Langfuse Instrumentation Setup

소개

This skill streamlines the process of adding observability to AI applications by guiding users through Langfuse SDK configuration, environment validation, and the implementation of best-practice tracing patterns. It provides specialized templates for various architectures—including RAG, agentic workflows, and multi-model pipelines—ensuring that LLM calls, tool executions, and spans are correctly captured. By identifying anti-patterns and offering pre-built scoring modules, it helps developers gain deep insights into their AI pipeline performance and debug SDK configurations effectively within the Claude Code environment.

주요 기능

• Anti-pattern detection for LLM instrumentation
• Automated environment and SDK validation scripts
• Implementation of custom scoring and evaluation modules
• Architecture-specific tracing templates for RAG and Agents
• 0 GitHub stars
• Verification testing with automated trace run validation

사용 사례

• Adding performance scoring to evaluate LLM output quality
• Debugging missing traces in complex agentic workflows
• Setting up production-grade observability for a RAG application