RRepoGEO

REPOGEO REPORT · LITE

BaranziniLab/KG_RAG

Default branch main · commit 01b9f6e6 · scanned 6/12/2026, 5:52:12 PM

GitHub: 939 stars · 112 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 BaranziniLab/KG_RAG, 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 concise, keyword-rich introductory sentence to the README

    Why:

    CURRENT
    The README excerpt begins with a Table of Contents, followed by a video and then the 'What is KG-RAG' section.
    COPY-PASTE FIX
    Add the following sentence as the very first line of text in the README (after any title/badges, before the Table of Contents or video): 'KG-RAG is a versatile, task-agnostic framework designed to enhance Large Language Models (LLMs) with structured knowledge graphs for improved factual accuracy and context in Retrieval-Augmented Generation (RAG) across various domains.'
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a link to a relevant project page, documentation, or a dedicated website for KG-RAG in the repository's 'About' section (homepage field).
  • mediumcomparison#3
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled 'Comparison with other RAG Frameworks (e.g., LangChain, LlamaIndex)' that highlights KG-RAG's unique strengths, particularly its deep integration with Knowledge Graphs and its 'task agnostic' nature.

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 BaranziniLab/KG_RAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. run-llama/llama_index · recommended 2×
  3. neo4j/neo4j · recommended 1×
  4. Amazon Neptune · recommended 1×
  5. huggingface/transformers · recommended 1×
  • CATEGORY QUERY
    How to improve large language model factual accuracy using external knowledge graphs?
    you: not recommended
    AI recommended (in order):
    1. Neo4j (neo4j/neo4j)
    2. LangChain (langchain-ai/langchain)
    3. LlamaIndex (run-llama/llama_index)
    4. Amazon Neptune
    5. Hugging Face Transformers (huggingface/transformers)
    6. Wikidata Query Service
    7. DBpedia
    8. PyTorch Geometric (pyg-team/pytorch_geometric)
    9. Deep Graph Library (DGL) (dmlc/dgl)

    AI recommended 9 alternatives but never named BaranziniLab/KG_RAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to integrate knowledge graphs into retrieval-augmented generation for LLMs.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Haystack (deepset-ai/haystack)
    4. Neo4j
    5. GraphRAG
    6. Kuzu (kuzudb/kuzu)

    AI recommended 6 alternatives but never named BaranziniLab/KG_RAG. 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 BaranziniLab/KG_RAG?
    pass
    AI named BaranziniLab/KG_RAG explicitly

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

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

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

Embed your GEO score

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite