Open-Source AI Agent Book Delivers 92 Hands-On Experiments and Architecture Principles
An open-source textbook breaks down AI agent engineering into 10 detailed chapters based on the formula Agent = LLM + Context + Tools. It includes 92 executable lab experiments covering Model Context Protocol, prompt distillation, and reinforcement learning.
Impact: High
Why it matters
Provides an end-to-end open-source roadmap for building, training, and evaluating production-grade AI agents across web, voice, and robotics.
TL;DR
- 01Open-source textbook with 10 chapters and 92 executable lab projects based on Agent = LLM + Context + Tools.
- 02Covers KV caching, Model Context Protocol, SFT/RL training trade-offs, and multimodal execution.
- 03Includes community translations across 7 languages under Apache License 2.0.
Key facts
- License
- Apache License 2.0
- Total Chapters
- 10 chapters
- Lab Experiments
- 92 executable projects
Core Agent Architecture
The text structures agent development across four main technical pillars: Context Management (KV Caching, Agent Skills, compression), Tool Integration (MCP, event-driven async agents), Training Stages (SFT vs RL trade-offs, tool-calling internalization), and Multi-Modal Execution (computer use, voice paradigms, robotics VLA).
Executable Lab Experiments
The repository bundles 92 practical hands-on labs, including:
chapter7/MiniMind-pretrain: Training small LLMs and VLMs from scratch.chapter8/prompt-distillation: Implementing cross-chapter prompt distillation pipelines.chapter9/browser-use: Integrating web browser automation agents.chapter10/TalkAct: Implementing dual-agent interactive architectures.