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langfuse

langfuse/langfuse · Updated

🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform.

langfuse/langfuse is a TypeScript repository in the Prompts category with 36,695 stars and 4,114 forks on GitHub, last updated today.

#analytics#autogen#evaluation#langchain#large-language-models#llama-index#llm#llm-evaluation
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How to install langfuse in your AI client

Setup for Claude Code, Claude Desktop, Cursor, Codex, Windsurf, Cline and VS Code — copy-paste ready.

How to install langfuse in Claude CodeAnthropic's agentic CLI

  1. Clone & open

    bash
    git clone https://github.com/langfuse/langfuse.git
cd langfuse
claude

  2. Let Claude read it

    Ask “/init” to generate a CLAUDE.md, then have Claude explain, run, or extend the codebase. It reads files on demand.

Documentation

Full README

<strong>Langfuse Cloud</strong> ·

<strong>Self Host</strong> ·

<strong>Demo</strong>

<strong>Docs</strong> · <strong>Report Bug</strong> · <strong>Feature Request</strong> · <strong>Changelog</strong> · <strong>Roadmap</strong> ·

Langfuse is an open source LLM engineering platform. It helps teams collaboratively develop, monitor, evaluate, and debug AI applications. Langfuse can be self-hosted in minutes and is battle-tested. Proudly made with ClickHouse open source database.

### 🧑‍💻 We're hiring Langfuse is growing fast (we doubled the team in the last 6 months) - since January 2026 we're part of ClickHouse, we're hiring engineering hybrid across the EU. We hire engineers who love open source and great developer experiences. [See open roles →](https://langfuse.com/careers?utm_source=github&utm_medium=readme&utm_campaign=hiring&utm_content=langfuse)

✨ Core Features

  • LLM Application Observability: Instrument your app and start ingesting traces to Langfuse, thereby tracking LLM calls and other relevant logic in your app such as retrieval, embedding, or agent actions. Inspect and debug complex logs and user sessions. Try the interactive demo to see this in action.
  • Prompt Management helps you centrally manage, version control, and collaboratively iterate on your prompts. Thanks to strong caching on server and client side, you can iterate on prompts without adding latency to your application.
  • Evaluations are key to the LLM application development workflow, and Langfuse adapts to your needs. It supports LLM-as-a-judge, Code evaluators, user feedback collection, manual labeling, and custom evaluation pipelines via APIs/SDKs.
  • Datasets enable test sets and benchmarks for evaluating your LLM application. They support continuous improvement, pre-deployment testing, structured experiments, flexible evaluation, and seamless integration with frameworks like LangChain and LlamaIndex.
  • LLM Playground is a tool for testing and iterating on your prompts and model configurations, shortening the feedback loop and accelerating development. When you see a bad result in tracing, you can directly jump to the playground to iterate on it.
  • Comprehensive API: Langfuse is frequently used to power bespoke LLMOps workflows while using the building blocks provided by Langfuse via the API. OpenAPI spec, Postman collection, and typed SDKs for Python, JS/TS are available.

📦 Deploy Langfuse

Excerpt — the full document is on GitHub.

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