* fix(litellm_pre_call_utils.py): add user agent tags to spend logs in standard logging payload logic
avoid clash when tag based routing is enabled
* test: remove redundant test
* test: rename oidc test to run earlier
quicker debuging
* fix(azure.py): return more detailed error message
* fix(azure/common_utils.py): use default scope, if scope is none
fixes oidc test
* fix: always default to cognitiveservices.azure.com
* test: update test
* feat: update the feature of ollama_embeddings to work on a sync api
Signed-off-by: Abinand P <abinand0911@gmail.com>
* lint:fixing of the lint file
Signed-off-by: Abinand P <abinand0911@gmail.com>
* fix:test
Signed-off-by: Abinand P <abinand0911@gmail.com>
* chore: added test for ollama embedding and refactored handler
Signed-off-by: Abinand P <abinand0911@gmail.com>
* fix:lint error
Signed-off-by: Abinand P <abinand0911@gmail.com>
---------
Signed-off-by: Abinand P <abinand0911@gmail.com>
* docs(index.md): add stable pip package
* fix(anthropic/chat/transformation.py): add 'none' tool choice mapping
Allows disabling anthropic tool calling
Maintain parity
* fix(transformation.py): if tool_choice="none" ignore 'disable_parallel_Tool_use'
unsupported param from anthropic - makes sense as the 'none' implies no tool calls are being made
* fix(anthropic/chat/transformation.py): append prefix to start of assistant response, if set
ensures assistant response contains complete response
* fix(anthropic/chat/transformation.py): add flag to allow user to opt out of enabling prefix in prompt
* fix(anthropic/chat/transformation.py): working e2e support for prefix prompt in assistant response
* feat(networking.tsx): always include model access groups on UI
show admin created access groups when giving key/user/team model permissions
* feat(add_model_tab.tsx): initial ui component for adding to an existing model access group
allows user to add model to an access group (simplify giving users/keys/teams model access)
* feat(proxy_server.py): add 'only_model_access_groups' flag support to `/v1/models`
simplifies listing available access groups on UI
* test: add e2e test for new only_model_access_groups param
* feat(add_model_tab.tsx): allow adding+viewing model access groups on models tab
make feature functional on UI
* feat(view_users.tsx): route edit user to user info page
more detailed user edit
* feat(columns.tsx): route edit user to user info page
more detailed user edit
* fix(columns.tsx): fix linting error
* build(ui/): fix linting errors
* feat(anthropic/passthrough): pass dynamic api key/api base params to litellm.completion
allows calls to work with config.yaml
* fix(responses_api/transformation): fix passing dynamic params to responses api from .completion()
Allows responses api to work with config.yaml
* fix(langfuse.py): fix responses api usage logging to langfuse
* refactor(litellm_logging.py): add more generic solution for responses api usage logging
ensures it works across all logging integrations
* fix(litellm_logging.py): patch for anthropic messages not returning a pydantic object
it should ideally return a pydantic object, which would simplify checks and reduce errors
* fix(handler.py): correctly bubble up empty choices errors to litellm.completion
causes downstream errors as it is expected there is at least one choice set
* fix(response_metadata.py): allow model_info to be none
* fix(litellm_logging.py): copy object before mutating
* fix: fix lint check
- Updated the `_add_reasoning_system_prompt_if_needed` method to maintain the original format of list content when prepending the reasoning prompt.
- Adjusted tests to verify that both string and list content types are correctly handled, ensuring the reasoning prompt is added without altering the content structure.
- Updated the `_add_reasoning_system_prompt_if_needed` method to convert list content to strings before prepending the reasoning prompt.
- Adjusted tests to verify that system messages with list content are correctly transformed into strings, ensuring original content is preserved.
- Revised the reasoning support indicators in the Mistral model documentation for clarity.
- Improved the `_add_reasoning_system_prompt_if_needed` method to handle both string and list content types for system messages, ensuring the reasoning prompt is correctly prepended.
- Added a new test case to verify the functionality of adding the reasoning system prompt when the existing content is a list.
* Added support for reasoning parameters in magistral models, including "reasoning_effort" and "thinking".
* Updated the MistralConfig class to handle reasoning system prompts.
* Implemented tests to verify reasoning functionality and ensure correct parameter mapping for magistral models.
* Enhanced the model prices JSON to reflect new reasoning capabilities.
* Checkpoint before follow-up message
* Add comprehensive tests for Deepgram transcription functionality
* clean up transform
* just use 1 test
* test cleanup
* test fix get_complete_url
* test rename file
* refactor deepgram URL construction
* add logging_obj.pre_call
* fix unused imports
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* fix(internal_user_endpoints.py): support user with `+` in email on user info
ensures user is correctly parsed from input
* fix(factory.py): support vertex function call args as None
handles empty string in args for vertex gemini calls
* docs(langfuse_integration.md): pin langfuse sdk version on docs
* fix(vertex_ai/): return empty dict, instead of none when empty string given
* refactor: reduce function size
* fix: fix linting errors
* fix: revert check
* fix(internal_user_endpoints.py): fix check
* test: update tests
* test: update tests
* fix: fixes for transfer encoding error on aiohttp transport
* Update tests/test_litellm/llms/custom_httpx/test_aiohttp_transport.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* feat(anthropic/): initial commit adding working mcp tool call support
pass in mcp tool via `tools` and litellm will handle translating it to the right anthropic param
* feat(anthropic/): map openai mcp tool to anthropic mcp tool
allows usage within responses api
* fix(databricks/transformation.py): fix databricks linting error
* test(test_anthropic_chat_transformation.py): fix test
* test: update test
* fix(anthropic/chat/transformation.py): add dummy tool call
* fix(vertex_and_google_ai_studio_gemini.py): add web search request tracking
Enables cost calculation for google web search
* fix(vertex_and_gemini): use common processing logic across stream / non-stream calls
* fix(vertex_And_google_ai_studio_Gemini.py): fix initial choice
* fix: fix linting error
* fix: add initial support for google search cost tracking
* fix(tool_call_cost_tracking.py): working tool cost tracking for gemini
* fix(vertex_ai/gemini/cost_calculator.py): add google web search tool cost tracking for vertex ai
Closes LIT-210
* fix: fix check
* build(model_prices_and_context_window.json): fix amazon nova max output tokens
Closes https://github.com/BerriAI/litellm/issues/11441
* fix: fix ruff check
* fix: using litellm with claude code bedrock
* fix: usage for bedrock with /messages
* fix: bedrock_sse_wrapper
* tests: test for test_chunk_parser_usage_transformation
* test fix
* fix(huggingface): use get() instead of pop() for input_type parameter
Fixes embedding generation for HuggingFace models where input_type override
is required (e.g. BAAI/bge-m3). The pop() method was mutating optional_params
and removing input_type before downstream functions could access it.
* Add unit tests to catch regression
* Move tests around
* fix(anthropic/): fix regression when passing file url's to the 'file_id' parameter
add test and ensure anthropic file url's are correctly sent as 'document' blocks
* fix(vertex_and_google_ai_studio.py): Use same usage calculation function as non-streaming
Closes https://github.com/BerriAI/litellm/issues/10667
* test(test_vertex_and_google_ai_studio_gemini.py): update test
* Add support for DataRobot as a router in LiteLLM
* Updates to logic
* Changes to make things work better
* Capitalize bearer
* Revert change
* Undo and simplify things
* Add basic testing
* Add some extra handling
* More tests
* Lowercase
* Fix
* Comment
* Add local test_completion for datarobot
* Sync repo to main
* Update get_complete_url to accept deployments
* Migrate to OpenAILike
* Mock datarobot in test
* Migrate everything to OpenAI LIke
* Apply fixes and changes from review
* Update docs
* Update env vars
* Move tests
* feat: Add Gemini TTS audio parameter support
- Add is_model_gemini_audio_model() method to detect TTS models
- Include 'audio' parameter in supported params for TTS models
- Map OpenAI audio parameter to Gemini speechConfig format
- Add _extract_audio_response_from_parts() method to transform audio
output to openai format
* updated unit-test to use pcm16
* - created typedict for speechconfig
- simplified gemini tts model detection
- moved gemini_tts test to test_litellm
* simplified is_model_gemini_audio_model more
* fix(utils.py): support non default params for audio transcription
allows passing provider specific params straight through on transcription calls
* fix(gpt_transformation.py): fix o_series model routing
call _transform_request on async event
* refactor: refactor tests
* test(test_azure_chat_o_series_transformation.py): add unit test for azure o series error
* test: update test
* test: update json
* fix: fix mutiple keyword error
* fix(anthropic/chat/handler.py): Fixes https://github.com/BerriAI/litellm/issues/10328
Adopts changes from https://github.com/BerriAI/litellm/pull/10329
* fix(vertex_and_google_ai_studio.py): don't set 'include thoughts' if thinking budget = 0
VertexAI raises errors
* fix(vertex_llm_base.py): new function for deciding the api base, handles 'global' api base
Fixes https://github.com/BerriAI/litellm/issues/11190
* fix(vertex_ai/partner_models): fix instrumentation for custom api base check
* refactor(vertex_ai/partner): refactor function to keep below 50 LOC
* fix(vertex_ai/gemini): remove parallel tool calls error for >1 tool - just ignore (prevent call from failing)
* fix: fix linting error
* fix(vertex_and_google_ai_studio_gemini.py): handle both camel case and underscores in the tool for vertex ai code execution
support vertex ai code execution
* docs(vertex.md): add code execution example to vertex ai
* fix(vertex_ai/common_utils.py): when anyof in field, just select anyof - don't include other k,v pairs - vertex throws error
Fixes https://github.com/BerriAI/litellm/issues/11164
* fix(common_utils.py): add title field inside anyof - to retain some description
Addresses https://github.com/BerriAI/litellm/issues/11164#issuecomment-2914728385
* fix: AiohttpResponseStream transport
* fix: use AiohttpResponseStream transport by default
* fix: AiohttpResponseStream transport
* fixes: mapping aiohttp exceptions
* fixes: aiohttp rollout
* fixes: add support ssl_verify for aiohttp
* fixes: add support ssl_verify for aiohttp
* fixes: remove duplicates
