Langfuse Selfhosting

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Overview

In this tutorial, you’ll learn how to run Langfuse locally using Docker Compose and integrate it with your LLM-based applications (e.g., those built with LangChain). Langfuse provides comprehensive tracking and observability for:

  • Token usage

  • Execution time and performance metrics

  • Error rates and unexpected behaviors

  • Agent and chain interactions

By the end of this tutorial, you’ll have a local Langfuse instance running on http://localhost:3000 and be ready to send tracking events from your application.

Table of Contents

References


Environment Setup

Set up the environment. You may refer to Environment Setup for more details.

[Note]

  • langchain-opentutorial is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials.

  • You can checkout the langchain-opentutorial for more details.

set environment variables is in .env.

Copy the contents of .env_sample and load it into your .env with the key you set.

Prerequisites

  1. Docker & Docker Compose

  • Docker Desktop (Mac/Windows) or

  • Docker Engine & Docker Compose plugin (Linux)

  1. Git

  • Make sure you can run git clone from your terminal or command prompt.

  1. Sufficient System Resources

  • Running Langfuse locally requires a few GB of memory available for Docker containers.

Clone the Langfuse Repository

open your terminal and run the following command:

This will download the Langfuse repository to your local machine. and move into the directory.

Start Langfuse with Docker Compose

Inside the langfuse directory, simply run:

What's happening?

  • Docker Compose will pull and start multiple containers for the Langfuse stack:

    • Database (Postgres)

    • Langfuse backend services

    • Langfuse web/UI

  • This is the simplest way to run Langfuse locally and explore its features.

Wait for Ready State

After 2-3 minutes, you should see logs indicating that the 'langfuse-web-1' container is Ready. At that point, open your browser to:

You will be greeted by the Langfuse login/setup sceen or homepage.

Initial Setup & Usage

Follow the on-screen prompts to configure your local Langfuse instance(e.g., create an initial admin user). Once the setup completes, you can log in to the Langfuse UI.

From here, you will be able to:

  • Create projects to track events from your LLM apps

  • Invite team members (if applicable)

  • Adjust various settings like API keys, encryption or networking

Sending Traces from Your Application

To start collecting trace data in your local Langfuse instance, you’ll need to send events from your application code:

  1. Obtain your Project Key / API Key from the Langfuse UI.

  2. Install the appropriate Langfuse SDK in your application environment (e.g., npm install @langfuse/node for Node.js, or pip install langfuse for Python—if available).

  3. Initialize the Langfuse client and pass in the host (pointing to your local instance) and the API key.

  4. Record events (e.g., chain steps, LLM requests, agent actions) in your code to send structured data to Langfuse.

(Refer to Langfuse’s official docs for language-specific integration examples.)

Managing & Monitoring Your Local Instance

Once you’re up and running:

  • UI: Go to http://localhost:3000 for the Langfuse dashboard.

  • Logs: Monitor the Docker logs in your terminal for any errors or system messages.

  • Configuration: Check the configuration guide for advanced setups:

    • Authentication & SSO

    • Encryption

    • Headless Initialization

    • Networking

    • Organization Creators

    • UI Customization

Stopping/Restarting

To stop the containers, press Ctrl + C in the same terminal window running Docker Compose. If you used a detached mode (-d), then:

Add the -v flag to remove volumes as well (which also deletes stored data).

Upgrading & Maintenance

To upgrade to the latest Langfuse version, simply:

This will pull the newest images and restart Langfuse. Refer to the upgrade guide for more detailed steps.

Set KEY and run LANGFUSE locally

You can try out langfuse by running the code below.

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