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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
📚 《Deep Agents 实战》—— LangChain 官方大使出品，基于 LangChain / LangGraph 生态，从零构建生产级 AI Agent 的完整指南
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Today
+8 stars today
This week
+54 stars this week
This month
+299 stars this month
Momentum
166.0
growth rate 1.91%/day
<div align="center">
# Deep Agents 实战
<p>
<a href="https://datawhalechina.github.io/deepagents-in-action/">
<img src="public/imgs/hero.png" alt="Deep Agents 实战 — 基于 LangChain / LangGraph 的中文开源课程，感谢 2,000+ GitHub Stars" width="800" />
</a>
</p>
[](https://datawhalechina.github.io/deepagents-in-action/)
[](https://space.bilibili.com/28357052/lists/7757577?type=season)
[](https://www.xiaohongshu.com/collection/item/69c4fd2a0072000000000001?xhsshare=&appuid=65032a0300000000120065e8&apptime=1778152909&share_id=2abb593f301a4e60a6e71fbbee3c8967)
[](https://docs.langchain.com/oss/python/deepagents/overview)
[](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh)
[](CONTRIBUTING.md)
<p>
<a href="https://trendshift.io/repositories/57851?utm_source=trendshift-badge&utm_medium=badge&utm_campaign=badge-trendshift-57851" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/57851/monthly?language=Astro" alt="datawhalechina%2Fdeepagents-in-action | Trendshift" width="250" height="55"/></a>
<a href="https://trendshift.io/developers/10200?utm_source=developer-badge&utm_medium=badge&utm_campaign=badge-developer-10200" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/developers/10200" alt="webup | Trendshift" width="250" height="55"/></a>
</p>
作者：**[沧海九粟](https://space.bilibili.com/28357052)** · LangChain 官方认证大使 · [LCAE 认证](https://certs.langchain.com/93ab2ac4-e237-4415-bcc8-46b27741f75a)
</div>
基于 LangChain / LangGraph 的中文开源课程，配套 **17 章图文与实验模板，并持续补充视频**。从第一个 Agent 开始，逐步掌握任务规划、文件系统、子 Agent 协作与上下文管理。
**从这里开始：** [环境准备](https://datawhalechina.github.io/deepagents-in-action/chapters/pre01-agentseek-create/) → [5 分钟上手](https://datawhalechina.github.io/deepagents-in-action/chapters/ch02-quickstart/) → [课程大纲](#课程大纲)
> [!NOTE]
> 课程从 0.5 起步，当前推荐使用最新的 **Deep Agents 0.7.x**。从 0.5 / 0.6 升级的读者，请先阅读 [v0.7 版本更新与迁移指南](https://datawhalechina.github.io/deepagents-in-action/chapters/release-v0-7/)。
<details>
<summary>版本要求与模型配置</summary>
旧章节和图片保留了当时的默认行为，紧邻它们的“v0.7 提醒”代表当前用法。进阶与 Beta 功能的依赖要求以各章说明为准。
| 功能 | 版本要求 |
| --- | --- |
| `FilesystemPermission` 基础权限 / `interrupt` 模式 | 分别需要 `deepagents>=0.5.2` / `deepagents>=0.6.8` |
| 第 13 章：`RubricMiddleware`（Beta） | 章节以 `deepagents==0.7.1` 验证 |
| 第 14 章：Event Streaming v3 | `deepagents>=0.6` |
| 第 15 章：Interpreters（Beta） | Python 3.11+、`langchain-quickjs>=0.2.0` |
| 第 17 章：Decision Model Harness | Python 3.11+；以 `deepagents==0.7.23`、`langchain-typesafe[experimental]==0.0.1a3` 验证 |
示例默认通过 [硅基流动](https://cloud.siliconflow.cn/i/Fq9zUwPf) 接入支持工具调用的模型，使用 `MODEL_NAME` 环境变量配置模型名。小模型可用于简单试跑；任务规划、上下文总结和多 Agent 编排需要更强的模型能力。
模型可用性、价格与免费额度会调整，使用前请查阅 [模型广场](https://cloud.siliconflow.cn/models)、[价格页](https://siliconflow.cn/pricing)和[更新公告](https://api-docs.siliconflow.cn/docs/release-notes/overview)。框架说明见 [Deep Agents 官方文档](https://docs.langchain.com/oss/python/deepagents/overview)。
</details>
---
## 课程大纲
**想先看 Deep Agents 的整体架构？** 第 1 章的[三层选择与组合](https://datawhalechina.github.io/deepagents-in-action/chapters/ch01-agent-harness/#三层如何选择与组合)解释 Deep Agents、LangChain 与 LangGraph 的分工；[整体架构图](https://datawhalechina.github.io/deepagents-in-action/chapters/ch01-agent-harness/#deep-agents-的技术全景)展示 Middleware、工具、Backend 和运行时的连接；第 2 章的[核心执行循环](https://datawhalechina.github.io/deepagents-in-action/chapters/ch02-quickstart/#核心执行循环)说明一次 Excerpt of 15,212 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:34be37f3bff06400, topic:ai-agents, topic:agentic-ai, topic:langgraph
matched fp:34be37f3bff06400, topic:tutorial, topic:course