RRepoGEO

REPOGEO REPORT · LITE

NVIDIA-NeMo/ProRL-Agent-Server

Default branch stable · commit 8bc67cc3 · scanned 6/13/2026, 5:27:36 PM

GitHub: 553 stars · 57 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 NVIDIA-NeMo/ProRL-Agent-Server, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README H1 to emphasize "Agent Server" and "Microservice"

    Why:

    CURRENT
    Polar is a RL rollout framework for real-world agent harnesses.
    COPY-PASTE FIX
    Polar is a **scalable RL Agent Server and microservice** for real-world agent harnesses, providing **Rollout as a Service** for production environments.
  • mediumhomepage#2
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    [Insert URL to project documentation, demo, or main product page here]

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 NVIDIA-NeMo/ProRL-Agent-Server
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ray RLLib
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ray RLLib · recommended 1×
  2. Stable Baselines3 · recommended 1×
  3. Celery · recommended 1×
  4. Dask · recommended 1×
  5. OpenAI Gym · recommended 1×
  • CATEGORY QUERY
    How to integrate existing agent environments into a scalable reinforcement learning rollout framework?
    you: not recommended
    AI recommended (in order):
    1. Ray RLLib
    2. Stable Baselines3
    3. Celery
    4. Dask
    5. OpenAI Gym
    6. Farama Gymnasium
    7. gRPC
    8. ZeroMQ
    9. DeepMind Acme
    10. TF-Agents

    AI recommended 10 alternatives but never named NVIDIA-NeMo/ProRL-Agent-Server. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What solutions enable efficient, distributed asynchronous reinforcement learning rollouts as a service?
    you: not recommended
    AI recommended (in order):
    1. Ray RLlib
    2. Acme
    3. OpenSpiel
    4. TensorFlow Agents (TF-Agents)
    5. PyTorch Lightning
    6. Kubernetes

    AI recommended 6 alternatives but never named NVIDIA-NeMo/ProRL-Agent-Server. 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 NVIDIA-NeMo/ProRL-Agent-Server?
    pass
    AI named NVIDIA-NeMo/ProRL-Agent-Server explicitly

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

  • If a team adopts NVIDIA-NeMo/ProRL-Agent-Server in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVIDIA-NeMo/ProRL-Agent-Server 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 NVIDIA-NeMo/ProRL-Agent-Server solve, and who is the primary audience?
    pass
    AI named NVIDIA-NeMo/ProRL-Agent-Server explicitly

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

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NVIDIA-NeMo/ProRL-Agent-Server — 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