# DeepAgents — Multi-Step Agent Framework by LangChain

> Agent harness built on LangGraph by the LangChain team. Features planning tools, filesystem backend, and sub-agent spawning for complex multi-step tasks like codebase refactoring. 16,500+ stars.

## Install

Save as a script file and run:

## Quick Use

```bash
pip install deepagents
```

```python
from deepagents import Agent

agent = Agent(model="claude-sonnet-4-20250514")
result = agent.run("Refactor the authentication module to use JWT tokens")
print(result)
```

Or use the CLI:
```bash
deepagents run "Add comprehensive tests for the API layer"
```

---

## Intro

DeepAgents is a multi-step agent framework built on LangGraph by the LangChain team with 16,500+ GitHub stars. It provides a planning tool, filesystem backend, and the ability to spawn sub-agents for complex tasks like codebase refactoring, research, and multi-file editing. Unlike simple chat-based agents, DeepAgents creates execution plans, tracks progress, and can delegate subtasks to specialized sub-agents. Best for developers building production AI agent systems that handle multi-step workflows. Works with: Claude, GPT-4, Gemini, any LangChain-supported model. Setup time: under 2 minutes.

---

## Core Architecture

### Planning Tool
Before executing, DeepAgents creates a structured plan:

```python
agent = Agent(model="claude-sonnet-4-20250514", planning=True)
result = agent.run("Migrate the codebase from Express to Fastify")

# Agent creates a plan:
# 1. Analyze current Express routes and middleware
# 2. Create Fastify equivalents
# 3. Migrate route handlers
# 4. Update tests
# 5. Verify all endpoints work
```

### Sub-Agent Spawning
Complex tasks get broken into subtasks, each handled by a specialized sub-agent:

```python
from deepagents import Agent, SubAgent

researcher = SubAgent(role="researcher", model="claude-sonnet-4-20250514")
coder = SubAgent(role="coder", model="claude-sonnet-4-20250514")
reviewer = SubAgent(role="reviewer", model="gpt-4o")

agent = Agent(
    sub_agents=[researcher, coder, reviewer],
    workflow="research -> code -> review"
)
result = agent.run("Build a rate limiting middleware")
```

### Filesystem Backend
Persistent state across runs — agents remember previous work:

```python
agent = Agent(
    model="claude-sonnet-4-20250514",
    backend="filesystem",
    workspace="./agent-workspace"
)
```

### NVIDIA OpenShell Integration
Secure code execution in sandboxed containers for untrusted operations.

### Key Stats
- 16,500+ GitHub stars
- Built on LangGraph by LangChain team
- Sub-agent spawning for complex tasks
- Filesystem persistence across runs
- TypeScript version available (deepagentsjs)

### FAQ

**Q: What is DeepAgents?**
A: DeepAgents is a multi-step agent framework by the LangChain team that uses planning, sub-agent delegation, and persistent state to handle complex development tasks beyond simple chat-based interactions.

**Q: Is DeepAgents free?**
A: Yes, open-source under MIT license.

**Q: How is DeepAgents different from LangChain?**
A: LangChain is a general LLM framework. DeepAgents is specifically designed for multi-step agentic tasks with built-in planning, sub-agent orchestration, and filesystem persistence.

---

## Source & Thanks

> Created by [LangChain AI](https://github.com/langchain-ai). Licensed under MIT.
>
> [deepagents](https://github.com/langchain-ai/deepagents) — ⭐ 16,500+

Thanks to the LangChain team for advancing the state of multi-step AI agents.

---

<!-- ZH -->

## 快速使用

```bash
pip install deepagents
```

```python
from deepagents import Agent

agent = Agent(model="claude-sonnet-4-20250514")
result = agent.run("重构认证模块使用 JWT")
print(result)
```

---

## 简介

DeepAgents 是 LangChain 团队基于 LangGraph 构建的多步骤 Agent 框架，GitHub 16,500+ stars。提供规划工具、文件系统后端和子 Agent 生成能力，用于处理代码重构、研究和多文件编辑等复杂任务。与简单聊天 Agent 不同，DeepAgents 创建执行计划、跟踪进度，并将子任务委派给专门的子 Agent。

---

## 来源与感谢

> Created by [LangChain AI](https://github.com/langchain-ai). Licensed under MIT.
>
> [deepagents](https://github.com/langchain-ai/deepagents) — ⭐ 16,500+

---
Source: https://tokrepo.com/en/workflows/ac820f80-41ff-4eaa-b3b1-da27653bd7a5
Author: Agent Toolkit