RRepoGEO

REPOGEO REPORT · LITE

EpistasisLab/KRAGEN

Default branch main · commit 149a62d9 · scanned 6/2/2026, 12:33:46 PM

GitHub: 683 stars · 44 forks

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 EpistasisLab/KRAGEN, 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
  • highabout#1
    Update the repository description for clarity

    Why:

    CURRENT
    Software to implement GoT with a weviate vectorized database
    COPY-PASTE FIX
    KRAGEN is a Knowledge Retrieval Augmented Generation Engine that combines knowledge graphs, RAG, and advanced prompting (Graph-of-Thoughts) to solve complex problems with natural language.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    knowledge-graph, rag, retrieval-augmented-generation, graph-of-thoughts, got, llm, large-language-models, vector-database, weaviate, natural-language-processing
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/EpistasisLab/KRAGEN

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 EpistasisLab/KRAGEN
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
neo4j/neo4j
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. neo4j/neo4j · recommended 1×
  2. langchain-ai/langchain · recommended 1×
  3. Amazon Neptune · recommended 1×
  4. run-llama/llama_index · recommended 1×
  5. vaticle/typedb · recommended 1×
  • CATEGORY QUERY
    How to leverage knowledge graphs with RAG for complex natural language problem solving?
    you: not recommended
    AI recommended (in order):
    1. Neo4j (neo4j/neo4j)
    2. LangChain (langchain-ai/langchain)
    3. Amazon Neptune
    4. LlamaIndex (run-llama/llama_index)
    5. Grakn/TypeDB (vaticle/typedb)
    6. Apache Jena (apache/jena)
    7. Stardog
    8. Google Cloud Knowledge Graph
    9. Google Cloud Vertex AI

    AI recommended 9 alternatives but never named EpistasisLab/KRAGEN. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to convert existing knowledge base into vector store for RAG with thought visualization?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. LangSmith
    4. Pinecone
    5. Chroma
    6. Weaviate
    7. Haystack
    8. PyPDF2
    9. python-docx
    10. BeautifulSoup
    11. pandas
    12. sentence-transformers
    13. FAISS
    14. Annoy
    15. Milvus
    16. Qdrant
    17. matplotlib
    18. seaborn
    19. Plotly
    20. Bokeh
    21. Gradio
    22. Streamlit

    AI recommended 22 alternatives but never named EpistasisLab/KRAGEN. 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 EpistasisLab/KRAGEN?
    pass
    AI named EpistasisLab/KRAGEN explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of EpistasisLab/KRAGEN. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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EpistasisLab/KRAGEN — 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