- Add new tutorial for integrating Qwen Code CLI with LiteLLM Proxy
- Update sidebar to include Qwen Code CLI in both AI Tools and main Tutorials sections
- Document environment variables for OpenAI-compatible configuration
- Include examples for routing to various providers (Anthropic, OpenAI, Bedrock)
* feat(langfuse-otel): Add comprehensive metadata support to Langfuse OpenTelemetry integration
This commit brings the langfuse_otel integration to feature parity with the vanilla Langfuse integration by adding support for all metadata fields.
Changes:
- Extended LangfuseSpanAttributes enum with all supported metadata fields:
- Generation-level: generation_name, generation_id, parent_observation_id, version, mask_input/output
- Trace-level: trace_user_id, session_id, tags, trace_name, trace_id, trace_metadata, trace_version, trace_release, existing_trace_id, update_trace_keys
- Debug: debug_langfuse
- Implemented metadata extraction and mapping in langfuse_otel.py:
- Added _extract_langfuse_metadata() helper to extract metadata from kwargs
- Support for header-based metadata (langfuse_* headers) via proxy
- Enhanced _set_langfuse_specific_attributes() to map all metadata to OTEL attributes
- JSON serialization for complex types (lists, dicts) for OTEL compatibility
- Updated documentation:
- Added 'Metadata Support' section explaining all fields are now supported
- Provided usage example showing how to pass metadata
- Clarified that traces are viewed in Langfuse UI (not generic OTEL backends)
- Added opentelemetry-exporter-otlp to required dependencies
This allows users to pass metadata like:
metadata={
'generation_name': 'my-generation',
'trace_id': 'trace-123',
'session_id': 'session-456',
'tags': ['prod', 'v1'],
'trace_metadata': {'user_type': 'premium'}
}
All metadata is exported as OpenTelemetry span attributes with 'langfuse.*' prefix for easy filtering and analysis in the Langfuse UI.
* Fix ruff linting error
* test(langfuse-otel): Fix failing test and add comprehensive metadata tests
- Fix test_set_langfuse_environment_attribute to use positional arguments
instead of keyword arguments when asserting safe_set_attribute calls
- Add test_extract_langfuse_metadata_basic to verify metadata extraction
from litellm_params
- Add test_extract_langfuse_metadata_with_header_enrichment to test
integration with header-based metadata using a stubbed LangFuseLogger
- Add test_set_langfuse_specific_attributes_full_mapping to comprehensively
test all metadata field mappings and JSON serialization of complex types
These tests ensure full coverage of the langfuse_otel metadata features
added in commit ab1dbe355 and fix the CI test failure.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
---------
Co-authored-by: Claude <noreply@anthropic.com>
* feat: Add Pillar Security guardrail integration
Implements comprehensive LLM security guardrails using Pillar Security API with support for prompt injection detection, PII/secret detection, content moderation, and multi-mode execution (pre_call, during_call, post_call). Includes complete documentation, testing, and configurable actions on flagged content.
* fix: Resolve MyPy type error in Pillar guardrail config
Restructure PillarGuardrailConfigModel to properly inherit from GuardrailConfigModel[T]
and resolve return type compatibility issue in get_config_model method.
* fix: Resolve MyPy type error in Pillar guardrail config
Restructure PillarGuardrailConfigModel to properly inherit from GuardrailConfigModel[T]
and resolve return type compatibility issue in get_config_model method.
* fix docs
* fix docs
* improved docs
* fix examples, READY
* feat(litellm_pre_call_utils.py): add num_retries to litellm data for backend call
allow user to pass in num retries via request headers
* test(test_litellm_pre_call_utils.py): add unit test
* docs(request_headers.md): document new request header
* fix(common_daily_activity.py): show spend breakdown by model group
Partial fix for https://github.com/BerriAI/litellm/issues/12887
* feat(new_usage.tsx): new tab switcher for viewing usage by model group vs. received model
Closes https://github.com/BerriAI/litellm/issues/12887
* fix(main.py): fix async retryer
Fixes https://github.com/BerriAI/litellm/issues/12830
* fix(forward_clientside_headers_by_model_group.py): filter out 'content-type' from forwardable headers
clientside content-type != proxy content type, can cause requests to hang
* test(tests/): update tests
Ishaan Jaff