RRepoGEO

REPOGEO REPORT · LITE

openai/universe

Default branch master · commit cc9ce6ec · scanned 5/27/2026, 4:42:32 AM

GitHub: 7,507 stars · 958 forks

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 openai/universe, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Add a concise historical context to the README's opening

    Why:

    CURRENT
    **This repository has been deprecated in favor of the Retro (https://github.com/openai/retro) library. See our Retro Contest (https://blog.openai.com/retro-contest) blog post for detalis.**
    COPY-PASTE FIX
    OpenAI Universe was a pioneering platform for training AI agents across diverse real-world applications and games, using a Gym interface. **This repository has been deprecated in favor of the Retro (https://github.com/openai/retro) library. See our Retro Contest (https://blog.openai.com/retro-contest) blog post for detalis.**
  • mediumcomparison#2
    Add a historical comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Historical Context and Comparison
OpenAI Universe pioneered a unique approach to AI training by enabling agents to interact with *any* existing software application or game through a virtual desktop interface (pixels, keyboard, mouse). This differentiated it from platforms like OpenAI Gym (which focused on standardized, API-driven environments) or Unity ML-Agents (which integrates directly with the Unity engine), by allowing broad generalization across arbitrary, real-world software.

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 openai/universe
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Farama-Foundation/Gymnasium
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Farama-Foundation/Gymnasium · recommended 2×
  2. Unity-Technologies/ml-agents · recommended 2×
  3. deepmind/lab · recommended 2×
  4. ray-project/ray · recommended 2×
  5. rlworkgroup/metaworld · recommended 1×
  • CATEGORY QUERY
    How to build AI agents that learn by interacting with diverse applications and games?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym / Gymnasium (Farama-Foundation/Gymnasium)
    2. Unity ML-Agents Toolkit (Unity-Technologies/ml-agents)
    3. DeepMind Lab (deepmind/lab)
    4. MetaWorld (rlworkgroup/metaworld)
    5. RLlib (Ray) (ray-project/ray)
    6. Minigrid (Farama-Foundation/Minigrid)

    AI recommended 6 alternatives but never named openai/universe. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a platform to train AI using visual input and simulated human controls.
    you: not recommended
    AI recommended (in order):
    1. Unity ML-Agents (Unity-Technologies/ml-agents)
    2. Unreal Engine
    3. AirSim (microsoft/AirSim)
    4. Stable Baselines3 (DLR-RM/stable-baselines3)
    5. Ray RLlib (ray-project/ray)
    6. Gymnasium (Farama-Foundation/Gymnasium)
    7. OpenAI Gym (openai/gym)
    8. PyBullet (bulletphysics/bullet3)
    9. MuJoCo (deepmind/mujoco)
    10. DeepMind Lab (deepmind/lab)
    11. Isaac Sim
    12. NVIDIA Omniverse
    13. ROS/ROS 2
    14. RoboSuite (StanfordVL/RoboSuite)

    AI recommended 14 alternatives but never named openai/universe. 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 openai/universe?
    pass
    AI named openai/universe explicitly

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

  • If a team adopts openai/universe in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/universe 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 openai/universe solve, and who is the primary audience?
    pass
    AI named openai/universe 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 openai/universe. 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)
[![RepoGEO](https://repogeo.com/badge/openai/universe.svg)](https://repogeo.com/en/r/openai/universe)
HTML
<a href="https://repogeo.com/en/r/openai/universe"><img src="https://repogeo.com/badge/openai/universe.svg" alt="RepoGEO" /></a>
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openai/universe — 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