* fixes for using bedrock guard
* fixes for output_content_bedrock guard
* test_convert_to_bedrock_format_input_source
* test_convert_to_bedrock_format_post_call_streaming_hook
* test bedrock guard
* Enhance Mistral API: Add support for parallel tool calls and refine name handling in tool messages. Plus, introduce a new test for parallel tool calls in the Mistral model.
* tests
* make mypy happy
* Refine name handling in Mistral chat transformation: clarify conditions for removing the 'name' field based on message role and content.
* refactor: streamline Mistral integration by removing deprecated references and adding a new handler
- Removed "mistral" from the list of compatible providers in constants.
- Updated the completion function in main.py to utilize the new Mistral handler.
- Deleted outdated Mistral chat and embedding files.
- Introduced a new handler for Mistral chat completions, implementing the llm_http_handler pattern.
- Added integration tests for the Mistral handler to ensure proper API base and key handling.
* lint
* fix: remove unneeded handler object
* add tests
* Addres PR comments
* fix(main.py): handle router custom azure model name for responses api bridge
* fix(responses/handler): ensure azure model name is stripped before sending to provider
Fixes model name error
* fix(google_genai/main.py): handle stream=true being set in kwargs
* docs: cleanup icons from sidebar
* fix(test-litellm.yml): add google-genai to test litellmyml
* fix(main.py): strip 'responses/' from bridge
* fix(main.py): fix linting errors
* fix(types/openai.py): allow item to be none
handle azure streaming response
* fix(base.py): allow extra fields + handle azure item = none value in response output item added event
* fix(main.py): correctly handle removing responses/
* test(test_main.py): add unit tests
When JSON_LOGS=True is set, error logs were not being formatted as JSON despite
the configuration. This was because the logging initialization code configured
individual loggers but failed to properly initialize all loggers with the JSON
formatter.
This fix ensures that when json_logs is enabled, the _initialize_loggers_with_handler()
function is called to:
- Configure all loggers (root, LiteLLM, Router, Proxy) with JSON formatter
- Disable logger propagation to prevent duplicate entries
- Set up exception handlers for JSON formatting
Fixes LIT-267
* add logos to callback list
* added logos
* minor
* cleanup console.log + remove unused functions + prettier
* more cleanup
* fix braintrust logo
* minor
* Enhance Mistral API: Add support for parallel tool calls and refine name handling in tool messages. Plus, introduce a new test for parallel tool calls in the Mistral model.
* tests
* make mypy happy
* Refine name handling in Mistral chat transformation: clarify conditions for removing the 'name' field based on message role and content.
* handle mistral returning '' instead of None
* fix(batches_endpoints/endpoints.py): support passing target model names for batch list as a query param
Fixes issue where cloud run fails calls because GET can't contain request body
* test(test_openai_batches_endpoints.py): add unit test
* docs(managed_batches.md): update docs
* feat(spend_tracking_utils.py): support STORE_PROMPTS_IN_SPEND_LOGS env var
ensures prompt is stored in spend logs
* fix(streaming_iterator.py): fix anthropic - completion streaming iterator to yield content block stop
ensures claude code renders messages
* test: skip local test
* fix(proxy_server.py): only rewrite server_root_path if path set
Fixes UI rendering issue on non-root images
* docs(custom_root_ui.md): clarify custom root path doesn't work on non-root images
* fix - tuple was never falsy so never triggered the exception
* test - add test suite for openmeter integration
* refactor - move tests for openmeter integration
* Move PANW Prisma AIRS test per feedback on PR #12116
- Move test to tests/test_litellm/proxy/guardrails/guardrail_hooks/
* Remove test file from old location
* Fix: Preserve full path structure for Gemini custom api_base (Fixes#11959)
This fix addresses an issue where custom api_base URLs (like Cloudflare AI Gateway)
were not working correctly with Google AI Studio (Gemini) models.
The problem was that the _check_custom_proxy method was simply appending the endpoint
to the custom base URL, resulting in malformed URLs like:
https://gateway.ai.cloudflare.com/v1/my-id/my-gateway/google-ai-studio:generateContent
Instead of the correct format:
https://gateway.ai.cloudflare.com/v1/my-id/my-gateway/google-ai-studio/v1beta/models/gemini-2.5-flash:generateContent
Changes:
- Modified _check_custom_proxy to preserve the full path structure from the original URL
- Extracts the path from the original Google AI URL and appends it to the custom base
- Maintains backward compatibility for Vertex AI models (unchanged behavior)
- Added comprehensive tests to verify the fix works correctly
Fixes#11959
* Fix: Update test to match actual Gemini URL format and fix double colon issue
- Fixed test expectation to include the full model path with 'gemini/' prefix
- Fixed double colon issue in Vertex AI URL construction when using custom api_base
- All tests now pass successfully
* fix(proxy_server.py): handle empty config yaml
Fixes https://github.com/BerriAI/litellm/issues/12163
* fix(gemini/common_utils.py): replace models/ as expected, instead of using 'strip'
Fixes https://github.com/BerriAI/litellm/issues/12160
* fix(anthropic/experimental_pass_through/messages/transformation.py): check for env var when selecting api key
* fix(anthropic/transformation.py): return tool_use content block start on anthropic bridge
Closes https://github.com/BerriAI/litellm/issues/12158
* fix(anthropic/streaming_iterator.py): fix setting index in block
ensure index is set just once and increments correctly when a new block is created
* fix(anthropic/adapters/handler.py): update logging obj with stream options value if set
* feat(anthropic/streaming_iterator.py): return usage from chat completion to messages bridge
enables usage tracking for non-anthropic models
Closes https://github.com/BerriAI/litellm/issues/12132
* fix(streaming_iterator.py): safely access usage chunk
* fix: suppress linting error
* test: update tests
* fix: fix streaming errors
The test_keys_delete_error_handling test was failing with:
- ConnectionError when the mock wasn't properly applied
- The test was checking str(result.exception) without first verifying exception exists
This fix adds an explicit check that result.exception is not None before
attempting to convert it to string, preventing potential AttributeError
and making the test more robust.
