RRepoGEO

REPOGEO REPORT · LITE

stair-lab/kg-gen

Default branch main · commit 6259b4c7 · scanned 5/24/2026, 12:02:03 AM

GitHub: 1,179 stars · 174 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 stair-lab/kg-gen, 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
    Add a clear license statement to the README

    Why:

    COPY-PASTE FIX
    ## License

This project is licensed under the [YOUR CHOSEN LICENSE, e.g., MIT License]. A copy of the license can be found in the `LICENSE` file.
  • highreadme#2
    Reposition the README's opening paragraph to highlight its research/benchmarking focus

    Why:

    CURRENT
    Welcome! `kg-gen` helps you extract knowledge graphs from any plain text using AI. It can process both small and large text inputs, and it can also handle messages in a conversation format.
    COPY-PASTE FIX
    Welcome! `kg-gen` is a **research-oriented framework** for **Knowledge Graph Generation from Any Text**, designed for **benchmarking and experimentation** across various LLM APIs. It helps you extract knowledge graphs from plain text, supporting both small and large inputs, and conversational formats.
  • mediumtopics#3
    Expand GitHub topics to include specific technologies and applications

    Why:

    CURRENT
    benchmark, knowledge-graph, llm
    COPY-PASTE FIX
    benchmark, knowledge-graph, llm, knowledge-graph-generation, llm-apis, dspy, litellm, rag, nlp, research-framework, text-to-graph

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 stair-lab/kg-gen
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Haystack
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Haystack · recommended 2×
  2. LangChain · recommended 2×
  3. Neo4j · recommended 1×
  4. spaCy · recommended 1×
  5. Prodigy · recommended 1×
  • CATEGORY QUERY
    How to convert plain text documents into a structured knowledge graph for RAG applications?
    you: not recommended
    AI recommended (in order):
    1. Haystack
    2. Neo4j
    3. spaCy
    4. Prodigy
    5. Amazon Neptune
    6. GraphDB
    7. LangChain

    AI recommended 7 alternatives but never named stair-lab/kg-gen. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source libraries can generate knowledge graphs from text using different LLM APIs?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Knowledge Graph-GPT (KG-GPT)
    4. OpenNRE (Open-source Neural Relation Extraction)
    5. Spacy-LLM
    6. Haystack
    7. GraphRAG (Microsoft Research)

    AI recommended 7 alternatives but never named stair-lab/kg-gen. 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 stair-lab/kg-gen?
    pass
    AI named stair-lab/kg-gen explicitly

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

  • If a team adopts stair-lab/kg-gen in production, what risks or prerequisites should they evaluate first?
    pass
    AI named stair-lab/kg-gen 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 stair-lab/kg-gen solve, and who is the primary audience?
    pass
    AI did not name stair-lab/kg-gen — 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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  • Brand-free category queries5 vs 2 in Lite
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