RRepoGEO

REPOGEO REPORT · LITE

Paitesanshi/LLM-Agent-Survey

Default branch main · commit c6503602 · scanned 6/27/2026, 11:33:43 AM

GitHub: 2,903 stars · 160 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)

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

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 Paitesanshi/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
  • highabout#1
    Add a concise 'About' description to the repository

    Why:

    COPY-PASTE FIX
    A comprehensive survey of LLM-based autonomous agents, covering their construction, applications, and evaluation methods. This repository provides insights and references for researchers and practitioners in the field.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-agents, autonomous-agents, large-language-models, llm-survey, ai-survey, research-paper, machine-learning, artificial-intelligence
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0) to clarify usage terms.

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 Paitesanshi/LLM-Agent-Survey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. run-llama/llama_index · recommended 1×
  3. Significant-Gravitas/AutoGPT · recommended 1×
  4. openai/openai-python · recommended 1×
  5. anthropics/anthropic-sdk-python · recommended 1×
  • CATEGORY QUERY
    What are the essential components for building autonomous agents powered by large language models?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. AutoGPT (Significant-Gravitas/AutoGPT)
    4. OpenAI API (openai/openai-python)
    5. Anthropic Claude (anthropics/anthropic-sdk-python)
    6. Google Gemini (google-gemini/gemini-api-python)
    7. Hugging Face Transformers (huggingface/transformers)
    8. Pinecone (pinecone-io/pinecone-python-client)
    9. Weaviate (weaviate/weaviate-python-client)
    10. Chroma (chroma-core/chroma)
    11. Qdrant (qdrant/qdrant)
    12. SerpAPI (serpapi/serpapi-python)
    13. Google Search API (googleapis/google-api-python-client)
    14. Selenium (SeleniumHQ/selenium)
    15. Playwright (microsoft/playwright-python)
    16. Redis (redis/redis-py)
    17. LangSmith (langchain-ai/langsmith-sdk)
    18. Weights & Biases (W&B Prompts) (wandb/wandb)

    AI recommended 18 alternatives but never named Paitesanshi/LLM-Agent-Survey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive survey of LLM-based autonomous agent applications and evaluation methods?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. Papers With Code
    4. ACL Anthology
    5. EMNLP Proceedings
    6. NeurIPS Proceedings
    7. ICML Proceedings
    8. Distill.pub
    9. Towards Data Science
    10. Medium

    AI recommended 10 alternatives but never named Paitesanshi/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
    fail

    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 Paitesanshi/LLM-Agent-Survey?
    pass
    AI named Paitesanshi/LLM-Agent-Survey explicitly

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

  • If a team adopts Paitesanshi/LLM-Agent-Survey in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Paitesanshi/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 Paitesanshi/LLM-Agent-Survey solve, and who is the primary audience?
    pass
    AI did not name Paitesanshi/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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Paitesanshi/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