- Add _verify_org_access to deprecated POST /organization/info endpoint
- Move get_user_object to module-level import in organization_endpoints.py
- Add tests for _verify_team_access 403 denial path
- Introduced a new method in `FileContentStreamingHandler` to resolve streaming request parameters, enhancing the routing logic based on credentials.
- Updated the `should_stream_file_content` method to check against supported providers.
- Cleaned up type hints and imports across multiple files for better organization and clarity.
- Added comprehensive tests to validate the new routing behavior and ensure original data integrity during streaming requests.
- Changed the import path for `upload_file_to_storage_backend` in test files to reflect the new module structure.
- Ensured consistency in mocking for storage backend service tests.
The raw SQL queries in spend_management_endpoints.py, spend_tracking_utils.py,
and analytics_endpoints.py cast date params to ::timestamptz while comparing
against the plain-timestamp "startTime" column. Postgres resolves the type
mismatch by promoting the column using the DB session timezone, which drifts
the filter window and date_trunc buckets whenever session TZ is not UTC —
silently dropping rows at UTC day boundaries and, for narrow windows, losing
rows entirely.
Wraps every such comparison with `AT TIME ZONE 'UTC'` so the param side
resolves to a plain timestamp matching the column type. Both sides end up
as plain timestamp, Postgres does no implicit conversion, and session TZ
plays no role in the query. The fix is constant-foldable so the existing
startTime index (PR #17504) remains usable.
Also marks strptime-produced datetimes as tz-aware UTC at the call sites
for intent clarity and consistency with parse_date.
Fixes#22529 (Logs page missing recent rows under non-UTC session TZ) and
the Global Usage single-day-returns-two-days symptom reported internally.
- Added validation to ensure credentials include a custom LLM provider before routing.
- Cleaned up type casting for better readability.
- Introduced a new test to verify behavior when a non-OpenAI provider is used, ensuring proper handling of streaming responses.
- Updated imports to include necessary modules for testing.
- Updated `FileContentStreamingHandler` to utilize `custom_llm_provider` from credentials for routing.
- Added error handling for missing `custom_llm_provider` in credentials.
- Introduced new tests to validate streaming behavior with routed providers and non-OpenAI providers.
- Cleaned up imports and ensured proper type casting for improved clarity.
- Static Methods for Streaming Handler Function
- Remove the afile_content_streaming wrapper function. Enabled with a stream boolean in afile_content
- Cleaned up test cases after refactor
- Removed unused imports and streamlined type hints in `litellm/utils.py` and `litellm/files/main.py`.
- Moved `FileContentStreamingResult` to a new `litellm/files/types.py` for better organization.
- Updated `FileContentStreamingResponse` in `litellm/files/streaming.py` to include asynchronous close methods and improved logging capabilities.
- Enhanced tests to ensure proper closure of streaming iterators in `tests/test_litellm/llms/openai/test_openai_file_content_streaming.py` and `tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py`.
- Introduced `afile_content_streaming` and `file_content_streaming` functions in `litellm/files/main.py` to handle asynchronous and synchronous file content streaming.
- Added `FileContentStreamingResponse` class in `litellm/files/streaming.py` to manage streaming responses with logging capabilities.
- Updated OpenAI API integration in `litellm/llms/openai/openai.py` to support new streaming methods.
- Enhanced file content retrieval in `litellm/proxy/openai_files_endpoints/files_endpoints.py` to route requests for streaming.
- Added unit tests for the new streaming functionality in `tests/test_litellm/llms/openai/test_openai_file_content_streaming.py` and `tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py`.
- Refactored type hints and imports for better clarity and organization across modified files.
The deployment hook now converts stream=True→False in wrapper_async's
scope so the streaming early-return path is skipped and logging executes.
logging_obj.stream is synced after the hook, and the original stream
intent is recovered for the short-circuit path.
Made-with: Cursor
Add Baseten Model API pricing entries for Nemotron, GLM, Kimi, GPT OSS, and DeepSeek models with validated model slugs. Include a focused regression test to assert provider and per-token pricing values.
Made-with: Cursor
* fix(vertex_ai): support pluggable (executable) credential_source for WIF auth (#24700)
The WIF credential dispatch in load_auth() only handled identity_pool and
aws credential types. When credential_source.executable was present (used
for Azure Managed Identity via Workload Identity Federation), it fell
through to identity_pool.Credentials which rejected it with MalformedError.
Add dispatch to google.auth.pluggable.Credentials for executable-type
credential sources, following the same pattern as the existing identity_pool
and aws helpers.
Fixes authentication for Azure Container Apps → GCP Vertex AI via WIF
with executable credential sources.
* feat(logging): add component and logger fields to JSON logs for 3rd p… (#24447)
* feat(logging): add component and logger fields to JSON logs for 3rd party filtering
* Let user-supplied extra fields win over auto-generated component/logger, tighten test assertions
* Feat - Add organization into the metrics metadata for org_id & org_alias (#24440)
* Add org_id and org_alias label names to Prometheus metric definitions
* Add user_api_key_org_alias to StandardLoggingUserAPIKeyMetadata
* Populate user_api_key_org_alias in pre-call metadata
* Pass org_id and org_alias into per-request Prometheus metric labels
* Add test for org labels on per-request Prometheus metrics
* chore: resolve test mockdata
* Address review: populate org_alias from DB view, add feature flag, use .get() for org metadata
* Add org labels to failure path and verify flag behavior in test
* Fix test: build flag-off enum_values without org fields
* Gate org labels behind feature flag in get_labels() instead of static metric lists
* Scope org label injection to metrics that carry team context, remove orphaned budget label defs, add test teardown
* Use explicit metric allowlist for org label injection instead of team heuristic
* Fix duplicate org label guard, move _org_label_metrics to class constant
* Reset custom_prometheus_metadata_labels after duplicate label assertion
* fix: emit org labels by default, remove flag, fix missing org_alias in all metadata paths
* fix: emit org labels by default, no opt-in flag required
* fix: write org_alias to metadata unconditionally in proxy_server.py
* fix: 429s from batch creation being converted to 500 (#24703)
* add us gov models (#24660)
* add us gov models
* added max tokens
* Litellm dev 04 02 2026 p1 (#25052)
* fix: replace hardcoded url
* fix: Anthropic web search cost not tracked for Chat Completions
The ModelResponse branch in response_object_includes_web_search_call()
only checked url_citation annotations and prompt_tokens_details, missing
Anthropic's server_tool_use.web_search_requests field. This caused
_handle_web_search_cost() to never fire for Anthropic Claude models.
Also routes vertex_ai/claude-* models to the Anthropic cost calculator
instead of the Gemini one, since Claude on Vertex uses the same
server_tool_use billing structure as the direct Anthropic API.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* fix(anthropic): pass logging_obj to client.post for litellm_overhead_time_ms (#24071)
When LITELLM_DETAILED_TIMING=true, litellm_overhead_time_ms was null for
Anthropic because the handler did not pass logging_obj to client.post(),
so track_llm_api_timing could not set llm_api_duration_ms. Pass
logging_obj=logging_obj at all four post() call sites (make_call,
make_sync_call, acompletion, completion). Add test to ensure make_call
passes logging_obj to client.post.
Made-with: Cursor
* sap - add additional parameters for grounding
- additional parameter for grounding added for the sap provider
* sap - fix models
* (sap) add filtering, masking, translation SAP GEN AI Hub modules
* (sap) add tests and docs for new SAP modules
* (sap) add support of multiple modules config
* (sap) code refactoring
* (sap) rename file
* test(): add safeguard tests
* (sap) update tests
* (sap) update docs, solve merge conflict in transformation.py
* (sap) linter fix
* (sap) Align embedding request transformation with current API
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) mock commit
* (sap) run black formater
* (sap) add literals to models, add negative tests, fix test for tool transformation
* (sap) fix formating
* (sap) fix models
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) commit for rerun bot review
* (sap) minor improve
* (sap) fix after bot review
* (sap) lint fix
* docs(sap): update documentation
* fix(sap): change creds priority
* fix(sap): change creds priority
* fix(sap): fix sap creds unit test
* fix(sap): linter fix
* fix(sap): linter fix
* linter fix
* (sap) update logic of fetching creds, add additional tests
* (sap) clean up code
* (sap) fix after review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) add a possibility to put the service key by both variants
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) update test
* (sap) update service key resolve function
* (sap) run black formater
* (sap) fix validate credentials, add negative tests for credential fetching
* (sap) fix validate credentials, add negative tests for credential fetching
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) lint fix
* (sap) lint fix
* feat: support service_tier in gemini
* chore: add a service_tier field mapping from openai to gemini
* fix: use x-gemini-service-tier header in response
* docs: add service_tier to gemini docs
* chore: add defaut/standard mapping, and some tests
* chore: tidying up some case insensitivity
* chore: remove unnecessary guard
* fix: remove redundant test file
* fix: handle 'auto' case-insensitively
* fix: return service_tier on final steamed chunk
* chore: black
* feat: enable supports_service_tier to gemini models
* Fix get_standard_logging_metadata tests
* Fix test_get_model_info_bedrock_models
* Fix test_get_model_info_bedrock_models
* Fix remaining tests
* Fix mypy issues
* Fix tests
* Fix merge conflicts
* Fix code qa
* Fix code qa
* Fix code qa
* Fix greptile review
---------
Co-authored-by: michelligabriele <gabriele.michelli@icloud.com>
Co-authored-by: Josh <36064836+J-Byron@users.noreply.github.com>
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Alperen Kömürcü <alperen.koemuercue@sap.com>
Co-authored-by: Vasilisa Parshikova <vasilisa.parshikova@sap.com>
Co-authored-by: Lin Xu <lin.xu03@sap.com>
Co-authored-by: Mark McDonald <macd@google.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
Fixes 'LLM Provider NOT provided' errors when models are configured with
custom_llm_provider but model names lack provider prefix (e.g., 'gpt-4.1-mini'
instead of 'azure/gpt-4.1-mini').
Changes:
- Router now passes deployment's custom_llm_provider to get_llm_provider()
- Fixes 6 code paths: file creation, file content, batch operations, vector store
- Adds regression tests for file creation and file content operations
Made-with: Cursor
* feat(triton): add embedding usage estimation for self-hosted responses
Populate Triton embedding usage from request input using token counting with a safe fallback so cost/observability flows work even when provider usage is missing.
Made-with: Cursor
* fix(triton): sum per-input embedding token counts for batches
Joining batch strings with newlines before token_counter added spurious
tokens. Count each input separately and sum, matching OpenAI-style usage.
Made-with: Cursor
