RRepoGEO

REPOGEO REPORT · LITE

openai/retro

Default branch master · commit 094531b1 · scanned 5/22/2026, 8:16:55 PM

GitHub: 3,588 stars · 535 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
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/retro, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    reinforcement-learning, gym, retro-gaming, emulation, ai-training, video-games, libretro, openai-gym-retro, gymnasium-retro
  • highreadme#2
    Strengthen the README's opening sentence for AI/RL context

    Why:

    CURRENT
    Gym Retro lets you turn classic video games into Gym environments for reinforcement learning and comes with integrations for ~1000 games.
    COPY-PASTE FIX
    Gym Retro is a powerful toolkit for reinforcement learning research, enabling AI agents to train on classic video games by turning them into standardized Gym environments. It provides integrations for ~1000 games and supports various emulators via the Libretro API.
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://retro.readthedocs.io/en/latest/

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/retro
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Gym Retro
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Gym Retro · recommended 1×
  2. Arcade Learning Environment (ALE) · recommended 1×
  3. ViZDoom · recommended 1×
  4. PyGame · recommended 1×
  5. Minicraft · recommended 1×
  • CATEGORY QUERY
    How can I set up classic video game environments for training AI models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym Retro
    2. Arcade Learning Environment (ALE)
    3. ViZDoom
    4. PyGame
    5. Minicraft
    6. Gym-SuperMarioBros
    7. Gym-DonkeyKong-v0

    AI recommended 7 alternatives but never named openai/retro. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python library to develop reinforcement learning agents using retro game emulators.
    you: not recommended
    AI recommended (in order):
    1. Gymnasium
    2. Stable Baselines3
    3. gymnasium-retro
    4. RLlib
    5. CleanRL
    6. Minigrid
    7. PyBullet

    AI recommended 7 alternatives but never named openai/retro. 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/retro?
    pass
    AI named openai/retro 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/retro in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/retro 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/retro solve, and who is the primary audience?
    pass
    AI named openai/retro 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/retro. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/openai/retro.svg)](https://repogeo.com/en/r/openai/retro)
HTML
<a href="https://repogeo.com/en/r/openai/retro"><img src="https://repogeo.com/badge/openai/retro.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

openai/retro — 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