RRepoGEO

REPOGEO REPORT · LITE

QwenLM/Qwen-AgentWorld

Default branch main · commit 354f7338 · scanned 6/27/2026, 10:37:52 PM

GitHub: 591 stars · 51 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 QwenLM/Qwen-AgentWorld, 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's opening to clearly state its role as a platform/benchmark

    Why:

    CURRENT
    Welcome to the GitHub repository of Qwen-AgentWorld. Here, you can find official information about Qwen-AgentWorld, post your questions (Issues), and share your ideas with the community (Discussions).
    COPY-PASTE FIX
    Welcome to the GitHub repository of Qwen-AgentWorld, a comprehensive **platform and benchmark** for developing, simulating, and evaluating general-purpose AI agents using language world models. Here, you can find official information about Qwen-AgentWorld, post your questions (Issues), and share your ideas with the community (Discussions).
  • mediumreadme#2
    Add a sentence to the README's intro highlighting AgentWorldBench as a core differentiator

    Why:

    COPY-PASTE FIX
    It uniquely features **AgentWorldBench**, a robust evaluation benchmark across 7 domains, enabling systematic assessment of LLM-powered agents within simulated 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 QwenLM/Qwen-AgentWorld
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4 · recommended 1×
  2. Claude 3 Opus · recommended 1×
  3. Gemini 1.5 Pro · recommended 1×
  4. GPT-3.5 Turbo · recommended 1×
  5. Llama 3 · recommended 1×
  • CATEGORY QUERY
    What are the best language models for building general-purpose AI agents?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Gemini 1.5 Pro
    4. GPT-3.5 Turbo
    5. Llama 3
    6. Mistral Large

    AI recommended 6 alternatives but never named QwenLM/Qwen-AgentWorld. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I simulate complex environments using large context language models for agentic reasoning?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI Gym/Gymnasium
    4. Meta's Light
    5. Microsoft's AutoGen
    6. DeepMind's AlphaCode 2
    7. Hugging Face's Transformers Agents

    AI recommended 7 alternatives but never named QwenLM/Qwen-AgentWorld. 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 QwenLM/Qwen-AgentWorld?
    pass
    AI named QwenLM/Qwen-AgentWorld explicitly

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

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

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

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QwenLM/Qwen-AgentWorld — 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