generative-ai-for-beginners vs hello-agents
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.
generative-ai-for-beginners
21-lesson course on building generative AI apps in Python and TypeScript
hello-agents
Chinese-language course on building LLM agents from scratch
Verdict
generative-ai-for-beginners ranks higher, 80 to 72. Of the 4 score parts we measure for both, generative-ai-for-beginners leads on 2, hello-agents on 1 and 1 is even.
- Adoptiongenerative-ai-for-beginners leads by 25 points, 96 to 71: a wide gap.
- FreshnessEven at 100.
- Maintenancehello-agents leads by 8 points, 70 to 62.
- Agent-readygenerative-ai-for-beginners leads by 30 points, 30 to 0: a wide gap.
Pick generative-ai-for-beginners if you are
Developers new to building LLM apps who know basic Python or TypeScript
- Build lessons ship code in both Python and TypeScript where possible
- Covers RAG, function calling, agents, fine-tuning and security in one sequence
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
| What we compare | generative-ai-for-beginners | hello-agents |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 96, widely used | 71, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 62, fair | 70, fair |
| 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 | 121.3k | 82.9k |
| License | MIT (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | None published | Jul 2026 |
| Language | Jupyter Notebook | Python |
| Docker | No | No |
| GPU | Not needed | Not stated |
| arm64 or Apple Silicon | Not stated | Not stated |
generative-ai-for-beginners
A Microsoft Cloud Advocates course of 21 lessons, each a README with a short video and code samples, split into "Learn" concept lessons and "Build" lessons. Topics include prompt engineering, text and chat apps, embeddings search, image generation, function calling, RAG, agents, fine-tuning, and small, Mistral and Meta models. Code targets Azure OpenAI, OpenAI API, Microsoft Foundry Models or Foundry Local.
Who it is for: Developers new to building LLM apps who know basic Python or TypeScript
Strengths
- Build lessons ship code in both Python and TypeScript where possible
- Covers RAG, function calling, agents, fine-tuning and security in one sequence
- Lessons are independent, so you can start at any number
- MIT license; translations into 50+ languages
Weaknesses
- Samples assume Azure OpenAI, OpenAI API or Foundry access, which may need paid accounts
- Several lessons list video as coming soon
- Cloning includes 50+ translations, so sparse checkout is needed for a small download
- GitHub Models lessons are tied to a service retiring end of July 2026
- no GPU
- Needs Azure OpenAI Service, OpenAI API, Microsoft Foundry Models, Foundry Local, GitHub account
- Models: Azure OpenAI, OpenAI, Microsoft Foundry Models, Foundry Local, Hugging Face open models
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
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