RRepoGEO

REPOGEO REPORT · LITE

darrencxl0301/StageRAG

Default branch main · commit 77bfc6a9 · scanned 5/15/2026, 5:18:53 AM

GitHub: 1,038 stars · 90 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 darrencxl0301/StageRAG, 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 topics to improve categorization

    Why:

    COPY-PASTE FIX
    rag, llm, hallucination-reduction, nlp, python, ai-framework, llama, confidence-scoring, multi-stage-rag
  • highreadme#2
    Clarify project maturity in README's opening paragraph

    Why:

    CURRENT
    StageRAG is a lightweight, production-ready RAG framework designed to give you precise control over the speed-versus-accuracy trade-off. It allows you to build high-factuality applications while gracefully managing uncertainty in LLM responses.
    COPY-PASTE FIX
    StageRAG is a lightweight, **production-ready blueprint** for RAG systems, designed to give you precise control over the speed-versus-accuracy trade-off. While **currently in active development**, it allows you to build high-factuality applications and gracefully managing uncertainty in LLM responses.
  • mediumreadme#3
    Add a sentence emphasizing unique features to the README's introduction

    Why:

    COPY-PASTE FIX
    It uniquely offers **dynamically switchable 3-step (Speed) and 4-step (Precision) pipelines**, optimized for **smaller Llama 3.2 models**, providing precise control over the speed-versus-accuracy trade-off.

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 darrencxl0301/StageRAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Haystack · recommended 2×
  4. Weaviate · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to build a RAG system that minimizes LLM hallucination and manages response uncertainty effectively?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Weaviate
    4. Pinecone
    5. Qdrant
    6. OpenAI API
    7. Haystack
    8. Cohere Rerank API
    9. Guardrails AI

    AI recommended 9 alternatives but never named darrencxl0301/StageRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework allows dynamic switching between RAG speed and precision for smaller LLMs?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Hugging Face Transformers
    5. DSPy

    AI recommended 5 alternatives but never named darrencxl0301/StageRAG. 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 darrencxl0301/StageRAG?
    pass
    AI named darrencxl0301/StageRAG explicitly

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

  • If a team adopts darrencxl0301/StageRAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named darrencxl0301/StageRAG 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 darrencxl0301/StageRAG solve, and who is the primary audience?
    pass
    AI named darrencxl0301/StageRAG 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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darrencxl0301/StageRAG — 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