REPOGEO REPORT · LITE

Marker-Inc-Korea/AutoRAG

Default branch main · commit e0a717b1 · scanned 5/20/2026, 2:27:10 PM

GitHub: 4,775 stars · 395 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

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 Marker-Inc-Korea/AutoRAG, 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

Strengthen the README's opening statement to emphasize automated RAG evaluation and optimization for custom data.

Why:

CURRENT

RAG AutoML tool for automatically finding an optimal RAG pipeline for your data.

COPY-PASTE FIX

AutoRAG is an open-source AutoML framework for **Retrieval-Augmented Generation (RAG) evaluation and optimization**, designed to automatically discover the optimal RAG pipeline for *your specific dataset and use-case*.

mediumcomparison#2

Add a 'Why AutoRAG? How We Compare' section to the README.

Why:

COPY-PASTE FIX

## Why AutoRAG? How We Compare

[Add a section explaining AutoRAG's unique AutoML-style optimization approach compared to RAG frameworks (e.g., LangChain, LlamaIndex) and pure evaluation/observability tools (e.g., Ragas, Phoenix, DeepEval).]

mediumreadme#3

Add a 'Key Features' section to highlight core capabilities.

Why:

COPY-PASTE FIX

## Key Features

*   Automated RAG pipeline evaluation and benchmarking
*   Optimization of RAG modules (retrievers, rankers, generators)
*   Support for custom datasets and evaluation metrics
*   AutoML-style hyperparameter tuning for RAG components
*   Integration with popular LLM and embedding models
*   Easy-to-use interface for RAG pipeline experimentation

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 Marker-Inc-Korea/AutoRAG

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×
Ragas · recommended 2×
Phoenix · recommended 2×
DeepEval · recommended 2×

CATEGORY QUERY
How to automatically find the best RAG pipeline for my specific dataset?
you: not recommended
AI recommended (in order):
1. LlamaIndex
2. LangChain
3. LangSmith
4. Ragas
5. Phoenix
6. DeepEval
7. Weights & Biases
8. W&B Prompts
AI recommended 8 alternatives but never named Marker-Inc-Korea/AutoRAG. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help evaluate and optimize retrieval-augmented generation performance for custom data?
you: not recommended
AI recommended (in order):
1. LlamaIndex
2. LangChain
3. Ragas
4. Arize AI
5. Phoenix
6. Weights & Biases
7. DeepEval
8. EleutherAI's LM Evaluation Harness
9. HELM
AI recommended 9 alternatives but never named Marker-Inc-Korea/AutoRAG. 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 Marker-Inc-Korea/AutoRAG?
pass
AI named Marker-Inc-Korea/AutoRAG explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Marker-Inc-Korea/AutoRAG in production, what risks or prerequisites should they evaluate first?
pass
AI named Marker-Inc-Korea/AutoRAG 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 Marker-Inc-Korea/AutoRAG solve, and who is the primary audience?
pass
AI named Marker-Inc-Korea/AutoRAG explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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Marker-Inc-Korea/AutoRAG — 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