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771 commits
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mateo-berri
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598fb8499c |
Merge branch 'main' into litellm_rag_team_provider_credentials
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Keeps main's Path import next to the branch's Final import in the batches endpoint tests, and takes the integration test's closed port from allocate_port so it follows main's TQ012 rule |
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yuneng-jiang
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910c30381b
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test(integration): fix seven CircleCI integration tests that race or assume a worker spread (#47001)
* test(integration): expect the tool search call replayed beside the function call for Anthropic targets * test(integration): wait for the team spend flush before killing a worker * test(integration): poll for the S3 attempt the batch sinks write concurrently * test(integration): queue the terminal object and its sibling into the same s3_v2 flush * test(integration): time the long Bedrock version against an equally long unversioned name * test(integration): pin each half of a burst to one worker instead of hoping the kernel spreads it * test(integration): assert parsed SSE events for live pings instead of transport chunk boundaries |
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Mateo Wang
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b39eadcb82
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feat(openai)!: honor per-deployment workload identity on non-chat routes (#39966)
* feat(anthropic): workload identity federation and pluggable identity sources Backend half of #38818 (internal copy of the fork PR #38013), rebuilt as one commit on top of litellm_internal_staging without the dashboard changes. Deployments on anthropic/ without a static api_key can exchange an OIDC workload assertion for a short-lived sk-ant-oat01 token through a shared RFC 7523 JWT-bearer engine. The assertion comes from a mounted token file, an env token, a LiteLLM-signed issuer, or Keycloak, chosen per deployment, per named credential, or through ANTHROPIC_IDENTITY_SOURCE. The federation fields are server-owned: refused inline in request bodies and on POST /model/new, proxy-admin only on credentials, and the token exchange is pinned to api.anthropic.com unless LITELLM_ANTHROPIC_WIF_ALLOWED_HOSTS adds a host. GET /credentials/{name}/jwks exports the public key set of a LiteLLM-signed credential for the Claude Console. The OpenAI federation trio from #39613 rides along on the backend side with the same server-owned handling. Fixes #28607 Resolves LIT-6107 Co-authored-by: derhornspieler <15236687+derhornspieler@users.noreply.github.com> * fix(anthropic): let batch-result downloads mint from deployment params and accept host:port allowlist entries The files handler enabled workload identity on batch-result downloads but never received the deployment's litellm_params, so a deployment authenticating through a named credential could only mint from process-wide env vars. It now threads litellm_params through to the auth header the way the batch retrieve path already does. LITELLM_ANTHROPIC_WIF_ALLOWED_HOSTS entries written as host:port were read by urlsplit as a scheme, so the allowlist kept the raw entry while the exchange compared bare hostnames and refused the gateway. Entries are now parsed as network locations whether or not they carry a scheme. * fix(types): move the WIF kwargs key sets to a leaf module so the kwargs funnel imports without a cycle * test(anthropic): pin case-insensitive matching of WIF exchange-host allowlist entries * fix(anthropic): end workload identity federation errors without a period so the router suffix reads cleanly * fix(proxy): decrypt stored litellm_params before the WIF write gate * fix(proxy): hide WIF secret references from /health output * fix(proxy): keep the proxy error shape on credential endpoint refusals * fix(proxy): hide identity token file paths from /health output * feat(openai): honor per-deployment workload identity on non-chat routes Image generation, audio speech and transcription, moderation, text completions, files, batches, and fine-tuning built their OpenAI client from the env vars alone, so a keyless deployment carrying openai_identity_provider_id, openai_service_account_id, and openai_identity_token_file failed with the api_key error on every route other than chat, Responses, and embeddings. Those handlers now build their client through the shared workload identity resolver, and the managed batch file-access credential extraction keeps the trio * fix(openai): cache workload identity clients and let deployment identity outrank process-wide keys Non-chat handlers built a fresh SDK client per call, so files, batches, fine-tuning, text completion and sync moderation exchanged the OIDC token on every request. The builders now reuse the cached workload identity client per identity, base, timeout, retries and organization; static-key clients are still built fresh. A process-wide OPENAI_API_KEY (or litellm.api_key / litellm.openai_key) no longer silently overrides a deployment's openai_identity_* params. An explicit distinct api_key on the deployment or call still wins. * fix(openai): keep shared workload identity clients on the process http session The workload identity client is cached and shared across requests, so it must never borrow the transport a single caller passed in. Static-key clients keep using the caller's http client; federated clients always ride the process-wide session. Also makes the http client helpers on BaseOpenAILLM public since the builder now needs them from outside the class. * test(openai): pin partial deployment identity beating the process key * test(openai): type the managed-object invoke callback * fix(anthropic): rename the federation workspace param so Bedrock's anthropic_workspace_id keeps working The Bedrock Claude Platform route already reads anthropic_workspace_id from optional_params, so banning that spelling as a server-owned federation parameter broke a pre-existing client capability. The federation field is now anthropic_federation_workspace_id (env ANTHROPIC_FEDERATION_WORKSPACE_ID), which restores the base branch's behavior for Bedrock callers, drops the Bedrock-specific hint from the refusal message, and deletes the unconditional ban constant that no longer had a reader * fix(auth): share one exchanged token across workers reading the same assertion Anthropic accepts each identity assertion exactly once, so two uvicorn workers reading the same token file both minting from it means the second exchange is denied with jti_reused. Minted tokens now land in a per-user 0700 cache directory guarded by a file lock, so workers on the same host reuse one exchange until the token expires or the assertion rotates. A 401 is only retried when the re-read assertion actually differs, and the denial hint explains jti_reused. LITELLM_TOKEN_EXCHANGE_CACHE_DIR moves the cache and an empty value disables it * feat(openai): honor deployment workload identity on image edits, vector stores, containers, videos, and evals The httpx-based OpenAI handlers built their Authorization header from the deployment api_key or OPENAI_API_KEY alone, so a keyless deployment carrying openai_identity_provider_id, openai_service_account_id, and openai_identity_token_file sent "Bearer None". They now resolve the bearer through resolve_openai_bearer_token, which exchanges the deployment's identity token the same way the SDK-based routes do. A deployment that carries only part of the identity trio now raises a 500 naming the missing field instead of silently falling back to the process key, and the SDK client builders take a factory for the static-key http client so the federated branch never touches litellm.client_session. * fix: keep anthropic federation from being shadowed or leaked An empty or whitespace-only ANTHROPIC_API_KEY counted as set, so a federated deployment sent an empty x-api-key on every call instead of minting a token. Blank values now read as unset, and a real static key on a federated deployment logs once that it outranks federation and nothing is being federated. The exchange-host allowlist matched hostnames only, so a second process on another port of an allowed host was trusted with the workload's identity token. An entry that names a port now trusts that port alone, while a bare host still trusts every port. The shared token store exists so the workers reading one projected token file do not each spend its single-use jti. A source that mints its own assertion per exchange shares nothing with another worker, so it no longer writes a live token to disk for a lookup that can never hit. * feat(openai): resolve per-deployment workload identity for the assistants routes OpenAIAssistantsAPI built its OpenAI and AsyncOpenAI clients from a locals() scrape, so a deployment carrying openai_identity_provider_id, openai_service_account_id and openai_identity_token_file never exchanged a token. Thread litellm_params through the class and build both clients with the shared workload identity builders. * fix: unlink a staged token file a failed write leaves behind The 401 denial hint now also says federation ignores ANTHROPIC_WORKSPACE_ID, which the Bedrock Claude platform provider already reads. * refactor: move anthropic jwks derivation behind a provider-owned tagged union * feat(openai): resolve per-deployment workload identity for image variations Build the image variations client through the shared workload identity builders instead of scraping locals(), and give the async path the async http session it was missing. Let a federation trio serve the model-less batch fallback, and hold the standard LRU size of exchanged tokens. * fix: unlink the staged token file when its write fails at close A buffered write only reaches the disk when the handle closes, so a full disk surfaces at close and left the staging file behind holding a usable token. * fix(anthropic): close the staging descriptor before writing the shared token file * fix(wif): judge federation writes by what they set, not what is stored The admin gate read the stored deployment, so a team admin lost edit, delete and Test Connection on any deployment carrying federation params. It now returns early unless the submitted fields touch the federation surface, and a Test Connection probe that points the deployment at its own api_base is still refused, with the 403 no longer wrapped into a 500 The rest of the same review pass: POST /model/new refuses only a blocking value of `blocked`, so a client that always sends `blocked: false` is not turned away; a request body can no longer pick which federated identity to mint as by naming a stored credential; an advisory refresh the executor refuses disarms the entry instead of wedging the identity until the follower timeout; the static-key shadow warning resolves its env fallback inside the cache instead of once per request; credential writes drop nulls before storing them; the token exchange validates the endpoint URL before reading an assertion and keeps refusing redirects across a client heal; /health hides every server-owned federation field from non-admins; and the async create_file and create_batch paths say which setting is missing when the provider resolves no URL * style(tests): keep two blank lines before the batch_cost_is_final block * test(batches): type the workload identity fallback test fixtures * fix(moderation): stop validating unrelated kwargs on the sync workload identity path * fix(auth): refuse server-owned workload identity keys in array form and inside a client router config * fix(openai): keep a deployment's own key ahead of workload identity on every route Thread the caller's own api_key into the identity resolver for embedding, transcription, moderation, image generation, and assistants so a deployment key equal to the process key still wins. A partial identity trio now falls back to a configured static key, unset max_retries keeps the SDK default, and /v1/decisions plus OpenAI count_tokens resolve the exchanged bearer for identity-only deployments * fix(containers): pass deployment params to container file auth headers Container file routes built their auth headers without the deployment's litellm_params, so a keyless deployment with an OpenAI workload identity sent Bearer None on upload, list, retrieve, delete and content calls * fix(openai): keep process-wide key precedence and move token exchange off the event loop Restore the merge base order where an explicit or process-wide OpenAI key wins over federation, so exporting OPENAI_API_KEY keeps working the way it did before. Run the header-building validate_environment calls (image edit, videos, containers, vector stores, vector store files) and the decisions and System One request preparation in a worker thread, so a slow token exchange no longer stalls the loop. Federate realtime websocket sessions, live sessions, realtime client secrets and transcription sessions, and the OpenAI moderation guardrail when no key is set. * fix(openai): answer 401 for a partial identity trio and federate realtime health checks A deployment with part of the workload identity trio and no static key now fails as an auth error naming the missing params, so the router does not retry it as a server error. The realtime health check sends the exchanged bearer for a keyless federated OpenAI deployment, and API token counting reads the same static keys as every other route before minting. * fix(openai): keep local token counting without an identity trio and federate the moderation guardrail from its own config * fix(openai): skip a partial-trio moderation guardrail and let vault keys win in token counting * test(integration): refuse caller-chosen identity params in user_config and bracketed form keys * fix(openai): send the honored secret manager key from SDK client builders A deployment whose OPENAI_API_KEY lives only in the secret manager now sends that key on image, assistants, files, batches, and other SDK-built routes instead of failing with no api_key, matching what the workload identity resolver already treats as a static key * test(openai): check the token exchange runs off the event loop instead of timing the loop --------- Co-authored-by: derhornspieler <15236687+derhornspieler@users.noreply.github.com> |
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ryan-crabbe-berri
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9cf09c18fe
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fix(providers): route Azure Responses past dated api versions, count Grok reasoning tokens on azure_ai, refresh provider sets after register_model, fix gpt-6.1-sol Azure rows (#46377)
* fix(azure): add responses_api_version so the Responses route stops using the deployment chat api_version * fix(azure_ai): fold Grok reasoning tokens into completion_tokens like the xai provider * fix(utils): refresh provider model sets after register_model * fix(cost-map): align azure gpt-6.1-sol reasoning effort flags with the openai row * test(e2e): cover Azure Responses on a deployment pinned to a dated chat api_version * fix(utils): keep router deployment ids out of provider model sets register_model now refreshes the provider sets, so the per-request and router registrations keyed by a deployment id put those opaque ids into wildcard model listings. Those callers now pass add_to_provider_model_sets=False. * fix(azure): carry responses_api_version through the chat to Responses bridge completion() dropped responses_api_version from the litellm_params it hands the Responses bridge, so azure/responses/<model> on a dated chat api_version still built the 404ing /openai/responses URL. Also cover the auto-router marker never forwarding the new param. * fix(azure): stop listing a wrong set of reasoning efforts when none is rejected The message said only low, medium and high are supported, which is wrong for models that also take xhigh such as gpt-6.1-sol. * test(azure): use the latest grok and gpt-5.4 rows in the new azure tests * fix(proxy): list each wildcard-expanded model once A provider set can hold a model both bare and provider-prefixed, which now happens after any priced request registers its openai/<model> key, so the openai/* listing showed that model twice. * fix(azure): route Responses past dated GA api_versions by default A deployment pinned to a dated GA api_version such as 2024-10-21 for chat now sends Responses calls (create, get, delete, cancel, input_items and the azure/responses/ bridge) to the default Responses version, because those dated versions have no Responses route on Azure and 404. responses_api_version still overrides it, -preview, v1, latest and preview versions are kept, an api-version in the api_base query still wins, and chat keeps api_version as before * chore(azure_ai): mark the grok chunk_parser dict as an override contract * fix(azure): rewrite only the route's /openai segment when moving a call to v1 The v1 rewrite in the Azure URL builder replaced every "/openai" substring in the path, so a gateway api_base like https://apim.example.com/openai-gw/team turned into /openai/v1-gw/team/openai/v1/responses. Dated GA Responses calls now land on the v1 route by default, which made this reachable for them too, so the rewrite now only touches the last /openai path segment. The Responses version resolver no longer duplicates the builder's default for an unset api_version. Tests cover the gateway api_base case, the env AZURE_API_BASE query-string precedence, and the e2e suite now has one test for the dated GA default and one where only responses_api_version can make the call work * test(integration): cover Azure Responses api_version, Grok reasoning spend, wildcard listing and reasoning_effort none Wire tests pin the Azure route and query for chat, Responses and the azure/responses bridge on a dated GA api_version, and check responses_api_version never reaches the request body. Cost cases check azure_ai Grok folds reasoning tokens into completion_tokens and spend, streaming and not. A config-driven proxy checks /v1/models next to an openai/* wildcard lists each id once and never a deployment id. Azure gpt-6.1-sol rejects reasoning_effort none before reaching upstream and drops it with drop_params * fix(utils): list ai21 chat and vertex code models under their provider wildcard register_model now files ai21 chat models into ai21_chat_models, which models_by_provider never read, so a registered ai21 chat model dropped out of ai21/* listings. models_by_provider now reads ai21_chat_models and the vertex code chat and code text sets too. * fix(main): keep caller-chosen model names out of provider wildcard listings --------- Co-authored-by: yuneng-jiang <yuneng@berri.ai> |
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Mateo Wang
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29cd11bec6
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fix(sso): concurrent first sign-ins land on the winner's account, worker-kill test stops waiting on the reap (#46855)
* fix(sso): concurrent first sign-ins land on the account the winner created, worker-kill test pins half its streams to each worker * test(integration): a worker kill returns once the worker is dead, the reap waits at block exit uvicorn can take a full worker healthcheck to reap a worker that died mid-ping, so kill() held the caller that long and peers held on the surviving worker hit their client timeout * fix(sso): a sign-in that loses the first-insert race joins the default teams The losing sign-in could read the winner's row before new_user added the default teams, so its CLI key was minted with no team. The race tests now run the real new_user against a fake user table |
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Mateo Wang
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d76ec3a47e
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fix(proxy)!: answer 400 for malformed tools on count_tokens instead of crashing (#46583)
* fix(proxy): answer 400 for malformed tools on count_tokens instead of crashing A non-list tools value crashed the vector store lookup during auth and the built-in tool cost lookup in the failure logging path, so the proxy answered an unhandled 500 and closed the keep-alive connection. count_tokens now answers a 400 invalid_request_error for a malformed body, drops malformed tools when drop_params is on, and both lookups skip values of the wrong shape. * fix(proxy): skip non-list tools in the tool allowlist check Also validate the count_tokens body without recursion, which the recursion detector rejects. * test(integration): restore the int tools case on count_tokens The proxy now answers a non-list tools value with a 400, so the case is deterministic again. * fix(proxy): reject malformed tools at the LLM route entry and keep vector store checks on malformed ids * fix(proxy): apply drop_params like core when dropping malformed tools, and reject malformed tool vector store ids Check tools after request setup so the Claude Code default and the logged call type apply, read the request, global, and model group drop_params the way core does, drop tool_choice and friends with tools, cover Gemini generateContent, and answer 400 for a malformed tools[].vector_store_ids with the strict flag off * fix(proxy): keep the request's call type when logging a gate rejection after request setup The body-shape fallback relabeled a /v1/messages rejection as acompletion and a list-input /v1/responses rejection as aembedding, so it now only applies when the logging object carries no known call type * fix(proxy): share the malformed tools check across tool routes and honor drop_params per entry * fix(proxy): accept null vector_store_ids, read every named store id in auth, check the responses websocket * refactor(proxy): leave the responses websocket out of the request-level tool check * fix(proxy): let a request's additional_drop_params replace the deployment list and keep compact's call type on rejections * fix(proxy): read router default drop lists and deployment ids in the malformed tools check * fix(proxy): leave malformed tools to the router when it can pick another deployment Fallbacks, auto-routers, model strings, comma batches and team model names let the router pick a deployment after the proxy checks the request, so the proxy now forwards a malformed tools field there as main does, and only rejects it when the deployment it checks is the one that serves. Token counting never reroutes, so count_tokens and input_tokens keep reading the group they count with. additional_drop_params names are read the way core reads them, so a list with a non-string entry still names its strings. * test(integration): cover malformed tools on reroutable models, router defaults and deployment ids * fix(proxy): resolve malformed tools at the deployment the router picks * fix(proxy): keep provider-repaired tool schemas, honor fallback_on_unsupported_params, skip cooldown on malformed tools Empty schemas and JSON-string schemas pass the tools check the way litellm's provider paths already read them. The rejection is a BadRequestError subclass carrying the deployment's provider, so fallback_on_unsupported_params: false keeps the 400, and the router never cools a deployment down for a request litellm rejected before calling it. Adds the audit's pre_call guardrail and cross-provider bridge cells. * test(integration): expect a 400 for an empty-string tools field on /v1/messages Real Anthropic rejects tools "" with a 400 on main too, so the case joins the malformed tools test * fix(router): keep web search routing for tools passed as a tuple * fix(router): keep a deployment's own tools when drop_params drops a malformed request tools * test(router): list _merged_tools as covered through the deployment tools merge tests * fix(router): drop the request's tool settings with its malformed tools when deployment tools replace them * fix(router): skip a tool whose type is not a string in the web search filter instead of raising TypeError * fix(prometheus): keep deployment health metrics clean on a malformed tools rejection * test: type the new malformed tools tests * test: mark new test locals Final and type kwargs dicts |
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Mateo Wang
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57e69ce713
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docs(agents): keep directory rules in nested AGENTS.md and pin integration tests to CircleCI (#46796)
* docs(agents): keep directory rules in nested AGENTS.md and pin integration tests to CircleCI * docs(agents): scope the CircleCI rule to the files it covers and ask agents to read nested AGENTS.md * ci(coverage): reject GITHUB_FILES integration contracts run from a test-unit.yml shard * ci(coverage): report integration ownership findings under their own header * ci(coverage): flag GITHUB_FILES contracts run from any non-dedicated workflow * docs(integration): say a pre-merge run is no reason to route an integration file to GitHub * ci(coverage): tie each dedicated integration workflow to the directory it owns |
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ryan-crabbe-berri
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0dac61a555
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fix(guardrails): accept Gemini tools in generic_guardrail_api, add opt-in forward_messages_as_sent, name the error type on fail-open (#46338)
* fix(guardrails): accept Gemini tools and Anthropic content blocks in generic_guardrail_api, name the error type on fail-open GuardrailToolParam required a type key, so Gemini native tools (function_declarations, googleSearch) failed validation inside the hook and either returned a 500 or skipped the scan when fail_on_error was false. The type key is now optional and left out of the forwarded tool when absent, so an echoing guardrail server cannot write type: null into the provider request. structured_messages was re-validated against the chat completion message types, which silently emptied tool_result, tool_use, image and document content blocks to content: [] and dropped undeclared keys before the guardrail saw them. The request model now forwards rows as opaque mappings, which also retires the write-back shim that restored the original rows for echoed-back entries. The fail-open critical log line now carries error_type so a timeout, a refused connection and an upstream HTTP status can be told apart without the traceback. * fix(httpx): retry aiohttp server disconnects the way httpx does aiohttp raises ServerDisconnectedError when a kept-alive connection is closed by the peer before a response arrives, which is what a load balancer idle timeout shorter than AIOHTTP_KEEPALIVE_TIMEOUT produces. The transport mapped it to httpx.ReadError, which the handler's connection-error retry does not cover, so the request failed instead of being resent on a fresh connection. It now maps to httpx.RemoteProtocolError, the error httpx itself raises for the same condition, so the existing retry fires. * test(httpx): build the stub server reply in one bytes literal * fix(httpx): resend a dropped connection's request as sent and keep a streamed resend readable The connection-error retry closed its fresh client before a streamed body was read, so an aiohttp disconnect now billed a second upstream request and still failed. It also skipped masking on a status error from the resend, resent PATCH and DELETE as POST, dropped multipart files and ignored the handler's ssl_verify. The retry now resends the request the first attempt built, on a client with the handler's TLS settings, closes that client when a streamed body is closed, and masks a status error the same way the first attempt does. * fix(guardrails): log HTTPStatusError, not the masking wrapper, as the fail-open error_type * fix(guardrails): resend a generic guardrail check cut before any response A load balancer that closes an idle keepalive connection made the guardrail POST fail with "Server disconnected", because #46336 only resends POST on ConnectError. AsyncHTTPHandler.post now takes a keyword-only idempotent flag, defaulting to False, that gives a RemoteProtocolError the same single fresh-client resend PUT and DELETE get. Only GenericGuardrailAPI.apply_guardrail opts in, since a guardrail check is a pure verdict over the request. Every other POST keeps the #46336 rule The fail-open log line now puts error_type at the end so the pre-PR text stays contiguous for existing alert matchers test_rows_reach_the_guardrail_verbatim now reads the body off a real local guardrail server instead of mock call args * fix(http): keep a mid-body server disconnect a ReadError and move socket tests to integration A ServerDisconnectedError raised while reading a response body now stays httpx.ReadError, as it was before this PR. Only a disconnect before any response maps to RemoteProtocolError, which is the case the resend rule is about. Stream readers that end on ReadError, like the SAP stream iterator, keep their old behavior The loopback-server disconnect, TLS and guardrail resend tests move to tests/integration/sdk/test_disconnect_resend_wire.py, since tests/unit is in-process only. Unit keeps in-process MockTransport checks for the idempotent POST resend, masking on the resend, streamed and buffered client closing, the guardrail resend, and the guardrail receiving rows verbatim * fix(guardrails): resend a reset guardrail check and keep typed echoes on texts An idempotent POST now resends once on any network error after connect (ReadError, WriteError, RemoteProtocolError), so a guardrail behind a load balancer that answers a stale keepalive with a TCP reset rides it out too. That makes the aiohttp ServerDisconnectedError remap unnecessary, so it goes back to ReadError. This stops PUT and DELETE from stacking litellm's resend on top of aiohttp's own idempotent retry (4 sends instead of 2) and stops Vigil Guard from replaying its analyze POST on a disconnect Every resend now runs inside the method's own error mapping, so a timeout on the resend is a litellm.Timeout like the first attempt and a status error is masked once by the caller A guardrail server that parses rows with its own message types and echoes them back without the keys those types lack (annotations, tool_calls[].index) is read as an echo again, so its texts redaction reaches the LLM. A row only counts as an echo when no value changed and every key the chat completion types declare is kept The wire tests for the PUT/DELETE resend, its masking and its TLS settings now run on the httpx transport, where litellm's resend is the only retry, and the POST and guardrail tests cover both transports and both cut styles * test(guardrails): black box wire tests for generic guardrail shapes, fail-open error type and idle cut resend Integration tests against an owned proxy and scripted peers. Gemini native tools reach the guardrail and Gemini as sent, the guardrail sees chat rows and an Anthropic image block as sent, an echoing guardrail leaves a /v1/messages request unchanged, the fail-open log line names ConnectError, Timeout or HTTPStatusError, and a guardrail check cut on an idle pooled connection is resent once on each worker. Each fails at the merge base and passes at the head * fix(guardrails): check echoed rows without recursion and keep pytest.raises to one statement * refactor(guardrails): move the dropped-connection guardrail resend out of this PR The resend on a dropped or hung generic guardrail call, and the shared AsyncHTTPHandler retry rework behind it, move to a follow-up PR that adds a per-guardrail num_retries setting. This PR keeps the request and response shape fixes and the error_type on the fail-open log line * test(guardrails): pick the closed guardrail port from the integration port allocator Reading a port back from a port-0 bind trips TQ012, so the fail-open connect error case now takes its unused port from allocate_port * feat(guardrails): forward structured_messages as sent only behind forward_messages_as_sent generic_guardrail_api keeps main's OpenAI-format rows and echo comparison by default. Setting forward_messages_as_sent: true on the guardrail sends rows as the client sent them and treats an echo that only drops undeclared keys as unchanged --------- Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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yuneng-jiang
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6530b329f4
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fix(proxy): give /team/delete transactions a 30s timeout (#46979)
Both interactive transactions in /team/delete used Prisma's 5s default. A lock wait or a short database stall past 5s expired the transaction after the keys were already deleted, so the call returned 503 and left the team row, memberships and team models behind |
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devin-ai-integration[bot]
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8bb8ac7f18
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fix(proxy): keep a retried /v1/messages stream's spend log by logging the superseded attempt as a failure (#45136)
* fix(proxy): keep a retried /v1/messages stream's spend log by logging the superseded attempt as a failure A /v1/messages stream the router retries before any content shares one logging object across both attempts. The superseded attempt's end-of-stream logging ran as a success (a usage-only or placeholder result) and claimed the request's one-shot success log, so the recovered stream's spend row was either missing or written under the superseded attempt's message id. The passthrough logging path now logs an Anthropic stream whose frames carry an `event: error` as a failure with whatever usage the provider reported, the way a dropped stream already does, and the router closes the superseded stream before it opens the retry so that logging is queued first. * fix(proxy): log a provider error frame through an attempt-scoped logging copy The error-frame failure stashed the superseded attempt's usage on the logging object the retry shares, so the retry's success log carried the failed attempt's token counts. The failure now logs through a copy of the logging object with its own model_call_details, and the helper moves into the Anthropic passthrough logging handler so the proxy streaming handler only routes by endpoint type. * fix(proxy): scope the error-frame failure log to streams closed at the frame A stream that ran to its end at the provider's error frame kept logging through the success route before this PR, and the attempt-scoped failure log is now taken only for a stream the router closed at that frame, with the copy made at the close so the retry's deployment never reaches it * chore: drop duplicate litellm import and mark the stream_ended rebind * fix(proxy): log only router-superseded attempts as error-frame failures A client that hangs up at a forwarded error frame closed the stream the same way the router closes a superseded attempt, so a non-retried stream lost its spend. The router now closes a superseded attempt through aclose_superseded_attempt, and only that close routes the attempt to the failure log * test: type the _settle helper's recorded events --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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AxelRay
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388251a5b8
|
fix(bedrock): drop dangerous-tool-use beta without safeguards (#44471)
* fix(bedrock): drop dangerous beta without safeguards * fix(bedrock): drop the dangerous-tool-use beta only next to structured outputs, keep it on Mantle Bedrock rejects the pair with 'invalid beta flag' on Claude models before Opus 4.7, while requests without structured outputs and Bedrock Mantle accept the beta, so the drop now only applies to Invoke requests with output_config.format or a strict tool and no safeguards. * fix(anthropic): keep the AnthropicThinkingParam import that main now uses * fix(bedrock): drop the client's dangerous-tool-use beta whenever safeguards is absent Bedrock rejects the beta with 'invalid beta flag' on Sonnet 4.5, Opus 4.5, and Opus 4.6 even without structured outputs, so dropping it only next to structured outputs left those beta-only requests failing. The beta does nothing without safeguards, so Invoke now drops it in every such request. Mantle keeps it. The thinking display helper goes back to main's version. * fix(bedrock): drop the client's dangerous-tool-use beta on Invoke chat without safeguards * test(bedrock): wire cells for the dangerous-tool-use beta on Invoke messages, chat and responses * test(bedrock): type the new dangerous-tool-use beta tests * test(bedrock_mantle): type the dangerous-tool-use beta Mantle test --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Irvallen Saragih
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d6b54ae2b0
|
fix(openai)!: route gpt-transcribe to GPT audio transcription config (#44021)
* fix(openai): route gpt-transcribe to GPT audio transcription config * test(openai): move gpt-transcribe tests under tests/unit * fix(openai): bill gpt-transcribe from provider usage seconds when duration is unknown Fall back to the provider-reported usage.seconds when neither a measured file duration nor a response duration is available, so json responses for undecodable uploads are no longer billed at zero. Move the routing test next to the other provider config tests and replace the structure test with a behavior test. * fix(openai): bill provider usage seconds only for gpt transcription models * fix(cost): decide provider-billed transcription seconds from the deployment model, not its pricing id * test(integration): cover gpt-transcribe default request, provider-billed spend, and health probe --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
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33b73207e3
|
test(cost): rekey vertex gemini, add bedrock mantle gpt and claude-opus-5-5, drop suite-only price fields (#46962)
* test(cost): remove price fields the real gemini and azure rows do not carry Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(cost): key vertex gemini cases on the vertex_ai prefixed rows and add gemini-4-argon Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(cost): add current gemini and vertex usage keys to stored responses Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(cost): add bedrock mantle claude-opus-5-5 cost tracking cases Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(cost): add bedrock mantle gpt cost tracking cases Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
|
c1a8575e58
|
test(integration): remove BUG skip for 1 test that passes on main (#46937)
test_two_identical_cache_hit_rejects_write_matching_spend_rows passed every run with the skip removed, so the dropped spend row it was guarding against no longer reproduces Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Kerry Lu
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1451aed726
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fix(tests): drain SDK logging before restoring callbacks (#46924)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
|
f811a5923e
|
test(cost): bill vertex gemini-3.8-flash reasoning tokens at the reasoning price (#46939)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
|
f8217d2a03
|
fix(count_tokens): apply context_management edits in the Gemini /v1/messages/count_tokens count (#46843)
* fix(count_tokens): apply context_management edits in the Gemini /v1/messages/count_tokens count /v1/messages runs clear_tool_uses context edits before it sends an Anthropic body to a Gemini or Vertex AI deployment, but /v1/messages/count_tokens counted the body unedited, so a request with older tool results cleared counted about 2.8x what inference bills. The count now runs the same history and context-edit step inference runs, honors additional_drop_params on the deployment, and skips compact edits so a count never makes a paid summary call. * fix(count_tokens): match the Gemini count to inference for compact edits and drop opt-outs Run compact edits in the count without the summary call so trigger checks and history slicing match /v1/messages, honor a request-level additional_drop_params, keep the full body when any deployment in the model group drops context_management, and answer 400 naming a block Gemini cannot count when an edit trigger fails on it * fix(count_tokens): count the unedited body when an edit fails and honor deployment ids in the drop check * fix(count_tokens): log a failed edit in the count at debug, since the send already logs it * fix(count_tokens): resolve a deployment id before wildcard routes in the drop check, as the router does * fix(count_tokens): honor the router-wide additional_drop_params opt-out in the drop check The router merges default_litellm_params between the deployment and the request, so a router-wide context_management opt-out makes /v1/messages send the full history. The count now reads it in the same order. Adds the audit's wire tests for the count's context edits. * fix(count_tokens): read the opt-out of a deployment the router serves by its litellm model name * fix(count_tokens): read the opt-out and drop_params from the deployments the router and completion_model send to * fix(messages): drop an invalid context_management spec under drop_params so the send matches the count * fix(count_tokens): give Claude Code and Codex counts the drop_params default /v1/messages gives them The proxy turns on drop_params for Claude Code and Codex user agents on /v1/messages. The count now applies the same default, so an invalid context_management spec drops on both endpoints instead of the count answering 400 while the send answers 200. The wire test also covers default_fallbacks, model_group_alias, and completion_model routes. * test(count_tokens): cover a coding agent that turns drop_params off and stop two count tests running as Claude Code |
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devin-ai-integration[bot]
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86a91f35de
|
fix(responses): log and bill every round of non-stream MCP auto-execute (#43667)
* fix(responses): log and bill every round of non-stream MCP auto-execute Both model rounds run as internal sub-calls on the request's logging object, and the outer call logs the response the client received with usage, cost, and cost breakdown summed across the rounds. The internal-call gate now also covers the sync success callbacks, so an inner round no longer takes their first-wins slot * fix(caching): let the parent call log and bill an internal sub-call's cache hit A round of non-stream MCP auto-execute that the response cache served fired the cache-hit callbacks on the shared logging object, so the spend log kept round 1's function_call row and dropped the billed row for the answer. Internal sub-call cache hits now fire no callbacks and carry a zero cost, and the summed cost breakdown leaves out rounds served from the cache * fix(logging): keep a parent row unique when its response came from the response cache A RAG query or an MCP turn whose final round is served from the cache hands back the cached response's id, which already belongs to the row that cached it, so SpendLogs dropped the new row as a duplicate. Such a row now gets the cache-hit id suffix, and it is logged as a cache hit when it cost nothing. * fix(logging): skip the zero-cost warning for a response served from the cache at no charge A non-stream MCP auto-execute request whose rounds the response cache served is priced at $0 before the parent marks it a cache hit, so it logged a false 'priced at $0' warning and counted in litellm_zero_cost_requests_total. The diagnostic now treats a response the cache served at no charge the same as a marked cache hit * fix(responses): bill the paid first MCP round when a later step of the request fails * fix(responses): log each MCP tool call under its own id so a failed follow-up keeps its spend row The non-stream auto-execute path passed the request's call id to the tool call, so the tool's spend row and the request's failure row shared one request_id and the failure row was dropped on insert, leaving the paid first round unbilled. * fix(responses): bill round 1 on a client disconnect, keep MCP tool rows in the request's session, and charge a fixed margin once per request * fix(responses): keep bridged MCP auto-execute in one session and trace and continue its conversation The bridged follow-up no longer chains on round 1, so a thinking model keeps its reasoning and the request stays in one session. The tool call log continues the request trace without renaming it, the tools are fetched once, and a later turn finds the MCP turn under the id the client got, skipping the tool rows. * fix(responses): log the non-stream MCP tool listing in the request's session and trace * fix(responses): find a bridged MCP turn by the id the client holds and log its listing in the bridged trace * fix(responses): derive the MCP tool row id from the request's call id The tool call's spend row now uses <call id>_<tool call id>, so it stays linked to the request without colliding with the request's failure row. Also tighten the internal cache-hit test to a fixed clock and typed params. * fix(responses): bill a retried MCP attempt for its own rounds A router retry or fallback reuses the request's logging object, so a failed attempt's stashed round 1 usage made the next attempt's success row log stale tokens. Each non-stream MCP attempt now starts without that stash. * fix(responses): bill every failed MCP attempt's paid first round on the request failure A retry or fallback now sets a failed attempt's stashed round 1 aside instead of dropping it, and the proxy's failure row adds it to the last attempt's own, so a request whose later attempt fails on its first call still bills the round the provider charged. A cached final round also keeps the paid first round's cost breakdown. * fix(responses): keep MCP tool catalog and results out of the bridged next-turn replay * fix(responses): keep MCP tool spend, client body keys, and Langfuse traces working on auto-execute Count the MCP tools an auto-executed Responses API call ran in the tool spend rollup, accept a client body key named previous_response_client_ids without a 500, and point MCP listing and tool spans at the trace Langfuse gives the parent request, including x-litellm-session-id requests * fix(responses): join MCP spans to the Langfuse trace when the request sends body metadata * test(integration): cover non-stream MCP auto-execute billing, failure rows, and bridged follow-ups --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
|
ee4860dd84
|
test(cost): add tier 1 cost tracking cases for gpt-5.6-terra, gemini-4-argon and gemini-3.1-pro-preview (#46931)
* test(cost): add tier 1 cost tracking cases for gpt-5.6-terra, gemini-4-argon and gemini-3.1-pro-preview Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(cost): add audit 3 shape cases for vertex_ai and azure_ai claude Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(cost): mark azure gpt-5.6-terra priority case as known bug Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
|
7ddc9f1c96
|
fix(bedrock)!: keep reasoning, thinking blocks and the json_mode finish_reason on fake streams (#46863)
* fix(bedrock): keep reasoning, thinking blocks and the json_mode finish_reason on fake streams A Bedrock Converse model without native structured output fakes the stream when response_format carries a json_schema. The fake stream transformed the response without json_mode and rebuilt a chunk from text, tool_use and token counts only, so it dropped reasoning_content, the signed thinking_blocks and the reasoning token count, and ended on finish_reason tool_calls. The three fake-stream sites now pass json_mode to _transform_response and stream the resulting ModelResponse through the shared convert_model_response_to_streaming converter, which now numbers each streamed thinking block with its index the way real streams do * fix(bedrock): parse text tool calls on Converse fake streams like the non-stream answer * fix(responses): stream the answer text when one chat chunk carries reasoning and the answer * fix(streaming): keep every field on the OpenAI and openai_like fake streams and split multi-block thinking Azure o-series and openai_like fake streams now use the shared MockResponseIterator, so extra tool calls, reasoning and cached and reasoning tokens survive; the Bedrock-local iterator is deleted. The /v1/responses bridge also splits a chunk whose thinking block is signed or redacted with no reasoning text, and the sync iterator closes the reasoning item before the answer opens, like the async one. The /v1/messages bridge gives each thinking block in a chunk its own content block, starts a new block when the thinking block index changes, and streams redacted thinking as redacted_thinking. Snowflake replays drop the stream index from thinking blocks. * fix(anthropic): stream each thinking block's text once on /v1/messages An unsigned thinking chunk now opens its content block with empty text, since its thinking_delta carries the text. A multi-block chunk keeps each piece's thinking block, so an unsigned block followed by a signed one keeps its own text and index. * docs(anthropic): describe which thinking block start carries its text * fix(streaming): close merged reasoning before the answer when one chunk carries both * test(streaming): cover fake-stream reasoning across the proxy bridges and type the Snowflake replay test |
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devin-ai-integration[bot]
|
e45f2737d3
|
fix(proxy)!: delete callbacks configured under callbacks or failure_callback (#42740)
* fix(proxy): delete callbacks configured under callbacks or failure_callback Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): assert on configured callback rows and use a callback absent from CI configs Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(e2e): follow logging suite conventions for callback delete tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): flatten remaining callbacks with a helper generator Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): type callback delete helpers to satisfy basedpyright budget Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): save callback config without mutating the loaded dict Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): delete callbacks from every litellm_settings list GET shows POST /config/callback/delete only searched success_callback while GET /get/config/callbacks also lists failure_callback and callbacks, so the Admin UI Delete returned 404 for those rows Remove the name from every list that holds it, matching entries case-insensitively, and skip lists the config file owns when the database owns another copy so the save is not refused Replace the e2e test with an integration test: the shared e2e stack config owns litellm_settings.callbacks, so it cannot exercise that list * test(proxy): cover config-owned success_callback on callback delete and wait on runtime state Add the mirror mixed-ownership case where the config file sets success_callback and the database owns failure_callback, which main refused with a 400, plus a string-valued list case Poll GET listings and /active/callbacks instead of reading them once, so a request served by a worker that has not synced yet cannot fail the test, and reuse the existing callback list key map in the route * test(proxy): annotate callback delete test helper locals with Final * fix(proxy): save callback delete through the config it read so concurrent settings edits are kept --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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ryan-crabbe-berri
|
977a581213
|
fix(router): stop queueing budget increments for Redis when no Redis is configured (#46831)
* fix(router): stop queueing budget increments for Redis when no Redis is configured Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(proxy): assert Redis-less model budget limiter tracks spend locally without queueing Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
|
859529ccbc
|
fix(realtime)!: keep turn_detection.type when a transcript guardrail disables auto-response (#44939)
* fix(realtime): keep turn_detection.type when a transcript guardrail disables auto-response * test(realtime): type the GA session.update helper and mark its locals Final * test(realtime): expect typed turn_detection on the voice guardrail session updates * test(realtime): accept either gate path for the client's first voice session.update * fix(gemini): drop VAD tuning when the guardrail turns auto-response off * fix(gemini): check create_response without a redundant isinstance * fix(vertex_ai): keep the client's VAD tuning on Vertex when auto-response is off * fix(realtime): keep the transcript-guardrail gate typeless on xAI so it does not auto-answer * fix(realtime): replace any client turn_detection with the untyped gate on xAI xAI answers on its own under a typed server_vad even with create_response off, so a client's typed turn_detection must not reach xAI while the transcript guardrail gates the turn. * fix(realtime): keep a client's early turn_detection and hide the guardrail cancel's error A session.update sent before session.created reached OpenAI ahead of the proxy's own gate, so the gate replaced the client's turn_detection. The proxy now re-sends the client's gated turn_detection after the gate when the two differ. The guardrail's response.cancel now carries a litellm_guardrail_ event_id, and the error OpenAI returns for it (response_cancel_not_active when nothing is answering) no longer reaches the client. * fix(realtime): send the guardrail gate before a client's early session.update and let Muse and Chirp fall back to server_vad under drop_params * fix(realtime): hide only the cancel errors that echo the guardrail's own response.cancel ids Track the event ids of the guardrail cancels this session sent and drop a backend error only when its error.event_id is one of them, so a client's own event id with the same prefix still gets its error. Cover the scripted response.cancel reply in the integration upstream, update the stale list-mode test to the typed gate, and mark the xAI cancel error cell as an expected failure, since xAI replies with its own event id. * test(integration support): deliver the disconnect a real server sends after the client goes away The scripted upstream now keeps reading frames after a reply hits a closed socket, so the unit test's fake ASGI receive has to end with the websocket.disconnect message a real server delivers. * fix(realtime): delete the blocked transcript item before the guardrail's refusal turn OpenAI GA answered the blocked question in some refusal turns because the blocked user item stayed in the conversation. The raw OpenAI-protocol path now sends conversation.item.delete for it right after the cancel. Provider config backends have no such event, so that path no longer passes an item id. Also bound the guardrail integration file by the shared graceful-stop timeout and make the xAI cancel-error xfail require an AssertionError. * fix(realtime): keep the blocked item on xAI, which cannot delete an audio input item * test(realtime): assert the xAI voice block sends no conversation.item.delete upstream --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Ayush Shekhar
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7930c0e434
|
feat(router): mid-stream fallback continuation for chat completions (#41127)
* feat(router): continue chat streams on fallback after a mid-stream break A chat-completions stream that breaks after content has been delivered was re-raised and the fallback deployment never ran (#40404), while the Responses-API path already continues via a prefilled assistant turn. Add the same for chat completions behind `enable_mid_stream_fallback_continuation` (opt-in, async-only), so a post-content break re-enters the fallback chain with the partial text as an assistant prefill instead of surfacing the error. Continuation is only attempted when it is safe: the stream emitted plain assistant text (no tool/function calls, thinking blocks, reasoning items, audio or images), the request is not constrained output (response_format or a forced tool_choice) and not merge-reasoning mode, and the fallback target's model supports assistant prefill. The target check runs as a deployment pre-call filter, so a chain with no prefill-capable deployment empties and the original error is surfaced rather than a duplicated or rejected request being sent. Plain reasoning_content is treated as out-of-band and does not block a continuation, matching the Responses-API path; Anthropic thinking is excluded because its signed thinking blocks cannot ride a text-only prefill. CustomStreamWrapper now latches whether a disqualifying delta was streamed and carries it on MidStreamFallbackError so the router can decide without re-scanning chunks. Default behavior is unchanged. * fix(router): harden mid-stream continuation eligibility and prefill folding Address review of the mid-stream continuation: - keep response_format={"type": "text"} eligible; only json_object / json_schema and other structured formats decline, since "text" is the unconstrained default - fold a new partial into an existing trailing assistant prefill instead of appending a second one, so a nested mid-stream break stays a single prefill turn even on providers that do not merge consecutive assistant messages - split the deployment prefill-capability lookup into two readable steps - cover the merge_reasoning decline branch, the text response_format pass, and the prefill-folding path with tests * fix(router): address review of mid-stream continuation - register the continuation deployment filter as a process-global singleton instead of tracking it per router, so discarding one router no longer removes the type-deduplicated filter that other live routers still depend on - disqualify reasoning_content: it reaches the caller as visible reasoning that a text-only prefill cannot carry, so a reasoning-capable fallback would re-derive it and produce an incoherent response - move MID_STREAM_CONTINUATION_KWARG to constants and lazy-import the filter class in the router, breaking the module-level import cycle CodeQL flagged - trim the added docstrings to the repository comment policy * fix(router): keep mid-stream continuation within the type-discipline budget The LIT* type-discipline gate counts mutable-collection use the ruff pass does not see. Annotate the reads with read-only Sequence/Mapping views, compare response_format against a frozen MappingProxyType sentinel, and mark the genuinely mutable litellm message and deployment-list boundaries with mutable-ok reasons, so LIT001 and LIT002 stay under their limits. * fix(router): clear basedpyright operator error and the import-cycle alert - narrow the accumulated stream delta content to str before appending, so the shared helper no longer trips reportOperatorIssue (str += object) - keep the continuation marker in the pre-call-check module and lazy-import it at the two router call sites instead of routing it through constants, so the router has no module-level dependency that closes an import cycle * fix(router): make the mid-stream continuation marker unforgeable The deployment filter is process-global and read the continuation marker as a truthy value from request_kwargs. Since the proxy can forward arbitrary request-body fields into the router, a client could send `"_mid_stream_continuation": true` on a normal request to filter a mixed model group down to prefill-capable deployments and steer their prompt onto that provider. Set a private sentinel object internally and check it by type instead of truthiness: a JSON request body cannot construct one, so a forged flag is ignored. Adds a regression test that forged client values (true, "true", 1, a dict) leave the deployment list untouched. * feat(router): honor per-deployment model_info for continuation prefill capability The deployment filter only read supports_assistant_prefill from the cost map, so a model configured with `model_info: {supports_assistant_prefill: true}` on the deployment (the usual way to declare a capability for a model that is not in the cost map, or to override it) was wrongly skipped. Check the deployment's model_info first (True or False both authoritative), then fall back to the cost map. Adds tests for the override in both directions. * fix(router): bound mid-stream continuation to the caller's output allowance A continuation handed the fallback the full max_tokens / max_completion_tokens even though part of the answer had already streamed, so the total could exceed the caller's limit and repeated interruptions could re-grant the allowance on every hop, amplifying spend. Reduce each configured ceiling by the tokens already emitted before re-entry, and decline the continuation (surface the original error) when the allowance is exhausted. The reductions compound across hops because the trimmed ceiling is what the next hop sees. Also add the missing explanatory comment on the model_info fall-through except. * refactor(router): extract mid-stream decline to stay within the complexity budget The output-ceiling branch pushed stream_with_fallbacks past the C901 limit. Both decline paths (ineligible after content, and exhausted allowance) re-raised the original provider error the same way, so pull that into _raise_original_mid_stream_error. It removes the duplicated branching, drops the function back under the complexity ceiling, and reads clearer. * fix(cost-map): mark Claude Sonnet 4.6 as not supporting assistant prefill The Anthropic API and Bedrock both reject an assistant-last message on claude-sonnet-4-6 with "This model does not support assistant message prefill", so the continuation filter must not route a mid-stream continuation to it. Flip the flag on every claude-sonnet-4-6 entry and on databricks' opus-4-6 entry, whose anthropic sibling already says false. The prefill check tests now register synthetic cost-map entries instead of pinning real models, which broke when claude-3-opus left the map. * refactor(router): type the streaming fallback kwargs and drop narration comments Removes an unused mutable-ok marker (LIT013) and the explanatory comments Greptile flagged, and types initial_kwargs on both streaming fallback iterators as dict[str, Any] like their Responses siblings so the new continuation helpers take a typed mapping and the basedpyright budget stays at or below main. * fix(router): run post_call_rules over the joined text of a continued stream Each provider stream wrapper validates only the fragment it produced, so a rule that trips on the primary's prefix plus the continuation's text never fired. The router now feeds litellm.post_call_rules the emitted text plus every continuation chunk before yielding it. Also drops a narrating test-helper docstring. * refactor(router): read a deployment's declared prefill support without an empty except * test(router): cover _raise_original_mid_stream_error directly for the router coverage gate * fix(router): fold the continuation prefill into a trailing assistant turn A request that already ends in an assistant message took the partial output as a second assistant turn, which prefill providers reject. The partial now extends that turn in place, with prefix set, so the continuation request never ends in two assistant messages. * fix(router): keep content blocks on a trailing assistant turn when extending it with the partial output A trailing assistant message whose content is a list of blocks was stringified before the partial output was appended, so the fallback received the Python repr of the blocks as its prefill. The partial now lands as one more text block, and a content shape that cannot be extended surfaces the original stream error instead of a garbled continuation. * fix(router): continue the trailing text block in place when extending a prefill with content blocks Anthropic strips trailing whitespace per content block, so appending the partial output as a new text block after an assistant turn that ends in "Sure, " would send the prefill as "Sure,here". The continuation now extends the last text block itself and only opens a new text block when the trailing block is not text (an image, say), so the prefill reads exactly as the text the caller already received * test(router): type the prefill check's deployment helper * fix(router): keep a continued stream on the primary's identity and widen what can continue Continuation chunks now keep the primary stream's id and created time and drop the fallback's second role delta. Requests with n > 1 no longer continue. Prefill support reads model_info.base_model and litellm_params.base_model before the deployment model. enable_mid_stream_fallback_continuation is runtime-updatable through update_settings and registers the prefill filter. The bridge rebuild of MidStreamFallbackError keeps the inner stream's disqualifier. A fallback that repeats the prefill is not doubled in the continuation input. * fix(router): decline mid-stream continuation after a content filter, merged reasoning, or a plain trailing assistant turn Also count the partial stream's completion tokens in the continued stream's usage, accept string values for enable_mid_stream_fallback_continuation, and list the new router helpers in the router coverage check * fix(router): drop re-emitted whitespace at the continuation join and sum both legs' usage consistently * fix(router): keep the full prefill so a nested break keeps the separator * fix(router): bill both calls of a continued stream and fix Bedrock Converse, Databricks, and Vertex DeepSeek prefill routes * test(router): integration cells for mid-stream fallback continuation Covers the continued stream the caller gets, the prefill turn and reduced max_tokens the fallback receives, the prefill-capability filter, the runtime toggle, repeated requests through the response cache, and the spend row of a continued stream * fix(router): log a continued stream under the caller's id, price each cut on its own prompt, and keep prefill flags per deployment * fix(router): price and log each cut leg before its continuation reuses the logging object Price the cut stream's partial and let its failure log finish (bounded) before the fallback overwrites a shared proxy logging object, and skip continuation when an output guardrail is registered or a request fallback carries its own messages. * test(router): integration cells for the output-guardrail and request-fallback declines and the cut call's failure log Cover a registered post_call guardrail and a request fallback carrying its own messages keeping the original error, an input-only guardrail leaving continuation on, and the cut call's failure event naming its own deployment and price. * fix(router): skip mid-stream continuation when a request fallback overrides request params A request-body fallback entry that sets anything besides model (stream, messages, max_tokens) changes the request the prefill continuation relies on, so a stream: false entry ended the answer with no error. * fix(router): skip mid-stream continuation for a fallback group target that overrides request params The override check read only top-level fallback entries, so a target inside a group's list, such as {"primary": [{"model": "backup", "stream": false}]} in the request, the config, or a key's router settings, still ended the answer with no error. * fix(router): check only the request's own fallback chain for request param overrides Scanning every group's chain turned continuation off for all requests when one unrelated group's target overrode a param. Resolve the chain the router will use for this request, the same way the fallback path does. * fix(router): keep the original error when request fallbacks are malformed The request-param override check resolved a standard-shape chain outside the router's own error handling, so an empty entry or a non-list chain raised StopIteration or TypeError and replaced the original mid-stream error. Skip empty entries and treat a non-list chain as no chain. * fix(router): read deployment-configured constraints, decline deployment-id fallbacks without prefill, strip only the last Bedrock text block * fix(router): read a deployment's tool_choice ahead of router defaults in the continuation check, and restore the renamed prefill test names * fix(router): filter continuation deployments on every routing strategy, read extra_body and fallback deployments' own params The deployment filter now also runs on the sync selection path that usage-based-routing v1 and custom strategies use, one predicate reads n, response_format, tool_choice and merge_reasoning_content_in_choices with extra_body laid over them, fallback deployments whose own config constrains the output are skipped, and continuation declines when the cut deployment's id is not in the router (per-request credential deployments) * fix(router): decline a cut mid-echo, reduce extra_body output limits, and check post_call_rules once on nested continuations --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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|
Deepanshu Pal
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d5ce7dbb8c
|
fix(proxy)!: honor caller litellm_session_id in spend log session_id (#44199)
* fix(responses): scope session history lookup to the referenced key * Implement SQLite-backed DB for session tests Added a SQLite-backed database class for testing and a new test to ensure session isolation between different API keys. * fix(proxy): honor caller litellm_session_id in spend log session_id Updated docstring to clarify session ID resolution logic for spend log rows. * test(proxy): caller litellm_session_id wins over the trace id * style(proxy): fix blank lines around new tests * fix(proxy): read the caller session from the logging payload for spend logs * fix(responses): match user and team too when replaying session history * fix(proxy): keep bridged Responses chains whole and scope session history by owner * fix(proxy): keep a metadata session next to a trace header and let session headers win * fix(proxy): send the trace-header session only to spend logs and let session headers win beside a trace header * fix(proxy): drop a client-sent caller session and keep it out of requester metadata * fix(proxy): keep the trace id as the spend log session when the caller session contains a NUL byte * fix(responses): keep a continued bridged chain's session under request_correlation_in_logs With the flag on, the caller session outranked the chain session the bridge sets for a continued Responses chain, so a chain whose session changed or first appeared on a later turn split. The continuation now logs under the chain's session and drops the turn's caller session key, as on main. Adds the /audit integration cells for the spend log caller session. * fix(responses): log a continued bridged chain under its session through the spend-log key only The previous commit set the chain session as the turn's litellm_session_id, which also moved the OTel conversation id of a continued turn away from its trace header. The bridge now writes the chain session into the internal spend-log session key of the metadata the spend log reads, so only the spend log session changes, as on main. Adds an OTel cell for a continued chain. * test(spend): cover bridged chains that share one caller session * test(spend): cover the caller session on configured pass-through routes * test(spend): type the SQLite fake and split stacked comprehensions in the caller session tests --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
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4e2dd65549
|
fix(interactions): keep background interaction bills through crashes, late restarts, and deployment keys (#45743)
* fix(interactions): keep background interaction bills through crashes, late restarts, and deployment keys A settlement claim is now a lease, so a row whose settler died between claim and outcome is claimable again after the lease and boot resumes it. A resumed poll gets the full poll window and fetches with the serving deployment's credentials from the live router. The stored create context keeps the request tags and the request metadata that the creating worker's own spend row used * fix(interactions): bill a lapsed claim's takeover only when no spend row exists A settler that billed and then died, or failed to record the outcome, left the row unsettled, so the replica that took over its lapsed claim billed the interaction a second time. A takeover now looks up the spend row keyed by the interaction id and records the row as billed when it finds one. * fix(interactions): settle Vertex AI and edited deployments, never double bill with spend logs off A takeover with spend logs off records the interaction unsettled instead of billing it again, and a release records its outcome before freeing the budget reservation. A resumed or live fetch carries the deployment's Vertex AI project, location, and credentials, and a stale deployment id falls back to the model group only when one same-provider deployment serves it. A poll stops at the first 400, 401, 403, or 404, string request tags no longer fail the create, and settled_at gets an index built concurrently. * fix(interactions): leave a live replica's interaction to it when a resumed poll cannot read it A replica that boots with credentials unable to read an interaction got a 404 on its resumed poll and settled the row as unsettled at once, so the replica that created it, still polling, found it settled and never billed it. A resumed poll that gets a 400, 401, 403, or 404 now stops fetching and keeps checking the row until its poll window ends, and settles it as unsettled only when no other replica did. * fix(interactions): keep the creator's metadata on a resumed bill and retry outcome writes inside the claim lease A bill another replica writes now carries the metadata the creating worker's own spend row is built from, so a client's metadata.model_group no longer relabels it. A failed outcome write is retried for half the claim lease before the budget reservation is left to a takeover, and the creating replica retries every fetch error for its whole window again, as main does. The resume integration case now checks that a resumed fetch uses the deployment's own key. * fix(interactions): resume background interaction polls only after database models load Boot resume ran before the proxy loaded the models stored in the database, so a resumed poll for a model added through /model/new or the Admin UI fetched with env credentials. A 4xx there stops a resumed poll, so the bill was lost. The settlement now installs after the database models load. The integration test resumes a database model with an eager first fetch and fails with the old startup order. * fix(interactions): renew a settling claim so a slow settle is never taken over and billed twice A settle runs every success callback in turn, and one slow callback could hold it past the 5 minute claim lease. A booting replica or a DELETE then took the live claim over, found no spend row yet, and billed the interaction a second time. The settler now renews its claim every third of the lease until it records the outcome, so only a settler that stopped renewing is taken over. A takeover also read the spend log after writing its claim, so a failed read left the row under the taker's own fresh claim, which its poll then took for another settler's and left unbilled until a reboot. The spend row is now read before the claim is written, so a failed read takes nothing and the claim is retried on the next poll. * test(spend): cover lapsed claim takeovers, rotated deployment keys, late restarts, and resumed bill tags --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
|
2dc6aafa6b
|
fix(proxy)!: count prompt-only token_counter requests with the provider when call_endpoint is set (#46839)
* fix(proxy): count prompt-only token_counter requests with the provider when call_endpoint is set * fix(proxy): keep the local count for a prompt-only token_counter request when the provider count fails * fix(proxy): log a prompt-only token count fallback as a one-line warning * fix(proxy): send a prompt-only token_counter request to the provider only on Gemini and Vertex AI * fix(proxy): count a token_counter prompt sent next to an empty messages or contents list * fix(proxy): count a prompt next to messages on the local token count instead of answering 500 * fix(proxy): answer 400 for a token count prompt next to a conversation unless drop_params is set With drop_params the conversation is counted without the prompt. On the local count, a prompt sent with system or tools is counted as a user message with them, like the messages form. * test(proxy): count tools with a prompt in the token counter fallback test * test(proxy): cover prompt counting on /utils/token_counter in the Gemini count wire tests * test(proxy): name the token counter fallback test by what it checks |
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Marcus
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642a2ad934
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fix(prompt-templates): treat initial_prompt_value and final_prompt_value as optional (#39762)
* feat(dd_span_tagger): emit litellm_user_email span tag for JWT-authenticated requests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(dd_span_tagger): use dotted litellm.user_email tag for consistency Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * feat(model_hub): surface model_info.description in model group info and Model Hub UI Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(router): aggregate model group description without in-place mutation Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(prompt-templates): treat initial_prompt_value and final_prompt_value as optional completion() only stores a custom prompt template key when its value is truthy, and custom_prompt() already defaults both prompt values to "". Seven call sites read them back with direct indexing, so a template that sets only roles raises KeyError instead of rendering, on ollama, petals, vllm, predibase, codestral, anthropic text completion, and response_schema_prompt. Five other providers already read defensively, so the same config works on sagemaker and 500s on ollama. Read both keys with .get(key, ""), matching those five providers and the defaults custom_prompt() already declares. Templates that set all three keys render exactly as before. * test(prompt-templates): drop the explanatory docstrings from the optional-keys regression test * fix(health): skip background health check DB writes when the latest-row read fails Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(prompt-templates): read every custom prompt template field through one typed helper Templates that omit roles, initial_prompt_value, or final_prompt_value now render with empty defaults across every provider that reads them, instead of failing with KeyError or TypeError. A field with the wrong type fails with a validation error naming it. * fix(prompt-templates): keep templates that rendered on main working and fail bad fields with a 400 Only the roles a request uses are checked, null fields mean unset, and extra role keys are ignored, so every template main rendered renders the same. A wrong-type field now raises a BadRequestError naming it. initial_prompt_value is declared litellm-owned so it stays out of provider request bodies, like final_prompt_value. The template tests move into the mapped factory test file, and the predibase test asserts the real rendered prompt * fix(prompt-templates): keep bad-template failover and render non-mapping role entries like main A wrong-type template field now raises a 500 InternalServerError naming the field, so the router still retries and cools down the bad deployment. A roles value or role entry that is not a mapping renders unwrapped as it did on main, and role keys are only checked for roles a request uses. * fix(prompt-templates): warn when a custom prompt template roles value is not a mapping * fix(prompt-templates): keep deployments in rotation on a bad template and reject a template with nothing to render * test(integration): audit custom prompt templates across endpoints, providers, and chaos --------- Co-authored-by: milan <milan@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: yassin <yassin@berri.ai> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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武本龍門
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43be1c2ae7
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fix(router)!: keep RPM, region, and param checks when token counting fails (#44544)
* fix(router): stop skipping rate-limit checks when token counting fails Router._pre_call_checks() caught any token_counter() exception and returned the full, unfiltered deployment list, bypassing the RPM, region, and invalid-params checks for every deployment - not just the context-window check that actually depends on the count. A content block token_counter() cannot parse (e.g. an invalid image_url detail value) silently disabled rate-limit enforcement for that request, with only a log line and no error surfaced to the caller. Now a counting failure only disables the context-window check; the other checks still run for every deployment as before. Input-token resolution is pulled into its own method and computed once before the loop, so nothing needs to track "did counting fail" with a mutable flag across iterations. * test(router): cover the async region check when token counting fails Replace the unit test of the private input-token resolver with a regression test through async_get_healthy_deployments, the path the proxy uses: an EU-pinned request whose content cannot be token-counted must not get the US deployment * test(router): cover the param filter when token counting fails and satisfy router coverage * fix(router): answer 400 when no deployment takes the requested param, honor allowlists and drop_params The pre-call param filter now reuses the router's param-acceptance helper, so allowed_openai_params, drop_params, and additional_drop_params keep a deployment instead of filtering it out. When every available deployment lacks a requested param, the router answers 400 UnsupportedParamsError right away instead of a retried 429. Deployment context windows are resolved once per call. * fix(router): keep the retried 429 when a cooling-down deployment takes the param * fix(router): steer to a deployment that takes the param before dropping it, keep context-window fallbacks * fix(router): steer by deployment capability, apply drop settings after region and RPM checks A deployment-level allowed_openai_params no longer counts as taking the param when another deployment does, the drop and allow settings now apply only when no deployment that passed the region, RPM, and context-window checks takes it, and an authenticating provider is checked by its declared model instead of always passing * test(router): make the github_copilot pre-call filter test hermetic in CI * test(router): cover pre-call region and response_format filters through the proxy * fix(router): judge the 400 against the request's own deployments, not a name lookup * fix(router): judge the 400 against the passed deployments when the group name lists none * fix(router): let a request's drop and allow settings replace the deployment's in the param check --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Aryan Pardeshi
|
a844dadcd2
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feat(gemini): allow disabling thought summaries while keeping thinking on (#36460)
* feat(gemini): allow disabling thought summaries while keeping thinking on Both reasoning-effort mappers hardcoded includeThoughts to True whenever thinking was on, so thinking ON with summaries OFF was unreachable even though that is the google SDK's own default. Summaries are the narration rather than the reasoning, and for callers that discard them they are pure added latency. Adds litellm.gemini_include_thoughts_default, defaulting to True so nothing changes for anyone who does not set it. It gates the includeThoughts=True branches in the thinkingBudget mapper, the thinkingLevel mapper, and the anthropic-style thinking param mapper. The disable and none paths still force includeThoughts=False: with thinking off there is nothing to summarise. * test(gemini): assert thought-summary flag through the outbound Gemini request * feat(gemini): let a request's thought summary ask override the gateway default thinking.display (summarized, updates, omitted) and a reasoning_effort object carrying summary now decide includeThoughts for that request; gemini_include_thoughts_default applies only when the request says nothing * feat(gemini): let a thought summary ask on /v1/messages and /v1/responses override the gateway default * fix(gemini): read a text gateway default as a boolean and leave display omitted to the flag Normalize gemini_include_thoughts_default through normalize_drop_params so a YAML or env value written as "false" or "0" turns thought text off and a null or unknown value keeps the default on. thinking.display "omitted" no longer counts as a per-request ask, so the gateway flag decides, matching /v1/messages. Adds proxy integration cells for chat, /v1/messages and /v1/responses that prove the flag, every per-request ask, streaming, and a burst across workers. * test(gemini): match the streamed Responses usage, which omits null token details * feat(gemini): read the AI SDK reasoning summary alias and thinking.summary as chat asks * fix(gemini): keep the logged reasoning_effort unchanged and key the response cache on the summary alias A dict reasoning_effort next to the AI SDK reasoning_summary alias now counts as a summary ask, and the alias is part of the cache key so a plain request and an alias ask never share a reply * fix(caching): key the response cache on the summary alias wherever the request carries it The cache key now reads reasoning_summary and reasoningSummary through the same reader as the summary fold, so an alias inside extra_body gets its own key too * test: type the new summary-ask unit tests and mark their locals Final * test: annotate the remaining summary-ask unit tests the PR touches --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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|
debanshd
|
1b1ddef1b2
|
fix: preserve non-null anyOf branches when stripping null types for Vertex (#41683)
* fix: preserve non-null anyOf branches when stripping null types for Vertex * refactor: build anyOf branches immutably and drop explanatory comments * style: apply ruff format * fix: annotate anyOf list construction for the LIT002 budget * fix(vertex_ai): send a null-only anyOf as one nullable null instead of raising * test(vertex_ai): pin empty anyOf passthrough * fix(vertex_ai): forward a non-list anyOf unchanged and add wire cells for the null fold * fix(vertex_ai): forward a non-object anyOf branch for Google to judge instead of crashing * fix(vertex_ai): fold null out of type arrays and union branches before sending to Gemini --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
|
4e49394ade
|
fix(anthropic): keep trailing user messages with None or empty list content (#41397)
* fix(anthropic): keep user messages with None or empty list content so requests do not end on an assistant turn Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): avoid mutable empty-list construction in sanitizer Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): avoid rebinding sanitizer input Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): keep a trailing user turn whose content key was dropped litellm.completion strips None fields from every message before the provider transform, so a user message sent with content null reaches the Anthropic converter with no content key and raised KeyError. Read the content with get so that turn gets the same placeholder as an empty list. * fix(anthropic): only fill user turns with empty content and keep their cache_control * fix(anthropic): fill each empty user message in place like an empty string * fix(anthropic): add the placeholder only for an empty turn or a cache breakpoint, in linear time * test(anthropic): cover the tool-result guard with cache_control and widen the linear-time bound * test(anthropic): cover empty user turns through the proxy on chat, responses and messages * test(anthropic): type the empty user turn tests and move the linear-time check to a benchmark --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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yucheng-berri
|
f281f77bc0
|
fix(guardrails): record every content filter match in logging_only and offer the mode in the UI (#46673)
* test(guardrails): pin content filter logging_only matches and UI modes in an integration test * fix(guardrails): record every content filter match in logging_only and offer the mode in the UI * style(tests): format the content filter logging_only integration test * refactor(guardrails): keep the logging_only predicate and scan override within the type gates * fix(guardrails): flag tool arguments a content filter would reject in logging_only and keep scanning * test(integration): expect guardrail_flagged for an observe-only content filter block in the scope config test * test(integration): pin logging_only flags for tool arguments an enforcing content filter rejects * refactor(guardrails): drop the content filter flagged branch no logging_only scan can reach * test(integration): pin OTel spans, Guardrails Monitor and a provider outage burst for a logging_only content filter * test(integration): pin MCP tool call verdicts and fail the Monitor cell when usage never rolls up * test(integration): pin category and competitor intent verdicts and the provider failure row in logging_only --------- Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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moyai-devin-berriai[bot]
|
a3251a3bfd
|
fix(responses): preserve provider request IDs in streaming failure logs (#45453)
* fix(responses): preserve provider request IDs in streaming failure logs * fix(bedrock): log the provider request id for in-stream errors on bridged Bedrock streams Bedrock models that run through the chat-completions bridge (Claude, converse, invoke) raise in-stream failures from the event-stream decoder as a BedrockError without headers, so their failure logs kept error_provider_request_id null. The decoder now passes the stream's request-id headers to those errors. Both the native Responses iterator and the decoder pass only the PROVIDER_REQUEST_ID_HEADERS subset, so error responses and failure logs pick up the request id and none of the 200 response's other headers. MidStreamFallbackError gets its response at construction instead of mutating it afterwards. --------- 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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Mateo Wang
|
b21e659c24
|
fix(proxy)!: count endpoints return errors in their source API shape instead of silent local counts (#46815)
* fix(count_tokens): convert Anthropic-format messages for the OpenAI counter the way inference does * test(count_tokens): check /v1/messages/count_tokens sends OpenAI the input /v1/messages inference sends * fix(count_tokens): build the Anthropic count request without a cast and sync dashboard API types * fix(count_tokens): count Anthropic-format requests for Gemini the way /v1/messages inference converts them * fix(count_tokens): return a 400 naming Anthropic blocks the OpenAI and Gemini count can't convert * test(count_tokens): compare tools by value in the public api test * fix(count_tokens): reject unconvertible blocks nested inside a tool_result * fix(count_tokens): count Anthropic bodies the way /v1/messages inference sends them to OpenAI Unwrap affinity-encoded reasoning, count the structured-output schema, flatten replayed web search results, join a list system prompt, and answer a message without a role with a 400 before calling OpenAI. messages_format is now internal to the Anthropic count route instead of a public /utils/token_counter field. * test(count_tokens): expect the image detail the adapter now sends after merging main * fix(count_tokens): fetch Gemini remote media without blocking the event loop * fix(count_tokens): type normalize_native_messages without the unimported Any On Python 3.10 to 3.13 the annotation is evaluated at import, so the proxy failed to start with NameError * chore(count_tokens): give the Gemini count helpers' list returns a reason * fix(count_tokens): normalize Anthropic messages for Gemini counts the way /v1/messages does * fix(count_tokens): count Gemini bodies after inference's history rewrites and answer converter errors with 400 * test(count_tokens): cover the Gemini count error paths and compaction with a text-block system * fix(proxy): count endpoints return missing-key and request errors in their source API shape * fix(proxy): drop imports the count error change left unused * fix(proxy): let Entra-authenticated Azure AI Claude count tokens and keep Google count errors in the Google envelope * fix(proxy): send Azure environment keys as x-api-key on Azure AI Claude counts and log unexpected Google count failures * fix(proxy): count with the global OpenAI and Anthropic keys chat already uses * fix(proxy): resolve the OpenAI count base with the key so a global key stays on its gateway * fix(proxy): keep unexpected Google count failures generic so internal details stay out of the response * fix(proxy): resolve the Azure AI Claude count base from the same settings chat reads * fix(proxy): count OpenAI tokens with the workload identity bearer chat already uses * fix(proxy): mint the OpenAI workload identity bearer inside the count error handling * fix(proxy): report a failed OpenAI workload identity exchange on the count endpoints as a 401 --------- Co-authored-by: Mrinal Chanshetty <mrinal@berri.ai> |
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Michelangelo Setaro
|
c288dc7da0
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fix(router): let an exact base_model widen a deployment's context window (#46197)
* fix(router): honor explicit bedrock base model capacity * fix(router): honor an exact base_model's capacity for every non-Azure provider Extends the Bedrock-only check to every non-Azure provider and only uses the base_model when it has its own exact cost-map entry, so an unmapped or wildcard base_model falls back to the route id instead of raising in the router. * test(router): call _maps_to_exact_model_info directly in its cost-map tests * fix(router): let an exact base_model only widen a deployment's context window A base_model used as a pricing stand-in with a smaller window no longer narrows the route's limit, and a route with no window never takes one from base_model * test(router): count the base_model window helpers as covered through get_router_model_info * fix(router): widen only from a model_info base_model naming the same model A stand-in base_model for a different, larger model widened a smaller deployment's window, so pre-call checks sent it prompts the provider rejects with 400. The window now widens only when model_info.base_model names the same provider model id as the route, which is also the field /model/info reads. * fix(router): match a base_model's model id across Bedrock region prefixes and @ qualifiers * fix(router): read litellm_params.base_model too and look the base row up as written * test(router): cover a same-model base_model window on chat, messages, responses, and complexity escalation --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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yucheng-berri
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cda19d6a29
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test(integration): wait for the gen-AI span before reading the excluded-services operator trace (#46846)
The operator trace wait stopped at the root and redis spans, but the gen-AI span is closed later by the async success callback. When it had not landed yet, the tenant mirror was built without it and the gen-AI assertion failed. Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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yucheng-berri
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b5870df967
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fix(bedrock-passthrough)!: return AWS-shaped errors so AWS SDKs read the code and message (#46522)
* fix(bedrock-passthrough): return AWS-shaped errors so AWS SDKs read the code and message A guardrail block on a /bedrock route now answers with x-amzn-errortype ValidationException and a top-level message, and an error Bedrock returned (such as a 429 ThrottlingException) is passed through with its status, x-amzn-errortype and body * fix(bedrock-passthrough): keep 5xx guardrail failures and failure-hook replacements unchanged * test(bedrock-passthrough): guardrail vendor failures and unknown models keep the litellm error * fix(bedrock-passthrough): only answer guardrail exceptions that blocked content as ValidationException * fix(bedrock-passthrough): pass Bedrock's content-type through with its error body * refactor(bedrock-passthrough): wrap the guardrail ProxyException instead of copying its fields Unit tests drive the error response with the real request processor and ProxyLogging * test(bedrock-passthrough): cover the direct model route, invoke, and auth, budget and rate-limit errors * fix(bedrock-passthrough): pass through only errors a Bedrock model operation returned A guardrail service's HTTP error that carries x-amzn-errortype keeps the litellm error * refactor(bedrock-passthrough): mark Bedrock's errors where the passthrough raises them instead of matching the URL A guardrail service's HTTP error never goes through the passthrough call, so it keeps the litellm error whatever its URL --------- Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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Mateo Wang
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79ca820933
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fix(count_tokens): return a 400 naming Anthropic blocks the OpenAI and Gemini count can't convert (#46772)
* fix(count_tokens): convert Anthropic-format messages for the OpenAI counter the way inference does * test(count_tokens): check /v1/messages/count_tokens sends OpenAI the input /v1/messages inference sends * fix(count_tokens): build the Anthropic count request without a cast and sync dashboard API types * fix(count_tokens): count Anthropic-format requests for Gemini the way /v1/messages inference converts them * fix(count_tokens): return a 400 naming Anthropic blocks the OpenAI and Gemini count can't convert * test(count_tokens): compare tools by value in the public api test * fix(count_tokens): reject unconvertible blocks nested inside a tool_result * fix(count_tokens): count Anthropic bodies the way /v1/messages inference sends them to OpenAI Unwrap affinity-encoded reasoning, count the structured-output schema, flatten replayed web search results, join a list system prompt, and answer a message without a role with a 400 before calling OpenAI. messages_format is now internal to the Anthropic count route instead of a public /utils/token_counter field. * test(count_tokens): expect the image detail the adapter now sends after merging main * fix(count_tokens): fetch Gemini remote media without blocking the event loop * fix(count_tokens): type normalize_native_messages without the unimported Any On Python 3.10 to 3.13 the annotation is evaluated at import, so the proxy failed to start with NameError * chore(count_tokens): give the Gemini count helpers' list returns a reason * fix(count_tokens): normalize Anthropic messages for Gemini counts the way /v1/messages does * fix(count_tokens): count Gemini bodies after inference's history rewrites and answer converter errors with 400 * test(count_tokens): cover the Gemini count error paths and compaction with a text-block system --------- Co-authored-by: Mrinal Chanshetty <mrinal@berri.ai> |
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devin-ai-integration[bot]
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91ae7d1b7d
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fix(cost): bill Responses API image generation and usage reported web search as tool usage cost (#41877)
* fix(cost): bill Responses API image_generation_call tool usage Price completed image_generation_call output items from tool_usage.image_gen token counts when present, otherwise per image from the quality and size on the item, with the image model taken from tools[].model (default gpt-image-1). Add the result into tool_usage_cost alongside web search and file search, and read tool_usage from the top level or usage.tool_usage. Squashed and rebased onto main * style(cost): annotate read-only views and mark casts for the type discipline gate * fix(cost): price Responses image tool from tools[].model only, no default model or provider * refactor(cost): drop redundant status check, unread usage fields and unused output_type literal * test(cost): integration case for Responses image_generation tool token billing --------- 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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dba86bec28
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fix(count_tokens): count Anthropic-format requests for Gemini the way /v1/messages inference converts them (#46767)
* fix(count_tokens): convert Anthropic-format messages for the OpenAI counter the way inference does * test(count_tokens): check /v1/messages/count_tokens sends OpenAI the input /v1/messages inference sends * fix(count_tokens): build the Anthropic count request without a cast and sync dashboard API types * fix(count_tokens): count Anthropic-format requests for Gemini the way /v1/messages inference converts them * test(count_tokens): compare tools by value in the public api test * fix(count_tokens): count Anthropic bodies the way /v1/messages inference sends them to OpenAI Unwrap affinity-encoded reasoning, count the structured-output schema, flatten replayed web search results, join a list system prompt, and answer a message without a role with a 400 before calling OpenAI. messages_format is now internal to the Anthropic count route instead of a public /utils/token_counter field. * test(count_tokens): expect the image detail the adapter now sends after merging main * fix(count_tokens): fetch Gemini remote media without blocking the event loop * fix(count_tokens): type normalize_native_messages without the unimported Any On Python 3.10 to 3.13 the annotation is evaluated at import, so the proxy failed to start with NameError * chore(count_tokens): give the Gemini count helpers' list returns a reason * fix(count_tokens): normalize Anthropic messages for Gemini counts the way /v1/messages does * fix(count_tokens): count Gemini bodies after inference's history rewrites and answer converter errors with 400 * test(count_tokens): cover the Gemini count error paths and compaction with a text-block system --------- Co-authored-by: Mrinal Chanshetty <mrinal@berri.ai> |
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Kerry Lu
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5ed3629242
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fix(responses): write one spend-log row per native WebSocket turn (#46768)
* fix(responses): write one spend-log row per native WebSocket turn Each response.create turn on a native Responses WebSocket connection logs and bills as its own call when its terminal event arrives, keyed by the resp_ id the client saw, instead of one row at socket close that sums every turn * test(responses): mock the websocket slot release and mark the per-turn start time naive * style(responses): keep the per-turn start time line within ruff format * refactor(responses): type websocket turn events as read-only mappings * fix(responses): settle the websocket budget reservation on the first turn only Every per-turn logging copy carried the connection's admission reservation, so two turns whose cost callbacks overlapped could both reconcile it. The first turn keeps the reservation and later turns charge their cost directly * fix(proxy): keep the legacy limiter's websocket slot until disconnect A websocket turn callback no longer decrements current_requests in the legacy limiter. The socket's slot is released once from arelease_max_parallel_requests_on_disconnect, which now dispatches to the legacy handler as well --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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yuneng-jiang
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cf1e6b19ab
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fix(sso): share SAML sign-in state over Redis without enable_redis_auth_cache (#46807)
* fix(sso): share SAML sign-in state over Redis without enable_redis_auth_cache SAML login tickets, the single-use assertion mark and cached IdP metadata lived in user_api_key_cache, which only gets Redis when enable_redis_auth_cache is on. With several replicas or workers the IdP callback often landed on a process that never saw the ticket and got a 401, and IdP-initiated sign-in was refused even with Redis configured. SAML state now has its own DualCache that _attach_redis_usage_cache always wires to Redis, like the CLI SSO session cache, and the SAML cache calls are async. * test(sso): drop docstring from the SAML proxy pair fixture * test(sso): cover the SAML login and metadata endpoints |
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Mateo Wang
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d25b58f744
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fix(count_tokens): count Anthropic-format requests for OpenAI the way /v1/messages inference converts them (#46759)
* fix(count_tokens): convert Anthropic-format messages for the OpenAI counter the way inference does * test(count_tokens): check /v1/messages/count_tokens sends OpenAI the input /v1/messages inference sends * fix(count_tokens): build the Anthropic count request without a cast and sync dashboard API types * test(count_tokens): compare tools by value in the public api test * fix(count_tokens): count Anthropic bodies the way /v1/messages inference sends them to OpenAI Unwrap affinity-encoded reasoning, count the structured-output schema, flatten replayed web search results, join a list system prompt, and answer a message without a role with a 400 before calling OpenAI. messages_format is now internal to the Anthropic count route instead of a public /utils/token_counter field. * test(count_tokens): expect the image detail the adapter now sends after merging main * fix(count_tokens): type normalize_native_messages without the unimported Any On Python 3.10 to 3.13 the annotation is evaluated at import, so the proxy failed to start with NameError * test(count_tokens): expect the converted Responses input in the budgeted-key count integration test --------- Co-authored-by: Mrinal Chanshetty <mrinal@berri.ai> |
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yucheng-berri
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627bcab42b
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fix(guardrails): honor streaming settings on the Noma v2 guardrail (#46606)
* fix(guardrails): honor streaming settings on the Noma v2 guardrail The Noma v2 initializer now passes streaming_buffer_until_moderated, streaming_end_of_stream_only, streaming_sampling_rate and streaming_buffer_release_on_scan from the guardrail config to NomaV2Guardrail, so the unified streaming hook applies them. The constructor validates them, so a custom guardrail class built on NomaV2Guardrail gets the same checks. Unset keys keep the hook defaults, and an invalid value is ignored with a warning naming the guardrail, so the guardrail still loads. Resolves LIT-9633 * test(guardrails): cover Noma v2 streaming settings across clients, failures and workers Adds integration cells for the custom-subclass loader, string and invalid values, unset and false keys, a Noma outage with and without block_failures, an upstream rejection, n=2 choices, pre_call and non-streaming guardrails, the OpenAI and Anthropic SDKs (sync and async), stored-guardrail info, a PATCH while traffic flows, a burst while Noma is down, a slow Noma, and replaced workers. Resolves LIT-9633 * test(guardrails): pin the PATCH probes to one worker at a time Each probe suspends the other worker and opens a fresh connection, so the test checks that every worker applies the patched setting. Resolves LIT-9633 * test(guardrails): check sampled scans by count, not window position /v1/messages hands the streaming hook raw network reads, so where the default sampling windows fall depends on timing; the base leg caught one shifted window. Check the scan count and the final full-text scan instead. Resolves LIT-9633 --------- Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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yucheng-berri
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250e6bd24b
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test(integration): build OpenAI and Anthropic SDK models before tests start threads (#46797)
K2 in test_logging_only_scope_chaos.py flaked on CircleCI with AttributeError: type object 'BaseModel' has no attribute '__pydantic_core_schema__' raised inside the OpenAI client while its 30-thread control burst parsed responses The SDKs define their response models with deferred schemas, so the first parse of each model rebuilds it, and pydantic's model_rebuild is not safe when several threads hit it at once. Every integration worker now builds those schemas in pytest_configure, before any test can start a thread pool Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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Mateo Wang
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b47f7a4712
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fix(responses): preserve explicit websocket API keys (internal copy of #40853) (#46724)
* fix(responses): preserve explicit websocket API keys Refresh the one-line fix from BerriAI/litellm#34253 against the current default branch Co-authored-by: puffer <389053104@qq.com> * test(responses): verify websocket authorization Cover explicit deployment keys, precedence over global keys, and the existing global fallback at the upstream WebSocket transport boundary * test(responses): type the authorization capture Replace the mutable header list with a typed, final Future carrying the upstream authorization value, addressing the review on #40853 * Refactor test_openai_websocket_authorization method * fix(responses): resolve websocket keys with the deployment's provider Pass the deployment custom_llm_provider to get_llm_provider so an api_base host never swaps in another provider's env key, and drop the OpenAI-only global key fallbacks so non-OpenAI websocket deployments resolve their own env keys the way the HTTP route does * fix(responses): let a deployment's own websocket key beat env keys A deployment with an api_base on a host litellm recognizes, and no declared provider, got that host's env key ahead of its own api_key. The explicit key now always wins, matching the HTTP Responses route. Drop the litellm_params api_key fallback, which could never be set because api_key is a named argument * fix(responses): keep openai deployments on other providers' known hosts on the HTTP stream An openai deployment whose api_base is a known OpenAI-compatible provider host (DeepSeek, Groq, ...) opened a native Responses WebSocket there and got a 405, because those hosts serve /responses over HTTP only. Route it through the managed HTTP stream with the deployment's own key instead. * fix(responses): resolve openai deployments on known provider hosts as that provider An openai deployment whose api_base is a known provider host (DeepSeek, Mistral, ...) now resolves to that host's provider on the Responses WebSocket again, as before this branch, so it keeps that provider's chat completions bridge, pricing and env key fallback. Its own api_key still wins over the env key. The known-host test now mocks the upstream HTTP route and checks the route, the bearer and response.completed. * test(responses): prove websocket deployment keys and declared providers reach the provider socket * test(responses): use the reported bare model shape for the websocket deployment key cell --------- Co-authored-by: Ysh <734645956@qq.com> Co-authored-by: puffer <389053104@qq.com> |
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mrinal-berri
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bd60cc8608
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fix(bedrock): forward Nova Sonic realtime tool calls with their arguments (#46214) | ||
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devin-ai-integration[bot]
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67bfb7a148
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fix(ollama): prevent reused response ids from dropping spend logs (#39227)
* fix(spend_tracking): key completion spend logs off the per-call id so duplicate provider response ids cannot drop rows Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(spend_tracking): narrow the provider response id with isinstance instead of cast Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(gcs_bucket): store request payloads under the spend log request id so the logs viewer can find them Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(spend_tracking): validate callback fields for current type gates * test(gcs_pubsub): assert provider response id in spend metadata * fix(ollama): normalize reused response ids automatically --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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yuneng-jiang
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b602209ca6
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test(integration): hand every harness server a port from one per-worker block (#46761)
* test(integration): hand every harness server a port from one per-worker block
About 45 places in the integration harness picked a port by binding port 0,
reading the number back, closing the socket and starting a server on that
number later. Another process could take the port in between, which surfaced
as "Address already in use" flakes. Earlier fixes retried around the race in
single call sites without removing it.
tests/integration/_support/ports.py now owns every port the harness picks
ahead of a server. Each pytest process claims a 200-port block between 10000
and 32767 by taking an exclusive flock on a per-block lock file in the system
temp directory, so parallel runs on one machine never share a block and the
lock goes away with the process. The range is above the fixed service ports
and below the Linux and macOS ephemeral ranges. Ports go out in order, are
never reused in a run, skip anything an outside process already holds, and
a process that exhausts its block claims the next free one.
The three _free_port copies, worker_endpoints' port-0 binds,
free_loopback_port, the owned proxy's lost-port-race retry and the owned
Redis retry loop are gone. Tests that restart a server on the same port now
allocate that port up front instead of reading it back from the first
server. TQ012 in scripts/check_test_quality.py rejects a port-0 bind read
back with getsockname anywhere under tests/integration.
* ci(integration): record listening sockets and the ephemeral port range
* Revert "ci(integration): record listening sockets and the ephemeral port range"
This reverts commit
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