RRepoGEO

REPOGEO REPORT · LITE

AuvaLab/itext2kg

Default branch main · commit 9eb8b8fd · scanned 6/15/2026, 8:33:06 AM

GitHub: 950 stars · 103 forks

AI VISIBILITY SCORE
28 /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
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 AuvaLab/itext2kg, 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
  • highabout#1
    Reposition 'About' description to clarify project identity

    Why:

    CURRENT
    We build KGs the way nature builds matter
    COPY-PASTE FIX
    ATOM (formerly iText2KG) is a few-shot, scalable approach for building and continuously updating Temporal Knowledge Graphs (TKGs) from unstructured texts using LLMs.
  • highreadme#2
    Add a clear disclaimer in the README about the name 'itext2kg'

    Why:

    COPY-PASTE FIX
    Add the following sentence right after the first paragraph in the README: "Note: This project is unrelated to the iText PDF library."
  • mediumhomepage#3
    Populate the 'Homepage' field in the repository settings

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2510.22590

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 AuvaLab/itext2kg
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. Amazon Neptune · recommended 2×
  3. deepset-ai/haystack · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. run-llama/llama_index · recommended 1×
  • CATEGORY QUERY
    How to build continuously updating knowledge graphs from unstructured text data using LLMs?
    you: not recommended
    AI recommended (in order):
    1. Haystack (deepset-ai/haystack)
    2. LangChain (langchain-ai/langchain)
    3. Neo4j
    4. LlamaIndex (run-llama/llama_index)
    5. Gradio (gradio-app/gradio)
    6. SpaCy (explosion/spaCy)
    7. Amazon Neptune
    8. Azure Cosmos DB for Gremlin
    9. Google Cloud Knowledge Graph

    AI recommended 9 alternatives but never named AuvaLab/itext2kg. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are scalable methods for constructing knowledge graphs from text efficiently with large language models?
    you: not recommended
    AI recommended (in order):
    1. Stanford OpenIE
    2. OpenNRE
    3. spaCy
    4. Hugging Face Transformers
    5. GPT-3.5
    6. GPT-4
    7. Llama 2
    8. Mistral
    9. Claude
    10. LangChain
    11. LlamaIndex
    12. Instructor
    13. OpenAI API
    14. Falcon
    15. Snorkel
    16. Prodigy
    17. Argilla
    18. Neo4j
    19. Amazon Neptune
    20. ArangoDB
    21. Regex

    AI recommended 21 alternatives but never named AuvaLab/itext2kg. 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 AuvaLab/itext2kg?
    pass
    AI named AuvaLab/itext2kg explicitly

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

  • If a team adopts AuvaLab/itext2kg in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AuvaLab/itext2kg 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 AuvaLab/itext2kg solve, and who is the primary audience?
    pass
    AI did not name AuvaLab/itext2kg — 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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AuvaLab/itext2kg — 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