From foundational theory to production-grade multi-agent applications
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If 2024 was the year of the Battle of Foundation Models, then 2025 is undeniably the Year of Agents.
The AI landscape is shifting fast — from training bigger models to building smarter agent systems. Yet systematic, hands-on learning resources remain scarce. Hello-Agents bridges that gap.
This is a complete, open-source curriculum that takes you from zero to building production-grade multi-agent systems. We don't just teach you to use agent frameworks — we teach you to build them from the ground up.
| Feature | Description |
|---|---|
| 🔬 First-principles approach | Understand why agents work, not just how to call APIs |
| 🏗️ Build your own framework | Implement a full agent framework from scratch using OpenAI native API |
| 🌐 Protocol-level coverage | Deep dives into MCP, A2A, ANP communication protocols |
| 🎓 Interview-ready | Curated agent interview questions from top tech companies |
| 💻 100% runnable code | Every chapter has tested, working code in the /code folder |
| 🤝 Community-driven | Active co-creation, open PRs, and community extra chapters |
👉 http://helloagents.org/ — No setup required. Start learning immediately.
# Clone the repository
git clone https://github.com/Reyzowter/Hello-Agents.git
cd Hello-Agents
# Install Python dependencies (for code examples)
pip install -r code/requirements.txt
# Run your first agent
python code/chapter4/react_agent.py📄 Get the complete tutorial as a beautifully formatted PDF:
The curriculum is divided into 5 structured parts — each one a solid step forward.
Hello-Agents
├── Part 1: Agent & LLM Fundamentals (Chapters 1–3)
├── Part 2: Building Your First LLM Agent (Chapters 4–7)
├── Part 3: Advanced Techniques (Chapters 8–12)
├── Part 4: Real-World Case Studies (Chapters 13–15)
└── Part 5: Capstone & Future Outlook (Chapter 16)
| # | Chapter | Topics | Status |
|---|---|---|---|
| 0 | Preface | Project origin, background, how to use this book | ✅ |
| Part 1 | Agent & Language Model Fundamentals | ||
| 1 | Introduction to Agents | Agent definition, types, paradigms, real-world applications | ✅ |
| 2 | History of Agents | Symbolic AI → neural nets → LLM-driven agents | ✅ |
| 3 | LLM Fundamentals | Transformer, prompting, mainstream LLMs and limitations | ✅ |
| Part 2 | Building Your LLM Agent | ||
| 4 | Classic Agent Paradigms | Implement ReAct, Plan-and-Solve, Reflection from scratch | ✅ |
| 5 | Low-Code Agent Platforms | Coze, Dify, n8n — no-code agent building | ✅ |
| 6 | Framework Development | AutoGen, AgentScope, LangGraph in practice | ✅ |
| 7 | Build Your Own Framework | Implement a full agent framework from zero | ✅ |
| Part 3 | Advanced Knowledge | ||
| 8 | Memory & Retrieval | Memory systems, RAG pipelines, vector storage | ✅ |
| 9 | Context Engineering | Contextual understanding for continuous interaction | ✅ |
| 10 | Agent Communication Protocols | MCP, A2A, ANP deep-dives | ✅ |
| 11 | Agentic-RL | LLM training: SFT → GRPO full pipeline | ✅ |
| 12 | Agent Evaluation | Metrics, benchmarks, evaluation frameworks | ✅ |
| Part 4 | Real-World Case Studies | ||
| 13 | Intelligent Travel Assistant | MCP + multi-agent collaboration in production | ✅ |
| 14 | Deep Research Agent | Reproducing and extending DeepResearch Agent | ✅ |
| 15 | Cyber Town Simulation | Agents + games, simulating social dynamics | ✅ |
| Part 5 | Capstone | ||
| 16 | Graduation Project | Build your complete multi-agent application | ✅ |
| # | Extra Chapter | Summary |
|---|---|---|
| 00 | Co-creation Capstone Projects | Community-built multi-agent applications |
| 01 | Agent Interview Questions & Answers | Top agent interview Q&A with detailed answers |
| 02 | Context Engineering Supplement | Extended deep-dive into context management |
| 03 | Dify Agent Step-by-Step Tutorial | Complete Dify agent creation walkthrough |
| 04 | Hello-Agents FAQ | Common questions from the learning community |
| 05 | Agent Skills vs MCP Comparison | Technical comparison of agent skill systems |
| 06 | GUI Agent: Principles & Practice | GUI-driven agent concepts and implementation |
| 07 | Environment Configuration Guide | Setting up your dev environment correctly |
| 08 | Writing Effective Agent Skills | Best practices for skill authoring |
| 09 | Agent Development Pitfalls | Real-world lessons from building a Code Agent |
| 10 | Agent Self-Evolution | Four closed loops of agent self-improvement |
| 11 | Web Agent: Principles & Practice | Web automation, anti-bot, HelloAgents integration |
| 12 | Trip Planner Post-Training | Fine-tuning a trip-planner for real-world use |
| 13 | Video Course Co-creation | Resources for video course contributors |
|
🧑💻 AI Developers Building agent-powered products and need systematic foundations |
👩🎓 Students & Researchers Exploring LLM agents for research or coursework |
🏗️ Software Engineers Transitioning into AI-native application development |
🎯 Job Seekers Preparing for AI engineer interviews at top companies |
Prerequisites: Basic Python · Familiarity with calling LLM APIs
We welcome every form of contribution — from fixing a typo to writing an entire Extra Chapter.
- Fork this repository
- Create your branch:
git checkout -b feat/your-contribution - Commit:
git commit -m 'feat: add chapter on X' - Push:
git push origin feat/your-contribution - Open a Pull Request
📋 Read the full Contributing Guide →
| Type | How |
|---|---|
| 🐛 Found a bug | Open an Issue |
| 💡 Feature idea | Start a Discussion |
| 📝 Improve content | Submit a Pull Request |
| 🌍 Translate | Open an issue to coordinate |
@misc{hello_agents2025,
title = {Hello-Agents: Building an AI Agent System from Scratch},
author = {Reyzowter and Hello-Agents Contributors},
year = {2025},
url = {https://github.com/Reyzowter/Hello-Agents},
note = {GitHub repository — http://helloagents.org/}
}Licensed under CC BY-NC-SA 4.0 — free for learning, not for commercial resale.
⭐ If Hello-Agents helps you, please star the repo — it helps others find it!
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