ai-engineering-from-scratch 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.
ai-engineering-from-scratch
Hands-on curriculum for building AI systems from math to agents
hello-agents
Chinese-language course on building LLM agents from scratch
Verdict
ai-engineering-from-scratch ranks higher, 75 to 72. Of the 4 score parts we measure for both, ai-engineering-from-scratch leads on 2, hello-agents on 1 and 1 is even.
- Adoptionhello-agents leads by 32 points, 71 to 39: a wide gap.
- FreshnessEven at 100.
- Maintenanceai-engineering-from-scratch leads by 25 points, 95 to 70: a wide gap.
- Agent-readyai-engineering-from-scratch leads by 45 points, 45 to 0: a wide gap.
Pick ai-engineering-from-scratch if you are
Developers learning to build LLM, RAG and agent systems from first principles
- Lessons ship runnable code; the first ones need only python3 and no extra dependencies
- Three staged projects with local graders (project_test.py --strict) and reference solutions
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 | ai-engineering-from-scratch | hello-agents |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 39, known | 71, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 95, healthy | 70, fair |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 45, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 68.4k | 82.9k |
| License | MIT (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Jul 2026 |
| Language | Python | Python |
| Docker | No | No |
| GPU | Not stated | Not stated |
| arm64 or Apple Silicon | Not stated | Not stated |
ai-engineering-from-scratch
A course of 528 lessons across 20 phases, covering math foundations, ML fundamentals, LLM engineering, agents, tools and protocols, and production infrastructure. Lessons are runnable code in Python, TypeScript, Rust and Julia, with three graded projects: retrieval evaluation, agent trace debugging and a tool-call firewall. It can be followed on the website, from a local clone, or through tutor skills installed into a coding agent.
Who it is for: Developers learning to build LLM, RAG and agent systems from first principles
Strengths
- Lessons ship runnable code; the first ones need only python3 and no extra dependencies
- Three staged projects with local graders (project_test.py --strict) and reference solutions
- Tutor skills install via npx skills and save progress to LEARNING.md
- Code in Python, TypeScript, Rust and Julia; README translated into 30+ languages
Weaknesses
- 528 lessons is a large scope; total study time is not stated in the visible README
- Project starters fail by design until you implement the stages
- Tutor skills need Node.js, npx and a skill-capable coding agent host
- GPU and RAM needs for later phases are not stated in the visible README
- Needs Python 3.10+, Node.js 22.18+ (TypeScript project), Rust (Tool Call Firewall project), npx (optional, tutor skills)
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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