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143 lines
9.7 KiB
Markdown
143 lines
9.7 KiB
Markdown
# 🦜️🔗 LangChain
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⚡ Build context-aware reasoning applications ⚡
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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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[](https://opensource.org/licenses/MIT)
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[](https://pypistats.org/packages/langchain-core)
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[](https://star-history.com/#langchain-ai/langchain)
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[](https://github.com/langchain-ai/langchain/issues)
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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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[](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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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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```bash
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pip install langchain
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```
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With conda:
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```bash
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conda install langchain -c conda-forge
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```
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## 🤔 What is LangChain?
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**LangChain** is a framework for developing applications powered by large language models (LLMs).
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For these applications, LangChain simplifies the entire application lifecycle:
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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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### 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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