RRepoGEO

REPOGEO REPORT · LITE

tkn-tub/ns3-gym

Default branch app-ns-3.36+ · commit cfff7f32 · scanned 6/8/2026, 6:22:07 PM

GitHub: 691 stars · 220 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 tkn-tub/ns3-gym, 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 to emphasize network protocol optimization

    Why:

    CURRENT
    ns3-gym OpenAI Gym is a toolkit for reinforcement learning (RL) widely used in research. The network simulator ns-3 is the de-facto standard for academic and industry studies in the areas of networking protocols and communication technologies. ns3-gym is a framework that integrates both OpenAI Gym and ns-3 in order to encourage usage of RL in networking research.
    COPY-PASTE FIX
    ns3-gym: The Playground for Reinforcement Learning in Networking Research. This framework integrates OpenAI Gym with the ns-3 network simulator, providing a powerful environment for applying machine learning agents to optimize network protocols and communication technologies. It's designed for researchers and students exploring RL in networking.
  • mediumhomepage#2
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/tkn-tub/ns3-gym
  • lowtopics#3
    Expand repository topics for better keyword matching

    Why:

    CURRENT
    gym-environment, ns3, openai-gym, reinforcement-learning, reinforcement-learning-environments
    COPY-PASTE FIX
    gym-environment, ns3, openai-gym, reinforcement-learning, reinforcement-learning-environments, network-simulation, network-optimization, protocol-optimization, rl-for-networking

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 tkn-tub/ns3-gym
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
omnetpp/omnetpp
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. omnetpp/omnetpp · recommended 2×
  2. mininet/mininet · recommended 2×
  3. NetSim · recommended 2×
  4. ray-project/ray · recommended 1×
  5. NS-3 · recommended 1×
  • CATEGORY QUERY
    How to use machine learning agents to optimize network protocols in simulations?
    you: not recommended
    AI recommended (in order):
    1. Ray RLLib (ray-project/ray)
    2. NS-3
    3. OMNeT++ (omnetpp/omnetpp)
    4. OpenAI Gym (openai/gym)
    5. Mininet (mininet/mininet)
    6. TensorFlow Agents (tensorflow/agents)
    7. PyTorch Lightning (Lightning-AI/lightning)
    8. NetSim
    9. INET Framework (inet-framework/inet)

    AI recommended 9 alternatives but never named tkn-tub/ns3-gym. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What simulation environments exist for applying reinforcement learning to networking research?
    you: not recommended
    AI recommended (in order):
    1. NS-3 (nsnam/ns-3-dev)
    2. OMNeT++ (omnetpp/omnetpp)
    3. Mininet (mininet/mininet)
    4. Gym-Network (network-gym/gym-network)
    5. SimPy (simpy/simpy)
    6. bmv2 (p4lang/behavioral-model)
    7. NetSim

    AI recommended 7 alternatives but never named tkn-tub/ns3-gym. 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 tkn-tub/ns3-gym?
    pass
    AI named tkn-tub/ns3-gym explicitly

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

  • If a team adopts tkn-tub/ns3-gym in production, what risks or prerequisites should they evaluate first?
    pass
    AI named tkn-tub/ns3-gym 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 tkn-tub/ns3-gym solve, and who is the primary audience?
    pass
    AI named tkn-tub/ns3-gym explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
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