Content
<div align="center">
<img src="readme_img.png" width="900" alt="Agent Learning Roadmap">
<br>
# 🤖 Agent Learning: Learn Agent Development from Scratch
**The complete open-source roadmap for learning AI Agents — from LLM basics to production-ready Agent systems.**
**Agent Learning** (`agent_learning`) is a systematic, practice-oriented AI Agent learning roadmap and hands-on tutorial covering LLM fundamentals, RAG, memory, tool use, function calling, agentic workflows, LangChain, LangGraph, MCP, multi-agent systems, evaluation, deployment, and agentic RL.
> If you want to learn how to build AI Agents — not just use ChatGPT, but understand how agents retrieve knowledge, remember context, call tools, plan actions, collaborate, and run safely in production — this project is for you.
**Daily auto-tracking of arXiv frontier papers — content stays cutting-edge, always.**
<br>
[](https://opensource.org/licenses/MIT)
[](https://github.com/Haozhe-Xing/agent_learning)
[](https://github.com/Haozhe-Xing/agent_learning/pulls)
[](https://rust-lang.github.io/mdBook/)
[](https://arxiv.org)
<br>
[<img src="https://img.shields.io/badge/📖_Read_Online_(中文)-4CAF50?style=for-the-badge" alt="Read Online Chinese">](https://Haozhe-Xing.github.io/agent_learning/zh/) [<img src="https://img.shields.io/badge/📖_Read_Online_(English)-2196F3?style=for-the-badge" alt="Read Online English">](https://Haozhe-Xing.github.io/agent_learning/en/)
<br>
<table>
<tr>
<td width="50%" align="center">
<img src="scripts/agent2.png" width="100%" alt="Agent Learning online book screenshot - frontier research chapter">
<br>
<sub>Daily updated Agentic-RL frontier research chapter</sub>
</td>
<td width="50%" align="center">
<img src="scripts/grpo_chapter.png" width="100%" alt="Agent Learning online book screenshot - GRPO chapter">
<br>
<sub>Step-by-step GRPO / GSPO learning content</sub>
</td>
</tr>
</table>
<br>
[🐛 Report Issues](https://github.com/Haozhe-Xing/agent_learning/issues) · [💬 Discussions](https://github.com/Haozhe-Xing/agent_learning/discussions) · [🇨🇳 中文版 README](README_ZH.md)
</div>
---
## 🚀 Auto-Tracking Frontier: Daily arXiv Paper Updates
<div align="center">
🤖 **This repository automatically searches arXiv for the latest AI Agent-related papers every day and updates the content accordingly — ensuring you always stay at the cutting edge of research!**
</div>
- 📡 **Daily Automated Search**: A scheduled pipeline scans arXiv daily for new papers on Agent architectures, tool use, memory systems, multi-agent collaboration, reinforcement learning for agents, and more.
- 📝 **Auto-Updated Content**: Relevant findings are automatically integrated into the corresponding chapters, keeping the book's frontier sections fresh and up-to-date.
- 🔔 **Never Miss a Breakthrough**: No need to manually track dozens of research feeds — this repo does it for you, so you can focus on learning and building.
> 💡 This means the content you read here is **not static** — it evolves continuously with the latest advances in the AI Agent field.
---
## 👥 Who Is This For?
- **Developers** who want to build real AI Agent applications instead of only prompting chatbots
- **Students and beginners** who need a structured path from LLM basics to Agent systems
- **LLM application engineers** working with RAG, tool calling, memory, LangGraph, MCP, and evaluation
- **Researchers and builders** who want to connect frontier Agent papers with engineering practice
- **Product and startup teams** exploring production-ready Agent workflows
---
## 🧭 Learning Paths
| Path | Start Here | Goal |
| ---- | ---------- | ---- |
| **Beginner Path** | LLM basics → Prompt Engineering → Function Calling → RAG → Memory → ReAct | Understand how an Agent works end to end |
| **Engineering Path** | Tool Layer → LangGraph → Evaluation → Security → Deployment → Observability | Build production-ready Agent systems |
| **Research Path** | ReAct → Reflexion → MemGPT → PPO / DPO / GRPO → Agentic RL | Follow and understand frontier Agent research |
| **Project Path** | Hello Agent → RAG QA Agent → Memory Agent → Data Analysis Agent → Coding Agent | Learn by building complete applications |
---
## ✨ 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
<table>
<tr>
<td width="50%" align="center">
**Perceive-Think-Act Loop (Chapter 1)**
<img src="src/en/svg/chapter_intro_03_loop.svg" width="420" alt="Perceive-Think-Act Loop">
<sub>Agent's core mechanism: Perceive environment → LLM reasoning → Execute action → Loop until goal achieved</sub>
</td>
<td width="50%" align="center">
**ReAct Reasoning Framework (Chapter 5)**
<img src="src/en/svg/chapter_planning_02_react_loop.svg" width="420" alt="ReAct Reasoning Framework">
<sub>Thought → Action → Observation alternating loop, enabling Agents to think while acting</sub>
</td>
</tr>
</table>
### 🛠️ Tool Calling & RAG
<table>
<tr>
<td width="50%" align="center">
**Function Calling Complete Flow (Chapter 3)**
<img src="src/en/svg/chapter_tools_02_function_calling.svg" width="480" alt="Function Calling Flow">
<sub>6-step complete flow from user input to tool invocation to final response, with message structure illustration</sub>
</td>
<td width="50%" align="center">
**RAG Retrieval-Augmented Generation (Chapter 6)**
<img src="src/en/svg/chapter_rag_01_rag_flow.svg" width="480" alt="RAG Workflow">
<sub>Offline indexing + Online retrieval dual-phase architecture, making LLM answers evidence-based</sub>
</td>
</tr>
</table>
### 💾 Memory System & Context Engineering
<table>
<tr>
<td width="50%" align="center">
**Three-Layer Memory Architecture (Chapter 4)**
<img src="src/en/svg/chapter_memory_01_memory_types.svg" width="480" alt="Three-Layer Memory Architecture">
<sub>Working memory → Short-term memory → Long-term memory, with important info sinking down and semantic retrieval pulling up</sub>
</td>
<td width="50%" align="center">
**Prompt Engineering vs Context Engineering (Chapter 7)**
<img src="src/en/svg/chapter_context_01_comparison.svg" width="480" alt="Prompt Engineering vs Context Engineering">
<sub>From "how to say it" to "what the LLM sees" — the paradigm shift of the Agent era</sub>
</td>
</tr>
</table>
### 🤝 Multi-Agent & Communication Protocols
<table>
<tr>
<td width="50%" align="center">
**Three Multi-Agent Communication Patterns (Chapter 15)**
<img src="src/en/svg/chapter_multi_agent_02_communication.svg" width="480" alt="Multi-Agent Communication Patterns">
<sub>Message Queue (async decoupling) / Shared Blackboard (data sharing) / Direct Call (real-time collaboration)</sub>
</td>
<td width="50%" align="center">
**MCP / A2A / ANP Protocol Comparison (Chapter 16)**
<img src="src/en/svg/chapter_protocol_03_three_protocols.svg" width="480" alt="Three Protocol Comparison">
<sub>Three-layer protocol stack: ANP for discovery → A2A for task collaboration → MCP for tool invocation</sub>
</td>
</tr>
</table>
### 🧪 Reinforcement Learning & Frameworks
<table>
<tr>
<td width="50%" align="center">
**GRPO Training Architecture (Chapter 10)**
<img src="src/en/svg/chapter_agentic_rl_03_grpo_architecture.svg" width="420" alt="GRPO Training Architecture">
<sub>No Critic model needed, computes advantage via intra-group normalization, only 1.5× model size in VRAM</sub>
</td>
<td width="50%" align="center">
**LangGraph Three Core Concepts (Chapter 12)**
<img src="src/en/svg/chapter_langgraph_02_state_node_edge.svg" width="480" alt="LangGraph Core Concepts">
<sub>State (shared state) · Node (processing unit) · Edge (execution flow control)</sub>
</td>
</tr>
</table>
<div align="center">
📖 **The above is just a selected preview** — For the full 120+ architecture diagrams + 5 interactive animations, please [**read online**](https://Haozhe-Xing.github.io/agent_learning)
</div>
---
## 🎬 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 5 | Shows the alternating Thought → Action → Observation process |
| 🔧 **Function Calling** | Chapter 3 | Complete tool invocation flow animation |
| 📚 **RAG Retrieval Flow** | Chapter 6 | 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](https://Haozhe-Xing.github.io/agent_learning). Local builds can also preview them.
---
## 🔥 Core Topics at a Glance
<table>
<tr>
<td width="50%">
**🧠 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
</td>
<td width="50%">
**💾 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
</td>
</tr>
</table>
---
## 🚀 Quick Start
### Local Build
```bash
# 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 repository
git clone https://github.com/Haozhe-Xing/agent_learning.git
cd agent_learning
# Build both Chinese and English versions and start unified server (default port 3000)
./serve.sh
```
After starting, visit:
- 🌐 **Language Selection Home**: `http://localhost:3000`
- 🇨🇳 **Chinese Version**: `http://localhost:3000/zh/`
- 🇺🇸 **English Version**: `http://localhost:3000/en/`
### Environment Setup (For Code Practice)
```bash
# Python 3.11+
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install core dependencies
pip install langchain langchain-openai langgraph openai anthropic
# Configure API Key
export OPENAI_API_KEY="your-key-here"
```
---
## 📊 Technology Stack
-191919?style=flat)
---
## 🤝 Contributing
All forms of contribution are welcome!
- 🐛 **Found a bug**: [Submit an Issue](https://github.com/Haozhe-Xing/agent_learning/issues)
- 💡 **Content suggestions**: [Start a Discussion](https://github.com/Haozhe-Xing/agent_learning/discussions)
- 📝 **Improve content**: Fork → Edit → Submit PR
- ⭐ **Support the project**: Give this repo a Star!
### Contributing Guide
```bash
# Fork and clone
git clone https://github.com/YOUR_USERNAME/agent_learning.git
# Create a feature branch
git checkout -b feature/improve-chapter-3
# Local preview
./serve.sh
# Commit and push
git commit -m "feat: improve Chapter 3 tool calling code examples"
git push origin feature/improve-chapter-3
```
### Content Organization Conventions
- Each chapter is placed in a separate directory `src/zh/chapter_xxx/` (Chinese) or `src/en/chapter_xxx/` (English)
- Chapter overview goes in `README.md`, sections are numbered as `01_xxx.md`, `02_xxx.md`
- Chinese SVG illustrations go in `src/zh/svg/`, English versions in `src/en/svg/`, naming format: `chapter_xxx_description.svg`
- Chinese interactive animations go in `src/zh/animations/`, English versions in `src/en/animations/`
### Paper Reading Template
All paper reading and frontier research sections should follow a consistent structure so that readers can quickly understand why a paper matters, what it contributed at the time, and how it connects to real Agent engineering.
Use the following template for each representative paper:
```markdown
### Paper Title: one-sentence explanation of the problem it solves
- **Paper link**:
- **Code / project link**:
- **Year / organization**:
- **Problem addressed at the time**:
- **Core contribution**:
- **Method breakdown**:
- **Engineering insight for Agent systems**:
- **Limitations**:
```
Quality requirements:
- **Link to the original source**: include the arXiv, conference, official blog, GitHub, or project page whenever available.
- **Explain historical contribution**: describe what problem the work solved when it appeared, not only what it does.
- **Connect to engineering practice**: explain how the idea affects Agent memory, tools, planning, evaluation, safety, training, or deployment.
- **State limitations**: clarify what the paper does not solve, where assumptions are strong, or whether the result is mainly benchmark-driven.
- **Avoid paper lists without synthesis**: after several papers, add a short comparison table or narrative summary explaining how the works relate to each other.
---
## 📄 License
This project is open-sourced under the [MIT License](LICENSE).
---
## 🗺️ Project Roadmap
- [x] Chinese / English online book powered by mdBook
- [x] 120+ original SVG architecture diagrams and flowcharts
- [x] Interactive animations for core Agent concepts
- [x] Paper reading sections for key Agent research
- [x] Agentic RL chapters covering PPO / DPO / GRPO
- [ ] Runnable Agent example projects and templates
- [ ] Agent glossary and keyword cheat sheet
- [ ] Agent architecture diagram gallery
- [ ] Interview questions and self-check exercises
- [ ] Production-ready Agent template with evaluation and observability
---
## ⭐ Star History
If this project helps you, please give it a Star ⭐ — it's the greatest encouragement for the author!
[](https://www.star-history.com/#Haozhe-Xing/agent_learning&Date)
---
<div align="center">
**Built with ❤️, so that every developer can master AI Agent development**
[⬆ Back to Top](#-learn-agent-development-from-scratch)
</div>
