langfuse

Trace and monitor LLM applications across LangChain, OpenAI, and custom pipelines.

1|Updated Dec 15, 2025
One-click install
npx skills add https://github.com/jokken79/YuKyuDATA-app1.0v --skill langfuse-jokken79
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: langfuse
Source: https://github.com/jokken79/YuKyuDATA-app1.0v/tree/main/.agent/skills/langfuse
Command: npx skills add https://github.com/jokken79/YuKyuDATA-app1.0v --skill langfuse-jokken79

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provide end-to-end observability for LLM-powered applications.

Core Features & Use Cases

  • LLM tracing and observability
  • Prompt management and versioning
  • Evaluation and scoring
  • Dataset management
  • Cost tracking
  • Performance monitoring
  • A/B testing prompts
  • Integrations with LangChain, LlamaIndex, and OpenAI

Quick Start

Install Langfuse, configure your credentials, and wrap your LLM calls to begin tracing.

Frequently Asked Questions about langfuse

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add LLM observability and tracing to my production application?

LLM observability is achieved by wrapping your LLM calls to enable end-to-end tracing, capturing performance insights, debugging data, and cost awareness across your custom pipelines.

Does Langfuse work with LangChain, OpenAI, and LlamaIndex integrations?

Yes, it supports multi-tool integration for monitoring models across LangChain, LlamaIndex, and OpenAI, allowing you to trace and observe LLM-powered applications within these frameworks.

How do I manage and A/B test versioned prompts for LLM monitoring?

You can manage and A/B test versioned prompts using built-in prompt management features, enabling systematic evaluation and scoring alongside dataset handling for your LLM applications.

What is the best way to track LLM cost and performance metrics in production?

Production ML deployments use scalable monitoring with configurable tracing and metrics to track cost and performance, providing necessary insights for debugging and maintaining LLM applications.

Can I use this for dataset handling and evaluation in custom ML pipelines?

Yes, it satisfies requirements for dataset handling and evaluation within custom pipelines, allowing you to score outputs and configure metrics for scalable monitoring of your models.

Why do I need configurable tracing for my LLM-powered applications?

Configurable tracing provides end-to-end observability for LLM-powered applications, enabling detailed debugging, cost tracking, and performance monitoring across production environments.