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<p align="center">
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<source media="(prefers-color-scheme: light)" srcset="docs/static/img/logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="docs/static/img/logo-dark.svg">
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<img alt="LangChain Logo" src="docs/static/img/logo-dark.svg" width="80%"> <img alt="LangChain Logo" src="docs/static/img/logo-dark.svg" width="80%">
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</p>
<div> <p align="center">
<br> The platform for reliable agents.
</div> </p>
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</p>
> [!NOTE] LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.
> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
LangChain is a framework for building LLM-powered applications. It helps you chain
together interoperable components and third-party integrations to simplify AI
application development — all while future-proofing decisions as the underlying
technology evolves.
```bash ```bash
pip install -U langchain pip install -U langchain
``` ```
To learn more about LangChain, check out ---
[the docs](https://python.langchain.com/docs/introduction/). If you’re looking for more
advanced customization or agent orchestration, check out **Documentation**: To learn more about LangChain, check out [the docs](https://python.langchain.com/docs/introduction/).
[LangGraph](https://langchain-ai.github.io/langgraph/), our framework for building
controllable agent workflows. If you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://langchain-ai.github.io/langgraph/), our framework for building controllable agent workflows.
> [!NOTE]
> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
## Why use LangChain? ## Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
interface for models, embeddings, vector stores, and more.
Use LangChain for: Use LangChain for:
- **Real-time data augmentation**. Easily connect LLMs to diverse data sources and - **Real-time data augmentation**. Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.
external/internal systems, drawing from LangChain’s vast library of integrations with - **Model interoperability**. Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.
model providers, tools, vector stores, retrievers, and more.
- **Model interoperability**. Swap models in and out as your engineering team
experiments to find the best choice for your application’s needs. As the industry
frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without
losing momentum.
## LangChain’s ecosystem ## LangChain’s ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
with any LangChain product, giving developers a full suite of tools when building LLM
applications.
To improve your LLM application development, pair LangChain with: To improve your LLM application development, pair LangChain with:
- [LangSmith](https://www.langchain.com/langsmith) - Helpful for agent evals and - [LangSmith](https://www.langchain.com/langsmith) - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain - [LangGraph](https://langchain-ai.github.io/langgraph/) - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
visibility in production, and improve performance over time. - [LangGraph Platform](https://docs.langchain.com/langgraph-platform) - Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
- [LangGraph](https://langchain-ai.github.io/langgraph/) - Build agents that can
reliably handle complex tasks with LangGraph, our low-level agent orchestration
framework. LangGraph offers customizable architecture, long-term memory, and
human-in-the-loop workflows — and is trusted in production by companies like LinkedIn,
Uber, Klarna, and GitLab.
- [LangGraph Platform](https://docs.langchain.com/langgraph-platform) - Deploy
and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across
teams — and iterate quickly with visual prototyping in
[LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
## Additional resources ## Additional resources
- [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with - [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with guided examples on getting started with LangChain.
guided examples on getting started with LangChain. - [How-to Guides](https://python.langchain.com/docs/how_to/): Quick, actionable code snippets for topics such as tool calling, RAG use cases, and more.
- [How-to Guides](https://python.langchain.com/docs/how_to/): Quick, actionable code - [Conceptual Guides](https://python.langchain.com/docs/concepts/): Explanations of key concepts behind the LangChain framework.
snippets for topics such as tool calling, RAG use cases, and more.
- [Conceptual Guides](https://python.langchain.com/docs/concepts/): Explanations of key
concepts behind the LangChain framework.
- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback. - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
- [API Reference](https://python.langchain.com/api_reference/): Detailed reference on - [API Reference](https://python.langchain.com/api_reference/): Detailed reference on navigating base packages and integrations for LangChain.
navigating base packages and integrations for LangChain.
- [Chat LangChain](https://chat.langchain.com/): Ask questions & chat with our documentation. - [Chat LangChain](https://chat.langchain.com/): Ask questions & chat with our documentation.