RRepoGEO

REPOGEO REPORT · LITE

luo-junyu/Awesome-Agent-Papers

Default branch main · commit 55afd290 · scanned 5/26/2026, 4:27:42 PM

GitHub: 2,716 stars · 101 forks

AI VISIBILITY SCORE
22 /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
1 / 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 luo-junyu/Awesome-Agent-Papers, 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 the README's opening paragraph to clarify the repo's role as a survey

    Why:

    CURRENT
    This repository contains a **comprehensive collection** of research papers on Large Language Model (LLM) agents.
    COPY-PASTE FIX
    This repository serves as the **official, up-to-date survey and comprehensive collection** of research papers on Large Language Model (LLM) agents, covering methodology, applications, and challenges.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of the MIT License.
  • mediumtopics#3
    Expand repository topics for better categorization

    Why:

    CURRENT
    agent, awesome-list, llm, llmagents
    COPY-PASTE FIX
    agent, awesome-list, llm, llmagents, llm-agents-survey, research-papers, ai-survey, large-language-models-research, survey-paper

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 luo-junyu/Awesome-Agent-Papers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. Google Scholar · recommended 1×
  3. Hugging Face · recommended 1×
  4. Towards Data Science · recommended 1×
  5. NeurIPS · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive survey of recent research on large language model agents?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. Hugging Face
    4. Towards Data Science
    5. NeurIPS
    6. ICML
    7. ICLR
    8. AAAI
    9. ACL
    10. GitHub

    AI recommended 10 alternatives but never named luo-junyu/Awesome-Agent-Papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the key methodologies and applications for building advanced LLM agents?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. Amazon CodeWhisperer

    AI recommended 2 alternatives but never named luo-junyu/Awesome-Agent-Papers. 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 luo-junyu/Awesome-Agent-Papers?
    pass
    AI did not name luo-junyu/Awesome-Agent-Papers — likely talking about a different project

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

  • If a team adopts luo-junyu/Awesome-Agent-Papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named luo-junyu/Awesome-Agent-Papers 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 luo-junyu/Awesome-Agent-Papers solve, and who is the primary audience?
    pass
    AI did not name luo-junyu/Awesome-Agent-Papers — likely talking about a different project

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

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luo-junyu/Awesome-Agent-Papers — 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