RRepoGEO

REPOGEO REPORT · LITE

superlinear-ai/raglite

Default branch main · commit 3b3db9ec · scanned 5/12/2026, 7:22:14 AM

GitHub: 1,158 stars · 102 forks

AI VISIBILITY SCORE
28 /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
2 / 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 superlinear-ai/raglite, 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 README opening to highlight core differentiator against alternatives

    Why:

    CURRENT
    RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL.
    COPY-PASTE FIX
    RAGLite is a lightweight, production-ready Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL. Unlike more complex frameworks such as LangChain or LlamaIndex, RAGLite focuses on simplicity, efficiency, and minimal dependencies, making it ideal for developers building robust RAG applications.
  • mediumabout#2
    Populate the 'Homepage' field in repository settings

    Why:

    COPY-PASTE FIX
    Add a URL to the 'Homepage' field in your repository settings that points to the official project website, documentation, or a dedicated landing page for RAGLite.
  • lowreadme#3
    Add a dedicated 'Why RAGLite?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why RAGLite?

(Elaborate on RAGLite's unique advantages, such as its lightweight nature, specific database support, advanced chunking, and minimal dependencies, in comparison to other RAG frameworks like LangChain or LlamaIndex.)

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 superlinear-ai/raglite
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. RAGatouille · recommended 2×
  5. Sentence Transformers · recommended 1×
  • CATEGORY QUERY
    Looking for a Python RAG library supporting DuckDB or PostgreSQL for vector and keyword search.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. RAGatouille
    5. Sentence Transformers

    AI recommended 5 alternatives but never named superlinear-ai/raglite. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a lightweight RAG framework with advanced document chunking and local LLM integration.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. llama-cpp-python
    3. transformers
    4. LangChain
    5. Ollama
    6. Haystack
    7. RAGatouille
    8. Sentence-Transformers
    9. NLTK
    10. spaCy

    AI recommended 10 alternatives but never named superlinear-ai/raglite. 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 superlinear-ai/raglite?
    pass
    AI did not name superlinear-ai/raglite — likely talking about a different project

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

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

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

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superlinear-ai/raglite — 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