隐私与广告选择
Git-Stars 会使用必要存储来保障网站运行。可选分析和广告测量脚本默认不加载，只有在你同意后，Google 等合作伙伴才可能按要求使用 Cookie 或类似标识符。 隐私政策
《深入理解 AI Agent：设计原理与工程实践》（李博杰 著）开源主仓库：全书正文、编译版 PDF 与按章配套代码
这个仓库详情页已经具备足够的原创分析、来源证据和同类对比，可以作为 Git-Stars 的可索引评测页。
100
评测分
解决的问题
This book solves the lack of a structured, practical, and open-source educational resource for building production-grade AI Agents. It bridges the gap between theoretical concepts and engineering implementation by providing a clear formula (Agent = LLM + Context + Tools) and hands-on code examples.
部署现实
当前可用的安装信号是：git clone https://github.com/bojieli/ai-agent-book.git。这只能作为起点，正式使用前建议让 Codex 或 Claude 检查 README、环境变量、运行时版本和部署目标。
商业可用性
当前记录的许可证是 Apache-2.0。这是重要的商业使用信号，但仍应核验许可证全文、依赖许可证、模型/API 条款和商标限制。
能力上限
Developers can build a wide range of AI Agent systems, from simple context-aware chatbots to complex multi-agent collaboration frameworks, coding agents, and multimodal interactive agents. Real-world use cases include customer support agents with memory, RAG-based knowledge assistants, automated code generation tools, and robotic control systems. The ceiling is production-level agents that can self-evolve and operate in real-time environments.
Git-Stars 是独立产品，不隶属于 GitHub 或该项目。 分析可能由 AI 辅助生成，依据公开仓库元数据和 README 的短摘要。 我们不镜像完整 README、文档、Issue 或社媒评论。
bojieli/ai-agent-book 被追踪为 Python 项目，主要属于 AI Agent, LLM Tool, MCP Server 方向。这个评估结合公开 GitHub 元数据、分类信号、短来源摘要和 Git-Stars 编辑规则，而不是复制项目文档。
增长检查：该仓库目前有 30k Star，今日 +0，本周 +9.3k，本月 +0。这些窗口用于区分持续采用信号和短期曝光峰值。
维护检查：当前活跃度为 一般；最近一次推送距今 72 天，未关闭 Issue 为 6，约占总 Star 的 0.02%。这只是采用信号，不替代工程尽调。
采用检查：3.2k Fork 和 30k Watcher 反映项目被复用和关注的程度。许可证信号：Apache-2.0。商业或内部使用前请核验许可证兼容性。
适用判断：当你需要「AI 原型、LLM 工作流和 Agent 类应用」时，这个项目更值得评估；如果「需要法律审查、安全审计或生产 SLA 保证」，则需要谨慎。
来源检查：Git-Stars 当前为这份报告保留了 2 个明确来源引用，近期增长信号为 9.3k。最终安装、安全和版本信息仍应以原始 GitHub 仓库为准。
原创分析
充分已经具备问题、能力和结论层面的原创判断。
来源
充分已附带仓库元数据和 README/来源引用。
许可证
充分已记录 Apache-2.0，可作为评估依据。
维护
充分仓库元数据中可以看到近期活跃信号。
同类对比
充分已有足够相近项目用于对比。
热度
30k 星标
复用
3.2k 复刻
关注
30k 关注者
维护
moderate
许可证
Apache-2.0
未解决 Issue
6
An open-source book that systematically explains AI Agent design principles and engineering practices across 10 chapters, with 88 accompanying experiments. It covers the core formula 'Agent = LLM + Context + Tools' and provides downloadable ebooks in multiple languages.
核心特性
- 10 chapters covering Agent fundamentals, context engineering, tools, coding agents, evaluation, post-training, self-evolution, multimodality, and multi-agent collaboration. - 88 accompanying projects (70+ runnable) with source code and ebooks in 5 languages. - Free PDF/EPUB downloads and community translations.
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
LLM Tool
Libraries and tools for LLM apps, RAG, prompts, and evals
MCP Server
Model Context Protocol servers, clients, and integrations
git clone https://github.com/bojieli/ai-agent-book.git70
健康评分
一般
提交活跃度
Sep 9, 2025
创建于
Aug 2, 2026
最近提交
每日同步积累数据后，Star 历史将在此显示。
复刻
未解决
关注者
bojieli
GitHub 主页obra/superpowers
An agentic skills framework & software development methodology that works.
affaan-m/ECC
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
NousResearch/hermes-agent
The agent that grows with you
mattpocock/skills
Skills for Real Engineers. Straight from my .agents directory.
multica-ai/andrej-karpathy-skills
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
✓
许可证
✓
Fork 情况
~ 一般
维护情况
解决的问题
This book solves the lack of a structured, practical, and open-source educational resource for building production-grade AI Agents. It bridges the gap between theoretical concepts and engineering implementation by providing a clear formula (Agent = LLM + Context + Tools) and hands-on code examples.
能力范围
Developers can build a wide range of AI Agent systems, from simple context-aware chatbots to complex multi-agent collaboration frameworks, coding agents, and multimodal interactive agents. Real-world use cases include customer support agents with memory, RAG-based knowledge assistants, automated code generation tools, and robotic control systems. The ceiling is production-level agents that can self-evolve and operate in real-time environments.
结论
This book is ideal for developers and researchers seeking a comprehensive, hands-on guide to building AI Agents from scratch. It is not for those looking for a quick, high-level overview or a specific framework tutorial. The key trade-off is depth versus breadth: it covers many topics thoroughly but requires significant time investment to work through all 88 projects.