langfuse

langfuse is a LLM observability tool - 🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23.

Overview

Langfuse uses GitHub Discussions for Support and Feature Requests.

We're hiring. Join us in product engineering and technical go-to-market roles.

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.

• 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.

langfuse is open-source, written primarily in TypeScript, with 28,667 GitHub stars under the Other license. The latest release is v3.178.0 (2026-06-02).

Key capabilities

From the project's documentation:

• VM: Run Langfuse on a single Virtual Machine using Docker Compose.
• Langfuse FAQs where the most common questions are answered.
• Use "Ask AI" to get instant answers to your questions.
• Request a feature on GitHub Discussions.
• Report a Bug on GitHub Issues.
• For time-sensitive queries, ping us via the in-app chat widget.

Install

A quick way to get started (always check the official docs for the latest):

pip install langfuse openai

How it fits a local-AI stack

langfuse runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Browse the full tools directory for alternatives.

Sources

• Source code & docs: langfuse/langfuse
• Official website: https://langfuse.com/docs

Stats from GitHub, 2026-06-08.