by Haozhe-Xing · MCP Server · ★ 248
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🤖 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 () 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. [<img src="https://img.sh
| Stars | 248 |
| Forks | 36 |
| Language | HTML |
| Category | MCP Server |
| License | MIT |
| Quality Score | 53.578/100 |
| Open Issues | 2 |
| Last Updated | 2026-06-21 |
| Created | 2026-03-13 |
| Platforms | mcp |
| Est. Tokens | ~19k |
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agent_learning is 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. It is categorized as a MCP Server with 248 GitHub stars.
agent_learning is primarily written in HTML. It covers topics such as agent-learning, agentic-workflow, ai-agent.
You can find installation instructions and usage details in the agent_learning GitHub repository at github.com/Haozhe-Xing/agent_learning. The project has 248 stars and 36 forks, indicating an active community.
agent_learning is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to agent_learning on Agent Skills Hub include flock, aser, c4-genai-suite. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.