RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/MLGym

Default branch main · commit 9d40c1b5 · scanned 6/17/2026, 7:52:45 AM

GitHub: 604 stars · 59 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 facebookresearch/MLGym, 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
    machine-learning, reinforcement-learning, ai-research, benchmark, gym-environment, ml-tasks, agent-training, research-framework, deep-learning
  • highhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://sites.google.com/view/mlgym
  • mediumreadme#3
    Clarify the project's license(s) in the README

    Why:

    COPY-PASTE FIX
    In the 'License' section of your README, add: "The MLGym framework is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/). Please refer to the `LICENSE` file for full details."

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 facebookresearch/MLGym
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Farama-Foundation/Gymnasium · recommended 1×
  2. deepmind/lab · recommended 1×
  3. rlworkgroup/metaworld · recommended 1×
  4. openai/procgen · recommended 1×
  5. Farama-Foundation/Minigrid · recommended 1×
  • CATEGORY QUERY
    What framework helps train reinforcement learning agents for diverse machine learning research tasks?
    you: not recommended
    Show full AI answer
  • CATEGORY QUERY
    Looking for a benchmark environment to evaluate AI agents on complex machine learning research problems.
    you: not recommended
    AI recommended (in order):
    1. Farama Gymnasium (Farama-Foundation/Gymnasium)
    2. DeepMind Lab (deepmind/lab)
    3. MetaWorld (rlworkgroup/metaworld)
    4. Procgen Benchmark (openai/procgen)
    5. MiniGrid (Farama-Foundation/Minigrid)
    6. Atari Learning Environment (ALE) (mgbellemare/Arcade-Learning-Environment)
    7. MuJoCo (google-deepmind/mujoco)
    8. PyBullet (bulletphysics/bullet3)

    AI recommended 8 alternatives but never named facebookresearch/MLGym. 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 facebookresearch/MLGym?
    pass
    AI named facebookresearch/MLGym explicitly

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

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

    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)
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facebookresearch/MLGym — 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