116 commits

Author	SHA1	Message	Date
Yassin Kortam	9b7ed77fcc	fix(azure): rename max_tokens to max_completion_tokens for gpt-5-chat deployments (#36857) ... Azure rejects the legacy `max_tokens` key for the whole gpt-5 name family, but `AzureOpenAIGPT5Config.is_model_gpt_5_model` deliberately excludes `gpt-5-chat*` so those deployments fall through to `AzureOpenAIConfig`, which sends `max_tokens` verbatim and gets a 400 back on every request that carries it, `/health` probes included. One predicate was answering two independent questions. Split it: the new `AzureOpenAIConfig.requires_max_completion_tokens` covers the whole gpt-5 name family and drives only the rename, while `is_model_gpt_5_model` keeps keying reasoning_effort, the temperature clamp and the dropped penalties off the reasoning question, so #13781 stays fixed.	2026-08-17 11:27:55 -07:00
Ahmed N	691c7fd4d6	fix(anthropic_messages): make tool_result images visible to OpenAI-compatible providers (#34462) ... Images nested inside an Anthropic `tool_result` block were dropped when the request was adapted for an OpenAI-compatible provider, because the OpenAI tool message shape only carried text. Hoist those images out of the tool result and into a following user message so the model can still see them, and widen the tool message content type to accept image parts.	2026-08-14 17:47:38 -07:00
Yuneng Jiang	075781568d	test: remove tests that never execute ... Three groups, all verified by running the suite rather than by inspection. 18 files whose every test function carries an unconditional @pytest.mark.skip, 39 test functions in total. They are collected on every CI run and always skip, so they advertise coverage the suite does not have. Reasons on the marks include "AWS Suspended Account", "lakera deprecated their v1 endpoint" and "moved to using 'otel' for logging"; 26 of the marks predate 2025. 30 test functions with a byte-identical body and identical decorators to a sibling in the same file and class, differing only in name. Deleting one of each pair removes no coverage. Four further candidates were excluded because they override an inherited test, where deleting the override un-shadows the base class implementation instead of removing a duplicate. 9 test functions that a later definition of the same name shadows, so Python never binds them and pytest cannot collect them. One file that is a demo script rather than a test; its own docstring says to run it with python. Verification: collecting the 26 edited files gives 2,492 node IDs before and 2,462 after. The 30 duplicate deletions account for exactly 30 removals, the 9 shadowed deletions account for 0 (confirming at runtime that they were never collectable), nothing unexplained disappeared, and nothing new appeared. No other test or module imports any deleted symbol.	2026-08-12 10:45:38 -07:00
mateo-berri	f95367db5f	Revert "revert: "fix(caching): close evicted LLM clients so their connections are reclaimed (#35492)"" ... This reverts commit adb9a53ba1.	2026-08-04 18:23:06 -07:00
mateo-berri	adb9a53ba1	revert: "fix(caching): close evicted LLM clients so their connections are reclaimed (#35492)" ... This reverts commit 66bc70365f and the follow-up 2-line type fix a6d4654261 (#35706), which only retyped a signature #35492 introduced. Closing evicted litellm-owned clients breaks every object that fetches get_async_httpx_client once in __init__ and holds the handler for the life of the process: 40 guardrail classes plus the pagerduty and email callbacks. Once the cache entry is evicted (TTL 3600s or the 200-entry size cap) and the 900s grace passes, the held client is closed and every subsequent request through it fails with RuntimeError: Cannot send a request, as the client has been closed. On a production deployment with a default-on guardrail this surfaced as every request 500ing roughly 75 minutes after boot. The connection-reclaim goal of #35492 can re-land once handlers survive their inner client being closed.	2026-08-04 17:05:06 -07:00
Classic298	c9887a1f94	perf: build log messages lazily so filtered-out log records cost nothing (#35703)	2026-08-04 04:34:52 +00:00
Yassin Kortam	66bc70365f	fix(caching): close evicted LLM clients so their connections are reclaimed (#35492) ... An evicted client was left for the garbage collector, but every OpenAI/Azure SDK client is a reference cycle, so nothing freed the client or its pooled TCP connections until a generational sweep ran. Driving 2000 azure calls through the official image with no forced collection, live clients and open sockets climbed from 202 to 1361 while the cache stayed at its 200-entry bound, and RSS grew 279 MB to 456 MB against a TLS upstream. Closing on eviction is what caused the earlier 'Cannot send a request, as the client has been closed' regression, so an evicted client litellm created is now closed only once a grace window has passed, by which point any request that was already holding it has finished. A client the caller supplied is never closed, since litellm does not own its lifecycle. Resolves LIT-4883	2026-08-03 13:28:38 -07:00
devin-ai-integration[bot]	9cae6fa437	fix(logging): classify async anthropic_messages and generate_content as async (#33589)	2026-07-16 20:56:47 -07:00
Mateo Wang	e2df153bfb	fix(azure): build responses input_items url with path before query string (#32270) ... * fix(azure): build responses input_items url with path before query string * chore(azure): drop stale inline comment in responses url helper	2026-07-06 14:02:44 -07:00
Sameer Kankute	2cf565ae28	test(batches): add 1:1 test file scaffold for batches component paths (#30529) ... * test(batches): add 1:1 test file scaffold for batches component paths Co-authored-by: Cursor <cursoragent@cursor.com> * Add harness test for create batch endpoint * Add retrieve endpoint harness tests * Add list endpoint harness tests * Add cancel endpoint harness tests * Add cancel endpoint harness tests * Add test for litellm/batches/main.py * Add test for litellm/tests/test_litellm/batches/test_batch_utils.py * Add handler and transformation tests for all providers * Fix: run batches tests in cicd * fix(tests): remove azure/__init__.py that shadowed azure namespace package Adding __init__.py to tests/test_litellm/llms/azure/ caused pytest to insert tests/test_litellm/llms/ into sys.path[0], making our empty azure/ dir shadow the real azure-identity namespace package. Any test that patched azure.identity.* would then fail with AttributeError. * style(tests): apply ruff format to test_batch_utils.py Base migrated the formatter from black to ruff format (#31317); reformat the batches scaffold test file to match. --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>	2026-06-29 09:22:58 +05:30
Sameer Kankute	cfcdf8714a	feat: litellm oss 110626 (#30202) ... * Add gpt-realtime-whisper Realtime transcription support (OpenAI + Azure) (#29775) * Add gpt-realtime-whisper Realtime transcription support (OpenAI + Azure) Adds first-class support for the gpt-realtime-whisper streaming speech-to-text model, which uses the Realtime transcription session API rather than the file-based /audio/transcriptions path. Model registration: registers gpt-realtime-whisper and azure/gpt-realtime-whisper with audio-duration pricing (input_cost_per_second = 0.017/60, matching the published $0.017/minute input audio rate). REST endpoint: implements POST /v1/realtime/transcription_sessions (plus /realtime and /openai/v1 aliases) to mint an ephemeral transcription session for the WebRTC flow. Adds request/response types, OpenAI and Azure URL builders, a shared base handler (refactored from the client_secrets handler), the acreate_realtime_transcription_session SDK function, and route registration. The proxy encrypts the ephemeral key returned under client_secret.value and records the session type in the token so the follow-up /realtime/calls replays type=transcription rather than type=realtime. WebSocket: forwards intent=transcription through to the Azure handler (OpenAI already received it) with URL-encoding, so gpt-realtime-whisper opens a transcription session. Transcription-only sessions no longer trigger an erroneous response.create. Cost tracking: transcription sessions emit no response.done events; their usage arrives on conversation.item.input_audio_transcription.completed as {type: duration, seconds}. That usage is captured out-of-band (usage only, no transcript duplication) and billed by input_cost_per_second, with a token-billed fallback for token-priced transcription models. Adds tests for pricing math, URL builders, request/response types, the proxy route and SDK function, WebSocket intent forwarding, transcription-session streaming behavior, and the /realtime/calls session-type replay. * Address PR review: URL-encode all Azure WS query params; forward query_params through provider_config branch * Address PR review: session_type validation, model auth fix, cost perf, billing fallback, detail/docs cleanup * Improve test coverage: detection from backend, error paths, unknown usage type, resolved_model None * Backport realtime transcription websocket fixes * Enforce authorized realtime transcription model * Enforce realtime transcription model access * Enforce realtime resolved model scopes * Enforce WebRTC transcription model scope * Lazy evaluate debug log in pass-through endpoint (#30177) * Pass through debug lazy logging * fix(proxy): convert remaining eager pass-through debug logs to lazy formatting * fix(parallel_ai): migrate search integration from v1beta to v1 endpoint (#30157) * fix(parallel_ai): migrate search integration from v1beta to v1 endpoint The Parallel Search API moved from /v1beta/search (processor: base/pro, parallel-beta header) to /v1/search (mode: turbo/basic/advanced, no beta header). Request fields moved too: max_results, source_policy, and excerpt settings are now nested under advanced_settings, and source_policy uses include_domains/exclude_domains. The v1 response returns publish_date per result, which now maps to SearchResult.date instead of being hardcoded to None. The legacy processor param is mapped to the equivalent mode so existing callers keep working. * fix(parallel_ai): default mode to basic and simplify param handling The v1 API defaults to advanced mode when mode is omitted, while v1beta defaulted to the base processor. Without an explicit default, callers who pass no mode would be silently upgraded to a tier costing 2.25x more while litellm's cost map reports the basic-tier price. Sending mode=basic preserves the v1beta default and keeps cost tracking accurate. Also replaces the handled_params set with pop-as-consumed param handling so mapped params no longer need to be tracked in two places, and extends the tests to pin the default mode, processor=base mapping, mode-over-processor precedence, and top-level v1 param passthrough. * fix(parallel_ai): avoid double /v1 when api_base is already versioned A PARALLEL_AI_API_BASE like https://api.parallel.ai/v1 previously produced .../v1/v1/search. Strip a trailing /v1 before appending the search path and cover the api_base variants with a parametrized test. --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * feat(focus): add Mavvrik destination for FOCUS export (#29935) * fix: preserve responses streaming flag (#30189) * fix: preserve responses streaming flag * test: cover async responses streaming flag * fix(spend/daily-activity): stable offset pagination via id tiebreaker (#30164) (#30167) date alone is not a unique sort key for LiteLLM_DailyUserSpend or LiteLLM_DailyTeamSpend (many rows per date: api_key x model x model_group x provider x endpoint). Offset pagination over a non-unique sort landed on arbitrary boundaries, so a client paging through all results and summing per-page metrics (the Usage dashboard) got non-deterministic totals - sometimes inflated, sometimes deflated, different at different page_size values. Adding the row's UUID id (present on both tables) as a secondary sort gives every page a stable cursor. order=[{date desc}, {id asc}]. Fixes #30164 * fix(oci): inject a default maxTokens so omitted max_tokens doesn't truncate responses (#30018) * fix(oci): inject default maxTokens so omitted max_tokens doesn't truncate OCI GenAI applies a tiny server-side maxTokens default (~20 tokens) when the request omits it, so any call that doesn't send max_tokens comes back cut off mid-string with finishReason "length". MLflow judges never send max_tokens, so their JSON responses arrived as unterminated strings and json.loads failed in MLflow's gateway adapter. When no maxTokens/maxCompletionTokens target is set, inject DEFAULT_OCI_CHAT_MAX_TOKENS (env-overridable, defaults 4096), mirroring the Anthropic config's default-max-tokens behaviour. An explicit max_tokens still wins, and reasoning models still route to maxCompletionTokens. Used a fixed default rather than the catalog max_output_tokens because the catalog value is unreliable for some models (grok-4 reports max_output_tokens equal to its context window, not a real output cap, which would risk 400s). Adds TestOCIDefaultMaxTokens covering Cohere and generic injection, the explicit-override case, and the reasoning maxCompletionTokens branch. * test(oci): e2e regression that omitted max_tokens isn't truncated Real-proxy integration test asserting a chat completion that omits max_tokens completes with finish_reason "stop" instead of being cut off at OCI's ~20-token server default. Fails before the maxTokens-default injection (finish_reason "length", ~19 tokens), passes after. * test(oci): update cohere default-params test for injected maxTokens test_cohere_default_parameters asserted no maxTokens was injected, encoding the old behaviour where OCI's ~20-token server default truncated responses. Now that transform_request injects DEFAULT_OCI_CHAT_MAX_TOKENS, assert maxTokens equals that default while the other params (topK/topP/frequencyPenalty) stay pass-through with no hardcoded default. * fix(oci): make DEFAULT_OCI_CHAT_MAX_TOKENS a plain constant Drop the os.getenv override. The env knob was not requested and introducing a new env var forced a cross-repo dependency on litellm-docs (test_env_keys.py validates every referenced env var against the docs table there). A plain 4096 constant keeps the PR self-contained; callers who want a different limit pass max_tokens explicitly per request. * fix(oci): route all OpenAI commercial models to maxCompletionTokens OCI serves OpenAI models (gpt-4.1, gpt-5.1 through 5.5, o-series) that the litellm catalog doesn't track, so the supports_reasoning lookup returned False for them and the provider sent maxTokens, which the reasoning families reject with HTTP 400. With the injected default maxTokens this broke every request to those models, not just ones with an explicit max_tokens. Route the whole openai.* vendor prefix to maxCompletionTokens since OpenAI accepts max_completion_tokens on every chat model; the openai.gpt-oss-* open weights are served by OCI's own stack and keep maxTokens. Verified live against gpt-5.2, gpt-5, gpt-4o, gpt-4.1, gpt-oss-120b, llama-3.3, command-a and grok-3-mini * test(oci): hoist transformation imports and drop unused ones Makes the generic-chat test file ruff-clean: the per-test local imports of OCIChatConfig/OCIVendors shadowed the module-level import (F811) and left it unused (F401), and json plus three OCI type imports were never referenced * fix(oci): translate response_format json_schema to OCI's accepted shape (#29691) * fix(oci): translate response_format json_schema to OCI's accepted shape OCI GenAI rejected every json_schema response_format with HTTP 400 "Please pass in correct format of request", which broke structured-output callers such as MLflow LLM judges (they always send a json_schema). The provider forwarded OpenAI's raw json_schema body unchanged. For GENERIC models OCI's ResponseJsonSchema accepts only name/description/schema/isStrict, so OpenAI's `strict` key (and any other extra) 400s the request; the key must be renamed to isStrict and the body whitelisted. For Cohere models there is no JSON_SCHEMA type at all; the schema has to ride on JSON_OBJECT as {"type": "JSON_OBJECT", "schema": ...}. Cohere type values must also be the canonical uppercase TEXT/JSON_OBJECT. _normalize_response_format now branches by vendor and emits the exact shape each one accepts (verified live against OCI GenAI for Cohere, Meta, Gemini and Grok). Drops the unused, incorrect Cohere response-format pydantic models. Two existing tests asserted the broken behavior (lowercase type, raw jsonSchema on Cohere); they are rewritten to assert the corrected shape, and generic/Cohere json_schema regression tests are added. * fix(oci): raise early on json_schema response_format with no body A GENERIC model request with {"type": "json_schema"} and no json_schema object fell through to the JSON_OBJECT branch and emitted a bodyless {"type": "JSON_SCHEMA"}, which OCI rejects with an opaque HTTP 400. Raise a descriptive 400 at translation time instead. Cohere is unaffected since it always maps to JSON_OBJECT. * test(oci): gateway integration test for response_format json_schema Added to tests/integration/ (the real-network integration suite) reusing the existing OCI proxy harness, not tests/llm_translation/ which is mock-only. --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(oci): accept default n=1 on Cohere instead of hard-failing (#29705) * fix(oci): accept default n=1 on Cohere instead of hard-failing Cohere on OCI has no numGenerations field, so n was mapped to False and map_openai_params raised "param `n` is not supported on OCI" whenever a client sent n. But n=1 (and None) is the OpenAI default single-generation request, which every OCI model produces anyway, so standard clients that always send n=1 (such as the MLflow gateway) were rejected with a 500. Drop n=1/None silently for Cohere; only n>1 is genuinely unsupported and still raises (or drops under drop_params). Generic models are unaffected and keep numGenerations, including n>1. * docs(oci): explain why n is not advertised for Cohere despite tolerating n=1 * test(oci): gateway integration test for Cohere default n=1 Added to tests/integration/ (the real-network integration suite) reusing the existing OCI proxy harness, not tests/llm_translation/ which is mock-only. --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(oci): drop max_retries instead of hard-failing on OCI (#29727) max_retries is a litellm-level control param (litellm applies retries itself), not a generation param OCI accepts. The provider mapped it to False and raised "param `max_retries` is not supported on OCI" whenever it was present. The litellm proxy injects max_retries on every request, so any OCI call through the proxy 500'd unless drop_params was set. Drop max_retries silently in map_openai_params. Adds a unit test (Cohere and generic) and a gateway integration test that a plain request succeeds through a proxy without drop_params. Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(spend-logs): rehydrate metadata JSONB text on ui_view_spend_logs (#29682) Fixes #29674. `/spend/logs/ui` raw-SQL path returns the JSONB metadata column as a string — prisma's query_raw skips the ORM-layer hydration. The UI reads metadata.status / metadata.error_information as object fields, so provider-failure rows look like successes. Fix: json.loads the metadata field right after query_raw, fall back to {} on malformed JSON. 3 existing error-code/error-message tests called json.loads on response.data[0]["metadata"] — they were leaning on the bug. Updated to read the dict directly. Plus 2 new regression tests (failure metadata roundtrip + invalid-json fallback). Reverting the fix makes both new tests fail with AssertionError: metadata should be dict, got <class 'str'>. * fix(proxy): release max_parallel_requests slot when a stream is cancelled mid-flight (#27955) (#30020) * fix(proxy): release max_parallel_requests slot when a stream is cancelled mid-flight (#27955) * fix: refund max_parallel_requests on disconnect from outer streaming generators The cancellation refund previously lived in async_post_call_streaming_iterator_hook, but that hook is nested inside the outer streaming generators and a nested async generator only receives GeneratorExit on garbage collection (non-deterministic). With only the v3 limiter enabled, /chat/completions also bypasses the hook entirely (needs_iterator_wrap() is false). Move the release into async_data_generator and async_streaming_data_generator, the generators Starlette closes on client disconnect, so the refund fires deterministically on every streaming route. Warn when no event loop is running, and document the window TTL refresh on the decrement * fix(mcp): propagate model into model_call_details for passthrough tool calls (#30122) * fix(mcp): propagate model into model_call_details for passthrough tool calls The @client decorator on call_mcp_tool creates the logging object via function_setup without a model kwarg, so model_call_details["model"] starts as None. execute_mcp_tool only set logging_obj.model as an instance attribute, which the spend-log writer never reads (it reads kwargs["model"] from model_call_details). MCP passthrough tools/call rows therefore persisted with model="" while list_tools rows showed "MCP: list_tools", degrading the Logs UI display and bucketing all MCP tool spend under an empty model in DailyUserSpend. Propagate the model into model_call_details alongside the existing attribute assignment so the StandardLoggingPayload and SpendLogs writer pick it up. Covers the /mcp passthrough, REST /mcp-rest/tools/call, and orchestrated paths (the latter already passed model into function_setup, so this is a no-op there). * test(mcp): trim regression test docstring * fix(mcp): surface upstream challenges for delegated OAuth (#30124) * fix(mcp): surface upstream challenges for delegated OAuth * docs(mcp): clarify delegated upstream auth comments * perf(benchmarks): add CPU timing metrics to streaming benchmark (#29980) * Add CPU timing metrics to streaming benchmark * Fix spacing around timing sample dataclass * fix(gemini): don't emit empty choices on metadata-only stream chunks (#29167) web_search + reasoning makes Gemini stream mid-chunks that carry only grounding/thought metadata — no content part, no finishReason. _process_candidates skips content-less candidates and the existing fallback only ran when finishReason was set, so choices stayed empty and the downstream streaming handler raised IndexError on choices[0]. Emit an empty-delta choice for content-less chunks regardless of finishReason. Fixes #28884 * fix(key): allow /key/update to clear budget_limits with [] or null (#30085) * Fix /key/update rejecting budget_limits clear requests with HTTP 400 Sending budget_limits: [] or null to /key/update returned HTTP 400, so once a key had budget windows the last one could never be removed. prepare_key_update_data only json.dumps'd budget_limits when the value was truthy, so [] and None passed through raw to the Prisma Json? column; jsonify_object only serializes dicts, and prisma-client-py has no DbNull sentinel for Json? writes, so Prisma rejected both shapes. Serialize the clear case explicitly as the JSON literal null, matching how memory_endpoints encodes metadata for the same column type. Truthy values keep the existing reset_at window initialization path. Fixes #30067. * Require admin access for budget_limits changes on /key/update Clearing budget_limits via [] or null is a budget mutation, but _validate_update_key_data only counted max_budget and spend as budget changes before deciding whether to skip _check_key_admin_access. A non-admin key owner or a team member with /key/update could therefore remove a key's per-window spend caps without admin authorization. Treat any explicit budget_limits value in the request (set, change, or clear) as a budget change so it gates through the same admin check as max_budget. model_fields_set is used because an explicit null is indistinguishable from an omitted field by value alone. * fix(proxy): persist guardrail info in spend logs for /v1/responses (#30092) Pre-call guardrail blocks on /v1/responses wrote guardrail_information as null in LiteLLM_SpendLogs because _handle_logging_proxy_only_error splits request_data by LoggedLiteLLMParams keys and litellm_metadata, where the Responses API stores request metadata including standard_logging_guardrail_information, was not among them. It fell into optional_params, so merge_litellm_metadata never saw it. Add litellm_metadata to LoggedLiteLLMParams so it routes into litellm_params the same way metadata does on the chat completions path Fixes #28971. * fix(proxy): handle non-standard SSE frames in Anthropic passthrough logging (#26000) Some third-party Anthropic-compatible providers emit non-standard SSE frames (OpenAI-style [DONE] sentinels, non-JSON keep-alive lines) in streaming responses. These caused json.JSONDecodeError in _build_complete_streaming_response, breaking the passthrough logging pipeline so the request was never logged or billed. Skip whole-line 'data: [DONE]' sentinels and catch JSONDecodeError per event. Matching the full line (not a substring) keeps a valid chunk whose text payload contains '[DONE]' from being dropped. Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Sameer Kankute <sameer@berri.ai> * feat(newrelic): Add New Relic extension (#26989) * initial New Relic integration. * Minor fixes for basic observability. * Implemented basic support for the success path. Generates New Relic custom events needed by the AI Monitorin interface. * Supportability metric is sent on first request. * Emit supportability metric every hour instead of once a day. * Add the start/end times to the messages before sending them so that the start time and end time reflect the correct time and both are not set to 'now'. * Make use of `turn_off_message_logging` configuration that is available by default from CustomLogger. * Enabling New Relic agent to be wired when docker container starts if an environment variable is set. * If we cannot find trace information, send the AI events without the trace ID attached. * Use a fake trace_id if we cannot find one. * Implementing a configuration so that users can use litellm configuration to disable sending LLM messages to New Relic. There is a second method to do this via New Relic env var. * Mised file. * Cleaning up logic to turn off recording content via either the LiteLLM configuration or an env var. * Removing debugging. Fixed logic / comments around how often to send supportability metric. * Initial version of public doc for New Relic. * Use a proper name for the doc file. * Updating newrelic.md document. * Updating LiteLLM documentation for New Relic extension. * Moving New Relic imports into the methods to support unit tests. * Adding unit tests for the New Relic extension. * Updating linting and the unit tests that are not running in the CI environment. * Address reviewer feedback on New Relic integration. - Fix _record_error_metric to use app.record_custom_metric() instead of module-level newrelic.agent.record_custom_metric() so the call works outside of an active transaction context - Remove unreachable except ImportError block in _get_trace_context - Update stale "23 hours" comment to "27 hours" (matches 97200s threshold) - Remove commented-out debug code from _process_success - Fix docs typo: NEW_RELIC_CUSTOM_INSIGHTS_EVENTS_MAX_SAMPLES_STOREDA -> NEW_RELIC_CUSTOM_INSIGHTS_EVENTS_MAX_SAMPLES_STORED - Update TestRecordErrorMetric to verify app.record_custom_metric call Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * Reformating for the linter. * Addressing additional automated feedback. - Removed a legacy comment about the New Relic header - Reordered imports in one file - Switched another file to use the import at the top of the file instead of inline when used - Added unit tests for untested methods that were identified * Addressing new feedback. - Proper handling of time to floats. Created a util method and updated code to use it. - added the missing guard to ensure the app is enabled * Addressing feedback. - When an error occurs, still check if the periodic supportability metric should be emitted - Added a check to ensure the extension is ready in the error handler to match _process_success * Updating the NR event timestamps to more accurately reflect when the messages were generated. * Addressing feedback for potential better practice. * Addressing feedback on accessing default values. Added tests for most of these cases. * Adding a new catch exception block based on feedback. * Addressing feedback about a potential issue around a timestamp for the supportability metric. * Addressing minor feedback on length of generated, fallback traceId. * Addressing feedback. - A few more cases were found where the dictionary access might not return the correct value. - Handling cases where `traceparent` is not lower cased * Addressed feedback where the newrelic options might not apply correctly. * Addressing some feedback. * Addressing feedback. * Validating testing / formatting for our changes. * Updating linting, adding tests, defining data type for UI. * Configuration for the logging callback definition. * Adding a newrelic image for the UI to use. * Putting the New Relic callback in proper alphabetic order. * Copying the logo to a committed output directory so it shows up in a locally built container. * Adding missing definition of new env vars that were causing a build failure. * Addressing automated feedback from greptile. * Adding a few more unit tests to increase the code coverage just a bit more. * Additional unit tests to push coverage to almost 90%. * Adding a custom newrelic docker image build process. This removes the need to add the newrelic agent to the core litellm container or dependencies. * Clarifying message when the New Relic agent is not installed and someone is trying to use the newrelic extension. Either use the proper image when using docker, or install the agent manually when running from source. * Ensuring pip is available to install the New Relic agent. * Updating the definition and handling of traceId (no spanId). Clarifying behavior of env vars vs UI configuration for the newrelic extension. * Removing entries from the New Relic logger configuraiton UI as these values must be set as part of running the image. * Removing a stale doc file that has moved to the litellm-docs repo. Cleanup of Dockerfile to remove a LABEL that was incorrect. * Updating container image name to be the best guess for the new name. * Addressing feedback from greptile. - Added a comment around token_count=0 - Updated the boolean parser to allow a wider set of options which matches existing patterns in other parts of LiteLLM. * Removing option for a separate New Relic container image. The agreement is to handle this in the New Relic integration docs. * Updating error message when New Relic agent is not available. * Wiring in the test message from the LiteLLM callback UX. * Missed saving one of the file conflicts. * Fixed a lint error I introduced. Somehow, I dropped another string and now added it back. * Adding newrelic to the schema definition. * Added an admin check on the call before sending test message as mentioned by the AI code review. * Updating to use should_redact_message_logging(kwargs) as part of the logic to determine if message content should be sent to New Relic or not. This still uses the `record_content` property as well, but both have to be true in order for content to be included. --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * Add Azure AI Foundry DeepSeek V3.1 and V4 Pro/Flash global pricing to cost map (#30134) Co-authored-by: Cursor <cursoragent@cursor.com> * fix(logging): translate Responses bridge result to ModelResponse for spend logs (#28985) PR #29394 fixed the AnthropicResponse.model_validate crash for the streaming anthropic_messages -> OpenAI Responses bridge by unwrapping terminal events and returning the inner ResponsesAPIResponse. The spend_logs row lands and usage/cost are correct, but the row's response field stores the Responses API shape (output[...].content[...].text). The proxy UI Logs tab reads response.choices[0].message via parseMessages in prettyMessagesUtils.ts with no fallback for the Responses shape, so the OutputCard renders "No response data available" for every cross-routed call. The same shape mismatch affects every downstream consumer of spend_logs that assumes the canonical chat-completion shape This change keeps the unwrap from #29394 but routes the resulting ResponsesAPIResponse (and the bare-response non-streaming path) through LiteLLMResponsesTransformationHandler.transform_response, which is the same conversion already used by the chat-completion Responses bridge. Spend_logs now stores a ModelResponse with choices[0].message.content, so the UI and other consumers see the assistant text. On a translation failure (eg. empty output on an incomplete response) the handler falls back to a minimal ModelResponse carrying model and usage so the row still lands rather than being dropped as a Non-Blocking error Also corrects a stale comment in the Responses adapter that implied the call type was reclassified to acompletion; the code preserves anthropic_messages and the success handler translates back to ModelResponse for the row Fixes #28595 * fix(anthropic-adapter): re-emit first delta on streaming content-block transitions (#30024) * fix(anthropic-adapter): re-emit first delta on streaming content-block transitions The `/v1/messages` -> `/v1/chat/completions` streaming adapter (`AnthropicStreamWrapper`) silently dropped the first non-empty delta of every content block that started via a *transition* (e.g. text -> tool_use -> text, text -> thinking). When an upstream chunk both triggers a new content block (its type differs from the active block) and carries that block's first delta, the wrapper emitted `content_block_stop` -> `content_block_start` and then only re-queued the trigger chunk when it was an `input_json_delta` (bundled tool args). The synthesized `content_block_start` always carries an empty body, so the first `text_delta` / `thinking_delta` was lost — the client output started from the second token (e.g. "Hi, how can I help you?" rendered as ", how can I help you?", or text resuming after a tool call lost its first sentence). This is especially visible with Claude Code-style clients that consume Anthropic Messages streaming events strictly. Fix: re-queue the trigger chunk's translated delta whenever it carries non-empty content (text/thinking/signature/tool args), via a shared `_trigger_delta_has_content` helper used by both the sync and async paths. Empty trigger deltas are still suppressed so no spurious empty `content_block_delta` is introduced. Fixes #30014 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * test(anthropic-adapter): cover all _trigger_delta_has_content branches Add a direct parametrized unit test for the re-emit predicate so every delta type (text/input_json/thinking/signature), the empty-payload guards, and the malformed/non-delta cases are exercised independently of upstream chunk translation. Raises patch coverage for the new helper. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> * feat: add opt-in healthy_only filter to GET /v1/models (#30130) * feat: add opt-in healthy_only filter to GET /v1/models Adds an opt-in `healthy_only=true` query parameter to GET /v1/models and GET /models that hides models whose backing deployments are all marked unhealthy by background health checks. - Add Router.async_get_fully_unhealthy_model_names(), mirroring the semantics of get_fully_blocked_model_names(): a model is hidden only when every backing deployment is unhealthy and the health state is not stale (fail open otherwise). - Reuses the existing DeploymentHealthCache populated by _run_background_health_check(), so no new health state is introduced. - No-op when allowed_fails_policy is set, mirroring _async_filter_health_check_unhealthy_deployments semantics. - team_public_model_name aliases are aggregated alongside model_name. - Hiding is presentation-only; default behavior is unchanged. Fixes #30128 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs: address Greptile review notes - Note team-alias asymmetry vs get_fully_blocked_model_names - Debug-log when healthy_only is set but no health state is available Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Claude Fable 5 <noreply@anthropic.com> * Dedupe team soft budget alerts by team_id instead of token (#30097) _team_soft_budget_check sends type="soft_budget" alerts with event_group=TEAM, but SoftBudgetAlert.get_id always returned the request token. The alert cache key was therefore scoped per virtual key, so every active key in a team over its soft budget fired its own alert within budget_alert_ttl. Branch on event_group so team-level alerts dedupe by team_id, matching TeamBudgetAlert, while key and project level alerts keep per-token dedupe. Fixes #27398. * feat(bedrock guardrails): support contextual grounding qualifiers (request-side) (#30057) * test: add failing tests for Bedrock contextual grounding (request-side) Drive the request-side of Bedrock contextual grounding: callers tag message content blocks as grounding_source/query, the post_call hook assembles an ApplyGuardrail(OUTPUT) call carrying source + query + response(guard_content), and the bedrock converse transform must render the tags as prompt text instead of silently dropping them. Non-grounding payloads must stay byte-identical. * feat(bedrock guardrails): support contextual grounding qualifiers Bedrock contextual grounding scores a model response against a reference source and the user query, expressed via a per-content-block `qualifiers` array on ApplyGuardrail. The guardrail hook previously sent plain text only, so grounding could not be driven through it even though the response-side contextualGroundingPolicy parsing already existed. Callers now tag message content blocks `{"type":"grounding_source"}` / `{"type":"query"}` (mirroring the existing `guarded_text` marker). On the generate path the bedrock converse transform renders them as plain text; at post_call the hook harvests them from the request and assembles one ApplyGuardrail(OUTPUT) call carrying grounding_source + query + the response (as guard_content). Requests without these tags produce a byte-identical payload, so existing behaviour is unchanged. * Feat(guardrail): Adding support for custom Ovalix guardrail (#21887) * Feat(guardrail): Adding support for custom Ovalix guardrail * Internal CR comments fixes * greptileai comments fixes * fix conflict * fixes * fix sha256 * clarify Ovalix actor-id hash is for normalization, not PII protection * fix(github_copilot): normalize per-event item_id in /responses streaming (#30072) GitHub Copilot's native /v1/responses stream assigns a different item_id to every event of a single output item (output_item.added, the part.added / delta / done events, and output_item.done). Spec-strict clients like the Vercel AI SDK key streaming parts by item_id and abort with "reasoning part <id> not found" / "text part <id> not found" when a delta references an unregistered id. Override transform_streaming_response in GithubCopilotResponsesAPIConfig to anchor every event of an output item to the id from its output_item.added. Copilot accepts that id paired with the final encrypted_content on the next turn, so multi-turn replay is unaffected. Fixes #30071 * feat: add /model/block and /model/unblock endpoints (#30125) * feat: add /model/block and /model/unblock endpoints Add dedicated proxy-admin POST /model/block and /model/unblock endpoints over the existing blocked flag on LiteLLM_ProxyModelTable, mirroring the /key/block and /key/unblock pattern. Calling a model whose deployments are all blocked now returns a clear 403 "Model is blocked" instead of a generic no-deployment error, including direct-dispatch route types (e.g. eval) via a pre-route guard. Includes audit-log entries for block/unblock and unit tests. Closes #29742 Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> * chore: regenerate dashboard API types for model block/unblock endpoints Regenerate ui/litellm-dashboard/src/lib/http/schema.d.ts from the proxy OpenAPI spec (npm run gen:api) so it includes the new endpoints. Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> * fix: widen router block-helper param type and add direct unit tests Type the _are_all_deployments_blocked deployments parameter to match its callers (DeploymentTypedDict) so mypy passes, and add tests/test_litellm/test_router_block_helpers.py with direct unit tests for the three block helper methods so router_code_coverage recognizes them. Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> * fix: restore type-ignore on messages arg after black reflow Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> * refactor: raise model-block 403 in proxy layer, not SDK Router Keep the SDK Router's documented behavior for blocked deployments (filtered -> "no healthy deployment") and move the 403 PermissionDeniedError into the proxy layer (route_llm_request), where model blocking is an admin concept. This avoids a backwards-incompatible 403 for SDK users who set blocked=True on their own deployments, per maintainer review. Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> --------- Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix: add week unit support to get_next_standardized_reset_time (#30100) * fix: add week unit support to get_next_standardized_reset_time The function handled d/h/m/s/mo units but silently fell through to the default next-midnight branch for the w (week) unit. This was inconsistent: _extract_from_regex already accepted w in its character class, and duration_in_seconds already returned value * 604800 for it. Add the missing elif unit == 'w' branch that delegates to _handle_day_reset with value * 7, which reuses the existing Monday- alignment logic for 1w and the generic N-day-from-midnight path for larger multiples. Add test_week_based_resets covering 1w from a Wednesday (expects next Monday) and 2w from a Monday (expects 14 days forward at midnight). Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> * test: exercise relative week semantics with non-Monday base dates + add docstring Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> --------- Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> * fix: black formatting and remove undocumented MAVVRIK_FOCUS_FREQUENCY env var * fix: black formatting with correct version and sync schema.d.ts for healthy_only param * fix: resolve mypy errors and add transcription_sessions to JSON schema endpoint enum * fix: restore MAVVRIK_FOCUS_FREQUENCY guard and exclude it from docs key scan * fix: address Greptile P2 comments - move constant, use UTC datetime, skip redundant team lookup * revert: restore original team lookup logic in can_key_call_resolved_model --------- Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com> Co-authored-by: nina-hu <nina.huuu@gmail.com> Co-authored-by: Sahith Jagarlamudi <104647530+s-jag@users.noreply.github.com> Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Praveen Ghuge <95286176+pghuge-cloudwiz@users.noreply.github.com> Co-authored-by: alex107ivanov <30668368+alex107ivanov@users.noreply.github.com> Co-authored-by: hcl <chenglunhu@gmail.com> Co-authored-by: Fede Kamelhar <federico.kamelhar@oracle.com> Co-authored-by: Armaan Sandhu <74664101+Ar-maan05@users.noreply.github.com> Co-authored-by: Teo Xian Zhong Augustine <35527068+auggie246@users.noreply.github.com> Co-authored-by: King Star <mcxin.y@gmail.com> Co-authored-by: Saksham Maggo <122939011+SakshamMaggo@users.noreply.github.com> Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com> Co-authored-by: Kelvin <leikaiwei@outlook.com> Co-authored-by: Josh Bonczkowski <josh.bonczkowski@gmail.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: M. Dennis Turp <mdturp@pm.me> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Piotr Minkina <piotrminkina@users.noreply.github.com> Co-authored-by: Martín Alcalá Rubí <martin@tryolabs.com> Co-authored-by: T. Kobayashi <13004314+nix-tkobayashi@users.noreply.github.com> Co-authored-by: João Costa <13508071+jpv-costa@users.noreply.github.com> Co-authored-by: Shalom <shalom@ovalix.io> Co-authored-by: codgician <15964984+codgician@users.noreply.github.com> Co-authored-by: FugoP <kim@pomsora.com> Co-authored-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>	2026-06-11 22:30:26 -07:00
Sameer Kankute	424db6a980	feat(azure_ai): add MAI-Image-2.5 image generation support (#29688) ... * feat(azure_ai): add MAI-Image-2.5 image generation support Route azure_ai MAI models to /mai/v1/images/generations and map OpenAI size to width/height for the serverless API. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(azure_ai): address MAI image generation review feedback Validate unsupported size values, default width/height independently, add MAI-Image-2.5 pricing, and expand test coverage. @greptileai Co-authored-by: Cursor <cursoragent@cursor.com> * feat(azure_ai): add MAI image edit and expand model cost map Add MAI image edit support with usage normalization for Azure response format, and register MAI-Image-2.5-Flash and MAI-Image-2e pricing in the model map. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(azure_ai): validate MAI edit size by consuming map iterator Greptile: lazy map() never evaluated int() so values like 1024xabc passed through. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(azure_ai): normalize MAI usage in generation response handler Apply normalize_mai_image_usage before building ImageResponse so token-based cost calculation works when Azure returns num_output_tokens fields. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(azure_ai): narrow MAI edit size param type for mypy Co-authored-by: Cursor <cursoragent@cursor.com> * Fix Azure MAI image response handling * Fix MAI image generation base model routing * fix(azure_ai): preserve zero num_output_tokens in MAI usage normalization * fix(azure_ai): wrap MAI generation response JSON parsing in error handling * fix(azure_ai): build MAI image edit URL correctly for /mai/ root bases * fix(azure_ai): build MAI image generation URL correctly for /mai/ root bases --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>	2026-06-08 18:27:04 -07:00
Sameer Kankute	cb041966bf	Litellm oss staging 040626 (#29671) ... * fix(azure): apply api_version fallback chain to image edit URL `AzureImageEditConfig.get_complete_url` only read `api_version` from `litellm_params`. When callers configured it via `litellm.api_version` or `AZURE_API_VERSION`, the constructed URL had no `?api-version=` and Azure responded `404 Resource not found`. Apply the same fallback chain the Azure chat path already uses in `common_utils.py`: litellm_params > litellm.api_version > AZURE_API_VERSION env > litellm.AZURE_DEFAULT_API_VERSION Adds 5 unit tests pinning each layer of the chain plus a regression guard for `api_base` that already carries `?api-version=`. * feat(mcp): core sampling and elicitation flow with security hardening - Add sampling_handler.py: full MCP sampling/createMessage flow with model selection (hint-based + priority-based), auth enforcement, budget checks, route restriction gates, and tag policy pre-auth - Add elicitation_handler.py: MCP elicitation/create relay with downstream client capability detection - Wire sampling/elicitation callbacks in mcp_server_manager.py gated behind allow_sampling/allow_elicitation config flags - Add allow_sampling/allow_elicitation fields to MCPServer type - Fix session lock deadlock: skip lock for JSON-RPC response POSTs (elicitation/sampling replies) with truncated-body heuristic - Extend client.py with sampling_callback and elicitation_callback - Security: RouteChecks gate, tag-budget bypass fix, x-forwarded-for spoofing fix, Latin-1 header encoding guard - Add 4 new test modules (model access, priority selection, request builder, tool conversion) + update existing MCP tests * fix(security): run pre-call guardrails before MCP sampling acompletion Without this, an upstream MCP server with allow_sampling enabled could send prompts that bypass every guardrail (content filtering, PII redaction, prompt-injection detection) configured on /chat/completions. - Call proxy_logging_obj.pre_call_hook(call_type='acompletion') before llm_router.acompletion so guardrails fire for sampling sub-calls - Add HTTPException to the re-raise list so guardrail rejections propagate correctly instead of being swallowed as generic errors * feat(bedrock_mantle): add Responses API support (/openai/v1/responses) (#29490) * feat(bedrock_mantle): add Responses API transformation config * test(bedrock_mantle): cover trailing-slash api_base normalization * feat(bedrock_mantle): export BedrockMantleResponsesAPIConfig * feat(bedrock_mantle): register gpt-5.x Responses config (gpt-oss unchanged) * feat(bedrock_mantle): add gpt-5.5/gpt-5.4 Responses price-map entries * refactor(bedrock_mantle): exclude gpt-oss instead of allow-listing gpt-5 for Responses routing Frontier OpenAI models on Bedrock Mantle are Responses-only on /openai/v1/responses; gpt-oss is the legacy family that also speaks chat-completions. Gate by excluding gpt-oss (which keeps its chat-completions emulation) and defaulting everything else to the native Responses config, so future frontier models (gpt-6, etc.) route correctly without a code change. Verified against the live us-east-2 Mantle endpoint: gpt-oss 400s on /openai/v1/responses while gpt-5.5 400s on both standard paths. * test(bedrock_mantle): cover supports_native_websocket opt-out Closes the one uncovered line flagged by codecov on the Responses config. The assertion documents that Mantle Responses has no realtime/websocket transport, so realtime routing must not attempt a socket it cannot serve. * fix(bedrock_mantle): route file_search through emulation instead of forwarding to Mantle BedrockMantleResponsesAPIConfig inherited supports_native_file_search() -> True from OpenAIResponsesAPIConfig but never overrode it. Mantle has no OpenAI vector stores, so a forwarded file_search tool is rejected with a 400 (verified upstream: Tool type 'file_search' is not supported). Opting out, like the existing supports_native_websocket override, routes the tool through LiteLLM's file_search emulation instead. * fix(bedrock_mantle): only route openai.gpt frontier models to Responses The previous gate excluded gpt-oss and routed every other model to the native Responses config. But on Mantle only the OpenAI gpt frontier models (gpt-5.x) are served on /openai/v1/responses; gpt-oss and the non-OpenAI families (nvidia, mistral, google, zai, ...) are chat-completions only and 400 on that path. Allow-list the openai.gpt- family (excluding gpt-oss) instead, so chat-only models fall through to the chat-completions emulation. Verified against the live us-east-2 endpoint: nvidia.nemotron-nano-9b-v2 returns 400 on /openai/v1/responses and 200 on /v1/chat/completions. * feat(custom_llm): allow streaming/astreaming to yield ModelResponseStream (#27580) * fix(custom_llm): allow streaming/astreaming to yield ModelResponseStream directly * fix(streaming): enhance ModelResponseStream handling for custom LLM providers * fix(streaming): strip finish_reason from content chunks and ensure tool_calls are preserved * fix(streaming): add type ignore for finish_reason assignment in CustomStreamWrapper * fix(proxy): strip stack trace from HTTP 503 responses (CWE-209) (#28330) * fix(proxy/cwe-209): strip Python traceback from HTTP 503 error responses The /cache/ping endpoint included a full Python traceback in its 503 error response body (inside the ProxyException message), leaking internal file paths, line numbers, and call stacks to any caller. Two MCP route handlers in proxy_server.py similarly interpolated str(e) into "Internal server error" detail strings. Fix: log the traceback server-side via verbose_proxy_logger.exception() and omit it from the ProxyException payload / HTTPException detail returned to clients. Tests updated to assert no "traceback" keyword or frame paths appear in the 503 body, with a new dedicated regression test. CWE-209: Generation of Error Message Containing Sensitive Information. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(proxy/cwe-209): apply Greptile P2 fixes and add MCP exception-path tests Greptile 4/5 review identified two remaining gaps and Codecov reported 0% coverage on the two MCP handler exception branches: 1. caching_routes.py — str(e) in "Service Unhealthy ({str(e)})" could still leak Redis hostnames/IPs; replaced with static "Service Unhealthy". HTTPException is now re-raised before the generic handler so the "cache not initialized" 503 still reaches callers with its detail. Removed the redundant str(e) arg from verbose_proxy_logger.exception() (exception() already appends the traceback automatically). 2. tests — two new unit tests cover the exception paths in dynamic_mcp_route and toolset_mcp_route that were previously at 0%: - test_dynamic_mcp_route_unexpected_exception_returns_500_without_traceback - test_toolset_mcp_route_unexpected_exception_returns_500_without_traceback All 25 tests pass (9 caching + 16 MCP). CWE-209: Generation of Error Message Containing Sensitive Information. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * test(caching_routes): restore precise assertion in test_cache_ping_no_cache_initialized The assertion was weakened to `"Cache not initialized" in str(data)`, which matches the raw string of the entire response dict and would pass even if the error moved to an unexpected field or changed structure. Restore a targeted check on the parsed response: assert the exact string in the correct field `data["detail"]`, matching FastAPI's HTTPException serialisation format {"detail": "<message>"}. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * test(caching_routes): restore precise assertion and add CWE-209 no-cache path test The assertion in test_cache_ping_no_cache_initialized was weakened to `"Cache not initialized" in str(data)`, which matched against the raw string representation of the entire response dict. This would pass silently even if the error message moved to an unexpected field or the structure changed. Restore a targeted assertion on the parsed field: assert data["detail"] == "Cache not initialized. litellm.cache is None" matching FastAPI's HTTPException serialisation format exactly. Add test_cache_ping_no_cache_does_not_expose_internals to show the code path is still working correctly after the CWE-209 fix: verifies that the HTTPException is re-raised as-is (no traceback, no source paths), and asserts the complete response structure is exactly {"detail": "Cache not initialized. litellm.cache is None"}. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(caching_routes): restore ProxyException envelope for null-cache 503 The except HTTPException: raise guard (added in the CWE-209 fix) caused the null-cache HTTPException to escape as FastAPI's {"detail": "..."} shape instead of the {"error": {...}} ProxyException envelope that callers expect. Move the null-cache guard before the try block and raise ProxyException directly so the response structure is consistent with all other /cache/ping 503s, and the except HTTPException: raise guard is only reachable by unexpected downstream HTTPExceptions. Update the two no-cache tests to assert the correct ProxyException envelope. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * Update utils.py (#26609) * feat(pricing): add Snowflake Cortex REST API model pricing (#26612) * feat(pricing): add Snowflake Cortex REST API model pricing ## Summary Adds pricing and context window information for 20+ Snowflake Cortex REST API models to `model_prices_and_context_window.json`. ## What's included - **7 Claude models** (sonnet-4-5, sonnet-4-6, 4-sonnet, 4-opus, haiku-4-5, 3-7-sonnet, 3-5-sonnet) — with prompt caching rates - **4 OpenAI models** (gpt-4.1, gpt-5, gpt-5-mini, gpt-5-nano) — with prompt caching rates - **5 Llama models** (3.1-8b, 3.1-70b, 3.1-405b, 3.3-70b, 4-maverick) - **1 DeepSeek model** (deepseek-r1) - **1 Mistral model** (mistral-large2) - **1 Snowflake model** (snowflake-llama-3.3-70b) - **2 Embedding models** (arctic-embed-l-v2.0, arctic-embed-m-v2.0) Each entry includes `input_cost_per_token`, `output_cost_per_token`, `cache_read_input_token_cost` (where applicable), `max_input_tokens`, `max_output_tokens`, and capability flags (`supports_function_calling`, `supports_vision`, `supports_prompt_caching`, `supports_reasoning`). ## Pricing source All prices are in USD per token, sourced from the official [Snowflake Service Consumption Table](https://www.snowflake.com/legal-files/CreditConsumptionTable.pdf) — Tables 6(b) (REST API with Prompt Caching) and 6(c) (REST API). ## Context The existing `snowflake/` provider has zero model entries in the pricing JSON, which means LiteLLM cannot track costs for Snowflake Cortex calls. This PR fills that gap. ## Related - Existing provider: `litellm/llms/snowflake/` - Cortex REST API docs: https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-rest-api * Update model_prices_and_context_window.json Fix the JSON parsing error * Update model_prices_and_context_window.json Removed the duplicate entry * fix(utils): copy extra_body before adding unknown params to prevent model config mutation (#29620) Fixes #29615. In add_provider_specific_params_to_optional_params, the line: extra_body = passed_params.pop("extra_body", None) or {} returns the original dict reference when extra_body is non-empty (truthy). Subsequent writes like extra_body[k] = passed_params[k] then mutate the shared model config object held by the router, poisoning /model/info and all subsequent requests for that deployment. The or {} short-circuit creates a new dict only when extra_body is falsy (None or {}), which is why the bug does not reproduce with extra_body: {}. Fix: wrap in dict() so we always work on a fresh shallow copy. * fix(vertex_ai): Bake tool_choice into Gemini CachedContent body to prevent silent drop (#29097) * fix(vertex_ai): bake tool_choice into Gemini CachedContent body to prevent silent drop * address greptile feedback on tool_choice cache test * adds test that uses ToolConfig(functionCallingConfig=FunctionCallingConfig(mode=ANY)) instead of a dict literal, mirroring what map_tool_choice_values actually produce * fix(gemini/veo): move image from parameters into instances[0] (#29501) * fix(gemini/veo): move image from parameters into instances[0] Veo's predictLongRunning schema puts image (and prompt) on the instances element; parameters is for aspectRatio/durationSeconds/etc. The Gemini path was leaving image in params_copy, so it ended up nested under parameters and the API silently ignored it. The Vertex path already builds the instance dict explicitly, so this just aligns the Gemini path with it. Fixes #29498 * address greptile: unconditional pop + BytesIO test - Pop `image` from params_copy unconditionally so it never reaches GeminiVideoGenerationParameters even when None, removing implicit reliance on Pydantic's extra-field-ignore. - Add test_transform_video_create_request_image_filelike_goes_to_instance covering the BytesIO path (_convert_image_to_gemini_format) — round-trips the base64 to confirm encoding. - Add test_transform_video_create_request_image_none_is_dropped covering the new None branch. * fix(huggingface): handle special token text in embedding usage (#29660) * fix(guardrails): recompile ToolPermissionGuardrail rules on update_in_memory_litellm_params (#29655) * fix(guardrails): recompile ToolPermissionGuardrail rules on update_in_memory_litellm_params ToolPermissionGuardrail builds self.rules and the compiled target/pattern maps only in __init__. The base update_in_memory_litellm_params re-sets raw attributes via setattr but never rebuilds those maps, so a guardrail updated in place (PUT /guardrails, or the immediate in-memory sync) keeps enforcing the construction-time rules until it is reinitialized (PATCH path, periodic DB poll, or restart). Extract the compile step into _load_rules and override update_in_memory_litellm_params to rebuild from it (dict- and model-safe), re-normalizing default_action / on_disallowed_action. Mirrors the existing PresidioGuardrail override of the same method. Adds regression tests. Fixes #29592. * fix(guardrails): handle dict params in ToolPermissionGuardrail in-memory update Delegate to super() only for LitellmParams input (the base setattr loop is model-only); apply the raw-dict case inline. Fixes the mypy arg-type error and makes the recompile work when the proxy passes the raw DB dict. * fix(guardrails): preserve tool-permission rules on a partial in-memory update A partial update (e.g. a LitellmParams whose rules field is None) ran through the generic setattr, which set self.rules to None, and the recompile was skipped, leaving the guardrail with no rules. Snapshot the previous rules and restore them when the update carries no rules; an explicit empty list still clears them. Adds a regression test for the rules-absent case. Addresses the Greptile review note on #29655. * fix(bedrock): stop base_model label from stripping tools/tool_choice (#29621) * fix(bedrock): stop base_model label from stripping tools/tool_choice A Router/proxy Bedrock deployment whose model_info.base_model is a friendly label (e.g. claude-haiku-4-5) silently lost tools/tool_choice: the outgoing Converse request was built without toolConfig, so the model behaved as if no tools were provided. Worked in v1.84.0, regressed in v1.85.0, and with drop_params=true it failed silently. Two changes compound into the bug. completion() passed model_info.base_model as the model argument to get_optional_params, so the real Bedrock model id never reached supported-param resolution; and get_supported_openai_params resolved the provider config's params from base_model or model, letting the label fully replace the real model. For Bedrock the label resolves to no tool support, so tools/tool_choice were dropped before transformation. completion() now keeps model as the real deployment model and threads the resolved base_model (kwarg or model_info) through separately, and get_supported_openai_params treats base_model as additive: it returns the union of the params supported by model and by base_model. A hint can only add capabilities, never strip ones the real model already exposes, which also preserves the original base_model behavior from #27717 and Azure's base_model driven model-type detection. Fixes #29618 * test(main): make base_model param test robust to new parametrize cases Restore an explicit per-case expected_model_param literal instead of hardcoding the gemini id, so a future case with a different model can't produce a misleading assertion failure. * fix(fireworks_ai): pass response_format json_schema through unchanged (#29606) FireworksAIConfig.map_openai_params was rewriting the OpenAI strict `{type: json_schema, json_schema: {name, strict, schema}}` shape into `{type: json_object, schema: ...}` before sending to Fireworks, dropping `strict` and `name` and changing the `type`. Per Fireworks' docs json_object means "force any valid JSON output (no specific schema)", so the schema constraint was effectively dropped and grammar-guided decoding never ran; model output silently violated the schema. The rewrite landed in #7085 (Dec 2024) when Fireworks did not yet accept native json_schema. Fireworks accepts the OpenAI strict shape natively now, so the rewrite has become a regression. Removes the rewrite. Passes response_format through unchanged. Updates the existing test_map_response_format to assert pass-through. Adds focused regression tests in tests/test_litellm/ covering preservation of type, strict, name, and schema body, plus that json_object alone still works. * fix(types): import Required from typing_extensions in gemini types * style: reformat sampling_handler.py for py312 black compat * refactor(mcp-sampling): extract helpers to fix PLR0915 too-many-statements in handle_sampling_create_message * fix(proxy-server): add explicit ProxyLogging type annotation to proxy_logging_obj to fix mypy inference * fix(mcp-sampling): suppress mypy assignment error on ImportError fallback for proxy_logging_obj * fix(test): use .value when comparing LlmProviders enum against string in test_default_api_base * fix(test): iterate LlmProviders enum in test_default_api_base to avoid str pollution from custom provider registration litellm.provider_list is a mutable global initialized to list(LlmProviders) but custom_llm_setup() appends plain provider strings to it. When a test_custom_llm.py test runs first in the same xdist worker, provider_list contains a str and calling .value on it raises AttributeError. Iterate the immutable LlmProviders enum instead, which is deterministic and what the check intends. * fix(mcp): depth-aware JSON-RPC response detection and neutral speed-priority fallback Replace the flat substring check in the truncated-body routing path with a top-level-key scan so a JSON-RPC response whose result payload nests a "method" field is still detected as a response and skips the session lock, removing a deadlock against the in-flight tool call awaiting it. Drop the inverse max_output_tokens speed proxy when no model exposes output_tokens_per_second; context-window size does not track latency, so a neutral score avoids biasing speedPriority toward the smallest-context model. * fix(guardrails): make ToolPermission rule reload atomic on invalid regex _load_rules appended each rule to self.rules before compiling its regex, so an invalid pattern raised mid-loop after the bad rule was already live but without a _compiled_rule_targets entry. _matches_regex reads a missing compiled target as a None pattern and returns True, turning the bad rule into a match-all that silently applies its decision to every tool. Via update_in_memory_litellm_params (PUT /guardrails) this corrupted the live guardrail. Build the parsed rules and compiled maps into locals and swap them in only after every regex compiles, and restore the previous ruleset if a live update is rejected, so an invalid regex now fails the update without leaving the guardrail enforcing a broken policy. * test(mcp): cover sampling conversion, model resolution, and elicitation relay paths The MCP sampling and elicitation handlers shipped with partial test coverage, leaving the response-to-MCP conversion, the model resolution fallback chain, completion-kwargs assembly, guardrail routing, and the entire elicitation relay untested. That pulled the PR's diff (patch) coverage below the codecov threshold even though overall project coverage rose. Add focused unit tests for _convert_openai_response_to_mcp_result, _convert_mcp_tools_to_openai, _convert_mcp_tool_choice_to_openai, image and audio content conversion, the hint-matching and fallback branches of _resolve_model_from_preferences, _build_completion_kwargs, the router and guardrail-rejection paths of _run_guardrails_and_call_llm, the handle_sampling_create_message success and error-propagation flows, the marker-hoisting fallback for tool content on unexpected roles, and the elicitation form/url/generic relay together with its decline paths --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: lengkejun <lengkejun@xd.com> Co-authored-by: Yug <yugborana000@gmail.com> Co-authored-by: Kent <72616338+kingdoooo@users.noreply.github.com> Co-authored-by: tanmay958 <53569547+tanmay958@users.noreply.github.com> Co-authored-by: DrishnaTrivedi <142084770+DrishnaTrivedi@users.noreply.github.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: Navnit Shukla <Navnit.shukla25@gmail.com> Co-authored-by: PRABHU KIRAN VANDRANKI <72809214+VANDRANKI@users.noreply.github.com> Co-authored-by: Adrian Lopez <109683617+adriangomez24@users.noreply.github.com> Co-authored-by: hcl <chenglunhu@gmail.com> Co-authored-by: JooHo Lee <96564470+BWAAEEEK@users.noreply.github.com> Co-authored-by: Dinesh Girbide <85330597+Dinesh-Girbide@users.noreply.github.com> Co-authored-by: cloudwiz <22098246+andrey-dubnik@users.noreply.github.com> Co-authored-by: Ahmad Khan <ahmadkhan2508@gmail.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>	2026-06-04 11:07:20 -07:00
Mateo Wang	96a2e8b16d	fix(azure): preserve AD token refresh in v1 OpenAI client path (#28627) ... * fix(azure): preserve AD token refresh in v1 OpenAI client path The /openai/v1/ code path (api_version in {"v1", "latest", "preview"}) constructs a plain OpenAI/AsyncOpenAI client, but only forwarded `api_key` from `azure_client_params`. When `enable_azure_ad_token_refresh` is set (or any AD-only auth), `api_key` is None and the client constructor raised "The api_key client option must be set...", breaking every Azure call with a v1 api_version. The OpenAI SDK (>=2.20.0) accepts a callable for `api_key` and re-invokes it on every request via `_refresh_api_key`, so we now forward `azure_ad_token_provider` directly — preserving the per-request token refresh behavior of the regular AzureOpenAI client and avoiding the expiry hole that resolving the token once at client-creation time would introduce. Static `azure_ad_token` strings fall through to `api_key`. For the async path we wrap the sync provider returned by azure-identity in an async function since AsyncOpenAI expects `Callable[[], Awaitable[str]]`. Fixes #27945 https://claude.ai/code/session_01UnzrDSFUUgp5T2wRoPMxq5 * fix(azure): offload sync token provider to thread in v1 async wrapper * fix(azure): include AD credential identity in v1 client cache key --------- Co-authored-by: Claude <noreply@anthropic.com>	2026-05-25 21:08:52 -07:00
ishaan-berri	203b529c9d	feat(azure): add speech transcription config support (#27482) ... Co-authored-by: oss-agent-shin <279349115+oss-agent-shin@users.noreply.github.com> Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com>	2026-05-23 12:16:01 -07:00
Sameer Kankute	e9f0eddbd1	Litellm oss staging 2 (#28582) ... * fix(anthropic): handle empty streaming tool calls (#28549) Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * [Feature][Bug Fix] Decouple Azure OpenAI Deployment ID from model name via base_model to fix gpt5 model routing (#28490) * feat(azure): decouple deployment ID from model name via base_model Azure OpenAI deployments have arbitrary names (deployment IDs) that may not match the underlying model. Previously, model-type detection (o-series, gpt-5, etc.) relied on substring matching against the deployment name, causing misrouted configs and rejected params when deployment names were non-standard (e.g. 'my-deployment-id' for gpt-5.2). This change extends the existing base_model field to drive model-type detection, config selection, supported param resolution, and param mapping throughout the Azure call path: - _get_azure_config() uses base_model for is_o_series/is_gpt_5 checks - get_provider_chat_config() threads base_model for Azure - get_supported_openai_params() accepts and uses base_model - get_optional_params() accepts base_model and passes it to all Azure config method calls (get_supported_openai_params, map_openai_params) - azure.py completion handler uses base_model for GPT-5 detection - Config internal methods (e.g. is_model_gpt_5_2_model) now receive base_model so features like logprobs are correctly enabled Fully backward compatible - when base_model is unset, behavior is identical. Existing o_series/ and gpt5_series/ prefix workarounds continue to work. Usage in proxy config: model_list: - model_name: my-gpt5 litellm_params: model: azure/my-deployment-id model_info: base_model: azure/gpt-5.2 Fixes: non-standard deployment names like 'prefix-gpt-5.2' rejecting logprobs/top_logprobs despite the underlying model supporting them. * Addressing Greptile comments. * gemini-3.1-flash-lite pricing (#27933) * feat(model_prices): add gemini-3.1-flash-lite pricing with standard/batch/flex/priority tiers * fix pricing * add service tier --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> * fix(openai-responses): strip Anthropic cache_control from Responses API requests (#28431) Squash-merged by litellm-agent from cwang-otto's PR. * Treat None litellm_provider as wildcard in _check_provider_match (#28523) Squash-merged by litellm-agent from adityasingh2400's PR. * fix greptile * fix: use _azure_detection_model in default Azure branch of get_supported_openai_params Co-authored-by: Yassin Kortam <yassin@berri.ai> * fix(openai-responses): strip cache_control on compact endpoint as well Co-authored-by: Yassin Kortam <yassin@berri.ai> --------- Co-authored-by: Felipe Garé <90070734+FelipeRodriguesGare@users.noreply.github.com> Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: withomasmicrosoft <withomas@microsoft.com> Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com> Co-authored-by: cwang-otto <chengxuan.wang@ottotheagent.com> Co-authored-by: Aditya Singh <60082699+adityasingh2400@users.noreply.github.com> Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Yassin Kortam <yassin@berri.ai>	2026-05-22 10:04:23 -07:00
ishaan-berri	b593b88ec6	Ishaan - May 13th Staging LiteLLM (#27877) ... * fix: strip Gemini thought-signature from tool_use.id in non-streaming path; example websearch config (#27873) - adapters/transformation.py: mirror the streaming path and strip the `__thought__<b64>` suffix off `tool_call.id` before building the AnthropicResponseContentBlockToolUse. Base64's `+ / =` characters violate Anthropic's `^[a-zA-Z0-9_-]+$` tool_use.id pattern, so when a conversation that flowed through Gemini is later replayed to an Anthropic-native provider (Bedrock or Anthropic API) the request 400s. - example_config_yaml/websearch_interception_config.yaml: register the interceptor under `callbacks:` not `success_callback:`. `success_callback` does not run pre-request hooks, so the tool-conversion step never fires on `/v1/messages` and the raw `web_search_20250305` tool is forwarded to Bedrock, which 400s. - adds a unit test pinning the non-streaming strip behavior and the surviving `^[a-zA-Z0-9_-]+$` shape of the resulting id. Co-authored-by: oss-agent-shin <279349115+oss-agent-shin@users.noreply.github.com> * Fix/azure image edit auth header (#27863) * fix(azure/image_edit): use api-key header instead of Authorization Bearer Delegate `AzureImageEditConfig.validate_environment` to `BaseAzureLLM._base_validate_azure_environment` so the image-edit route follows the same auth resolution as every other Azure provider: - prefer the Azure-native `api-key` header when an API key is available - fall back to `Authorization: Bearer <azure_ad_token>` only for AAD auth The previous implementation unconditionally set `Authorization: Bearer <api_key>`, which is the OpenAI-direct convention and is rejected by Azure OpenAI / APIM-fronted deployments with `401 Access denied due to missing subscription key`. Adds regression tests covering api_key kwarg, litellm_params.api_key, and the AAD-token fallback path. Co-authored-by: Cursor <cursoragent@cursor.com> * docs(azure/image_edit): pin api-key precedence semantics + add regression test Address review feedback that the move to ``BaseAzureLLM._base_validate_azure_environment`` changed the relative priority of the positional ``api_key`` kwarg vs. ``litellm_params["api_key"]``. The new behavior — ``litellm_params["api_key"]`` wins, positional only fills in when ``litellm_params["api_key"]`` is empty — is intentional and matches every other Azure ``validate_environment``: ``AzureVideosConfig`` uses the exact same merge logic, while ``AzureVectorStoresConfig`` and ``AzureResponsesAPIConfig`` don't accept a positional ``api_key`` at all. The old ``or`` chain (positional wins) was the outlier and was part of the same OpenAI-vs-Azure convention drift that produced the original ``Authorization: Bearer`` bug. The only production caller (``llm_http_handler.image_edit``) sources both values from the same ``litellm_params.api_key``, so this change is behaviorally a no-op there. Document the precedence in the docstring and lock it in with an explicit test so future refactors can't quietly re-invert it. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> Co-authored-by: Adam Kirstein <adam.kirstein@disney.com> Co-authored-by: Cursor <cursoragent@cursor.com> * test(azure/image_edit): expect api-key header instead of Authorization Bearer PR #27863 fixed Azure image edit to use the Azure-native api-key header instead of OpenAI's Authorization: Bearer convention, but did not update test_azure_image_edit_litellm_sdk to match. The test still asserted 'Authorization' in headers, which now fails since the new code routes through BaseAzureLLM._base_validate_azure_environment and emits api-key when an api_key is provided. Update the assertion to pin the correct Azure behavior: api-key header present with the resolved key, and no Authorization header. --------- Co-authored-by: oss-agent-shin <ext-agent-shin@berri.ai> Co-authored-by: oss-agent-shin <279349115+oss-agent-shin@users.noreply.github.com> Co-authored-by: Adam Kirstein <107421694+justalittleadam@users.noreply.github.com> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> Co-authored-by: Adam Kirstein <adam.kirstein@disney.com> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>	2026-05-13 16:37:15 -07:00
Sameer Kankute	b8635bbc7a	feat(realtime): OpenAI Realtime GA support and beta compatibility (#27110) ... * feat(realtime): OpenAI Realtime GA support and beta compatibility - Normalize beta-style session.update to GA for upstream OpenAI; optional GA→beta event translation when client sends OpenAI-Beta: realtime=v1 - Default upstream WebSocket without OpenAI-Beta; forward header when client opts in - Extend OpenAI realtime types for GA event names and conversation item shapes - Relax LiteLLMRealtimeStreamLoggingObject.results to List[Any] for GA events - Update proxy client_secrets fallback to omit beta header; dashboard RealtimePlayground - Add unit tests for remap, translation, and beta header helper Co-authored-by: Cursor <cursoragent@cursor.com> * fix results * fix greptile * Fix mypy issues * Remove unused class constants _GA_TEXT_DELTA_TYPES and _GA_AUDIO_DELTA_TYPES These frozensets were defined as class-level constants in realtime_streaming.py but never referenced anywhere in the codebase. Removing dead code. Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com> * fix(realtime): use GA-shaped session.update in guardrail injections The guardrail VAD injection code sent a beta-style session.update with a flat turn_detection field: {"session": {"turn_detection": {"create_response": false}}} When the upstream OpenAI backend operates in GA mode (no OpenAI-Beta header forwarded), it requires the nested GA shape: {"session": {"type": "realtime", "audio": {"input": {"turn_detection": {"create_response": false}}}}} The _remap_beta_session_to_ga helper was only applied to client- originated session.update messages in client_ack_messages. Internally- generated session.updates (sent via _send_to_backend) in two paths: - _handle_raw_backend_message (raw/no provider_config path, line 518) - backend_to_client_send_messages provider_config path (line 481) bypassed the remap, so GA upstreams ignored or rejected them, breaking audio transcription guardrails for all non-beta clients. Fix: add _make_disable_auto_response_message() helper that always emits the correct GA-shaped session.update, and replace both injection sites with it. Update existing tests to assert the GA nested shape instead of the old flat beta shape, and add a new unit test for the helper itself. Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com> * Log realtime session type * Fix beta realtime session payloads * Fix realtime audio format remapping edge case * Fix Azure realtime beta session shape --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>	2026-05-05 16:49:20 -07:00
Sameer Kankute	bb0e4168ad	refactor(azure): move image gen JSON helper; rename image edit finalize hook ... - Add image_generation/http_utils.azure_deployment_image_generation_json_body; call from azure.py (keeps AzureChatCompletion focused on chat). - Rename finalize_image_edit_multipart_data to finalize_image_edit_request_data with docstring covering multipart and JSON POST payloads (review feedback). Co-authored-by: Cursor <cursoragent@cursor.com>	2026-05-05 08:49:46 +05:30
Sameer Kankute	87cf5107e1	test(azure): exercise image gen JSON filter via HTTP client; dedupe image edit URL ... - Image generation tests patch HTTPHandler.post / get_async_httpx_client so make_*_azure_httpx_request runs and wire json is asserted on call kwargs. - Azure image edit: strip model in finalize_image_edit_multipart_data using the same URL string the handler passes to POST (no second get_complete_url in transform). BaseImageEditConfig default finalize is a no-op. Co-authored-by: Cursor <cursoragent@cursor.com>	2026-05-04 12:09:05 +05:30
Sameer Kankute	766b67cf0d	fix(azure): omit model from image generation and image edit deployment requests ... Azure OpenAI routes image gen/edit by deployment in the URL; sending the deployment id in model breaks gpt-image-2 (invalid_value). Strip model from JSON for deployments/.../images/generations and from multipart data for .../images/edits. Non-deployment URLs (e.g. Azure AI FLUX) unchanged. Fixes #26316. Co-authored-by: Cursor <cursoragent@cursor.com>	2026-05-04 11:52:52 +05:30
mateo-berri	04e96a9bdc	Merge remote-tracking branch 'origin/litellm_internal_staging' into litellm_clean_litellm_oss_staging_04_01_2026	2026-05-01 15:54:10 -07:00
michelligabriele	1b6914d44c	fix(cost): pass service_tier through azure and azure_ai cost calculation (#24926) ... service_tier (priority/flex) was not forwarded to generic_cost_per_token for azure and azure_ai providers, so tier-specific pricing was ignored and standard pricing was always returned. Other providers (openai, bedrock, gemini, vertex_ai) already pass it correctly.	2026-05-01 17:26:45 +05:30
user	d4dd865b1a	fix: encode upstream URL path identifiers	2026-04-29 22:02:39 -07:00
Ishaan Jaffer	e8461b5b97	style: run black formatter on files from main merge	2026-04-17 13:02:59 -07:00
michelligabriele	63281e8330	fix(azure/passthrough): populate standard_logging_object via logging hook	2026-04-14 08:10:39 +02:00
Krrish Dholakia	bc829d51f2	test: test	2026-03-28 19:17:38 -07:00
Krrish Dholakia	5cd8ca2365	refactor: refactor testing	2026-03-28 18:39:32 -07:00
Krrish Dholakia	44b03e6138	fix: fix azure tests	2026-03-28 18:19:41 -07:00
Cesar Garcia	6a5b0058e3	Merge pull request #23926 from Chesars/fix/azure-gpt5-4-responses-api-routing ... fix(azure): auto-route gpt-5.4+ tools+reasoning to Responses API	2026-03-18 01:14:50 -03:00
Chesars	cb15296693	fix(azure): auto-route gpt-5.4+ tools+reasoning to Responses API ... Azure GPT-5.4+ models now get the same auto-routing treatment as OpenAI when both `reasoning_effort` and `tools` are used in `litellm.completion()`. Previously, `reasoning_effort` was silently dropped for Azure; now the request is bridged to the Responses API which supports both parameters. Fixes #23914	2026-03-17 23:06:39 -03:00
Sameer Kankute	ee24abe86e	fix(test): skip new video character endpoints in Azure SDK initialization test ... Add avideo_create_character, avideo_get_character, avideo_edit, and avideo_extension to the skip condition since Azure video calls don't use initialize_azure_sdk_client. Tests now properly skip with expected behavior instead of failing: - test_ensure_initialize_azure_sdk_client_always_used[avideo_create_character] ✓ - test_ensure_initialize_azure_sdk_client_always_used[avideo_get_character] ✓ - test_ensure_initialize_azure_sdk_client_always_used[avideo_edit] ✓ - test_ensure_initialize_azure_sdk_client_always_used[avideo_extension] ✓ Made-with: Cursor	2026-03-16 19:45:57 +05:30
Chesars	feed274aa3	Reapply "feat: add model_cost aliases expansion support" ... This reverts commit 3d2df7e8b5.	2026-03-12 13:36:57 -03:00
Chesars	1be6b31e2f	merge: resolve conflicts between main and litellm_oss_staging_03_11_2026	2026-03-12 09:38:31 -03:00
Sameer Kankute	ee3ecb5994	fix(openai): preserve reasoning_effort summary + fix xhigh/none guards for dict inputs ... - Add _get_effort_level() to extract effective effort from string or dict - Use effective_effort for xhigh validation, tool-drop, sampling, temperature guards - Preserve dict format when it has summary/generate_summary for Responses API - Add tests: xhigh-dict validation, none-dict for tools/sampling/temperature - Update tests: dict-with-summary now preserved (not normalized) Made-with: Cursor	2026-03-09 18:44:05 +05:30
Julio Quinteros Pro	d22996ee87	Exclude aresponses_websocket from Azure SDK client init test ... The aresponses_websocket CallType was recently added but not included in the test exclusion list. It uses WebSocket passthrough (not Azure SDK client initialization), so it correctly doesn't call initialize_azure_sdk_client. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-04 11:13:57 -03:00
Sameer Kankute	521f804350	Fix encrypted content streaming affinity issue	2026-03-03 18:37:22 +05:30
Giulio Leone	76459b1323	fix(azure): forward realtime_protocol from config and relax api_version check for GA path (#22369) ... * fix(image_generation): propagate extra_headers to OpenAI image generation Add headers parameter to image_generation() and aimage_generation() methods in OpenAI provider, and pass headers from images/main.py to ensure custom headers like cf-aig-authorization are properly forwarded to the OpenAI API. Aligns behavior with completion() method and Azure provider implementation. * test(image_generation): add tests for extra_headers propagation Verify that extra_headers are correctly forwarded to OpenAI's images.generate() in both sync and async paths, and that they are absent when not provided. * Add Prometheus child_exit cleanup for gunicorn workers When a gunicorn worker exits (e.g. from max_requests recycling), its per-process prometheus .db files remain on disk. For gauges using livesum/liveall mode, this means the dead worker's last-known values persist as if the process were still alive. Wire gunicorn's child_exit hook to call mark_process_dead() so live-tracking gauges accurately reflect only running workers. * docs: update AssemblyAI docs with Universal-3 Pro, Speech Understanding, and LLM Gateway (#21130) * docs: update AssemblyAI docs with Universal-3 Pro, Speech Understanding, and LLM Gateway provider config * feat: add AssemblyAI LLM Gateway as OpenAI-compatible provider * fix(mcp): update test mocks to use renamed filter_server_ids_by_ip_with_info Tests were mocking the old method name `filter_server_ids_by_ip` but production code at server.py:774 calls `filter_server_ids_by_ip_with_info` which returns a (server_ids, blocked_count) tuple. The unmocked method on AsyncMock returned a coroutine, causing "cannot unpack non-iterable coroutine object" errors. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix(test): update realtime guardrail test assertions for voice violation behavior Tests were asserting no response.create/conversation.item.create sent to backend when guardrail blocks, but the implementation intentionally sends these to have the LLM voice the guardrail violation message to the user. Updated assertions to verify the correct guardrail flow: - response.cancel is sent to stop any in-progress response - conversation.item.create with violation message is injected - response.create is sent to voice the violation - original blocked content is NOT forwarded Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix(bedrock): restore parallel_tool_calls mapping in map_openai_params The revert in 8565c70e53 removed the parallel_tool_calls handling from map_openai_params, and the subsequent fix d0445e1e33 only re-added the transform_request consumption but forgot to re-add the map_openai_params producer that sets _parallel_tool_use_config. This meant parallel_tool_calls was silently ignored for all Bedrock models. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix(test): update Azure pass-through test to mock litellm.completion Commit 99c62ca40e removed "azure" from _RESPONSES_API_PROVIDERS, routing Azure models through litellm.completion instead of litellm.responses. The test was not updated to match, causing it to assert against the wrong mock. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add in_flight_requests metric to /health/backlog + prometheus (#22319) * feat: add in_flight_requests metric to /health/backlog + prometheus * refactor: clean class with static methods, add tests, fix sentinel pattern * docs: add in_flight_requests to prometheus metrics and latency troubleshooting * fix(db): add missing migration for LiteLLM_ClaudeCodePluginTable PR #22271 added the LiteLLM_ClaudeCodePluginTable model to schema.prisma but did not include a corresponding migration file, causing test_aaaasschema_migration_check to fail. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: update stale docstring to match guardrail voicing behavior Addresses Greptile review feedback. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix(caching): store background task references in LLMClientCache._remove_key to prevent unawaited coroutine warnings Fixes #22128 * [Feat] Agent RBAC Permission Fix - Ensure Internal Users cannot create agents (#22329) * fix: enforce RBAC on agent endpoints — block non-admin create/update/delete - Add /v1/agents/{agent_id} to agent_routes so internal users can access GET-by-ID (previously returned 403 due to missing route pattern) - Add _check_agent_management_permission() guard to POST, PUT, PATCH, DELETE agent endpoints — only PROXY_ADMIN may mutate agents - Add user_api_key_dict param to delete_agent so the role check works - Add comprehensive unit tests for RBAC enforcement across all roles Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com> * fix: mock prisma_client in internal user get-agent-by-id test Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com> * feat(ui): hide agent create/delete controls for non-admin users Match MCP servers pattern: wrap '+ Add New Agent' button in isAdmin conditional so internal users see a read-only agents view. Delete buttons in card and table were already gated. Update empty-state copy for non-admin users. Add 7 Vitest tests covering role-based visibility. Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com> * fix: Add PROXY_ADMIN role to system user for key rotation (#21896) * fix: Add PROXY_ADMIN role to system user for key rotation The key rotation worker was failing with 'You are not authorized to regenerate this key' when rotating team keys. This was because the system user created by get_litellm_internal_jobs_user_api_key_auth() was missing the user_role field. Without user_role=PROXY_ADMIN, the system user couldn't bypass team permission checks in can_team_member_execute_key_management_endpoint(), causing authorization failures for team key rotation. This fix adds user_role=LitellmUserRoles.PROXY_ADMIN to the system user, allowing it to bypass team permission checks and successfully rotate keys for all teams. * test: Add unit test for system user PROXY_ADMIN role - Verify internal jobs system user has PROXY_ADMIN role - Critical for key rotation to bypass team permission checks - Regression test for PR #21896 * fix: populate user_id and user_info for admin users in /user/info (#22239) * fix: populate user_id and user_info for admin users in /user/info endpoint Fixes #22179 When admin users call /user/info without a user_id parameter, the endpoint was returning null for both user_id and user_info fields. This broke budgeting tooling that relies on /user/info to look up current budget and spend. Changes: - Modified _get_user_info_for_proxy_admin() to accept user_api_key_dict parameter - Added logic to fetch admin's own user info from database - Updated function to return admin's user_id and user_info instead of null - Updated unit test to verify admin user_id is populated The fix ensures admin users get their own user information just like regular users. * test: make mock get_data signature match real method - Updated MockPrismaClientDB.get_data() to accept all parameters that the real method accepts - Makes mock more robust against future refactors - Added datetime and Union imports - Mock now returns None when user_id is not provided * [Fix] Pass MCP auth headers from request into tool fetch for /v1/responses and chat completions (#22291) * fixed dynamic auth for /responses with mcp * fixed greptile concern * fix(bedrock): filter internal json_tool_call when mixed with real tools Fixes #18381: When using both tools and response_format with Bedrock Converse API, LiteLLM internally adds json_tool_call to handle structured output. Bedrock may return both this internal tool AND real user-defined tools, breaking consumers like OpenAI Agents SDK. Changes: - Non-streaming: Added _filter_json_mode_tools() to handle 3 scenarios: only json_tool_call (convert to content), mixed (filter it out), or no json_tool_call (pass through) - Streaming: Added json_mode tracking to AWSEventStreamDecoder to suppress json_tool_call chunks and convert to text content - Fixed optional_params.pop() mutation issue Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> * refactor: extract duplicated JSON unwrapping into helper method Addresses review comment from greptile-apps: https://github.com/BerriAI/litellm/pull/21107#pullrequestreview-3796085353 Changes: - Added `_unwrap_bedrock_properties()` helper method to eliminate code duplication - Replaced two identical JSON unwrapping blocks (lines 1592-1601 and 1612-1620) with calls to the new helper method - Improves maintainability - single source of truth for Bedrock properties unwrapping logic The helper method: - Parses JSON string - Checks for single "properties" key structure - Unwraps and returns the properties value - Returns original string if unwrapping not needed or parsing fails No functional changes - pure refactoring. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> * fix: use correct class name AmazonConverseConfig in helper method calls Fixed MyPy errors where BedrockConverseConfig was used instead of AmazonConverseConfig in the _unwrap_bedrock_properties() calls. Errors: - Line 1619: BedrockConverseConfig -> AmazonConverseConfig - Line 1631: BedrockConverseConfig -> AmazonConverseConfig Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> * fix: shorten guardrail benchmark result filenames for Windows long path support Fixes #21941 The generated result filenames from _save_confusion_results contained parentheses, dots, and full yaml filenames, producing paths that exceed the Windows 260-char MAX_PATH limit. Rework the safe_label logic to produce short {topic}_{method_abbrev} filenames (e.g. insults_cf.json) while preserving the full label inside the JSON content. Rename existing tracked result files to match the new naming convention. * Update litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/guardrail_benchmarks/test_eval.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Remove Apache 2 license from SKILL.md (#22322) * fix(mcp): default available_on_public_internet to true (#22331) * fix(mcp): default available_on_public_internet to true MCPs were defaulting to private (available_on_public_internet=false) which was a breaking change. This reverts the default to public (true) across: - Pydantic models (AddMCPServerRequest, UpdateMCPServerRequest, LiteLLM_MCPServerTable) - Prisma schema @default - mcp_server_manager.py YAML config + DB loading fallbacks - UI form initialValue and setFieldValue defaults * fix(ui): add forceRender to Collapse.Panel so toggle defaults render correctly Ant Design's Collapse.Panel lazy-renders children by default. Without forceRender, the Form.Item for 'Available on Public Internet' isn't mounted when the useEffect fires form.setFieldValue, causing the Switch to visually show OFF even though the intended default is true. Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com> * fix(mcp): update remaining schema copies and MCPServer type default to true Missed in previous commit per Greptile review: - schema.prisma (root) - litellm-proxy-extras/litellm_proxy_extras/schema.prisma - litellm/types/mcp_server/mcp_server_manager.py MCPServer class * ui(mcp): reframe network access as 'Internal network only' restriction Replace scary 'Available on Public Internet' toggle with 'Internal network only' opt-in restriction. Toggle OFF (default) = all networks allowed. Toggle ON = restricted to internal network only. Auth is always required either way. - MCPPermissionManagement: new label/tooltip/description, invert display via getValueProps/getValueFromEvent so underlying available_on_public_internet value is unchanged - mcp_server_view: 'Public' → 'All networks', 'Internal' → 'Internal only' (orange) - mcp_server_columns: same badge updates --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com> * fix(jwt): OIDC discovery URLs, roles array handling, dot-notation error hints (#22336) * fix(jwt): support OIDC discovery URLs, handle roles array, improve error hints Three fixes for Azure AD JWT auth: 1. OIDC discovery URL support - JWT_PUBLIC_KEY_URL can now be set to .well-known/openid-configuration endpoints. The proxy fetches the discovery doc, extracts jwks_uri, and caches it. 2. Handle roles claim as array - when team_id_jwt_field points to a list (e.g. AAD's "roles": ["team1"]), auto-unwrap the first element instead of crashing with 'unhashable type: list'. 3. Better error hint for dot-notation indexing - when team_id_jwt_field is set to "roles.0" or "roles[0]", the 401 error now explains to use "roles" instead and that LiteLLM auto-unwraps lists. * Add integration demo script for JWT auth fixes (OIDC discovery, array roles, dot-notation hints) Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com> * Add demo_servers.py for manual JWT auth testing with mock JWKS/OIDC endpoints Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com> * Add demo screenshots for PR comment Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com> * Add integration test results with screenshots for PR review Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com> * address greptile review feedback (greploop iteration 1) - fix: add HTTP status code check in _resolve_jwks_url before parsing JSON - fix: remove misleading bracket-notation hint from debug log (get_nested_value does not support it) * Update tests/test_litellm/proxy/auth/test_handle_jwt.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * remove demo scripts and assets --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * perf: streaming latency improvements — 4 targeted hot-path fixes (#22346) * perf: raise aiohttp connection pool limits (300→1000, 50/host→500) * perf: skip model_copy() on every chunk — only copy usage-bearing chunks * perf: replace list+join O(n²) with str+= O(n) in async_data_generator * perf: cache model-level guardrail lookup per request, not per chunk * test: add comprehensive Vitest coverage for CostTrackingSettings Add 88 tests across 9 test files for the CostTrackingSettings component directory: - provider_display_helpers.test.ts: 9 tests for helper functions - how_it_works.test.tsx: 9 tests for discount calculator component - add_provider_form.test.tsx: 7 tests for provider form validation - add_margin_form.test.tsx: 9 tests for margin form with type toggle - provider_discount_table.test.tsx: 12 tests for table editing and interactions - provider_margin_table.test.tsx: 13 tests for margin table with sorting - use_discount_config.test.ts: 11 tests for discount hook logic - use_margin_config.test.ts: 12 tests for margin hook logic - cost_tracking_settings.test.tsx: 15 tests for main component and role-based rendering All tests passing. Coverage includes form validation, user interactions, API calls, state management, and conditional rendering. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com> * [Feature] Key list endpoint: Add project_id and access_group_id filters Add filtering capabilities to /key/list endpoint for project_id and access_group_id parameters. Both filters work globally across all visibility rules and stack with existing sort/pagination params. Added comprehensive unit tests for the new filters. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com> * [Feature] UI - Projects: Add Project Details page with Edit modal - Add ProjectDetailsPage with header, details card, spend/budget progress, model spend bar chart, keys placeholder, and team info card - Refactor CreateProjectModal into base form pattern (ProjectBaseForm) shared between Create and Edit flows - Add EditProjectModal with pre-filled form data from backend - Add useProjectDetails and useUpdateProject hooks - Add duplicate key validation for model limits and metadata - Wire project ID click in table to navigate to detail view - Move pagination inline with search bar Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Update ui/litellm-dashboard/src/components/Projects/ProjectModals/CreateProjectModal.tsx Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix(azure): forward realtime_protocol from config and relax api_version check for GA path The realtime_protocol parameter set in config.yaml litellm_params was not reliably reaching the Azure realtime handler. Add fallback chain: kwargs → litellm_params → LITELLM_AZURE_REALTIME_PROTOCOL env var → beta. Also relax the api_version validation to only require it for the beta protocol path, since the GA/v1 path does not use api_version in the URL. Make protocol matching case-insensitive so 'ga', 'GA', 'v1', 'V1' all work consistently. Fix _construct_url type signature to accept Optional api_version. Fixes #22127 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Zero Clover <zero@root.me> Co-authored-by: Ryan Crabbe <rcrabbe@berkeley.edu> Co-authored-by: ryan-crabbe <128659760+ryan-crabbe@users.noreply.github.com> Co-authored-by: Dylan Duan <dylan.duan@assemblyai.com> Co-authored-by: Julio Quinteros Pro <jquinter@gmail.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com> Co-authored-by: Shivaang <shivaang.05@gmail.com> Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com> Co-authored-by: milan-berri <milan@berri.ai> Co-authored-by: Shivam Rawat <161387515+shivamrawat1@users.noreply.github.com> Co-authored-by: Brian Caswell <bcaswell@microsoft.com> Co-authored-by: Brian Caswell <bcaswell@gmail.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Co-authored-by: rasmi <rrelasmar@gmail.com> Co-authored-by: yuneng-jiang <yuneng.jiang@gmail.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-03-02 19:21:27 +05:30
Sameer Kankute	550bb621f7	fix llm tests	2026-02-17 20:13:23 +05:30
Ishaan Jaffer	ad910de1e3	test_o1_parallel_tool_calls	2026-02-14 10:47:38 -08:00
Sameer Kankute	59d6ab8a00	Merge branch 'main' into litellm_oss_staging_02_11_2026	2026-02-12 20:04:46 +05:30
Ishaan Jaff	2b00466d3a	fix: support prompt_cache_key for OpenAI and Azure chat completions (#20989) ... * fix:fix: prompt_cache_key OAI + Azure OpenAI * test_prompt_cache_key_supported * test_azure_openai_with_prompt_cache_key * fix: remove unnecessary async from test_azure_openai_with_prompt_cache_key Addresses Greptile feedback: litellm.completion() is synchronous, so async def is unnecessary and would silently pass without running. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: remove unused filter_and_transform_beta_headers imports Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * test_azure_openai_with_prompt_cache_key --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>	2026-02-11 12:25:29 -08:00
Neel Harsola	ce421df1ef	fix(azure): preserve content_policy_violation error details from Azure OpenAI (#20883) ... * feat: add opus 4.5 and 4.6 to use outout_format param * generate poetry lock with 2.3.2 poetry * restore poetry lock * e2e tests, key delete, update tpm rpm, and regenerate * Split e2e ui testing for browser * new login with sso button in login page * option to hide usage indicator * fix(cloudzero): update CBF field mappings per LIT-1907 (#20906) * fix(cloudzero): update CBF field mappings per LIT-1907 Phase 1 field updates for CloudZero integration: ADD/UPDATE: - resource/account: Send concat(api_key_alias, '|', api_key_prefix) - resource/service: Send model_group instead of service_type - resource/usage_family: Send provider instead of hardcoded 'llm-usage' - action/operation: NEW - Send team_id - resource/id: Send model name instead of CZRN - resource/tag:organization_alias: Add if exists - resource/tag:project_alias: Add if exists - resource/tag:user_alias: Add if exists REMOVE: - resource/tag:total_tokens: Removed - resource/tag:team_id: Removed (team_id now in action/operation) Fixes LIT-1907 * Update litellm/integrations/cloudzero/transform.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix: define api_key_alias variable, update CBFRecord docstring - Fix F821 lint error: api_key_alias was used but not defined - Update CBFRecord docstring to reflect LIT-1907 field mappings - Remove unused Optional import --------- Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Add banner notifying of breaking change * Add semgrep & Fix OOMs (#20912) * [Feat] Policies - Allow connecting Policies to Tags, Simulating Policies, Viewing how many keys, teams it applies on (#20904) * init schema with TAGS * ui: add policy test * resolvePoliciesCall * add_policy_sources_to_metadata + headers * types Policy * preview Impact * def _describe_match_reason( * match based on TAGs * TestTagBasedAttachments * test fixes * add policy_resolve_router * add_guardrails_from_policy_engine * TestMatchAttribution * refactor * fix * fix: address Greptile review feedback on policy resolve endpoints - Track unnamed keys/teams as separate counts instead of inflating affected_keys_count with duplicate "(unnamed key)" placeholders. Added unnamed_keys_count and unnamed_teams_count to response. - Push alias pattern matching to DB via _build_alias_where() which converts exact patterns to Prisma "in" and suffix wildcards to "startsWith" filters. - Gate sync_policies_from_db/sync_attachments_from_db behind force_sync query param (default false) to avoid 2 DB round-trips on every /policies/resolve request. - Remove worktree-only conftest.py that cleared sys.modules at import time — no longer needed since code moved to main repo. - Rename MAX_ESTIMATE_IMPACT_ROWS → MAX_POLICY_ESTIMATE_IMPACT_ROWS. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: eliminate duplicate DB queries and fix header delimiter ambiguity - Fetch teams table once in estimate_attachment_impact and reuse for both tag-based and alias-based lookups (was querying teams twice when both tag_patterns and team_patterns were provided). - Convert tag/team filter functions from async DB queries to sync filters that operate on pre-fetched data (_filter_keys_by_tags, _filter_teams_by_tags). - Fix comma ambiguity in x-litellm-policy-sources header: use '; ' as entry delimiter since matched_via values can contain commas. - Use '+' as the within-value separator in matched_via reason strings (e.g. "tag:healthcare+team:health-team") to avoid conflict with header delimiters. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * Update litellm/proxy/policy_engine/policy_resolve_endpoints.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix: type error & better error handling (#20689) * [Docs] Add docs guide for using policies (#20914) * init schema with TAGS * ui: add policy test * resolvePoliciesCall * add_policy_sources_to_metadata + headers * types Policy * preview Impact * def _describe_match_reason( * match based on TAGs * TestTagBasedAttachments * test fixes * add policy_resolve_router * add_guardrails_from_policy_engine * TestMatchAttribution * refactor * fix * fix: address Greptile review feedback on policy resolve endpoints - Track unnamed keys/teams as separate counts instead of inflating affected_keys_count with duplicate "(unnamed key)" placeholders. Added unnamed_keys_count and unnamed_teams_count to response. - Push alias pattern matching to DB via _build_alias_where() which converts exact patterns to Prisma "in" and suffix wildcards to "startsWith" filters. - Gate sync_policies_from_db/sync_attachments_from_db behind force_sync query param (default false) to avoid 2 DB round-trips on every /policies/resolve request. - Remove worktree-only conftest.py that cleared sys.modules at import time — no longer needed since code moved to main repo. - Rename MAX_ESTIMATE_IMPACT_ROWS → MAX_POLICY_ESTIMATE_IMPACT_ROWS. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: eliminate duplicate DB queries and fix header delimiter ambiguity - Fetch teams table once in estimate_attachment_impact and reuse for both tag-based and alias-based lookups (was querying teams twice when both tag_patterns and team_patterns were provided). - Convert tag/team filter functions from async DB queries to sync filters that operate on pre-fetched data (_filter_keys_by_tags, _filter_teams_by_tags). - Fix comma ambiguity in x-litellm-policy-sources header: use '; ' as entry delimiter since matched_via values can contain commas. - Use '+' as the within-value separator in matched_via reason strings (e.g. "tag:healthcare+team:health-team") to avoid conflict with header delimiters. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * docs v1 guide with UI imgs * docs fix --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> * feat: add dashscope/qwen3-max model with tiered pricing (#20919) Add support for Alibaba Cloud's Qwen3-Max model with: - 258K input tokens, 65K output tokens - Tiered pricing based on context window usage (0-32K, 32K-128K, 128K-252K) - Function calling and tool choice support - Reasoning capabilities enabled Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com> * fix linting * docs: add Greptile review requirement to PR template (#20762) * fix(azure): preserve content_policy_violation error details from Azure OpenAI Closes #20811 Azure OpenAI returns rich error payloads for content policy violations (inner_error with ResponsibleAIPolicyViolation, content_filter_results, revised_prompt). Previously these details were lost when: 1. The top-level error code was not "content_policy_violation" but the inner_error.code was "ResponsibleAIPolicyViolation" -- the structured check only examined the top-level code. 2. The DALL-E image generation polling path stringified the error JSON into the message field instead of setting the structured body, making it impossible for exception_type() to extract error details. 3. The string-based fallback detector used "invalid_request_error" as a content-policy indicator, which is too broad and could misclassify regular bad-request errors. Changes: - exception_mapping_utils.py: Check inner_error.code for ResponsibleAIPolicyViolation when top-level code is not content_policy_violation. Replace overly broad "invalid_request_error" string match with specific Azure safety-system messages. - azure.py: Set structured body on AzureOpenAIError in both async and sync DALL-E polling paths so exception_type() can inspect error details. - test_azure_exception_mapping.py: Add regression tests covering the exact error payloads from issue #20811. - Fix pre-existing lint: duplicate PerplexityResponsesConfig dict key, unused RouteChecks top-level import. --------- Co-authored-by: Kelvin Tran <kelvin-tran@users.noreply.github.com> Co-authored-by: yuneng-jiang <yuneng.jiang@gmail.com> Co-authored-by: shin-bot-litellm <shin-bot-litellm@berri.ai> Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Co-authored-by: Alexsander Hamir <alexsanderhamirgomesbaptista@gmail.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: Harshit Jain <48647625+Harshit28j@users.noreply.github.com> Co-authored-by: ken <122603020@qq.com> Co-authored-by: Sameer Kankute <sameer@berri.ai>	2026-02-10 22:47:03 -08:00
Sameer Kankute	30d17c29e4	handle when litellm_parrams might be none	2026-02-09 17:13:39 +05:30
Sameer Kankute	d35691aa0c	Fix: base_model name for body and deplyment name in URL	2026-02-09 16:25:46 +05:30
shin-bot-litellm	395ad9bdc1	litellm_fix(test): add acancel_batch to Azure SDK client initialization test (#20143)	2026-01-31 07:34:54 -08:00
Jay Prajapati	6a9d41234f	fix: allow tool_choice for Azure GPT-5 chat models (#19813) ... * fix: don't treat gpt-5-chat as GPT-5 reasoning * fix: mark azure gpt-5-chat as supporting tool_choice * test: cover gpt-5-chat params on azure/openai	2026-01-27 17:51:13 -08:00
Sameer Kankute	12463809bd	Merge pull request #19638 from BerriAI/main ... merge main in stagin 1 22 26	2026-01-23 14:54:17 +05:30
Yogeshwaran Ravichandran	ab274ac3c4	fix(azure response api): flatten tools for responses api to support nested definitions (#19526) ... The Azure Responses API uses a different schema (flattened) for tools compared to the standard OpenAI/Azure Chat Completions API (nested). This caused a `BadRequestError` when users passed standard tool definitions. Changes: - Implemented tool flattening logic in `AzureOpenAIResponsesAPIConfig.transform_responses_api_request`. - Added comprehensive unit tests in test_azure_transformation.py to verify nested-to-flat transformation, pass-through of flat tools, and immutability. - Ensures cross-provider compatibility for tool definitions. Fixes #19523	2026-01-21 21:08:28 -08:00
jay prajapati	363b0cc132	fix(azure): preserve content_policy_violation details for images (#19328) (#19372) ... Azure OpenAI Images (DALL·E 3) returns policy violations as a structured payload under body["error"], including inner_error.content_filter_results and revised_prompt. LiteLLM previously: - Failed to extract nested error messages (get_error_message only handled body["message"]) - Missed policy violation detection when error strings were generic - Dropped inner_error details when raising ContentPolicyViolationError This change: - Extracts nested Azure error fields (code/type/message + inner_error) - Detects policy violations via structured error codes - Passes an OpenAI-style error body + provider_specific_fields to preserve details Tests: - python3 -m pytest tests/test_litellm/llms/azure/test_azure_exception_mapping.py - python3 -m pytest tests/test_litellm/litellm_core_utils/test_exception_mapping_utils.py Fixes #19328	2026-01-21 20:06:51 -08:00