RRepoGEO

REPOGEO REPORT · LITE

openai/multiagent-competition

Default branch master · commit b2e081a1 · scanned 6/14/2026, 11:12:57 PM

GitHub: 835 stars · 158 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/multiagent-competition, 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
  • highlicense#1
    Add a standard open-source license file

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, choosing a standard open-source license like Apache-2.0 or MIT, and include its text.
  • hightopics#2
    Expand repository topics to improve category visibility

    Why:

    CURRENT
    paper
    COPY-PASTE FIX
    multi-agent, reinforcement-learning, competition, environments, gym, research, ai
  • mediumabout#3
    Update the repository description for better clarity

    Why:

    CURRENT
    Code for the paper "Emergent Complexity via Multi-agent Competition"
    COPY-PASTE FIX
    Environments and code for simulating and studying emergent behaviors in competitive multi-agent reinforcement learning scenarios.

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/multiagent-competition
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NetLogo
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NetLogo · recommended 1×
  2. projectmesa/mesa · recommended 1×
  3. Unity · recommended 1×
  4. Unity-Technologies/ml-agents · recommended 1×
  5. openai/gym · recommended 1×
  • CATEGORY QUERY
    How can I simulate competitive multi-agent interactions to study emergent behaviors?
    you: not recommended
    AI recommended (in order):
    1. NetLogo
    2. Mesa (projectmesa/mesa)
    3. Unity
    4. Unity ML-Agents (Unity-Technologies/ml-agents)
    5. OpenAI Gym (openai/gym)
    6. Farama Foundation Gymnasium (Farama-Foundation/Gymnasium)
    7. GAMA Platform
    8. Anylogic

    AI recommended 8 alternatives but never named openai/multiagent-competition. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python environments are available for training agents in competitive scenarios?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym
    2. Gymnasium
    3. PettingZoo
    4. DeepMind Lab
    5. Unity ML-Agents Toolkit
    6. StarCraft II Learning Environment (SC2LE)
    7. Google Football Environment (GFootball)
    8. MAgent
    9. OpenSpiel

    AI recommended 9 alternatives but never named openai/multiagent-competition. 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/multiagent-competition?
    pass
    AI named openai/multiagent-competition 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/multiagent-competition in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/multiagent-competition 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/multiagent-competition solve, and who is the primary audience?
    pass
    AI named openai/multiagent-competition explicitly

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

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openai/multiagent-competition — 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