The test_sso_key_generate_shows_deprecation_banner test was failing in CI
with a 403 Forbidden error because the SSO endpoint checks for premium_user
at line 297 in ui_sso.py.
The fix adds a monkeypatch for premium_user at its source location
(litellm.proxy.proxy_server.premium_user) to bypass the enterprise check
during testing.
Fixes the intermittent test failure where the endpoint would return 403
instead of the expected 200 status code.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit addresses two issues:
1. **Merge conflict resolution**: Resolved merge conflict in litellm/integrations/opentelemetry.py
that was preventing imports from working. The conflict was in the OpenTelemetry SDK
LogRecord import section.
2. **Test flakiness fix**: Fixed intermittent failures in test_bedrock_converse_budget_tokens_preserved
by properly configuring mock objects to avoid unawaited coroutine warnings.
The test was failing in CI with "Expected 'post' to have been called once. Called 0 times."
The root cause was improper mock setup where AsyncMock was creating async child methods
(raise_for_status, json) that returned unawaited coroutines, causing unreliable behavior
across different Python versions and test environments.
**Changes:**
- Set raise_for_status() and json() as explicit MagicMock instances on the response
- Use AsyncMock explicitly for the post() method via patch.object's 'new' parameter
- This ensures response methods are synchronous while the HTTP call remains async
**Testing:**
- Test now passes consistently across 5 consecutive runs
- RuntimeWarnings about unawaited coroutines eliminated (18 warnings → 16 warnings)
- Request JSON verification shows budget_tokens correctly preserved
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
The manual sys.modules restoration code was redundant because
patch.dict.stop() automatically handles the cleanup. This simplifies
the tearDown method and removes the now-unused _original_langfuse_module
instance variable.
Addresses review comment: https://github.com/BerriAI/litellm/pull/21214#pullrequestreview-3802348462
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Fixes test_log_langfuse_v2_handles_null_usage_values flaky test failure
by properly cleaning up sys.modules['langfuse'] in tearDown.
Changes:
- Store original langfuse module in setUp before mocking
- Restore original or remove mock in tearDown to prevent state pollution
- Remove invalid print_verbose parameter from log_event_on_langfuse
Root Cause:
The tearDown method was not cleaning up sys.modules['langfuse'] after
each test, causing mock state to leak between tests. This caused
intermittent failures in CI, especially when tests run in parallel or
in different orders.
Impact:
This test has a long history of flakiness with multiple attempted fixes
(#20475, #17599, #17594, #17591, #17588). The missing sys.modules cleanup
was the underlying issue causing continued failures despite those patches.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Allow passing enable_json_schema_validation as a parameter to completion()
and acompletion() instead of only relying on the global
litellm.enable_json_schema_validation flag. The per-request value takes
priority when provided; otherwise falls back to the global (backward
compatible). This makes JSON schema validation safe for concurrent usage
in FastAPI and other multi-threaded environments.
Address three additional root causes that prevented connection cleanup
on client disconnect: Starlette/Uvicorn disconnect detection gap,
content= vs stream= no-op wrapper in aiohttp transport, and anyio
CancelledError interrupting cleanup awaits.
- Add `definitions` handling alongside `$defs` in schema normalization
(older JSON Schema drafts use `definitions` instead of `$defs`)
- Fall back to tool-call approach when `response_format: {type: json_object}`
has no explicit schema, since the native API requires one
- Add tests for both cases
* fix(content_filter.py): fix filter on toxic keywords
* feat: improve toxic/abusive language detection
* fix: additional improvements to nsfw filters
* feat: more improvements to nsfw filter
* feat(content_filter.json): add new australia specific nsfw content filter
ensure complete coverage for australia nsfw
* fix: cleanup policy templates
* fix(index.tsx): alert notice
* fix(index.tsx): add disclaimer notice
* feat(harmful_child_safety.yaml): new child safety content filter
ensure we catch inappropriate, child-specific content
* feat(policy_templates.json): add child safety and self harm filters
* fix(content_filter.py): improve racial bias filter to use a similar identifier + block word pattern and cover a wider range of ethnicities
* feat(policy_templates.json): add racial bias to nsfw policy template
* feat: add json content viewer
* fix(model_cost): add missing supports_system_messages and supports_tool_choice to bedrock/moonshotai.kimi-k2.5
* fix(streaming): ensure role=assistant is set on first streaming chunk via strip_role_from_delta
* fix(vertex_ai): ensure role=assistant on first streaming chunk for Llama models
Add VertexAILlama3StreamingHandler that injects role='assistant' into the
first streaming chunk delta when the Vertex AI Llama API omits it.
* fix(anthropic): filter unsupported JSON schema constraints for structured outputs
Fixes 400 error when using Anthropic models with structured outputs that have
min/max constraints.
The Anthropic API doesn't support these JSON schema constraints:
- minimum/maximum (numeric)
- exclusiveMinimum/exclusiveMaximum (numeric)
- minLength/maxLength (string)
- minItems/maxItems (array)
This mirrors the transformation done by the official Anthropic Python SDK.
See: https://platform.claude.com/docs/en/build-with-claude/structured-outputs#how-sdk-transformation-works
Adds tests for the schema filtering function.
* fix: update descriptions with removed constraint info in filter_anthropic_output_schema
Address review feedback: the function now appends removed constraint
information to the description field (matching Anthropic SDK behavior),
rather than silently dropping constraints.
---------
Co-authored-by: OpenClaw <openclaw@users.noreply.github.com>
- Add aclose() to CustomStreamWrapper to delegate to underlying stream
- Add finally block in async_data_generator to release HTTP connections
- Thread shared_session through async_streaming to reuse connection pool
- Set finite default timeout (600s) in _get_openai_client
