/langfuse-integration

name: langfuse-integration
description: LangFuse LLM observability integration for tracing, analytics, and cost tracking
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graph:
  domains: [domain:software-engineering]
  specializations: [specialization:ai-agents-conversational]
  skillAreas: [skill-area:agent-debugging-logging, skill-area:model-monitoring-drift-detection]
  roles: [role:ml-engineer, role:backend-engineer]
  workflows: [workflow:ml-model-lifecycle, workflow:feature-development]

LangFuse Integration Skill

Capabilities

• Set up LangFuse tracing for LLM calls
• Configure cost tracking and analytics
• Implement prompt management
• Set up evaluation datasets
• Design custom trace metadata
• Create dashboards and alerts

Target Processes

• llm-observability-monitoring
• cost-optimization-llm

Implementation Details

Core Features

1. **Tracing**: Track LLM calls, chains, and agents 2. **Prompts**: Version and manage prompts 3. **Analytics**: Usage, latency, cost metrics 4. **Datasets**: Evaluation and testing data 5. **Scores**: Track output quality

Integration Methods

• LangChain callback handler
• Direct SDK integration
• OpenAI drop-in replacement
• Decorator-based tracing

Configuration Options

• Public/secret keys
• Host URL (cloud or self-hosted)
• Sampling rate
• Metadata configuration
• User tracking

Best Practices

• Consistent trace naming
• Meaningful metadata
• Regular prompt versioning
• Set up alerting

Dependencies

• langfuse
• langchain (for callback integration)