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| # 🦜️🔗 LangChain | |
| ⚡ Build context-aware reasoning applications ⚡ | |
| [](https://github.com/langchain-ai/langchain/releases) | |
| [](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml) | |
| [](https://opensource.org/licenses/MIT) | |
| [](https://pypistats.org/packages/langchain-core) | |
| [](https://star-history.com/#langchain-ai/langchain) | |
| [](https://libraries.io/github/langchain-ai/langchain) | |
| [](https://github.com/langchain-ai/langchain/issues) | |
| [](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain) | |
| [](https://codespaces.new/langchain-ai/langchain) | |
| [](https://discord.gg/6adMQxSpJS) | |
| [](https://twitter.com/langchainai) | |
| Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs). | |
| To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com). | |
| [LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications. | |
| Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team. | |
| ## Quick Install | |
| With pip: | |
| ```bash | |
| pip install langchain | |
| ``` | |
| With conda: | |
| ```bash | |
| conda install langchain -c conda-forge | |
| ``` | |
| ## 🤔 What is LangChain? | |
| **LangChain** is a framework for developing applications powered by large language models (LLMs). | |
| For these applications, LangChain simplifies the entire application lifecycle: | |
| - **Open-source libraries**: Build your applications using LangChain's [modular building blocks](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel) and [components](https://python.langchain.com/v0.2/docs/concepts/#components). Integrate with hundreds of [third-party providers](https://python.langchain.com/v0.2/docs/integrations/platforms/). | |
| - **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence. | |
| - **Deployment**: Turn any chain into a REST API with [LangServe](https://python.langchain.com/v0.2/docs/langserve/). | |
| ### Open-source libraries | |
| - **`langchain-core`**: Base abstractions and LangChain Expression Language. | |
| - **`langchain-community`**: Third party integrations. | |
| - Some integrations have been further split into **partner packages** that only rely on **`langchain-core`**. Examples include **`langchain_openai`** and **`langchain_anthropic`**. | |
| - **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture. | |
| - **[`LangGraph`](https://langchain-ai.github.io/langgraph/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. | |
| ### Productionization: | |
| - **[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. | |
| ### Deployment: | |
| - **[LangServe](https://python.langchain.com/v0.2/docs/langserve/)**: A library for deploying LangChain chains as REST APIs. | |
|  | |
| ## 🧱 What can you build with LangChain? | |
| **❓ Question answering with RAG** | |
| - [Documentation](https://python.langchain.com/v0.2/docs/tutorials/rag/) | |
| - End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain) | |
| **🧱 Extracting structured output** | |
| - [Documentation](https://python.langchain.com/v0.2/docs/tutorials/extraction/) | |
| - End-to-end Example: [SQL Llama2 Template](https://github.com/langchain-ai/langchain-extract/) | |
| **🤖 Chatbots** | |
| - [Documentation](https://python.langchain.com/v0.2/docs/tutorials/chatbot/) | |
| - End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain) | |
| And much more! Head to the [Tutorials](https://python.langchain.com/v0.2/docs/tutorials/) section of the docs for more. | |
| ## 🚀 How does LangChain help? | |
| The main value props of the LangChain libraries are: | |
| 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 | |
| 2. **Off-the-shelf chains**: built-in assemblages of components for accomplishing higher-level tasks | |
| Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones. | |
| ## LangChain Expression Language (LCEL) | |
| LCEL is the foundation of many of LangChain's components, and is a declarative way to compose chains. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains. | |
| - **[Overview](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel)**: LCEL and its benefits | |
| - **[Interface](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface)**: The standard Runnable interface for LCEL objects | |
| - **[Primitives](https://python.langchain.com/v0.2/docs/how_to/#langchain-expression-language-lcel)**: More on the primitives LCEL includes | |
| - **[Cheatsheet](https://python.langchain.com/v0.2/docs/how_to/lcel_cheatsheet/)**: Quick overview of the most common usage patterns | |
| ## Components | |
| Components fall into the following **modules**: | |
| **📃 Model I/O** | |
| This includes [prompt management](https://python.langchain.com/v0.2/docs/concepts/#prompt-templates), [prompt optimization](https://python.langchain.com/v0.2/docs/concepts/#example-selectors), a generic interface for [chat models](https://python.langchain.com/v0.2/docs/concepts/#chat-models) and [LLMs](https://python.langchain.com/v0.2/docs/concepts/#llms), and common utilities for working with [model outputs](https://python.langchain.com/v0.2/docs/concepts/#output-parsers). | |
| **📚 Retrieval** | |
| Retrieval Augmented Generation involves [loading data](https://python.langchain.com/v0.2/docs/concepts/#document-loaders) from a variety of sources, [preparing it](https://python.langchain.com/v0.2/docs/concepts/#text-splitters), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/v0.2/docs/concepts/#retrievers) it for use in the generation step. | |
| **🤖 Agents** | |
| 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. LangChain provides a [standard interface for agents](https://python.langchain.com/v0.2/docs/concepts/#agents) along with the [LangGraph](https://github.com/langchain-ai/langgraph) extension for building custom agents. | |
| ## 📖 Documentation | |
| Please see [here](https://python.langchain.com) for full documentation, which includes: | |
| - [Introduction](https://python.langchain.com/v0.2/docs/introduction/): Overview of the framework and the structure of the docs. | |
| - [Tutorials](https://python.langchain.com/docs/use_cases/): 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. | |
| - [How-to guides](https://python.langchain.com/v0.2/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. | |
| - [Conceptual guide](https://python.langchain.com/v0.2/docs/concepts/): Conceptual explanations of the key parts of the framework. | |
| - [API Reference](https://api.python.langchain.com): Thorough documentation of every class and method. | |
| ## 🌐 Ecosystem | |
| - [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Tracing and evaluating your language model applications and intelligent agents to help you move from prototype to production. | |
| - [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Creating stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives. | |
| - [🦜🏓 LangServe](https://python.langchain.com/docs/langserve): Deploying LangChain runnables and chains as REST APIs. | |
| - [LangChain Templates](https://python.langchain.com/v0.2/docs/templates/): Example applications hosted with LangServe. | |
| ## 💁 Contributing | |
| 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. | |
| For detailed information on how to contribute, see [here](https://python.langchain.com/v0.2/docs/contributing/). | |
| ## 🌟 Contributors | |
| [](https://github.com/langchain-ai/langchain/graphs/contributors) | |