Contribute Integrations

Integrations are a core component of LangChain. LangChain provides standard interfaces for several different components (language models, vector stores, etc) that are crucial when building LLM applications.

Why contribute an integration to LangChain?

• Discoverability: LangChain is the most used framework for building LLM applications, with over 20 million monthly downloads. LangChain integrations are discoverable by a large community of GenAI builders.
• Interoperability: LangChain components expose a standard interface, allowing developers to easily swap them for each other. If you implement a LangChain integration, any developer using a different component will easily be able to swap yours in.
• Best Practices: Through their standard interface, LangChain components encourage and facilitate best practices (streaming, async, etc)

Components to Integrate

info

See the Conceptual Guide for an overview of all components supported in LangChain

While any component can be integrated into LangChain, there are specific types of integrations we encourage more:

Integrate these ✅	Not these ❌
Chat Models Tools/Toolkits Retrievers Vector Stores Embedding Models	LLMs (Text-Completion Models) Document Loaders Key-Value Stores Document Transformers Model Caches Graphs Message Histories Callbacks Chat Loaders Adapters Other abstractions

How to contribute an integration

In order to contribute an integration, you should follow these steps:

1. Confirm that your integration is in the list of components we are currently encouraging.
2. Implement your package and publish it to a public github repository.
3. Implement the standard tests for your integration and successfully run them.
4. Publish your integration by publishing the package to PyPi and add docs in the docs/docs/integrations directory of the LangChain monorepo.
5. [Optional] Open and merge a PR to add documentation for your integration to the official LangChain docs.
6. [Optional] Engage with the LangChain team for joint co-marketing (see below).

Co-Marketing

With over 20 million monthly downloads, LangChain has a large audience of developers building LLM applications. Besides just adding integrations, we also like to show them examples of cool tools or APIs they can use.

While traditionally called "co-marketing", we like to think of this more as "co-education". For that reason, while we are happy to highlight your integration through our social media channels, we prefer to highlight examples that also serve some educational purpose. Our main social media channels are Twitter and LinkedIn.

Here are some heuristics for types of content we are excited to promote:

• Integration announcement: If you announce the integration with a link to the LangChain documentation page, we are happy to re-tweet/re-share on Twitter/LinkedIn.
• Educational content: We highlight good educational content on the weekends - if you write a good blog or make a good YouTube video, we are happy to share there! Note that we prefer content that is NOT framed as "here's how to use integration XYZ", but rather "here's how to do ABC", as we find that is more educational and helpful for developers.
• End-to-end applications: End-to-end applications are great resources for developers looking to build. We prefer to highlight applications that are more complex/agentic in nature, and that use LangGraph as the orchestration framework. We get particularly excited about anything involving long-term memory, human-in-the-loop interaction patterns, or multi-agent architectures.
• Research: We love highlighting novel research! Whether it is research built on top of LangChain or that integrates with it.

Further Reading

To get started, let's learn how to implement an integration package for LangChain.