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AI Agent Analysis
AB

bojieli/ai-agent-book

《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

stars
30k
Language
Python
GitHub
Source and compliance noteLast synced: Aug 3, 2026

Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.

Original GitHub sourceMethodologyEditorial Policy

Overview

This is an open-source book repository titled 'Deep Understanding of AI Agents: Design Principles and Engineering Practice' by Bojie Li. It provides a comprehensive 10-chapter guide covering AI Agent fundamentals, context engineering, tools, coding agents, evaluation, model post-training, self-evolution, multimodality, and multi-agent collaboration, with 88 accompanying code projects and multi-language translations.

Installation

Clone the repo and optionally run `cd book && bash build_pdf.sh` to compile the PDF (requires pandoc, xelatex, ElegantBook).

Problem solved

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.

What you can build

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.

Community sentiment

Positive

No community feedback yet.

Concerns

No concerns documented yet.

Bottom line

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.

Analyzed by Git-Stars - 7/22/2026