* 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
Fixes#17517
- Fixed bug where only the first matching blocked keyword was masked
- Now iterates through ALL blocked keywords and masks each one
- Added 3 regression tests for multiple keyword masking
- Fix bug where model names without slash (e.g., 'gpt-5') couldn't
match providers in polling_via_cache list
- Look up model in llm_router.model_name_to_deployment_indices
- Check ALL deployments for matching provider (supports load balancing)
- Check custom_llm_provider first, then extract from model string
- Add comprehensive tests for provider resolution logic
Committed-By-Agent: cursor
* init schema.prisma
* init LiteLLM_ObjectPermissionTable with agents and agent_access_groups
* TestAgentRequestHandler
* refatctor agent list
* add AgentRequestHandler
* fix agent access controls by key/team
* feat - new migration for LiteLLM_AgentsTable
* fix add LiteLLM_ObjectPermissionBase with agent and agent groups
* add agent routes to llm api routes
* add agent routes as llm route
- Create new background_streaming.py in response_polling/
- Update endpoints.py to import from new location
- Update __init__.py to export background_streaming_task
- Add tests for module imports and structure
Committed-By-Agent: cursor
Fixes issue where LiteLLM used the json_tool_call workaround for all Groq
models, even those that support native json_schema (e.g., gpt-oss-120b,
llama-4, kimi-k2). This caused errors when users passed their own tools
alongside structured outputs.
Changes:
- Check `supports_response_schema()` before using the workaround
- Only use json_tool_call workaround for models without native support
- Add clear error message when using workaround with user-provided tools
- Update model config to set `supports_response_schema: false` for models
that don't support native json_schema
- Add unit tests for structured output handling
Add `ocr` and `aocr` entries to the CallTypes enum to fix the
ValueError that occurs when using the /v1/ocr endpoint with
guardrails enabled.
The OCR endpoint uses route_type="aocr", but the CallTypes enum
was missing these values, causing guardrail hooks to fail when
trying to instantiate CallTypes("aocr").
Fixes#17381
When translating system messages for the Anthropic API, empty text
content blocks cause the error "messages: text content blocks must be
non-empty". This fix skips empty string content and empty text blocks
in list content to prevent this error.
Fixes issue with Vertex AI Anthropic API calls.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude <noreply@anthropic.com>
* 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
