Learn AI Agents from Scratch

A visual, bilingual, and engineering-first textbook for building reliable LLM Agents.

From Function Calling, memory, planning, RAG, and context engineering to Agentic RL, multi-agent systems, evaluation, security, and production deployment.

中文说明 · Complete directory · Runnable reference agent · Contributing

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What is this repository?

agent_learning is an open-source AI Agent textbook and learning repository. It is designed for the gap between "I can call an LLM API" and "I can build, evaluate, secure, and deploy an Agent system."

Instead of teaching isolated framework APIs, the book builds one connected mental model:

LLM fundamentals → tools → memory → planning → RAG → context → harness → skills → Agentic RL → multi-agent → evaluation → security → deployment

The repository includes:

• 23 chapters across foundations, core capabilities, frameworks, multi-agent systems, production, and capstone projects.
• 188 Markdown pages per language, maintained in English and Chinese.
• 330+ original SVG diagrams and 5 interactive demos for architecture, state, sequence, and training flows.
• Paper-to-practice explanations covering ReAct, Reflexion, MemGPT/Letta, GraphRAG, GRPO, MCP, A2A, and more.
• reference-agent/, a small runnable Agent baseline with tools, memory, security gates, evaluation, an MCP server, a FastAPI service, and 16 tests.

This is not an awesome-list and not a framework manual. It is a structured path from first principles to production engineering.

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Complete directory

• Book source
  • src/en/ — English mdBook source
  • src/zh/ — Chinese mdBook source
  • src/en/SUMMARY.md — English table of contents
  • src/zh/SUMMARY.md — Chinese table of contents
• Foundations
  • 1. What Is an Agent?
  • 2. LLM Fundamentals
• Core capabilities
  • 3. Tools
  • 4. Memory
  • 5. Planning
  • 6. RAG
  • 7. Context Engineering
  • 8. Harness Engineering
  • 9. Skills
  • 10. Agentic RL
  • 11. Self-Evolving Agents
• Framework practice
  • 12. LangChain
  • 13. LangGraph
  • 14. Agent Frameworks
  • 15. Claude Code
• Multi-agent systems
  • 16. Multi-Agent Collaboration
  • [17. Agent Protocols](https://Haozhe-Xing.github.io/agent_learning/en/ch