WenyuChiou/awesome-agentic-ai-zh

This is a curated, trilingual (Traditional Chinese, Simplified Chinese, English) learning roadmap for agentic AI, covering LLM basics to multi-agent systems. It features 8 stages, 145+ curated projects, and hands-on exercises, making it a comprehensive resource for learning AI agents. The project has strong traction on GitHub.

How you'd build it

1. 1Outline core topics for an 'Agentic AI Mastery' course, focusing on practical implementation with specific AI tools (e.g., Claude, LlamaIndex, LangChain).
2. 2Curate 50-75 high-quality open-source projects or tutorials, linking them to specific learning objectives in each stage.
3. 3Develop 10-15 hands-on coding exercises, each with a clear problem statement, expected output, and a reference solution. Use a mix of Python notebooks and CLI-based projects.
4. 4Structure the content into a multi-language (English primary, then add French/Spanish) online learning platform using Astro and a simple database (e.g., Supabase) for content management.
5. 5Integrate a 'progress tracking' feature and a simple 'challenge submission' system for exercises, potentially offering a certificate of completion.
6. 6Market to developers interested in AI agent development, emphasizing practical skills and a clear path to building agentic applications.

Risks & moats

• The rapid pace of AI agent development means content can become outdated quickly, requiring constant maintenance.
• Competition from existing learning platforms and direct documentation from AI providers (e.g., Anthropic, OpenAI) is high.
• Translating complex technical concepts accurately and maintaining quality across multiple languages is challenging and time-consuming.
• Curating and vetting 145+ projects for quality and relevance is a significant ongoing effort.

Reach out to this community of AI agent developers and learners to offer tools like MCP server starters, agent evaluation pipelines, AI usage monitors, and dev automation kits that complement their learning and building journey.

global→n/a

The core content of AI agent development is globally relevant; localization to specific markets would primarily involve language translation, which is already a feature of the original project. No specific geo-arbitrage advantage for a rebuild.

Original context

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。 Topics: agentic-ai, ai-agents, awesome-list, claude-code, claude-skills, cli, learning-roadmap, llm-agents, mcp, model-context-protocol, trilingual, tutorial.

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