PR #19809 changed stateless=True to stateless=False to enable progress
notifications for MCP tool calls. This caused the mcp library to enforce
mcp-session-id headers on all non-initialize requests, breaking MCP
Inspector, curl, and any client without automatic session management.
Revert to stateless=True to restore compatibility with all MCP clients.
The progress notification code already handles missing sessions gracefully
(defensive checks + try/except), so no other changes are needed.
Fixes#20242
* Fix tool params reported as supported for models without function calling (#21125)
JSON-configured providers (e.g. PublicAI) inherited all OpenAI params
including tools, tool_choice, function_call, and functions — even for
models that don't support function calling. This caused an inconsistency
where get_supported_openai_params included "tools" but
supports_function_calling returned False.
The fix checks supports_function_calling in the dynamic config's
get_supported_openai_params and removes tool-related params when the
model doesn't support it. Follows the same pattern used by OVHCloud
and Fireworks AI providers.
* Style: move verbose_logger to module-level import, remove redundant try/except
Address review feedback from Greptile bot:
- Move verbose_logger import to top-level (matches project convention)
- Remove redundant try/except around supports_function_calling() since it
already handles exceptions internally via _supports_factory()
When a shared ClientSession is passed to LiteLLMAiohttpTransport,
calling aclose() on the transport would close the shared session,
breaking other clients still using it.
Add owns_session parameter (default True for backwards compatibility)
to AiohttpTransport and LiteLLMAiohttpTransport. When a shared session
is provided in http_handler.py, owns_session=False is set to prevent
the transport from closing a session it does not own.
This aligns AiohttpTransport with the ownership pattern already used
in AiohttpHandler (aiohttp_handler.py).
Add s3_use_virtual_hosted_style parameter to support AWS S3 virtual-hosted-style URL format (bucket.endpoint/key) alongside the existing path-style format (endpoint/bucket/key).
This enables compatibility with S3-compatible services like MinIO and aligns with AWS S3 official terminology.
The test was creating fresh mocks but not fully isolating from setUp state,
causing intermittent CI failures with 'Expected generation to be called once.
Called 0 times.'
Instead of creating fresh mocks, properly reset the existing setUp mocks to
ensure clean state while maintaining proper mock chain configuration.
Bedrock rejects thinking.budget_tokens values below 1024 with a 400
error. This adds automatic clamping in the LiteLLM transformation
layer so callers (e.g. router with reasoning_effort="low") don't
need to know about the provider-specific minimum.
Fixes#21297
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The module litellm.videos.main is already imported at the top of
the file (line 21), so the import inside the test method is redundant.
Addresses Greptile feedback (minor style issue).
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
The test test_proxy_config_state_post_init_callback_call was failing with:
```
ValidationError: 2 validation errors for TeamCallbackMetadata
callback_vars.langfuse_public_key
Input should be a valid string [type=string_type, input_value=None, input_type=NoneType]
```
Root cause: The test uses environment variable references like
"os.environ/LANGFUSE_PUBLIC_KEY" which get resolved at runtime. In
parallel execution with --dist=loadscope, these environment variables
may not be set in all worker processes, causing the resolution to
return None, which fails Pydantic validation expecting strings.
Solution: Use monkeypatch to set the required environment variables
before the test runs. This ensures consistent behavior across all
test execution environments (local, CI, parallel workers).
Fixes test failure exposed by PR #21277.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Two MCP server tests were failing when run with pytest-xdist parallel
execution (--dist=loadscope):
- test_mcp_routing_with_conflicting_alias_and_group_name
- test_oauth2_headers_passed_to_mcp_client
Both tests showed assertion failures where mocks weren't being called
(0 times instead of expected 1 time).
Root cause: These tests rely on global_mcp_server_manager singleton
state and complex async mocking that doesn't work reliably with
parallel execution. Each worker process can have different state
and patches may not apply correctly.
Solution:
1. Added autouse fixture to clean up global_mcp_server_manager registry
before and after each test for better isolation
2. Added @pytest.mark.no_parallel to these specific tests to ensure
they run sequentially, avoiding parallel execution issues
This approach maintains test reliability while allowing other tests
in the file to still benefit from parallelization.
Fixes test failures exposed by PR #21277.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
The test was failing with AuthenticationError because the mock wasn't
intercepting the actual HTTP handler calls. This caused real API calls
with no API key, resulting in 401 errors.
Root cause: The test was patching the wrong target using string path
'litellm.videos.main.base_llm_http_handler' instead of using patch.object
on the actual handler instance. Additionally, it was mocking the sync
method instead of async_video_generation_handler.
Solution: Use patch.object with side_effect pattern on the correct
async handler method, following the same pattern used in
test_video_generation_async().
Fixes test failure in PR #21277 when running with --dist=loadscope.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
The cost calculation log level tests were failing when run with pytest-xdist
parallel execution because caplog doesn't work reliably across worker processes.
This causes "ValueError: I/O operation on closed file" errors.
Solution: Replace caplog fixture with a custom LogRecordHandler that directly
attaches to the logger. This approach works correctly in parallel execution
because each worker process has its own handler instance.
Fixes test failures in PR #21277 when running with --dist=loadscope.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Based on Greptile feedback:
- Removed autouse fixture (applied too broadly to unrelated tests)
- Removed setUpClass (redundant since setUp runs before every test)
- Kept module-level clear and setUp() method (sufficient for test isolation)
- Added blank line for proper formatting
The simplified approach still ensures test isolation under --dist=loadscope
while avoiding unnecessary complexity.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
The _select_tokenizer_helper function is decorated with @lru_cache, which
causes test failures when tests run sequentially with --dist=loadscope.
Previous tests' cached results prevent from_pretrained from being called,
causing mock assertions to fail.
Implemented triple-layer cache clearing:
1. Module-level clear on import
2. Class-level clear in setUpClass
3. Function-level clear in setUp + pytest fixture
This ensures test isolation while allowing --dist=loadscope to provide better
overall CI stability (70% pass rate vs 40% without loadscope).
Fixes the intermittent failure in TestTokenizerSelection where
'from_pretrained' mock was never called due to cache hits.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
The test_vertex_ai_gpt_oss_simple_request and test_vertex_ai_gpt_oss_reasoning_effort
tests were failing in CI with 401 authentication errors. This was because the
vertexai module import was triggering authentication attempts even though the
_ensure_access_token method was mocked.
Added patch.dict('sys.modules', ...) to mock the vertexai module entirely,
preventing it from trying to authenticate when imported. This ensures tests
are fully isolated and don't attempt real API calls regardless of environment
variables or test execution order.
This follows the same pattern used in other Vertex AI tests and works in
combination with the autouse fixture that clears environment variables.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
The test_watsonx_gpt_oss_prompt_transformation was using return_value to mock
an async method (AsyncHTTPHandler.post), which doesn't work correctly with
async/await. This could cause intermittent failures in CI due to test ordering.
Changed to use side_effect with an async function (mock_post_func) to properly
mock the async post method, following the same pattern used in other async
tests like test_vertex_ai_gpt_oss_reasoning_effort.
This ensures the mock is always called correctly regardless of test execution
order or parallel test execution.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Add autouse pytest fixture to clear Google/Vertex AI environment
variables before each test, preventing authentication errors in CI.
Previous tests may set GOOGLE_APPLICATION_CREDENTIALS or other Vertex
environment variables and not clean them up, causing this test to
attempt real Google authentication instead of using mocks.
This fix:
- Adds clean_vertex_env fixture with autouse=True
- Saves and clears Google/Vertex env vars before each test
- Restores them after each test
- Prevents "AuthenticationError: Request had invalid authentication
credentials" (401) in CI when run with other tests
Same fix pattern as PR #21268 (rerank) and PR #21272 (GPT-OSS).
Related: test was failing on PR #21217, but NOT caused by PR #21217
(which only modifies test_anthropic_structured_output.py). This is
another test isolation issue.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Add autouse pytest fixture to clear Google/Vertex AI environment
variables before each test, preventing authentication errors in CI.
Previous tests may set GOOGLE_APPLICATION_CREDENTIALS or other Vertex
environment variables and not clean them up, causing this test to
attempt real Google authentication instead of using mocks.
This fix:
- Adds clean_vertex_env fixture with autouse=True
- Saves and clears Google/Vertex env vars before each test
- Restores them after each test
- Prevents "AuthenticationError: Request had invalid authentication
credentials" in CI when run with other tests
Test makes real API calls in CI without this fix, gets 401 error.
Locally fails with "No module named 'vertexai'" (expected).
Related: test was failing on PR #21217, but NOT caused by PR #21217
(which only modifies test_anthropic_structured_output.py). This is
another test isolation issue.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
PR #20813 changed the Anthropic schema filter to remove string and numeric constraints
(minLength/maxLength, minimum/maximum) per Anthropic API requirements, but forgot to
update the corresponding test.
The new behavior (per Anthropic SDK):
1. Remove unsupported constraints from schema (Anthropic API doesn't support them)
2. Add constraint info to description field (e.g., "Note: minimum length: 1")
**Changes:**
- Updated test to expect constraints REMOVED from schema
- Added assertions to verify constraints are added to description
- Updated docstring to explain the new behavior
**Testing:**
- ✅ test_other_constraints_preserved now passes
- ✅ All 4 tests in test_anthropic_structured_output.py pass
**Related:**
- Fixes test broken by PR #20813
- Aligns with Anthropic API requirements documented in commit 84934a7258
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Update test expectations to match the current code behavior where
reasoning_effort is transformed from a string to a dict with
'effort' and 'summary' fields.
The transformation happens in:
litellm/llms/anthropic/experimental_pass_through/adapters/handler.py:72-74
When reasoning_effort is a string like "minimal", it's converted to:
{"effort": "minimal", "summary": "detailed"}
The test was expecting just the string "minimal", causing it to fail.
Test now passes ✅
Related: test was failing on PR #21217, but NOT caused by PR #21217
(which only modifies test_anthropic_structured_output.py). This is a
pre-existing broken test that also fails on main branch.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Add setup_method and teardown_method to clean up Google/Vertex AI
environment variables that may be left by previous tests.
Previous tests may set GOOGLE_APPLICATION_CREDENTIALS or other Vertex
environment variables and not clean them up, causing this test to
attempt real Google authentication instead of using mocks.
This fix:
- Saves and clears Google/Vertex env vars in setup_method
- Restores them in teardown_method
- Prevents "DefaultCredentialsError" in CI when run with other tests
Test passes in isolation but fails in CI due to test ordering. This
is another test isolation issue, NOT related to PR #21217.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
batch_cost_calculator only checked the global cost map, ignoring
deployment-level custom pricing (input_cost_per_token_batches etc.).
Add optional model_info param through the batch cost chain and pass
it from CheckBatchCost.
The manual cache flush is redundant since the autouse fixture
clear_client_cache (lines 21-32) already flushes the cache before
and after every test in this module.
The manual flush was added in Jan 2026 before the autouse fixture
existed. Now that the fixture handles it, the manual flush is unnecessary.
Related: greptile review comment on PR #21255
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
