CONTRIBUTING.md

Contributing to LangChain

👋 Hi there! Thank you for even being interested in contributing to LangChain. As an open source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation.

To contribute to this project, please follow a "fork and pull request" workflow. Please do not try to push directly to this repo unless you are a maintainer.

Quick Links

Not sure what to work on?

If you are not sure what to work on, we have a few suggestions:

• Look at the issues with the help wanted label. These are issues that we think are good targets for new contributors. If you are interested in working on one of these, please comment on the issue so that we can assign it to you. And any questions let us know, we're happy to guide you!
• At the moment our main focus is reaching parity with the Python version across both integrations and features. If you are interested in working on a specific integration or feature, just pick anything from those lists not done yet, please let us know and we can help you get started.

New abstractions

We are currently trying to keep API parity between the Python and JS versions of LangChain, where possible. As such we ask that if you have an idea for a new abstraction, please open an issue first to discuss it. This will help us make sure that the API is consistent across both versions. If you're not sure what to work on, we recommend looking at the links above first.

Want to add a specific integration?

LangChain supports several different types of integrations with third-party providers and frameworks, including LLM providers (e.g. OpenAI), vector stores (e.g. FAISS), document loaders (e.g. Apify) persistent message history stores (e.g. Redis), and more.

We welcome such contributions, but ask that you read our dedicated integration contribution guide for specific details and patterns to consider before opening a pull request.

🗺️Contributing Guidelines

🚩GitHub Issues

Our issues page is kept up to date with bugs, improvements, and feature requests. There is a taxonomy of labels to help with sorting and discovery of issues of interest. These include:

• prompts: related to prompt tooling/infra.
• llms: related to LLM wrappers/tooling/infra.
• chains
• utilities: related to different types of utilities to integrate with (Python, SQL, etc.).
• agents
• memory
• applications: related to example applications to build

If you start working on an issue, please assign it to yourself.

If you are adding an issue, please try to keep it focused on a single modular bug/improvement/feature. If the two issues are related, or blocking, please link them rather than keep them as one single one.

We will try to keep these issues as up to date as possible, though with the rapid rate of develop in this field some may get out of date. If you notice this happening, please just let us know.

🙋Getting Help

Although we try to have a developer setup to make it as easy as possible for others to contribute (see below) it is possible that some pain point may arise around environment setup, linting, documentation, or other. Should that occur, please contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is smooth for future contributors.

In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase. If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help - we do not want these to get in the way of getting good code into the codebase.

🏭Release process

TODO:

As of now, LangChain has an ad hoc release process: releases are cut with high frequency via by a developer and published to npm.

LangChain follows the semver versioning standard. However, as pre-1.0 software, even patch releases may contain non-backwards-compatible changes.

If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)! If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.

🚀Quick Start

Tooling

This project uses the following tools, which are worth getting familiar with if you plan to contribute:

• yarn (v3.4.1) - dependency management
• eslint - enforcing standard lint rules
• prettier - enforcing standard code formatting
• jest - testing code
• TypeDoc - reference doc generation from comments
• Docusaurus - static site generation for documentation

Now, you should be able to run the common tasks in the following section.

✅Common Tasks

Our primary goal is to make it as easy as possible for you to contribute to this project. To that end, we have configured the most common actions to be directly runnable from the root of the project (unless otherwise noted).

Setup

To get started, you will need to install the dependencies for the project. To do so, run:

yarn

Linting

We use eslint to enforce standard lint rules. To run the linter, run:

yarn lint

To automatically fix linting errors, run:

yarn lint:fix

Formatting

We use prettier to enforce code formatting style. To run the formatter, run:

yarn format

To just check for formatting differences, without fixing them, run:

yarn format:check

Testing

Tests should be added within a tests/ folder alongside the modules they are testing.

To run all tests, run:

yarn test

Unit tests cover modular logic that does not require calls to outside APIs.

If you add new logic, please add a unit test. Unit tests should be called *.test.ts.

To run only unit tests, run:

yarn test:unit

Integration tests cover logic that requires making calls to outside APIs (often integration with other services).

If you add support for a new external API, please add a new integration test. Integration tests should be called *.int.test.ts.

To run only integration tests, run:

yarn test:int

Note that many integration tests require credentials or other setup. You may need to set up a langchain/.env file like the example here.

Environment tests test whether LangChain works across different JS environments, including Node.js (both ESM and CJS), Edge environments (eg. Cloudflare Workers), and browsers (using Webpack).

To run the environment tests with Docker run:

yarn test:exports:docker

Running a single test

To run a single test, run:

yarn test:single ./path/to/yourtest.test.ts

Building

To build the project, run:

yarn build

Running examples

If you add a new major piece of functionality, it is helpful to add an example to showcase how to use it. Most of our users find examples to be the most helpful kind of documentation.

Examples can be added in the examples/src directory, e.g. examples/src/path/to/example and should export a run function. This example can then be invoked with yarn example path/to/example at the top level of the repo.

Adding an Entrypoint

LangChain exposes multiple subpaths the user can import from, e.g.

import { OpenAI } from "langchain/llms/openai";

We call these subpaths "entrypoints". In general, you should create a new entrypoint if you are adding a new integration with a 3rd party library. If you're adding self-contained functionality without any external dependencies, you can add it to an existing entrypoint.

In order to declare a new entrypoint that users can import from, you should edit the langchain/scripts/create-entrypoints.js script. To add an entrypoint tools that imports from tools/index.ts you'd add the following to the entrypoints variable:

const entrypoints = {
  // ...
  tools: "tools/index",
};

This will make sure the entrypoint is included in the published package, and in generated documentation.

Documentation

Contribute Documentation

Docs are largely autogenerated by TypeDoc from the code.

For that reason, we ask that you add good documentation to all classes and methods.

Similar to linting, we recognize documentation can be annoying. If you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.

Build Documentation Locally

You can run a hot-reloading dev version of the docs static site by running:

To generate and view the documentation locally, run:

yarn docs