{"data":{"slug":"adongwanai-agentguide","name":"AgentGuide","tagline":"AI Agent development guide with job interview preparation","github_url":"https://github.com/adongwanai/AgentGuide","owner":"adongwanai","repo":"AgentGuide","owner_avatar_url":"https://avatars.githubusercontent.com/u/54011546?v=4","primary_language":"HTML","stars":8484,"forks":835,"topics":["agenticrag","ai-agent","crewai","graphrag","grpo","interview","job-hunting","langchain","llm","multi-agent","rag","sft","tutorial"],"archived":false,"github_pushed_at":"2026-08-04T06:02:35+00:00","maintenance_label":"Active","stars_delta_30d":1368,"url":"https://www.graphcanon.com/tools/adongwanai-agentguide","markdown_url":"https://www.graphcanon.com/tools/adongwanai-agentguide.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/adongwanai-agentguide","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=adongwanai-agentguide","description":"https://adongwanai.github.io/AgentGuide | AI Agent开发指南 | LangGraph实战 | 高级RAG | 转行大模型 | 大模型面试 | 算法工程师 | 面试题库 | 强化学习｜数据合成","homepage_url":null,"license":null,"open_issues":36,"watchers":20,"ai_summary":"A comprehensive guide for AI Agent development, including tutorials, project practices, and job interview preparation. It covers topics such as LangGraph, RAG, and reinforcement learning.","readme_excerpt":"# AgentGuide\n\n\n<div align=\"center\">\n    <img src=\"https://img.shields.io/badge/Agent-%E5%BC%80%E5%8F%91%E6%8C%87%E5%8D%97-blue.svg?style=for-the-badge\" alt=\"Agent开发指南\">\n    <img src=\"https://img.shields.io/badge/%E9%9D%A2%E8%AF%95-%E6%B1%82%E8%81%8C%E5%AF%BC%E5%90%91-green.svg?style=for-the-badge\" alt=\"求职导向\">\n    <img src=\"https://img.shields.io/badge/%E9%A1%B9%E7%9B%AE-%E5%AE%8C%E5%85%A8%E5%BC%80%E6%BA%90-orange.svg?style=for-the-badge\" alt=\"完全开源\">\n    \n<br/>\n    \n<a href=\"https://github.com/adongwanai/AgentGuide\">\n        <img src=\"https://img.shields.io/github/stars/adongwanai/AgentGuide.svg?style=for-the-badge&logo=github&label=Stars\" alt=\"GitHub stars\">\n    </a>\n    <a href=\"https://github.com/adongwanai/AgentGuide/network/members\">\n        <img src=\"https://img.shields.io/github/forks/adongwanai/AgentGuide.svg?style=for-the-badge&logo=github&label=Forks\" alt=\"GitHub forks\">\n    </a>\n    \n<br/>\n    \n<h2>🔥 AI Agent 开发 × 面试求职 = 一站式解决方案</h2>\n    \n<p>\n        <strong>对标 JavaGuide 的 AI Agent 学习指南</strong><br/>\n        <strong>从入门到拿 Offer，系统化 + 实战化 + 求职导向</strong>\n    </p>\n</div>\n\n---\n\n## 💡 核心理念\n\n> **📌 本项目定位：资源整合 + 系统化路径 + 实战导向**\n>\n> 🎯 **我们的原则**：\n> - ✅ **站在巨人的肩膀上** - 互联网已有的优质资源（课程、教程、论文），我们直接引用，不重复造轮子\n> - ✅ **只分享干货** - （坚持更新中，欢迎催更）\n> - ✅ **提供系统化路径** - 将碎片化资源串联成完整学习路线，告诉你先学什么、再学什么\n> - ✅ **求职导向** - 每个知识点都标注\"面试怎么考\"、\"简历怎么写\"\n>\n> 💪 **AgentGuide 的独特价值**：不是简单的资源堆砌，而是**系统化 + 求职导向 + 实战验证**的完整解决方案！\n\n## 📑 目录\n\n**🎯 核心内容**：\n- [💡 关于本项目](#-关于本项目) - Agent开发指南、转行大模型、高级RAG、大模型面试\n- [🆕 求职新范式](#-求职新范式做出什么--学过什么) - 1-2-5框架、个人品牌、投递策略\n- [🧭 Agent 求职通关 Todo List](#-agent-求职通关-todo-list新增) - 当前优先级、8阶段学习产出、项目落地5步法\n- [🚦 6步学习路径](#-从零到offer的完整路径快速导航) - 从岗位选择到拿Offer\n- [🔬 算法岗 vs 🛠️ 开发岗](#-第一步确定你的目标岗位) - 岗位选择决策树\n- [📚 学习路线图](#-第三步基于岗位的学习路线) - 算法岗10-15周 | 开发岗8-12周\n- [💼 实战项目](#-第四步完成实战项目可写进简历) - 开源优质项目合集+N X Agent项目\n- [📖 技术教程](#-第五步系统学习-agent-技术技术准备) - LangGraph、RAG、上下文工程、监督微调、强化学习\n- [🎯 面试题库](#-第六步面试准备与-offer-冲刺) - 1500+题/面经、系统设计、编程题\n\n**🛠️ 快速导航**：\n- ⭐ 阿东作品推荐：[**learn-workbuddy**](https://github.com/adongwanai/learn-workbuddy) - 从 0 搭建 WorkBuddy-style Desktop Agent Harness，clean-room 教学复现 Agent Loop、工具调用、上下文工程、长期记忆、Sidecar、权限审计和真实模型评测\n- [🚀 10分钟快速开始](#-快速开始) | [💬 加入学习社群](#-联系作者--加入社群) | [❓ 常见问题](./FAQ.md)\n- [🧭 新手快速开始](./docs/00-getting-started/README.md) | [🧭 2026 Agent 求职路线](./docs/05-roadmaps/agent-job-ready-roadmap-2026.md) | [🛠️ Agent 项目落地方法](./docs/03-practice/05-ship-agent-project.md) | [🧩 Agent Harness Engineering](./docs/02-tech-stack/27-agent-harness-engineering.md)\n- [🔬 前沿算法完整路线](./docs/05-roadmaps/algorithm-complete-learning-guide.md) | [🤖 具身智能/VLA路线](./docs/05-roadmaps/embodied-ai-vla-learning-guide.md) | [💻 算法+AI手撕题库](./docs/04-interview/22-algorithm-ai-coding-question-bank.md) | [📋 小红书AI算法岗面经](./docs/04-interview/19-xiaohongshu-ai-algorithm-interview-bank.md)\n- [📄 Paper Agent](./projects/01-paper-agent/README.md) | [🧳 Travel Agent](./projects/02-travel-agent/README.md) | [🌐 Web Agent](./projects/03-web-agent/README.md) | [🖼️ Multimodal RAG](./resources/multimodal/README.md)\n\n---\n\n## 📖 关于本项目\n\n> **3 分钟了解为什么你需要 AgentGuide**\n\n### 😰 你是否正在经历这些痛点？\n\n- ❌ **学了一堆 LLM API 调用，但不知道 Agent 和普通对话有什么区别**\n- ❌ **看了无数篇 LangChain 文档，却依然不知道从哪里开始**\n- ❌ **做了一些 Demo 项目，但简历上写不出亮点，面试讲不清楚**\n- ❌ **想转 AI Agent 方向，但不知道算法岗和开发岗应该准备什么**\n- ❌ **网上资料又多又杂，缺少一条清晰的学习路线**\n\n**`AgentGuide` 是什么？**\n\n> **AI Agent 开发学习指南 | 转行大模型 | LangGraph 实战 | 高级RAG  | 大模型面试**\n\n一份系统化、求职导向的 AI Agent 学习与面试指南，涵盖：\n- **Agent 工程**：Agent Loop、LangGraph / OpenAI Agents SDK、MCP、Skills、权限与状态管理\n- **Context Engineering**：上下文分层、Memory、Tool Loadout、长任务压缩、成本与缓存优化\n- **RAG / Multimodal RAG**：文档解析、Embedding、Rerank、GraphRAG、Agentic RAG、视觉文档检索\n- **Eval / Observability / Safety**：Agent 评测集、trace、LLM-as-judge、红队、安全边界与 human-in-the-loop\n- **Post-training / Agent RL**：SFT、偏好优化、GRPO/DPO、工具调用数据合成、轨迹数据训练\n- **实战与求职**：Paper Agent、Travel Agent、Web Agent、项目复盘、简历表达与面试题库\n\n### 🗺️ AgentGuide 在 LLM 生态中的定位\n\n**我们覆盖 AI Agent 开发的完整技术栈** - 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