* chore(pangea-guardrail): Fix typo in debug message.
* docs(pangea-guardrail): Fix YAML example in pangea.md (README)."
* docs(pangea-guardrail): Update pangea.md (README).
* chore(pangea-guardrail): Format with Black.
* build(model_prices_and_context_window.json): add 'claude-opus-4' on vertexai (no @)
* build(model_prices_and_context_window.json): add claude sonnet 4 without 2
*@
* feat(internal_user_endpoints.py): assign user to orgs on user creation
allows user to be a member of orgs on creation - work to enable default orgs on UI
* fix(internal_user_endpoints.py): fix http_request
* docs(deploy.md): move docker recommendation to `main-stable`
* feat(enterprise/internal_user_endpoints.py): expose endpoint for checking available premium users
* feat(usage_indictor.tsx): add new element to help track remaining premium users
* feat(usage_indicator.tsx): show premium user remaining usage
allows users with user caps to know how much is left
* fix(vertex_and_google_ai_studio_gemini.py): bubble up stream is not finished, even if stop reason is given
prevents early completion of stream
Closes https://github.com/BerriAI/litellm/issues/11549
* fix(streaming_handler.py): respect is_finished = False in hidden params
internal logic for preventing ending stream early
* fix(litellm_license.py): add function to check if user is over limit
* fix(internal_user_endpoints.py): add function to check if user is over limit
* refactor: move test
* docs(customer_endpoints.py): document new param
* Feature/lasso guardrail (#9002)
* first version of lasso guardrail in litellm
* update to the new Lasso API
* change prod api_base and kill the request when lasso detect issue.
* change test for now api, local test pass
* add async tests
* all tests pass
* add docs for the new lasso guardrail
* Remove support for modes other than pre_call in Lasso guardrail
* code structure and naming
* only pre_call docs
* fix lint errors
* move test to the new location follows the same directory structure as litellm/.
* add lasso guard
* docs lasso docs
* add lasso guardrail
* fix lasso guardrail
---------
Co-authored-by: oroxenberg <oro@lasso.security>
* 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>
* Update web_search.md to include new supported providers and models, enhance web search options, and improve documentation for using web search with various AI models.
* Update LiteLLM version in web_search.md to reflect the latest stable release.
* Fix formatting in web_search.md for model declaration consistency.
* docs(web_search.md): add configuration options for web search in config.yaml
This update introduces sections for setting default and custom web search options in the proxy config file, including examples for different models and context sizes. A note clarifies that users can override these settings in API requests.
* fix(streaming_handler.py): maintain same 'created' across all chunks
Fixes https://github.com/BerriAI/litellm/issues/11437
* test: add unit test to ensure created is always the same across all chunks
* fix(types/utils.py): set a tool call id, if missing in delta tool call
Ensures stream chunk builder can reconstruct tool calls correctly
Fixes https://github.com/BerriAI/litellm/issues/11262
* fix(responses/transformation.py): support passing mcp server tool call to anthropic
allows switching between openai and anthropic for mcp tool calling
* fix(ollama/chat/transformation.py): set tool call id's when missing
* fix(onboarding_link.tsx): fix adding ui/invitation id
* fix(onboarding_link.tsx): update invitation link function to handle w/ and w/out custom server path cases
* fix(model_checks.py): ensure team only models returned when all proxy models set for team
* 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
* refactor: comment out circuit breaker
causes incorrect rate limiting in high traffic
* fix(base_routing_strategy.py): don't reset value if redis val is lower than current in-memory value
Fixes issue where redis might be trailing in-memory value
* fix(parallel_request_limiter_v2.py): if in-memory higher than redis, don't reset value; add previous slot keys to redis increment to correctly 'get' them
* fix(parallel_request_limiter_v3.py): v3 implementation of parallel request limiter
does not use background redis syncing - increments redis in call
simplify rate limiting logic, to improve accuracy
* fix: fix ruff errors
* fix(parallel_request_limiter_v3.py): don't decrement limit on post call success - causes double decrements
* fix(parallel_request_limiter_v3.py): working accurate multi-instance logic
ensured just 100 requests allowed on 100 users, 10 ramp up, 100 rpm limit key, 2 instances
* fix(parallel_request_limiter_v3.py): working accurate rate limiting with time window resets
allows rate limiting to work across multiple windows
* test: add unit tests for v3 rate limiter
* fix(parallel_request_limiter_v3.py): return window value into in-memory cache
allows in-memory cache checks to be used correctly
* refactor(parallel_request_limiter_v3.py): refactor rate limiting to work for multiple window/counter key pairs
enables using for user/team/model rate limiting
* feat(parallel_request_limiter_v3.py): working rate limiting, across key/user/team/end-user
* fix(parallel_request_limiter_v3.py): add model specific rate limiting
* fix(parallel_request_limiter_v3.py): ignore if no rate limits set
skip unecessary rate limit checks - if no limits set
* fix(parallel_request_limiter_v3.py): initial commit bringing token rate limits back
* fix(parallel_request_limiter_v3.py): increment by value in list + update assertions to handle tokens + max parallel requests
* test(parallel_request_limiter_v3.py): more testing
* fix(parallel_request_limiter.py): working in-memory cache limiter
* fix(redis_cache.py): ignore linting error - use safe hasattr
* fix(parallel_request_limiter_v3.py): fix linting error
* refactor: remove redundant parallel_Request_limiter_v2.py
old / inaccurate implementation
* test: update tests
* style: cleanup
* test: update test
* docs(config_settings.md): document new env var
* test(test_base_routing_strategy.py): update test
* Update web_search.md to include new supported providers and models, enhance web search options, and improve documentation for using web search with various AI models.
* Update LiteLLM version in web_search.md to reflect the latest stable release.
* Fix formatting in web_search.md for model declaration consistency.
