ai-agent-book: an open-source book and experimental codebase for mastering AI agent design and engineering

What it solves

It addresses the gap between theoretical understanding and practical engineering in the field of AI Agents. The project provides a comprehensive guide to moving from basic LLM interactions to building production-ready agents using the formula: Agent = LLM + Context + Tools.

How it works

The project is structured as a 10-chapter book covering topics from context engineering and RAG to coding agents, model fine-tuning, and multi-agent collaboration. It accompanies the text with 92 practical experiments and code implementations, including 70+ independently runnable projects that demonstrate real-world applications like browser automation, robotic control, and autonomous coding.

Who it’s for

• AI engineers and developers looking to implement agentic workflows.
• Researchers interested in agent evaluation, fine-tuning, and multi-agent systems.
• Students and enthusiasts wanting to learn AI agent principles through hands-on coding experiments.

Highlights

• 10 chapters covering the full lifecycle from fundamentals to production.
• 92 accompanying experiments and code samples.
• Multilingual support in Chinese, English, Russian, Tamil, Vietnamese, and Japanese.
• Practical coverage of advanced topics like MCP protocols, RAG, and Sim2Real robotics.