* fix(main.py): fix async retryer
Fixes https://github.com/BerriAI/litellm/issues/12830
* fix(forward_clientside_headers_by_model_group.py): filter out 'content-type' from forwardable headers
clientside content-type != proxy content type, can cause requests to hang
* test(tests/): update tests
* fix(prompt_templates/factory.py): handle anthropic cache control on individual tool results
Fixes issue where cache control on individual tool result was being ignored
* test(test_vertex_And_google_ai_studio_gemini.py): initial unit test covering translation for grounding metadata on streaming chunk
* fix(vertex_and_google_ai_studio.py): ensure grounding metadata is preserved on streaming
Closes https://github.com/BerriAI/litellm/issues/10237
* fix(core_helpers.py): include usage in expected openai keys
* refactor(aim.py): refactor to support adding aim guardrails on UI
* fix(base.py): add ui_friendly_name to config model
* feat(ui/): support loading new guardrails from backend api call
removes need to onboard each guardrail to ui
* fix: don't show optional params if not set and don't show ui_friendly_name (internal param0
* fix(ui/add_guardrail_form.tsx): ensure dynamic provider value is used
* fix(ui/): just one-time update the provider map dictionary
* fix(ui/): show masked api base / api key on guardrail update
* refactor(aporia_ai/): refactor to show on UI
* feat(aporia_ai/): add aporia ai guardrail to UI
* refactor(guardrails_ai/): refactor to add via UI
* refactor(lasso.py): refactor to enable adding lasso guardrails via UI
* feat(pangea.py): add pangea guardrail on UI
* feat(panw): add panw prisma airs through UI
* test: update tests
* fix: fix ruff linting error
* test: update tests
* fix: add missing docs
* fix: fix guardrail init
* fix: suppress linting errors
* fix(proxy_server.py): fix linting error
* 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
* 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
* 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
* 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
* Refactor get_end_user_id_from_request_body to support user ID retrieval from custom headers and multiple request body formats. Enhance tests to cover various scenarios including header precedence and fallback mechanisms.
* Refactor get_end_user_id_from_request_body function to accept request_body as the first parameter, improving clarity and flexibility. Update tests for compatibility and add new cases to ensure correct functionality across various request body formats.
* Update _user_api_key_auth_builder and user_api_key_auth to pass request object to get_end_user_id_from_request_body, enhancing user ID retrieval from request data.
* refactor(auth_utils.py): update get_end_user_id_from_request_body to accept request_headers instead of request, and adjust related function calls in user_api_key_auth and tests
* refactor(tests): update mock request handling in LLM pass-through endpoint tests
- Replaced the Request object with a Mock for better flexibility in testing.
- Enhanced mock setup to include user API key handling and virtual key retrieval.
- Updated test calls to reflect changes in mock request structure and added necessary patches for new dependencies.
* refactor(vertex_and_google_ai_studio_gemini.py): remove redundant variable declaration for url_context_metadata, linting error
Krish Dholakia