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20617 commits
| Author | SHA1 | Message | Date | |
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joshua-berri
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440744f967
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feat(mcp): enable verified stateless protocol capabilities (#46264)
* feat(mcp): advertise verified stateless capabilities * fix(mcp): honor rate limits during capability discovery * test(mcp): cover protocol interactions and scoped enablement rollback * fix(mcp): preserve legacy capabilities and bound discovery probes * fix(mcp): keep discovery results single-assignment * ci(mcp): distribute acceptance cases across existing workers * ci: balance MCP integration cases across idle workers * ci: isolate MCP interaction acceptance workload * test(mcp): acknowledge fixture notifications after dispatch * test(mcp): drain session dispatch before fixture deletion * test(mcp): preserve deletion of streaming fixture requests --------- Co-authored-by: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> |
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Mateo Wang
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d489c04554
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fix(anthropic): drop unknown legacy thinking keys under drop_params when rebuilding adaptive thinking (#46741)
* fix(anthropic): drop unknown legacy thinking keys under drop_params when rebuilding adaptive thinking * fix(bedrock): drop unknown thinking keys under drop_params on display updates rebuilds Bedrock Invoke copied every caller thinking key when adding display updates, so an unknown key still reached the provider with drop_params on. Filter that copy the same way, and cover each chat call site plus the global litellm.drop_params path in tests * fix(anthropic): drop budget_tokens from rebuilt adaptive thinking under drop_params * test(anthropic): cover drop_params sources for rebuilt adaptive thinking on /v1/messages and Bedrock Invoke |
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devin-ai-integration[bot]
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8007773ec5
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fix(mcp): request offline access from Google upstream OAuth providers (#41197)
* fix(mcp): request offline access from Google upstream OAuth providers Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style(mcp): format authorize_with_server redirect Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(mcp): type empty OAuth authorization defaults --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
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1ddaa54793
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fix(mcp): scope gateway OAuth consent to x-mcp-servers and add explicit scope setting (#44957)
Co-authored-by: yassin <yassin@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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7090588bdf
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feat(gemini)!: auto-continue gemini-4-argon long decodes with continuationToken (#46240)
* feat(gemini): continue MAX_TOKENS decodes with continuationToken across chat, streaming, and interactions * fix(gemini): continue long decode on CONTINUATION and cancel interactions at /cancel * feat(gemini): add public gemini-4-argon cost-map rows for AI Studio and Vertex AI Add gemini/gemini-4-argon and vertex_ai/gemini-4-argon alongside the existing early-access row, carrying the same limits and capabilities. The AI Studio row mirrors the early-access entry; the Vertex row differs only in provider and pricing source. Prices follow once Google publishes them. Extend the mapped metadata test to cover all three ids, that each routes to its provider, that its backup copy matches, and that the public rows carry the same limits as the early-access row. * feat(gemini): raise gemini-4-argon to the 1M long decode cap and report initial_prompt_tokens across hops * chore(prices): build on the merged gemini-4-argon rows and only raise the long decode cap * fix(gemini): follow continuation hops in a loop instead of recursion The unary hop collectors and the stream iterator called themselves once per continuation request, which the recursion check in CI rejects and which would hit the interpreter's call depth on a very long output. They now loop, and a test follows more hops than the interpreter allows nested calls * fix(gemini): bill Google Search grounding from every continuation request Google sends search grounding once, on the request that ran the search, and does not repeat it on a continuation request. The merged response kept only the last request's grounding, so usage.web_search_requests and the per-search fee dropped every earlier search. Unary merges and the stream iterator now carry the grounding of every request in the chain * fix(gemini): continue long gemini-4-argon answers on streams and Vertex AI A continued stream stopped after the first chunk of its second request. The stream wrapper asks the iterator for a fresh start before every chunk, and that reset it to the first request's finished stream. The iterator now reads from the newest request's stream On Vertex AI the continuation request went to the v1 API version, which rejects continuationToken with a 400. A request that carries the token now goes to v1beta1, which accepts it, including a token that v1 issued On a stream, initial_prompt_tokens came from a usage chunk in the middle of the stream, which reports a smaller prompt count than the last chunk. It is now the count the first request's stream ends with, the same number a unary call reports continuation_token is no longer forwarded on the Interactions API, which answers 400 for the field. It is dropped there as it was before this branch * chore(gemini): name the type errors the dual-mode stream iterator trips The stream iterator holds a sync or an async line iterator in one untyped attribute. Pointing it at a continuation request's stream gave the type checker a concrete union for it, so six existing lines that only ever run in one of the two modes started to report errors This adds a rule-named suppression with a reason on each of those lines and changes no code. Splitting the class into a sync and an async iterator would remove them and is left for a follow-up * fix(gemini): continue only when Google ends a request with CONTINUATION, and stop streaming mid-answer tokens A long decode now sends the next request only when Google's finish reason is CONTINUATION, so an answer cut at the caller's own max_tokens returns in one request with its continuation_token instead of drawing a rejected extra request A request for more than one choice is never continued, since Google rejects a continuation with more than one candidate Chat-format streams no longer carry the tokens Google sends mid-answer. Only a stream that ends cut off carries a continuation_token, on its last content chunk The response cache key now includes continuation_token, so a resend with a token is not served the earlier segment * fix(gemini): keep one response id across a continued answer A continued chat stream changed its chunk id each time the next request to Google started, and a unary answer came back under the last request's id. Every chunk and the unary answer now carry the first request's id, including the chunk that ends a cut-off stream A request whose n or max_tokens arrives as a string is sent once and never continued, where it used to fail with a 500 on the comparison. An empty continuation_token no longer changes the cache key * test(gemini): add integration cells for long decode continuation Covers continued answers on chat, /v1/messages and /v1/responses, streamed and not, on the Gemini API and Vertex AI against a scripted Google, plus concurrency, outage and worker restart runs Also moves the Google stand-in cancel route to /cancel, the URL this PR now calls for Interactions cancel, and checks the cancel test posts there exactly once * fix(vertex_ai): fetch the access token again before each continuation request A long Gemini 4 answer on Vertex AI can run for over half an hour of follow-up requests, and every one of them reused the bearer token fetched before the first request. Once that token expired Google answered 401 and the caller got a cut-off answer with finish_reason length Each Google request in a chain now asks for the token again, which is a cache hit until the token nears expiry, on the streamed and unary paths, sync and async. A refresh that fails counts as a failed follow-up, so the caller still gets the answer so far with its continuation token. A caller's own Authorization in extra_headers still wins on every request, and the Gemini API key path is unchanged * fix(vertex_ai): start each continuation stream with an empty parser buffer When a Google stream ended on an unfinished frame, its leftover text stayed in the stream parser and every frame of the next continuation stream was appended to it. None of them could be decoded, so the whole continuation was billed and dropped without an error Each continuation stream now starts with a fresh buffer, and a test covers a stream that ends on an unfinished frame, sync and async * fix(vertex_ai): treat a continuation stream with no readable frame as a failed follow-up A continuation stream that answered 200 and then sent Google's error frame first, sent a body that is not JSON, or dropped before its first frame ended the caller's stream with `stop` or an error and no token, so a cut answer looked complete or was lost. It now ends the way a refused follow-up does, with the answer so far, `finish_reason` length and that request's token Every continuation stream request now also gets the caller's per-request timeout, which #46349 added to the first one, so a follow-up that never answers ends at that timeout the same way * fix(vertex_ai): keep every follow-up's citations and URL context in a continued answer A non-streamed answer that took several Google requests kept citationMetadata and urlContextMetadata from the last request only, and left every later request's offsets counting from that request's own text. So a URL read on the first request was missing from the answer, citations from earlier requests were dropped, and a later request's citations and search supports pointed at the wrong part of the joined answer The joined answer now keeps every request's citations (Vertex AI's `citations` and AI Studio's `citationSources`) and URL context, and shifts each later request's citation and search support offsets by the UTF-8 bytes of the answer text sent before it, thought parts excluded, since Google counts these offsets in bytes * fix(vertex_ai): answer when a candidate sends null citationMetadata or urlContextMetadata A Gemini candidate whose citationMetadata or urlContextMetadata was JSON null made the call fail on every Gemini model, non-streamed with a 422 or 500 and streamed with an error event, because the per-request metadata helper handed the null back as is and the caller tried to extend a list with it. A continued answer also failed when a follow-up's citation or search support came with a null startIndex A null field is now kept as one entry on a single request, as it was before this branch, a follow-up's null URL context adds nothing to a continued answer, and a null startIndex counts from the start of that request's text. Tests cover missing, null and empty fields on one request and on a continued answer, streamed and not * fix(vertex_ai): pass through spans with no end index and point a continued stream's citations into the whole answer A later request's citation or search support with no endIndex made the whole non-streamed answer fail with a 500, so a span Google sends without an end index now comes back as Google sent it, the way one request for the whole answer returns it. A span with an end index but no start index still counts from 0 A continued stream handed callers each later request's citation and search support offsets counted from that request's own text. The stream now adds the answer bytes of every earlier request, so the offsets point into the whole answer like the non-streamed path and one request for the whole answer Adds the audit's wire tests for both, which fail 24 cells before this change * test(vertex_ai): cover a later request whose cited span starts at its first byte with no start index Google leaves out a zero startIndex, so the second request of a continued answer can cite its own opening with only an endIndex. These wire cells check that span lands where one request for the whole answer puts it, on Gemini and Vertex AI, chat and Responses, unary and streamed * fix(vertex_ai): log a continued answer's raw response as one object A non-streamed answer joined from several Google requests logged its raw response as a JSON list of every request's body. The OpenTelemetry logger reads that raw response as one Gemini body to set its `llm.gemini.*` attributes, so on a continued answer it hit a validation error and set none of them It now logs the merged body, which carries only the last request's token, and a single request still logs Google's body unchanged. The new unit test fails with the change reverted Also adds integration cells for a cut chat stream's token chunk, an answer joined from 41 requests, a caller that gave up on a non-streamed call, counts and caps that are not numbers or are sent twice, `n` sent as true, a partial answer cached after a refused continuation, the token in another provider's cache key, and the sync SDK path with its Vertex token refetch * test(integration): check a continued answer logs one merged raw response A non-streamed Gemini answer that took several Google requests now hands logging one merged response, so the OpenTelemetry span carries the llm.gemini attributes. These 12 cells fail on the merge base and on the commit before that fix, and pass on the branch * test(vertex_ai): cover a follow-up tool call and its thought signature in long decode * test(gemini): pin that a one-request answer logs Google's body unchanged |
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Yassin Kortam
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a02f1b4939
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fix(proxy): preserve AWS session tokens for RDS IAM auth (#46559)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Yassin Kortam
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e4df98272e
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fix(mcp): resolve /mcp/proxy tool_id from the owning server instead of relisting every server (#46545)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Kerry Lu
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8e92a47ff3
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test(integration): assert the streamed usage.cost matches the spend row on every cost case (#46762)
* test(integration): assert the streamed usage.cost matches the spend row on every cost case * test(integration): type the streamed usage frames as JsonValue * test(integration): only require streamed usage.cost when the client received a usage frame --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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84ff9f84a9
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fix(anthropic): send detail on input_image parts bridged from /v1/messages to Responses (#46720)
* fix(anthropic): send detail on input_image parts bridged from /v1/messages to Responses * fix(ui): send detail on playground Responses image parts and code snippets * test(messages): cover image detail on the Responses bridge wire |
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Fede Kamelhar
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68759a239f
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fix(oci): drop non-function tools under drop_params and gate reasoning_effort on declared levels (#40732)
* fix(oci): skip non-function tools and gate reasoning_effort on models that reject it Fixes #31449. Two OCI GENERIC request-building defects that made Grok-on-OCI unusable for tool-using coding-agent clients (Codex over the Responses API, Claude Code over the Anthropic Messages API): 1. `adapt_tool_definition_to_oci_standard` raised `OCIError("OCI only supports function tools")` for any tool whose type is not "function", aborting the whole request when a client mixed a built-in/server tool in with its function tools. Non-function tools are now skipped with a warning; if none remain, `tools`/`toolChoice` are omitted from the request instead of sending an empty array. 2. The GENERIC param map forwarded `reasoning_effort` to every non-Cohere model, but OCI rejects it for all served xAI Grok models (`Model grok-4.20 does not support parameter reasoningEffort`) and for OpenAI's non-reasoning models (`Unrecognized request argument supplied: reasoning_effort` on gpt-4o / gpt-4.1); gpt-5, the o-series, gpt-oss-* and Meta/Google models accept it. `_model_supports_reasoning_effort` encodes that, `get_supported_openai_params` stops advertising the param for those models, and `map_openai_params` follows the standard unsupported-param contract (dropped under drop_params, clear error otherwise) so `drop_params` / `additional_drop_params` now work as documented instead of the 400 surfacing from the service. Behaviour verified against OCI GenAI in us-chicago-1 (2026-09-11) per model. The two existing reasoning_effort normalisation tests move from a Grok model to openai.gpt-5, which is the case they exercise. 21 new tests; the OCI suite is 406 passed. * fix(oci): drive reasoning_effort gating from the model catalog and drop tool_choice for skipped tools Review follow-ups on #40732: - `_model_supports_reasoning_effort` now reads the `supports_reasoning` flag of the model's `oci/` catalog entry through `get_model_info` instead of hardcoding xAI / gpt-oss branches. Catalogued models without the flag (xAI Grok, Meta Llama) no longer receive `reasoningEffort`; models absent from the catalog keep the historical pass-through. - Catalog: flag `oci/google.gemini-2.5-{flash,pro,flash-lite}` as reasoning models and add `oci/openai.gpt-oss-120b` / `oci/openai.gpt-oss-20b` (pricing from Oracle's public price list, 128K context per OCI docs). - `_model_uses_max_completion_tokens` routes on the `openai.*` prefix only. OCI-hosted Gemini accepts `maxCompletionTokens` but ignores it (the output cap silently disappears) and Llama rejects it with 400, so the catalog's reasoning flag must not drive that switch. - After tool adaptation, a `FUNCTION` tool_choice naming a tool that was skipped (built-in / server tools) is dropped with a warning instead of being sent to OCI, which rejects the unmatched choice. Verified live against OCI GenAI (us-chicago-1): Grok with mixed tools plus a forced built-in now completes and still calls the function tool; Gemini and gpt-oss forward reasoningEffort with maxTokens honoured; Llama drops it under drop_params; gpt-5 keeps maxCompletionTokens. * style(oci): use an immutable set for the tool-name lookup Keeps the type-discipline gate (LIT002, mutable collections) within its budget; the lookup is read-only. * fix(oci): fail closed when a forced tool_choice names a tool OCI will not receive Review follow-ups on #40732: - When tool_choice forces a function that is not among the adapted tools (a skipped built-in or a misspelled name), send `toolChoice: NONE` instead of removing the choice. Falling back to the service default would let the model call any other supplied function even though the caller forced a specific one, which an agent processing untrusted content must not do. - Move the #31449 regression tests into the mapped test module (`test_oci_chat_transformation.py`) instead of a separate file. Verified live against OCI GenAI: Grok and Llama with a forced skipped built-in now complete with no tool call; a forced kept function is still honoured. * test(oci): point reasoning_effort comments at the in-module gating tests The referenced module was folded into this file. * fix(oci): drop non-function tools only under drop_params and gate reasoning_effort on declared levels Non-function tools now follow the unsupported-param contract: a 400 naming drop_params without it, and dropped with a warning under it, with a forced dropped tool turning tool calls off. reasoning_effort is gated on the model's reasoning_effort_levels catalog entry, so OCI Llama keeps forwarding it and the Grok 4.20 models OCI serves, now in the catalog with their Oracle prices, stop sending a param the service rejects * fix(oci): gate reasoning_effort on oci/xai.grok-4.20, which rejects it * fix(oci): disable tool calls when tool_choice forces a dropped custom tool by name * fix(oci): drop tool_choice that forces a dropped tool and keep a kept function's name * fix(oci): keep Gemini 2.5 Flash Lite unflagged for reasoning so released proxies keep its max_tokens cap * fix(oci): answer 400 when a request depends on a dropped tool and leave Gemini max_tokens routing to its own PR Under drop_params, non-function tools still drop when function tools remain and tool_choice does not force a dropped tool. A request whose every tool is dropped, or whose tool_choice forces one, now answers 400 instead of a tool-less reply the model can fill with invented results, or a swap to another tool. The maxTokens routing change for OCI Gemini goes back to base, since it is outside this fix and an open PR carries it. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Yassin Kortam
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ba3fce2bda
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feat(ui): scope the MCP server selector to the team, user and access groups it edits (#46565)
GET /v1/mcp/server takes an optional user_id (admin, or the user themselves) and returns the union of the servers each of that user's teams reaches plus allow_all_keys servers. A user in no team gets the unscoped list, a deleted team still listed on the user is skipped, an unknown user is a 404 and a restricted virtual key is refused like for team_id. team_id keeps precedence when both are passed. The dashboard MCPServerSelector passes the team and user it edits (key edit sends keyData.team_id and user_id, key create sends the picked team and owner, user edit sends the user) and narrows the server options to members of the access groups selected in the same control, keeping allow_all_keys servers and servers already granted so they can still be removed. Resolves LIT-7784 Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Yassin Kortam
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92fb55522a
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feat(mcp): add mcp_disabled_capabilities to stop advertising prompts and resources (#46563)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Yassin Kortam
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82977e2465
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fix(mcp): accept validated JWT subjects after virtual-key mapping (#46549)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Yassin Kortam
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04645d0a07
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fix(mcp): persist the first DCR client for config-declared servers regardless of who registers (#46544)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Yassin Kortam
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40e27812a7
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fix(proxy): route SSO token exchange and userinfo calls through litellm's httpx client (#46547)
fastapi_sso opens a bare httpx.AsyncClient for the non-PKCE token exchange and userinfo call, so litellm_settings.ssl_verify and SSL_VERIFY never applied and an IdP on an internal CA failed with an opaque 503. A LiteLLMHttpClientSSO mixin replays that exchange on litellm's shared SSO client for the generic, Google and Microsoft providers and surfaces IdP connection and TLS failures as a 401 ProxyException naming the endpoint. Resolves LIT-9445 Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Yassin Kortam
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40187731b7
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fix(proxy): chain RDS IAM DB auth through the IRSA role when the DB role trusts only the pod role (#46540)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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rameswaran-mohan
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e65e5abc4e
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feat(proxy): name the successor model for deprecating deployments (#41670)
* feat(proxy): name the successor model for deprecating deployments * chore(ui): add successor_model to the generated deprecation api types * fix(proxy): keep the cost map successor when a deployment overrides only the date * fix(proxy): read the cost map successor from metadata.successor The cost map schema rejects a top-level successor_model key, and curated entries already carry the successor under metadata.successor. Read that key, and look it up even when the cost map entry has no deprecation date, so a deployment that sets only its own date still names the successor. * fix(proxy): keep the first deployment when deduping, let a blank successor hide the cost map one, and name a served successor by its model group * fix(proxy): name only the deployment's own successor_model, since cost map successors are LiteLLM ids rather than served model names * fix(proxy): leave out a successor_model the deployment's callers cannot call, with a warning * fix(proxy): report successor_model as the admin set it and only warn when no model matches it --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Dushyant Acharya
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06efa5de85
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fix(proxy): honor disable_end_user_cost_tracking in spend logs and end-user tables (#40945)
* fix(proxy): strictly honor disable_end_user_cost_tracking in SpendLogs * fix(proxy): drop end-user id on failed requests and ClickHouse rows when end-user cost tracking is off * fix(clickhouse): drop the end-user id from spend log metadata when end-user cost tracking is off * test(spend): cover disable_end_user_cost_tracking across endpoints, failures, cache hits and chaos --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Yassin Kortam
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d113e4a0dd
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fix(proxy-extras): hold only Prisma's lock key during migrate deploy so it never waits on the request log index build (#46660)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Yassin Kortam
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1ac9cb2a1d
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fix(proxy-extras): accept unencoded DB passwords in the v2 migration lock and redact its errors (#46592)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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62af39aaf8
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fix(anthropic): count a replayed server tool call's cache mark the way modify_params sends it (#46591)
* fix(anthropic): count a replayed server tool call's cache mark the way modify_params sends it With modify_params on, a trailing tool search turn whose call has a kept result got a placeholder answer from the factory and went out as a marked tool_use, but the cache control hook counted marks before that placeholder existed and skipped the mark, so a request at the cap reached Anthropic with 5 marks and failed with a 400. The hook now finds client answers on the sanitized messages, the same list the factory reads. Also make the replay checks cheaper (prefix check before validation, early return without provider_specific_fields, and look up client answers only when a tool call carries cache_control), drop the two audit cells that cover old gaps outside the tool search fix, and derive the pass-through test timeout from the graceful stop budget. * fix(anthropic): keep the duplicate tool result warning out of the cache control hook The hook finds client answers with only the step of the modify_params sanitize pass that pairs tool calls with their results, so a request with a duplicate tool result logs the warning once again instead of once per mark count. Also restore the Snowflake non-stream and cached-stream tool search cells as known bugs with pytest.skip instead of deleting them, and put the Anthropic pass-through test timeout back to 240 seconds. * fix(anthropic): count cache marks next to a message the pairing step cannot read With modify_params on and a marked tool call, the cache control hook paired tool calls with their results before counting. A message without a get method (a stray string or null in extra_body.messages, or an openai SDK ChatCompletionMessage) made that pairing raise AttributeError before any provider call, where main counted the marks and let the request through. The hook now pairs only when every message can be read the way the pairing step reads it, and otherwise counts on the messages as sent, like main did. * fix(anthropic): count cache marks after placing a later system message the way the request builder does * test(anthropic): cover a leading system prompt and a system message before the answer in the cache mark census * test(anthropic): align the tool search replay cells with main and cover a system message after the search turn The malformed kept result cells now expect the unanswered search call to be dropped, as main does since #45882. The stream cut cell checks streamed tool call names, since the kept search result names get_weather as a tool reference. The streamed response cache hit cell runs again because #46104 fixed it. The modify_params cap cell expects the four client marks, since the placeholder answer sends the search call as a plain tool_use with its mark, and a new cell covers a system message after the trailing search turn in both stream modes * fix(anthropic): count marks on the raw messages when they cannot be placed, and find client answers once per request * test(anthropic): cover marked tool calls next to a malformed tool id and a later system message * perf(anthropic): look up client tool answers only when a marked tool call is a server tool call Answered ids only decide whether a marked srvtoolu_ call is rebuilt as server_tool_use, so a request whose marked tool calls are all client calls no longer places system messages or runs the modify_params pairing step to count marks * test(anthropic): reach the client answer lookup with a marked search call in the malformed tool id cell * perf(anthropic): place mid-conversation system messages in linear time for models that keep them * fix(anthropic): count cache marks with the model's own system message placement and pair tool results in linear time * test(anthropic): cover modify_params dropping a tool answer that answers no call of the latest assistant turn * fix(anthropic): count marks with in-place system placement when the model lookup cannot confirm the flag * test(anthropic): cover a bare model with custom_llm_provider filling the cache mark budget through litellm.completion and Router * fix(anthropic): give unmapped models the request builder's converting placement and survive any model lookup failure * test(anthropic): cover an unmapped model name counting cache marks with the request builder's converting placement * fix(anthropic): count server tool call cache marks the request builder's way only on routes that build Anthropic Messages requests Databricks, OpenRouter and Bedrock Converse send every marked tool call as is, so the hook keeps the census main uses there. The seeding step carries custom_llm_provider on the injection points when a server tool call is marked, so a bare model name still resolves its route. * test(anthropic): cover two answered marked server tool calls before a system message on Databricks, OpenRouter and Bedrock Converse * fix(anthropic): keep main's cache mark count on GitHub Copilot and ChatGPT routes without resolving them, so a sync call with no stored login never starts the device flow * test(anthropic): cover a marked server tool call reaching the GitHub Copilot and ChatGPT login hosts like an unmarked one |
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mgeorgaklis
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b3c9d4c1f7
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fix(prompt_templates): do not mutate messages in-place in function_call_prompt (#41676)
* fix(prompt_templates): do not mutate messages in-place in function_call_prompt * refactor(test): avoid local list mutation and satisfy type discipline gate * style(prompt_templates): keep line length under 120 chars for ruff format * style(prompt_templates): type _with_function_prompt with AllMessageValues for ANN401 * style(prompt_templates): eliminate basedpyright errors in function_call_prompt * fix(prompt_templates): keep tuple and None system content when adding the function prompt Missing system content becomes the function prompt alone, a tuple keeps its parts, and the old test that pinned the caller-side mutation now asserts the caller's messages stay unchanged * fix(prompt_templates): reject unreadable system content without echoing it Only list and tuple content is read as parts. Any other non-string content raises a TypeError that names the type, so error logs never carry the system prompt * fix(prompt_templates): leave non-list system content failing as before None and tuple content fail the same way they did before this change, so the only behavior change is that the caller's messages are no longer mutated * fix(prompt_templates): answer 400 for unreadable system content, send the function prompt alone under drop_params * test(prompt_templates): pin the function prompt on every system message * test(integration): cover function prompt fallbacks, retries, cache and drop_params on ollama --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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835038cbbc
|
fix(websearch_interception): intercept the bare web_search tool type on chat completions (#46736)
* fix(websearch_interception): intercept the bare web_search tool type on chat completions * fix(websearch_interception): match the bare web_search type only on chat completions /v1/messages keeps its existing detector, so a non-Anthropic model there no longer short-circuits a bare web_search tool into raw search results * test(websearch_interception): cover the bare web_search type on chat and through the messages adapter |
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yuneng-jiang
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46913be92b
|
test(integration): wait on the right signal in five flaky integration contracts (#46737)
* test(integration): wait on the right signal in five flaky integration contracts The Prometheus shared-label tests waited for the latency metric and then read the request counter, which the logger writes after an await. They now wait for the counter The auto-router session poll returned on the first 200 while the caller expected two turns, so it now waits for the expected turn count The S3 queue cap test let its failing sink recover on a 15s clock that could expire mid-flush. The sink now stays down until a flush fails after every event is queued K3 normally takes 168-175s against a 180s limit, so the limit is now 300s The MCP env var browser spec reused an idle keep-alive socket that the proxy closes after 5s, so its API calls now send Connection: close * test(integration): add until_asserted so a wait polls the exact asserts it guards eventually() takes a predicate separate from the asserts that follow it, which lets a test wait on one value and then assert on another written later. until_asserted reruns a block of asserts until they all pass and re-raises the last failure on timeout The Prometheus shared-label tests and the auto-router session check now use it |
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Priyansh Nandwana
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391296ffdd
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fix(vector_stores): stop sharing one list across default-constructed registries (#38879)
* fix(vector_stores): stop sharing one list across default-constructed registries VectorStoreIndexRegistry and VectorStoreRegistry took a mutable list as their default argument. Python evaluates that once at class definition, so every registry built without arguments shared one list. The proxy builds one that way during startup, so an upsert into it leaked into every registry created after, and into the class default itself. Default to None and build a fresh list per instance. A caller-supplied list is still used as given, including an empty one, so nothing that passes its own list changes behaviour. Fixes #38874 * test(vector_stores): annotate locals Final and mutate registries through their public API The leak tests now go through add_vector_store_to_registry and upsert_vector_store_index rather than appending to the registry's list directly, which is closer to how the proxy hits the bug. * fix(vector_stores): drop the last mutable list default and identity-only tests --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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joshua-berri
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4a4a0e8d3c
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feat(mcp): relay authorized modern upstream subscriptions (#46153)
* feat(mcp): relay authorized modern upstream subscriptions * fix(mcp): close subscriptions when caller identity changes * fix(mcp): preserve subscription scope and setup changes * fix(mcp): preserve subscriptions across managed token renewal * fix(mcp): forward legacy notifications with subscription-owned authorization * test(mcp): verify modern method boundaries and unsupported legacy resources * test(mcp): model SDK context fields in cleanup regressions --------- Co-authored-by: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> |
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Kerry Lu
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fbd078bcbe
|
test(e2e): perf rig (out-of-process mocks, PerformanceTestCase runner, per-request limits) and the small_bodies_5rps case (#46713)
* test(e2e): out-of-process mock OpenAI, Anthropic, Bedrock and guardrail servers for the perf cases Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): coding-agent request corpus and real provider captures for the perf mocks Request bodies come from a deterministic coding-agent session (system prompt, Read/Edit/Write/Bash/Grep/Glob tools, tool_use and tool_result turns carrying real source files picked by the request marker) in OpenAI chat, Responses and Anthropic formats. captures/ holds one real response per provider, route, shape and stream mode (gpt-6-luna, claude-sonnet-5, us.anthropic.claude-sonnet-5), cold prompt cache, body only, raw stream chunks with time offsets Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): replay module for the perf mocks, captured provider responses picked by route, mode and request size Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): perf mocks replay the captured provider responses, knobs shrink to flake and speed The OpenAI, Anthropic and Bedrock mocks answer with the captured body or stream chunks for their route, mode and nearest request size, ids swapped per request, on the captured clock scaled by the p knob. The synthetic reply builders, the per-token stream pacing and the hand-rolled Bedrock event-stream encoder are gone Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): perf mock request log names its provider Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): perf guardrail mock remembers the request marker by call id for the response hook Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): perf mock captures retaken cold, one marker and system-prompt prefix per cell Run 2 shared one marker across cells, so every stream capture was served from the provider's prompt cache (cache_read_input_tokens up to 523946) and the json cells had small prefix hits. Every cell now carries its own marker, also prepended to the system prompt, and all 30 captures report zero cached tokens Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): perf corpus reads tests/e2e files so the lane's runner image has them * test(e2e): perf long_context json captures for Claude retaken with a 16k output cap, no longer cut mid-thinking * test(e2e): perf mocks keep only what the replay path and the test read * test(e2e): perf mocks interpolate the provider's time to first byte from the captures around the request size * test(e2e): perf captures at 100k (short) and 500k (long) tokens with tools, the fixed-size set removed * test(e2e): PerformanceTestCase schema, deterministic request plan and wire records for the perf cases RequestShape names a corpus session (input_tokens, tools, output_tokens) and wire.request_body builds it with setup/corpus/session.py, so a 300k request is 85 file reads of real source in the endpoint's own format. The plan no longer predicts reply text, the mocks replay captured provider responses and the runner joins what the client saw against the mock's log by marker Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): perf Metric gains time_to_first_token_seconds Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): perf User loses the unused role field, every JWT user is an internal_user * test(e2e): perf RequestShape sizes requests from an input-token range, short and long stock shapes * test(e2e): perf case runner (provision, Locust plan replay, observe) and the 50 rps mixed-provider case provision.py checks the deployed config against the case and builds the callers, run.py replays the plan from a separate Locust process (locustfile.py) recording status, header, first-byte, first-token and total time per request, observe.py joins the records with what the mocks received and the workers' RSS to score every Limit, and compares each served body with the capture the mock replayed for it as a diagnostic. The case sends 15,000 requests at 50 rps over three model groups, two guardrails and the three request shapes Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): perf case asserts time_to_first_token_seconds at percentile 50 and 99 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): perf provision creates every JWT user as internal_user * test(e2e): perf observe reads only the mock log fields that remain * test(e2e): 50 rps case sends short and long coding-agent sessions sized per request * test(e2e): perf run keeps the per-request records as perf-records.jsonl under E2E_ARTIFACT_DIR * test(e2e): the mixed small-gateway case runs at 25 rps (50 saturated one 4-worker pod on main and on v1.105.1) * test(e2e): small_bodies_high_rate perf case, the github issue 46276 shape (11 to 17 KB chat bodies, keys only, 200 rps) * test(e2e): the mixed small-gateway case runs at 10 rps for 6,000 requests (25 rps still saturated one pod on v1.105.1) * ci: python lint job gets 20 minutes, the e2e harness phase (625 tests plus the replay harness suite) pushes a run past 15 when tests/e2e changes * test(e2e): small-bodies perf case runs at 5 rps so gateway_added_seconds is per-request overhead, not queue wait * test(e2e): mixed-provider perf case runs at 5 rps x 3,000 requests from 100 users * test(e2e): mixed-provider perf case sends happy-path traffic only (no flaky deployment, no guardrail interventions or blocks) * test(e2e): perf gateway model_list is three happy-path deployments, one per provider * test(e2e): Postgres and Redis work per request as perf case limits Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): perf mocks wait a case-level latency with jitter instead of replaying captured timings, cpu_seconds_per_request limit from metrics-server * test(e2e): perf suite PR carries the small_bodies_5rps case only, the mixed-provider case moves to its own PR * test(e2e): small_bodies_5rps limits from the ten v1.105.1 baselines (1.5x on datastore counts, 2x on latency and cpu, absolute on errors and memory) * test(e2e): perf mocks keep only the short captures the small case reads, captured timings and the Bedrock invoke route removed * test(e2e): perf mocks generate each reply from the request (provider schema, usage, ids, event order, chunk sizes) instead of replaying captures --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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|
Weihong Xu
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b4a63d3b6a
|
fix(s3): bound object key filenames (#40825)
* fix(s3): bound object key filenames * Update litellm/constants.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * test(s3): pin the 1024-byte full key boundary next to the filename limit --------- Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
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da2f5a498f
|
feat(agents): support Databricks Agent Platform agents in the agent gateway (#45681)
* feat(agents): support Databricks Agent Platform agents in the agent gateway Add a databricks_agent provider that speaks the MLflow ResponsesAgent wire (Responses-shaped input, output_text items, output_text.delta SSE) to Model Serving endpoints (/invocations or the unified /serving-endpoints/responses) and Databricks Apps (/responses), with a PAT or OAuth M2M client credentials Register it through the agents config block or the Admin UI agent catalog and call it through /a2a/<agent> (message/send, message/stream) or model: a2a/<agent> chat completions. The chat route now runs every completion-bridge agent through its own provider, and attaches the minted Databricks OAuth token for both bridge and URL agents. Consumed OAuth settings never reach the provider call * fix(agents): harden databricks_agent input, output, and OAuth handling Non-text content parts answer 400 unless drop_params is set, a reply without an output list answers 502, flat OAuth fields resolve an os.environ api_base, the bridge debug log no longer echoes the request model, routing tests stub only the HTTP boundary, and the catalog description fits the dropdown * fix(agents): merge agent and client extra_headers on the a2a chat route, and reject an empty databricks_oauth block * fix(agents): bridge only databricks_agent agents on the a2a chat route, keep the card url over a client api_base, and require workspace_url for a Databricks App * refactor(agents): read the a2a chat bridge providers from a constants set instead of branching on a provider name * fix(databricks_agent): let the configured credential win, reject malformed client extra_headers, and keep a missing endpoint name empty * test(agents): drive the flat OAuth a2a send through the real bridge * fix(agents): keep an empty databricks_oauth block ignored on url agents, as before this change * fix(agents): accept bare App hosts, env service principals, and os.environ refs on Databricks agents * fix(databricks_agent): address review caveats on chat bridging, params, and auth Map max_tokens and max_completion_tokens to max_output_tokens and pass temperature, top_p, and user, so /v1/messages works without drop_params. Send A2A message metadata only with string values, answering 400 or dropping the rest under drop_params. Join message items with a blank line in both stream and non-stream replies. Cache the env service principal token until shortly before it expires. Keep databricks_agent replies out of the response cache. On the chat bridge, let request fields win over the agent's stored params while pinning the agent's api_base and api_key, and apply the agent's static_headers between client and agent extra_headers, as /a2a does. Resolve os.environ/ only for databricks_agent api_base and api_key instead of every bridge provider. Ignore an unusable databricks_oauth block on URL agents on chat, as at the merge base, instead of answering 400. * test(internal_context): list the databricks agent token cache as process-local * fix(databricks_agent): keep the agent's endpoint, input, and target out of client control extra_body model and input answer 400, or are dropped under drop_params. A client api_base or base_url never replaces the agent's card URL on the bridge. Chat to a URL agent keeps its merge-base call, and an incomplete databricks_oauth block on a URL agent keeps its merge-base ValueError. * test(databricks): expect the merge-base 400 for a client api_base on agent chat * fix(databricks): keep agent-owned inputs and identity headers out of a chat caller's reach * fix(databricks): bound the agent-over-caller merge to two levels so it needs no recursion --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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yucheng-berri
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d9b5d9e19f
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Revert "feat(proxy): add LITELLM_FIPS_MODE startup gate with provider assertion and loud password migration failure (#42700)" (#46740)
This reverts commit
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Mateo Wang
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1938f537bf
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fix(decisions): read a deployment's own supports_box_questions flag before the cost map (#46696)
* fix(decisions): read a deployment's own supports_box_questions flag before the cost map The box-question check only read the shared cost-map row and required a decisions mode there, so an OpenAI-format deployment whose model_info sets supports_box_questions was refused with a 400, which kept the wire test from #46633 red on main * fix(decisions): keep the box flag on its deployment and out of request bodies A model_info that is not a plain dict no longer fails the call, the proxy decisions endpoint drops a caller-sent model_info, and the router stops copying supports_box_questions onto the shared cost-map key. * fix(decisions): keep the box flag shared with same-model deployments like main Scoping supports_box_questions to one deployment turned box questions on an unflagged sibling of the same backend model from reaching the provider into a 400. Siblings inherit the flag through the shared cost-map row again, as they do on main. Also formats two new tests. * test(decisions): pin box flag inheritance by an unflagged same-model deployment * test(decisions): audit box-question deployment flag across providers, routes, and chaos cases |
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Kerry Lu
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fac0135d82
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fix(search): bill an unpriced fallback search tool at its own price, not the failed primary's (#46734)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
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777478425e
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test(integration): drop inherited provider API keys from the integration runner env (#46733)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Kerry Lu
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11e9fcc033
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fix(cost): price bridged /v1/messages reasoning tokens from the chat completion, not the served Anthropic body (#46726)
* fix(cost): price bridged /v1/messages reasoning tokens from the chat completion, not the served Anthropic body * fix(cost): keep the type gate flat for the bridged /v1/messages billing path --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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67d75522ed
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fix(test): wait for the wedged Redis to be reached instead of a fixed 0.1s sleep in team roster sync tests (#46727)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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joshua-berri
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1fc456917f
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fix(mcp): preserve progress and cancel upstream work promptly (#46134)
* fix(mcp): preserve progress and cancel upstream work promptly * fix(mcp): apply output guardrails to progress messages --------- Co-authored-by: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
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3d526bfe3f
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test(integration): remove BUG skips for 3 tests that pass on main (#46725)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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ryan-crabbe-berri
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f470e418ea
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fix(anthropic): keep signed empty thinking blocks and honor thinking.block_binding (#46342)
* fix(anthropic): treat adaptive thinking as enabled when mapping response_format
is_thinking_enabled only recognised thinking.type=enabled, so a request that
paired thinking.type=adaptive with response_format took the synthetic JSON
tool path with a forced tool_choice, which Anthropic rejects alongside
thinking. The same check also gates the Bedrock Converse and Databricks
forced tool_choice downgrades, so they now see adaptive thinking too.
* fix(anthropic): keep signed thinking blocks with empty text on chat completions
Models that default to display: omitted return signed thinking blocks whose
text is empty, and Anthropic rejects the next turn when they are not replayed
unchanged. is_unsignable_thinking_block dropped them along with unsigned
blocks, and the inline content path required non-empty text, so a tool-use
turn on Opus 4.7+ or the 5 families lost its thinking blocks and 400ed on the
tool-result turn. Only a missing signature now drops a block, and the
mid-conversation system placement mirrors that rule. The /v1/messages strip
helper is left to #41362.
* fix(anthropic): skip the thinking-strip retry when block_binding asks for an error
The /v1/messages retry loop strips thinking blocks and resends on any
invalid-thinking 400, including the binding mismatch a caller opted into
with thinking.block_binding.prefix_mismatch_behavior: error. The retry
decision now sees the request body and surfaces that 400 instead of
overriding the caller's choice. #42888 covers the litellm_params side.
* fix(bedrock): carry thinking.block_binding through the Invoke thinking rebuilds
Bedrock Invoke rebuilt the thinking param from scratch when display: updates
was requested, on both the /v1/messages and chat completions paths, and the
legacy enabled-to-adaptive translation plus the clear_thinking injection did
the same, so thinking.block_binding never reached Bedrock. Every rebuild now
spreads the caller's thinking and only overrides the keys it owns, and
AnthropicThinkingParam types block_binding. The thinking-binding-controls
beta was already on the Bedrock allowlist.
* fix(anthropic): keep forcing the response_format tool under adaptive thinking
This reverts
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devin-ai-integration[bot]
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e344dbd8db
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feat(proxy): split Azure PTU deployments across teams with per-team PTU ceilings and PTU-hour reporting (#43043)
* feat(proxy): split Azure PTU deployments across teams by share
A PTU deployment's model_info takes ptu_shares (team_id -> whole PTUs adding up to
ptu_count) in place of a single team_id. With LITELLM_ENABLE_PTU_COST_ATTRIBUTION on,
routing hides the deployment from teams holding no share, the v3 limiter enforces each
share as a per-team TPM ceiling from Azure's per-model TPM-per-PTU sizing, the daily
flat-cost rollup splits the hourly cost by share, and /team/daily/activity plus the Team
Usage page report PTU-hours next to tokens.
* fix(proxy): weight cached input in PTU ceilings and size team-scoped deployments by public name
The PTU flag is read from the process environment on every check instead of
through the secret-manager path, so a proxy with a hosted secret manager does
not pay a round trip per request. A team's ceiling now carries the model's
cached-input ratio and settlement charges cache reads at that ratio, matching
how Azure sizes a PTU. A deployment registered for one team is found by its
team_public_model_name for PTU-hours and the ceiling, since its model_name is
rewritten to a unique internal name. ptu_shares are StrictInt on ModelInfo and
parsed_ptu_shares refuses booleans, strings, and non-string team ids, a
fractional ptu_count reserves nothing, and models_processed counts distinct
deployments rather than holdings.
* fix(proxy): reserve PTU ceilings in normalized tokens, keep share routing flag-independent, and warn only shared or Azure reservations
* fix(proxy): size the uncapped output floor against the PTU share in raw output tokens and escape the exported team id
* fix(ptu): refuse ptu_shares naming a team that does not exist
* refactor(ptu): keep Azure detection under llms/azure and check share teams with one query
* fix(proxy): drop the reverted TeamIdSearchFilter import after merging main
Main reverted the team key search change that added TeamIdSearchFilter, so the PTU share team check types its where clause locally
* fix(proxy): chunk the ptu_shares team lookup under the IN-list bound
* fix(proxy): count a request naming a shared PTU deployment directly against its group's ceiling
* fix(proxy): resolve every name a shared PTU deployment is served under to its group's ceiling
* fix(ptu): refuse ptu_shares declared without ptu_count and cost_per_ptu_per_hour
* fix(ui): seed total_ptu_hours in the empty daily activity metadata
* fix(ptu): size usage rows by the name the request used
* fix(ptu): size a team's usage rows by the deployment it is served from
* fix(ptu): never size a team's usage row by another team's reservation
* fix(ptu): reserve an uncapped request against the cap the proxy writes, not its prompt length
* fix(ptu): read a share map only through the terms registration requires
A ptu_shares map stored without ptu_count, cost_per_ptu_per_hour and
ptu_effective_from, or one that does not add up to ptu_count, is refused at
registration but was enforced once loaded: every other team got the 400 and
the share holder a ceiling while no flat cost accrued. _deployment_shares now
reads the shares through ptu_terms, so such a row reserves nothing, the same
as before the map was read.
* fix(ptu): keep a declared split as the access rule and check a config split at boot with the flag off
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moyai-devin-berriai[bot]
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3151c47960
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fix(guardrails): resolve decision models in caller team scope (#46085)
* fix(guardrails): resolve decision and judge models in caller team scope * fix(guardrails): scope guardrail model calls to the caller's team only where it owns the deployment Read the team only from the auth object the proxy attached to the request, never from caller JSON, and use it only when the router's route to the guardrail model for that team reaches a deployment a teamless caller cannot. Shared names, aliases, legacy team tags and wildcards keep resolving and billing as before. Decision calls pass the team in litellm_metadata only when it is used, so the logged model group stays intact. Adds integration cells for chat, responses and messages, forged team ids, a team deployment that shares a name with a shared model or wildcard, and billing, plus unit cases for legacy team tags and aliases. * fix(guardrails): route team guardrail calls to the team deployment without charging the team Guardrail calls on a team's own model now go to the deployment the router picks for that team, by its id, with no team on the call. Like every other guardrail call, they write no spend row and add nothing to the team's or the key's spend * fix(guardrails): pin the team deployment only for the judge guardrail, not shadow eval judge calls * fix(guardrails): keep the teamless route when only a team wildcard covers the guardrail model * refactor(guardrails): scope the team model fix to the decision model guardrail only * fix(guardrails): send decision calls on a team's own model with the team, and keep team wildcards out The decision call on a team's own model now carries the caller's team instead of being pinned to a deployment id, so it is charged to that team and goes through the team's normal routing: an access window that reserves the deployment for the team lets the call through, and pre-call checks see the input and skip a deployment too small for it. A team deployment listed under a wildcard public name, such as team_public_model_name openai/*, no longer captures a guardrail model the team never named, whatever model it points at. Adds unit and integration cells for the reserved deployment, mixed context windows under enable_pre_call_checks, a team wildcard name over a fixed model, and the owner team's spend. * fix(guardrails): log the owner team's name on decision calls charged to it The decision call on a team's own model now carries the team alias beside the team id, so the spend row and the Logs page name the team the call was charged to. * fix(decision_model): use the caller's team only for names nothing else serves A team model named like a shared deployment, a shared wildcard or an SDK public name took over the guardrail's decision calls for that team, so a team admin could point a proxy-wide guardrail at their own endpoint. The team is now used only when a caller without a team reaches no model under the name. * refactor(decision_model): drop the team route subset check the served check covers Once nothing serves the name without a team, every deployment a caller without a team can reach is a wildcard, so the subset check only ever rejected an empty team route. Pin team wildcards over a name nothing else serves. * fix(decision_model): use the caller's team only when judge_target finds nothing without a team The served check rebuilt the router's rules and missed a shared wildcard whose provider comes from custom_llm_provider, so a team model under the guardrail's name took over that team's decision calls. Ask judge_target, the same lookup the teamless dispatch makes, instead --------- Co-authored-by: moyai-devin-berriai[bot] <336287033+moyai-devin-berriai[bot]@users.noreply.github.com> Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
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30ada9d26a
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feat(batches): store each batch line's request and response in S3 and GCS (#41691)
* feat(batches): emit per-line JSONL batch records to callbacks
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): only emit line items for final batches
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(deps): bump soupsieve to 2.9.2 for GHSA-gjv8-xp57-g29c and GHSA-j934-xhv5-fg8f
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(batches): satisfy ruff format for line item logging
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(ui): regenerate schema.d.ts for store_batch_line_items_in_callbacks
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(batches): cover line-item edge shapes and general settings propagation
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): keep deployment credentials out of callback-visible params
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(batches): declare credential transport attribute on Logging
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(batches): integration contract for per-line batch callback events
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): resolve store_batch_line_items_in_callbacks through the settings store
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(batches): assert per-line events beside the aggregate and create events
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): drop resurrected use_legacy_interactions_schema shim from merge
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(batches): assert line item events carry no credentials
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): reset batch line-item flag when the setting is absent
Removing store_batch_line_items_in_callbacks from the DB general settings left the module flag at its previous value because the effect early-returned when the key was neither in the DB nor owned by YAML. The proxy owns the global, so an absent resolved value now writes False.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): map anthropic batch lines through the anthropic message transformer
Anthropic result lines carry a message-shaped body, so ModelResponse(**body) produced empty choices and a usage key that blocked the batch-rate usage attach. Extract the existing AnthropicResponse -> ModelResponse conversion used for /v1/messages logging into anthropic_message_to_model_response and reuse it for anthropic line items.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style: tag the anthropic result cast with a cast-ok reason
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style: shorten cast-ok reason so ruff format passes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): skip line-item callbacks for providers without a line reconstruction
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): reconstruct bedrock and mistral batch lines through their provider transformations
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style: wrap widened provider literals so ruff format passes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): pair bedrock lines by recordId and type them from the reconstructed response
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): keep line item events out of proxy limiters and strip credentials from child params
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): keep cast-ok tags on the flagged cast lines for the discipline gate
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): skip failed line item events in parallel request limiter failure hooks
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(batches): tolerate lifecycle spend rows when asserting no line item rows
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): place mutable-ok tags on the lines the discipline gate flags
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): skip router budget accounting for batch line item events
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): keep anthropic line errors and skip native vertex batch rows
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(batches): restore test-quality tags on line item tests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test: move batch line-item unit test to tests/unit after merging main
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): emit batch line-item callbacks once per batch
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): release line-item claim when fan-out emitted nothing
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): make the line-item claim release ownership-safe
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): keep bodyless failed batch lines reaching callbacks
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(logging): seed batch line hidden params in get_hidden_params defaults
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(batches): reuse fetched batch result files for line-item logging
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): release line-item claim when nothing was emitted
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): type batch line-item callback sites to satisfy basedpyright budget
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style: wrap batch hidden-params cast for ruff format
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): reason suppressions for batch line-item lint deltas
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore: drop stale mutable-ok suppressions after LIT002 removal
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(batches): skip batch line-item events in ClickHouse spend sink and router quota counters (#44486)
* fix(batches): skip batch line-item events in ClickHouse spend sink and router quota counters
Batch line-item callbacks (call_type=acompletion + litellm_params.batch_parent_id)
bypassed the call-type-only batch guards:
- ClickHouseSpendLogger wrote one spend row per line on top of the aggregate
aretrieve_batch row, billing every batch twice with the flag on.
- Router deployment callbacks and the routing-strategy usage handlers charged the
historical batch's tokens/rpm to the live TPM/RPM counters, so after
Router.aretrieve_batch the next chat raised RouterRateLimitError.
Skip these events wherever the aggregate batch retrieve is already skipped.
* fix(logging): keep internal litellm_params authoritative in model_call_details
A request body key named litellm_params lands in optional_params, and
update_environment_variables merged **optional_params after the internal
entry, so a caller could overwrite the logging kwargs' litellm_params and
forge markers like batch_parent_id. Live-reproduced: a Gemini chat whose
body carried {"litellm_params": {"batch_parent_id": ...}} returned 200
with no ClickHouse spend row. Re-assert the internal litellm_params after
the merge so every sink (ClickHouse, Postgres, router quota counters)
reads the same trusted value, and assert the aggregate aretrieve_batch
row still writes in the ClickHouse line-item test (review finding).
* fix(clickhouse): skip failed batch line items in the spend sink too
A failed JSONL line is part of the aggregate aretrieve_batch row's
request counts; writing it its own spend_logs row inflated request
counts with the line-item flag on. Extend the batch line-item guard to
the failure handler (review finding) and pin both directions: a failed
line item writes no row, a genuine live failure still does.
* fix(batches): declare batch_line_items service family on claim calls
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(integrations): keep batch line items out of the built-in metering sinks
With store_batch_line_items_in_callbacks on, per-line batch events carry a
real response_cost and reached six metering sinks that lacked the
is_batch_line_item_event guard, so a completed batch metered aggregate +
per-line (roughly 2x true spend): prometheus (litellm_spend_metric and
request counters), openmeter, lago, datadog cost management (FOCUS
BilledCost), newrelic metrics, and the OTel v1/v2 gen_ai.usage.cost
metrics. Skip line-item events in each sink's success/failure metering
entry points, matching the guards already on the Postgres and ClickHouse
spend sinks. OTel spans for line items are still emitted; only their
cost/token metrics are skipped. Tests drive real sink instances on the
real dispatch lists through a completed 2-line batch and assert each
sink meters exactly the aggregate.
* style: format metering-sinks test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(otel): skip synthetic line-item durations in v2 metrics; isolate prometheus test
Follow-ups from review on the sink guards: record_failure() now skips
batch line items too (a failed line's interval measures retrieval and
emission, not its model call), and the prometheus guard test snapshots
and restores the process-wide registry collectors instead of leaving
them unregistered. The OTel v2 test is now a flag on/off A/B over the
whole metric snapshot: cost and usage values must match exactly and no
metric may gain datapoints with the flag on.
* chore: finish main merge conflict resolution for batch line items
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style: sort imports in litellm_logging after main merge
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(batches): use the public hook class names main now exports
* fix(batches): only the batch writer can mark a callback event as a line item
amoderation, transcription and speech build litellm_params from every request kwarg,
so a body field named batch_parent_id reached Logging.litellm_params and every
spend, budget and rate-limit hook skipped the request. update_environment_variables
now drops an incoming batch_parent_id; the line-item writer sets it through
Logging.mark_batch_line_item. Batch line hidden params are emitted only when set.
* chore(batches): keep the lint gates within main's ceilings after the merge
Typed the helpers main now types (Mapping litellm_params, Any Span), replaced the
banned cast with a literal match, dropped isinstance checks main made redundant,
and moved test-quality reasons onto the lines the gate reads.
* fix(anthropic): read the message id off the validated message
The anthropic_messages logging path can hand over an already parsed message object,
which has no .get; main read its id through getattr, so take it from the validated
AnthropicResponse instead.
* fix(batches): an unreadable error file no longer drops the output line events
Costing counts an error file it could not fetch as no failed lines; line logging
refetched it and the raise aborted the whole fan-out, so the successful lines were
never emitted. The error file now reads as empty with a warning.
* test(batches): pin billing for batch_parent_id on every endpoint and value shape
Chat completions, embeddings, responses and messages join moderations, speech and
transcriptions, and speech is driven with an empty, int, list, object and 5 KB value.
* test(batches): bill every request of a concurrent burst mixing forged and plain bodies
* revert: keep the line-item fan-out all-or-nothing on an unreadable error file
|
||
|
ryan-crabbe-berri
|
1f041a3265
|
fix(openai): strip prompt_cache_breakpoint under drop_params for OpenAI models that cannot read it (#46453)
A chat completions request to gpt-4.1 (or any OpenAI model older than 5.6) with drop_params on still sent the marker inside messages and tools. It is now removed there, while drop_params off keeps sending it through and models that read it keep it either way Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
yujonglee
|
c0d7264e8b
|
feat(rust): add native OpenRouter Responses (#46000)
The Responses route now resolves openrouter alongside openai. The OpenRouter config reuses the OpenAI request and response transforms and owns only its bearer credential (OPENROUTER_API_KEY, then OR_API_KEY) and base URL (OPENROUTER_API_BASE). Wire model ids follow Python's get_llm_provider, including the openrouter/auto native id with an explicit provider. The Python route host no longer hands litellm.openai_key to OpenRouter. The OpenRouter live test lives in tests/live/openrouter.rs and returns early when the provider key is not exported, so cargo test stays offline by default instead of hiding it behind --ignored. Co-authored-by: nate-berri <nate@berri.ai> Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com> |
||
|
devin-ai-integration[bot]
|
fbc2e358bc
|
fix(responses): bill a background response once, on the first read or poll that sees it finished (#45703)
* fix(responses): bill a background response once, from the poll job
Reading a completed background response with GET /v1/responses/{id} wrote a
full-cost spend row on every read, on top of the row the background cost poll
writes. Reads of a Responses object (GET, DELETE, cancel) now never bill, and
the poll job is the single biller.
The poll job now bills with the creating request's attribution (key hash, team,
org, user, aliases, tags) read off the managed object row, through a helper it
shares with the batch cost poll. The background create persists the creating
key and request tags so that attribution survives until the poll runs.
* test(proxy): a background response read reports zero cost
* fix(responses): bill a background response once, on the first read or poll that sees it finished
A conditional update moves the stored row out of queued or in_progress so only one worker or pod bills it, through one re-read stamped with the creator's attribution. A failed billing read puts the row back for the next poll
* fix(responses): keep the caller's response id out of the billing failure warning
* fix(responses): keep the creating client's User-Agent tags on the one background charge
* fix(responses): keep managed background reads free and bill each response once
The managed files hook marks reads and cancels of proxy-issued response ids, so only those stay free. SDK and database-less reads of a background response still bill, extra_body background creates are tracked, and the poll's status read no longer cools down a deployment on a 404.
* fix(responses): bill finished background responses from the fetched body and store rows Postgres accepts
* fix(responses): give the background billing event its own spend log row and bill responses the create left untracked
* fix(responses): narrow the managed files hook with a protocol instead of an enterprise import
* fix(responses): leave a read the caller retargets with client-side credentials to the cost poll
* fix(responses): bill background responses on the creator's end user and report the charge on the settling read
The read that settles the bill returns the charge in x-litellm-response-cost, the billed event
carries the creator's end user and the key, team, and user budget fields, and its spend row is
named after the id the client got. Override reads, cancels, and deletes settle from the
configured deployment, a streamed create marks its row billed, stored rows keep no model
output, and model insights counts the billed row's spend without a request.
* test(responses): cover background response billing across proxies, reads and chaos
* fix(responses): settle background billing only for callers allowed to address the response
A delete now settles the bill only after the response id access check passes, a streamed
create is marked billed when it sets background inside extra_body or its completed event
arrives after the last chunk, the billing event keeps the deployment's litellm_params
base_model, a key with no user and no team bills no user instead of its owner scope, and the
enterprise cost check import is optional
* fix(responses): record the bound on the pending status IN filters
* fix(responses): send the billed cost split on the settling read and keep the batch cost event as it was
The read that settles a background response now sends the whole cost header
family from the billed event, so the breakdown headers add up to the charge.
Batch cost events go back to main's attribution (creator id as the user, no
budget fields); only background responses get the end user and budgets.
Integration tests expect the charge on the settling read (or none when the
poll billed first), one charge for a streamed create, and SDK flows that poll
with the id each read returns.
* fix(responses): bill each background response once across upgrades, streams, and failed settles
Rows stored before this billing model are found by the issued id and rekeyed by the
provider id, and the cost poll retires one a read already billed. A streamed background
create is free on its own and bills the finished response through the same managed row
before the client sees response.completed, so a racing read cannot bill it again. A
settle that fails retries in the background, a retargeted read with no row tracks and
bills from the configured deployment, and a delete keeps its billed cost header.
Reverts the unrelated json.dumps narrowing in the completion transformation.
* fix(responses): bill cache-polled background streams and match poll rows by the issued id
* test(responses): cover deletes, failed settles, budgets, base models, and cache-polled background billing
Adds integration cells for the background response billing paths the audit
inventory lists: a DELETE that settles the bill and refuses a stranger, a
settle the database refuses that retries once it recovers, the Prometheus
remaining budget, base_model pricing from model_info and litellm_params, the
daily model usage rollup, the end user, the settling read's cost split, stored
rows without model output, a forged managed marker on the create, streamed
creates with background in extra_body, and the cache-polled create
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
|
||
|
Yusuf Efe
|
c9ce9a733d
|
fix(proxy): allow model instances in LiteLLM_EndUserTable validation (#41677)
* fix(proxy): allow model instances in LiteLLM_EndUserTable validation * fix(proxy): preserve property-backed attributes when coercing spend * refactor(proxy): default null end-user spend with a field validator * fix(proxy): drop model-wide from_attributes from the end-user table * test(proxy): unskip the /customer/block integration contracts --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
|
Mateo Wang
|
622daf3cdf
|
fix(scim): write a group's roster in one locked transaction instead of one member at a time (#45197)
* fix(scim): write a group's roster in one locked transaction instead of one member at a time POST, PUT and PATCH /scim/v2/Groups resolve every member in one query and write the roster, the membership rows and the users' team tags in one transaction under the team's advisory lock, so a 500-member push runs a fixed number of statements instead of thousands. PUT now answers with the roster it just wrote * test(scim): assert the roster the IdP gets back and boot the bulk roster cell with two workers * fix(scim): skip the team lock for a rename-only PATCH and lock member rows in id order before the roster write A PATCH naming no members opened the roster transaction and took the team advisory lock for nothing. A SIGKILLed worker then left its orphaned query engine holding that lock until the 60 s transaction timeout, and /team/delete, which takes the same lock, timed out behind it. An empty delta is now answered from a plain team read, with no transaction and no lock Two groups trading members in parallel locked user rows in opposite orders, and one request died with a Postgres deadlock. Every user row a push touches is now locked with SELECT ... ORDER BY user_id FOR UPDATE before the first write, so the two requests serialize The bulk roster cell gets a 120 s client through a new owned_proxy_process client_timeout, so an unfixed push fails on the statement count instead of the harness clock, requires positive counts on both pushes, and gains a parallel roster-trade test that answered 500 before the lock * fix(scim): evict the cached team on a group rename and read a no-op roster from the writer A rename-only PUT or PATCH wrote the new alias and left the team cached under its id and old alias on every worker, so keys kept logging spend against the old team alias until the TTL expired. The group write now evicts the cached team (and broadcasts the eviction) right after the alias or metadata update. sync_team_roster reads the team from the writer when the plan names nobody, so a replica cannot hand back the pre-rename row; a plan already matching the roster returns inside the lock without writing, evicting, or emitting the members metric; the response is built from the written row, so lastModified reflects the roster write; and detaching a removed member bumps the user's updated_at like every other user write. * fix(scim): bump detached members' updated_at in UTC The detach UPDATE wrote updated_at = CURRENT_TIMESTAMP, which Postgres casts into Prisma's naive timestamp column in the session timezone, so on a database whose session is not UTC the removed member's row landed hours away from the UTC instants Prisma itself writes. Write NOW() AT TIME ZONE 'UTC' instead, and have the bulk roster cell assert the bumped row sits between two reads of the database's own UTC clock taken around the replace. * fix(scim): chunk the group write's member lookups under the bind cap Every Prisma "in" filter the group write issues bound one parameter per member with nothing bounding the list, which check_unbounded_in_lists reports as 11 new findings. The single-field lookups now go through litellm.repositories.chunked_in (caller_transaction inside the roster transaction, per_chunk_ok for the role recompute), and the one cross-field OR read slices its values at IN_LIST_CHUNK_SIZE and records the bound. A push of IN_LIST_CHUNK_SIZE + 1 members now reads in two slices, and a member named only in the last slice still resolves. * fix(proxy): send bulk cache evictions in slices so the broadcaster drops none Veria pointed out that the invalidation publisher caps its pending tasks at 1,024 and drops the excess with a warning, and that a group push now detaches every removed member's keys in one call instead of one member at a time, so a removal of more than roughly 340 members (three cache keys each) silently left stale key objects on the other replicas. The merge base never hit the cap because each member was a separate request. Bulk evictions (team, user, organization, and session key deletes, plus the roster settle) now publish in slices of 256 and wait, bounded at two seconds, for the backlog already handed to the event loop before each slice. A Redis that never answers costs a bulk caller at most that wait per slice instead of wedging the request, and single-key callers are untouched. Regression tests push 1,500 keys and a 500-member removal through a slow publisher and assert every eviction reaches Redis; both fail against the previous tip. * fix(proxy): clear this worker's cache before pacing bulk eviction broadcasts Greptile pointed out that pacing the broadcasts in slices also paced the local eviction: a bulk delete waited for the publish backlog before dropping the next slice from this worker's own memory, so on a Redis that accepts connections but never answers a key deleted from the database kept authenticating on the handling worker for up to two seconds per slice. The merge base cleared every key locally at once. Bulk evictions now drop every affected entry from this worker's memory first, through the cache's own partition lookup since key objects live apart from the other auth objects, and only then publish in paced slices. The roster settle does the same for the deleted tokens, the JWT mapping keys and every changed member's user and membership entries before any step that can wait. Regression tests run both paths against a publisher that never answers and check that nothing is still cached on this worker before the first bound elapses; both fail with the local eviction removed. * fix(proxy): drop the Redis copies of bulk-evicted auth objects before pacing the broadcasts * docs(proxy): drop the module docstring that repeated the PR description * fix(scim): bound bulk cache evictions to one Redis round trip and close lock-order gaps Bulk key evictions now drop entries from memory, delete them from Redis in one chunked DEL, and broadcast every key in one pipelined publish, so a large roster removal no longer overflows the publisher's pending cap or costs a Redis round trip per member. A push waits for in-flight membership loads before evicting. User rows are locked in byte order to match the spend writer, a bulk user delete takes every team lock before its first user write, and a POST whose roster write fails deletes the empty team so the IdP retry creates it again. * test(team): observe delete broadcasts at the Redis layer * fix(scim): lock user rows in id order on every multi-row writer and create unprovisioned members concurrently * fix(team): chunk the email lookup behind the member row lock * fix(scim): create a member deleted mid-push again instead of failing the group write * fix(scim): refuse strict-mode mid-push deletes with 400 and check license seats once per batch A member deleted mid-push is created again only when scim_upsert_user allows it, under the verb's own created_via. New group members are checked against the license seat limit as one batch before any is created. Auth cache publish drops log the key count instead of the key. * fix(scim): lock member rows while recomputing admin roles after a group push * fix(users): re-read teams under the row locks in bulk user delete so a racing group push keeps no deleted member * fix(users): take /user/delete and SCIM DELETE /Users users off their current teams under the row locks so a racing group push keeps no deleted member * fix(scim): keep retrying a push while deletes race it, lock rows in member bulk delete, evict removed keys from Redis first A group push gave up after one retry when SCIM user deletes kept landing between attempts, answering 500 in 9 of 10 race rounds. It now retries while each attempt finds newly deleted members, and gives up only when a member it created again goes missing a second time. The team member bulk delete now locks the removed members' user rows in the shared order and takes the team off their teams arrays in one array_remove statement, so it no longer writes back a teams array read before a concurrent push added another team. A push that removes members now drops their deleted team keys from memory and Redis together with the members' other cache entries before the team eviction, so a removed key cannot refill from Redis mid-cleanup. * fix(user-delete): keep the team alias label on the team members gauge after removals * test(integration): count relay statements over plaintext so an SSL-enabled Postgres does not hide them * fix(user-delete): skip a team deleted while the user delete waited on its lock instead of answering 404 * fix(scim): answer 404 and roll back when the team is deleted during a roster write * test(integration): audit cells for SCIM bulk roster writes, user delete locking and the eviction broadcast |
||
|
Thijmen Stavenuiter
|
a76e723b26
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feat(proxy): report when each per-model budget window resets (#45797)
* feat(proxy): report when each per-model budget window resets model_max_budget_usage on /key/info, /user/info and /team/info now carries budget_reset_at per model, the open window's start plus its period, or null when no window is open. The window start is read in the same batched lookup as the spend counter * fix(proxy): count a monthly budget window from its own start when reporting its reset duration_in_seconds measures Nmo from now, so a window opened on January 15 and read on February 10 reported February 12, then null while the counter still refused requests until February 15. duration_in_seconds_from measures the month from the window start, and duration_in_seconds now shares that month arithmetic * fix(proxy): measure months from timezone-aware datetimes and assert whole usage values datetime.fromtimestamp without a tz tripped DTZ006. The month arithmetic now runs on an aware local datetime, which keeps the same wall-clock result. The duration tests move to parametrized functions, since subTest payloads carrying datetimes cannot cross the xdist worker boundary, and the new usage tests compare the whole entry * fix(budgets): end a monthly budget window a calendar month after it opens Each charge recounted the month from the charge date, so a monthly window opened in a month of another length expired days before or after the reset /key/info reports. The window is now measured from its start, by the same helper the usage report uses. A period ending after year 9999 reports no reset instead of failing the read. * fix(budgets): count monthly budget windows in UTC for every router budget Months were counted on the host's local calendar, so replicas with different TZ settings disagreed on when a monthly window ends. Provider, deployment, tag and per-model budgets now all measure a window from its start in UTC, and a test covers the router budgets. A reset past year 9999 is documented as null. * test(budgets): cover monthly tag budgets in the router window test * test(budgets): add integration cells for budget_reset_at and monthly budget windows * test(budgets): find a monthly window in another span on every calendar day --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Kerry Lu
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7faa1a24bf
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test: extend runtime regression contracts for Messages and SDK (#46641)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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yujonglee
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dd0c023338
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refactor(rust-bridge): remove the LoggerRule concept from the catalog (#46711)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Kerry Lu
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1138f8a203
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test(cost): pin litellm's own prompt count over a tool-call history when the stream carries no usage (#46710)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |