RRepoGEO

REPOGEO REPORT · LITE

qhjqhj00/MemoRAG

Default branch main · commit 7c23dfa8 · scanned 6/27/2026, 1:12:02 PM

GitHub: 2,249 stars · 156 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
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
    Reposition the core value proposition to the top of the README

    Why:

    CURRENT
    **MemoRAG** is an innovative RAG framework built on top of a highly efficient, super-long memory model. Unlike standard RAG, which primarily handles queries with explicit information needs, MemoRAG leverages its memory model to achieve a global understanding of the entire database. By recalling query-specific clues from memory, MemoRAG enhances evidence retrieval, resulting in more accurate and contextually rich response generation.​
    COPY-PASTE FIX
    Move the first paragraph of the 'Overview' section to immediately follow the main tagline/description at the very top of the README, before any other sections or headings.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2409.05591
  • mediumreadme#3
    Add a 'Comparison' or 'Why MemoRAG?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Why MemoRAG? Differentiating from Standard RAG Frameworks', that explicitly compares MemoRAG's memory-based approach and global understanding capabilities against traditional RAG systems (like LangChain or LlamaIndex), highlighting its unique advantages.

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
LangChain
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 3×
  2. LlamaIndex · recommended 2×
  3. Haystack · recommended 2×
  4. Sentence Transformers · recommended 1×
  5. Cohere Rerank · recommended 1×
  • CATEGORY QUERY
    How can I improve RAG systems for deeper context and more accurate responses?
    you: not recommended
    AI recommended (in order):
    1. Sentence Transformers
    2. LangChain
    3. LlamaIndex
    4. Cohere Rerank
    5. Hugging Face Transformers
    6. Elasticsearch
    7. OpenAI GPT-3.5/4
    8. Haystack
    9. Hugging Face `sentence-transformers`
    10. OpenAI Embeddings (Ada-002)
    11. Neo4j
    12. LangChain

    AI recommended 12 alternatives but never named qhjqhj00/MemoRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What RAG framework uses a memory model for global database understanding and long-context LLMs?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. RAGatouille
    5. DSPy

    AI recommended 5 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