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Contributing to Langfuse

First off, thanks for taking the time to contribute! ❤️

The best ways to contribute to Langfuse:

  • Submit and vote on Ideas
  • Create and comment on Issues
  • Open a PR.

We welcome contributions through GitHub pull requests. This document outlines our conventions regarding development workflow, commit message formatting, contact points, and other resources. Our goal is to simplify the process and ensure that your contributions are easily accepted.

We gratefully welcome improvements to documentation (docs repo), the core application (this repo) and the SDKs (Python, JS).

The maintainers are available on Discord in case you have any questions.

And if you like the project, but just don't have time to contribute code, that's fine. There are other easy ways to support the project and show your appreciation, which we would also be very happy about:

  • Star the project;
  • Tweet about it;
  • Refer to this project in your project's readme;
  • Submit and vote on Ideas;
  • Create and comment on Issues;
  • Mention the project at local meetups and tell your friends/colleagues.

Making a change

Before making any significant changes, please open an issue. Discussing your proposed changes ahead of time will make the contribution process smooth for everyone. Large changes that were not discussed in an issue may be rejected.

Once we've discussed your changes and you've got your code ready, make sure that tests are passing and open your pull request.

A good first step is to search for open issues. Issues are labeled, and some good issues to start with are labeled: good first issue.

Project Overview

Technologies we use

  • Application (this repository)
    • NextJS 14, pages router
    • NextAuth.js / Auth.js
    • tRPC: Frontend APIs
    • Prisma ORM
    • Zod
    • Tailwind CSS
    • shadcn/ui tailwind components (using Radix and tanstack)
    • Fern: generate OpenAPI spec and Pydantic models
  • JS SDK (langfuse/langfuse-js)
    • openapi-typescript to generated types based on OpenAPI spec
  • Python SDK (langfuse/langfuse-python)
    • Pydantic for input validation, models generated by fern

Architecture Overview

flowchart TB
   subgraph s4["Clients"]
      subgraph s2["langfuse/langfuse-python"]
         Python["Python SDK"]
         OAI["OpenAI drop-in replacement"] -->|extends| Python
         LCPYTHON["Langchain Python Integration"] -->|extends| Python
         Langflow -->|uses| LCPYTHON
         LiteLLM -->|uses| Python
      end
      subgraph s3["langfuse/langfuse-js"]
         JS["JS SDK"]
         LCJS["Langchain JS Integration"]  -->|extends| JS
         Flowise -->|uses| LCJS
      end
   end

   DB[Postgres Database]
	subgraph s1["Application (langfuse/langfuse)"]
      API[Public HTTP API]
      G[TRPC API]
      I[NextAuth]
      H[React Frontend]
      Prisma[Prisma ORM]
      H --> G
      H --> I
      G --> I
      G --- Prisma
      API --- Prisma
      I --- Prisma
	end
   Prisma --- DB
   JS --- API
   Python --- API
Loading

Database Overview

The diagram below may not show all relationships if the foreign key is not defined in the database schema. For instance, trace_id in the observation table is not defined as a foreign key to the trace table to allow unordered ingestion of these objects, but it is still a foreign key in the application code.

Full database schema: prisma/schema.prisma

Infrastructure & Network Overview

flowchart LR
   Browser ---|Web UI & TRPC API| App
   Integrations/SDKs ---|Public HTTP API| App
   subgraph i1["Application Network"]
      App["Langfuse Application (Docker or Serverless)"]
   end
   subgraph i2["Database Network"]
      DB["Postgres Database"]
   end
   App --- DB
Loading

Development Setup

Requirements

  • Node.js 20 as specified in the .nvmrc
  • Docker to run the database locally

Steps

  1. Fork the the repository and clone it locally

  2. Install dependencies

    npm install

  3. Run the development database

    docker-compose -f docker-compose.dev.yml up -d

  4. Create an env file

     cp .env.dev.example .env

  5. Run the migrations

    npm run db:migrate

# Optional: seed the database
# npm run db:seed
# npm run db:seed:examples

  6. Start the development server

     npm run dev

Note

If you frequently switch branches, use npm run dx instead of npm run dev. This command will install dependencies, reset the database (wipe and apply all migrations), and run the database seeder with example data before starting the development server.

Commit messages

On the main branch, we adhere to the best practices of conventional commits. All pull requests and branches are squash-merged to maintain a clean and readable history. This approach ensures the addition of a conventional commit message when merging contributions.

Test the public API

The API is tested using Jest. With the development server running, you can run the tests with:

Run all

npm run test

Run interactively in watch mode

npm run test:watch

These tests are also run in CI.

CI/CD

We use GitHub Actions for CI/CD, the configuration is in .github/workflows/pipeline.yml

CI on main and pull_request

  • Check Linting
  • E2E test of API using Jest
  • E2E tests of UI using Playwright

CD on main

  • Publish Docker image to GitHub Packages if CI passes. Done on every push to main branch. Only released versions are tagged with latest.

Staging environment

We run a staging environment at https://staging.langfuse.com that is automatically deployed on every push to main branch.

The same environment is also used for preview deployments of pull requests. Limitations:

  • SSO is not available as dynamic domains are not supported by most SSO providers.
  • When making changes to the database, migrations to the staging database need to be applied manually by a maintainer. If you want to interactively test database changes in the staging environment, please reach out.

You can use the staging environment end-to-end with the Langfuse integrations or SDKs (host: https://staging.langfuse.com). However, please note that the staging environment is not intended for production use and may be reset at any time.

Production environment

When a new release is tagged on the main branch (excluding prereleases), it triggers a production deployment. The deployment process consists of two steps:

  1. The Docker image is published to GitHub Packages with the version number and latest tag.
  2. The deployment is carried out on Langfuse Cloud. This is done by force pushing the main branch to the production branch during every release, using the release.yml GitHub Action.

License

Langfuse is MIT licensed, except for ee/ folder. See LICENSE and docs for more details.

When contributing to the Langfuse codebase, you need to agree to the Contributor License Agreement. You only need to do this once and the CLA bot will remind you if you haven't signed it yet.