Skip to main content

Contribute Integrations

To begin, make sure you have all the dependencies outlined in guide on Contributing Code.

There are a few different places you can contribute integrations for LangChain:

  • Community: For lighter-weight integrations that are primarily maintained by LangChain and the Open Source Community.
  • Partner Packages: For independent packages that are co-maintained by LangChain and a partner.

For the most part, new integrations should be added to the Community package. Partner packages require more maintenance as separate packages, so please confirm with the LangChain team before creating a new partner package.

In the following sections, we'll walk through how to contribute to each of these packages from a fake company, Parrot Link AI.

Community package

The langchain-community package is in libs/community and contains most integrations.

It can be installed with pip install langchain-community, and exported members can be imported with code like

from langchain_community.chat_models import ChatParrotLink
from langchain_community.llms import ParrotLinkLLM
from langchain_community.vectorstores import ParrotLinkVectorStore

The community package relies on manually-installed dependent packages, so you will see errors if you try to import a package that is not installed. In our fake example, if you tried to import ParrotLinkLLM without installing parrot-link-sdk, you will see an ImportError telling you to install it when trying to use it.

Let's say we wanted to implement a chat model for Parrot Link AI. We would create a new file in libs/community/langchain_community/chat_models/ with the following code:

from langchain_core.language_models.chat_models import BaseChatModel

class ChatParrotLink(BaseChatModel):
"""ChatParrotLink chat model.

.. code-block:: python

from langchain_community.chat_models import ChatParrotLink

model = ChatParrotLink()

API Reference:BaseChatModel

And we would write tests in:

  • Unit tests: libs/community/tests/unit_tests/chat_models/
  • Integration tests: libs/community/tests/integration_tests/chat_models/

And add documentation to:

  • docs/docs/integrations/chat/parrot_link.ipynb

Partner package in LangChain repo

Partner packages can be hosted in the LangChain monorepo or in an external repo.

Partner package in the LangChain repo is placed in libs/partners/{partner} and the package source code is in libs/partners/{partner}/langchain_{partner}.

A package is installed by users with pip install langchain-{partner}, and the package members can be imported with code like:

from langchain_{partner} import X

Set up a new package

To set up a new partner package, use the latest version of the LangChain CLI. You can install or update it with:

pip install -U langchain-cli

Let's say you want to create a new partner package working for a company called Parrot Link AI.

Then, run the following command to create a new partner package:

cd libs/partners
langchain-cli integration new
> Name: parrot-link
> Name of integration in PascalCase [ParrotLink]: ParrotLink

This will create a new package in libs/partners/parrot-link with the following structure:

langchain_parrot_link/ # folder containing your package
docs/ # bootstrapped docs notebooks, must be moved to /docs in monorepo root
scripts/ # scripts for CI
LICENSE # fill out with information about your package
Makefile # default commands for CI
pyproject.toml # package metadata, mostly managed by Poetry
poetry.lock # package lockfile, managed by Poetry

Implement your package

First, add any dependencies your package needs, such as your company's SDK:

poetry add parrot-link-sdk

If you need separate dependencies for type checking, you can add them to the typing group with:

poetry add --group typing types-parrot-link-sdk

Then, implement your package in libs/partners/parrot-link/langchain_parrot_link.

By default, this will include stubs for a Chat Model, an LLM, and/or a Vector Store. You should delete any of the files you won't use and remove them from

Write Unit and Integration Tests

Some basic tests are presented in the tests/ directory. You should add more tests to cover your package's functionality.

For information on running and implementing tests, see the Testing guide.

Write documentation

Documentation is generated from Jupyter notebooks in the docs/ directory. You should place the notebooks with examples to the relevant docs/docs/integrations directory in the monorepo root.

(If Necessary) Deprecate community integration

Note: this is only necessary if you're migrating an existing community integration into a partner package. If the component you're integrating is net-new to LangChain (i.e. not already in the community package), you can skip this step.

Let's pretend we migrated our ChatParrotLink chat model from the community package to the partner package. We would need to deprecate the old model in the community package.

We would do that by adding a @deprecated decorator to the old model as follows, in libs/community/langchain_community/chat_models/

Before our change, our chat model might look like this:

class ChatParrotLink(BaseChatModel):

After our change, it would look like this:

from langchain_core._api.deprecation import deprecated

since="0.0.<next community version>",
class ChatParrotLink(BaseChatModel):
API Reference:deprecated

You should do this for each component that you're migrating to the partner package.

Additional steps

Contributor steps:

  • Add secret names to manual integrations workflow in .github/workflows/_integration_test.yml
  • Add secrets to release workflow (for pre-release testing) in .github/workflows/_release.yml

Maintainer steps (Contributors should not do these):

  • set up pypi and test pypi projects
  • add credential secrets to Github Actions
  • add package to conda-forge

Partner package in external repo

Partner packages in external repos must be coordinated between the LangChain team and the partner organization to ensure that they are maintained and updated.

If you're interested in creating a partner package in an external repo, please start with one in the LangChain repo, and then reach out to the LangChain team to discuss how to move it to an external repo.

Was this page helpful?

You can leave detailed feedback on GitHub.