RRepoGEO

REPOGEO REPORT · LITE

VectifyAI/OpenKB

Default branch main · commit a65b612f · scanned 6/22/2026, 1:32:41 AM

GitHub: 2,577 stars · 286 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
40 /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
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 VectifyAI/OpenKB, 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
    Elevate core definition to immediately follow the main title

    Why:

    CURRENT
    The current structure where the detailed definition is under `## 📑 What is OpenKB` after a `details` block.
    COPY-PASTE FIX
    Move the sentence "**OpenKB (Open Knowledge Base)** is an open-source system (in CLI) that compiles raw documents into a structured, interlinked wiki-style knowledge base using LLMs, powered by **PageIndex**'s vectorless, reasoning-based retrieval for long documents." to immediately follow the main H1 and subtitle.
  • hightopics#2
    Refine topics to emphasize LLM-driven wiki and differentiate from traditional KGs

    Why:

    CURRENT
    agents, ai, knowledge-base, llm, rag, retrieval
    COPY-PASTE FIX
    ai, llm, llm-knowledge-base, wiki, document-compilation, reasoning-based-retrieval, vectorless, knowledge-management
  • mediumreadme#3
    Simplify README's initial structure for better AI parsing

    Why:

    CURRENT
    The current structure with `div`, `p align`, `details`, `summary` tags around key sections.
    COPY-PASTE FIX
    Convert HTML-heavy sections like the intro and "What is OpenKB" into standard Markdown (e.g., `## What is OpenKB`, plain paragraphs, bullet points).

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 VectifyAI/OpenKB
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 1×
  2. LangChain · recommended 1×
  3. Weaviate · recommended 1×
  4. Pinecone · recommended 1×
  5. Chroma · recommended 1×
  • CATEGORY QUERY
    How can I build a self-updating, wiki-style knowledge base from documents using LLMs?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Weaviate
    4. Pinecone
    5. Chroma
    6. Confluence
    7. Notion
    8. OpenAI API
    9. Anthropic Claude API
    10. Google Gemini API

    AI recommended 10 alternatives but never named VectifyAI/OpenKB. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an open-source knowledge base system with reasoning-based retrieval, avoiding vector databases.
    you: not recommended
    AI recommended (in order):
    1. TypeDB
    2. Apache Jena
    3. AllegroGraph
    4. Stardog
    5. RDF4J

    AI recommended 5 alternatives but never named VectifyAI/OpenKB. 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 VectifyAI/OpenKB?
    pass
    AI named VectifyAI/OpenKB explicitly

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

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

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

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VectifyAI/OpenKB — 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