ai-agents-for-beginners vs ai-agent-book
Two of the top projects in Learning and cookbooks, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
ai-agents-for-beginners
18-lesson course on building AI agents with Microsoft Agent Framework
ai-agent-book
Open book on AI agent design, with 109 runnable companion experiments
Verdict
Both score 77; ai-agents-for-beginners ranks higher on stars, 77,229 to 54,403. Of the 4 score parts we measure for both, ai-agents-for-beginners leads on 2, ai-agent-book on 1 and 1 is even.
- Adoptionai-agents-for-beginners leads by 12 points, 88 to 76.
- FreshnessEven at 100.
- Maintenanceai-agent-book leads by 21 points, 81 to 60: a wide gap.
- Agent-readyai-agents-for-beginners leads by 30 points, 30 to 0: a wide gap.
Pick ai-agents-for-beginners if you are
Developers new to building AI agents, comfortable with Python
- Each lesson is standalone, so you can start at any topic
- Covers design patterns plus MCP, A2A, NLWeb, memory and deployment
Pick ai-agent-book if you are
Engineers learning to build and evaluate LLM agents, Chinese readers first
- 109 companion experiments mapped to chapters, installable per chapter via uv.lock or pip
- PDF and EPUB in 15 languages, plus an online reader
| What we compare | ai-agents-for-beginners | ai-agent-book |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 88, widely used | 76, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 60, fair | 81, healthy |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 77.2k | 54.4k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Sep 2026 |
| Last release | None published | None published |
| Language | Jupyter Notebook | Python |
| Docker | No | No |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
ai-agents-for-beginners
A Microsoft course of 18 lessons on building AI agents, each with a written README, a short video for most lessons, and Python code samples in Jupyter notebooks. Topics include tool use, agentic RAG, planning, multi-agent patterns, memory, MCP/A2A/NLWeb protocols, computer-use agents, local agents and securing agents. Samples use Microsoft Agent Framework with Microsoft Foundry Agent Service V2, and some also support OpenAI-compatible providers such as MiniMax.
Who it is for: Developers new to building AI agents, comfortable with Python
Strengths
- Each lesson is standalone, so you can start at any topic
- Covers design patterns plus MCP, A2A, NLWeb, memory and deployment
- Text, short videos and runnable Python samples per lesson
- 50+ language translations kept current by GitHub Action
Weaknesses
- Samples are built around Microsoft Agent Framework and Foundry, which need an Azure account
- Some later lessons have no video
- Cloning pulls large translation folders unless sparse checkout is used
- Alternative provider support is only partial, per the README
- no GPU
- Needs Microsoft Foundry (Azure account), Microsoft Agent Framework, Microsoft Foundry Agent Service V2
- Models: Microsoft Foundry models, OpenAI-compatible providers (MiniMax)
ai-agent-book
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
- 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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