* Add Amazon Nova as a first party provider
* Added new provider folder under llms/ to outline the openai supported params
* Updated supported endpoints on the documnetation
- Remove network dependency by mocking HuggingFace template fetch
- Use mock template that produces correct format for test validation
- Test now focuses on transformation logic, not network calls
- Fixes flaky test failures due to network timeouts/rate limits
The test verifies that prompt transformation occurs (not simple
concatenation), which doesn't require the actual HuggingFace template.
Mocking makes the test deterministic and faster while still validating
the core behavior.
Fixed three flaky tests that were intermittently failing in CI:
1. test_no_duplicate_spend_logs (test_litellm/responses/test_no_duplicate_spend_logs.py)
Problem: Used await asyncio.sleep(1) to wait for async logging completion,
which created race conditions. The async logging worker queues tasks
in the background, and sleep() doesn't guarantee completion.
Fix: Replaced sleep() with GLOBAL_LOGGING_WORKER.flush() which properly waits
for the logging queue to empty, ensuring all async logging tasks complete
before assertions run.
2. test_log_langfuse_v2_handles_null_usage_values (test_litellm/integrations/test_langfuse.py)
Problem: Used datetime.datetime.now() twice for start_time and end_time, which
could cause timing inconsistencies between test runs, especially in
CI environments with variable execution speeds.
Fix: Use fixed timestamps instead of datetime.now() to ensure consistent timing
across all test runs, eliminating timing-related flakiness.
3. test_watsonx_gpt_oss_prompt_transformation (test_litellm/llms/watsonx/test_watsonx.py)
Problem: Directly accessed mock_post.call_args without checking if it exists,
which could be None if the mock wasn't called or if an exception
occurred before the POST request. The test catches exceptions and
continues, making this a potential failure point.
Fix: Added proper assertions and use call_args_list[0] for safer access:
- Assert that call_args_list has at least one call
- Assert that call_args is not None
- Assert that 'data' key exists in kwargs
This ensures the test fails with clear error messages rather than
intermittent AttributeError exceptions.
All fixes maintain the original test intent while making them deterministic
and reliable in CI environments.
The previous implementation incorrectly used `thoughtSignature` as the criterion
to detect thinking blocks. However, per Google's docs:
- `thought: true` indicates that a part contains reasoning/thinking content
- `thoughtSignature` is just a token for multi-turn context preservation
(a part can have thoughtSignature without thought:true, e.g., function calls)
This caused functionCall data to leak into reasoning_content when using
Gemini 2.5 Pro with streaming + tools enabled.
Changes:
- _extract_thinking_blocks_from_parts now checks `part.get("thought") is True`
- Extract actual text content instead of json.dumps(part)
- Include signature only when present (optional in Gemini 2.5)
Refs:
- https://ai.google.dev/gemini-api/docs/thinking
- https://ai.google.dev/gemini-api/docs/thought-signatures
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(github_copilot): preserve encrypted_content in reasoning items for multi-turn conversations
GitHub Copilot uses encrypted_content in reasoning items to maintain conversation
state across turns. The parent class (OpenAIResponsesAPIConfig._handle_reasoning_item)
strips this field when converting to OpenAI's ResponseReasoningItem model, causing
"encrypted content could not be verified" errors on multi-turn requests.
This override preserves encrypted_content while still filtering out status=None
which OpenAI's API rejects.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* chore: regenerate poetry.lock
* Revert "chore: regenerate poetry.lock"
This reverts commit 8796dc8f96.
---------
Co-authored-by: Claude <noreply@anthropic.com>
* fix: handle none content
* fix: defensive check on none value
* Fix test failures: Azure OCR skip, None content handling, PublicAI JSON config
- Skip aocr/ocr call types in Azure test (they don't use Azure SDK client)
- Handle None content in Responses API transformation (skip message creation)
- Update PublicAI tests to use JSON-based configuration system
- Add None check in PublicAI test fixture to fix type error
* 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
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
* 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
* Support for Custom Vertex AI Models via PSC Endpoint with api_base
* Add docs related psc
* remove not needed files
* remove print statemnt
* fix mypy errors
* update databricks pricing and add DBU<>USD test
* Refactor test_databricks_pricing.py
Removed unnecessary sys.path modification and cleaned up comments.
