To get started with Langfuse, users can opt for a managed deployment by the Langfuse team or run it on localhost. The Langfuse callback handler automatically instruments Langchain applications, with support for Python and JS/TS. Users can also manually instrument applications using SDKs. Additionally, Langfuse allows users to add scores/evaluations to traces. Contributions to Langfuse are welcomed and can be made through PRs, raising a GitHub issue, or via email.
Key takeaways:
- Langfuse is an open-source observability and analytics solution for LLM-based applications, providing a platform for monitoring and debugging these applications.
- It offers an admin UI for exploring ingested data, with features like nested view of LLM app executions, segment execution traces by user feedback, and detailed analytics reporting.
- Langfuse can be run on a server, either through a managed deployment by the Langfuse team, on localhost with Docker and Node.js, or self-hosted with Docker.
- Langfuse is MIT licensed and encourages community contributions through PRs, raising GitHub issues, or via email.