#88th of 18 in Learning and cookbooks
ai-agent-book
Open book on AI agent design, with 109 runnable companion experiments
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- Stars
- 54.2k
- License
- Apache-2.0
- Last commit
- Sep 2026
- Language
- Python
82 on 25 Mar 202654,239 on 14 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
A Chinese-language book, "Understanding AI Agents in Depth", with 10 chapters built on the formula Agent = LLM + context + tools. It covers context engineering, memory and RAG, tools and MCP, coding agents, interaction, evaluation, post-training and multi-agent collaboration. The repo holds the book sources, PDF and EPUB builds in 15 languages, and 109 Python experiments installed per chapter with uv or pip.
Who it is for: Engineers learning to build and evaluate LLM agents, Chinese readers first
Strengths
- 109 companion experiments mapped to chapters, installable per chapter via uv.lock or pip
- PDF and EPUB in 15 languages, plus an online reader
- Covers evaluation and post-training (SFT/RL), not only prompting and tool use
- External repos are pinned to commit SHAs for reproducible setups
Weaknesses
- Original text is Chinese; the other 14 translations are community-made and may lag
- README says experiment code is agent-generated and not read line by line by the author
- Installing or cloning does not mean an experiment was run; status is tracked separately
- Most experiments need provider API keys; some need CUDA, browsers or external repos
What it needs
- GPU optional
- Needs Python 3.11-3.13, uv or pip, LLM provider API keys, pandoc and xelatex (only to build the PDF)
- Models: Kimi, GLM, DeepSeek, OpenRouter, Siliconflow
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| 2 | generative-ai-for-beginners21-lesson course on building generative AI apps in Python and TypeScript | 81 out of 100 |
| 3 | llms-from-scratchCode for the book on building a GPT-style LLM in PyTorch | 76 out of 100 |
| #4 | hello-agentsChinese-language course on building LLM agents from scratch | 76 out of 100 |
| #5 | ml-for-beginners12-week, 26-lesson classic machine learning course using Scikit-learn | 75 out of 100 |