RRepoGEO

REPOGEO REPORT · LITE

qhjqhj00/MemoRAG

Default branch main · commit 7c23dfa8 · scanned 5/16/2026, 3:36:53 PM

GitHub: 2,242 stars · 156 forks

AI VISIBILITY SCORE
35 /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
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 qhjqhj00/MemoRAG, 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
    Clarify MemoRAG's role as an LLM RAG framework in the README's opening.

    Why:

    CURRENT
    The current README's detailed explanation of MemoRAG as a framework is in the 'Overview' section.
    COPY-PASTE FIX
    Add the following sentence immediately after the main title and description in the README: "MemoRAG is an advanced *RAG framework* specifically designed for *Large Language Models* (LLMs) to enhance context understanding and evidence retrieval through its innovative memory model."
  • mediumhomepage#2
    Add a homepage URL to the repository's About section.

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2409.05591
  • mediumtopics#3
    Expand repository topics with more specific RAG/LLM framework terms.

    Why:

    CURRENT
    long-llm, memory, rag
    COPY-PASTE FIX
    long-llm, memory, rag, llm-framework, retrieval-augmented-generation, context-understanding

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 qhjqhj00/MemoRAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Neo4j · recommended 1×
  4. Amazon Neptune · recommended 1×
  5. ArangoDB · recommended 1×
  • CATEGORY QUERY
    How to improve RAG system context understanding with long-term memory capabilities?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. Amazon Neptune
    3. ArangoDB
    4. Pinecone
    5. Weaviate
    6. Milvus
    7. LlamaIndex
    8. LangChain
    9. Redis Stack

    AI recommended 9 alternatives but never named qhjqhj00/MemoRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework uses memory models to enhance RAG evidence retrieval and contextual responses?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. RAGatouille
    5. OpenAI Assistants API
    6. Microsoft Semantic Kernel

    AI recommended 6 alternatives but never named qhjqhj00/MemoRAG. 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 qhjqhj00/MemoRAG?
    pass
    AI named qhjqhj00/MemoRAG explicitly

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

  • If a team adopts qhjqhj00/MemoRAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named qhjqhj00/MemoRAG 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 qhjqhj00/MemoRAG solve, and who is the primary audience?
    pass
    AI named qhjqhj00/MemoRAG explicitly

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

Embed your GEO score

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MARKDOWN (README)
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qhjqhj00/MemoRAG — 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