RRepoGEO

REPOGEO REPORT · LITE

OpenGVLab/ScaleCUA

Default branch main · commit 5d92feea · scanned 6/24/2026, 5:41:47 PM

GitHub: 1,118 stars · 79 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)

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

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 OpenGVLab/ScaleCUA, 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 the README's opening paragraph to state ScaleCUA's identity directly

    Why:

    CURRENT
    Vision-Language Models (VLMs) have enabled computer use agents (**CUAs**) that operate GUIs autonomously with great potential. However, developing robust CUAs requires extensive in-domain knowledge about software interfaces and operations. Unlike image–text pairs that are widely available on the Internet, computer-use data, particularly operation trajectories, are rare, costly to collect. Consequently, progress in this field remains constrained by both data scale and the limited transferability of existing VLMs. In this work, we introduce **ScaleCUA**, a step toward scaling open-source CUAs. It offers a large-scale dataset spanning 6 operating systems and 3 task domains, via a closed-loop pipeline uniting automated agents with human experts. Trained on this scaled-up data, ScaleCUA can operate seamlessly across platforms.
    COPY-PASTE FIX
    ScaleCUA is an open-source framework for developing and evaluating **cross-platform computer use agents (CUAs)** that autonomously operate graphical user interfaces (GUIs) across Windows, macOS, Ubuntu, and Android. While Vision-Language Models (VLMs) have shown great potential for CUAs, their development is often constrained by data scale and limited transferability. ScaleCUA addresses this by introducing a large-scale dataset spanning 6 operating systems and 3 task domains, enabling models trained on this data to operate seamlessly across platforms.
  • mediumabout#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2509.15221
  • lowtopics#3
    Add more specific topics to differentiate from generic automation tools

    Why:

    CURRENT
    computer-use-agents, data, gui-agents, models, online-evaluation-suite, scalecua
    COPY-PASTE FIX
    computer-use-agents, data, gui-agents, models, online-evaluation-suite, scalecua, autonomous-agents, vlm-agents, gui-automation, cross-platform-automation

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 OpenGVLab/ScaleCUA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pyautogui/pyautogui
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. pyautogui/pyautogui · recommended 1×
  2. pywinauto/pywinauto · recommended 1×
  3. appium/appium-desktop · recommended 1×
  4. appium/appium · recommended 1×
  5. microsoft/playwright · recommended 1×
  • CATEGORY QUERY
    What tools enable building AI agents to automate tasks across Windows, macOS, and Android?
    you: not recommended
    AI recommended (in order):
    1. pyautogui (pyautogui/pyautogui)
    2. pywinauto (pywinauto/pywinauto)
    3. Appium Desktop (appium/appium-desktop)
    4. Appium (appium/appium)
    5. Playwright (microsoft/playwright)
    6. Robot Framework (robotframework/robotframework)
    7. AutoItLibrary (robotframework/AutoItLibrary)
    8. SeleniumLibrary (robotframework/SeleniumLibrary)
    9. AppiumLibrary (robotframework/AppiumLibrary)
    10. UIPath
    11. UIPath Assistant
    12. UIPath Mobile Automation
    13. AutoHotkey (AutoHotkey/AutoHotkey)
    14. AppleScript
    15. Tasker
    16. Selenium WebDriver (SeleniumHQ/selenium)

    AI recommended 16 alternatives but never named OpenGVLab/ScaleCUA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an open-source framework for developing autonomous agents that interact with diverse GUIs.
    you: not recommended
    AI recommended (in order):
    1. Playwright
    2. Selenium WebDriver
    3. PyAutoGUI
    4. Appium
    5. Robot Framework
    6. AutoIt
    7. SikuliX

    AI recommended 7 alternatives but never named OpenGVLab/ScaleCUA. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 OpenGVLab/ScaleCUA?
    pass
    AI named OpenGVLab/ScaleCUA explicitly

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

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

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

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OpenGVLab/ScaleCUA — 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