langfuse-integration

name	langfuse-integration
description	LangFuse LLM observability integration for tracing, analytics, and cost tracking
allowed-tools	["Read","Write","Edit","Bash","Glob","Grep"]
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)