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本页 24 段还没译成Español,下面显示的是英文原文。Contributing integrations
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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. Implementing a new integration helps expand LangChain's ecosystem and makes your service discoverable to millions of developers.
Warning
New integrations are not accepted as PRs to any langchain-ai repository. All new integrations must be published as independent packages to PyPI (e.g., langchain-yourprovider). The only PR you should open to a langchain-ai repo is to list your published package in the docs: either a YAML row for the download table, or a hosted guide if you meet the eligibility criteria.
Why implement a LangChain integration?
Discoverability
LangChain is the most used framework for building LLM applications, with over 200 million monthly downloads.
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.) that improve developer experience and application performance.
Components to integrate
While any component can be integrated into LangChain, there are specific types of integrations we encourage more:
Integrate these ✅:
- Chat Models: Most actively used component type
- Tools/Toolkits: Enable agent capabilities
- Retrievers: Core to RAG applications
- Embedding Models: Foundation for vector operations
- Vector Stores: Essential for semantic search
- Middleware: Extend agent behavior with hooks
- Sandboxes: Run code safely with Deep Agents
Not these ❌:
- LLMs (Text-Completion Models): Deprecated in favor of Chat Models
- Document Loaders: High maintenance burden
- Key-Value Stores: Limited usage
- Document Transformers: Niche use cases
- Model Caches: Infrastructure concerns
- Graphs: Complex abstractions
- Message Histories: Storage abstractions
- Callbacks: System-level components
- Chat Loaders: Limited demand
- Adapters: Edge case utilities
How to contribute an integration
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Implement your package
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Pass standard tests
If applicable, implement support for LangChain's standard test suite for your integration and successfully run them.
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Publish integration
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List your integration
Open a PR in the LangChain docs repo so users can find your package. Hosted guides are limited; most integrations are listed via YAML.
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Co-marketing
(Optional) Engage with the LangChain team for joint co-marketing.