REPOGEO REPORT · LITE

openai/multi-agent-emergence-environments

Default branch master · commit bafaf1e1 · scanned 6/29/2026, 4:17:42 PM

GitHub: 1,807 stars · 325 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

2 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 to clearly state its domain and purpose

Why:

CURRENT

# Multiagent emergence environments
Environment generation code for Emergent Tool Use From Multi-Agent Autocurricula (blog)

COPY-PASTE FIX

# Multi-Agent Emergence Environments: Physics-Based Reinforcement Learning for Emergent Behavior Research
This repository contains the environment generation code for physics-based multi-agent reinforcement learning experiments, specifically developed for the paper "Emergent Tool Use From Multi-Agent Autocurricula" (blog link). It provides customizable simulation environments to study complex emergent behaviors and tool use in AI agents.

mediumhomepage#2
Add a homepage URL linking to the associated blog post
Why:
COPY-PASTE FIX
```
[URL of the "Emergent Tool Use From Multi-Agent Autocurricula" blog post]
```

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

Of all named tools, what % are you?

Top rival

projectmesa/mesa

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

projectmesa/mesa · recommended 1×
NetLogo/NetLogo · recommended 1×
gama-platform/gama · recommended 1×
mason/mason · recommended 1×
Anylogic · recommended 1×

CATEGORY QUERY
How to simulate complex multi-agent interactions for emergent behavior research in AI?
you: not recommended
AI recommended (in order):
1. Mesa (projectmesa/mesa)
2. NetLogo (NetLogo/NetLogo)
3. GAMA Platform (gama-platform/gama)
4. MASON (mason/mason)
5. Anylogic
6. OpenSpiel (deepmind/open_spiel)
AI recommended 6 alternatives but never named openai/multi-agent-emergence-environments. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help create dynamic physics-based environments for multi-agent reinforcement learning experiments?
you: not recommended
AI recommended (in order):
1. Unity (Unity-Technologies/ml-agents)
2. MuJoCo (google-deepmind/mujoco)
3. Isaac Gym (NVIDIA-Omniverse/IsaacSim)
4. PyBullet (bulletphysics/bullet3)
5. Gazebo (osrf/gazebo)
6. RoboSuite (ARISE-Initiative/robosuite)
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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MARKDOWN (README)

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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