Langfuse Instrumentation Setup

概要

This skill streamlines the integration of Langfuse observability into Python projects by analyzing code structure and providing tailored instrumentation patterns. It guides users through the correct implementation of traces, spans, and generations for various architectures including RAG and autonomous agents. By following its interactive workflow, developers can avoid common anti-patterns, ensure proper context propagation, and implement sophisticated scoring modules for production-grade AI monitoring and debugging.

主な機能

• Best-practice guidance for context propagation and data flushing
• 0 GitHub stars
• Tailored templates for RAG, Agentic, and Multi-model pipelines
• Automated environment and API key validation
• Correct mapping of observations to traces, spans, and generations
• Integration of automated scoring and LLM-as-judge metrics

ユースケース

• Debugging nested tool calls in autonomous agent workflows
• Implementing automated quality and cost monitoring for LLM applications
• Adding production-grade tracing to a complex RAG pipeline