RRepoGEO

REPOGEO REPORT · LITE

Tongyi-MAI/MAI-UI

Default branch main · commit 94154d62 · scanned 5/12/2026, 3:42:54 AM

GitHub: 1,800 stars · 177 forks

AI VISIBILITY SCORE
40 /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
3 / 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 Tongyi-MAI/MAI-UI, 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
  • highreadme#1
    Reposition README opening to clarify AI agent framework, not UI automation

    Why:

    CURRENT
    We present **MAI-UI**, a family of GUI agent foundation models spanning the full spectrum of sizes, including **2B**, **8B**, **32B**, and **235B-A22B** variants.
    COPY-PASTE FIX
    We present **MAI-UI**, a cutting-edge framework for building and deploying **GUI agent foundation models** (2B to 235B-A22B variants). Unlike traditional UI automation or testing tools, MAI-UI focuses on enabling AI agents to intelligently interact with and navigate graphical user interfaces, offering advanced capabilities like device-cloud collaboration and dynamic RL scaling.
  • mediumabout#2
    Update repository description to emphasize AI agent framework

    Why:

    CURRENT
    MAI-UI: Real-World Centric Foundation GUI Agents ranging from 2B to 235B
    COPY-PASTE FIX
    MAI-UI: A framework for real-world centric **AI agent foundation models** (2B-235B) enabling advanced GUI interaction and device-cloud automation, distinct from traditional UI testing tools.
  • mediumtopics#3
    Add explicit AI agent and foundation model topics

    Why:

    CURRENT
    device-cloud-collaboration, gui-agent, gui-grounding, gui-navigation, mcp
    COPY-PASTE FIX
    ai-agents, foundation-models, large-language-models, gui-agents, gui-automation, device-cloud-collaboration, gui-grounding, gui-navigation, mcp

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 Tongyi-MAI/MAI-UI
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Playwright
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Playwright · recommended 1×
  2. Selenium WebDriver · recommended 1×
  3. Appium · recommended 1×
  4. PyAutoGUI · recommended 1×
  5. Puppeteer · recommended 1×
  • CATEGORY QUERY
    Need a framework for AI agents to interact with and navigate graphical user interfaces.
    you: not recommended
    AI recommended (in order):
    1. Playwright
    2. Selenium WebDriver
    3. Appium
    4. PyAutoGUI
    5. Puppeteer
    6. Taiko

    AI recommended 6 alternatives but never named Tongyi-MAI/MAI-UI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a solution to build large-scale AI models for device-cloud GUI automation.
    you: not recommended
    AI recommended (in order):
    1. Playwright (microsoft/playwright)
    2. Selenium WebDriver (SeleniumHQ/selenium)
    3. Appium (appium/appium)
    4. Cypress (cypress-io/cypress)
    5. Puppeteer (puppeteer/puppeteer)
    6. Robot Framework (robotframework/robotframework)

    AI recommended 6 alternatives but never named Tongyi-MAI/MAI-UI. 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 Tongyi-MAI/MAI-UI?
    pass
    AI named Tongyi-MAI/MAI-UI explicitly

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

  • If a team adopts Tongyi-MAI/MAI-UI in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Tongyi-MAI/MAI-UI 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 Tongyi-MAI/MAI-UI solve, and who is the primary audience?
    pass
    AI named Tongyi-MAI/MAI-UI explicitly

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

Embed your GEO score

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MARKDOWN (README)
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Tongyi-MAI/MAI-UI — 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