RRepoGEO

REPOGEO REPORT · LITE

JayLZhou/GraphRAG

Default branch master · commit 4e87938e · scanned 5/9/2026, 2:12:43 AM

GitHub: 1,522 stars · 97 forks

AI VISIBILITY SCORE
55 /100
Needs work
Category recall
1 / 2
Avg rank #4.0 when recommended
Rule findings
1 pass · 0 warn · 1 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 JayLZhou/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

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

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of your chosen open-source license (e.g., MIT, Apache-2.0). If you intend a custom license, state it clearly in the README.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    Add the following topics: `graph-rag`, `retrieval-augmented-generation`, `knowledge-graphs`, `llms`, `nlp`, `research`, `framework`, `deep-analysis`.
  • mediumreadme#3
    Clarify the project's focus in the README's opening paragraph

    Why:

    CURRENT
    > **GraphRAG** is a popular 🔥🔥🔥 and powerful 💪💪💪 RAG system! 🚀💡 Inspired by systems like Microsoft's, graph-based RAG is unlocking endless possibilities in AI. > Our project focuses on **modularizing and decoupling** these methods 🧩 to **unveil the mystery** 🕵️‍♂️🔍✨ behind them and share fun and valuable insights! 🤩💫 Our project🔨 is included in Awesome Graph-based RAG.
    COPY-PASTE FIX
    > **GraphRAG** is a research framework and in-depth study focusing on **modularizing and decoupling** methods within graph-based Retrieval-Augmented Generation (RAG) systems. Inspired by powerful systems like Microsoft's, our project aims to **unveil the mystery** 🕵️‍♂️🔍✨ behind these techniques, offering valuable insights and a platform for experimentation. It's included in Awesome Graph-based RAG.

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
1 / 2
50% of queries surface JayLZhou/GraphRAG
Avg rank
#4.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
Amazon Neptune
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Amazon Neptune · recommended 2×
  2. neo4j/neo4j · recommended 1×
  3. vaticle/typedb · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. run-llama/llama_index · recommended 1×
  • CATEGORY QUERY
    How can I leverage graph structures for advanced retrieval-augmented generation in AI applications?
    you: not recommended
    AI recommended (in order):
    1. Neo4j (neo4j/neo4j)
    2. Amazon Neptune
    3. TypeDB (vaticle/typedb)
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. OpenSearch (opensearch-project/OpenSearch)
    7. Elasticsearch (elastic/elasticsearch)
    8. StellarGraph (stellargraph/stellargraph)
    9. PyTorch Geometric (pyg-team/pytorch_geometric)
    10. DGL (dmlc/dgl)

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

    Show full AI answer
  • CATEGORY QUERY
    Looking for tools to analyze and build modular graph-based RAG systems effectively.
    you: #4
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Neo4j
    4. GraphRAG ← you
    5. Amazon Neptune
    6. ArangoDB
    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 JayLZhou/GraphRAG?
    pass
    AI named JayLZhou/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 JayLZhou/GraphRAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named JayLZhou/GraphRAG 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 JayLZhou/GraphRAG solve, and who is the primary audience?
    pass
    AI named JayLZhou/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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JayLZhou/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