ai-engineering-from-scratch 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.
ai-engineering-from-scratch
Hands-on AI engineering curriculum with 542 lessons across 20 phases
ai-agents-for-beginners
18-lesson course on building AI agents with Microsoft Agent Framework
Verdict
ai-engineering-from-scratch ranks higher, 86 to 77. Of the 4 score parts we measure for both, ai-engineering-from-scratch leads on 2, ai-agents-for-beginners on 1 and 1 is even.
- Adoptionai-agents-for-beginners leads by 6 points, 85 to 79.
- FreshnessEven at 100.
- Maintenanceai-engineering-from-scratch leads by 34 points, 94 to 60: a wide gap.
- Agent-readyai-engineering-from-scratch leads by 15 points, 45 to 30: a wide gap.
Pick ai-engineering-from-scratch if you are
Developers learning to build AI systems from first principles
- 542 lessons in 20 phases, each with runnable code in the repo
- Three staged projects with reference solutions and local graders
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 | ai-engineering-from-scratch | ai-agents-for-beginners |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 79, popular | 85, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 94, healthy | 60, fair |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 45, minimal | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 69.1k | 77.2k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | None published |
| Language | Python | Jupyter Notebook |
| Docker | No | No |
| GPU | Not stated | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
ai-engineering-from-scratch
A course where learners implement model internals, retrieval pipelines and agent runtimes in Python, TypeScript, Rust and Julia, from math foundations to LLM engineering and agents. Lessons can be read on the website, taught by coding-agent skills installed with `npx skills add`, or run locally. Three projects (retrieval evaluation, agent trace debugger, tool call firewall) ship with staged starters and local graders.
Who it is for: Developers learning to build AI systems from first principles
Strengths
- 542 lessons in 20 phases, each with runnable code in the repo
- Three staged projects with reference solutions and local graders
- Agent skills act as a tutor with placement and per-phase quizzes
- Website lessons need no setup; early local lessons are dependency-free
Weaknesses
- Tutor skills need Node.js, npx, python3 and a supported coding-agent host
- Hardware needs for later phases (GPU, RAM) are unknown from the README excerpt
- Large curriculum with no single linear path; learner must pick a route
- Projects need different toolchains: Python, Node.js 22.18+ and Rust
- Needs Python 3.10+, Node.js 22.18+ (agent trace debugger project), Rust (tool call firewall project), npx (tutor skills)
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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