile < 4/5 or QA proof removed) -> remove label + "what's missing"
comment, PR stays open
- recover after a regression -> "all clear again" comment + re-add the label
- fail & untagged, < 24h old -> one-time "what's missing" notice (grace window)
- fail & untagged, > 24h old -> close + comment (reopen via @agent-shin reconsider)
The label itself is the persisted state, so comments fire only on transitions
(never on every scheduled run). All side effects are gated behind --close, so
the dry-run contract matches the existing triage flow. Lifecycle comments use
hidden HTML markers and deliberately avoid the auto-close marker so they never
trip the reconsider provenance check.
Relocates the shared Greptile helpers (extract_greptile_score, SCORE_PATTERN,
GREPTILE_BOT_LOGINS, parse_iso8601) into triage_with_llm.py so the daily sweep
and the review gate read the score through one implementation, and adds the
review_gate.yml workflow (dry-run unless AGENT_SHIN_ENABLED=true) plus 18 unit
tests covering every branch and a full pass->regress->recover cycle.
https://claude.ai/code/session_01XyyWa8t2VYmoGd6mKMEqkZ
* Port review-gate feature from #28758 onto #28147 triage scripts
Adds the "ready for review" label lifecycle (originally PR #28758) on top
of #28147's refactored triage_with_llm.py. The original commit was
authored against an older snapshot of #28117 and could not be applied
cleanly, so the additions were re-applied surgically:
- New constants: READY_FOR_REVIEW_LABEL, DEFAULT_GRACE_DAYS,
DEFAULT_MIN_GREPTILE_SCORE, READY/REGRESSED/WITHIN_GRACE markers,
GREPTILE_BOT_LOGINS, SCORE_PATTERN, AGENT_SHIN_AUTO_CLOSE_MARKER.
- New helpers: add_label, remove_label, extract_greptile_score,
parse_iso8601 (the latter two mirrored from close_low_quality_prs.py
so the daily sweep and the review gate read the score through the
same logic).
- New comment formatters: format_ready_for_review_comment,
format_all_clear_comment, format_regression_comment,
format_within_grace_comment.
- New entry point: review_gate() implementing the pass/regress/recover
state machine, with the label itself acting as persisted state so
transition comments fire only on actual transitions.
- main() learns --review-gate, --grace-days, --min-greptile-score and
dispatches to review_gate() when the flag is set.
Verified via tests/test_litellm/test_github_review_gate.py (18 tests)
and the existing triage suites (144 more) — all 162 pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* agent_shin: extract shared constants/helpers; cover review_gate.yml in guardrail tests
Bug 1: `triage_with_llm.py` and `close_low_quality_prs.py` each defined
their own copies of `extract_greptile_score`, `parse_iso8601`,
`GREPTILE_BOT_LOGINS`, `SCORE_PATTERN`, `GRACE_COMMENT_MARKER`,
`GRACE_PERIOD_SECONDS`, `IMMEDIATE_CLOSE_LOGINS`, and
`AGENT_SHIN_DEFAULT_BOT_LOGIN`. The comments explicitly said the two
copies had to stay in sync, but nothing enforced it. A future change to
one (e.g. extending `SCORE_PATTERN` for a new Greptile output format)
would silently diverge from the other and the daily sweep and the LLM
judge would disagree on which PRs have low scores.
Extract these to `.github/scripts/agent_shin_shared.py` and re-export
them from each script so the existing test attribute access
(`triage_module.GRACE_COMMENT_MARKER`, etc.) keeps working without
any test changes.
Bug 2: `review_gate.yml` is a destructive workflow (close PRs, add/remove
labels, post comments) with the same gating philosophy as the others
(`AGENT_SHIN_ENABLED = "true"` + a per-run `CLOSE_FLAG = "true"`),
but it was missing from `DESTRUCTIVE_GATE_ENV` in the guardrail tests.
Add it so a future regression (e.g. flipping to `!= "false"`) is
caught by the same parameterized invariants as every other workflow.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* agent_shin: fix bug bundle (gated LLM key, author-filtered marker dedup, dedup gh/grace helpers)
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* agent_shin: fix review_gate close-after-regression and case-insensitive label match
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* feat(triage): add one-shot 7-day heads-up sweep for Agent Shin rollout
Adds a rollout-day workflow that comments on every open external PR/issue
that the new triage bot WOULD auto-close, giving contributors 7 days to
fix their description before any destructive action runs.
Why now: merging this PR enables Agent Shin in dry-run. The follow-up
"enact" PR (next Monday) flips the destructive paths on. Without this
heads-up, contributors would get a close-comment on day 8 with no prior
warning. The heads-up names the cutoff date, lists the rubric, calls out
each PR/issue's specific missing pieces, and explains the recovery paths
(@agent-shin reconsider for PRs, edit + reopen for issues).
Files
- .github/scripts/_agent_shin_actions.py — thin maybe_post_comment /
maybe_close_* / maybe_add_label / etc. wrappers. Each is a single
`if dry_run: log; return; else: call_through()` so a dry-run preview
differs from the real run in exactly one call site per mutation. The
call-through goes via `triage_with_llm.<name>` (module-qualified) so
monkeypatching the underlying function in tests is reflected here.
- .github/scripts/triage_rollout_heads_up.py — the sweep. Iterates every
open PR + issue via `gh pr list` / `gh issue list`, runs the future
rubric (review_gate for PRs, triage(kind="issue") for issues), and
posts the heads-up on any item that would be auto-closed. Idempotent
via a `<!-- agent-shin:rollout-heads-up -->` marker. Defaults to dry-
run; --close opts in to real posts. --close-on overrides the cutoff
date (defaults to today + 7 days).
- .github/workflows/triage_rollout_heads_up.yml — one-shot workflow.
Triggers on push to litellm_internal_staging filtered to the script
path (fires on rollout merge) plus workflow_dispatch with a dry_run
input that defaults to "true" for safe manual re-runs.
- tests/test_litellm/test_triage_rollout_heads_up.py — 28 unit tests
covering: the dry-run wrappers (each maybe_* gates correctly), the
_would_be_closed predicate for PR vs. issue results, the comment
formatter (cutoff/rubric/marker/recovery wording), per-item dispatch
(skip-not-open, skip-internal-author, skip-already-notified,
skip-passing, would-post/posted), and the sweep loop end-to-end.
Local preview (no GitHub mutations):
python3 .github/scripts/triage_rollout_heads_up.py --repo BerriAI/litellm
Real run (what the workflow does):
python3 .github/scripts/triage_rollout_heads_up.py --repo BerriAI/litellm --close
TODO: replace the placeholder ROLLOUT_BLOG_URL with the canonical
docs URL once the litellm-docs PR ships.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* fix: gate reconsider workflow OPENAI_API_KEY + remove dead actions wrappers
- Mirror sibling Agent Shin workflows by only exposing OPENAI_API_KEY in
triage_reconsider.yml when vars.AGENT_SHIN_ENABLED == 'true'. Previously
the secret was unconditionally exposed, so any PR/issue author could
trigger paid LLM calls by commenting '@agent-shin reconsider' even while
the bot was supposed to be in dry-run.
- Remove the six unused dry-run wrappers (maybe_close_pr, maybe_close_issue,
maybe_reopen_pr, maybe_reopen_issue, maybe_add_label, maybe_remove_label)
from _agent_shin_actions.py — only maybe_post_comment is used by rollout
scripts. Drop the associated tests that exercised the now-removed
functions.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix: address triage script edge cases
- triage_rollout_heads_up.py: replace %-d strftime specifier (GNU-only)
with portable day formatting so the script doesn't crash on Windows.
- close_low_quality_prs.py: skip malformed JSON lines in fetch_pr_comments
instead of letting one bad line abort the daily sweep, matching the
pattern in triage_with_llm._iter_paginated_json.
- triage_with_llm.py: move has_linked_issue short-circuit before
build_pr_prompt to avoid unnecessary prompt construction on PRs that
link an issue.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(scripts): per-PR error isolation and limit grace warnings in close_low_quality_prs
- Wrap per-PR processing in try/except so a transient GitHub API failure
on one PR no longer aborts the entire daily sweep (mirrors the pattern
already used in triage_rollout_heads_up.py).
- Have --limit bound *all* destructive write actions (closures and grace
warnings combined), not just closures. Prevents a backlog of newly
failing PRs from flooding contributors with comments in a single run.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(agent-shin): remove 1000-PR cap on bulk sweeps; sweep entire backlog
Both bulk-sweep scripts hardcoded `gh {pr,issue} list --limit 1000`, and gh
lists newest-first — so the OLDEST ~900 PRs and ~380 issues were silently
dropped. That's exactly the stale backlog the daily closer and one-shot
rollout heads-up exist to catch.
Extract a single `list_open_items(kind, *, repo, fields)` helper into
`agent_shin_shared.py` with `GH_LIST_ALL_LIMIT = 100_000` — a ceiling far
above any realistic open backlog so gh paginates until the queue is
exhausted. `fetch_open_prs` and `_list_open_numbers` both delegate to it,
so the limit lives in exactly one place going forward.
Verified live against BerriAI/litellm:
- `fetch_open_prs` -> 1981 PRs (was 1000)
- `_list_open_numbers(issue)` -> 1382 issues (was 1000)
- `_list_open_numbers(pr)` -> 1981 PRs (was 1000)
Adds 7 regression tests asserting the new limit is passed, the dedicated
`gh {pr,issue} list` command + fields are used per kind, bad kind raises
ValueError, and both callers delegate to the shared helper.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(agent-shin): require non-mocked end-to-end QA proof for PR pass
The PR rubric previously passed any PR with a linked issue, regardless
of whether it showed the fix actually working. Sample spot-check found
21/25 recent external PRs passing, including ones that linked an issue
but provided zero QA evidence.
Tighten the rubric so a pass now requires BOTH:
(1) CONTEXT — a linked issue OR a clear problem description with
expected-vs-actual behavior.
(2) END-TO-END QA PROOF — at least one of:
(a) screenshot(s) of the fix working,
(b) screen recording / video,
(c) specific commands actually run, paired with their real
output, against the real system.
Mocked unit tests, generic 'I tested it' claims, 'all tests pass'
without output, and the linked issue itself are explicitly excluded
from QA proof.
Also add 'qa_proof_type' to the JSON schema so the per-PR report
surfaces which kind of proof (or 'none') the judge saw.
Re-sample on the same 25 recent external PRs shifts the verdict
distribution from 21 pass / 4 fail to 4 pass / 21 fail, with zero
prior-fails now passing — the stricter rule catches PRs that ship
only with unit-test claims and no real integration evidence.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(agent-shin): link blog explainer from every action-required bot comment
Adds "What's this and why am I getting it?" links to docs.litellm.ai/blog/
agent-shin-triage from the four comments contributors actually read when
something went wrong: PR close, PR grace warning, issue close, issue grace
warning. PR comments also link the rubric section directly from the
QA-proof bullet so contributors can self-serve "what counts as proof"
without pinging a maintainer.
Pins the new guarantees in tests: blog link must appear in all four
comments, and the PR close comment must continue to flag mocked-dependency
unit tests as insufficient proof.
The linked blog post is in BerriAI/litellm-docs PR #240; the URL will 404
until that lands.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(review_gate): raise sweep limit from 1000 to 100000 to match GH_LIST_ALL_LIMIT
gh lists newest-first, so capping at 1000 silently drops the oldest open
PRs — exactly the stale ones the daily sweep is meant to reconcile. Use
the same ceiling as agent_shin_shared.GH_LIST_ALL_LIMIT so the workflow
sees the entire backlog.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* Fix three Agent Shin triage edge cases
- review_gate: expire the regression-marker short-circuit after grace_days
so PRs that were regressed and then abandoned can eventually be closed.
- review_gate: when the rubric short-circuits to pass via the linked-issue
regex but Greptile drags the PR below the bar, replace the synthetic
'LLM was not called' explanation with the real Greptile shortfall so
regression / close comments are not misleading.
- triage_rollout_heads_up._comments_have_marker: drop the unused 'kind'
parameter and filter by bot author so a contributor quoting the
heads-up via 'Quote reply' cannot trick the idempotency check, matching
the pattern in triage_with_llm._has_marker.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix: pass min_greptile_score through to ready-for-review comment text
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* feat(agent-shin): warmer triage comments — bullet-train emoji, 'what you got right' section, softer 'park this for later' framing
User feedback on the auto-triage comments contributors will see:
1. Tone — the previous 'You have 1 day to address this before this PR is
auto-closed' framing reads as an ultimatum. Replace with: 'If the
description isn't updated in the next 1 day, I'll auto-close this PR.
That's not us saying we don't care about the change — we want the
open-PR list to mirror what a maintainer can act on right now, so
contributors don't get lost in a backlog. A closed PR is a soft "park
this for later," not a rejection. Take your time.'
2. Positive feedback — the previous comments only listed what was missing.
Now every close + grace-warning comment opens with a 'What you got
right:' section rendered from the judge's per-field flags. Contributors
see a checkmark for everything they got right (linked issue, problem
description, expected/actual, QA proof for PRs; runnable repro,
screenshot/log, expected/actual, motivation+example for issues) before
the gaps. The block is omitted entirely when nothing is present so
we never render 'What you got right: (nothing).'
3. Reconsider trigger — the previous grace warning told contributors to
comment '@agent-shin reconsider' during the grace window. They don't
need to — the bot re-checks on every sweep. The new copy says 'just
update the description, no need to ping me' for the grace path, and
reserves '@agent-shin reconsider' for the post-close recovery path.
4. Bullet-train emoji — replace 👋 with 🚄 (Shinkansen, the symbol of
Agent Shin) across every action-required comment: PR close, PR grace
warning, issue close, issue grace warning, within-grace, Greptile-
closer grace warning, rollout heads-up. Pinned in tests so a future
refactor can't silently revert.
5. Greptile-post-close — the @greptileai bullet now explicitly says 'a
low Greptile score isn't a blocker either,' since the previous copy
buried the fact that @greptileai works after auto-close.
Comment templates updated: format_pr_close_comment,
format_issue_close_comment, format_grace_warning_pr_comment,
format_grace_warning_issue_comment, format_within_grace_comment
(triage_with_llm.py); format_grace_warning_comment
(close_low_quality_prs.py); format_heads_up_comment header
(triage_rollout_heads_up.py).
New helpers: _format_present_for_pr / _format_present_for_issue /
_format_present_block, driven off the existing per-field flags the
LLM judge already emits — no prompt change needed.
New tests pin: bullet-train emoji in every action-required comment;
'What you got right' appears with ✅ bullets when fields are present;
the block is omitted when no fields are present; 'park this for
later' / 'not a rejection' softer framing; grace warnings tell the
contributor 'no need to ping' during the grace window (reconsider is
the post-close path only).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(agent-shin): gate triage on a dogfood allowlist
Add ALLOWLIST_LOGINS to agent_shin_shared so Agent Shin only acts on the
named accounts while the set is non-empty. mateo-berri and SwiftWinds are
allowlisted for the dogfood rollout; everyone else is skipped with
skip-not-allowlisted across all four entrypoints (triage, review gate, the
daily low-quality sweep, and the rollout heads-up).
For an allowlisted author the usual internal/external classification is
bypassed, so a maintainer's own org account still gets triaged during
testing. Emptying the set lifts the restriction and restores full triage
for the public rollout. The gate is dependency-injected via an `allowlist`
parameter defaulting to the constant, so the internal/external-skip paths
stay testable.
* feat(agent-shin): tighten QA-proof and issue rubrics, ack reconsider with reactions
Reorder the end-to-end QA proof options to video, then screenshots, then
exact commands with their real output across the PR template, the LLM judge
prompts, and every contributor-facing comment, and spell out that mocked or
stubbed runs (including pytest on the repo's own unit tests, which mock the
provider, DB, and network) never count as proof. QA proof is now required of
all contributors, not just external ones.
Tighten the issue bug-report rubric to require end-to-end evidence of the bug
(the "before" half: a video, screenshot, or command paired with real output)
plus expected vs. actual behavior, drop the bias toward PASS, and collapse the
separate has_repro/has_proof flags into a single has_repro signal.
Standardize the bullet-train emoji and strip em dashes from the bot's
public-facing messages, and route issue recovery through @agent-shin
reconsider since GitHub doesn't let OSS authors reopen an issue a bot closed.
Acknowledge an @agent-shin reconsider the moment it's accepted with an eyes
reaction and a thumbs-up once the run finishes, both gated on
AGENT_SHIN_ENABLED so dry-run leaves no trace.
* fix(agent-shin): shorten auto-close grace to 2 hours and drop the instant-close bypass
Two dogfooding changes to the Agent Shin grace window. First, the warn-then-close
grace (GRACE_PERIOD_SECONDS) drops from a day to 2 hours so the "fix it before it
closes" loop can be exercised in one sitting; the constant carries a note to bump
it back up for the public rollout.
Second, remove IMMEDIATE_CLOSE_LOGINS entirely. SwiftWinds (the external dogfood
account) used to skip the grace window and close on first detection, which also
meant closing real PRs even during a scheduled dry run because the per-PR
override flipped dry_run off. It now follows the same warn-then-close path as
every other author, so a low-quality PR is warned first and only closed once the
2-hour window elapses. This also closes the Greptile finding that the sweep could
mutate real PRs while AGENT_SHIN_ENABLED was still off.
The review gate's separate age-based grace (DEFAULT_GRACE_DAYS) is left unchanged.
Regression tests pin that SwiftWinds now warns-grace instead of closing instantly,
and that a dry-run sweep over a closeable PR reports "would close" without making
any GitHub mutation.
* fix(agent-shin): gate reconsider reopen on an Agent Shin close marker
was_closed_by_agent_shin only checked that the most recent close actor was
the bot identity. That identity defaults to github-actions[bot], which is
shared by every workflow in the repo (stale/duplicate sweeps included), so a
contributor could @agent-shin reconsider an item another workflow closed and,
if the description passed the rubric, get it reopened even though Agent Shin
was never the closer.
Require a second, Agent-Shin-specific signal alongside the actor check: an
auto-close comment stamped with a hidden AGENT_SHIN_CLOSE_MARKER. Both close
paths (the grace-period close and the review-gate close) flow through
format_pr_close_comment / format_issue_close_comment, so stamping the marker
there covers every real close while leaving the grace warnings unmarked. The
guard stays fail-closed: no marker, no reopen.
This also replaces the unused AGENT_SHIN_AUTO_CLOSE_MARKER constant (a visible
phrase the guard never consulted) with the hidden marker the guard now relies
on.
* fix(agent-shin): stamp close marker on sweep closes and disclose regression deadline
The daily Greptile sweep's close comment advertised `@agent-shin reconsider`
but never stamped AGENT_SHIN_CLOSE_MARKER, so the reconsider reopen guard
(was_closed_by_agent_shin), which now also requires that marker, silently
rejected every sweep-closed PR with `skip-not-bot-closed`. Move the marker into
agent_shin_shared so both close paths share one source of truth, extract
format_close_comment so the sweep close comment is unit-testable, and stamp the
marker there.
Also disclose the grace_days deadline in the review-gate regression comment; it
promised "the PR stays open" without mentioning that a still-failing PR is
auto-closed grace_days after the notice, which would surprise contributors with
a close they were never warned about.
* fix(triage): tighten Agent Shin reconsider reopen guards
The bot-closed guard accepted any historical Agent Shin marker comment
on the thread as proof that Agent Shin owned the latest close, so a
post-reopen close by another workflow under the shared
`github-actions[bot]` identity could still satisfy the gate and let
`@agent-shin reconsider` reopen a PR that Agent Shin did not close
this cycle. `fetch_last_close_event` now also returns the latest
`closed` event timestamp, and `was_closed_by_agent_shin` requires
the most recent Agent Shin marker comment to sit at (or just before)
that timestamp, with a small skew window for clock drift between the
events and comments APIs.
In the same path the LLM verdict check used `decision != "fail"` to
choose the reopen branch, which treated a missing, empty, or typo
verdict as a pass. Reopen is destructive, so the check now requires an
explicit `decision == "pass"` and ambiguous verdicts fall through
to the "still failing" branch instead.
* style(agent-shin): black-format reconsider guard hardening
* docs(agent-shin): scope dry-run wrapper docstring to the single existing helper
The module docstring claimed it wrapped every Agent Shin mutation and
referenced post_comment/close_pr/etc., but only maybe_post_comment exists.
Describe the single helper accurately while keeping the dry-run pattern
guidance for any future wrapper.
* chore(agent-shin): defer issue/PR template changes to the rollout PR
The triage and review-gate automation is gated to the allowlisted authors
(mateo-berri, SwiftWinds) and AGENT_SHIN_ENABLED, so during this rollout it
only acts on internal PRs/issues. The issue and PR templates have no such
gate; they change for every contributor on merge and advertise that an LLM
bot auto-closes external submissions, which won't happen while the allowlist
is the sole author gate. Revert bug_report.yml, feature_request.yml, and
pull_request_template.md to base so the public-facing messaging lands with
the rollout flip instead of ahead of it. The scripts embed their own rubric
and never read these files, so triage behavior is unchanged.
* ci(agent-shin): hash-pin the openai install in privileged triage workflows
The triage workflows install the OpenAI client with `pip install
"openai>=1.40.0"`, a floating lower bound that resolves openai and its
whole transitive tree to whatever PyPI serves at run time. These jobs run
under pull_request_target with a write-scoped GITHUB_TOKEN, and the
install plus the triage run happen on every PR open regardless of the
AGENT_SHIN_ENABLED dry-run gate (that gate only withholds the LLM key and
the destructive --close path), so a compromised release would execute
during install or import while the token is in scope.
Install instead from a new .github/scripts/triage-requirements.txt that
pins openai==2.33.0 and every transitive dependency to an exact version
with sha256 hashes, via pip --require-hashes. The workflows already
sparse-checkout .github/scripts from the base repo (never fork code), so
the pinned file is trusted. Add static guardrails to
test_github_triage_workflows.py that fail if any installer workflow
reverts to a floating openai install or if the requirements file loses
its exact pins or hashes.
* ci(agent-shin): gate rollout heads-up real run behind manual dispatch
The rollout heads-up workflow fired its real `--close` sweep on every push
to litellm_internal_staging that touched the script, and exposed
OPENAI_API_KEY unconditionally, unlike every sibling triage workflow which
only exposes the key on an enabled or dispatched run. That made merging the
script post real heads-up comments (bounded only by the dogfood allowlist),
which contradicts the inert-by-default safety invariant; once the allowlist
is cleared for the public rollout, any later edit to the file would sweep
the whole open backlog with real writes.
The heads-up cannot be gated on AGENT_SHIN_ENABLED: its whole job is to warn
contributors before that flag flips on, so it has to run while the flag is
still off. Instead the automatic push trigger now stays dry-run, and the
real one-shot sweep is a deliberate manual workflow_dispatch with
dry_run=false, the sole path that adds `--close`. OPENAI_API_KEY is exposed
only on that dispatch, matching the sibling workflows.
Add static guardrails that fail if the push path regains a `--close`, if the
dispatch gate stops fail-closing on the exact string "false", or if the key
is exposed unconditionally again.
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Mateo <mateo@Mateos-MacBook-Pro.local>
|
||
|
yuneng-jiang
|
e122dac0db
|
fix(cost): stop non-string response service_tier from dropping cost tracking (#30706)
completion_cost extracted service_tier from the response object and the usage object without an isinstance guard, so a non-string value (e.g. a dict) flowed straight into _get_service_tier_cost_key and raised AttributeError on service_tier.lower(). completion_cost re-raises, so the request's cost was lost. PR #30690 fixed only the request-level optional_params path. This extends the same guard to the response and usage paths by normalizing each extracted value: a non-string tier (and the routing-only "auto" sentinel) is not billable, so it coerces to None and pricing defers to the next concrete tier the provider served, falling back to standard pricing when none is present. Adds two regression tests driving a dict service_tier through completion_cost, one on the response object (defers to the served usage tier) and one on the usage object (prices at standard); both raise AttributeError before the fix. |
||
|
ryan-crabbe-berri
|
e568d8bffb
|
refactor(ui): remove orphaned pass-through-settings route (#30692)
The `page == "pass-through-settings"` arm in (dashboard)/page.tsx is unreachable: it isn't a sidebar item and nothing in the app sets ?page=pass-through-settings. The Pass-Through Endpoints UI lives as a tab inside the Models + Endpoints view (ModelsAndEndpointsView renders PassThroughSettings), so the standalone switch arm is dead code. Remove it, its now-unused import, and the matching enum member in the e2e pages fixture. |
||
|
ryan-crabbe-berri
|
d97b17b161
|
feat(ui): migrate models page to App Router path route (#30677)
* feat(ui): migrate models page to App Router path route
Cut the Models + Endpoints page over from the legacy ?page=models switch
in (dashboard)/page.tsx to a path route at (dashboard)/models-and-endpoints.
Adding the MIGRATED_PAGES entry repoints the sidebar link and redirects old
?page=models bookmarks to /ui/models-and-endpoints.
ModelsAndEndpointsView already sourced identity from useAuthorized() and its
own data via useModelsInfo(), so the token/keys/modelData/setModelData props
were dead; drop them from ModelDashboardProps (and the parent's now-unused
setModelData state) to sever the last of the shared-state coupling.
* test(ui): scope migration smoke's shell probe to the exact sidebar link
The migration smoke used a loose `locator("a", { hasText: "Virtual Keys" })`
to assert the dashboard shell rendered. The Models + Endpoints page content
itself links to the "Virtual Keys page", so on that route the substring filter
matched two anchors and tripped Playwright strict mode. Match the sidebar link
by its exact accessible name instead, which resolves to just the nav item.
|
||
|
Mateo Wang
|
9b1c1e9894
|
ci(windows): pin uv to Python 3.11 so it ignores the preinstalled 3.14 (#30704) | ||
|
Mateo Wang
|
556e8f89c8
|
ci: run a local fake OpenAI endpoint instead of the shared Railway mock (#30695)
Several CI jobs run the proxy against a model whose api_base is a shared "fake OpenAI endpoint" hosted on Railway (exampleopenaiendpoint-production.up.railway.app) so the E2E runs return canned responses without paying for or depending on a live provider. When that single deployment is down, every one of those jobs fails with "404 Application not found" even though nothing in the PR is broken; the whole repo is coupled to the uptime of one free external service. This adds tests/_fake_openai_endpoint_server.py, a small canned-response OpenAI-shaped server (chat, text, embeddings, streaming with usage, and the "429" rate-limit special case), and a reusable start_fake_openai_endpoint CircleCI command that runs it on host port 8190 and waits until healthy. The affected jobs now inject FAKE_OPENAI_API_BASE pointing at the local server, and the example configs they mount resolve api_base from that env var. The intentionally bad fallback URL in proxy_server_config.yaml is left untouched so the fallback test still exercises a failing upstream. Wired into build_and_test, litellm_router_testing, db_migration_disable_update_check, proxy_logging_guardrails_model_info_tests, proxy_spend_accuracy_tests, proxy_multi_instance_tests, proxy_store_model_in_db_tests, and proxy_build_from_pip_tests. |
||
|
Yassin Kortam
|
187b205b34
|
fix(pod_lock): release cron lock by matching async_set_cache JSON encoding (#30600)
acquire_lock stores the pod_id through async_set_cache, which JSON-encodes the value, so Redis holds the quoted string "<pod_id>". release_lock's Lua compare-and-delete compared the raw pod_id, so the equality check never matched and the lock was never deleted; it only cleared on TTL expiry. That stalled the spend-update drain whenever the leader pod restarted, letting the litellm_daily_*_spend_update_buffer lists grow unbounded in Redis. Compare against json.dumps(self.pod_id) so the release matches the stored value. The GET+DEL fallback already round-trips through async_get_cache and is unaffected. Co-authored-by: Claude <noreply@anthropic.com> |
||
|
tin-berri
|
ba29657d09
|
feat(proxy): warn at startup when custom_auth skips common_checks enforcement (#30665)
When general_settings.custom_auth is configured but custom_auth_run_common_checks is not set, project/team/org enforcement (budgets, model-level rate limits, and model-access lists) silently does nothing for custom-auth requests, since the centralized common_checks gate returns early for custom auth. Emit a startup warning pointing operators at the flag so the misconfiguration is visible instead of failing silently. |
||
|
Mateo Wang
|
43dadc5138
|
fix(cost): stop non-string service_tier from silently dropping cost tracking (#30690)
completion_cost read service_tier straight from the request optional_params and called service_tier.lower() on it, so a non-string value (dict/int/list, reachable via allowed_openai_params/drop_params) raised AttributeError. _response_cost_calculator swallowed that and returned response_cost=None, so the request's cost was silently lost. The isinstance guard alone is not enough: a surviving dict would crash again downstream in _get_service_tier_cost_key, which also calls .lower(). A request-level service_tier is only meaningful for pricing when it is a concrete billable tier string, so coerce any non-string value to None and defer to the tier the provider reports on the response usage, the same way "auto" already does. Adds a regression test driving a dict service_tier through completion_cost; it raises AttributeError before the fix and prices at the served tier after. |
||
|
Mateo Wang
|
4ccc32312d
|
test(pass_through): harden vertex spendlog poll against transient empty reads (#30683)
test_basic_vertex_ai_pass_through_with_spendlog failed intermittently on litellm_internal_staging (pipelines 82155, 82196, 82209, 82230) with "Spend should be greater than before after 120s". Spend logging is async and batched, so the pass-through call's cost sometimes had not landed within the 120s poll window; one run ended on spend_after 0.0 because the final /global/spend/logs read returned nothing and "or 0.0" recorded that as zero spend. Widen the poll window to 240s and skip a transient empty read instead of treating it as 0.0, so a momentary endpoint hiccup on the last poll no longer fails an otherwise-billed call. The spend_after > spend_before assertion is unchanged, so a genuinely unbilled call still fails the test |
||
|
Mateo Wang
|
654e354ebd
|
test: harden remaining pass-through CI flakes (image-gen spend poll, ruby assistants timeout) (#30685)
* test(proxy): poll for image-gen spend instead of a fixed 5s sleep test_key_info_spend_values_image_generation failed once on litellm_internal_staging (pipeline 82282) with "spend did not increase on an identical repeat image call" (assert 0.24966 > 0.24966). The test made the second image call, slept 5s, then read the key's spend once. Response caching is commented out in proxy_server_config.yaml and no sibling test enables it, so the likely cause is async/batched spend logging not having flushed the repeat call's cost within 5s, which the build_and_test job aggravates by running every tests/test_*.py against one shared proxy under pytest -n 4. Poll the key's spend for up to 60s and break as soon as it grows. This removes the timing flake while preserving the canary: if the repeat were genuinely unbilled (for example the proxy response cache being on), spend never grows, the poll times out, and the assertion still fails. * test(pass_through): raise ruby assistants client request_timeout to 600s The streaming assistants example in openai_assistants_passthrough_spec.rb hit Net::ReadTimeout on litellm_internal_staging (pipeline 82280), failing at roughly 125s which is ruby-openai's default request_timeout of 120s. An assistants run with the code_interpreter tool can occasionally take longer than that to stream its first content back through the pass-through. Raise the client's request_timeout to 600s, matching the 600s timeout the Python pass-through e2e tests already use, so a slow-but-healthy streaming run no longer trips the default read timeout. |
||
|
Mateo Wang
|
c51ba34294
|
fix(health): correct bedrock embedding health checks (#30583)
* fix(health): correct bedrock embedding health checks
Health checks for Bedrock embedding deployments failed in two ways. A
deployment configured without an explicit model_info.mode was probed as
chat, so max_tokens was injected and Bedrock embeddings rejected it with
400 "extraneous key [max_tokens]". Separately, stripping the bedrock/
routing prefix dropped the provider, so a cross-region inference-profile
id like us.cohere.embed-v4:0 failed downstream with "LLM Provider NOT
provided".
Resolve the deployment mode from the model cost map (which understands
the bedrock/ and us./eu./apac. prefixes) before deciding whether to
inject max_tokens, and pin custom_llm_provider to bedrock when stripping
the prefix so the bare model id still resolves. ahealth_check now accepts
any string mode so the resolved embedding mode routes the probe to the
embedding handler.
* fix(health): preserve explicit custom_llm_provider on bedrock probe
The bedrock prefix-strip pinned custom_llm_provider to bedrock
unconditionally, so a deployment that set custom_llm_provider:
bedrock_converse had it overwritten at health-check time and the probe
hit the Invoke endpoint instead of Converse, a different request format
that can report a spurious failure. Only fill in bedrock when the
deployment left the provider blank, which still resolves bare
cross-region ids like us.cohere.embed-v4:0 while leaving an explicit
provider untouched.
* test(health): assert resolved mode reaches the ahealth_check probe
The existing tests check _resolve_health_check_mode and the params builder
in isolation, but nothing verified that _run_model_health_check actually
threads the resolved mode into litellm.ahealth_check. Without that, a
refactor that probed with model_info.get("mode") again would reintroduce
the chat fallback for embedding deployments while every test stayed green.
This drives _run_model_health_check with a bedrock embedding deployment and
asserts the probe is called with mode=embedding and the embedding params.
* fix(health): resolve probe mode once for reasoning_effort and audio_speech
The reasoning_effort and audio_speech branches read model_info.mode
directly, so an embedding deployment declared without an explicit mode (the
case this PR targets) was still treated as chat-like: a configured
health_check_reasoning_effort got injected into the embedding probe, which
embeddings reject as an unknown field, and an auto-detected audio_speech
deployment never had its voice set. Resolve the effective mode once from the
cost map and reuse it for the max_tokens, reasoning_effort, and audio_speech
decisions so they all agree with the mode threaded into ahealth_check.
|
||
|
Yassin Kortam
|
39ab43c10a
|
feat(proxy): add --max_requests_before_restart_jitter to stagger worker restarts (#30601)
Setting --max_requests_before_restart alone recycles every worker at almost the same time once they have served a similar number of requests, which under sustained load can drop a whole pod's capacity at once roughly every 7-10 days. This exposes a jitter knob that adds a random amount in [0, jitter] to the restart threshold per worker so restarts are staggered. It maps to uvicorn's limit_max_requests_jitter and gunicorn's max_requests_jitter. uvicorn only gained limit_max_requests_jitter in 0.41.0 while litellm still allows uvicorn>=0.33.0, so the uvicorn path feature-detects the parameter via the Config signature and warns instead of crashing on older versions. The flag has no effect without --max_requests_before_restart, so the kwarg is not forwarded in that case and a warning is printed on both the uvicorn and gunicorn paths. Resolves LIT-3774 |
||
|
Shivam Rawat
|
6c8b60d50d
|
fix(proxy): resolve list files credentials from team BYOK deployments (#30495)
* fix(proxy): resolve list files credentials from team BYOK deployments GET /v1/files without target_model_names now prefers the team's own deployment (model_info.team_id) over shared global provider keys, so JWT team auth lists files against the correct upstream account. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(proxy): scope list files credential lookup to team allowlist Remove the unrestricted deployment scan that could leak global provider keys to teams without access, normalize all-proxy-models to the team-scoped model list, and fix TID251 violations by using dict instead of Dict/Any. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com> |
||
|
Yassin Kortam
|
78a7d0b210
|
feat(guardrails): surface OpenAI moderation violation_categories on guardrail traces (#30659)
The OpenAI moderation guardrail (and the ai-platform-moderation guardrail built on it) stamped the whole moderation model response into the guardrail trace as guardrail_response. That blob carries the full category_scores map plus categories and category_applied_input_types, which on OTEL backends that index span attributes (for example ELK, which caps indexed attribute values at 1024 chars) overflows the limit and gets truncated, so the violated categories cannot be reliably searched. Extract the flagged category names from the moderation response and pass them through tracing_detail to add_standard_logging_guardrail_information_to_request_data, mirroring the Bedrock hook. Both the legacy and v2 OTEL integrations already read violation_categories off the standard logging guardrail information and emit it as a short, queryable guardrail_violation_categories attribute, so dashboards can group and filter by violation category without parsing the large guardrail_response blob. Resolves LIT-3801 |
||
|
Mateo Wang
|
b8d79d1e0c
|
ci: drop mypy entirely, standardize type checking on basedpyright (#30648)
* ci: drop redundant mypy type-check gate, standardize on basedpyright Type checking ran both mypy (via the pydantic.mypy plugin) and basedpyright. pydantic v2 emits dataclass_transform, so basedpyright understands models natively with no plugin, and its gated rules already cover what the mypy pass caught (no-untyped-def, no-any-return, valid-type, import-not-found all map to basedpyright equivalents). Running both meant two checkers, two budgets, and a plugin only mypy could load. This removes the mypy type-check gate: the lint-mypy/lint-mypy-budget-update Makefile targets, the CI MyPy step, mypy-code-budget.json, the budget-ratchet entry, and the vestigial [tool.mypy] pydantic plugin block (the gating pass used litellm/mypy.ini, which never loaded the plugin). type_check_gate.py is specialized to basedpyright since the mypy parsing path is now unused. mypy stays a dev dependency because the Any-discipline gate (scripts/check_any_discipline.py) imports it as a library to detect Any-typed values; it is no longer run as a type checker. * ci: remove the Any-discipline gate, rely on basedpyright's reportAny The Any-discipline gate (scripts/check_any_discipline.py) was the last consumer of mypy: it imported mypy as a library to detect values whose inferred type contains Any, gated per-file against any-discipline-budget.json. basedpyright already reports the same class of finding through reportAny/reportExplicitAny, which are gated tree-wide in basedpyright-code-budget.json, so the separate gate (and the mypy dependency behind it) is redundant. Removes the gate end to end: check_any_discipline.py and its test, the any-discipline CI job, the lint-any/lint-any-budget-update Makefile targets, any-discipline-budget.json, litellm/mypy.ini, the .mypy_cache_any references, and mypy from the dev dependencies. budget_ratchet_check.py drops the any-discipline entry and the now-unused zero-floor mechanism (rewritten as a comprehension). check_type_discipline.py drops the any-ok suppression token, since # any-ok suppressed only the deleted gate; the 134 now-orphaned # any-ok comments across 14 files are stripped (they never affected basedpyright, which uses # pyright: ignore). uv.lock is intentionally left untouched: uv still considers it consistent with the mypy-removed pyproject (uv lock --check and uv sync --frozen both pass), and a relock bumps 30+ unrelated packages because of the moving exclude-newer window. A future intentional relock will prune the now-unreferenced mypy entry. * build: relock to drop mypy from uv.lock CI's uv 0.10.9 honors the repo's exclude-newer window and correctly flags the lockfile as out of sync once mypy leaves pyproject; my earlier local uv 0.8.17 could not parse exclude-newer and silently passed --check. Relocking with the pinned CI version removes only mypy and its transitive librt, with no other version changes. |
||
|
ishaan-berri
|
60f4c01b74
|
fix(proxy): list public team model name in /v1/models (#30588)
* fix(proxy): optionally surface public team model name in /v1/models
Behind general_settings.use_team_public_model_name (default False). When
enabled, /v1/models and /models surface the public team_public_model_name
for team-scoped (BYOK) models instead of the internal routing key
model_name_{team_id}_{uuid} -- consistent with /v1/model/info and
OpenAI-compatible. Off by default so the listing's model ids stay
backward-compatible for callers that scripted against the internal name;
routing by the internal name is unchanged regardless of the flag.
Presentation-layer only: access-group, auth, and routing semantics are
unchanged; non-team models are pass-through.
* fix(proxy): default team model listings to public names
* test(proxy): cover team model listing metadata
* test(proxy): cover empty team listing deployments
* refactor(proxy): simplify team model listing translation
* fix(proxy): resolve public team model name on GET /v1/models/{id}
The listing endpoints advertise team_public_model_name, but the retrieve
endpoint validated and looked up by the raw id, so a public name 404'd.
Resolve the public name back to the internal routing key (scoped to the
caller's accessible models so colliding names never cross teams), look up
by it, and echo the public name back as the response id.
* test(proxy): cover public-name resolution on model retrieve
* refactor(proxy): extract team model-name translation into TeamModelNameTranslator
Move the team-scoped (BYOK) listing/retrieve name translation out of
proxy_server.py into a dedicated common_utils module. Static methods with
general_settings injected so the logic is unit-testable without globals and
proxy_server.py stays thin.
* refactor(proxy): use TeamModelNameTranslator in model_list and model_info
* test(proxy): target TeamModelNameTranslator for model-name translation
* fix(proxy): type create_model_info_response return as dict[str, object]
* fix(proxy): keep internal routing key for team model listing metadata lookup
Add listing_entries returning (public response id, internal lookup id) so
include_metadata=true resolves fallbacks against the routing key the router
indexes by, instead of the translated public name (which never matches).
* fix(proxy): build /v1/models metadata from internal key, show public id
* test(proxy): cover team listing fallback metadata via internal key
* fix(proxy): use builtin dict generics in create_model_info_response (UP006)
---------
Co-authored-by: Tushar More <tusharmore8408@gmail.com>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
|
||
|
Mateo Wang
|
cee6c9c724
|
feat: add dev and wildcard proxy configs for local testing (#30556) | ||
|
Mateo Wang
|
b638bc2248
|
fix(anthropic): price and surface response service_tier in cost tracking (#30558) | ||
|
Mateo Wang
|
cf2db415b8
|
fix(audio): don't override explicit response_format with verbose_json (#30599)
* fix(audio): don't override explicit response_format with verbose_json
* fix(audio): handle plain-text response body for response_format=text
* fix(audio): only swallow non-JSON transcription body when not declared JSON
Guard the plain-text fallback in transform_audio_transcription_response with
the response Content-Type: a body that fails json() but is labelled
application/json is a genuine upstream error and is re-raised, while
text/plain bodies (response_format=text) are still returned as-is. Prevents
a malformed JSON 2xx from silently becoming a transcription of garbled bytes.
* fix: normalize content-type header case in whisper transcription fallback
* test(audio): lock in case-insensitive content-type guard for transcription fallback
Adds a regression test that a mixed-case 'Application/JSON' content-type still
re-raises a malformed JSON body, covering the case-insensitivity fix in
|
||
|
Mateo Wang
|
17b88719a2
|
ci(lint): grandfather any-discipline with a per-file ratchet budget (50% headroom) (#30582)
* ci(lint): grandfather any-discipline with a per-file ratchet budget (50% headroom) The any-discipline gate previously failed on any Any-typed value touched on a changed line, which tripped on merely editing a legacy `X | Any` line. Switch it to a per-file budget: `any-discipline-budget.json` records each file's current Any count and a changed file fails only when its count exceeds `baseline + slack` (50% headroom, rounded up). New/unbudgeted files have baseline 0, so they stay airtight, while editing legacy files no longer forces cleaning pre-existing debt. Only changed files are re-type-checked (per-PR cost unchanged); the whole-tree scan to recapture the budget runs under `--update` (`make lint-any-budget-update`). The budget is a one-way ratchet guarded by `budget_ratchet_check.py`, matching the ruff/mypy/basedpyright budgets, and folds into `make lint-budget-update`. Also fixes a RecursionError in `contains_any` (recursive type aliases yield fresh objects per unfold, defeating the id() cycle guard) by walking iteratively with a depth cap, exposed by the whole-tree scan. * chore: make CLAUDE.md more concise * chore: rearrange Makefile * ci(lint): make any-budget --update git-failure-safe; clarify over-budget message all_litellm_py_files now returns None when git is unavailable (mirroring changed_line_map) instead of letting CalledProcessError/FileNotFoundError escape as a raw traceback, and update_budget reports a clean setup error (exit 2) for that case. The list-files dependency is injected so the path is unit-testable without monkeypatching. The over-budget diagnostic now reads "N value(s) total, over budget" so the count isn't misread as the excess over the ceiling. * ci(lint): exempt the file-keyed any-discipline budget from the ratchet's dropped-entry rule budget_ratchet_check treats a vanished budget entry as a loosening (an untracked rule whose ceiling is now unbounded). That holds for the rule-keyed budgets, but the any-discipline budget is keyed by file and its gate treats an absent file as ceiling 0 (the file must be Any-free). Cleaning a file to zero drops its entry on the next --update, so the generic rule flagged that as a regression: a false- positive red on exactly the cleanup the ratchet exists to encourage. Exempt the file-keyed budget from the dropped-entry rule while still catching a raised ceiling. |