Chapter 1 · Agent Fundamentals

Starting from the new paradigm of "Model as Agent," establishes the core formula Agent = LLM + Context + Tools, and introduces Harness engineering—all engineering capabilities beyond the model are the true competitive advantage.

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Companion Projects

Exp.	Project	Type	Description
1-1	context	✅	Demonstrates the importance of various Agent context components through systematic ablation experiments. Supports multiple LLM providers (SiliconFlow Qwen, ByteDance Doubao, Moonshot Kimi), allowing configuration of different context modes to observe changes in Agent behavior.
1-2	web-search-agent	✅	Implements an Agent with basic deep search capabilities, capable of multi-round searching and information integration.
1-3	search-codegen	✅	Builds an Agent with basic deep search and code sandbox capabilities, utilizing tools like web search and code execution for complex analysis.
7-1, 7-2	learning-from-experience	✅	Compares traditional reinforcement learning (Q-learning) with LLM-based in-context learning, reproducing key insights from Shunyu Yao's "The Second Half" blog post. Demonstrates how LLMs can surpass traditional RL with 250-400x sample efficiency through a treasure hunt game.
## Project Types

Icon	Type	Meaning
✅	Standalone	Full code in this repo, runs after configuring API Key
📖	Reproduction Guide	Detailed doc depending on external repos to git clone
🚧	Design Doc	Architecture/implementation plan only, runnable code still WIP