REPOGEO REPORT · LITE

superlinear-ai/raglite

Default branch main · commit 6a540e1b · scanned 6/22/2026, 1:32:52 PM

GitHub: 1,187 stars · 105 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

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 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

Emphasize "lightweight" and "no heavy dependencies" in the README's opening.

Why:

CURRENT

RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL.

COPY-PASTE FIX

RAGLite is a lightweight Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL, designed for speed and permissiveness without heavy dependencies like PyTorch or LangChain.

mediumabout#2
Add a homepage URL to the repository's "About" section.
Why:
COPY-PASTE FIX
```
https://[YOUR_PROJECT_HOMEPAGE_URL_HERE]
```

lowcomparison#3

Add a "Comparison to Alternatives" section in the README.

Why:

COPY-PASTE FIX

## Comparison to Alternatives

RAGLite differentiates itself from frameworks like LlamaIndex, LangChain, and Haystack by focusing on being a lightweight, production-ready toolkit with minimal dependencies. Unlike these broader frameworks, RAGLite prioritizes simplicity, performance, and specific database integrations (DuckDB/PostgreSQL) without requiring heavy libraries like PyTorch. It offers advanced features like optimal chunking and adaptive retrieval in a self-contained package, making it ideal for projects seeking a focused RAG solution without the overhead of larger ecosystems.

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

Of all named tools, what % are you?

Top rival

LlamaIndex

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LlamaIndex · recommended 2×
LangChain · recommended 2×
Haystack · recommended 2×
DuckDB · recommended 1×
PostgreSQL · recommended 1×

CATEGORY QUERY
Looking for a Python RAG toolkit using DuckDB or PostgreSQL, without heavy dependencies.
you: not recommended
AI recommended (in order):
1. LlamaIndex
2. DuckDB
3. PostgreSQL
4. pgvecto.rs
5. pgvector
6. LangChain
7. langchain-community
8. psycopg2-binary
9. sqlalchemy
10. Haystack
11. RAGatouille
12. duckdb-engine
AI recommended 12 alternatives but never named superlinear-ai/raglite. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to build a RAG system with advanced PDF processing and flexible LLM/reranker options?
you: not recommended
AI recommended (in order):
1. LlamaIndex
2. Unstructured.io
3. PyMuPDF
4. Ollama
5. vLLM
6. OpenAI
7. Anthropic
8. Hugging Face
9. Cohere Rerank
10. BGE Reranker
11. LangChain
12. pypdf
13. pdfminer.six
14. LayoutParser
15. Haystack
16. Sentence Transformers
17. faiss
18. chroma
AI recommended 18 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 named superlinear-ai/raglite explicitly
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?

Embed your GEO score

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/superlinear-ai/raglite"><img src="https://repogeo.com/badge/superlinear-ai/raglite.svg" alt="RepoGEO" /></a>

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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