RRepoGEO

REPOGEO REPORT · LITE

neo4j/neo4j-graphrag-python

Default branch main · commit 0508e085 · scanned 5/16/2026, 11:12:08 AM

GitHub: 1,158 stars · 206 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 neo4j/neo4j-graphrag-python, 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 highlight unique graph-RAG value

    Why:

    CURRENT
    The official Neo4j GraphRAG package for Python enables developers to build graph retrieval augmented generation (GraphRAG) applications using the power of Neo4j and Python.
    COPY-PASTE FIX
    The official Neo4j GraphRAG package for Python enables developers to build advanced Retrieval Augmented Generation (RAG) applications by uniquely combining the power of Neo4j's graph traversal with vector search. This first-party library offers a robust, feature-rich, and high-performance solution for enhancing LLM responses with structured graph information, with the added assurance of long-term support and maintenance directly from Neo4j.
  • mediumtopics#2
    Add more specific RAG and graph-AI related topics

    Why:

    CURRENT
    ai, cypher, genai, graph-database, graphrag, neo4j, python, python3, rag
    COPY-PASTE FIX
    ai, cypher, genai, graph-database, graphrag, neo4j, python, python3, rag, llm, vector-search, graph-traversal, retrieval-augmented-generation
  • lowreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [Insert specific license name(s) here, e.g., Apache 2.0 and MIT]. Please see the [LICENSE](LICENSE) file for full 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 neo4j/neo4j-graphrag-python
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GraphRAG
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GraphRAG · recommended 2×
  2. llamaindex/llamaindex · recommended 1×
  3. langchain-ai/langchain · recommended 1×
  4. deepset-ai/haystack · recommended 1×
  5. networkx/networkx · recommended 1×
  • CATEGORY QUERY
    Looking for a Python framework to enhance LLM responses with structured graph information.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (llamaindex/llamaindex)
    2. LangChain (langchain-ai/langchain)
    3. GraphRAG
    4. Haystack (deepset-ai/haystack)
    5. NetworkX (networkx/networkx)
    6. PyTorch Geometric (pyg-team/pytorch_geometric)
    7. Deep Graph Library (dgl-team/dgl)

    AI recommended 7 alternatives but never named neo4j/neo4j-graphrag-python. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for combining vector search with graph traversal for enhanced RAG applications?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. Neo4j AuraDS
    3. LangChain
    4. LlamaIndex
    5. ArangoDB
    6. ArangoSearch
    7. Weaviate
    8. Pinecone
    9. Qdrant
    10. Milvus
    11. Amazon Neptune
    12. Azure Cosmos DB for Gremlin
    13. GraphRAG
    14. NetworkX

    AI recommended 14 alternatives but never named neo4j/neo4j-graphrag-python. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 neo4j/neo4j-graphrag-python?
    pass
    AI named neo4j/neo4j-graphrag-python explicitly

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

  • If a team adopts neo4j/neo4j-graphrag-python in production, what risks or prerequisites should they evaluate first?
    pass
    AI named neo4j/neo4j-graphrag-python 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 neo4j/neo4j-graphrag-python solve, and who is the primary audience?
    pass
    AI named neo4j/neo4j-graphrag-python explicitly

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

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neo4j/neo4j-graphrag-python — 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