RRepoGEO

REPOGEO REPORT · LITE

PKU-Alignment/omnisafe

Default branch main · commit 15603dd7 · scanned 5/14/2026, 3:17:07 AM

GitHub: 1,114 stars · 155 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 PKU-Alignment/omnisafe, 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
    Move core value proposition to the very top of README

    Why:

    CURRENT
    The current README excerpt shows badges and navigation links before the core description.
    COPY-PASTE FIX
    Move the sentence "OmniSafe is an infrastructural framework designed to accelerate safe reinforcement learning (RL) research. It provides a comprehensive and reliable benchmark for safe RL algorithms, and also an out-of-box modular toolkit for researchers." to be the absolute first text in the README, above any badges or center-aligned divs.
  • mediumabout#2
    Enhance the 'About' description to highlight benchmarking and toolkit features

    Why:

    CURRENT
    JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.
    COPY-PASTE FIX
    JMLR: OmniSafe is an infrastructural framework, comprehensive benchmark, and modular toolkit for accelerating SafeRL research.
  • mediumcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled "Comparison with Alternatives" or "Why OmniSafe?" that explains how OmniSafe differs from general RL libraries (e.g., Stable Baselines3, RLlib) by being specifically designed for SafeRL, and how it provides a unified framework and benchmark compared to other SafeRL-specific tools.

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 PKU-Alignment/omnisafe
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Safe Reinforcement Learning (Safe-RL) Library
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Safe Reinforcement Learning (Safe-RL) Library · recommended 2×
  2. Safety Gym · recommended 1×
  3. RLlib · recommended 1×
  4. OpenAI Gym/Farama Foundation Gymnasium · recommended 1×
  5. PyBullet · recommended 1×
  • CATEGORY QUERY
    What are the best frameworks for developing and benchmarking safe reinforcement learning algorithms?
    you: not recommended
    AI recommended (in order):
    1. Safety Gym
    2. Safe Reinforcement Learning (Safe-RL) Library
    3. RLlib
    4. OpenAI Gym/Farama Foundation Gymnasium
    5. PyBullet
    6. MuJoCo

    AI recommended 6 alternatives but never named PKU-Alignment/omnisafe. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a toolkit for developing safety-critical deep reinforcement learning applications with constraints.
    you: not recommended
    AI recommended (in order):
    1. Safe Reinforcement Learning (Safe-RL) Library
    2. Ray RLlib
    3. OpenAI Baselines
    4. Stable Baselines3
    5. PyTorch-DRL
    6. TensorFlow Agents (TF-Agents)
    7. DeepMind's Acme

    AI recommended 7 alternatives but never named PKU-Alignment/omnisafe. 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 PKU-Alignment/omnisafe?
    pass
    AI named PKU-Alignment/omnisafe explicitly

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

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

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

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PKU-Alignment/omnisafe — 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