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.