Top AI Repos — open-source AI, indexed and scored
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.
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.
AI Agent 教学仓库 | 系统化 LangChain、RAG、LangGraph、MCP 全栈实战代码 | 万字博客详解 | 开源可运行示例 | 从零构建智能体
| Date | Stars |
|---|---|
| 2026-07-24 | 394 |
| 2026-07-25 | 395 |
| 2026-07-28 | 395 |
| 2026-07-30 | 395 |
| 2026-07-31 | 400 |
| 2026-08-01 | 401 |
| 2026-08-02 | 404 |
| 2026-08-03 | 404 |
| 2026-08-04 | 404 |
| 2026-08-05 | 407 |
| 2026-08-06 | 407 |
Today
— stars today
This week
+12 stars this week
This month
— stars this month
Momentum
0.0
growth rate 3.04%/day
# 🤖 Agent Craft —— 从零构建全栈AI智能体 > ✅ **系统性开源教学项目**,手把手带你用 Python 构建可运行的 AI Agent,涵盖 Prompt、LLM、LangChain、RAG、LangGraph、MCP 到部署发布全流程。 [](https://github.com/Annyfee/agent-craft/actions/workflows/ci.yml) ## 📘 项目简介 如果你正在学习 AI 智能体开发,却卡在“知道概念但不会动手”或“会调 API 但不懂原理”的阶段—— **Agent Craft 就是为你设计的学习路径。** 我们从最基础的 LLM 调用开始,一步步带你构建完整的 Agent 能力栈: - 让模型调用外部工具(Function Calling) - 接入私有知识库实现问答(RAG) - 用 LangGraph 实现带状态、可调试的决策流程 - 整合记忆、工具与检索,打造真正可用的智能体 所有内容围绕两个原则展开: - 💻 **代码可运行**:每个模块独立、注释清晰、本地一键复现 - 📖 **原理可理解**:配套 CSDN 博客详解设计思路与关键机制 > 💡 **我们不重复造轮子,但也不止于调用框架**—— > 而是在 LangChain、LangGraph 等主流工具中,看清 Agent 是如何思考、决策与行动的。 --- ## 🧭 模块总览(15 模块) > 💡 每个模块对应独立目录(含代码、说明与示例),可独立运行与学习。 > 🔥 已更新至 13 Streamlit 快速入门,持续更新中! | 章节 | 模块 | 博客 | 核心关键词 | 难度 | |:-----------:|:---------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------:|:-------------------------------------------------------------|:-----:| | 🏗️ **基础篇** | [01 Agent 入门 & 环境搭建](https://github.com/Annyfee/agent-craft/tree/main/m01_agent_introduction) | [🏠](https://blog.csdn.net/2401_87328929/article/details/153729555) | OpenAI API | ⭐ | | | [02 LLM 基础调用](https://github.com/Annyfee/agent-craft/tree/main/m02_llm_fundamentals) | [🏠](https://blog.csdn.net/2401_87328929/article/details/153735431) | LLM API 调用 · prompt · 上下文记忆 | ⭐ | | | [03 Function Calling 与工具调用](https://github.com/Annyfee/agent-craft/tree/main/m03_function_calling_tools) | [🏠](https://blog.csdn.net/2401_87328929/article/details/153866573) | Function Call · 工具函数封装 | ⭐⭐ | | ⚙️ **框架篇** | [04 LangChain 基础篇](https://github.com/Annyfee/agent-craft/tree/main/m04_langchain_basics) | [🏠](https://blog.csdn.net/2401_87328929/article/details/153978186) | LLM · Prompt · Chain · Memory | ⭐⭐ | | | [05 LangChain 进阶篇](https://github.com/Annyfee/agent-craft/tree/main/m05_langchain_advanced) | [🏠](https://blog.csdn.net/2401_87328929/article/details/154064397) | Agents · 缓存 · 流式输出 | ⭐⭐⭐ | | | [06 RAG 基础篇](https://github.com/Annyfee/agent-craft/tree/main/m06_rag_basics) | [🏠](https://blog.csdn.net/2401_87328929/article/details/154230067) | RAG概念 · Split · Embedding · FAISS · RAG 链 | ⭐⭐ | | | [07 RAG 进阶篇](https://github.com/Annyfee/agent-craft/tree/main/m07_rag_advanced) | [🏠](https://blog.csdn.net/2401_87328929/article/details/154408744) | Chroma · Reranker · RAG工具化 · 集成 | ⭐⭐⭐ | | | [08 LangGraph 基础篇](https://github.com/Annyfee/agent-craft/tree/main/m08_langgraph_basics) | [🏠](https://blog.csdn.net/2401_87328929/article/details/154576812) | Langgraph三要素 · LangSmith · ReAct · 提示词安全注入 | ⭐⭐⭐ | | | [09 LangGraph 进阶篇](https://github.com/Annyfee/agent-craft/tree/main/m09_langgraph_advanced) | [🏠](https://blog.csdn.net/2401_87328929/article/details/154803618) | Human-in-the-Loop · Graph-as-a-Tool · Multi-Agent 多智能体编排 | ⭐⭐⭐⭐ | | 🧠 **智能篇** | [10 MCP 基础 (Server)](https://github.com/Annyfee/agent-craft/tree/main/m10_mcp_basics) | [🏠](https://blog.csdn.net/2401_87328929/article/details/155660129) | Stdio · Streamable HTTP · FastMCP框架 · Transport通信 · 构建私有 MCP | ⭐⭐ | | | [11 M
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2df876b1646bb165, topic:ai-agent, topic:multi-agent, topic:langgraph