RRepoGEO

REPOGEO REPORT · LITE

deepseek-ai/Engram

Default branch main · commit fb7f84a2 · scanned 5/14/2026, 1:22:31 PM

GitHub: 4,400 stars · 334 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 deepseek-ai/Engram, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm-architecture, conditional-memory, sparse-models, n-gram-embeddings, deep-learning, large-language-models, ai-research
  • highreadme#2
    Add a concise positioning statement at the top of the README

    Why:

    CURRENT
    The README currently starts with a div for badges, then "## 1. Introduction" and a link to the paper.
    COPY-PASTE FIX
    This repository provides the official implementation of Engram, a novel architectural component for Large Language Models that introduces conditional memory via scalable lookup. Engram offers a new axis of sparsity, distinct from RAG or vector database systems, to enhance LLM capacity and performance by modernizing N-gram embeddings.
  • mediumhomepage#3
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    https://www.deepseek.com/

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 deepseek-ai/Engram
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. Pinecone · recommended 1×
  4. Weaviate · recommended 1×
  5. Milvus · recommended 1×
  • CATEGORY QUERY
    How to enhance large language model capacity using efficient memory lookup mechanisms?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Pinecone
    4. Weaviate
    5. Milvus
    6. Qdrant
    7. FAISS
    8. RETRO
    9. Differentiable Neural Computers (DNC)

    AI recommended 9 alternatives but never named deepseek-ai/Engram. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for methods to improve LLM performance beyond traditional Mixture-of-Experts approaches.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers
    4. GPT-4
    5. Claude 3
    6. GPT-3.5
    7. Llama 2
    8. Mistral
    9. PaLM 2
    10. Hugging Face Accelerate

    AI recommended 10 alternatives but never named deepseek-ai/Engram. 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 deepseek-ai/Engram?
    pass
    AI named deepseek-ai/Engram explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of deepseek-ai/Engram. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/deepseek-ai/Engram.svg)](https://repogeo.com/en/r/deepseek-ai/Engram)
HTML
<a href="https://repogeo.com/en/r/deepseek-ai/Engram"><img src="https://repogeo.com/badge/deepseek-ai/Engram.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

deepseek-ai/Engram — 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