# πŸ¦œοΈπŸ”— LangChain ⚑ Build context-aware reasoning applications ⚑ [![Release Notes](https://img.shields.io/github/release/langchain-ai/langchain)](https://github.com/langchain-ai/langchain/releases) [![CI](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml) [![Downloads](https://static.pepy.tech/badge/langchain-core/month)](https://pepy.tech/project/langchain-core) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS) [![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain) [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/langchain) [![GitHub star chart](https://img.shields.io/github/stars/langchain-ai/langchain?style=social)](https://star-history.com/#langchain-ai/langchain) [![Dependency Status](https://img.shields.io/librariesio/github/langchain-ai/langchain)](https://libraries.io/github/langchain-ai/langchain) [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langchain)](https://github.com/langchain-ai/langchain/issues) 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 ``` ## πŸ€” 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/docs/expression_language/) and [components](https://python.langchain.com/docs/modules/). Integrate with hundreds of [third-party providers](https://python.langchain.com/docs/integrations/platforms/). - **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://python.langchain.com/docs/langsmith/) so that you can constantly optimize and deploy with confidence. - **Deployment**: Turn any chain into a REST API with [LangServe](https://python.langchain.com/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://python.langchain.com/docs/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://python.langchain.com/docs/langsmith)**: 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/docs/langserve)**: A library for deploying LangChain chains as REST APIs. ![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/svg/langchain_stack.svg "LangChain Architecture Overview") ## 🧱 What can you build with LangChain? **❓ Question answering with RAG** - [Documentation](https://python.langchain.com/docs/use_cases/question_answering/) - 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/docs/use_cases/extraction/) - End-to-end Example: [SQL Llama2 Template](https://github.com/langchain-ai/langchain-extract/) **πŸ€– Chatbots** - [Documentation](https://python.langchain.com/docs/use_cases/chatbots) - 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 [Use cases](https://python.langchain.com/docs/use_cases/) section of the docs for more. ## πŸš€ 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 β€œprompt + LLM” chain to the most complex chains. - **[Overview](https://python.langchain.com/docs/expression_language/)**: LCEL and its benefits - **[Interface](https://python.langchain.com/docs/expression_language/interface)**: The standard interface for LCEL objects - **[Primitives](https://python.langchain.com/docs/expression_language/primitives)**: More on the primitives LCEL includes ## Components Components fall into the following **modules**: **πŸ“ƒ Model I/O:** This includes [prompt management](https://python.langchain.com/docs/modules/model_io/prompts/), [prompt optimization](https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/), a generic interface for [chat models](https://python.langchain.com/docs/modules/model_io/chat/) and [LLMs](https://python.langchain.com/docs/modules/model_io/llms/), and common utilities for working with [model outputs](https://python.langchain.com/docs/modules/model_io/output_parsers/). **πŸ“š Retrieval:** Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/modules/data_connection/document_loaders/) from a variety of sources, [preparing it](https://python.langchain.com/docs/modules/data_connection/document_loaders/), [then retrieving it](https://python.langchain.com/docs/modules/data_connection/retrievers/) for use in the generation step. **πŸ€– 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 done. LangChain provides a [standard interface for agents](https://python.langchain.com/docs/modules/agents/), a [selection of agents](https://python.langchain.com/docs/modules/agents/agent_types/) to choose from, and examples of end-to-end agents. ## πŸ“– Documentation Please see [here](https://python.langchain.com) for full documentation, which includes: - [Getting started](https://python.langchain.com/docs/get_started/introduction): installation, setting up the environment, simple examples - [Use case](https://python.langchain.com/docs/use_cases/) walkthroughs and best practice [guides](https://python.langchain.com/docs/guides/) - Overviews of the [interfaces](https://python.langchain.com/docs/expression_language/), [components](https://python.langchain.com/docs/modules/), and [integrations](https://python.langchain.com/docs/integrations/providers) You can also check out the full [API Reference docs](https://api.python.langchain.com). ## 🌐 Ecosystem - [πŸ¦œπŸ› οΈ LangSmith](https://python.langchain.com/docs/langsmith/): Tracing and evaluating your language model applications and intelligent agents to help you move from prototype to production. - [πŸ¦œπŸ•ΈοΈ LangGraph](https://python.langchain.com/docs/langgraph): Creating stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives. - [πŸ¦œπŸ“ LangServe](https://python.langchain.com/docs/langserve): Deploying LangChain runnables and chains as REST APIs. - [LangChain Templates](https://python.langchain.com/docs/templates/): Example applications hosted with LangServe. ## πŸ’ 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/docs/contributing/). ## 🌟 Contributors [![langchain contributors](https://contrib.rocks/image?repo=langchain-ai/langchain&max=2000)](https://github.com/langchain-ai/langchain/graphs/contributors)