RRepoGEO

REPOGEO REPORT · LITE

taichengguo/LLM_MultiAgents_Survey_Papers

Default branch main · commit dbfd618f · scanned 5/21/2026, 2:58:10 PM

GitHub: 1,265 stars · 65 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
15 /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
0 / 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 taichengguo/LLM_MultiAgents_Survey_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
  • highlicense#1
    Add a LICENSE file to clarify usage rights

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0, or CC-BY-4.0 for content) and ensure its presence in the repository root.
  • highabout#2
    Clarify the 'About' description to emphasize the repo's official companion role

    Why:

    CURRENT
    Large Language Model based Multi-Agents: A Survey of Progress and Challenges (In IJCAI 2024)
    COPY-PASTE FIX
    Official companion repository for 'Large Language Model based Multi-Agents: A Survey of Progress and Challenges' (IJCAI 2024), featuring categorized papers and updates.
  • mediumreadme#3
    Update README H1 to explicitly state its role as the official survey companion

    Why:

    CURRENT
    <h1 align="center"> 🤖 Awesome LLM-based Multi-Agents Papers </h1>
    COPY-PASTE FIX
    <h1 align="center"> 🤖 Official Companion Repository for "Large Language Model based Multi-Agents: A Survey of Progress and Challenges" (IJCAI 2024) </h1>

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 taichengguo/LLM_MultiAgents_Survey_Papers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AutoGen
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. AutoGen · recommended 2×
  2. A Survey on Large Language Model based Multi-Agent Systems · recommended 1×
  3. The Rise and Potential of Large Language Model Based Agents: A Survey · recommended 1×
  4. AgentVerse · recommended 1×
  5. MetaGPT · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive survey of progress in large language model multi-agent systems?
    you: not recommended
    AI recommended (in order):
    1. A Survey on Large Language Model based Multi-Agent Systems
    2. The Rise and Potential of Large Language Model Based Agents: A Survey
    3. AgentVerse
    4. MetaGPT
    5. Awesome-LLM-Agents
    6. LangChain
    7. AutoGen

    AI recommended 7 alternatives but never named taichengguo/LLM_MultiAgents_Survey_Papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the current challenges and architectural frameworks for LLM-based multi-agent development?
    you: not recommended
    AI recommended (in order):
    1. LangChain Agents
    2. AutoGen
    3. CrewAI
    4. LlamaIndex
    5. SPADE
    6. RabbitMQ
    7. Kafka
    8. Redis
    9. PostgreSQL

    AI recommended 9 alternatives but never named taichengguo/LLM_MultiAgents_Survey_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 taichengguo/LLM_MultiAgents_Survey_Papers?
    pass
    AI did not name taichengguo/LLM_MultiAgents_Survey_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 taichengguo/LLM_MultiAgents_Survey_Papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name taichengguo/LLM_MultiAgents_Survey_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?

  • In one sentence, what problem does the repo taichengguo/LLM_MultiAgents_Survey_Papers solve, and who is the primary audience?
    pass
    AI did not name taichengguo/LLM_MultiAgents_Survey_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?

Embed your GEO score

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

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taichengguo/LLM_MultiAgents_Survey_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