ai-agent-book: a comprehensive engineering guide and code library for designing and implementing production-grade AI agents

What it solves

This repository serves as the comprehensive open-source companion to the book "Understanding AI Agents: Design Principles and Engineering Practice." It bridges the gap between theoretical agent design and practical implementation by providing a structured learning path, full book text, and a vast library of executable examples covering the entire agent lifecycle—from basic prompt engineering to complex multi-agent orchestration.

How it works

The project is organized around the core formula Agent = LLM + Context + Tools. It provides a modular collection of projects categorized by chapter:

• Context Engineering: Implements KV Cache optimization, context compression, and prompt injection defense.
• Memory & Knowledge: Builds RAG pipelines, long-term user memory systems, and structured indexing (GraphRAG/RAPTOR).
• Tooling: Implements Model Context Protocol (MCP) servers for perception, execution, and collaboration, alongside asynchronous agent frameworks.
• Coding Agents: Features production-grade coding assistants and tools that use code to solve math and logic problems.
• Evaluation: Provides benchmarks for terminal automation (Terminal-Bench), software engineering (SWE-bench), and OS-level tasks (OSWorld).
• Post-Training: Includes experiments on SFT, RLHF, and adaptive reasoning depth (AdaptThink).
• Self-Evolution: Implements loops for learning from successful trajectories and autonomous tool discovery.
• Multimodal Interaction: Covers real-time voice dialogue, "Computer Use" browser automation, and end-to-end speech models.
• Multi-Agent Collaboration: Demonstrates architectures where specialized agents (e.g., a phone agent and a computer agent) communicate directly to solve tasks.

Who it’s for

• AI Engineers looking to move from simple prompts to production-ready agentic systems.
• Researchers interested in the engineering trade-offs of RAG, tool-use, and model post-training.
• Developers wanting a hands-on guide to building autonomous agents with real-world code examples.

Highlights

• Comprehensive Scope: Covers everything from basic LLM serving to advanced self-evolving agents.
• Practical Implementation: Includes "ready-to-run" demos that integrate with real LLM APIs.
• Diverse Tooling: Extensive use of the Model Context Protocol (MCP) for standardized tool integration.
• Evaluation-Driven: Strong focus on quantitative benchmarks and cost analysis for agent performance.
• Multilingual Support: Book content available in Chinese, English, Vietnamese, and Tamil.