RRepoGEO

REPOGEO REPORT · LITE

dylanhogg/llmgraph

Default branch main · commit 02d96fad · scanned 6/16/2026, 11:32:42 PM

GitHub: 508 stars · 31 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 dylanhogg/llmgraph, 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 README opening to emphasize dedicated library status

    Why:

    CURRENT
    Create knowledge graphs with LLMs.
    COPY-PASTE FIX
    **llmgraph is a dedicated Python library for directly generating knowledge graphs from text using large language models.**
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://pypi.org/project/llmgraph/
  • lowreadme#3
    Add a 'Why llmgraph?' or 'Comparison' section to README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, perhaps after 'Features', titled 'Why llmgraph?'. Include text such as: 'While general LLM frameworks like LangChain or LlamaIndex offer broad orchestration capabilities, and graph databases like Neo4j manage graph data, `llmgraph` provides a focused, dedicated library for the direct generation of knowledge graphs from text using LLMs. It streamlines the process of extracting structured entities and relationships into standard graph formats, rather than requiring users to build this generation pipeline from scratch within a larger framework.'

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 dylanhogg/llmgraph
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. Haystack · recommended 2×
  3. Neo4j · recommended 1×
  4. OpenAI Function Calling · recommended 1×
  5. GraphRAG · recommended 1×
  • CATEGORY QUERY
    What's the best way to automatically generate knowledge graphs from unstructured text using large language models?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. LangChain
    3. OpenAI Function Calling
    4. GraphRAG
    5. Haystack
    6. Kuzu

    AI recommended 6 alternatives but never named dylanhogg/llmgraph. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a library to programmatically generate graph data from text using LLMs for visualization.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. spaCy
    4. Haystack
    5. Graphistry

    AI recommended 5 alternatives but never named dylanhogg/llmgraph. 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 dylanhogg/llmgraph?
    pass
    AI named dylanhogg/llmgraph explicitly

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

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

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

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dylanhogg/llmgraph — 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