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docs: update readme (#30239)
Co-authored-by: Vadym Barda <vadym@langchain.dev>
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# 🦜️🔗 LangChain
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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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⚡ Build context-aware reasoning applications ⚡
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<div>
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<br>
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</div>
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[](https://github.com/langchain-ai/langchain/releases)
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[](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml)
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@ -12,131 +18,65 @@
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[](https://codespaces.new/langchain-ai/langchain)
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[](https://twitter.com/langchainai)
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Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
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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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To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com).
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[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.
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Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team.
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## Quick Install
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With pip:
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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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```bash
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pip install langchain
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pip install -U langchain
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```
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With conda:
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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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```bash
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conda install langchain -c conda-forge
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```
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## Why use LangChain?
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## 🤔 What is 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** is a framework for developing applications powered by large language models (LLMs).
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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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For these applications, LangChain simplifies the entire application lifecycle:
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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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To improve your LLM application development, pair LangChain with:
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- **Open-source libraries**: Build your applications using LangChain's open-source
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[components](https://python.langchain.com/docs/concepts/) and
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[third-party integrations](https://python.langchain.com/docs/integrations/providers/).
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Use [LangGraph](https://langchain-ai.github.io/langgraph/) to build stateful agents with first-class streaming and human-in-the-loop support.
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- **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
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- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/).
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- [LangSmith](http://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://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) - Deploy
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and scale agents effortlessly with a purpose-built deployment platform for long
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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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### Open-source libraries
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- **`langchain-core`**: Base abstractions.
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- **Integration packages** (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers.
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- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
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- **`langchain-community`**: Third-party integrations that are community maintained.
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- **[LangGraph](https://langchain-ai.github.io/langgraph)**: LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more. Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
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### Productionization:
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- **[LangSmith](https://docs.smith.langchain.com/)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
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### Deployment:
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- **[LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/)**: Turn your LangGraph applications into production-ready APIs and Assistants.
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## 🧱 What can you build with LangChain?
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**❓ Question answering with RAG**
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- [Documentation](https://python.langchain.com/docs/tutorials/rag/)
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- End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain)
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**🧱 Extracting structured output**
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- [Documentation](https://python.langchain.com/docs/tutorials/extraction/)
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- End-to-end Example: [LangChain Extract](https://github.com/langchain-ai/langchain-extract/)
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**🤖 Chatbots**
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- [Documentation](https://python.langchain.com/docs/tutorials/chatbot/)
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- End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
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And much more! Head to the [Tutorials](https://python.langchain.com/docs/tutorials/) section of the docs for more.
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## 🚀 How does LangChain help?
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The main value props of the LangChain libraries are:
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1. **Components**: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not.
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2. **Easy orchestration with LangGraph**: [LangGraph](https://langchain-ai.github.io/langgraph/),
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built on top of `langchain-core`, has built-in support for [messages](https://python.langchain.com/docs/concepts/messages/), [tools](https://python.langchain.com/docs/concepts/tools/),
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and other LangChain abstractions. This makes it easy to combine components into
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production-ready applications with persistence, streaming, and other key features.
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Check out the LangChain [tutorials page](https://python.langchain.com/docs/tutorials/#orchestration) for examples.
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## Components
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Components fall into the following **modules**:
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**📃 Model I/O**
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This includes [prompt management](https://python.langchain.com/docs/concepts/prompt_templates/)
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and a generic interface for [chat models](https://python.langchain.com/docs/concepts/chat_models/), including a consistent interface for [tool-calling](https://python.langchain.com/docs/concepts/tool_calling/) and [structured output](https://python.langchain.com/docs/concepts/structured_outputs/) across model providers.
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**📚 Retrieval**
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Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/concepts/document_loaders/) from a variety of sources, [preparing it](https://python.langchain.com/docs/concepts/text_splitters/), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/docs/concepts/retrievers/) it for use in the generation step.
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**🤖 Agents**
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Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. [LangGraph](https://langchain-ai.github.io/langgraph/) makes it easy to use
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LangChain components to build both [custom](https://langchain-ai.github.io/langgraph/tutorials/)
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and [built-in](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/)
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LLM agents.
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## 📖 Documentation
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Please see [here](https://python.langchain.com) for full documentation, which includes:
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- [Introduction](https://python.langchain.com/docs/introduction/): Overview of the framework and the structure of the docs.
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- [Tutorials](https://python.langchain.com/docs/tutorials/): If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
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- [How-to guides](https://python.langchain.com/docs/how_to/): Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
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- [Conceptual guide](https://python.langchain.com/docs/concepts/): Conceptual explanations of the key parts of the framework.
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- [API Reference](https://python.langchain.com/api_reference/): Thorough documentation of every class and method.
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## 🌐 Ecosystem
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- [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
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- [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
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- [🦜🕸️ LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): Deploy LLM applications built with LangGraph into production.
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## 💁 Contributing
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As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
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For detailed information on how to contribute, see [here](https://python.langchain.com/docs/contributing/).
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## 🌟 Contributors
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[](https://github.com/langchain-ai/langchain/graphs/contributors)
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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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- [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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