RRepoGEO

REPOGEO REPORT · LITE

yoheinakajima/prettygraph

Default branch main · commit 72b0c0df · scanned 6/9/2026, 9:18:56 AM

GitHub: 779 stars · 57 forks

AI VISIBILITY SCORE
35 /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
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 yoheinakajima/prettygraph, 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 prettygraph's core function and technology in the README's opening

    Why:

    CURRENT
    **prettygraph** is a Python-based web application developed by @yoheinakajima to demonstrate a new UI pattern for text-to-knowledge graph generation.
    COPY-PASTE FIX
    **prettygraph** is a Python-based Flask web application that provides an interactive UI for real-time text-to-knowledge graph generation, dynamically highlighting text input.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    knowledge-graph, text-to-graph, nlp, flask, litellm, ui, visualization, python
  • mediumhomepage#3
    Add a homepage URL if a live demo or project page exists

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    [URL of live demo or project page, e.g., https://prettygraph.example.com]

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 yoheinakajima/prettygraph
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Graphlit
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Graphlit · recommended 1×
  2. Kùzu · recommended 1×
  3. Neo4j AuraDS · recommended 1×
  4. Neo4j Desktop · recommended 1×
  5. APOC · recommended 1×
  • CATEGORY QUERY
    What tools help generate interactive knowledge graphs directly from text input?
    you: not recommended
    AI recommended (in order):
    1. Graphlit
    2. Kùzu
    3. Neo4j AuraDS
    4. Neo4j Desktop
    5. APOC
    6. Graph Data Science Library
    7. Ontotext GraphDB
    8. Stardog
    9. Haystack
    10. LangChain

    AI recommended 10 alternatives but never named yoheinakajima/prettygraph. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Python library for real-time text input highlighting and knowledge graph visualization?
    you: not recommended
    AI recommended (in order):
    1. Streamlit
    2. spaCy
    3. NetworkX
    4. Plotly
    5. Dash
    6. Jupyter Widgets (ipywidgets)
    7. pyvis
    8. Flask
    9. Django
    10. D3.js
    11. mpld3
    12. altair
    13. Gradio
    14. Matplotlib
    15. Textual
    16. rich

    AI recommended 16 alternatives but never named yoheinakajima/prettygraph. 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 yoheinakajima/prettygraph?
    pass
    AI named yoheinakajima/prettygraph explicitly

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

  • If a team adopts yoheinakajima/prettygraph in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yoheinakajima/prettygraph 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 yoheinakajima/prettygraph solve, and who is the primary audience?
    pass
    AI named yoheinakajima/prettygraph 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 yoheinakajima/prettygraph. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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yoheinakajima/prettygraph — 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