RRepoGEO

REPOGEO REPORT · LITE

neo4j/neo4j-graphrag-python

Default branch main · commit 49b2694a · scanned 6/27/2026, 7:47:02 AM

GitHub: 1,197 stars · 222 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)

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

AI VISIBILITY SCORE
33 /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
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 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
    Strengthen README's opening sentence to highlight unique GraphRAG 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 empowers developers to effortlessly build advanced graph retrieval augmented generation (GraphRAG) applications, leveraging Neo4j's unique graph capabilities to provide richer, context-aware LLM responses beyond what vector-only RAG can achieve.
  • mediumtopics#2
    Add more specific topics to improve categorization

    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-integration, knowledge-graph-rag, semantic-search, graph-ai, retrieval-augmented-generation-python
  • mediumlicense#3
    Clarify the project's license(s) in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [Specify License Name(s) and link to LICENSE file if applicable]. 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
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Neo4j · recommended 2×
  4. neo4j · recommended 2×
  5. NetworkX · recommended 2×
  • CATEGORY QUERY
    What Python libraries help build retrieval augmented generation applications with knowledge graphs?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Neo4j
    4. neo4j
    5. neo4j-graph-data-science
    6. RDFLib
    7. NetworkX
    8. Gensim

    AI recommended 8 alternatives but never named neo4j/neo4j-graphrag-python. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to improve large language model responses using graph traversal and vector search in Python?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Neo4j
    4. neo4j
    5. langchain-neo4j
    6. llamaindex-neo4j
    7. NetworkX
    8. FAISS
    9. OpenAI
    10. Hugging Face Transformers
    11. Weaviate
    12. Pinecone
    13. Qdrant

    AI recommended 13 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 did not name neo4j/neo4j-graphrag-python — 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?

  • 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