RRepoGEO

REPOGEO REPORT · LITE

inclusionAI/AReaL

Default branch main · commit 13353cc6 · scanned 6/17/2026, 6:47:12 PM

GitHub: 5,316 stars · 522 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 inclusionAI/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
    Add LLM agent application context directly to the README's main heading

    Why:

    CURRENT
    <h1 align="center">
    <em>AReaL</em>: A Large-Scale Asynchronous Reinforcement Learning System
    </h1>
    COPY-PASTE FIX
    <h1 align="center">
    <em>AReaL</em>: A Large-Scale Asynchronous Reinforcement Learning System for LLM-based Agent Applications
    </h1>
    <p align="center">
    <b>The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible.</b>
    </p>
  • hightopics#2
    Add specific topics for distributed/asynchronous RL systems and LLM agent frameworks

    Why:

    CURRENT
    agent, llm, llm-agent, llm-reasoning, machine-learning-systems, mlsys, reinforcement-learning, rl
    COPY-PASTE FIX
    agent, llm, llm-agent, llm-reasoning, machine-learning-systems, mlsys, reinforcement-learning, rl, distributed-rl, asynchronous-rl, large-scale-rl, llm-ops, agent-framework
  • mediumabout#3
    Refine the 'About' description to emphasize 'system' and 'scalable asynchronous' for LLM agents

    Why:

    CURRENT
    The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible.
    COPY-PASTE FIX
    A scalable, asynchronous reinforcement learning system designed as an RL bridge for large-scale LLM-based agent applications.

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 inclusionAI/AReaL
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. Hugging Face TRL · recommended 1×
  3. Axolotl · recommended 1×
  4. Gymnasium · recommended 1×
  5. Stable Baselines3 · recommended 1×
  • CATEGORY QUERY
    How to integrate reinforcement learning effectively for training large language model agents?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face TRL
    3. Axolotl
    4. Gymnasium
    5. Stable Baselines3
    6. LangChain
    7. LlamaIndex
    8. GPT-4
    9. Claude
    10. Diffusers

    AI recommended 10 alternatives but never named inclusionAI/AReaL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a scalable asynchronous reinforcement learning system for developing agentic AI applications.
    you: not recommended
    AI recommended (in order):
    1. Ray RLlib (ray-project/ray)
    2. Acme (deepmind/acme)
    3. OpenSpiel (deepmind/open_spiel)
    4. Tianshou (thu-ml/tianshou)
    5. CleanRL (vwxyzjn/cleanrl)

    AI recommended 5 alternatives but never named inclusionAI/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 inclusionAI/AReaL?
    pass
    AI named inclusionAI/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 inclusionAI/AReaL in production, what risks or prerequisites should they evaluate first?
    pass
    AI named inclusionAI/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 inclusionAI/AReaL solve, and who is the primary audience?
    pass
    AI named inclusionAI/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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inclusionAI/AReaL — 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