RRepoGEO

REPOGEO REPORT · LITE

koulanurag/ma-gym

Default branch master · commit 1f0aa3d9 · scanned 6/15/2026, 4:02:15 PM

GitHub: 632 stars · 114 forks

AI VISIBILITY SCORE
40 /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
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 koulanurag/ma-gym, 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 sentence to clarify purpose and audience

    Why:

    CURRENT
    It's a collection of multi agent environments based on OpenAI gym.
    COPY-PASTE FIX
    ma-gym provides a focused collection of lightweight multi-agent reinforcement learning (MARL) environments, built directly on the OpenAI Gym API, ideal for research and development of collaborative AI models.
  • mediumtopics#2
    Expand repository topics with more specific MARL keywords

    Why:

    CURRENT
    collaborative, environment, gym, multi-agent, openai-gym, reinforcement-learning
    COPY-PASTE FIX
    collaborative, environment, gym, marl, multi-agent, multi-agent-rl, openai-gym, reinforcement-learning, simulation, gym-environments
  • mediumcomparison#3
    Add a 'Comparison' or 'Why ma-gym?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why ma-gym?
While projects like PettingZoo offer a broad range of multi-agent environments, ma-gym focuses on providing a minimalist and direct extension of the standard OpenAI Gym API. This design choice makes it particularly suitable for researchers and developers who prefer a lightweight, familiar interface for developing and testing multi-agent reinforcement learning algorithms without extensive framework overhead.

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 koulanurag/ma-gym
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Farama-Foundation/PettingZoo
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Farama-Foundation/PettingZoo · recommended 2×
  2. SMAC · recommended 1×
  3. PettingZoo · recommended 1×
  4. MPE · recommended 1×
  5. openai/gym · recommended 1×
  • CATEGORY QUERY
    What are good multi-agent reinforcement learning environments for training collaborative AI models?
    you: not recommended
    AI recommended (in order):
    1. SMAC
    2. PettingZoo
    3. MPE

    AI recommended 3 alternatives but never named koulanurag/ma-gym. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a collection of multi-agent simulation environments compatible with standard reinforcement learning frameworks.
    you: not recommended
    AI recommended (in order):
    1. PettingZoo (Farama-Foundation/PettingZoo)
    2. OpenAI Gym (openai/gym)
    3. Farama Foundation's Gymnasium (Farama-Foundation/Gymnasium)
    4. Stable Baselines3 (DLR-RM/stable-baselines3)
    5. RLlib (ray-project/ray)
    6. CleanRL (vwxyzjn/cleanrl)
    7. MAgent (microsoft/MAgent)
    8. Multi-Agent Particle Environments (MPE) (Farama-Foundation/PettingZoo)
    9. Google Research Football (google-research/football)
    10. StarCraft II Learning Environment (SC2LE) (deepmind/pysc2)
    11. Unity ML-Agents (Unity-Technologies/ml-agents)
    12. OpenSpiel (deepmind/open_spiel)

    AI recommended 12 alternatives but never named koulanurag/ma-gym. 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 koulanurag/ma-gym?
    pass
    AI named koulanurag/ma-gym explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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