Providers like Cerebras return delta.reasoning in streaming responses
for gpt-oss models, but LiteLLM's Delta class expects reasoning_content.
This causes reasoning content to be silently dropped during streaming.
Fixes#13300
Add global media_resolution support for Gemini 2.x models (2.0, 2.5) when
using OpenAI's detail parameter on images. Previously, the detail parameter
was only working for Gemini 3+ models (per-part) and was silently ignored
for older Gemini models.
- Add _get_highest_media_resolution() and _extract_max_media_resolution_from_messages()
to extract highest detail from all images/files in a request
- Update _transform_request_body() to add mediaResolution to generationConfig
for Gemini 2.x models only (not 1.x which doesn't support it, not 3+ which
uses per-part)
- Add mediaResolution field to GenerationConfig TypedDict
- Support detail extraction from both image_url and file content types
- Add comprehensive unit tests and update documentation
Add MistralAudioTranscriptionConfig for Mistral's /v1/audio/transcriptions
endpoint, enabling litellm.transcription() with mistral/voxtral-mini-latest
and other Voxtral models. Supports multipart form-data with OpenAI-compatible
params (language, temperature, response_format, timestamp_granularities)
plus Mistral-specific params like diarize.
* azure content enhancement...
* rafactored to increase confidence score
* improvements based on additional feedback
* removed unused import
* Force-split any word longer than max length allowed
* preserve whitespace in text splitting
* moving common initialization to base class
* consolidate enforcement into async_make_request as single point, remove redundant caller-side checks, extract shared init/HTTP logic into base, and fix stale log messages
* clean up
* clean up tests
PR #22785 used pytest.importorskip which causes exit code 5 (all
skipped) in CI. Instead, add tenacity to the CI workflow pip install
and restore direct imports.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add cache_read_input_token_cost_per_audio_token, supports_code_execution,
and supports_file_search to the JSON schema used by the model prices
validation test.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
tenacity is not in pyproject.toml dependencies, causing ImportError
during test collection. Use pytest.importorskip to gracefully skip
when tenacity is not available.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The _encrypt_response_id method now receives request_cache=None as a
keyword argument from async_post_call_success_hook. Updated the mock
assertion to expect this parameter.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Tests used call_policy throughout but the actual API model uses
input_policy and output_policy. Updated _make_tool_row helper,
list filter query param, and policy update request/response assertions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
The test_streaming_mcp_events_validation test was flaky because:
1. It didn't mock the nested aresponses() call inside the iterator's
_create_initial_response_iterator(), causing real API calls that fail
without credentials
2. The iterator silently swallowed exceptions and set phase="finished",
discarding pre-generated MCP discovery events
3. The _execute_tool_calls mock had wrong signature (missing tool_server_map)
Production fix: MCPEnhancedStreamingIterator no longer sets phase="finished"
on LLM call failure — it falls through to emit MCP discovery events first.
Test fix: Added mock for litellm.responses.main.aresponses returning a fake
async streaming iterator, fixed mock signatures, removed try/except that
masked failures.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fix translate_thinking_to_reasoning in responses_adapters/transformation.py
to make summary opt-in (was hardcoded to "detailed")
- Update e2e test to mock litellm.responses (new OpenAI routing path)
- Add tests for Responses API adapter summary preservation
- Resolve merge conflict in test file
Remove hardcoded summary="detailed" injection — summary is opt-in per
OpenAI spec and increases costs. Users opt-in per-request via LiteLLM
extension: thinking={"type": "enabled", "budget_tokens": N, "summary": "concise"}.
Also preserve summary in translate_thinking_for_model() which previously
dropped it when converting thinking → reasoning_effort for non-Claude models.
Fixes#20998
- test_proxy_e2e_azure_batches: e2e managed batch test with delete retry for batch_processed
- test_fixtures_smoke: smoke test for fixtures
- validate_e2e_setup: setup validation script
Made-with: Cursor
- conftest: mock server + proxy server fixtures, log capture, health check fix
- base_integration_test: fix key_alias format (replace @ and . for API validation)
- test_managed_files_base: S3 callback wait with early exit, delete retry logic
Made-with: Cursor
Add test_parallel_tool_calls_comprehensive_streaming_integration which
synthesizes the full 10-event Responses API SSE sequence with split
argument deltas and asserts all fix invariants together:
1. output_item.done emits no finish_reason (no premature stream end)
2. Each call_id appears exactly once (no duplicate tool_call chunks)
3. Split argument deltas assemble to correct final JSON
4. Exactly one finish event, at the terminal response.completed chunk
5. Parallel tool calls have distinct indices (output_index 0 and 1)
All 24 unit tests pass.
* add explicit caching to litellm proxy for gemini models via injection
* fix: add missing `supports_function_calling` for deepinfra models
All 55 deepinfra models that had `supports_tool_choice: true` were
missing the `supports_function_calling` flag, causing
`litellm.supports_function_calling()` to incorrectly return False.
Fixes#22619
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Managed batches - Address PR bot comments from #22464
* feat(togetherai): add support for TogetherAI Qwen3.5-397B-A17B model
* Agent Tracing - support context_id based trace id propogation + nested llm calls (#22626)
* style(ui/): distinguish agent calls from llm calls on ui
* feat: initial grouping working
* feat: set stable contextid for a2a calls - allows for easily passing to downstream llm/mcp calls
* feat(a2a_endpoints.py): fix tracing to avoid recreating logging objects for the same call
allows stable trace id usage
* fix(guardrail_endpoints): handle string ui_type values in _build_field_dict
_build_field_dict unconditionally called .value on ui_type, which crashes
for guardrail configs that use plain strings (e.g. BlockCodeExecutionGuardrailConfigModel
uses "multiselect" and "percentage"). Now checks with hasattr before calling .value.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: propagate trace/session id from headers in MCP server calls
Cherry-picked mcp_server/server.py fixes from 6feb9bab: adds
get_chain_id_from_headers to extract x-litellm-trace-id /
x-litellm-session-id from raw headers, and uses it in call_tool
and list_tools to keep spend logs and tracing consistent with A2A.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* [Feat] UI - Add Open in New Tab on leftnav Bar (#22731)
* Add minimal dev_config.yaml for proxy development
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* feat(ui): wrap left nav items in <a> tags for open-in-new-tab support
Nav items are now rendered as <a> elements with proper href attributes,
enabling right-click → 'Open in new tab', Ctrl/Cmd+click, and
middle-click to open any sidebar page in a new browser tab.
Normal clicks continue to use SPA navigation (no full page reload).
Applied to both leftnav.tsx (query-param routing) and Sidebar2.tsx
(Next.js file-based routing).
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>
* [Feat] Add Tool Policies for AI Gateway (#22732)
* fix: fix ui render
* fix: fix minor bugs
* refactor: use prisma functions instead of raw sql (safer)
* fix(add-new-tiles-to-tool-policies): allow developer to see what's available
* feat: ensure tool allowlist runs correctly for tool names + mcp's
* refactor: more ui improvements
* feat: working key tool blocking
* feat(tools): show tool logs
* refactor: backend code improvements
* refactor: improve log viewer for tools
* fix: address PR review feedback for tool access control
- Add missing blocked_tools column to root schema.prisma (schema drift)
- Invalidate ToolPolicyRegistry after policy mutations so changes take effect immediately
- Remove dead code: unused get_effective_policies, get_tool_policies_cached, and helpers
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: race condition in permission resolution and remove duplicate allowlist check
- Use atomic update_many with object_permission_id=None to prevent concurrent
requests from creating orphaned permission rows and losing tool blocks
- Remove duplicate allowed_tools enforcement from guardrail (already enforced
in auth layer via check_tools_allowlist)
- Move inline uuid import to module level
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* update to account for userAgent
* UI - Add ToolDetails
* input/output policy
* LiteLLM_PolicyAttachmentTable
* LiteLLM_PolicyAttachmentTable
* fix: add _enqueue_tool_registry_upsert
* fix: tool mgmt endpoints
* tool mgmt endpoints
* Update tests/test_litellm/proxy/db/test_tool_registry_writer.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Update tests/test_litellm/proxy/db/test_tool_registry_writer.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Update tests/test_litellm/proxy/db/test_tool_registry_writer.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix: sync root schema.prisma and fix test_tool_registry_writer for input/output policy
- Migrate root schema.prisma LiteLLM_ToolTable from call_policy to
input_policy/output_policy, add missing user_agent and last_used_at columns
(now consistent with litellm/proxy/schema.prisma and litellm-proxy-extras)
- Fix SpendLogToolIndex comment across all three schema files
- Fix all call_policy references in test_tool_registry_writer.py:
swapped update_tool_policy arguments, wrong get_tools_by_names return type
assertions, _mock_tool_row setting call_policy instead of input_policy
Addresses Greptile review feedback on PR #22732.
Made-with: Cursor
---------
Co-authored-by: Krrish Dholakia <krrishdholakia@gmail.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>
* feat(proxy): add key_alias, key_hash, requested_model DD APM span tags (#22710)
* feat(proxy): add key_alias, key_hash, requested_model tags to DD APM spans
* refactor(proxy): consolidate DD APM tag helpers into DDSpanTagger class
* refactor(proxy): move DDSpanTagger to its own file litellm/proxy/dd_span_tagger.py
---------
Co-authored-by: liweiguang <codingpunk@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Ephrim Stanley <ephrim.stanley@point72.com>
Co-authored-by: Varad Khonde <varadkhonde@gmail.com>
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
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>
* feat(proxy): add key_alias, key_hash, requested_model tags to DD APM spans
* refactor(proxy): consolidate DD APM tag helpers into DDSpanTagger class
* refactor(proxy): move DDSpanTagger to its own file litellm/proxy/dd_span_tagger.py
* style(ui/): distinguish agent calls from llm calls on ui
* feat: initial grouping working
* feat: set stable contextid for a2a calls - allows for easily passing to downstream llm/mcp calls
* feat(a2a_endpoints.py): fix tracing to avoid recreating logging objects for the same call
allows stable trace id usage
* fix(guardrail_endpoints): handle string ui_type values in _build_field_dict
_build_field_dict unconditionally called .value on ui_type, which crashes
for guardrail configs that use plain strings (e.g. BlockCodeExecutionGuardrailConfigModel
uses "multiselect" and "percentage"). Now checks with hasattr before calling .value.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: propagate trace/session id from headers in MCP server calls
Cherry-picked mcp_server/server.py fixes from 6feb9bab: adds
get_chain_id_from_headers to extract x-litellm-trace-id /
x-litellm-session-id from raw headers, and uses it in call_tool
and list_tools to keep spend logs and tracing consistent with A2A.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
- Change status codes from 400 to 500 for team metadata misconfig errors
(callers can't fix admin-set config, 400 is misleading)
- Add anchor value validation to batch endpoint (matching files endpoint)
- Coerce seconds to int to handle string values from metadata
- Add error-path tests: missing keys, invalid anchor, status code assertions
- Add happy-path test: team injects expiry when caller sends nothing
