---
title: "langchain"
type: "tool"
slug: "langchain-ai-langchain"
canonical_url: "https://www.graphcanon.com/tools/langchain-ai-langchain"
github_url: "https://github.com/langchain-ai/langchain"
homepage_url: "https://docs.langchain.com/langchain/"
stars: 141208
forks: 23472
primary_language: "Python"
license: "MIT"
categories: ["vector-databases", "llm-frameworks", "ai-agents"]
tags: ["agents", "ai", "deepagents", "chatgpt", "anthropic", "framework", "ai-agents", "enterprise"]
updated_at: "2026-07-07T17:30:27.991723+00:00"
---

# langchain

> The agent engineering platform.

The agent engineering platform.

## Facts

- Repository: https://github.com/langchain-ai/langchain
- Homepage: https://docs.langchain.com/langchain/
- Stars: 141,208 · Forks: 23,472 · Open issues: 404 · Watchers: 891
- Primary language: Python
- License: MIT
- Last pushed: 2026-07-07T15:40:51+00:00

## Categories

- [Vector Databases](/categories/vector-databases.md)
- [LLM Frameworks](/categories/llm-frameworks.md)
- [AI Agents](/categories/ai-agents.md)

## Tags

agents, ai, deepagents, chatgpt, anthropic, framework, ai-agents, enterprise

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## README (excerpt)

```text
<div align="center">
  <a href="https://docs.langchain.com/oss/python/langchain/overview">
    <picture>
      <source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-dark.svg">
      <source media="(prefers-color-scheme: light)" srcset=".github/images/logo-light.svg">
      <img alt="LangChain Logo" src=".github/images/logo-dark.svg" width="50%">
    </picture>
  </a>
</div>

<div align="center">
  <h3>The agent engineering platform.</h3>
</div>

<div align="center">
  <a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/langchain" alt="PyPI - License"></a>
  <a href="https://pypistats.org/packages/langchain" target="_blank"><img src="https://img.shields.io/pepy/dt/langchain" alt="PyPI - Downloads"></a>
  <a href="https://pypi.org/project/langchain/#history" target="_blank"><img src="https://img.shields.io/pypi/v/langchain?label=%20" alt="Version"></a>
  <a href="https://x.com/langchain_oss" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain_oss.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
</div>

<br>

LangChain is a framework for building agents and 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.

> [!TIP]
> Just getting started? Check out **[Deep Agents](http://docs.langchain.com/oss/python/deepagents/)** — a higher-level package built on LangChain for agents that have built-in capabilites for common usage patterns such as planning, subagents, file system usage, and more.

## Quickstart

```bash
uv add langchain
```

```python
from langchain.chat_models import init_chat_model

model = init_chat_model("openai:gpt-5.5")
result = model.invoke("Hello, world!")
```

If you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://github.com/langchain-ai/langgraph), our framework for building controllable agent workflows.

For an equivalent JS/TS library, check out [LangChain.js](https://github.com/langchain-ai/langchainjs).

> [!TIP]
> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).

## LangChain 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.

- **[Deep Agents](http://docs.langchain.com/oss/python/deepagents/)** — Build agents that can plan, use subagents, and leverage file systems for complex tasks
- **[LangGraph](https://docs.langchain.com/oss/python/langgraph/overview)** — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
- **[Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview)** — Chat & embedding models, tools & toolkits, and more
- **[LangSmith](https://www.langchain.com/langsmith)** — Agent evals, observability, and debugging for LLM apps
- **[LangSmith Deployment](https://docs.langchain.com/langsmith/deployments)** — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows

## Why use LangChain?

LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.

- **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
- **Rapid prototyping** —
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/langchain-ai-langchain`](/api/graphcanon/tools/langchain-ai-langchain)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_