RRepoGEO

REPOGEO REPORT · LITE

KruxAI/ragbuilder

Default branch main · commit 5b084512 · scanned 5/10/2026, 9:02:15 PM

GitHub: 1,532 stars · 126 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 KruxAI/ragbuilder, 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
    Add specific RAG optimization and evaluation topics

    Why:

    CURRENT
    developer-tools, genai, rag
    COPY-PASTE FIX
    developer-tools, genai, rag, hyperparameter-tuning, rag-optimization, rag-evaluation, mlops
  • highreadme#2
    Refine README's opening sentence to emphasize RAG optimization

    Why:

    CURRENT
    RagBuilder is a toolkit that helps you create optimal Production-ready Retrieval-Augmented-Generation (RAG) setup for your data automatically.
    COPY-PASTE FIX
    RagBuilder is a specialized toolkit for **automating the optimization and evaluation of Production-ready Retrieval-Augmented-Generation (RAG) setups** for your data.
  • mediumreadme#3
    Add a comparison section highlighting RagBuilder's unique focus

    Why:

    COPY-PASTE FIX
    ## Why RagBuilder?
While frameworks like LangChain and LlamaIndex offer comprehensive RAG development, RagBuilder specializes in providing a modular, component-based toolkit for fine-grained control and extensibility, specifically designed for hyperparameter tuning and optimizing RAG pipelines for production. It focuses on evaluating and refining your RAG setup, rather than just building it.

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 KruxAI/ragbuilder
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. ChromaDB · recommended 1×
  3. Pinecone · recommended 1×
  4. Weaviate · recommended 1×
  5. OpenAI Embeddings · recommended 1×
  • CATEGORY QUERY
    How can I optimize RAG configurations for production-grade AI applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. ChromaDB
    3. Pinecone
    4. Weaviate
    5. OpenAI Embeddings
    6. Cohere Embeddings
    7. OpenAI GPT-4
    8. Anthropic Claude 3
    9. Google Gemini
    10. Haystack
    11. Weights & Biases
    12. MLflow
    13. RAGAS

    AI recommended 13 alternatives but never named KruxAI/ragbuilder. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help automate RAG hyperparameter tuning and evaluate different chunking strategies?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (wandb/wandb)
    2. MLflow (mlflow/mlflow)
    3. Ragas (explodinggradients/ragas)
    4. LlamaIndex (run-llama/llama_index)
    5. LangChain (langchain-ai/langchain)
    6. LangSmith
    7. Haystack (deepset-ai/haystack)

    AI recommended 7 alternatives but never named KruxAI/ragbuilder. 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 KruxAI/ragbuilder?
    pass
    AI named KruxAI/ragbuilder explicitly

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

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

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

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
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KruxAI/ragbuilder — RepoGEO report