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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 they involve new features, improved infrastructure, better documentation, or bug fixes.

πŸ—ΊοΈ Guidelines​

πŸ‘©β€πŸ’» Contributing Code​

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

Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.

Pull requests cannot land without passing the formatting, linting, and testing checks first. See Testing and Formatting and Linting for how to run these checks locally.

It's essential that we maintain great documentation and testing. If you:

  • Fix a bug
    • Add a relevant unit or integration test when possible. These live in tests/unit_tests and tests/integration_tests.
  • Make an improvement
    • Update any affected example notebooks and documentation. These live in docs.
    • Update unit and integration tests when relevant.
  • Add a feature
    • Add a demo notebook in docs/docs/.
    • Add unit and integration tests.

We are a small, progress-oriented team. If there's something you'd like to add or change, opening a pull request is the best way to get our attention.

🚩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. Please use these to help organize issues.

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 two issues are related, or blocking, please link them rather than combining them.

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to keep these issues as up-to-date as possible, though with the rapid rate of development in this field some may get out of date.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

πŸ™‹Getting Help​

Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, 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.

πŸš€ Quick Start​

This quick start guide explains how to run the repository locally. For a development container, see the .devcontainer folder.

Dependency Management: Poetry and other env/dependency managers​

This project utilizes Poetry v1.6.1+ as a dependency manager.

❗Note: Before installing Poetry, if you use Conda, create and activate a new Conda env (e.g. conda create -n langchain python=3.9)

Install Poetry: documentation on how to install it.

❗Note: If you use Conda or Pyenv as your environment/package manager, after installing Poetry, tell Poetry to use the virtualenv python environment (poetry config virtualenvs.prefer-active-python true)

Core vs. Experimental​

This repository contains two separate projects:

  • langchain: core langchain code, abstractions, and use cases.
  • langchain.experimental: see the Experimental README for more information.

Each of these has its own development environment. Docs are run from the top-level makefile, but development is split across separate test & release flows.

For this quickstart, start with langchain core:

cd libs/langchain

Local Development Dependencies​

Install langchain development requirements (for running langchain, running examples, linting, formatting, tests, and coverage):

poetry install --with test

Then verify dependency installation:

make test

If the tests don't pass, you may need to pip install additional dependencies, such as numexpr and openapi_schema_pydantic.

If during installation you receive a WheelFileValidationError for debugpy, please make sure you are running Poetry v1.6.1+. This bug was present in older versions of Poetry (e.g. 1.4.1) and has been resolved in newer releases. If you are still seeing this bug on v1.6.1, you may also try disabling "modern installation" (poetry config installer.modern-installation false) and re-installing requirements. See this debugpy issue for more details.


some test dependencies are optional; see section about optional dependencies.

Unit tests cover modular logic that does not require calls to outside APIs. If you add new logic, please add a unit test.

To run unit tests:

make test

To run unit tests in Docker:

make docker_tests

There are also integration tests and code-coverage available.

Formatting and Linting​

Run these locally before submitting a PR; the CI system will check also.

Code Formatting​

Formatting for this project is done via ruff.

To run formatting for docs, cookbook and templates:

make format

To run formatting for a library, run the same command from the relevant library directory:

cd libs/{LIBRARY}
make format

Additionally, you can run the formatter only on the files that have been modified in your current branch as compared to the master branch using the format_diff command:

make format_diff

This is especially useful when you have made changes to a subset of the project and want to ensure your changes are properly formatted without affecting the rest of the codebase.


Linting for this project is done via a combination of ruff and mypy.

To run linting for docs, cookbook and templates:

make lint

To run linting for a library, run the same command from the relevant library directory:

cd libs/{LIBRARY}
make lint

In addition, you can run the linter only on the files that have been modified in your current branch as compared to the master branch using the lint_diff command:

make lint_diff

This can be very helpful when you've made changes to only certain parts of the project and want to ensure your changes meet the linting standards without having to check the entire codebase.

We recognize linting 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.


Spellchecking for this project is done via codespell. Note that codespell finds common typos, so it could have false-positive (correctly spelled but rarely used) and false-negatives (not finding misspelled) words.

To check spelling for this project:

make spell_check

To fix spelling in place:

make spell_fix

If codespell is incorrectly flagging a word, you can skip spellcheck for that word by adding it to the codespell config in the pyproject.toml file.

# Add here:
ignore-words-list = 'momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure'

Working with Optional Dependencies​

Langchain relies heavily on optional dependencies to keep the Langchain package lightweight.

You only need to add a new dependency if a unit test relies on the package. If your package is only required for integration tests, then you can skip these steps and leave all pyproject.toml and poetry.lock files alone.

If you're adding a new dependency to Langchain, assume that it will be an optional dependency, and that most users won't have it installed.

Users who do not have the dependency installed should be able to import your code without any side effects (no warnings, no errors, no exceptions).

To introduce the dependency to the pyproject.toml file correctly, please do the following:

  1. Add the dependency to the main group as an optional dependency
    poetry add --optional [package_name]
  2. Open pyproject.toml and add the dependency to the extended_testing extra
  3. Relock the poetry file to update the extra.
    poetry lock --no-update
  4. Add a unit test that the very least attempts to import the new code. Ideally, the unit test makes use of lightweight fixtures to test the logic of the code.
  5. Please use the @pytest.mark.requires(package_name) decorator for any tests that require the dependency.

Adding a Jupyter Notebook​

If you are adding a Jupyter Notebook example, you'll want to install the optional dev dependencies.

To install dev dependencies:

poetry install --with dev

Launch a notebook:

poetry run jupyter notebook

When you run poetry install, the langchain package is installed as editable in the virtualenv, so your new logic can be imported into the notebook.


While the code is split between langchain and langchain.experimental, the documentation is one holistic thing. This covers how to get started contributing to documentation.

From the top-level of this repo, install documentation dependencies:

poetry install

Contribute Documentation​

The docs directory contains Documentation and API Reference.

Documentation is built using Docusaurus 2.

API Reference are largely autogenerated by sphinx 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​

In the following commands, the prefix api_ indicates that those are operations for the API Reference.

Before building the documentation, it is always a good idea to clean the build directory:

make docs_clean
make api_docs_clean

Next, you can build the documentation as outlined below:

make docs_build
make api_docs_build

Finally, run the link checker to ensure all links are valid:

make docs_linkcheck
make api_docs_linkcheck

Verify Documentation changes​

After pushing documentation changes to the repository, you can preview and verify that the changes are what you wanted by clicking the View deployment or Visit Preview buttons on the pull request Conversation page. This will take you to a preview of the documentation changes. This preview is created by Vercel.

🏭 Release Process​

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

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

🌟 Recognition​

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 through another means.