generative-ai-for-beginners 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.
generative-ai-for-beginners
21-lesson course on building generative AI apps in Python and TypeScript
ai-agents-for-beginners
18-lesson course on building AI agents with Microsoft Agent Framework
Verdict
generative-ai-for-beginners ranks higher, 81 to 77. Of the 4 score parts we measure for both, generative-ai-for-beginners leads on 2, ai-agents-for-beginners on none and 2 are even.
- Adoptiongenerative-ai-for-beginners leads by 13 points, 98 to 85.
- FreshnessEven at 100.
- Maintenancegenerative-ai-for-beginners leads by 2 points, 62 to 60: close to even.
- Agent-readyEven at 30.
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 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 | generative-ai-for-beginners | ai-agents-for-beginners |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 98, widely used | 85, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 62, fair | 60, fair |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 121.4k | 77.2k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | None published | None published |
| Language | Jupyter Notebook | Jupyter Notebook |
| Docker | No | No |
| GPU | Not needed | Not needed |
| 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
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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