RRepoGEO

REPOGEO REPORT · LITE

VectifyAI/OpenKB

Default branch main · commit 6fcce46f · scanned 5/11/2026, 7:47:52 PM

GitHub: 1,744 stars · 180 forks

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
    Clarify OpenKB's unique 'vectorless, compiled wiki' positioning in the README's opening paragraph

    Why:

    CURRENT
    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 for vectorless long document retrieval.
    COPY-PASTE FIX
    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. It provides a **vectorless alternative to traditional RAG and vector databases**, powered by PageIndex for long document retrieval, where knowledge compounds over time instead of being re-derived on every query.
  • mediumtopics#2
    Add more specific topics to highlight the 'vectorless' and 'compiled wiki' differentiation

    Why:

    CURRENT
    agents, ai, knowledge-base, llm, rag, retrieval
    COPY-PASTE FIX
    agents, ai, knowledge-base, llm, rag, retrieval, vectorless, wiki, knowledge-compilation, persistent-knowledge
  • lowreadme#3
    Add a dedicated 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## 🆚 OpenKB vs. Alternatives

While traditional RAG systems rediscover knowledge on every query and vector databases store embeddings, OpenKB compiles knowledge once into a persistent, interlinked wiki. This approach differs significantly from frameworks like LlamaIndex or LangChain, which often rely on vector stores, and general-purpose databases like PostgreSQL or Elasticsearch, which lack native LLM-driven knowledge compilation and cross-referencing.

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
PostgreSQL
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PostgreSQL · recommended 1×
  2. mongodb/mongo · recommended 1×
  3. elastic/elasticsearch · recommended 1×
  4. neo4j/neo4j · recommended 1×
  5. Amazon Neptune · recommended 1×
  • CATEGORY QUERY
    What are alternatives to vector databases for building a persistent LLM knowledge base?
    you: not recommended
    AI recommended (in order):
    1. PostgreSQL
    2. MongoDB (mongodb/mongo)
    3. Elasticsearch (elastic/elasticsearch)
    4. Neo4j (neo4j/neo4j)
    5. Amazon Neptune
    6. Redis (redis/redis)
    7. RocksDB (facebook/rocksdb)
    8. S3
    9. Lucene
    10. Whoosh (mchaput/whoosh)

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

    Show full AI answer
  • CATEGORY QUERY
    How to build an LLM-powered knowledge base for very long documents with structured reasoning?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Weaviate
    4. Neo4j
    5. Pinecone
    6. OpenSearch
    7. Haystack

    AI recommended 7 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?

Embed your GEO score

Drop this badge into the README of VectifyAI/OpenKB. 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/VectifyAI/OpenKB.svg)](https://repogeo.com/en/r/VectifyAI/OpenKB)
HTML
<a href="https://repogeo.com/en/r/VectifyAI/OpenKB"><img src="https://repogeo.com/badge/VectifyAI/OpenKB.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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