RRepoGEO

REPOGEO REPORT · LITE

HelixDB/helix-db

Default branch main · commit 8a8ef37d · scanned 5/16/2026, 11:07:05 PM

GitHub: 4,524 stars · 241 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
33 /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
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 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
    Clarify HelixDB's core data model and secondary capabilities in the README

    Why:

    CURRENT
    HelixDB primarily operates with a graph + vector data model, but it can also support KV, documents, and relational data.
    COPY-PASTE FIX
    HelixDB's core is a powerful graph + vector data model, designed to unify AI application backends. It seamlessly integrates support for key-value, document, and relational data *within this unified framework*, eliminating the need for separate databases.
  • mediumcomparison#2
    Add a 'Comparison to Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    A new section in the README, such as '## Comparison to Alternatives' or '## Why HelixDB?', directly addressing how HelixDB differs from common alternatives like Neo4j, ArangoDB, TigerGraph, and pg_vector, focusing on its unified graph-vector approach for AI/RAG.
  • mediumexamples#3
    Add RAG-specific examples to the README

    Why:

    COPY-PASTE FIX
    A new section in the README, such as '## RAG Application Examples' or '## Building AI with HelixDB,' showcasing code snippets or architectural diagrams for common RAG patterns using HelixDB.

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
Neo4j
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Neo4j · recommended 2×
  2. ArangoDB · recommended 2×
  3. TigerGraph · recommended 2×
  4. PostgreSQL · recommended 2×
  5. pg_vector · recommended 2×
  • CATEGORY QUERY
    How to simplify AI application backend with a single database for graph and vector data?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. ArangoDB
    3. TigerGraph
    4. PostgreSQL
    5. pg_vector
    6. Apache AGE
    7. DataStax Astra DB
    8. Apache Cassandra
    9. Apache TinkerPop

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

    Show full AI answer
  • CATEGORY QUERY
    Looking for a unified database solution combining vector search and graph capabilities for RAG.
    you: not recommended
    AI recommended (in order):
    1. Neo4j AuraDS
    2. Neo4j
    3. Graph Data Science Library
    4. ArangoDB
    5. TigerGraph
    6. Weaviate
    7. PostgreSQL
    8. pg_vector
    9. Apache AGE
    10. Milvus
    11. Zilliz

    AI recommended 11 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 named HelixDB/helix-db explicitly

    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?

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite