Add BFL models to model_prices_and_context_window.json with pricing:
- flux-kontext-pro: $0.04/image
- flux-kontext-max: $0.08/image
- flux-pro-1.0-fill: $0.05/image
- flux-pro-1.0-expand: $0.05/image
Add black_forest_labs_models set to __init__.py for model discovery.
Add native integration for Black Forest Labs image editing models
(flux-kontext-pro, flux-kontext-max, flux-pro-1.0-fill, flux-pro-1.0-expand).
Changes:
- Add BlackForestLabsImageEditConfig for BFL API transformation
- Add BLACK_FOREST_LABS to LlmProviders enum
- Add use_multipart_form_data() to BaseImageEditConfig for JSON vs form-data
- Modify image_edit_handler to support JSON request bodies
- Add comprehensive unit tests
Closes#11401
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
The AllModelsTab component was passing onRowClick to AllModelsDataTable
but the prop was not defined in the interface, causing the TypeScript
build to fail.
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 Mistral to the supported providers list in audio_transcription.md
and add Audio Transcription section to the Mistral provider page with
SDK usage, optional params, diarize support, and proxy configuration.
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
1. Okta SSO docs (admin_ui_sso.md):
- Rewrite Step 3 to document both Org Auth Server (free) and
Custom Auth Server (paid SKU) as tabbed options
- Add Step 4 for GENERIC_CLIENT_STATE and PKCE configuration
(moved from troubleshooting into the main guide)
- Clarify no_matching_policy error only applies to Custom Auth Server
- Deduplicate troubleshooting section to reference Step 4
2. Custom SSO handler (custom_sso.py + custom_sso.md):
- Replace broken user_info() call with prisma_client.get_data()
- user_info() is a FastAPI route handler requiring Request and
UserAPIKeyAuth params, cannot be called directly
- Keep new_user/add_new_member as commented-out import references
in docs for customers who need them
The linting workflow force-installed openai==1.100.1 which conflicts
with litellm's requirement of openai>=2.8.0, causing pip dependency
resolver errors and CI cancellation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
PR #22732 changed the ToolTable schema (renamed call_policy to
input_policy, added output_policy/user_agent/last_used_at columns,
updated indexes) but didn't include a migration for these changes.
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 asend_message function accessed request.params.message.context_id
using attribute syntax, but message can be either a dict or an object.
Handle both cases using isinstance check, matching the existing pattern
in litellm/a2a_protocol/utils.py.
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 redundant isinstance(thinking, dict) check in handler.py since
early return on line 64 guarantees thinking is a dict at that point
- Preserve summary in translate_anthropic_to_openai() for consistency
across all code paths (adapter, guardrail, main.py)
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
