RRepoGEO

REPOGEO REPORT · LITE

HelixDB/helix-db

Default branch main · commit 7b36e3e5 · scanned 6/27/2026, 8:56:56 PM

GitHub: 5,514 stars · 307 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
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 HelixDB/helix-db, 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 README's opening statement to emphasize OLTP and unified platform

    Why:

    CURRENT
    <b>HelixDB</b>: a graph-vector database for knowledge graphs and AI memory. Built from scratch in Rust.
    COPY-PASTE FIX
    <b>HelixDB</b>: an **OLTP graph-vector database** for knowledge graphs and AI memory, built from scratch in Rust. It simplifies building AI applications by unifying graph, vector, and relational data in a single platform.
  • highreadme#2
    Add a comparison section to the README against traditional database stacks

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., 'Why HelixDB? A Unified Platform for AI' or 'HelixDB vs. Traditional Stacks', that explicitly contrasts HelixDB's single-platform approach with the need for multiple databases (like Neo4j for graph, pgvector for vector, PostgreSQL for relational) in traditional AI application architectures.
  • mediumreadme#3
    Create a dedicated 'Key Differentiators' section in the README

    Why:

    COPY-PASTE FIX
    Add a new 'Key Differentiators' section to the README with bullet points, explicitly listing:
*   **OLTP Graph-Vector Database:** Combines transactional graph and vector capabilities.
*   **Unified Data Model:** Supports graph, vector, KV, documents, and relational data in one system.
*   **AI Memory & Knowledge Graphs:** Designed for federated access for AI agents and knowledge graph applications.
*   **Built in Rust:** Leverages Rust for performance, safety, and concurrency.

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 HelixDB/helix-db
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TigerGraph
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TigerGraph · recommended 2×
  2. Neo4j AuraDB · recommended 1×
  3. Neo4j Graph Data Science (GDS) · recommended 1×
  4. PostgreSQL · recommended 1×
  5. pgvector · recommended 1×
  • CATEGORY QUERY
    What database can simplify managing graph, vector, and relational data for AI applications?
    you: not recommended
    AI recommended (in order):
    1. Neo4j AuraDB
    2. Neo4j Graph Data Science (GDS)
    3. PostgreSQL
    4. pgvector
    5. Apache AGE
    6. ArangoDB
    7. TigerGraph
    8. DataStax Astra DB
    9. Apache Cassandra

    AI recommended 9 alternatives but never named HelixDB/helix-db. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an OLTP database combining graph and vector capabilities for real-time AI memory.
    you: not recommended
    AI recommended (in order):
    1. Neo4j (neo4j/neo4j)
    2. Neo4j AuraDS
    3. ArangoDB (arangodb/arangodb)
    4. TigerGraph
    5. PostgreSQL (postgres/postgres)
    6. pgvector (pgvector/pgvector)
    7. Apache AGE (apache/age)
    8. Milvus (milvus-io/milvus)
    9. Zilliz Cloud

    AI recommended 9 alternatives but never named HelixDB/helix-db. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 HelixDB/helix-db?
    pass
    AI did not name HelixDB/helix-db — 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?

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

Embed your GEO score

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MARKDOWN (README)
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HelixDB/helix-db — 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