ai-agent-book is a comprehensive open-source textbook that teaches AI agent design principles and engineering practices through theory and hands-on code examples.
The book addresses how to build effective AI agents by grounding them in a core formula: Agent equals LLM plus context plus tools. It structures this understanding across ten chapters that progress from foundational concepts to production-ready implementation. The approach combines theoretical explanation with practical experimentation, providing ninety-five accompanying code examples that readers can execute themselves to reinforce learning.
The project suits developers and engineers who want to move beyond basic LLM usage and understand how to architect agents that can reason, maintain state, and interact with external systems. It works well for teams building multi-agent systems, implementing retrieval-augmented generation, or integrating language models with tools and memory mechanisms. The book covers agent memory, context engineering, reinforcement learning approaches, and multimodal capabilities alongside practical coding patterns. The material is available in multiple formats including PDF, EPUB, and online reading with full-text search, making it accessible whether you prefer offline study or interactive browsing.
The project maintains active development with regular updates to both the textbook content and accompanying experiments. The codebase includes examples across multiple implementation approaches, reflecting evolving best practices in the field. Documentation spans thirteen languages, indicating sustained effort to reach a global audience. The repository structure separates the core textbook content from experimental code, allowing readers to study theory independently or dive into practical examples as needed.
