10 GitHub Repos That Shaped My AI‑Agent Playbook

🤔 Curiosity: The Question

Over the last year, I’ve built and sold AI agents across real production workflows. The question I kept coming back to was simple: what repos actually teach you the right instincts for agents? Not just theory—the stuff that helps you ship.

This post is my answer: 10 GitHub repos that shaped the way I build agents today, each covering a distinct layer of the agent stack.

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📚 Retrieve: The Knowledge

1) Hands‑On Large Language Models

• Why it matters: end‑to‑end LLM fundamentals + fine‑tuning workflows
• Repo: Hands-On-Large-Language-Models

2) AI Agents for Beginners (Microsoft)

• Why it matters: structured onboarding for your first working agent
• Repo: AI Agents for Beginners

3) GenAI Agents (NirDiamant)

• Why it matters: practical agent patterns from basic to advanced
• Repo: GenAI_Agents

4) Made With ML

• Why it matters: how to design, deploy, and iterate production‑grade ML systems
• Repo: Made With ML

5) Prompt Engineering Guide

• Why it matters: the most comprehensive prompt/RAG/agent resource hub
• Repo: Prompt Engineering Guide

6) Hands‑On AI Engineering

• Why it matters: LLM‑powered apps you can clone, modify, and ship
• Repo: Hands-On AI Engineering

7) Awesome Generative AI Guide

• Why it matters: curated map of tools, research, and notebooks
• Repo: Awesome Generative AI Guide

8) Designing Machine Learning Systems (DMLS)

• Why it matters: systems thinking for production ML
• Repo: DMLS Book Resources

9) Machine Learning for Beginners (Microsoft)

• Why it matters: solid ML foundations for new teammates
• Repo: ML for Beginners

10) LLM Course

• Why it matters: roadmaps + notebooks that connect learning to building
• Repo: LLM Course

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Quick map: what each repo teaches best

Layer	Best Repo	Why
LLM fundamentals	Hands‑On LLM	full stack from basics → fine‑tuning
Agent patterns	GenAI Agents	concrete implementations
Production ML	Made With ML	deployment + iteration
Prompting/RAG	Prompt Engineering Guide	deepest resource hub
Team onboarding	AI Agents for Beginners	structured curriculum
Systems thinking	DMLS	production constraints

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💡 Innovation: The Insight

What these repos collectively teach

1) Agents are systems, not demos. The best repos emphasize deployment, monitoring, and iteration. 2) Prompting is a skill, but routing is the leverage. The more models you use, the more orchestration matters. 3) Shipping is the real curriculum. The best learning path is building something that survives contact with users.

What I’d build first (if starting today)

1) A single‑purpose agent (one workflow, one clear output) 2) Add memory + tools (RAG, file I/O, a single external API) 3) Introduce routing + fallback (cost and reliability control)

New Questions This Raises

• What’s the minimal agent stack that still feels “production‑ready”?
• How should we benchmark agent reliability beyond model accuracy?
• Can we standardize a shared “agent curriculum” across teams?

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References

1. Hands‑On Large Language Models
2. AI Agents for Beginners
3. GenAI Agents
4. Made With ML
5. Prompt Engineering Guide
6. Hands‑On AI Engineering
7. Awesome Generative AI Guide
8. Designing ML Systems (DMLS)
9. ML for Beginners
10. LLM Course