hello-agents vs ai-agents-for-beginners
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.
hello-agents
Chinese-language course on building LLM agents from scratch
ai-agents-for-beginners
18-lesson course on building AI agents with Microsoft Agent Framework
Verdict
Both score 77; hello-agents ranks higher on stars, 83,127 to 77,155. Of the 4 score parts we measure for both, hello-agents leads on 2, ai-agents-for-beginners on 1 and 1 is even.
- Adoptionhello-agents leads by 3 points, 88 to 85: close to even.
- FreshnessEven at 100.
- Maintenancehello-agents leads by 10 points, 70 to 60.
- Agent-readyai-agents-for-beginners leads by 30 points, 30 to 0: a wide gap.
Pick hello-agents if you are
Python developers and students learning to build LLM agents, Chinese readers
- 16 chapters from theory to capstone, with runnable code in the code folder
- Includes an Agentic-RL chapter going from SFT to GRPO training
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
| What we compare | hello-agents | ai-agents-for-beginners |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 88, widely used | 85, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 70, fair | 60, fair |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 0, none | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 83.1k | 77.2k |
| License | custom license (read the license) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Jul 2026 | None published |
| Language | Python | Jupyter Notebook |
| Docker | No | No |
| GPU | Not stated | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
hello-agents
A 16-chapter Chinese tutorial from Datawhale covering agent fundamentals, ReAct, Plan-and-Solve and Reflection, low-code platforms (Coze, Dify, n8n) and frameworks (AutoGen, AgentScope, LangGraph). Later chapters build a custom framework on the OpenAI API and cover memory, RAG, context engineering, MCP/A2A/ANP, Agentic-RL (SFT to GRPO) and evaluation. Chapter code is in the repo's code folder.
Who it is for: Python developers and students learning to build LLM agents, Chinese readers
Strengths
- 16 chapters from theory to capstone, with runnable code in the code folder
- Includes an Agentic-RL chapter going from SFT to GRPO training
- Builds a custom agent framework on the OpenAI API, not only framework usage
- Free online reading and PDF download, plus extra chapters on interviews and skills
Weaknesses
- Written in Chinese; an English README is linked separately
- License is CC BY-NC-SA 4.0, which bars commercial use
- Assumes basic Python and experience calling an LLM API
- Hardware and model requirements for the exercises are unknown
- Needs OpenAI-compatible LLM API
- Models: OpenAI API
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)
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