* feat(anthropic/chat/transformations): for claude-4-5-sonnet and opus-4-1 support passing structured output to anthropic api
* docs: document new feature
* fix: fix output format
* fix: cleanup
* fix(transformation.py): conditionally pass in json tool call
* fix: support ARIZE_SPACE_ID instead of ARIZE_SPACE_KEY
* docs(arize_integration.md): cleanup arize docs
* feat(callback_info_helpers.tsx): allow setting arize space id via ui
* fix: fix linting error
* fix(opentelemetry.py): working arize phoenix root span tracing
- 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
This commit fixes two critical test failures and two test isolation issues
in the SSL configuration tests.
## Critical Test Failures Fixed
### 1. test_get_ssl_configuration
**Problem:** Test was failing with assertion error that ssl.create_default_context
was never called (expected 1 call, got 0).
**Root Cause:** The get_ssl_configuration() function uses a caching mechanism
(_ssl_context_cache) to avoid creating duplicate SSL contexts with the same
configuration. When tests run in sequence, a previous test may have created an
SSL context with the same configuration (same cafile, ssl_security_level,
ssl_ecdh_curve). When this test runs, it retrieves the cached context instead
of creating a new one, so ssl.create_default_context() is never called, causing
the mock assertion to fail.
**Fix:** Clear the SSL context cache at the start of the test to ensure a fresh
context is created, allowing the mock to be called and verified.
### 2. test_ssl_ecdh_curve
**Problem:** Test was failing with assertion error that set_ecdh_curve was
never called (expected 1 call, got 0).
**Root Cause:** Same caching issue as above. Additionally, the test needed to
use a real SSLContext instance instead of a MagicMock because _create_ssl_context
calls methods like set_ciphers() and minimum_version that require a real context.
**Fix:**
- Clear the SSL context cache at the start of the test
- Use a real SSLContext instance and patch set_ecdh_curve on it specifically
- Added explanatory comment about why a real context is needed
## Test Isolation Issues Fixed
### 3. test_ssl_security_level
**Problem:** Test was failing because it expected LiteLLMAiohttpTransport but
got httpx.AsyncHTTPTransport instead.
**Root Cause:** Test isolation issue. Other tests in the file (test_force_ipv4_transport,
test_aiohttp_disabled_transport) set litellm.disable_aiohttp_transport = True
but don't restore the original value. When this test runs after those tests,
aiohttp transport is disabled, causing it to use httpx transport instead.
**Fix:** Explicitly enable aiohttp transport at the start of the test and restore
the original value in a finally block, ensuring the test works regardless of
test execution order.
### 4. test_ssl_verification_with_aiohttp_transport
**Problem:** Same as above - expected LiteLLMAiohttpTransport but got
httpx.AsyncHTTPTransport.
**Root Cause:** Same test isolation issue - aiohttp transport disabled by
previous tests.
**Fix:** Same approach - explicitly enable aiohttp transport and restore
original value in finally block.
## Why These Fixes Work
1. **Cache clearing:** By clearing _ssl_context_cache before each test, we
ensure that get_ssl_configuration() creates a fresh SSL context, allowing
mocks to be properly called and verified.
2. **Test isolation:** By saving and restoring the disable_aiohttp_transport
setting, tests are independent of each other and work correctly regardless
of execution order.
These are minimal, targeted fixes that address the root causes without
modifying production code or affecting other functionality.
* 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 openai metadata filed in the request
* Add docs related to openai metadata
* Add utils
* test_completion_openai_metadata[True]
* Added support for though signature for gemini 3 in responses api (#16872)
* Added support for though signature for gemini 3
* Update docs with all supported endpoints and cost tracking
* Added config based routing support for batches and files
* fix lint errors
* Litellm anthropic image url support (#16868)
* Add image as url support to anthropic
* fix mypy errors
* fix tests
* Fix: Populate spend_logs_metadata in batch and files endpoints (#16921)
* Add spend-logs-metadata to the metadata
* Add tests for spend logs metadata in batches
* use better names
* Remove support for penalty param for gemini 3 (#16907)
* Remove support for penalty param
* remove halucinated model names
* fix mypy/test errors
* fix tests
* fix too many lines error
* fix too many lines error
* Add config for cicd test case
* Fix final tests
* fix batch tests
* fix batch tests
* though signature tool call id
* [stripe] refactor and tests
* [stripe] remove md and move to factory
* [stripe] remove redudant test
* [stripe] ran black formatting
* [stripe] add thought signature docs
* [stripe] remove unused import
* Add thought signature support to v1/messages api
* update the thinking level handling logic
* update the thinking level handling logic
* Add streaming support
* fix intalling litellm error
Gemini 3 models require 'includeThoughts: True' in the thinkingConfig to return the actual thought text. Previously, using reasoning_effort set the 'thinkingLevel' but missed the boolean flag, resulting in empty reasoning_content.
This fix:
1. Updates `_map_reasoning_effort_to_thinking_level` to include `includeThoughts: True` for low/medium/high.
2. Adds unit tests to verify the config mapping.
Fixes#16805
When using Gemini models (2.5/3.0) with streaming + tools enabled,
the reasoning_content field was missing from stream chunks, even though
thinking_blocks were present in non-streaming responses.
Changes:
- Convert thinking_blocks to reasoning_content for streaming responses
- Extract "thinking" field from each thinking_block
- Concatenate multiple thinking parts with newlines
- Assign to reasoning_content in chat_completion_message for streaming
Testing:
- Added test_streaming_chunk_with_tool_calls_includes_reasoning_content
- Test verifies reasoning_content appears with tool calls in streaming
- All 39 existing Gemini tests pass
- Implement GithubCopilotResponsesAPIConfig for /responses endpoint
- Add support for models requiring responses API (e.g., gpt-5.1-codex)
- Auto-detect vision requests and set X-Initiator header
- Follow OpenAI Responses API compatibility pattern
- Add comprehensive unit tests (16 tests passing)
Fixes#16820
* 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
Fixes#16613
The issue was caused by two test files having the same module name
(test_transformation.py) in different directories, which caused pytest
to fail with an import file mismatch error.
Changes:
- Renamed tests/test_litellm/llms/xai/responses/test_transformation.py
to test_xai_responses_transformation.py
- Renamed tests/test_litellm/llms/openai_like/chat/test_transformation.py
to test_openai_like_chat_transformation.py
Both files now have unique, descriptive names that reflect their
specific test purposes and prevent module name collisions.
* 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
* add memory tool in anthropic.py
* add memory tool test
* make format
* update transformation
* adding memory to hosted tools
* add test
* make format
