AI Infrastructure · GitHub ·195 ★

agent_learning

A systematic AI Agent development tutorial covering LLM agents, RAG, tool use, memory systems, multi-agent systems, LangChain, LangGraph, MCP, and agentic RL.｜从零开始学 AI Agent 开发 | 系统、全面、实战导向的 Agent 开发教程 | 每日自动追踪 arXiv 最新论文 | Learn AI Agent Development from Scratch

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Details

Author
Haozhe-Xing
Category
AI Infrastructure
Platform
GitHub
Framework
langchain
Language
html
Stars
195
First indexed
2026-05-28
Last active
2026-05-27
Directory sync
2026-05-28
@Haozhe-Xing

Overview

A systematic AI Agent development tutorial covering LLM agents, RAG, tool use, memory systems, multi-agent systems, LangChain, LangGraph, MCP, and agentic RL.｜从零开始学 AI Agent 开发 | 系统、全面、实战导向的 Agent 开发教程 | 每日自动追踪 arXiv 最新论文 | Learn AI Agent Development from Scratch

Quick start

git

git clone https://github.com/Haozhe-Xing/agent_learning

Snippet generated from the published metadata; check the source page for full setup, configuration, and prerequisites.

What agent_learning can do

llmrag
  • Llm — llm task automation.
  • Rag — Retrieves grounded context before answering.

Frequently asked questions

What is agent_learning?
A systematic AI Agent development tutorial covering LLM agents, RAG, tool use, memory systems, multi-agent systems, LangChain, LangGraph, MCP, and agentic RL.｜从零开始学 AI Agent 开发 | 系统、全面、实战导向的 Agent 开发教程 | 每日自动追踪 arXiv 最新论文 | Learn AI Agent Development from Scratch
How do I install agent_learning?
Use git: `git clone https://github.com/Haozhe-Xing/agent_learning`. Full setup details on the source page linked above.
Is agent_learning open source?
agent_learning is published on GitHub.
What are alternatives to agent_learning?
Comparable agents include awesome, openclaw, superpowers. Browse the full MeshKore directory to find more by category, framework, or language.

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Source & freshness

Profile data for agent_learning is sourced from GitHub, published by Haozhe-Xing.

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