RRepoGEO

REPOGEO REPORT · LITE

gusye1234/nano-graphrag

Default branch main · commit acb35c06 · scanned 6/23/2026, 7:47:30 AM

GitHub: 3,891 stars · 419 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)

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

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 gusye1234/nano-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
  • hightopics#1
    Expand repository topics for better query matching

    Why:

    CURRENT
    gpt, gpt-4o, graphrag, learning-by-doing, llm, rag
    COPY-PASTE FIX
    gpt, gpt-4o, graphrag, learning-by-doing, llm, rag, knowledge-graph, graph-based-rag, graph-llm
  • mediumabout#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/gusye1234/nano-graphrag
  • mediumreadme#3
    Refine README's main heading to clarify niche and differentiation

    Why:

    CURRENT
    A simple, easy-to-hack GraphRAG implementation
    COPY-PASTE FIX
    A simple, easy-to-hack GraphRAG implementation for LLMs, offering a focused, lightweight, and performant alternative to general RAG frameworks and complex official solutions.

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 gusye1234/nano-graphrag
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
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. GraphRAG · recommended 2×
  3. run-llama/llama_index · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. neo4j/neo4j-python-driver · recommended 1×
  • CATEGORY QUERY
    Looking for a lightweight and hackable RAG implementation using graph structures for LLMs.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (run-llama/llama_index)
    2. LangChain (langchain-ai/langchain)
    3. Neo4j (neo4j/neo4j-python-driver)
    4. Amazon Neptune
    5. transformers (huggingface/transformers)
    6. openai (openai/openai-python)
    7. GraphRAG
    8. NetworkX (networkx/networkx)

    AI recommended 8 alternatives but never named gusye1234/nano-graphrag. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a simplified, asynchronous knowledge graph RAG system that is easy to integrate.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. Neo4j
    3. Memgraph
    4. LangChain
    5. GraphRAG
    6. Amazon Neptune
    7. Haystack
    8. AWS Lambda
    9. AWS Step Functions
    10. DGL (Deep Graph Library)

    AI recommended 10 alternatives but never named gusye1234/nano-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
    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 gusye1234/nano-graphrag?
    pass
    AI named gusye1234/nano-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 gusye1234/nano-graphrag in production, what risks or prerequisites should they evaluate first?
    pass
    AI named gusye1234/nano-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 gusye1234/nano-graphrag solve, and who is the primary audience?
    pass
    AI named gusye1234/nano-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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gusye1234/nano-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