RRepoGEO

REPOGEO REPORT · LITE

robert-mcdermott/ai-knowledge-graph

Default branch main · commit 40b70197 · scanned 5/11/2026, 6:22:19 AM

GitHub: 2,263 stars · 336 forks

AI VISIBILITY SCORE
27 /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
1 / 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 robert-mcdermott/ai-knowledge-graph, 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
    Reposition the README's opening to emphasize end-to-end system

    Why:

    CURRENT
    This system takes an unstructured text document, and uses an LLM of your choice to extract knowledge in the form of Subject-Predicate-Object (SPO) triplets, and visualizes the relationships as an interactive knowledge graph.
    COPY-PASTE FIX
    This project is an **end-to-end AI system for generating interactive knowledge graphs directly from unstructured text documents.** It leverages Large Language Models (LLMs) to extract Subject-Predicate-Object (SPO) triplets and visualize complex relationships, offering a complete solution unlike generic NLP libraries or standalone graph visualization tools.
  • hightopics#2
    Refine repository topics for better categorization

    Why:

    CURRENT
    artificial-intelligence, knowledge-distillation, knowledge-graph, llm, networkx, pyvis, visualization
    COPY-PASTE FIX
    ai-knowledge-graph, llm-applications, text-to-graph, knowledge-extraction, graph-generation, interactive-visualization, python
  • mediumabout#3
    Update the repository description for clarity and differentiation

    Why:

    CURRENT
    AI Powered Knowledge Graph Generator
    COPY-PASTE FIX
    An end-to-end Python system that uses LLMs to automatically extract structured knowledge from unstructured text and generate interactive knowledge graphs.

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 robert-mcdermott/ai-knowledge-graph
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
spaCy
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. spaCy · recommended 1×
  2. Hugging Face Transformers · recommended 1×
  3. OpenAI API · recommended 1×
  4. Google Cloud Natural Language API · recommended 1×
  5. Amazon Comprehend · recommended 1×
  • CATEGORY QUERY
    How to automatically extract structured knowledge from unstructured text documents using AI?
    you: not recommended
    AI recommended (in order):
    1. spaCy
    2. Hugging Face Transformers
    3. OpenAI API
    4. Google Cloud Natural Language API
    5. Amazon Comprehend
    6. Microsoft Azure AI Language
    7. Prodigy

    AI recommended 7 alternatives but never named robert-mcdermott/ai-knowledge-graph. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good Python tools for generating interactive knowledge graphs from LLM outputs?
    you: not recommended
    AI recommended (in order):
    1. NetworkX (networkx/networkx)
    2. Dash (plotly/dash)
    3. Plotly (plotly/plotly.py)
    4. Pyvis (WestbrookJ/pyvis)
    5. vis.js (visjs/vis-network)
    6. Graphistry (graphistry/pygraphistry)
    7. Neo4j (neo4j/neo4j)
    8. py2neo (py2neo/py2neo)
    9. neo4j-driver (neo4j/neo4j-python-driver)
    10. Neo4j Bloom
    11. Neo4j Browser
    12. Streamlit (streamlit/streamlit)

    AI recommended 12 alternatives but never named robert-mcdermott/ai-knowledge-graph. 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 robert-mcdermott/ai-knowledge-graph?
    pass
    AI did not name robert-mcdermott/ai-knowledge-graph — 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?

  • If a team adopts robert-mcdermott/ai-knowledge-graph in production, what risks or prerequisites should they evaluate first?
    pass
    AI named robert-mcdermott/ai-knowledge-graph 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 robert-mcdermott/ai-knowledge-graph solve, and who is the primary audience?
    pass
    AI did not name robert-mcdermott/ai-knowledge-graph — 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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robert-mcdermott/ai-knowledge-graph — RepoGEO report