RRepoGEO

REPOGEO REPORT · LITE

xinzhel/LLM-Agent-Survey

Default branch main · commit d3814c7f · scanned 6/4/2026, 9:03:05 PM

GitHub: 506 stars · 20 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 xinzhel/LLM-Agent-Survey, 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 specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm-agents, llm-survey, large-language-models, artificial-intelligence, nlp, research-paper, reading-list, coling-2025
  • highreadme#2
    Reposition README H1 to emphasize 'survey'

    Why:

    CURRENT
    A Reading List for LLM-Agents (Last Major Updated: 14 Mar 2025)
    COPY-PASTE FIX
    A Comprehensive Survey on LLM Agents (CoLing 2025)
  • mediumhomepage#3
    Add homepage URL to repository metadata

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2406.05804

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 xinzhel/LLM-Agent-Survey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
A Survey on Large Language Model based Autonomous Agents
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. A Survey on Large Language Model based Autonomous Agents · recommended 1×
  2. The Rise and Potential of Large Language Model Based Agents: A Survey · recommended 1×
  3. Generative Agents: Interactive Simulacra of Human Behavior · recommended 1×
  4. AgentBench: Evaluating LLMs as Agents · recommended 1×
  5. LLM Powered Autonomous Agents · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive survey of current LLM agent paradigms and research?
    you: not recommended
    AI recommended (in order):
    1. A Survey on Large Language Model based Autonomous Agents
    2. The Rise and Potential of Large Language Model Based Agents: A Survey
    3. Generative Agents: Interactive Simulacra of Human Behavior
    4. AgentBench: Evaluating LLMs as Agents
    5. LLM Powered Autonomous Agents
    6. Awesome-LLM-Agents

    AI recommended 6 alternatives but never named xinzhel/LLM-Agent-Survey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the major architectural approaches and design patterns for developing LLM agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGen (Microsoft)
    4. DSPy (Stanford NLP)
    5. Weaviate
    6. Pinecone
    7. Chroma
    8. OpenAI Function Calling
    9. CrewAI
    10. Redis
    11. PostgreSQL (with pgvector)
    12. BabyAGI
    13. Auto-GPT

    AI recommended 13 alternatives but never named xinzhel/LLM-Agent-Survey. 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 xinzhel/LLM-Agent-Survey?
    pass
    AI did not name xinzhel/LLM-Agent-Survey — 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 xinzhel/LLM-Agent-Survey in production, what risks or prerequisites should they evaluate first?
    pass
    AI named xinzhel/LLM-Agent-Survey 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 xinzhel/LLM-Agent-Survey solve, and who is the primary audience?
    pass
    AI did not name xinzhel/LLM-Agent-Survey — 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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xinzhel/LLM-Agent-Survey — 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