RRepoGEO

REPOGEO REPORT · LITE

CaviraOSS/OpenMemory

Default branch main · commit de39bcd7 · scanned 5/21/2026, 9:46:38 AM

GitHub: 4,139 stars · 470 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 CaviraOSS/OpenMemory, 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
    Reposition core value proposition and project status to the very top of README

    Why:

    CURRENT
    # 🚧 This project is currently being rewritten.
    Expect breaking changes and potential bugs.
    ### Contributers needed
    To contribute, visit https://github.com/CaviraOSS/OpenMemory/tree/rewrite branch.
    If you find an issue, please open a GitHub issue with details so it can be tracked and resolved.
    ## OpenMemory
    > **Real long-term memory for AI agents. Not RAG. Not a vector DB. Self-hosted, Python + Node.**
    COPY-PASTE FIX
    # OpenMemory: Real Long-Term Memory for AI Agents (Not RAG, Not a Vector DB)
    > **🚧 This project is currently being rewritten.** Expect breaking changes and potential bugs. However, OpenMemory is a real, self-hosted cognitive memory engine for LLMs and agents, offering true long-term memory beyond traditional RAG or vector database approaches.
    ### Contributers needed
    To contribute, visit https://github.com/CaviraOSS/OpenMemory/tree/rewrite branch.
    If you find an issue, please open a GitHub issue with details so it can be tracked and resolved.
  • mediumreadme#2
    Integrate 'Not RAG, Not a Vector DB' more explicitly into the project's definition

    Why:

    CURRENT
    OpenMemory is a **cognitive memory engine** for LLMs and agents.
    COPY-PASTE FIX
    OpenMemory is a **cognitive memory engine** for LLMs and agents, designed to provide real long-term memory that goes beyond traditional RAG or vector database implementations. It enables your AI applications to retain context and learn over time, making them truly stateful.
  • lowtopics#3
    Add specific topics to highlight distinction from RAG/vector databases

    Why:

    CURRENT
    ai, ai-agents, ai-infrastructure, ai-memory, artificial-intelligence, cognitive-architecture, embeddings, gemini, llm, long-term-memory, memory, memory-engine, memory-retrieval, ollama, one-line, openai, openmemory, rag, supermemory, vector-database
    COPY-PASTE FIX
    ai, ai-agents, ai-infrastructure, ai-memory, artificial-intelligence, cognitive-architecture, embeddings, gemini, llm, long-term-memory, memory, memory-engine, memory-retrieval, ollama, one-line, openai, openmemory, rag, supermemory, vector-database, beyond-rag, cognitive-ai

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 CaviraOSS/OpenMemory
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. facebookresearch/faiss · recommended 1×
  3. chroma-core/chroma · recommended 1×
  4. SQLite · recommended 1×
  5. nmslib/hnswlib · recommended 1×
  • CATEGORY QUERY
    How to implement real long-term memory for AI agents beyond basic RAG or vector databases?
    you: not recommended
    Show full AI answer
  • CATEGORY QUERY
    What self-hosted local memory solutions are available for Python LLM applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Faiss (facebookresearch/faiss)
    3. Chroma (chroma-core/chroma)
    4. SQLite
    5. Hnswlib (nmslib/hnswlib)
    6. DiskANN (microsoft/DiskANN)

    AI recommended 6 alternatives but never named CaviraOSS/OpenMemory. 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 CaviraOSS/OpenMemory?
    pass
    AI named CaviraOSS/OpenMemory explicitly

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

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

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

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CaviraOSS/OpenMemory — 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