# Langfuse

LLM engineering platform for observability, evals, prompt management, datasets, and traces.

## Summary
Langfuse is a source-available LLM engineering platform that helps teams observe, evaluate, and improve LLM and agent applications with traces, metrics, prompts, datasets, and integrations.


## Guide
Langfuse is a source-available LLM engineering platform that helps teams observe, evaluate, and improve LLM and agent applications with traces, metrics, prompts, datasets, and integrations.

### What it is
Langfuse is listed on OpenAgent.bot as a tools resource for open AI builders.

### Why it matters
Agent products need more than a model call. They need traces, evaluations, prompt versions, and production feedback loops. Langfuse is one of the most visible open LLM observability stacks for that layer.

### How it works
Start from the official source links, then validate the project against your deployment needs, license requirements, and maintenance expectations.


### Getting Started
- Review the repository: https://github.com/langfuse/langfuse
- Homepage: https://langfuse.com/docs
## Why It Matters
Agent products need more than a model call. They need traces, evaluations, prompt versions, and production feedback loops. Langfuse is one of the most visible open LLM observability stacks for that layer.


## Best For
- Teams operating LLM or agent applications
- Builders who need traces, prompt management, and datasets
- Developers comparing open observability options for AI products

## Not For
- Tiny prototypes that do not need observability yet
- Teams that want a purely local CLI-only evaluation workflow

## What It Actually Does
- Workflow: Langfuse surfaces workflow as a core capability in its published project metadata and source links.
  - Why it matters: This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs.
- State: Langfuse surfaces state as a core capability in its published project metadata and source links.
  - Why it matters: This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs.

## Typical Use Cases
- Self hosted ai: Use it as a candidate for self hosted ai when the project facts, license, and official links match your deployment requirements.

## How It Compares
- When to choose Langfuse: Compare it with nearby tools by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need.

## Facts
- Category: tools
- Resource type: tool
- Open source: no
- License: See repository
- Last verified: 2026-06-02
- GitHub repo: langfuse/langfuse
- GitHub stars: 28327

## Capabilities
- workflow
- state

## Structured Use Case Tags
- self-hosted-ai

## Getting Started
- Review the repository: https://github.com/langfuse/langfuse
- Homepage: https://langfuse.com/docs

## Links
- GitHub: https://github.com/langfuse/langfuse
- Homepage: https://langfuse.com/docs

## Structured Outputs
- JSON: https://www.openagent.bot/tools/langfuse.json
- Markdown: https://www.openagent.bot/tools/langfuse.md
- Canonical: https://www.openagent.bot/tools/langfuse