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Make your integration available to the community.
Important: New integrations should be standalone packages, not PRs to the LangChain monorepo.While LangChain maintains a small subset of first-party and high-usage integrations (like OpenAI, Anthropic, and Ollama) in the main repository, new integrations should be published as separate PyPI packages and repositories (e.g., langchain-yourservice) that users install alongside the core LangChain packages. You should not submit a PR to add your integration directly to the main LangChain repository.
Now that your package is implemented and tested, you can publish it and add documentation to make it discoverable by the community.

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Publishing your package

This guide assumes you have already implemented your package and written tests for it. If you haven’t, please refer to the implementation guide and testing guide.
For the purposes of this guide, we’ll be using PyPI as the package registry. You may choose to publish to other registries if you prefer; instructions will vary.

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Setup credentials

First, make sure you have a PyPI account:
1

Create account

Go to the PyPI website and create an account
2

Verify email

Verify your email address by clicking the link that PyPI emails to you
3

Enable 2FA

Go to your account settings and click “Generate Recovery Codes” to enable 2FA. To generate an API token, you must have 2FA enabled
4

Generate token

Go to your account settings and generate a new API token

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Build and publish

How to publish a package

Helpful guide from uv on how to build and publish a package to PyPI.

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Adding documentation

To add documentation for your package to this site under the integrations tab, you will need to create the relevant documentation pages and open a PR in the LangChain docs repository.

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Writing docs

Depending on the type of integration you have built, you will need to create different types of documentation pages. LangChain provides templates for different types of integrations to help you get started.

Chat models

Tools/toolkits

Retrievers

Vector stores

Embedding models

To reference existing documentation, you can look at the list of integrations and find similar ones to yours.To view a given documentation page in raw markdown, use the dropdown button next to “Copy page” on the top right of the page and select “View as Markdown”.

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Submitting a PR

Make a fork of the LangChain docs repository under a personal GitHub account, and clone it locally. Create a new branch for your integration. Copy the template and modify them using your favorite markdown text editor. Make sure to refer to and follow the documentation guide when writing your documentation.
We may reject PRs or ask for modification if:
Please be patient as we handle a large volume of PRs. We will review your PR as soon as possible and provide feedback or merge it. Do not repeatedly tag maintainers about your PR.

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Next steps

Congratulations! Your integration is now published and documented, making it available to the entire LangChain community.

Co-marketing

Get in touch with the LangChain marketing team to explore co-marketing opportunities.
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