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langfuse-observability

Query Langfuse REST API for LLM traces, generations, costs, and metrics.

2|Updated Feb 19, 2026
Search Tags:#debugging#llm#observability#traces#langfuse#generations
One-click install
npx skills add https://github.com/ddnetters/homelab-agent-skills --skill langfuse-observability-ddnetters
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langfuse-observability
Source: https://github.com/ddnetters/homelab-agent-skills/tree/main/langfuse-observability
Command: npx skills add https://github.com/ddnetters/homelab-agent-skills --skill langfuse-observability-ddnetters

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of understanding and debugging complex LLM applications by providing access to detailed observability data from Langfuse.

Core Features & Use Cases

  • Query Traces: Retrieve detailed information about individual LLM interactions, including inputs, outputs, costs, and latency.
  • Analyze Generations: Inspect specific LLM calls (generations) to understand model behavior and identify issues.
  • Monitor Metrics: Access daily metrics for cost, usage, and error rates to track performance and identify trends.
  • Use Case: Debug a failing LLM application by querying traces for a specific user ID, examining the generations within those traces, and identifying the point where the error occurred.

Quick Start

Use the langfuse-observability skill to list the 10 most recent traces.

Frequently Asked Questions about langfuse-observability

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

FAQPage Schema
How do I debug LLM pipelines using Langfuse traces?▼

To debug LLM pipelines, query Langfuse traces to retrieve detailed interaction data including inputs, outputs, costs, and latency to identify where errors occurred.

What is LLM observability data and how does it help monitor model performance?▼

LLM observability data provides access to traces, generations, daily cost metrics, usage, and error rates to track model performance and identify trends in your applications.

Do I need Langfuse API keys to query generations and traces?▼

Yes, you need Langfuse API keys and a host URL for authentication and access to query generations, traces, and metrics via the REST API.

How do I analyze LLM generations to identify model behavior issues?▼

You analyze LLM generations by inspecting specific LLM calls within traces via the REST API to understand model behavior and pinpoint issues in your pipeline.

Can I retrieve the 10 most recent traces to monitor application costs?▼

Yes, you can retrieve the 10 most recent traces to monitor application costs and access daily metrics for usage and error rates to track performance trends.

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