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langfuse

Instrument LLM pipelines for latency, token usage, and hallucination tracking with Langfuse.

1|Updated Sep 11, 2025
Search Tags:#evaluation#tracing#latency#langfuse#prompt-management#llm observability
One-click install
npx skills add https://github.com/Dhumitech/DHUMI-AI-RESOURCE --skill langfuse-dhumitech
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langfuse
Source: https://github.com/Dhumitech/DHUMI-AI-RESOURCE/tree/main/AI-Engineer-planner-Skills/01-ai-core/langfuse
Command: npx skills add https://github.com/Dhumitech/DHUMI-AI-RESOURCE --skill langfuse-dhumitech

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Langfuse provides end-to-end observability for LLM pipelines, enabling latency tracing, token usage monitoring, and hallucination detection to drive reliable AI applications.

Core Features & Use Cases

  • LLM tracing and observability across prompts, models, and deployments
  • Prompt management, versioning, and evaluation
  • Dataset management, cost tracking, and performance monitoring
  • A/B testing prompts and LangChain integrations for Langfuse callbacks

Quick Start

Run the Langfuse client and start tracing by initializing with your public and secret keys, then instrument an LLM call.

Frequently Asked Questions about langfuse

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

FAQPage Schema
How do I track LLM latency and token usage in my application?▼

You can track LLM latency and token usage by instrumenting your pipelines with Langfuse. This provides end-to-end observability across prompts, models, and deployments to monitor performance and costs.

How does LLM observability help detect hallucinations?▼

LLM observability detects hallucinations by tracing pipeline executions and evaluating model outputs against expected datasets. Langfuse enables this through comprehensive evaluation and dataset management features.

Can I use Langfuse tracing with my existing LangChain integrations?▼

Yes, Langfuse supports LangChain integrations through its callback mechanisms. You can instrument your existing chains to automatically capture traces, token usage, and latency metrics.

Do I need a Langfuse account to start tracing my LLM pipelines?▼

Yes, you need a Langfuse account with API keys to initialize the client. You also require Python or TypeScript/JavaScript and access to your LLM APIs to enable tracing and prompt management.

What is the best way to manage and A/B test LLM prompts?▼

The best way to manage and A/B test prompts is using a prompt management system with versioning. Langfuse allows you to track prompt versions, evaluate performance, and run A/B tests.

Does this observability approach support cost tracking for different models?▼

Yes, this observability approach supports cost tracking across different models. Langfuse monitors token usage and calculates associated costs, providing performance monitoring for your entire LLM application.

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