RRepoGEO

REPOGEO REPORT · LITE

BAI-LAB/MemoryOS

Default branch main · commit 1d717060 · scanned 6/28/2026, 7:06:58 AM

GitHub: 1,487 stars · 143 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 BAI-LAB/MemoryOS, 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
    Strengthen README's opening to clarify 'memory operating system' positioning

    Why:

    CURRENT
    MemoryOS is designed to provide a memory operating system for personalized AI agents, enabling more coherent, personalized, and context-aware interactions. Drawing inspiration from memory management principles in operating systems, it adopts a hierarchical storage architecture with four core modules: Storage, Updating, Retrieval, and Generation, to achieve comprehensive and efficient memory management.
    COPY-PASTE FIX
    MemoryOS is a novel **memory operating system** for personalized AI agents, offering a comprehensive, system-level approach to memory management. Unlike standalone vector databases or generic RAG frameworks, MemoryOS provides a hierarchical storage architecture with integrated Storage, Updating, Retrieval, and Generation modules, inspired by traditional OS memory management principles, to enable more coherent, personalized, and context-aware interactions.
  • mediumtopics#2
    Add more specific topics to reinforce memory management and system design

    Why:

    CURRENT
    agent, language-model, llm, long-term-memory, operating-system, personalization, rag, retrieval-augmented-generation
    COPY-PASTE FIX
    agent, language-model, llm, long-term-memory, operating-system, personalization, rag, retrieval-augmented-generation, memory-management, context-management, ai-memory, agent-memory
  • mediumcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison with Alternatives' or 'Why MemoryOS?' that clearly outlines how MemoryOS differs from and complements solutions like vector databases (e.g., Pinecone, Weaviate) and general LLM orchestration frameworks (e.g., LangChain, LlamaIndex), emphasizing its 'memory operating system' approach.

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 BAI-LAB/MemoryOS
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 3×
  2. Weaviate · recommended 3×
  3. Chroma · recommended 3×
  4. Qdrant · recommended 3×
  5. LangChain · recommended 2×
  • CATEGORY QUERY
    How to implement long-term memory and personalization for AI agents using an LLM?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pinecone
    3. Weaviate
    4. Chroma
    5. Qdrant
    6. LlamaIndex
    7. Pinecone
    8. Weaviate
    9. Chroma
    10. Qdrant
    11. OpenAI Embeddings
    12. Cohere
    13. Hugging Face `sentence-transformers`
    14. Pinecone
    15. Weaviate
    16. Chroma
    17. Qdrant
    18. Faiss
    19. Redis
    20. Redis Stack
    21. RedisJSON
    22. RediSearch
    23. Redis Vector Search
    24. PostgreSQL
    25. pgvector
    26. OpenAI's API

    AI recommended 26 alternatives but never named BAI-LAB/MemoryOS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to manage memory and context for personalized LLM-powered agents efficiently.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Semantic Kernel
    5. AgentVerse

    AI recommended 5 alternatives but never named BAI-LAB/MemoryOS. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 BAI-LAB/MemoryOS?
    pass
    AI named BAI-LAB/MemoryOS explicitly

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

  • If a team adopts BAI-LAB/MemoryOS in production, what risks or prerequisites should they evaluate first?
    pass
    AI named BAI-LAB/MemoryOS 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 BAI-LAB/MemoryOS solve, and who is the primary audience?
    pass
    AI named BAI-LAB/MemoryOS explicitly

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

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BAI-LAB/MemoryOS — 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