RRepoGEO

REPOGEO REPORT · LITE

Graph-RAG/GraphRAG

Default branch main · commit ca288017 · scanned 5/31/2026, 1:22:57 AM

GitHub: 578 stars · 29 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 Graph-RAG/GraphRAG, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    A curated list of resources, papers, tools, and data sources on Retrieval-Augmented Generation (RAG) with Graphs, categorized by applied graph domain.
  • highlicense#2
    Add a LICENSE file and clarify in README

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root. Additionally, add a line to the README, such as: "This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details."

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 Graph-RAG/GraphRAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Neo4j
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Neo4j · recommended 2×
  2. LangChain · recommended 2×
  3. LlamaIndex · recommended 2×
  4. Amazon Neptune · recommended 2×
  5. TypeDB · recommended 2×
  • CATEGORY QUERY
    How to improve RAG system performance using graph-based knowledge representation?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. LangChain
    3. LlamaIndex
    4. Amazon Neptune
    5. TypeDB
    6. ArangoDB
    7. Apache Jena
    8. Stardog
    9. PyTorch Geometric
    10. Deep Graph Library (DGL)

    AI recommended 10 alternatives but never named Graph-RAG/GraphRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest research papers and tools for graph-enhanced retrieval augmented generation?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. LangChain
    3. LlamaIndex
    4. TypeDB
    5. Amazon Neptune
    6. GraphRAG (Microsoft)
    7. Hugging Face Transformers/Datasets
    8. Memgraph
    9. DGL (Deep Graph Library)
    10. PyG (PyTorch Geometric)

    AI recommended 10 alternatives but never named Graph-RAG/GraphRAG. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 Graph-RAG/GraphRAG?
    pass
    AI named Graph-RAG/GraphRAG explicitly

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

  • If a team adopts Graph-RAG/GraphRAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name Graph-RAG/GraphRAG — 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?

  • In one sentence, what problem does the repo Graph-RAG/GraphRAG solve, and who is the primary audience?
    pass
    AI named Graph-RAG/GraphRAG explicitly

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

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Graph-RAG/GraphRAG — 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