Does it support prompt management?

Yes, it guides you through managing and versioning prompts centrally within Langfuse, allowing you to decouple prompt engineering from your core codebase.

How does this skill help with OpenAI integrations?

It provides patterns for automatic tracing using a drop-in replacement for the OpenAI SDK, capturing all interactions and metadata without manual boilerplate.

Why is 'flushing' important in serverless environments?

Since Langfuse batches traces for efficiency, you must explicitly call the flush method in serverless functions to ensure data isn't lost when the process terminates.

Can I use this with LangChain?

Yes, this skill includes specific implementation patterns for LangChain's callback handlers to monitor agents, chains, and retrievers seamlessly.

Langfuse is an open-source observability platform designed specifically for LLM applications to track traces, costs, and quality metrics in production.

Langfuse LLM Observability

Name: Langfuse LLM Observability
Author: claudiodearaujo

byclaudiodearaujo

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分析と監視

Instruments and monitors LLM applications with advanced tracing, prompt management, and evaluation metrics.

Langfuse LLM Observability enables developers to gain deep insights into their AI applications by providing a comprehensive suite for tracing, prompt management, and performance monitoring. This skill helps you implement production-grade observability for LangChain, LlamaIndex, and OpenAI integrations, allowing you to track costs, evaluate outputs, and debug complex LLM chains. By utilizing traces, spans, and metrics, it ensures your LLM implementations are reliable, cost-effective, and performance-optimized while catching regressions early in the development lifecycle.

主な機能

01Automated LLM tracing and span observability

021 GitHub stars

03Seamless integration with LangChain, OpenAI, and LlamaIndex

04Dataset management for LLM evaluation and scoring

05Real-time cost, latency, and performance monitoring

06Centralized prompt management and versioning

ユースケース

01Debugging complex multi-step LLM chains and autonomous agents

02Monitoring production LLM usage costs and user feedback scores

03Managing and A/B testing prompts without redeploying application code

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/sistema-de-narra-o-de-livro langfuse

For use in Claude.ai and ChatGPT

Download Skill