- Add 8 content PRs that merged directly to the release branch outside the listed staging PRs: #23769 (Ramp callback), #25252 (JWT OAuth2 override), #25254 (AWS GovCloud mode), #25258 (batch-limit cleanup), #25334 (router custom_llm_provider), #25345 (Triton embeddings), #25347 (tag-based routing), #25358 (Baseten pricing attribution)
- Add @kedarthakkar to new contributors (first-ever PR via #23769)
- Update RELEASE_NOTES_GENERATION_INSTRUCTIONS: require walking git log range between release tags in addition to staging PRs, and verify new-contributor status per author rather than trusting the GH release body floor
The cookbook example pinned litellm==1.61.15 which has 3 known
vulnerabilities (CVE-2026-35029, CVE-2026-35030, and a password
hash exposure issue), all patched in 1.83.0.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* update bedrock models in tests
* updated more tests and model_prices_and_context_window
* fix model id and pricing
* replace more sonnet models
* update tests
* git push
* update pricing
* flaky total cost
* monkey patch
* relax the cost change
* fix and revert some changes
* revert the pricing
* chore: move cost/pricing changes to bedrock-cost-fixes branch
* chore: split Bedrock file-api beta stripping to separate branch
Removes strip_unsupported_file_api_betas_for_bedrock_invoke from this branch;
see litellm_bedrock_invoke_strip_file_api_betas for that fix.
Made-with: Cursor
The file was at the repo root and excluded from pip distributions. Moving it to litellm/proxy/public_endpoints/ alongside the other provider JSON files ensures it is packaged correctly. Updates all references in the endpoint handler, coverage tests, and release notes instructions.
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
* feat: initial commit adding prompt management api
* feat: initial commit adding prompt management api
* fix: refactoring to make sure get prompt is async
* fix: additional fixes
* fix(generic_guardrail_api.py): add 'structured_messages' support
allows guardrail provider to know if text is from system or user
* fix(generic_guardrail_api.md): document 'structured_messages' parameter
give api provider a way to distinguish between user and system messages
* feat(anthropic/): return openai chat completion format structured messages when calls made via `/v1/messages` on Anthropic
* feat(responses/guardrail_translation): support 'structured_messages' param for guardrails
structured openai chat completion spec messages, for guardrail checks when using /v1/responses api
allows guardrail checks to work consistently across APIs
* fix(unified_guardrail.py): support during_call event type for unified guardrails
allows guardrails overriding apply_guardrails to work 'during_call'
* feat(generic_guardrail_api.py): support new 'tool_calls' field for generic guardrail api
returns the tool calls emitted by the LLM API to the user
* fix(generic_guardrail_api.py): working anthropic /v1/messages tool call response
send llm tool calls to guardrail api when called via `/v1/messages` API
* fix(responses/): run generic_guardrail_api on responses api tool call responses
* fix: fix tests
* test: fix tests
* fix: fix tests
* fix(unified_guardrail.py): correctly map a v1/messages call to the anthropic unified guardrail
* fix: add more rigorous call type checks
* fix(anthropic_endpoints/endpoints.py): initialize logging object at the beginning of endpoint
ensures call id + trace id are emitted to guardrail api
* feat(anthropic/chat/guardrail_translation): support streaming guardrails
sample on every 5 chunks
* fix(openai/chat/guardrail_translation): support openai streaming guardrails
* fix: initial commit fixing output guardrails for responses api
* feat(openai/responses/guardrail_translation): handler.py - fix output checks on responses api
* fix(openai/responses/guardrail_translation/handler.py): ensure responses api guardrails work on streaming
* test: update tests
* test: update tests
* fix: support multiple kinds of input to the guardrail api
* feat(guardrail_translation/handler.py): support extracting tool calls from openai chat completions for guardrail api's
* feat(generic_guardrail_api.py): support extracting + returning modified tool calls on generic_guardrails_api
allows guardrail api to analyze tool call being sent to provider - to run any analysis on it
* fix(guardrails.py): support anthropic /v1/messages tool calls
* feat(responses_api/): extract tool calls for guardrail processing
* docs(generic_guardrail_api.md): document tools param support
* docs: generic_guardrail_api.md
improve documentation
* fix(unified_guardrail.py): correctly map a v1/messages call to the anthropic unified guardrail
* fix: add more rigorous call type checks
* fix(anthropic_endpoints/endpoints.py): initialize logging object at the beginning of endpoint
ensures call id + trace id are emitted to guardrail api
* feat(anthropic/chat/guardrail_translation): support streaming guardrails
sample on every 5 chunks
* fix(openai/chat/guardrail_translation): support openai streaming guardrails
* fix: initial commit fixing output guardrails for responses api
* feat(openai/responses/guardrail_translation): handler.py - fix output checks on responses api
* fix(openai/responses/guardrail_translation/handler.py): ensure responses api guardrails work on streaming
* test: update tests
* test: update tests
* test: update tests
* fix(bedrock_guardrails.py): fix post call streaming iterator logic
* fix: fix return
* fix(bedrock_guardrails.py): fix
* refactor(generic_guardrail_api.py): refactor to update to new guardrail api logic
* refactor: refactor llm api integrations to support passing in text as a list[str] instead of one at a time
* refactor: fix linting errors
* refactor: pass request type to guardrail api
allows request vs. response processing to occur
* feat: pass user api key dict information to the guardrail api
* fix: pass user api key dict information to the guardrail api
* feat: pass litellm call id + trace id, if present
* docs: update docs
* feat(generic_guardrail_api.py): new generic api for guardrails
Allows guardrail providers to work with litellm for guardrails without needing to make a PR to LiteLLM
* docs(generic_guardrail_api.md): document new generic guardrail api
* Fix: Improve PII detection and guardrail API integration
Co-authored-by: krrishdholakia <krrishdholakia@gmail.com>
* feat: correctly extract raw request from guardrail api
* docs(generic_guardrail_api.md): document this is a beta feature
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* fix: use fastuuid helper across the codebase
First batch of changes, simple drop in replacement.
* second batch of changes
* fixed: script mistake on helper file
* fix: cli auth with SSO okta
* fix: add LITTELM_CLI_SERVICE_ACCOUNT_NAME
* fix: get_litellm_cli_user_api_key_auth
* use existing_key CLI
* fix: use existing key
* test auth commands
* test_cli_sso_callback_regenerate_vs_create_flow
* feat: add CLI Token Utilities
* fix: get_stored_api_key
* move file
* fix: get_valid_models
* fix config.yaml
* TestCLITokenUtils
* TestGetValidModelsWithCLI
* fix: tie user id to keys created through CLI
* fix: add teams interface to CLI
* add /keys/update to the list client commands
* fix /sso/cli/poll to return the user_id
* fix: working TeamsManagementClient
* fix CLI Login command
* fixes for auth
* Potential fix for code scanning alert no. 3400: Clear-text logging of sensitive information
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* ruff fix
---------
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* fix: add follow_redirects=True,
* test_pass_through_with_httpbin_redirect
* cook book veo video
* docs Veo Video Generation with Google AI Studio
* add veo-3.0-generate-preview cost tracking details
* track vertex_video_models
* Add new model provider Novita AI (#7582)
* feat: add new model provider Novita AI
* feat: use deepseek r1 model for examples in Novita AI docs
* fix: fix tests
* fix: fix tests for novita
* fix: fix novita transformation
* ci: fix ci yaml
* fix: fix novita transformation and test (#10056)
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Co-authored-by: Jason <ggbbddjm@gmail.com>
This commit updates the Grafana dashboard configuration to include a datasource template variable. This allows users to dynamically select the datasource directly within the Grafana dashboard, improving flexibility and user experience.
