RRepoGEO

REPOGEO REPORT · LITE

OpenSPG/KAG

Default branch master · commit fdab15b3 · scanned 5/9/2026, 10:46:57 AM

GitHub: 8,722 stars · 676 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 OpenSPG/KAG, 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
    Add a concise tagline to the README's opening

    Why:

    COPY-PASTE FIX
    Add this line immediately after the H1: 'An advanced RAG framework for reliable, logical Q&A in specialized domains, powered by knowledge graphs.'
  • hightopics#2
    Add 'rag-framework' and 'domain-specific-qa' to repository topics

    Why:

    CURRENT
    knowledge-graph, large-language-model, logical-reasoning, multi-hop-question-answering, trustfulness
    COPY-PASTE FIX
    knowledge-graph, large-language-model, logical-reasoning, multi-hop-question-answering, trustfulness, rag-framework, domain-specific-qa
  • mediumreadme#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## 3. KAG vs. Traditional RAG and Knowledge Graph Databases', detailing how KAG improves upon or differs from these common approaches by leveraging knowledge graphs for logical reasoning.

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 OpenSPG/KAG
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. Amazon Neptune · recommended 2×
  3. LlamaIndex · recommended 1×
  4. Haystack · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to improve RAG accuracy and handle complex multi-hop questions in domain knowledge bases?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. Haystack
    3. Neo4j
    4. Amazon Neptune
    5. Pinecone
    6. Weaviate
    7. Cohere Rerank
    8. Hugging Face Transformers
    9. OpenAI Fine-tuning API
    10. LangChain Agents

    AI recommended 10 alternatives but never named OpenSPG/KAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help build reliable Q&A systems for specialized domains using knowledge graphs and LLMs?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Neo4j
    4. Amazon Neptune
    5. Google Cloud Knowledge Graph
    6. Hugging Face Transformers (huggingface/transformers)
    7. Hugging Face Datasets (huggingface/datasets)
    8. OpenAI API
    9. Azure OpenAI Service

    AI recommended 9 alternatives but never named OpenSPG/KAG. 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 OpenSPG/KAG?
    pass
    AI named OpenSPG/KAG explicitly

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

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

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

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OpenSPG/KAG — 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