RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/meta-agents-research-environments

Default branch main · commit 79463674 · scanned 5/30/2026, 11:23:01 PM

GitHub: 505 stars · 66 forks

AI VISIBILITY SCORE
27 /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
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 facebookresearch/meta-agents-research-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
  • highreadme#1
    Strengthen README's opening sentence to emphasize unique value

    Why:

    CURRENT
    Meta Agents Research Environments (ARE) is a platform designed to evaluate AI agents in dynamic, realistic scenarios.
    COPY-PASTE FIX
    Meta Agents Research Environments (ARE) is the leading platform for evaluating AI agents, particularly meta-agents, in dynamic, evolving scenarios that demand multi-step reasoning and adaptation.
  • mediumtopics#2
    Add more specific topics to clarify evaluation focus

    Why:

    CURRENT
    agents, ai, autonomous-agents, benchmark, evaluation, large-language-models, llm, meta, multi-agent-systems, natural-language-processing, reinforcement-learning, rl, simulation
    COPY-PASTE FIX
    agents, ai, autonomous-agents, benchmark, evaluation, large-language-models, llm, meta, multi-agent-systems, natural-language-processing, reinforcement-learning, rl, simulation, meta-agent-evaluation, dynamic-environments, adaptive-ai, real-world-simulation, agent-adaptation
  • lowcomparison#3
    Add a 'Comparison to Alternatives' section in README

    Why:

    COPY-PASTE FIX
    Add a new top-level section to the README, for example, right after 'Background', with a heading like `## Comparison to Alternatives` and an introductory sentence such as: `Unlike general-purpose reinforcement learning environments or LLM agent frameworks, Meta Agents Research Environments (ARE) is specifically designed for the rigorous evaluation of meta-agents in dynamic, evolving scenarios, focusing on adaptation and multi-step reasoning.`

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 facebookresearch/meta-agents-research-environments
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Gym
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Gym · recommended 2×
  2. DeepMind Lab · recommended 1×
  3. Unity ML-Agents · recommended 1×
  4. Gazebo · recommended 1×
  5. CARLA · recommended 1×
  • CATEGORY QUERY
    How to evaluate autonomous AI agents in dynamic, evolving environments?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym
    2. DeepMind Lab
    3. Unity ML-Agents
    4. Gazebo
    5. CARLA
    6. AirSim
    7. Optimizely
    8. Split
    9. LaunchDarkly
    10. CleverHans
    11. Foolbox

    AI recommended 11 alternatives but never named facebookresearch/meta-agents-research-environments. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What platforms provide realistic simulation benchmarks for multi-agent LLM systems?
    you: not recommended
    AI recommended (in order):
    1. MetaGPT
    2. AutoGPT
    3. LangChain
    4. OpenAI Gym
    5. PettingZoo
    6. MAgent

    AI recommended 6 alternatives but never named facebookresearch/meta-agents-research-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
    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 facebookresearch/meta-agents-research-environments?
    pass
    AI did not name facebookresearch/meta-agents-research-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 facebookresearch/meta-agents-research-environments in production, what risks or prerequisites should they evaluate first?
    pass
    AI named facebookresearch/meta-agents-research-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 facebookresearch/meta-agents-research-environments solve, and who is the primary audience?
    pass
    AI did not name facebookresearch/meta-agents-research-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?

Embed your GEO score

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facebookresearch/meta-agents-research-environments — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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