# WenyuChiou/awesome-agentic-ai-zh

A trilingual (繁中 / English / 简中) learning roadmap for agentic AI: from LLM basics to multi-agent systems, with 240+ curated resources and hands-on examples. 中文 AI agent 學習地圖。

Repository: https://github.com/WenyuChiou/awesome-agentic-ai-zh
Canonical: https://ross.abutalabs.com/products/awesome-agentic-ai-zh
Homepage: https://wenyuchiou.github.io/awesome-agentic-ai-zh/
Language: Python
License: MIT
License Family: permissive
Topics: agentic-ai, ai-agents, awesome-list, claude-code, claude-skills, learning-roadmap, llm-agents, mcp, model-context-protocol, cli, tutorial, trilingual, agentic-workflows, ai-agent, chinese-llm, llm, multi-agent-systems, prompt-engineering, rag
Last push: 2026-08-24T04:43:18+00:00

## Health v2 (maintenance only)
Score: 81/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 99, longevity 8
- inputs: {"age_days": 121, "days_push": 9, "days_rel": 10, "gap_med": 0, "n_releases_24m": 28}
- flags: young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6416, forks 857 (observed 2026-08-28T04:09:43.216529+00:00)

## What it is
A trilingual (Traditional Chinese, Simplified Chinese, English) learning roadmap for agentic AI, structured into 8 stages from LLM basics to multi-agent systems, with 240+ curated resources and 23 hands-on exercises. It includes a glossary, setup guide, and an online documentation site, covering topics like prompt engineering, tool calling, MCP, RAG, and Claude Code.

## Use cases
- learn how to build AI agents from scratch
- find a structured roadmap for agentic AI
- curated list of LLM agent resources in Chinese
- understand MCP and Claude Code ecosystem
- learn multi-agent system design
- beginner-friendly LLM learning path
- hands-on exercises for building agents
- compare agent frameworks like LangGraph and AutoGen

## When to choose
- you want a structured, stage-by-stage path from LLM basics to multi-agent systems
- you prefer Chinese-language explanations with English terminology mapping
- you want curated, vetted resources instead of searching scattered tutorials
- you learn best with small hands-on starter exercises per stage

## When to avoid
- you need production-ready agent code or a framework to deploy
- you want a comprehensive reference rather than a guided learning path
- you need deep coverage of a single advanced topic like RAG internals
- you only read English and find the trilingual layout noisy

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: artificial-intelligence, large-language-models, tutorials, awesome-lists, education
- platform: python, cross-platform
- tags: awesome-list, learning-roadmap, agentic-ai, mcp, prompt-engineering, rag, multi-agent-systems, trilingual, chinese, claude-code, ai-agents, web-server

## Member repositories
- WenyuChiou/awesome-agentic-ai-zh (main) score 81

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:43.216529+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:44:54.777065+00:00, confidence not recorded.
  - readme: https://github.com/WenyuChiou/awesome-agentic-ai-zh (fetched 2026-08-28T04:09:43.216529+00:00, sha 8c6824b34789)
  - homepage: https://wenyuchiou.github.io/awesome-agentic-ai-zh/ (fetched 2026-08-29T08:41:41.777642+00:00, sha d094f2975820)
- Data as of 2026-08-30T08:39:29.467469+00:00.