awesome-agentic-ai-zh — MCP Server by WenyuChiou

by WenyuChiou · MCP Server · ★ 1.7k

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About awesome-agentic-ai-zh

繁體中文 English awesome-agentic-ai-zh 🤖 AI Agent 學習地圖 — 從基本 LLM 概念到自己打造多 agent 系統 結構化 7 階段學習路徑,從「LLM 是什麼、token 怎麼算」一路到 multi-agent 編排、本地部署,每階段都有必做的 動手練習、必修閱讀、精選 project 🎯 專案介紹 這個專案是為想學習 AI 或 AI agent 的人設計的。 本 repo 把網路上散落各處的高品質專案、教材、動手練習、必修閱讀蒐集起來,按從零開始、循序漸進的順序整理成 7 個階段——每階段都會清楚指出該學什麼、必做哪些 動手練習、推薦哪幾個 project、進入下一階段前該檢查什麼。 走完整條路線,你會從「LLM 使用者」進階到「agent 系統建構者」——能看懂 framework 在做什麼、能設計多 agent 協作、能寫自己的 MCP server。 📋 目錄 🎯 專案介紹 📚 快速開始 線上閱讀 本地下載 ✨ 你會收穫什麼? 🗺️ 學習地圖(兩條學習路徑) 💡 如何學習 📚 相關資源 🤝 如何貢獻 🙏 致謝 🎓 引用 License 📚 快速開始 線上閱讀

agentic-aiai-agentsawesome-listclaude-codeclaude-skillsclilearning-roadmapllm-agentsmcpmodel-context-protocol

Quick Facts

Stars1,715
Forks188
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score46.866/100
Open Issues1
Last Updated2026-05-25
Created2026-05-04
Platformsclaude-code, cli, mcp, python
Est. Tokens~7404k

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Frequently Asked Questions

What is awesome-agentic-ai-zh?

awesome-agentic-ai-zh is A structured, trilingual (繁中 / 简中 / English) learning roadmap for agentic AI — from LLM basics to multi-agent systems. 8 stages · 145+ curated projects · hands-on exercises. 中文 AI agent 學習地圖。. It is categorized as a MCP Server with 1.7k GitHub stars.

What programming language is awesome-agentic-ai-zh written in?

awesome-agentic-ai-zh is primarily written in Python. It covers topics such as agentic-ai, ai-agents, awesome-list.

How do I install or use awesome-agentic-ai-zh?

You can find installation instructions and usage details in the awesome-agentic-ai-zh GitHub repository at github.com/WenyuChiou/awesome-agentic-ai-zh. The project has 1.7k stars and 188 forks, indicating an active community.

What license does awesome-agentic-ai-zh use?

awesome-agentic-ai-zh is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to awesome-agentic-ai-zh?

The top alternatives to awesome-agentic-ai-zh on Agent Skills Hub include ios-simulator-skill, cursor-talk-to-figma-mcp, Unity-MCP. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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