RRepoGEO

REPOGEO REPORT · LITE

areal-project/AReaL

Default branch main · commit 1fab24a2 · scanned 5/24/2026, 8:32:20 PM

GitHub: 5,210 stars · 504 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 areal-project/AReaL, 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
    Explicitly clarify the 'AReaL' acronym in the README's first paragraph

    Why:

    CURRENT
    AReaL is a reinforcement learning (RL) infrastructure designed to bridge foundation model training with modern agent-based applications.
    COPY-PASTE FIX
    AReaL (Asynchronous Reinforcement Learning) is a reinforcement learning (RL) infrastructure, *distinct from Augmented Reality (AR) applications*, designed to bridge foundation model training with modern agent-based applications.
  • mediumabout#2
    Update the repository description to disambiguate 'AReaL'

    Why:

    CURRENT
    The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible.
    COPY-PASTE FIX
    AReaL: The Asynchronous Reinforcement Learning (RL) Bridge for LLM-based Agent Applications. *Distinct from Augmented Reality (AR) projects.* Made Simple & Flexible.
  • lowcomparison#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Existing RL Frameworks
AReaL differentiates itself from frameworks like Ray, RLlib, DeepMind's Acme, and OpenAI Baselines by focusing on a fully asynchronous RL training paradigm specifically optimized for large-scale reasoning and agentic models, bridging foundation model training with modern agent-based applications. Our emphasis is on accessibility, efficiency, and cost-effectiveness for LLM-based agent development, offering a unique blend of scalability and flexibility.

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 areal-project/AReaL
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 3×
  2. Hugging Face Transformers · recommended 1×
  3. Hugging Face Accelerate · recommended 1×
  4. DeepMind's Acme · recommended 1×
  5. RLlib · recommended 1×
  • CATEGORY QUERY
    How to efficiently train large-scale LLM-based agents using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Accelerate
    3. DeepMind's Acme
    4. RLlib
    5. OpenAI Baselines
    6. Stable Baselines3
    7. PyTorch FSDP
    8. Colossal-AI
    9. DeepSpeed

    AI recommended 9 alternatives but never named areal-project/AReaL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What infrastructure supports scalable asynchronous reinforcement learning for complex agentic models?
    you: not recommended
    AI recommended (in order):
    1. Ray (ray-project/ray)
    2. RLlib (ray-project/ray)
    3. Ray Tune (ray-project/ray)
    4. Kubernetes (kubernetes/kubernetes)
    5. Kubeflow (kubeflow/kubeflow)
    6. MetaFlow (Netflix/metaflow)
    7. Argo Workflows (argoproj/argo-workflows)
    8. Google Cloud ML Engine
    9. AI Platform
    10. AWS SageMaker
    11. Azure ML
    12. PyTorch Lightning (Lightning-AI/lightning)
    13. TensorFlow (tensorflow/tensorflow)
    14. OpenSpiel (deepmind/open_spiel)

    AI recommended 14 alternatives but never named areal-project/AReaL. 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 areal-project/AReaL?
    pass
    AI named areal-project/AReaL explicitly

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

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

    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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MARKDOWN (README)
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areal-project/AReaL — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite