RRepoGEO

REPOGEO REPORT · LITE

lucidrains/RETRO-pytorch

Default branch main · commit d0866131 · scanned 6/16/2026, 6:56:40 AM

GitHub: 877 stars · 110 forks

AI VISIBILITY SCORE
28 /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
2 / 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 lucidrains/RETRO-pytorch, 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 README's opening to emphasize LLM application

    Why:

    CURRENT
    Implementation of RETRO, Deepmind's Retrieval based Attention net, in Pytorch.
    COPY-PASTE FIX
    This library provides a PyTorch implementation of DeepMind's RETRO (Retrieval-Augmented Transformer), specifically designed for building parameter-efficient large language models (LLMs) with integrated retrieval.
  • mediumtopics#2
    Add specific LLM and RAG-related topics

    Why:

    CURRENT
    artificial-intelligence, attention-mechanism, deep-learning, retrieval, transformers
    COPY-PASTE FIX
    artificial-intelligence, attention-mechanism, deep-learning, retrieval, transformers, large-language-models, retrieval-augmented-generation
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/lucidrains/RETRO-pytorch

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 lucidrains/RETRO-pytorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. Haystack · recommended 2×
  3. LangChain · recommended 1×
  4. FAISS · recommended 1×
  5. Sentence Transformers · recommended 1×
  • CATEGORY QUERY
    How to build large language models with retrieval for better parameter efficiency in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Haystack
    3. LangChain
    4. FAISS
    5. Sentence Transformers
    6. Pinecone
    7. Weaviate
    8. Qdrant
    9. Hugging Face Embeddings
    10. PyTorch

    AI recommended 10 alternatives but never named lucidrains/RETRO-pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a PyTorch library for implementing retrieval-augmented deep learning attention networks.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Haystack
    3. PyTorch-Lightning
    4. Faiss
    5. Sentence-Transformers

    AI recommended 5 alternatives but never named lucidrains/RETRO-pytorch. 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 lucidrains/RETRO-pytorch?
    pass
    AI named lucidrains/RETRO-pytorch explicitly

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

  • If a team adopts lucidrains/RETRO-pytorch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lucidrains/RETRO-pytorch 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 lucidrains/RETRO-pytorch solve, and who is the primary audience?
    pass
    AI did not name lucidrains/RETRO-pytorch — 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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lucidrains/RETRO-pytorch — 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