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
- Add database and Redis setup to litellm_mapped_tests_proxy job in CircleCI
- Create shared test helpers in tests/test_litellm/proxy/conftest.py for proxy test setup
- Refactor health endpoint tests to use shared helpers from conftest
- Support automatic Redis cache configuration when REDIS_HOST is set
- Ensure minimal config is created when Redis/database is needed
Fixes#17821
The `is_cached_message` function crashed with TypeError when message
content was a string instead of a list of content blocks.
Changes:
- Add explicit `isinstance(content, list)` check before iteration
- Add `isinstance(content_item, dict)` check inside loop to skip non-dict items
- Use `.get()` for safer nested dict access
- Follow same pattern as `extract_ttl_from_cached_messages` (same module)
Tests:
- Add TestIsCachedMessage class with 9 test cases covering:
- String content (the reported bug)
- None content
- Missing content key
- Empty list content
- List with/without cache_control
- Mixed content types (strings + dicts)
- Wrong cache_control type
Moved speechConfig from RequestBody to GenerationConfig TypedDict so that
TTS configuration survives the filtering in _transform_request_body().
This fixes the 400 INVALID_ARGUMENT error when using Gemini TTS models
(gemini-2.5-flash-tts, gemini-2.5-flash-preview-tts, etc.) with both
vertex_ai and gemini providers.
Fixes: speechConfig was being created correctly in map_openai_params()
but then filtered out because GenerationConfig.__annotations__.keys()
didn't include it.
Tested with both preview and non-preview TTS model names and both
vertex_ai and gemini providers.
When using litellm.completion() with model="openai/responses/...", images
in tool message content were not being transformed from Chat Completion
format to Responses API format.
Chat Completion format: {"type": "image_url", "image_url": {"url": "..."}}
Responses API format: {"type": "input_image", "image_url": "..."}
This caused OpenAI to reject the request with error 400 since "image_url"
is not a valid type for function_call_output content.
This fix addresses two issues with Anthropic web search streaming:
1. Fix trailing {} in tool call arguments
- web_search_tool_result blocks have input_json_delta events that were
incorrectly emitted as tool calls
- Added current_content_block_type tracking to only emit tool calls for
tool_use and server_tool_use blocks
2. Capture web_search_tool_result for multi-turn
- The web_search_tool_result content comes ALL AT ONCE in content_block_start
- Now captured in provider_specific_fields.web_search_results
- stream_chunk_builder combines these for final message
- Allows multi-turn conversations to work with streaming web search
Add support for the Bedrock Converse API serviceTier parameter to allow
specifying processing tier (priority, default, or flex).
Changes:
- Add ServiceTierBlock type in litellm/types/llms/bedrock.py
- Add serviceTier to CommonRequestObject
- Add serviceTier to get_config_blocks() in AmazonConverseConfig
- Add comprehensive tests for serviceTier functionality
- Add documentation for serviceTier usage
This allows users to configure service tier via:
- litellm_params in proxy config
- optional_params in SDK calls
* fix(azure_ai): Remove unsupported params from Azure AI Anthropic requests
Azure AI Anthropic endpoint rejects max_retries and stream_options parameters
with "Extra inputs are not permitted" error. These are LiteLLM-internal
parameters that should not be sent to the API.
Fixes 400 Bad Request error when using azure_ai/claude-sonnet-4-5 and other
Azure AI Anthropic models.
* test(azure_ai): Add test for unsupported params removal in Azure AI Anthropic
Verifies that max_retries, stream_options, and extra_body are properly
removed from the request before sending to Azure AI Anthropic endpoint.
Ishaan Jaffer