REPOGEO REPORT · LITE

xlang-ai/OSWorld

Default branch main · commit 83e85344 · scanned 7/1/2026, 4:16:56 AM

GitHub: 2,985 stars · 484 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

33 /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

2 / 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/OSWorld, 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 to clarify its unique category

Why:

COPY-PASTE FIX

OSWorld is a pioneering benchmark for evaluating multimodal AI agents on complex, open-ended tasks directly within *real, sandboxed operating system environments*. Unlike traditional UI automation tools or simulated environments, OSWorld provides a robust platform for assessing agent performance in authentic desktop and CLI scenarios.

mediumabout#2

Enhance the repository description with key differentiators

Why:

CURRENT

[NeurIPS 2024] OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments

COPY-PASTE FIX

[NeurIPS 2024] OSWorld: The definitive benchmark for evaluating multimodal AI agents on open-ended tasks in real computer environments, supporting both GUI and CLI interactions.

lowcomparison#3

Add a 'Comparison with Alternatives' section to the README

Why:

COPY-PASTE FIX

Add a new section titled 'Comparison with Alternatives' or 'Why OSWorld?' that explicitly contrasts OSWorld with common UI automation frameworks (e.g., Playwright, Selenium, Appium) and general reinforcement learning environments (e.g., OpenAI Gym, Gymnasium). Emphasize OSWorld's unique focus on *agent evaluation* in *real, sandboxed operating systems* rather than just UI testing or simulated environments.

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/OSWorld

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI Gym

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

OpenAI Gym · recommended 1×
Farama Foundation Gymnasium · recommended 1×
Microsoft Playwright · recommended 1×
Selenium · recommended 1×
Appium · recommended 1×

CATEGORY QUERY
How to evaluate performance of multimodal AI agents on complex desktop tasks?
you: not recommended
AI recommended (in order):
1. OpenAI Gym
2. Farama Foundation Gymnasium
3. Microsoft Playwright
4. Selenium
5. Appium
6. AutoHotkey
7. SikuliX
8. OpenCV
9. Robocorp
10. UiPath
AI recommended 10 alternatives but never named xlang-ai/OSWorld. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Tools for developing and testing large language model agents interacting with graphical interfaces?
you: not recommended
AI recommended (in order):
1. Playwright (microsoft/playwright)
2. Selenium WebDriver (SeleniumHQ/selenium)
3. Puppeteer (puppeteer/puppeteer)
4. Appium (appium/appium)
5. AutoIt
6. SikuliX (RaiMan/SikuliX1)
7. PyAutoGUI (asweigart/pyautogui)
AI recommended 7 alternatives but never named xlang-ai/OSWorld. 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/OSWorld?
pass
AI named xlang-ai/OSWorld 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/OSWorld in production, what risks or prerequisites should they evaluate first?
pass
AI named xlang-ai/OSWorld 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/OSWorld solve, and who is the primary audience?
pass
AI did not name xlang-ai/OSWorld — 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?

Embed your GEO score

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/xlang-ai/OSWorld.svg)](https://repogeo.com/en/r/xlang-ai/OSWorld)

HTML

<a href="https://repogeo.com/en/r/xlang-ai/OSWorld"><img src="https://repogeo.com/badge/xlang-ai/OSWorld.svg" alt="RepoGEO" /></a>

Pro

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xlang-ai/OSWorld — 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