RRepoGEO

REPOGEO REPORT · LITE

openai/multi-agent-emergence-environments

Default branch master · commit bafaf1e1 · scanned 5/18/2026, 10:13:14 AM

GitHub: 1,796 stars · 324 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
22 /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
1 / 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/multi-agent-emergence-environments, 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 specific topics for multi-agent research environments

    Why:

    COPY-PASTE FIX
    multi-agent-systems, reinforcement-learning, emergent-behavior, tool-use, physics-simulation, research-environments, archived
  • highreadme#2
    Expand README's opening to clarify purpose and scope

    Why:

    CURRENT
    Environment generation code for Emergent Tool Use From Multi-Agent Autocurricula (blog)
    COPY-PASTE FIX
    This repository provides the environment generation code used in the paper "Emergent Tool Use From Multi-Agent Autocurricula". It offers a framework for creating complex, physics-based multi-agent environments designed to study emergent behaviors, tool use, and social learning in reinforcement learning research.
  • mediumabout#3
    Update repository description for broader context

    Why:

    CURRENT
    Environment generation code for the paper "Emergent Tool Use From Multi-Agent Autocurricula"
    COPY-PASTE FIX
    Environment generation code for multi-agent reinforcement learning research, specifically designed for studying emergent behaviors and tool use, as featured in the paper "Emergent Tool Use From Multi-Agent Autocurricula".

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/multi-agent-emergence-environments
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Unity
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Unity · recommended 2×
  2. Isaac Gym / Isaac Sim · recommended 1×
  3. MuJoCo · recommended 1×
  4. PyBullet · recommended 1×
  5. ML-Agents Toolkit · recommended 1×
  • CATEGORY QUERY
    How to generate complex physics-based environments for multi-agent reinforcement learning research?
    you: not recommended
    AI recommended (in order):
    1. Isaac Gym / Isaac Sim
    2. MuJoCo
    3. PyBullet
    4. Unity
    5. ML-Agents Toolkit
    6. Gazebo
    7. Gymnasium

    AI recommended 7 alternatives but never named openai/multi-agent-emergence-environments. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for simulating multi-agent interactions to observe emergent behaviors and tool use?
    you: not recommended
    AI recommended (in order):
    1. NetLogo (ccl/netlogo)
    2. Mesa (projectmesa/mesa)
    3. GAMA Platform (gama-platform/gama)
    4. Repast Simphony (Repast/RepastSimphony)
    5. Anylogic
    6. Unity

    AI recommended 6 alternatives but never named openai/multi-agent-emergence-environments. 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/multi-agent-emergence-environments?
    pass
    AI did not name openai/multi-agent-emergence-environments — likely talking about a different project

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

  • If a team adopts openai/multi-agent-emergence-environments in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/multi-agent-emergence-environments 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/multi-agent-emergence-environments solve, and who is the primary audience?
    pass
    AI did not name openai/multi-agent-emergence-environments — likely talking about a different project

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

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openai/multi-agent-emergence-environments — 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