RRepoGEO

REPOGEO REPORT · LITE

xlang-ai/OpenCUA

Default branch main · commit dfc91ba8 · scanned 6/7/2026, 2:21:58 PM

GitHub: 776 stars · 102 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 xlang-ai/OpenCUA, 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
    Add a concise value proposition paragraph to the README intro

    Why:

    CURRENT
    The README immediately follows navigation links with "📢 Updates".
    COPY-PASTE FIX
    Insert the following paragraph immediately after the navigation links and before "📢 Updates":

```
OpenCUA provides a comprehensive, open-source foundation for developing advanced AI agents capable of interacting with diverse computer applications through their user interfaces. It includes foundational models, extensive datasets, and robust benchmarks to accelerate research and development in general-purpose, language-agnostic computer-use agents, moving beyond simple GUI automation or isolated vision-language models.
```
  • mediumtopics#2
    Expand GitHub topics for better categorization

    Why:

    CURRENT
    benchmark, computer-use-agent, dataset, foundation-models, gui, vision-language-model
    COPY-PASTE FIX
    ai-agents, agent-framework, computer-use-agent, multimodal-ai, human-computer-interaction, foundation-models, vision-language-model, benchmark, dataset, gui-automation, agent-development, large-language-models
  • lowabout#3
    Refine the repository description

    Why:

    CURRENT
    [NeurIPS 2025 Spotlight] OpenCUA: Open Foundations for Computer-Use Agents
    COPY-PASTE FIX
    [NeurIPS 2025 Spotlight] OpenCUA: Open Foundations for Computer-Use Agents, including models, datasets, and benchmarks for general-purpose computer interaction.

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 xlang-ai/OpenCUA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/playwright
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/playwright · recommended 1×
  2. SeleniumHQ/selenium · recommended 1×
  3. asweigart/pyautogui · recommended 1×
  4. opencv/opencv · recommended 1×
  5. UiPath · recommended 1×
  • CATEGORY QUERY
    How can I develop AI agents that can interact with graphical user interfaces?
    you: not recommended
    AI recommended (in order):
    1. Playwright (microsoft/playwright)
    2. Selenium WebDriver (SeleniumHQ/selenium)
    3. PyAutoGUI (asweigart/pyautogui)
    4. OpenCV (opencv/opencv)
    5. UiPath
    6. Blue Prism
    7. Automation Anywhere
    8. Appium (appium/appium)
    9. Microsoft UI Automation

    AI recommended 9 alternatives but never named xlang-ai/OpenCUA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for open-source foundation models to build robust vision-language computer interaction agents.
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. BLIP-2
    3. InstructBLIP
    4. OpenFlamingo
    5. MiniGPT-4
    6. Qwen-VL

    AI recommended 6 alternatives but never named xlang-ai/OpenCUA. 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 xlang-ai/OpenCUA?
    pass
    AI named xlang-ai/OpenCUA explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of xlang-ai/OpenCUA. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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xlang-ai/OpenCUA — 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