* Get the basics of the integration working.
* Cleanup bytez integration.
* Update user agent for Bytez integration.
* Use the config class directly. Create the start of the docs.
* Finish up bytez documentation. Include a provider integration guide.
* Fix typing bug in custom_logger_utils. Add tests for bytez integration.
* Add token tracking for model usage for Bytez integration.
* Create a units test for the Bytez config.
* Make changes to Bytez transformation code per PR feedback.
* Cleanup coment in Bytez transformation test.
* Remove LRU usage for bytez integration.
* Consolidate Bytez tests into a single file. Conform to project structure for tests.
* Fix linting error with Bytez impl.
* Added dashscope as a provider
* Fix some leftover references on nebius
* Porting the dashscope api endpoit international version
* explicit tool_choice = True in config
- Added 'size' to supported parameters for vertex_ai in get_optional_params_image_gen
- Implemented mapping from OpenAI size format (e.g., '1024x1024') to Vertex AI aspectRatio format (e.g., '1:1')
- Supports common aspect ratios: 1:1 (square), 16:9 (landscape), 9:16 (portrait)
- Added comprehensive test coverage for the size parameter mapping
Fixes LIT-279: Vertex AI Image Generation Aspect Ratio Support
* fix(proxy_server.py): handle empty config yaml
Fixes https://github.com/BerriAI/litellm/issues/12163
* fix(gemini/common_utils.py): replace models/ as expected, instead of using 'strip'
Fixes https://github.com/BerriAI/litellm/issues/12160
* fix(anthropic/experimental_pass_through/messages/transformation.py): check for env var when selecting api key
* fix(anthropic/transformation.py): return tool_use content block start on anthropic bridge
Closes https://github.com/BerriAI/litellm/issues/12158
* fix(anthropic/streaming_iterator.py): fix setting index in block
ensure index is set just once and increments correctly when a new block is created
* fix(anthropic/adapters/handler.py): update logging obj with stream options value if set
* feat(anthropic/streaming_iterator.py): return usage from chat completion to messages bridge
enables usage tracking for non-anthropic models
Closes https://github.com/BerriAI/litellm/issues/12132
* fix(streaming_iterator.py): safely access usage chunk
* fix: suppress linting error
* test: update tests
* fix: fix streaming errors
* fix: support Cursor IDE tool_choice format {"type": "auto"}
- Update validate_chat_completion_tool_choice to normalize {"type": "auto"} to "auto"
- Handles Cursor IDE sending non-standard tool_choice format
- Add comprehensive tests for tool choice validation
Fixes#12098
* fix: return full tool_choice object for Cursor IDE format
Based on PR feedback, updated validate_chat_completion_tool_choice to return
the full tool_choice dictionary instead of just extracting the type string.
This maintains consistency with downstream code that expects the full object
structure.
- Changed behavior: {"type": "auto"} now returns {"type": "auto"} instead of "auto"
- Updated tests to reflect the new expected behavior
- Ensures compatibility with code that passes tool_choice to optional_params
Addresses feedback from PR #12168
* feat: initial commit adding bedrock support via the new sdk passthrough logic
ensures correct sequencing of tasks (pre call checks etc. can run before signing request)
* fix(route_llm_requests.py): passthrough to allm_passthrough_route if no model found
* feat(bedrock/passthrough): working bedrock passthrough via sdk support
* fix(passthrough/main.py): re-add data and json
* feat(passthrough/main): support async passthrough calls to bedrock
* feat(passthrough/main.py): async streaming + completion support
* feat(llm_passthrough_endpoints.py): migrate bedrock passthrough calls to to new bedrock passthrough sdk
Enables calls to work correctly
* fix: fix linting errors
* test: update test
* init litellm google gen ai methods
* feat init structure of functions for generate content
* add init
* add BaseGoogleGenAIGenerateContentConfig
* add generate_content_handler
* add get_provider_google_genai_generate_content_config
* fixes for generate content
* add get_vertex_ai_project etc to base
* use VertexBase
* fixes for BaseGoogleGenAIGenerateContentConfig
* working validate env for google gemini
* feat - add transform google response
* fixes for transform_generate_content_request
* fix get_supported_generate_content_optional_params
* add BaseGoogleGenAITest
* working e2e test
* fixes init config
* use correct types
* fix test for google gen ai
* fix types
* add sync_get_auth_token_and_url
* fixes for transform
* add llm http handler for google
* working non-streaming google endpoints
* add BaseGoogleGenAIGenerateContentStreamingIterator
* add GoogleGenAIGenerateContentStreamingIterator
* fix working sync stream
* fixes for litellm logging obj
* working async streaming
* add google gen ai types
* fix - required imports
* fix readme
* fix deps
* fix deps
* fix ruff code QA checks
* fix linting
* fixes TYPE_CHECKING
* fixes for typing
* add google gemini methods to litellm router
* [Feat] Add initial endpoints for using Gemini SDK (gemini-cli) with LiteLLM (#12040)
* init with google endpoints
* add Depends
* feat - add gemini endpoints
* google_generate_content
* fix init
* fixes import
* fixes for streaming
* fixes for sync/async
* working streaming with google gemini cli
* add google endpoints to llm api routes
* add VertexAIGoogleGenAIConfig
* use aiter_bytes
* use common request for streaming data
* re-use logic for anthropic streaming
* add GoogleAIStudioDataGenerator
* init litellm google gen ai methods
* feat init structure of functions for generate content
* add init
* add BaseGoogleGenAIGenerateContentConfig
* add generate_content_handler
* add get_provider_google_genai_generate_content_config
* fixes for generate content
* add get_vertex_ai_project etc to base
* use VertexBase
* fixes for BaseGoogleGenAIGenerateContentConfig
* working validate env for google gemini
* feat - add transform google response
* fixes for transform_generate_content_request
* fix get_supported_generate_content_optional_params
* add BaseGoogleGenAITest
* working e2e test
* fixes init config
* use correct types
* fix test for google gen ai
* fix types
* add sync_get_auth_token_and_url
* fixes for transform
* add llm http handler for google
* working non-streaming google endpoints
* add BaseGoogleGenAIGenerateContentStreamingIterator
* add GoogleGenAIGenerateContentStreamingIterator
* fix working sync stream
* fixes for litellm logging obj
* working async streaming
* add google gen ai types
* fix - required imports
* fix readme
* fix deps
* fix deps
* fix ruff code QA checks
* fix linting
* fixes TYPE_CHECKING
* fixes for typing
* feat: add citation_cost_per_token and search_queries_cost_per_1000 fields to ModelInfoBase
- Add citation_cost_per_token field to ModelInfoBase for Perplexity citation token costs
- Add search_queries_cost_per_1000 field to ModelInfoBase for Perplexity search query costs
- Update _get_model_info_helper to include these fields in model info responses
- Enables proper cost calculation for Perplexity-specific usage metrics
* feat: update Perplexity sonar-deep-research model pricing configuration
- Update input/output token costs to / per million tokens respectively
- Add reasoning token cost at per million tokens
- Add citation_cost_per_token at per million tokens (same as input)
- Add search_queries_cost_per_1000 at /bin/zsh.005 per 1000 search queries
- Remove deprecated search_context_cost_per_query structure
- Aligns with Perplexity's updated pricing model for deep research capabilities
* feat: implement Perplexity-specific cost calculator
- Create cost_per_token function for Perplexity provider
- Calculate standard input/output token costs
- Add citation token cost calculation using citation_cost_per_token rate
- Add reasoning token cost calculation with fallback to completion_tokens_details
- Add search query cost calculation using search_queries_cost_per_1000 rate
- Return separate prompt_cost and completion_cost for accurate billing
- Handles all Perplexity-specific usage metrics: citation_tokens, num_search_queries, reasoning_tokens
* feat: integrate Perplexity cost calculator with main cost calculation system
- Import perplexity_cost_per_token function in main cost calculator
- Add perplexity provider case to cost_per_token function
- Enables automatic routing of Perplexity cost calculations to provider-specific logic
- Maintains compatibility with existing cost calculation patterns
- Supports all Perplexity-specific cost metrics through unified interface
* feat: enhance Perplexity response transformation to extract cost-related fields
- Override transform_response method to extract Perplexity-specific usage fields
- Add _enhance_usage_with_perplexity_fields method to process API responses
- Extract citation_tokens from citations array using character-based estimation (~4 chars/token)
- Extract num_search_queries from both usage field and root level with priority handling
- Create usage object when none exists to ensure cost fields are always captured
- Handle empty citations and missing fields gracefully
- Enables automatic extraction of cost metrics from Perplexity API responses
* test: add comprehensive test suite for Perplexity cost calculation features
Add 82 comprehensive tests across 3 test files:
- test_perplexity_cost_calculator.py (59 tests):
* Cost calculation with citation tokens, search queries, reasoning tokens
* Various combinations and edge cases
* Integration with main cost calculator
* Model info access and validation
* Zero values and missing fields handling
- test_perplexity_chat_transformation.py (12 tests):
* Citation token extraction from API responses
* Search query extraction from usage and root fields
* Priority handling and field aggregation
* Empty citations and missing fields handling
* Token estimation accuracy validation
- test_perplexity_integration.py (11 tests):
* End-to-end cost calculation workflows
* High-volume and edge case scenarios
* Model info integration validation
* Case-insensitive provider matching
* Transformation preservation of existing fields
Ensures reliability and correctness of all Perplexity cost features with comprehensive coverage of happy path, edge cases, and error conditions.
* fix: remove unused Union import from Perplexity transformation
- Remove unused typing.Union import from litellm/llms/perplexity/chat/transformation.py
- Fixes F401 linting error: 'typing.Union imported but unused'
- Maintains only necessary imports: Any, List, Optional, Tuple
* Fix JSON schema validation and use web_search_requests field
- Add citation_cost_per_token and search_queries_cost_per_1000 to JSON schema
- Update Perplexity transformation to use web_search_requests in PromptTokensDetailsWrapper
- Update Perplexity cost calculator to read from web_search_requests field
- Maintain backward compatibility while using standard LiteLLM fields
* Fix type errors in Perplexity cost calculator
- Add null checks for token counts and cost values to prevent None multiplication errors
- Use .get() with fallback values instead of direct dictionary access
- Ensure all arithmetic operations handle None values safely
This fixes the failing job 44517525148 type errors.
* Refactor Perplexity cost calculation tests to improve accuracy and consistency
- Replace absolute difference assertions with math.isclose for better precision in cost comparisons
- Update tests to utilize PromptTokensDetailsWrapper for handling web search requests
- Ensure all test cases correctly reflect the new structure of usage fields, enhancing clarity and maintainability
* fix: address type hinting issues in PerplexityChatConfig usage handling
- Add type ignore comments to model_response.usage assignments to resolve type checking errors
- Ensures compatibility with type definitions while maintaining existing functionality
* Update model pricing configuration in JSON backup
- Add citation_cost_per_token and search_queries_cost_per_1000 fields to enhance cost tracking
- Remove deprecated search_context_cost_per_query structure to streamline pricing model
- Aligns with recent updates in Perplexity's pricing strategy
* Update search queries cost structure in model_prices_and_context_window.json to use search_context_cost_per_query
* Refactor search queries cost structure in model_prices_and_context_window_backup.json and update related code to use search_queries_cost_per_query. Remove deprecated search_queries_cost_per_1000 references across model info and tests.
* Enhance cost calculation in cost_calculator.py by introducing a safe float casting function to handle potential None and invalid values. Update cost calculations for input, citation, output, reasoning, and search query tokens to use this new function, ensuring more robust handling of model pricing data.
* Refactor cost calculation in cost_calculator.py to support both legacy and current search cost keys. Enhance handling of search cost values by accommodating both dictionary and float formats, ensuring robust cost computation for search queries.
* Update test cases to reflect changes in cost structure, renaming search_queries_cost_per_query to search_context_cost_per_query for consistency with recent refactor. Ensure assertions in tests align with updated cost keys.
* Update test_perplexity_integration.py to rename search_queries_cost_per_query to search_context_cost_per_query, ensuring consistency with recent cost structure changes. Adjust assertions to align with updated cost keys.
* add test_anthropic_messages_litellm_router_streaming_with_logging to base tests
* move test
* fixes for base ant tests
* working bedrock ant logging
* use BaseAnthropicMessagesStreamingIterator
* use common iterator for messages streaming
* TestAnthropicDirectAPI
* test_anthropic_claude3_transformation.py
* fix code QA checks
* fix logging for anthropic messages in SLP
* fix TestAnthropicOpenAIAPI
* remove hard coded usage for adapter
* test_anthropic_messages_litellm_router_streaming_with_logging
* fix(utils.py): convert stringified numbers to numbers
Closes https://github.com/BerriAI/litellm/issues/11266
* fix(convert_dict_to_model_response_object/): bubble up azure content_filter_results
* fix: fix linting error
* fix: fix linting errors
* fix(types/utils.py): ensure choices is correctly set
* fix: delete field if not set
* fix: expand scope of choicelogprobs value
* Add tests for function calling support in LiteLLM proxy models
- Introduced a new test script `test_proxy_function_calling.py` to validate function calling capabilities for both direct and proxied models.
- Created a comprehensive test suite in `tests/litellm_utils_tests/test_proxy_function_calling.py` using pytest, covering various model configurations and edge cases.
- Implemented parameterized tests to ensure consistency between direct and proxied model function calling support.
- Added tests for specific proxy models, edge cases, and import verification for the `supports_function_calling` function.
- Included a demonstration test to highlight the current issue with proxy model resolution.
* feat: add fallback handling for litellm_proxy models in model info retrieval
* feat: enhance proxy function calling tests with custom model name handling and documentation
* fix: add type ignore comments for custom logger callback initialization
* fix: remove styling diff
* fix: style
* fix(utils.py): remove outdated comment regarding litellm_proxy models
* feat(utils.py): add proxy model handling for underlying model extraction
* feat(utils.py): enhance model name handling for litellm_proxy integration
* refactor(utils.py): remove unused _handle_proxy_model_names function
* 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
* 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(utils.py): prevent leaking sensitive keys to langfuse
Fixes https://github.com/BerriAI/litellm/issues/11150
* test(langfuse/): unit test preventing future bedrock key leaks
Fixes https://github.com/BerriAI/litellm/issues/11150
* test(test_langfuse_e2e_test.py): add unit test for vertex - make sure no key leaks occur
* ci(test-litellm.yml): add pytest retry to github workflow
* fix(proxy_server.py): support forwarding `/sso/key/generate` to the server root path url
Fixes https://github.com/BerriAI/litellm/issues/10761
* fix(proxy_server.py): don't rewrite absolute path (PROXY_BASE_URL) with relative path (SERVER_ROOT_PATH)
This causes issues when using a custom path with sso, when doing redirects
* fix(utils.py): ignore token - will mistakenly redact 'max_tokens' as well
* fix(main.py): use processed non-default-params as standard input params for langfuse
Fixes https://github.com/BerriAI/litellm/issues/11072
Fixes https://github.com/BerriAI/litellm/issues/11096
* fix(main.py): rename variable to be more accurate
* test(test_langfuse_e2e_test.py): add router unit test for langfuse e2e testing
Prevent https://github.com/BerriAI/litellm/issues/11072 from happening again
* build: update lock
* fix(utils.py): refactor optional params function
make it easier to get the standardized non default params
* fix(utils.py): improve process non default params function
* fix(main.py): include provider specific params in processed non default params used in logging
ensures user can see any provider specific params on langfuse
ensures user can see any provider specific params on langfus e
* Add support for supports_computer_use in model info
* Corrected list of supports_computer_use models
* Further fix computer use compatible claude models, fix existing test that predated supports_computer_use in the model list
* Move computer use test case into existing test_utils file
* Moved tests in to test_utils.py
- docs/my-website/docs/providers/lm_studio.md: add Structured Output section with JSON schema and Pydantic examples
- litellm/llms/lm_studio/chat/transformation.py: extend map_openai_params to handle `response_format` mappings (`json_schema`, `json_object`) and move them to optional_params
- litellm/utils.py: include `LM_STUDIO` in `supports_response_schema` list
- tests/litellm/llms/lm_studio/test_lm_studio_chat_transformation.py: add tests for Pydantic model and dict-based JSON schema handling
Co-authored-by: Earl St Sauver <estasuver@gmail.com>
