🤖 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.
🐛 Report Issues · 💬 Discussions · 🇨🇳 中文版 README
🚀 Auto-Tracking Frontier: Daily arXiv Paper Updates
🤖 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!
- 📡 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
Perceive-Think-Act Loop (Chapter 1)
Agent's core mechanism: Perceive environment → LLM reasoning → Execute action → Loop until goal achieved
ReAct Reasoning Framework (Chapter 5)
Thought → Action → Observation alternating loop, enabling Agents to think while acting
🛠️ Tool Calling & RAG
Function Calling Complete Flow (Chapter 3)
6-step complete flow from user input to tool invocation to final response, with message structure illustration
RAG Retrieval-Augmented Generation (Chapter 6)
Offline indexing + Online retrieval dual-phase architecture, making LLM answers evidence-based
💾 Memory System & Context Engineering
Three-Layer Memory Architecture (Chapter 4)
Working memory → Short-term memory → Long-term memory, with important info sinking down and semantic retrieval pulling up
Prompt Engineering vs Context Engineering (Chapter 7)
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 15)
Message Queue (async decoupling) / Shared Blackboard (data sharing) / Direct Call (real-time collaboration)
MCP / A2A / ANP Protocol Comparison (Chapter 16)
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 |
|---|---|---|