langfuse-observability

What problem does it solve?

This Skill addresses the challenge of understanding and debugging complex LLM applications by providing robust observability, tracing, and evaluation capabilities.

Core Features & Use Cases

• End-to-end Tracing: Track LLM calls, agentic workflows, and spans across your application.
• Prompt Management: Version, manage, and retrieve prompts directly from Langfuse.
• Cost & Latency Monitoring: Monitor token usage, costs, and latency per user or tenant.
• Evaluation: Build and run evaluation datasets to score LLM performance.
• Use Case: Integrate Langfuse into your FastAPI application to automatically trace all LLM interactions, tag them with user and tenant IDs, and visualize performance metrics in the Langfuse UI.

Quick Start

Integrate Langfuse into your Python application by following the setup instructions in the documentation.