RRepoGEO

REPOGEO REPORT · LITE

EmergenceAI/Emergence-World

Default branch main · commit 7613dcb6 · scanned 6/29/2026, 10:23:08 AM

GitHub: 504 stars · 66 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 EmergenceAI/Emergence-World, 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
  • highabout#1
    Refine repository description to highlight unique value

    Why:

    CURRENT
    Emergence World: A world designed to reveal what no benchmark can: emergent intelligence.
    COPY-PASTE FIX
    Emergence World: A persistent, living world for studying emergent AI intelligence, distinct from traditional benchmarks or training environments.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    ai-agents, simulation
    COPY-PASTE FIX
    ai-agents, simulation, emergent-intelligence, multi-agent-systems, persistent-world, open-ended-simulation
  • mediumreadme#3
    Add a 'Why Emergence World?' or 'Comparison' section to README

    Why:

    COPY-PASTE FIX
    ## Why Emergence World?

Emergence World is explicitly designed for the study and analysis of emergent behaviors within complex multi-agent systems. Unlike traditional simulation platforms (e.g., Unity ML-Agents, Mesa, NetLogo) or reinforcement learning environments (e.g., OpenAI Gym), Emergence World provides a persistent, open-ended environment with real constraints and consequences, where agents truly build, govern, and evolve without scripts, resets, or fixed outcomes. Our focus is on revealing what no benchmark can: true emergent intelligence.

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 EmergenceAI/Emergence-World
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Unity-Technologies/ml-agents
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Unity-Technologies/ml-agents · recommended 2×
  2. Unreal Engine · recommended 2×
  3. projectmesa/mesa · recommended 1×
  4. NetLogo/NetLogo · recommended 1×
  5. Farama-Foundation/Gymnasium · recommended 1×
  • CATEGORY QUERY
    How can I set up a persistent simulation for observing emergent intelligence in AI agents?
    you: not recommended
    AI recommended (in order):
    1. Unity ML-Agents (Unity-Technologies/ml-agents)
    2. Mesa (projectmesa/mesa)
    3. NetLogo (NetLogo/NetLogo)
    4. OpenAI Gym/Farama Gymnasium (Farama-Foundation/Gymnasium)
    5. Stable Baselines3 (DLR-RM/stable-baselines3)
    6. Ray RLlib (ray-project/ray)
    7. Unreal Engine
    8. TensorFlow (tensorflow/tensorflow)
    9. PyTorch (pytorch/pytorch)
    10. GAMA Platform (gama-platform/gama)

    AI recommended 10 alternatives but never named EmergenceAI/Emergence-World. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Frameworks for building and testing autonomous AI agents in dynamic virtual worlds?
    you: not recommended
    AI recommended (in order):
    1. Unity ML-Agents Toolkit (Unity-Technologies/ml-agents)
    2. Unreal Engine
    3. AirSim (microsoft/AirSim)
    4. DeepMind Lab (deepmind/lab)
    5. OpenAI Gym (openai/gym)
    6. Isaac Sim
    7. CARLA Simulator (carla-simulator/carla)

    AI recommended 7 alternatives but never named EmergenceAI/Emergence-World. 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 EmergenceAI/Emergence-World?
    pass
    AI named EmergenceAI/Emergence-World explicitly

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

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

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

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EmergenceAI/Emergence-World — 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