RRepoGEO

REPOGEO REPORT · LITE

huggingface/OpenEnv

Default branch main · commit fed98a23 · scanned 5/30/2026, 8:32:03 AM

GitHub: 1,902 stars · 357 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 huggingface/OpenEnv, 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 H1 to clarify 'execution environments' for RL

    Why:

    CURRENT
    An e2e framework for creating, deploying and using isolated execution environments for agentic RL training, built using Gymnasium style simple APIs.
    COPY-PASTE FIX
    OpenEnv is an end-to-end framework for creating, deploying, and using *interactive AI environments* for agentic RL training, built using Gymnasium-style simple APIs, and distinct from general system-level environment management tools.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    reinforcement-learning, rl, agentic-ai, gymnasium, environment-simulation, huggingface-hub, ai-agents, machine-learning
  • mediumabout#3
    Update the repository description for clarity and differentiation

    Why:

    CURRENT
    An interface library for RL post training with environments.
    COPY-PASTE FIX
    An end-to-end framework for creating, deploying, and sharing interactive AI environments for agentic RL training, with Gymnasium-style APIs and deep integration with the Hugging Face Hub.

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 huggingface/OpenEnv
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
moby/moby
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. moby/moby · recommended 1×
  2. conda/conda · recommended 1×
  3. pypa/virtualenv · recommended 1×
  4. venv · recommended 1×
  5. kubernetes/kubernetes · recommended 1×
  • CATEGORY QUERY
    How to create and manage isolated execution environments for agentic reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Docker (moby/moby)
    2. Conda (conda/conda)
    3. Virtualenv (pypa/virtualenv)
    4. venv
    5. Kubernetes (kubernetes/kubernetes)
    6. Google Cloud AI Platform
    7. AWS SageMaker
    8. Azure Machine Learning
    9. Singularity (apptainer/apptainer)

    AI recommended 9 alternatives but never named huggingface/OpenEnv. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for deploying RL agents with Gymnasium-like environment interfaces.
    you: not recommended
    AI recommended (in order):
    1. RLlib
    2. Stable Baselines3
    3. Tianshou
    4. CleanRL
    5. ACME
    6. Keras-RL2

    AI recommended 6 alternatives but never named huggingface/OpenEnv. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 huggingface/OpenEnv?
    pass
    AI named huggingface/OpenEnv explicitly

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

  • If a team adopts huggingface/OpenEnv in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huggingface/OpenEnv 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 huggingface/OpenEnv solve, and who is the primary audience?
    pass
    AI named huggingface/OpenEnv 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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huggingface/OpenEnv — 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