RRepoGEO

REPOGEO REPORT · LITE

OpenSPG/KAG

Default branch master · commit fdab15b3 · scanned 6/19/2026, 6:02:04 AM

GitHub: 8,833 stars · 689 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
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
    Reposition the README's opening to highlight unique logical reasoning and structured knowledge capabilities

    Why:

    CURRENT
    KAG is a logical reasoning and Q&A framework based on the OpenSPG engine and large language models, which is used to build logical reasoning and Q&A solutions for vertical domain knowledge bases. KAG can effectively overcome the ambiguity of traditional RAG vector similarity calculation and the noise problem of GraphRAG introduced by OpenIE. KAG supports logical reasoning and multi-hop fact Q&A, etc., and is significantly better than the current SOTA method.
    COPY-PASTE FIX
    KAG is a **logical form-guided reasoning and retrieval framework** built on the OpenSPG engine and LLMs, specifically designed to build **robust Q&A and logical reasoning solutions for professional domain knowledge bases**. It uniquely overcomes the limitations of traditional RAG's vector similarity and GraphRAG's noise by integrating **structured knowledge graphs with deep logical reasoning capabilities**, enabling superior multi-hop factual Q&A and complex inference.
  • mediumtopics#2
    Add more specific topics to clarify its role as a specialized RAG framework

    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, knowledge-augmented-generation, semantic-property-graph
  • lowreadme#3
    Add a 'Comparison' section to the README highlighting differentiators

    Why:

    COPY-PASTE FIX
    ## 4. Why KAG? / Comparison with Alternatives

KAG differentiates itself by providing a unified, schema-driven **Semantic Property Graph (SPG)** for representing complex, multi-modal enterprise knowledge with explicit semantics and relationships. Unlike pure vector databases, KAG offers a deeply structured approach to knowledge, enabling advanced logical reasoning and precise multi-hop Q&A that goes beyond simple semantic similarity.

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
Cohere Rerank
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Cohere Rerank · recommended 1×
  2. bge-reranker-large · recommended 1×
  3. ColBERT · recommended 1×
  4. Neo4j · recommended 1×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    How to improve RAG accuracy and trustfulness for complex multi-hop questions on domain data?
    you: not recommended
    AI recommended (in order):
    1. Cohere Rerank
    2. bge-reranker-large
    3. ColBERT
    4. Neo4j
    5. LangChain
    6. Grakn
    7. Vaticle's TypeDB
    8. Elasticsearch
    9. Weaviate
    10. Sentence-BERT (SBERT)
    11. Instructor-XL
    12. OpenAI Embeddings
    13. LlamaIndex

    AI recommended 13 alternatives but never named OpenSPG/KAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for building robust Q&A systems requiring deep logical reasoning over structured knowledge.
    you: not recommended
    AI recommended (in order):
    1. TypeDB (vaticle/typedb)
    2. Soufflé (souffle-lang/souffle)
    3. Datafrog (rust-lang/datafrog)
    4. SWI-Prolog (SWI-Prolog/swipl-devel)
    5. Apache Jena (apache/jena)
    6. Pellet
    7. HermiT (hermit-reasoner/hermit-reasoner)
    8. LogicBlox
    9. Neo4j (neo4j/neo4j)
    10. APOC procedures (neo4j-contrib/neo4j-apoc-procedures)

    AI recommended 10 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