RRepoGEO

REPOGEO REPORT · LITE

jdagdelen/hyperDB

Default branch main · commit bf3b1c48 · scanned 5/22/2026, 8:11:54 PM

GitHub: 1,408 stars · 87 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
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 jdagdelen/hyperDB, 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 relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    vector-database, llm-agents, local-database, python, vector-search, embeddings, in-memory, machine-learning
  • highabout#2
    Rewrite the repository description to be purely technical

    Why:

    CURRENT
    A hyper-fast local vector database for use with LLM Agents. Now accepting SAFEs at $135M cap.
    COPY-PASTE FIX
    HyperDB is a hyper-fast, lightweight, local vector database designed for efficient similarity search and storage of vector embeddings, optimized for use with LLM agents and Python applications.
  • mediumreadme#3
    Remove investment pitch from README's opening paragraph

    Why:

    CURRENT
    A hyper-fast local vector database for use with LLM Agents. Not entirely a joke. Want to invest? Now accepting SAFEs ($135M cap minimum.)
    COPY-PASTE FIX
    A hyper-fast, lightweight, local vector database for efficient similarity search and storage of vector embeddings, optimized for use with LLM Agents and Python applications.

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 jdagdelen/hyperDB
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
chroma-core/chroma
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. chroma-core/chroma · recommended 1×
  2. lancedb/lancedb · recommended 1×
  3. facebookresearch/faiss · recommended 1×
  4. nmslib/hnswlib · recommended 1×
  5. qdrant/qdrant · recommended 1×
  • CATEGORY QUERY
    What's a fast local vector database for LLM agent applications?
    you: not recommended
    AI recommended (in order):
    1. Chroma (chroma-core/chroma)
    2. LanceDB (lancedb/lancedb)
    3. FAISS (facebookresearch/faiss)
    4. Hnswlib (nmslib/hnswlib)
    5. Qdrant (qdrant/qdrant)

    AI recommended 5 alternatives but never named jdagdelen/hyperDB. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a high-performance Python library for local vector embedding storage and retrieval.
    you: not recommended
    AI recommended (in order):
    1. Faiss
    2. Hnswlib
    3. Annoy
    4. ScaNN
    5. NMSLIB

    AI recommended 5 alternatives but never named jdagdelen/hyperDB. 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 jdagdelen/hyperDB?
    pass
    AI named jdagdelen/hyperDB explicitly

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

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

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

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jdagdelen/hyperDB — 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