Langfuse

Open Source LLM Engineering Platform

5.0•52 reviews•

2.1K followers

Open Source LLM Engineering Platform

5.0•52 reviews•

2.1K followers

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Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications. All platform features are natively integrated to accelerate the development workflow. Langfuse is open. It works with any model, any framework, allows for complex nesting, and has open APIs to build downstream use cases. Docs: https://langfuse.com/docs Github: https://github.com/langfuse/langfuse

Langfuse reviews

The community submitted 52 reviews to tell us what they like about Langfuse, what Langfuse can do better, and more.

5.0

Based on 52 reviews

Review Langfuse?

Langfuse is highly praised for its robust LLM observability and debugging capabilities, making it a preferred choice for developers. Makers from Lingo.dev and Touring commend its reliability and utility in monitoring LLM pipelines. Truva makers appreciate its role in managing AI agents. Users highlight its seamless integration, open-source nature, and detailed analytics, which streamline development and debugging processes. Langfuse's flexibility and supportive community further enhance its appeal in the LLM space.

+49

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Pros

LLM observability (6)

monitoring capabilities (2)

Pros

Cons

Reviews

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Max Prilutskiy

used Langfuse to build

Lingo.dev

(1,091 points)

Extremely relilable LLM Engineering Platform.

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6mo ago

Nitesh Sharma

used Langfuse to build

Draft Alpha

(423 points)

Thanks for the generous free tier and excellent LLM observability 🙌

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9mo ago

Dan

used Langfuse to build Yapify

Yapify

(663 points)

Without Langfuse, we would have been flying blind with our drafting agent. This platform is critical not just for measuring performance, but for understanding exactly what context gets pulled into our drafting agent. The key benefits of Langfuse are: 1) (Real-time) visibility into agent performance 2) Detailed tracing that shows our system's decision-making process including the context 3) Open source with self-hosting options (huge win for us) Being able to host Langfuse on our own infrastructure while getting enterprise-grade LLM observability is exactly what we needed. Highly recommended for teams serious about understanding how their LLM systems actually work and to figure out how to improve them.

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1mo ago

Michael Weissenboeck

used Langfuse to build

MCP-Builder.ai

(129 points)

We use Langfuse to keep track of our LLM prompts while building MCP-Builder.ai. It’s a great tool that makes it easy to monitor and analyze prompt performance, helping us improve quickly and efficiently.

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1mo ago