RRepoGEO

REPOGEO REPORT · LITE

mckaywrigley/ai-brainstore

Default branch main · commit 8f84207e · scanned 6/9/2026, 7:38:20 AM

GitHub: 715 stars · 96 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 mckaywrigley/ai-brainstore, 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 relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    ai-agent, knowledge-base, vector-database, llm, web-browsing, memory, proof-of-concept
  • highreadme#2
    Reposition the README H1 and opening paragraph to clarify its purpose as an agent

    Why:

    CURRENT
    # AI Brainstore

This is a proof-of-concept of a brain for an AI agent.
    COPY-PASTE FIX
    # AI Brainstore: A Self-Learning AI Agent with Persistent Memory

This is a proof-of-concept for an autonomous AI agent that learns from web searches, builds a persistent knowledge base, and recalls information from its own "brain."
  • mediumreadme#3
    Add a 'Why AI Brainstore?' section to highlight its core differentiator

    Why:

    COPY-PASTE FIX
    ## Why AI Brainstore?

Unlike generic LLM frameworks or standalone vector databases, AI Brainstore provides a complete, self-contained system for an AI agent to actively learn from the web, store new knowledge, and recall specific memories. It's designed as a functional proof-of-concept to demonstrate persistent, adaptive AI memory.

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 mckaywrigley/ai-brainstore
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. PostgreSQL · recommended 2×
  3. LangChain · recommended 1×
  4. OpenAI GPT-4 · recommended 1×
  5. GPT-3.5 Turbo · recommended 1×
  • CATEGORY QUERY
    How can I implement an AI agent that learns from web searches and retains information?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI GPT-4
    3. GPT-3.5 Turbo
    4. Pinecone
    5. ChromaDB
    6. Google Search
    7. SerpApi
    8. BeautifulSoup
    9. Playwright
    10. LlamaIndex
    11. PostgreSQL
    12. pgvector
    13. requests
    14. Scrapy
    15. Haystack
    16. Elasticsearch
    17. OpenSearch
    18. Hugging Face Models
    19. NLTK
    20. spaCy
    21. SQLite
    22. MongoDB
    23. OpenAI API
    24. You.com API
    25. Perplexity AI API

    AI recommended 25 alternatives but never named mckaywrigley/ai-brainstore. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools are available for building a persistent knowledge base for autonomous AI agents?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Chroma
    4. Qdrant
    5. Milvus
    6. Redis
    7. PostgreSQL

    AI recommended 7 alternatives but never named mckaywrigley/ai-brainstore. 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 mckaywrigley/ai-brainstore?
    pass
    AI did not name mckaywrigley/ai-brainstore — 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 mckaywrigley/ai-brainstore in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mckaywrigley/ai-brainstore 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 mckaywrigley/ai-brainstore solve, and who is the primary audience?
    pass
    AI did not name mckaywrigley/ai-brainstore — 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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mckaywrigley/ai-brainstore — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite