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 →
✨ 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
Langfuse Cloud
Managed deployment by the Langfuse team, generous free-tier, no credit card required.
Self-Host Langfuse
Run Langfuse on your own infrastructure:
Local (docker compose): Run Langfuse on your own machine in 5 minutes using Docker Compose.
# Get a copy of the latest Langfuse repository
git clone --depth=1 https://github.com/langfuse/langfuse.git
cd langfuse
# Run the langfuse docker compose
docker compose up
VM: Run Langfuse on a single Virtual Machine using Docker Compose.
Kubernetes (Helm): Run Langfuse on a Kubernetes cluster using Helm. This is the preferred production deployment.
[!TIP]
Self-hosting Langfuse? Subscribe to the self-hosting update list to get an email about important features and new releases for open source Langfuse — self-hosting updates only, no marketing.
Multi agent framework for agent collaboration and tool use.
🚀 Quickstart
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.
Create new API credentials in the project settings
2️⃣ Log your first LLM call
The @observe() decorator makes it easy to trace any Python LLM application. In this quickstart we also use the Langfuse OpenAI integration to automatically capture all model parameters.
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Not using OpenAI? Visit our documentation to learn how to log other models and frameworks.
pip install langfuse openai
LANGFUSE_SECRET_KEY="sk-lf-..."
LANGFUSE_PUBLIC_KEY="pk-lf-..."
LANGFUSE_BASE_URL="https://cloud.langfuse.com" # 🇪🇺 EU region
# LANGFUSE_BASE_URL="https://us.cloud.langfuse.com" # 🇺🇸 US region
from langfuse import observe
from langfuse.openai import openai # OpenAI integration
@observe()
def story():
return openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "What is Langfuse?"}],
).choices[0].message.content
@observe()
def main():
return story()
main()
3️⃣ See traces in Langfuse
See your language model calls and other application logic in Langfuse.
Our documentation is the best place to start looking for answers. It is comprehensive, and we invest significant time into maintaining it. You can also suggest edits to the docs via GitHub.
Langfuse FAQs where the most common questions are answered.
Use "Ask AI" to get instant answers to your questions.
Support Channels:
Ask any question in our public Q&A on GitHub Discussions. Please include as much detail as possible (e.g. code snippets, screenshots, background information) to help us understand your question.
Open a PR - see CONTRIBUTING.md for details on how to setup a development environment.
🥇 License
This repository is MIT licensed, except for the ee folders. See LICENSE and docs for more details.
Dependencies
We deploy this code base in Docker containers based on the Linux Alpine Image (source). You may find the Dockerfiles in web/Dockerfile and worker/Dockerfile.
⭐️ Star History
❤️ Open Source Projects Using Langfuse
Top open-source Python projects that use Langfuse, ranked by stars (Source):
We take data security and privacy seriously. Please refer to our Security and Privacy page for more information.
Telemetry
By default, Langfuse automatically reports basic usage statistics of self-hosted instances to a centralized server (PostHog).
This helps us to:
Understand how Langfuse is used and improve the most relevant features.
Track overall usage for internal and external (e.g. fundraising) reporting.
The telemetry does not include raw traces, prompts, observations, scores, or dataset contents. We document the exact fields that are collected, where they are sent, and the implementation reference in our telemetry docs.
For Langfuse OSS, you can opt out by setting TELEMETRY_ENABLED=false.
