# Langfuse

> Open-source LLM observability — trace every agent run, score outputs, and catch regressions

**Category:** Monitoring & Observability  
**Pricing:** open-source  
**Status:** active  
**Tags:** open-source, self-hostable, managed  
**Website:** https://langfuse.com  
**Canonical:** https://www.frameworkr.com/tools/langfuse

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## What it is

Langfuse is an open-source observability and evaluation platform for LLM applications. It captures traces of every agent run — which model was called, with what prompt, what tool was invoked, and what the output was — and lets you score outputs manually or automatically. It integrates natively with LangChain, LlamaIndex, and the OpenAI SDK with minimal instrumentation.

## Best for

Teams that need to understand why their agent behaved a certain way, track quality regressions across prompt changes, and build evaluation datasets from production traces.

## Who it's for

Any team shipping agents to production users. Open-source and self-hostable; Langfuse Cloud removes operational burden. Essential once agents are doing real work and failures have real consequences.

## Agent architecture fit

Langfuse is the observability layer that wraps your entire agent blueprint. Every call your agent makes — to the model, to tools, to memory — gets recorded as a span in a trace. This gives you a complete picture of each agent run: what it decided, what it called, and what it returned. Without this layer, debugging agent failures is guesswork. In production blueprints, Langfuse is non-negotiable.

## Alternatives

- **Helicone** — when you need lightweight proxy-based LLM logging with minimal integration and a strong cost analytics dashboard
- **LangSmith** — when you're already deep in the LangChain ecosystem and want first-party tracing and dataset management


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_Reviewed by Frameworkr — https://www.frameworkr.com/tools/langfuse_