RRepoGEO

REPOGEO REPORT · LITE

Tongyi-MAI/MAI-UI

Default branch main · commit 94154d62 · scanned 6/22/2026, 9:57:59 AM

GitHub: 1,821 stars · 177 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
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 'foundation model for GUI agents'

    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. Our core contribution includes:
    COPY-PASTE FIX
    **MAI-UI** is a family of **real-world centric foundation models for intelligent GUI agents**, spanning sizes from 2B to 235B. Unlike generic LLM UIs or web automation tools, MAI-UI provides the core intelligence for agents to perform GUI grounding and autonomous navigation, leveraging device-cloud collaboration and dynamic RL scaling. Our models (2B, 8B, 32B, 235B-A22B) establish new SOTA results across GUI grounding and navigation tasks.
  • mediumtopics#2
    Add broader topics for foundation models and LLM agents

    Why:

    CURRENT
    device-cloud-collaboration, gui-agent, gui-grounding, gui-navigation, mcp
    COPY-PASTE FIX
    device-cloud-collaboration, gui-agent, gui-grounding, gui-navigation, mcp, foundation-model, llm-agent, autonomous-agent, ai-agent
  • lowreadme#3
    Add a 'Comparison' section to differentiate from generic LLMs and web automation

    Why:

    COPY-PASTE FIX
    ## 💡 Why MAI-UI?
Unlike general-purpose Large Language Models (LLMs) such as GPT-4o or Gemini, MAI-UI is specifically designed as a **foundation model for GUI agents**, providing specialized intelligence for tasks like GUI grounding and autonomous navigation. While tools like Playwright or Selenium offer web automation, MAI-UI focuses on the *agentic intelligence* to understand and interact with any GUI, not just web interfaces, and integrates device-cloud collaboration for optimal execution. It is not a generic UI for managing LLMs, but rather the core intelligence for building advanced GUI-centric AI agents.

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
OpenAI GPT-4o
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI GPT-4o · recommended 1×
  2. Google Gemini · recommended 1×
  3. Microsoft Copilot · recommended 1×
  4. Llama 3 · recommended 1×
  5. Claude 3 · recommended 1×
  • CATEGORY QUERY
    Looking for a foundation model to develop intelligent GUI agents with device-cloud execution.
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4o
    2. Google Gemini
    3. Microsoft Copilot
    4. Llama 3
    5. Claude 3
    6. OpenAI GPT-3.5 Turbo
    7. Hugging Face Transformers ecosystem (huggingface/transformers)
    8. BLIP-2
    9. LLaVA (haotian-liu/LLaVA)
    10. Mistral 7B Instruct
    11. Mixtral 8x7B Instruct

    AI recommended 11 alternatives but never named Tongyi-MAI/MAI-UI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable robust GUI grounding and autonomous navigation for large language models?
    you: not recommended
    AI recommended (in order):
    1. Playwright
    2. Selenium WebDriver
    3. Appium
    4. AutoGPT
    5. BabyAGI
    6. AgentGPT
    7. Puppeteer
    8. PyAutoGUI
    9. OpenAI Function Calling

    AI recommended 9 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?

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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