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# 🦜️🔗 LangChain
<picture>
<source media="(prefers-color-scheme: light)" srcset="docs/static/img/logo-dark.svg">
<source media="(prefers-color-scheme: dark)" srcset="docs/static/img/logo-light.svg">
<img alt="LangChain Logo" src="docs/static/img/logo-dark.svg" width="80%">
</picture>
⚡ Build context-aware reasoning applications ⚡
<div>
<br>
</div>
[![Release Notes](https://img.shields.io/github/release/langchain-ai/langchain?style=flat-square)](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)
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[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/langchain)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
> [!NOTE]
> 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:
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.
```bash
pip install langchain
pip install -U langchain
```
With conda:
To learn more about LangChain, check out
[the docs](https://python.langchain.com/docs/introduction/). 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.
```bash
conda install langchain -c conda-forge
```
## Why use LangChain?
## 🤔 What is LangChain?
LangChain helps developers build applications powered by LLMs through a standard
interface for models, embeddings, vector stores, and more.
**LangChain** is a framework for developing applications powered by large language models (LLMs).
Use LangChain for:
- **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.
- **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.
For these applications, LangChain simplifies the entire application lifecycle:
## LangChain’s ecosystem
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.
To improve your LLM application development, pair LangChain with:
- **Open-source libraries**: Build your applications using LangChain's open-source
[components](https://python.langchain.com/docs/concepts/) and
[third-party integrations](https://python.langchain.com/docs/integrations/providers/).
Use [LangGraph](https://langchain-ai.github.io/langgraph/) to build stateful agents with first-class streaming and human-in-the-loop support.
- **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 your LangGraph applications into production-ready APIs and Assistants with [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/).
- [LangSmith](http://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.
- [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.
- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#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/).
### Open-source libraries
- **`langchain-core`**: Base abstractions.
- **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.
- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
- **`langchain-community`**: Third-party integrations that are community maintained.
- **[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).
### 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:
- **[LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/)**: Turn your LangGraph applications into production-ready APIs and Assistants.
![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/svg/langchain_stack_112024.svg#gh-light-mode-only "LangChain Architecture Overview")
![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/svg/langchain_stack_112024_dark.svg#gh-dark-mode-only "LangChain Architecture Overview")
## 🧱 What can you build with LangChain?
**❓ Question answering with RAG**
- [Documentation](https://python.langchain.com/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/docs/tutorials/extraction/)
- End-to-end Example: [LangChain Extract](https://github.com/langchain-ai/langchain-extract/)
**🤖 Chatbots**
- [Documentation](https://python.langchain.com/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/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. **Easy orchestration with LangGraph**: [LangGraph](https://langchain-ai.github.io/langgraph/),
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/),
and other LangChain abstractions. This makes it easy to combine components into
production-ready applications with persistence, streaming, and other key features.
Check out the LangChain [tutorials page](https://python.langchain.com/docs/tutorials/#orchestration) for examples.
## Components
Components fall into the following **modules**:
**📃 Model I/O**
This includes [prompt management](https://python.langchain.com/docs/concepts/prompt_templates/)
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.
**📚 Retrieval**
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.
**🤖 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. [LangGraph](https://langchain-ai.github.io/langgraph/) makes it easy to use
LangChain components to build both [custom](https://langchain-ai.github.io/langgraph/tutorials/)
and [built-in](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/)
LLM agents.
## 📖 Documentation
Please see [here](https://python.langchain.com) for full documentation, which includes:
- [Introduction](https://python.langchain.com/docs/introduction/): Overview of the framework and the structure of the docs.
- [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.
- [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.
- [Conceptual guide](https://python.langchain.com/docs/concepts/): Conceptual explanations of the key parts of the framework.
- [API Reference](https://python.langchain.com/api_reference/): Thorough documentation of every class and method.
## 🌐 Ecosystem
- [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
- [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
- [🦜🕸️ LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): Deploy LLM applications built with LangGraph into production.
## 💁 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)
## Additional resources
- [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with
guided examples on getting started with LangChain.
- [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.
- [Conceptual Guides](https://python.langchain.com/docs/concepts/): Explanations of key
concepts behind the LangChain framework.
- [API Reference](https://python.langchain.com/api_reference/): Detailed reference on
navigating base packages and integrations for LangChain.

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