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chore(infra): rfc README.md for better presentation (#33172)
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Mason Daugherty
GitHub
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README.md
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README.md
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<picture>
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<p align="center">
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<picture>
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<source media="(prefers-color-scheme: light)" srcset="docs/static/img/logo-dark.svg">
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<source media="(prefers-color-scheme: dark)" srcset="docs/static/img/logo-light.svg">
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<img alt="LangChain Logo" src="docs/static/img/logo-dark.svg" width="80%">
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</picture>
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</picture>
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</p>
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<div>
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<br>
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</div>
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<p align="center">
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The platform for reliable agents.
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</p>
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[](https://opensource.org/licenses/MIT)
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[](https://pypistats.org/packages/langchain-core)
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[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
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[<img src="https://github.com/codespaces/badge.svg" alt="Open in Github Codespace" title="Open in Github Codespace" width="150" height="20">](https://codespaces.new/langchain-ai/langchain)
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[](https://codspeed.io/langchain-ai/langchain)
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[](https://twitter.com/langchainai)
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<p align="center">
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<a href="https://opensource.org/licenses/MIT" target="_blank">
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<img src="https://img.shields.io/pypi/l/langchain-core?style=flat-square" alt="PyPI - License">
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</a>
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<a href="https://pypistats.org/packages/langchain-core" target="_blank">
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<img src="https://img.shields.io/pepy/dt/langchain" alt="PyPI - Downloads">
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</a>
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<a href="https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain" target="_blank">
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<img src="https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode&style=flat-square" alt="Open in Dev Containers">
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</a>
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<a href="https://codespaces.new/langchain-ai/langchain" target="_blank">
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<img src="https://github.com/codespaces/badge.svg" alt="Open in Github Codespace" title="Open in Github Codespace" width="150" height="20">
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</a>
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<a href="https://codspeed.io/langchain-ai/langchain" target="_blank">
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<img src="https://img.shields.io/endpoint?url=https://codspeed.io/badge.json" alt="CodSpeed Badge">
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</a>
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<a href="https://twitter.com/langchainai" target="_blank">
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<img src="https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI" alt="Twitter / X">
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</a>
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</p>
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> [!NOTE]
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> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
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LangChain is a framework for building LLM-powered applications. It helps you chain
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together interoperable components and third-party integrations to simplify AI
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application development — all while future-proofing decisions as the underlying
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technology evolves.
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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.
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```bash
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pip install -U langchain
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```
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To learn more about LangChain, check out
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[the docs](https://python.langchain.com/docs/introduction/). If you’re looking for more
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advanced customization or agent orchestration, check out
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[LangGraph](https://langchain-ai.github.io/langgraph/), our framework for building
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controllable agent workflows.
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---
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**Documentation**: To learn more about LangChain, check out [the docs](https://python.langchain.com/docs/introduction/).
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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.
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> [!NOTE]
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> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
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## Why use LangChain?
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LangChain helps developers build applications powered by LLMs through a standard
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interface for models, embeddings, vector stores, and more.
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LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
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Use LangChain for:
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- **Real-time data augmentation**. Easily connect LLMs to diverse data sources and
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external/internal systems, drawing from LangChain’s vast library of integrations with
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model providers, tools, vector stores, retrievers, and more.
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- **Model interoperability**. Swap models in and out as your engineering team
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experiments to find the best choice for your application’s needs. As the industry
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frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without
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losing momentum.
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- **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.
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- **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.
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## LangChain’s ecosystem
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While the LangChain framework can be used standalone, it also integrates seamlessly
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with any LangChain product, giving developers a full suite of tools when building LLM
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applications.
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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.
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To improve your LLM application development, pair LangChain with:
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- [LangSmith](https://www.langchain.com/langsmith) - Helpful for agent evals and
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observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain
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visibility in production, and improve performance over time.
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- [LangGraph](https://langchain-ai.github.io/langgraph/) - Build agents that can
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reliably handle complex tasks with LangGraph, our low-level agent orchestration
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framework. LangGraph offers customizable architecture, long-term memory, and
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human-in-the-loop workflows — and is trusted in production by companies like LinkedIn,
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Uber, Klarna, and GitLab.
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- [LangGraph Platform](https://docs.langchain.com/langgraph-platform) - Deploy
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and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across
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teams — and iterate quickly with visual prototyping in
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[LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
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- [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.
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- [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.
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- [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/).
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## Additional resources
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- [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with
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guided examples on getting started with LangChain.
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- [How-to Guides](https://python.langchain.com/docs/how_to/): Quick, actionable code
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snippets for topics such as tool calling, RAG use cases, and more.
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- [Conceptual Guides](https://python.langchain.com/docs/concepts/): Explanations of key
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concepts behind the LangChain framework.
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- [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with guided examples on getting started with LangChain.
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- [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.
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- [Conceptual Guides](https://python.langchain.com/docs/concepts/): Explanations of key concepts behind the LangChain framework.
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- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
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- [API Reference](https://python.langchain.com/api_reference/): Detailed reference on
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navigating base packages and integrations for LangChain.
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- [API Reference](https://python.langchain.com/api_reference/): Detailed reference on navigating base packages and integrations for LangChain.
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- [Chat LangChain](https://chat.langchain.com/): Ask questions & chat with our documentation.
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