- Add ttl parameter to cache_control for Gemini models
- Support Google's TTL format (e.g., '3600s', '7200s')
- Implement robust TTL extraction and validation
- Extract TTL before system message transformation to handle all cases
- Add comprehensive test suite with 17 test cases in tests/test_litellm/
- Update documentation with TTL usage examples
- Maintain backward compatibility with existing cache_control usage
Fixes#9810
* fix(batches_endpoints/endpoints.py): support passing target model names for batch list as a query param
Fixes issue where cloud run fails calls because GET can't contain request body
* test(test_openai_batches_endpoints.py): add unit test
* docs(managed_batches.md): update docs
* feat(spend_tracking_utils.py): support STORE_PROMPTS_IN_SPEND_LOGS env var
ensures prompt is stored in spend logs
* fix(streaming_iterator.py): fix anthropic - completion streaming iterator to yield content block stop
ensures claude code renders messages
* test: skip local test
* 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(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(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
* build(model_prices_and_context_window.json): add vertex ai gemini-2.5-flash pricing
* build(model_prices_and_context_window.json): add gemini reasoning token pricing
* fix(vertex_and_google_ai_studio_gemini.py): support counting thinking tokens for gemini
allows accurate cost calc
* fix(utils.py): add reasoning token cost calc to generic cost calc
ensures gemini-2.5-flash cost calculation is accurate
* build(model_prices_and_context_window.json): mark gemini-2.5-flash as 'supports_reasoning'
* feat(gemini/): support 'thinking' + 'reasoning_effort' params + new unit tests
allow controlling thinking effort for gemini-2.5-flash models
* test: update unit testing
* feat(vertex_and_google_ai_studio_gemini.py): return reasoning content if given in gemini response
* test: update model name
* fix: fix ruff check
* test(test_spend_management_endpoints.py): update tests to be less sensitive to new keys / updates to usage object
* fix(vertex_and_google_ai_studio_gemini.py): fix translation
* fix(internal_user_endpoints.py): cleanup unused variables on beta endpoint
no team/org split on daily user endpoint
* build(model_prices_and_context_window.json): gemini-2.0-flash supports audio input
* feat(gemini/transformation.py): support passing audio input to gemini
* test: fix test
* fix(gemini/transformation.py): support audio input as a url
enables passing google cloud bucket urls
* fix(gemini/transformation.py): support explicitly passing format of file
* fix(gemini/transformation.py): expand support for inferred file types from url
* fix(sagemaker/completion/transformation.py): fix special token error when counting sagemaker tokens
* test: fix import
* build(model_prices_and_context_window.json): add gemini-1.5-flash context caching
* fix(context_caching/transformation.py): just use last identified cache point
Fixes https://github.com/BerriAI/litellm/issues/6738
* fix(context_caching/transformation.py): pick first contiguous block - handles system message error from google
Fixes https://github.com/BerriAI/litellm/issues/6738
* fix(vertex_ai/gemini/): track context caching tokens
* refactor(gemini/): place transformation.py inside `chat/` folder
make it easy for user to know we support the equivalent endpoint
* fix: fix import
* refactor(vertex_ai/): move vertex_ai cost calc inside vertex_ai/ folder
make it easier to see cost calculation logic
* fix: fix linting errors
* fix: fix circular import
* feat(gemini/cost_calculator.py): support gemini context caching cost calculation
generifies anthropic's cost calculation function and uses it across anthropic + gemini
* build(model_prices_and_context_window.json): add cost tracking for gemini-1.5-flash-002 w/ context caching
Closes https://github.com/BerriAI/litellm/issues/6891
* docs(gemini.md): add gemini context caching architecture diagram
make it easier for user to understand how context caching works
* docs(gemini.md): link to relevant gemini context caching code
* docs(gemini/context_caching): add readme in github, make it easy for dev to know context caching is supported + where to go for code
* fix(llm_cost_calc/utils.py): handle gemini 128k token diff cost calc scenario
* fix(deepseek/cost_calculator.py): support deepseek context caching cost calculation
* test: fix test
* fix(key_management_endpoints.py): fix user-membership check when creating team key
* docs: add deprecation notice on original `/v1/messages` endpoint + add better swagger tags on pass-through endpoints
* fix(gemini/): fix image_url handling for gemini
Fixes https://github.com/BerriAI/litellm/issues/6897
* fix(teams.tsx): fix member add when role is 'user'
* fix(team_endpoints.py): /team/member_add
fix adding several new members to team
* test(test_vertex.py): remove redundant test
* test(test_proxy_server.py): fix team member add tests
