RRepoGEO

REPOGEO REPORT · LITE

lmgame-org/GamingAgent

Default branch main · commit 996d848a · scanned 6/10/2026, 10:28:29 AM

GitHub: 936 stars · 103 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 lmgame-org/GamingAgent, 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
  • hightopics#1
    Add comprehensive topics to the repository

    Why:

    COPY-PASTE FIX
    llm-agents, vlm-agents, gaming-ai, ai-benchmarking, game-ai, interactive-agents, model-evaluation, computer-use-agents, llm, vlm
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://lmgame.org/#/gaming_agent
  • mediumreadme#3
    Add a 'Why GamingAgent?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why GamingAgent?

Unlike general reinforcement learning toolkits or game engines, GamingAgent is specifically designed for developing, evaluating, and benchmarking LLM/VLM-based agents, focusing on human-like cognitive gameplay and model evaluation in diverse interactive gaming environments.

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 lmgame-org/GamingAgent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Unity ML-Agents Toolkit
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Unity ML-Agents Toolkit · recommended 1×
  2. Unreal Engine · recommended 1×
  3. UnrealCV · recommended 1×
  4. AirSim · recommended 1×
  5. Gymnasium · recommended 1×
  • CATEGORY QUERY
    How can I benchmark large language models or vision models in interactive video games?
    you: not recommended
    AI recommended (in order):
    1. Unity ML-Agents Toolkit
    2. Unreal Engine
    3. UnrealCV
    4. AirSim
    5. Gymnasium
    6. Minetest
    7. Minecraft
    8. Mineflayer
    9. MCPI
    10. Pygame

    AI recommended 10 alternatives but never named lmgame-org/GamingAgent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to develop and deploy LLM-based agents for playing PC games.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym/Gymnasium
    2. Stable Baselines3
    3. Ray RLlib
    4. Unity ML-Agents
    5. MineRL
    6. Acme
    7. Dopamine
    8. LangChain
    9. LlamaIndex

    AI recommended 9 alternatives but never named lmgame-org/GamingAgent. 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 lmgame-org/GamingAgent?
    pass
    AI named lmgame-org/GamingAgent explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of lmgame-org/GamingAgent. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/lmgame-org/GamingAgent.svg)](https://repogeo.com/en/r/lmgame-org/GamingAgent)
HTML
<a href="https://repogeo.com/en/r/lmgame-org/GamingAgent"><img src="https://repogeo.com/badge/lmgame-org/GamingAgent.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

lmgame-org/GamingAgent — 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