Langfuse Overview

Langfuse is an open-source LLM engineering platform (GitHub) 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, self-hostable, and extensible (why langfuse?).

Tracing

Tracing is essential for understanding and debugging LLM applications. Unlike traditional software, LLM applications involve complex, non-deterministic interactions that can be challenging to monitor and debug. Langfuse provides comprehensive tracing capabilities that help you understand exactly what’s happening in your application.

• Traces include all LLM and non-LLM calls, including retrieval, embedding, API calls, and more
• Support for tracking multi-turn conversations as sessions and user tracking
• Agents can be represented as graphs
• Capture traces via our native SDKs for Python/JS, 50+ library/framework integrations, OpenTelemetry, or via an LLM Gateway such as LiteLLM

Want to see an example? Play with the interactive demo.

Prompt Management

Prompt Management is critical in building effective LLM applications. Langfuse provides tools to help you manage, version, and optimize your prompts throughout the development lifecycle.

• Get started with prompt management
• Manage, version, and optimize your prompts throughout the development lifecycle
• Test prompts interactively in the LLM Playground
• Run prompt experiments against datasets in order to test new prompt versions directly within Langfuse

Evaluations

Evaluations are crucial for ensuring the quality and reliability of your LLM applications. Langfuse provides flexible evaluation tools that adapt to your specific needs, whether you’re testing in development or monitoring production performance.

• Get started with different evaluation methods: LLM-as-a-judge, user feedback, manual labeling, or custom
• Identify issues early by running evaluations on production traces
• Create and manage Datasets for systematic testing in development that ensure your application performs reliably across different scenarios

Where to start?

Setting up the full process of online tracing, prompt management, production evaluations to identify issues, and offline evaluations on datasets requires some time. This guide is meant to help you figure out what is most important for your use case.

Simplified lifecycle from PoC to production:

Community & Contact

We actively develop Langfuse in open source together with our community:

• Contribute and vote on the Langfuse roadmap.
• Ask questions on GitHub Discussions or private support channels.
• Report bugs via GitHub Issues.
• Chat with the Langfuse maintainers and community on Discord.
• Why people choose Langfuse

Langfuse evolves quickly, check out the changelog for the latest updates. Subscribe to the mailing list to get notified about new major features: