When using http:// api_base (converted to ws://), the websockets library
throws "ssl argument is incompatible with a ws:// URI". Only pass SSL
context for secure wss:// connections.
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* fix(text_completion): support token IDs (list of integers) as prompt
Add support for passing token IDs (list of integers) to the text_completion
endpoint for OpenAI-compatible providers (openai, azure, vllm, etc.).
Fixes#17118
* test(text_completion): replace live test with mock test for token IDs
Move token IDs test from local_testing to test_litellm with mocks
per PR review feedback.
* fix(generic-guardrail-api): fix SerializationIterator error on multimodal requests
When sending multimodal messages (with images) through the Generic Guardrail API,
the `model_dump()` call fails with "Object of type SerializationIterator is not
JSON serializable" error.
Root cause: The `ChatCompletionAssistantMessage` type defines `content` as an
`Iterable` (not just `List`), and Pydantic's `model_dump()` creates a
`SerializationIterator` for iterables which is not JSON serializable.
Fix: Use `model_dump(mode="json")` which properly converts all iterables to
lists and ensures all complex objects are JSON serializable.
* fix(guardrails): pass tools (function definitions) to guardrail inputs
The unified guardrail handler was not passing the `tools` parameter
(function definitions) from the request to the guardrail inputs.
This meant guardrails could not inspect or validate tool definitions.
Added extraction of `data.get("tools")` and inclusion in the
GenericGuardrailAPIInputs passed to `apply_guardrail()`.
* test(guardrails): add tests for tools passed to guardrail
Added tests verifying that tools (function definitions) are correctly
passed to guardrails in the unified guardrail handler:
- test_tools_passed_to_guardrail
- test_multiple_tools_passed_to_guardrail
- test_no_tools_in_request
- test_tools_and_tool_calls_both_passed
* fix(unified_guardrails.py): send all chunks on completion of final stream
* feat(generic_guardrail_api.py): handle tool call response on streaming LLM responses
* fix(anthropic/chat/guardrail_translation): initial commit adding anthropic tool response streaming guardrails
enables guardrail checks on tool response from llm's to work via `/v1/messages`
* feat(anthropic/): working guardrail checks on tool response from LLMs
ensures guardrail checks on anthropic /v1/messages works as expected
* feat(responses/guardrail_translation): support tool call response guardrails on streaming for /v1/responses
ensures complete coverage of tool call responses
* refactor(openai.py): refactor to use consistent pydantic model for responses api tool response on streaming
enables non-openai model tool call response to work correctly with guardrail checks on /v1/responses
* test: update tests
* fix: fix linting error
* fix: fix failing tests
* fix: fix import errors
* fix(openai/chat/guardrail_transformation): fix final chunk returned on streaming
Add support for the 'xhigh' reasoning effort level on all gpt-5.2 model
variants, not just gpt-5.2-pro. This enables deeper reasoning capabilities
for the base gpt-5.2 model.
Changes:
- Add is_model_gpt_5_2_model() method to detect gpt-5.2 variants
- Update xhigh validation to allow gpt-5.2 models
- Update documentation with gpt-5.2 reasoning_effort support
- Update tests to reflect new behavior
The 'user' parameter was being ignored when using responses API models
(e.g., model="openai/responses/gpt-4.1") because the model name check
in get_supported_openai_params() didn't account for the "responses/" prefix.
Fix: Normalize the model name by stripping "responses/" prefix before
checking if the model is in the list of supported OpenAI models.
This is a minimal, non-breaking change that:
- Adds 2 lines of code in gpt_transformation.py
- Only affects the parameter support check, not the model variable itself
- Includes unit and integration tests
Add support for OpenAI's gpt-5.1-codex-max model, their most intelligent
coding model optimized for long-horizon agentic coding tasks.
- 400k context window, 128k max output tokens
- $1.25/1M input, $10/1M output, $0.125/1M cached input
- Only available via /v1/responses endpoint
- Supports vision, function calling, reasoning, prompt caching
* 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
* fix(initial-commit): adding a way to get the right response type based on the api route
* feat(unified_guardrail.py): support streaming guardrails
* test: update tests
* fix: fix linting errors
* test: update tests
* Enhance error handling in OpenAIResponsesAPIConfig to coalesce null error codes into a default string, preventing validation errors and improving stability during streaming iterations.
* Add test for coalescing null error codes in streaming responses
This test ensures that when a streaming error event has error.code set to None, the system correctly transforms it to 'unknown_error' and returns an ErrorEvent instance without raising a ValidationError.
---------
Co-authored-by: Ku Wei Chiet <chietkuw@fpt.com>
- Add scope and url attributes to WebSocket mock in test_user_api_key_auth_websocket
- Add shared_realtime_ssl_context initialization in realtime handler test
* Cache realtime websocket request body
Move the realtime request payload builder out of the websocket handler and wrap it with an LRU cache so repeated connections reuse the same bytes object. This keeps the JSON formatting cost down while bounding memory usage.
* Optimize realtime websocket caching
Refactored /v1/realtime to use cached helpers for both the JSON body and query params, introduced a reusable request-scope template, and optimized header handling to avoid redundant work.
* Refine realtime websocket header handling
* Reuse websocket scope headers in auth
* Refactor realtime request body helper
Move the realtime request body formatter into proxy common utils so it can be reused across modules. Reuse it in the websocket auth flow to share LRU caching and avoid ad hoc byte builders.
* fix: revert to old pattern
The old pattern was necessary, we can just return the optimized function instead.
* Reuse SSL context for realtime
Create a shared SSLContext for OpenAI realtime websocket dials and pass it into websockets.connect so we stop re-reading verify paths on every session.
* feat: reuse shared TLS context for realtime websockets
- add `SHARED_REALTIME_SSL_CONTEXT` helper so all realtime websocket clients share the same TLS settings
- wire the shared context into OpenAI, Azure, custom HTTPX handlers, and realtime health checks
- update realtime tests to assert that the expected SSL context is passed to `websockets.connect`
This keeps TLS configuration consistent and avoids recreating SSL contexts per connection.
* Reuse HTTP SSL context for realtime
Remove the standalone realtime SSL helper, expose a shared context directly from the HTTP handler, and point all realtime websocket clients and tests to it. Add the websocket header comparison tool.
* Lazy-load shared realtime SSL context
Fix circular imports introduced by eagerly instantiating the shared TLS context. Make the HTTP handler lazily create the context and have realtime clients/tests fetch it on demand, keeping configuration consistent without breaking startup.
* add: unit test for realtime LRU caches
* fix: merge conflict with imports
* Add support for vector store files endpoints (#16490)
* Add base code for vector store integration
* fix azure related tests and linting error
* fix mypy errors
* Add vector store files documentation
* fix mapped tests
* Add bytedance and ideogram support in fal ai (#16636)
* Add fal ai flux pro v1.1 support (#16578)
* Add fal ai flux pro v1.1 support
* Add tests and docs
---------
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
OpenAI's GPT-5 model family supports a verbosity parameter to control
the length and detail of responses. This parameter accepts three values:
'low', 'medium', or 'high'.
Changes:
- Added verbosity parameter to completion() and acompletion() signatures
- Added verbosity to DEFAULT_CHAT_COMPLETION_PARAM_VALUES in constants.py
- Added verbosity to get_optional_params() in utils.py
- Added verbosity to GPT-5 supported params list
- Updated OpenAI docs with verbosity usage examples
- Added comprehensive test for verbosity parameter
Supported models: gpt-5, gpt-5.1, gpt-5-mini, gpt-5-nano, gpt-5-codex, gpt-5-pro
* feat(openai): Add support for reasoning_effort='none' in GPT-5.1
OpenAI's GPT-5.1 introduced a new reasoning effort parameter 'none'
which replaces the previous 'minimal' setting for faster, lower-latency
responses. This is now the default setting for GPT-5.1.
Changes:
- Updated REASONING_EFFORT type to include 'none' value
- Added GPT-5.1, GPT-5-mini, and GPT-5-nano to documentation
- Updated docs to reflect 'none' as GPT-5.1's default reasoning effort
- Added test to verify reasoning_effort='none' passes through correctly
Fixes#16633
* feat(responses): Add support for reasoning_effort='none' in Responses API transformation
* fix(presidio.py): handle content as a list of texts
covers openai + anthropic messages api
* fix(presidio.py): safe get messages
* test: add unit testing for presidio guardrails
* fix(unified_guardrail.py): initial commit
* fix(enkryptai.py): implement apply_guardrail to enkrypt guardrail
* fix(unified_guardrail.py): support unified guardrail on input
* feat(unified_guardrail.py): add post call success hook implementation
allows us to just have 1 place to handle llm translation to guardrail api spec
* refactor: refactor initial unified guardrail component
* refactor: more refactoring
* feat(responses/): add guardrails to responses api
allows existing guardrails to work for new llm endpoints
* docs(adding_guardrail_support.md): document new guardrail endpoint support
* test: add unit tests
* feat(image_generation/): add guardrail support for image generation endpoint
* feat(openai/text_completion): support guardrails on `/v1/completions` API
* docs: document guardrails support on new endpoints
* docs: clarify when guardrails run
* feat(openai/speech): add guardrail support for input
* docs(rerank/): add guardrail support on input query
* fix: fix ruff check
* fix response api for litellm proxy
* Add test for checking if status is getting removed
* add test in correct file
* remove hardcoded fields
* Make the handling simpler
* fix lint error:
* fix intent params
* Add responses
* fix unrelated test
* test fix - fireworks API endpoint is down
* test fix fireworks ai is having an active outage
* test_completion_cost_databricks
* dbrx fix test API currently not responding
* Update OpenAI Realtime handler to use the correct endpoint and include all query parameters. Adjusted error messages for missing API base and key. Updated health check URL construction to pass model as a query parameter.
* Enhance OpenAI Realtime handler tests to ensure model parameter inclusion in WebSocket URL. Added new tests to verify correct URL construction with model and additional parameters, preventing 'missing_model' errors. Updated existing tests for consistency.
* Remove debug print statements for API base and key in OpenAIRealtime handler to clean up the code.
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* (#13284) add avector_store_create to route_type which doesn't require model
* (#13284) exclude hidden params in metadata when create vector store
* (#13284) fix lint error
* (#13284) keep metadata None if metadata is None(not empty dict)
* (#13284) add test code
* (#13284) change test code name
* (#13284) add avector_store_search to route_type which doesn't require model
