💡 This means the content you read here is not static — it evolves continuously with the latest advances in the AI Agent field.

✨ Key Features

• 🎯 Step by Step: From LLM fundamentals to multi-Agent systems, each chapter has a clear knowledge progression
• 💻 Code First: Every core concept comes with runnable Python code examples
• 🎨 Rich Illustrations: 120+ hand-drawn SVG architecture diagrams / flowcharts / sequence diagrams for intuitive understanding
• 🎬 Interactive Animations: 5 built-in interactive HTML animations (Perceive-Think-Act cycle, ReAct reasoning, Function Calling, RAG flow, GRPO sampling)
• 🔬 Paper Reviews: Key chapters include frontier paper deep-dives (ReAct, Reflexion, MemGPT, GRPO, etc.)
• 🏗️ Complete Projects: 3 comprehensive hands-on projects (AI Coding Assistant, Intelligent Data Analysis Agent, Multimodal Agent)
• 🛡️ Production Ready: Covers security, evaluation, deployment, and other production essentials
• 🧪 Cutting Edge: Covers Context Engineering, Agentic-RL (GRPO/DPO/PPO), MCP/A2A/ANP, and other 2025–2026 latest advances
• 📐 Formula Support: KaTeX-rendered math formulas for clear reading of policy gradient, KL divergence derivations in RL chapters
• 🔄 Continuously Updated: Tracking the latest changes in LangChain, LangGraph, MCP, and other frameworks

📸 Selected Content Preview

Below are selected showcases from the book's 120+ hand-drawn SVG illustrations, all original to this book.

🧠 Agent Core Architecture

Perceive-Think-Act Loop (Chapter 1)

Agent's core mechanism: Perceive environment → LLM reasoning → Execute action → Loop until goal achieved

ReAct Reasoning Framework (Chapter 6)

Thought → Action → Observation alternating loop, enabling Agents to think while acting

🛠️ Tool Calling & RAG

Function Calling Complete Flow (Chapter 4)

6-step complete flow from user input to tool invocation to final response, with message structure illustration

RAG Retrieval-Augmented Generation (Chapter 7)

Offline indexing + Online retrieval dual-phase architecture, making LLM answers evidence-based

💾 Memory System & Context Engineering

Three-Layer Memory Architecture (Chapter 5)

Working memory → Short-term memory → Long-term memory, with important info sinking down and semantic retrieval pulling up

Prompt Engineering vs Context Engineering (Chapter 8)

From "how to say it" to "what the LLM sees" — the paradigm shift of the Agent era

🤝 Multi-Agent & Communication Protocols

Three Multi-Agent Communication Patterns (Chapter 14)

Message Queue (async decoupling) / Shared Blackboard (data sharing) / Direct Call (real-time collaboration)

MCP / A2A / ANP Protocol Comparison (Chapter 15)

Three-layer protocol stack: ANP for discovery → A2A for task collaboration → MCP for tool invocation

🧪 Reinforcement Learning & Frameworks

GRPO Training Architecture (Chapter 10)

No Critic model needed, computes advantage via intra-group normalization, only 1.5× model size in VRAM

LangGraph Three Core Concepts (Chapter 12)

State (shared state) · Node (processing unit) · Edge (execution flow control)

📖 The above is just a selected preview — For the full 120+ architecture diagrams + 5 interactive animations, please read online

🎬 Interactive Animations

This book includes 5 interactive HTML animations to help you intuitively understand the dynamic processes of core concepts:

| Animation | Chapter | Description | | ------------------------------ | ---------- | --------------------------------------------------------------------------- | | 🔄 Perceive-Think-Act Cycle | Chapter 1 | Dynamic demonstration of Agent's core loop | | 💡 ReAct Reasoning Process | Chapter 6 | Shows the alternating Thought → Action → Observation process | | 🔧 Function Calling | Chapter 4 | Complete tool invocation flow animation | | 📚 RAG Retrieval Flow | Chapter 7 | From document chunking to vector retrieval to answer generation | | 🎯 GRPO Sampling Process | Chapter 10 | Visualization of intra-group multi-output sampling and reward normalization |

💡 Interactive animations are only available in the online e-book. Local builds can also preview them.

🔥 Core Topics at a Glance

🧠 Agent Core Architecture

• Perceive → Think → Act Loop
• ReAct Reasoning Framework
• Task Decomposition & Planning
• Reflection & Self-Correction

🛠️ Tools & Skills

• Function Calling Mechanism
• Custom Tool Design
• Skill System Construction
• Tool Description Best Practices

🧪 Reinforcement Learning Training

• SFT + LoRA Basic Training
• PPO / DPO / GRPO Algorithm Deep-Dive
• Complete Training Pipeline Hands-on
• 2025–2026 Latest Research Advances

💾 Memory, Knowledge & Context

• Short-term / Long-term / Working Memory
• Vector Databases (Chroma / FAISS)
• RAG Retrieval-Augmented Generation
• Context Engineering & Attention Budget

🤝 Multi-Agent Collaboration & Communication

• MCP / A2A / ANP Protocol Stack
• Supervisor vs Decentralized Patterns
• CrewAI / AutoGen Frameworks
• LangGraph Stateful Agents

🛡️ Production Full Pipeline

• Evaluation Benchmarks (GAIA / SWE-bench)
• Security Defense & Sandbox Isolation
• Containerized Deployment & Streaming
• Observability & Cost Optimization

🚀 Quick Start

Local Build

# Install mdBook (choose one)
cargo install mdbook
# Or macOS: brew install mdbook

# Install mdbook-katex plugin (for math formula rendering)
cargo install mdbook-katex

# Clone the reposit