RRepoGEO

REPOGEO REPORT · LITE

OpenRL-Lab/openrl

Default branch main · commit 4c92aa44 · scanned 6/2/2026, 8:21:57 PM

GitHub: 834 stars · 81 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 OpenRL-Lab/openrl, 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
    Reposition README's opening statement

    Why:

    CURRENT
    Welcome to OpenRL Crafting Reinforcement Learning Frameworks with Passion, Your Valuable Insights Welcome. OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agen
    COPY-PASTE FIX
    OpenRL is a unified, high-performance reinforcement learning framework designed for scalable and distributed training, with strong support for multi-agent reinforcement learning (MARL), embodied AI, robotics, and LLM agent development, all built on PyTorch.
  • mediumreadme#2
    Reorganize README to prioritize core value proposition

    Why:

    COPY-PASTE FIX
    Move the core value proposition (e.g., the text from the previous action item) to immediately follow the main '## Welcome to OpenRL' heading, before any extensive badges, build status, or navigation links.
  • lowreadme#3
    Elaborate on 'Unified' aspect in README

    Why:

    COPY-PASTE FIX
    Add a sentence or short paragraph in the README, perhaps after the opening statement, explaining: 'OpenRL unifies support for diverse reinforcement learning paradigms, including single-agent and multi-agent setups, across a wide range of environments from Atari and Mujoco to embodied AI and LLM 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 OpenRL-Lab/openrl
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 1×
  2. google-research/open_spiel · recommended 1×
  3. deepmind/acme · recommended 1×
  4. thu-ml/tianshou · recommended 1×
  5. DLR-RM/stable-baselines3 · recommended 1×
  • CATEGORY QUERY
    Looking for a unified reinforcement learning framework supporting distributed training and multi-agent setups.
    you: not recommended
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. OpenSpiel (google-research/open_spiel)
    3. Acme (deepmind/acme)
    4. Tianshou (thu-ml/tianshou)
    5. Stable Baselines3 (DLR-RM/stable-baselines3)

    AI recommended 5 alternatives but never named OpenRL-Lab/openrl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best reinforcement learning libraries for embodied AI, robotics, and LLM agent development?
    you: not recommended
    AI recommended (in order):
    1. RLlib
    2. Stable Baselines3
    3. Tianshou
    4. Acme
    5. CleanRL
    6. Surreal

    AI recommended 6 alternatives but never named OpenRL-Lab/openrl. 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 OpenRL-Lab/openrl?
    pass
    AI named OpenRL-Lab/openrl explicitly

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

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

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

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OpenRL-Lab/openrl — 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