RRepoGEO

REPOGEO REPORT · LITE

WenyuChiou/awesome-agentic-ai-zh

Default branch main · commit f19b973d · scanned 6/17/2026, 5:07:19 PM

GitHub: 2,887 stars · 400 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface WenyuChiou/awesome-agentic-ai-zh, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Explicitly state 'awesome list' in the 'About' description

    Why:

    CURRENT
    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。
    COPY-PASTE FIX
    An awesome list and trilingual 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。
  • hightopics#2
    Add more specific compound topics for learning paths and awesome lists

    Why:

    CURRENT
    agentic-ai, agentic-workflows, ai-agent, ai-agents, awesome-list, chinese-llm, claude-code, claude-skills, cli, learning-roadmap, llm, llm-agents, mcp, model-context-protocol, multi-agent-systems, prompt-engineering, rag, trilingual, tutorial
    COPY-PASTE FIX
    agentic-ai, agentic-workflows, ai-agent, ai-agents, awesome-list, chinese-llm, claude-code, claude-skills, cli, learning-roadmap, llm, llm-agents, mcp, model-context-protocol, multi-agent-systems, prompt-engineering, rag, trilingual, tutorial, ai-agent-roadmap, llm-agent-learning, awesome-agentic-ai
  • mediumreadme#3
    Add a direct, explicit statement of the repo's type after the H1

    Why:

    CURRENT
    # 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

<p><em><b>學習路線圖 + 240+ 資源 curation + 簡單 illustrative 案例</b><br/>結構化 8 階段、從「LLM 是什麼、token 怎麼算」走到 multi-agent 編排、Computer Use / Browser Use / Sandbox</em></p>
    COPY-PASTE FIX
    # 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

這是一個精選的 AI Agent 學習路線圖與資源列表 (Awesome List)，旨在幫助您從 LLM 基礎知識逐步掌握多 Agent 系統的建構。

<p><em><b>學習路線圖 + 240+ 資源 curation + 簡單 illustrative 案例</b><br/>結構化 8 階段、從「LLM 是什麼、token 怎麼算」走到 multi-agent 編排、Computer Use / Browser Use / Sandbox</em></p>

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface WenyuChiou/awesome-agentic-ai-zh
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. langchain-ai/langchain · recommended 2×
  3. run-llama/llama_index · recommended 2×
  4. microsoft/autogen · recommended 2×
  5. OpenAI API · recommended 1×
  • CATEGORY QUERY
    I need a structured learning roadmap to go from LLM basics to multi-agent system development.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. OpenAI API
    3. Google Gemini API
    4. PaLM API
    5. LangChain (langchain-ai/langchain)
    6. LlamaIndex (run-llama/llama_index)
    7. Pinecone
    8. Chroma (chroma-core/chroma)
    9. Weaviate (weaviate/weaviate)
    10. FastAPI (tiangolo/fastapi)
    11. CrewAI (joaomdmoura/crewai)
    12. AutoGen (microsoft/autogen)
    13. Python's `asyncio`
    14. Mesa (projectmesa/mesa)
    15. Docker
    16. Kubernetes (kubernetes/kubernetes)
    17. LangSmith
    18. Weights & Biases

    AI recommended 18 alternatives but never named WenyuChiou/awesome-agentic-ai-zh. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find curated resources and practical examples for building AI agentic applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. OpenAI Cookbook (openai/openai-cookbook)
    4. Awesome-AGI (mr-ai/awesome-agi)
    5. Hugging Face Transformers Agents (huggingface/transformers)
    6. AutoGen (microsoft/autogen)
    7. DeepLearning.AI

    AI recommended 7 alternatives but never named WenyuChiou/awesome-agentic-ai-zh. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of WenyuChiou/awesome-agentic-ai-zh?
    pass
    AI did not name WenyuChiou/awesome-agentic-ai-zh — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts WenyuChiou/awesome-agentic-ai-zh in production, what risks or prerequisites should they evaluate first?
    pass
    AI named WenyuChiou/awesome-agentic-ai-zh explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo WenyuChiou/awesome-agentic-ai-zh solve, and who is the primary audience?
    pass
    AI did not name WenyuChiou/awesome-agentic-ai-zh — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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WenyuChiou/awesome-agentic-ai-zh — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite