PixelRAG is an open-source visual Retrieval-Augmented Generation system developed by UC Berkeley SkyLab and BAIR. It indexes documents, PDFs, and web pages as high-resolution screenshot tiles rather than parsed plain text, allowing vision-language models to reason over tables, charts, diagrams, and layout hierarchy.

How does pixelbrowse give Claude Code eyes?

pixelbrowse is a plugin that captures a web page screenshot via the pixelshot CLI tool (Playwright/CDP) and feeds the rendered visual tiles directly to Claude's vision context, enabling accurate reading of infographics, pricing tables, and UI layouts.

PIXEL_RAG_VISUAL.exe

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PixelRAG: Visual Document Search & Giving AI Coding Agents Eyes ⚡

Why web screenshots beat plain text for Retrieval-Augmented Generation: preserving tables, layout, and infographics across 8.28M documents with UC Berkeley's PixelRAG.

⚡ TL;DR

Traditional text-based RAG strips away CSS, layout structure, tables, and charts during HTML-to-text parsing.
PixelRAG (UC Berkeley SkyLab / BAIR) renders documents directly to screenshot tiles and executes vector retrieval over the images themselves.
This preserves 100% of spatial formatting, allowing vision models (Claude 3.5 Sonnet, GPT-4o) to accurately answer questions about complex diagrams, financial tables, and UI states.

Quickstart & API Usage

# Install CLI tool
pip install pixelrag

# Render any web page or PDF to screenshot tiles
pixelshot https://en.wikipedia.org/wiki/Artificial_intelligence --output ./tiles

# Search the hosted 8.28M Wikipedia visual index
curl -X POST https://api.pixelrag.ai/search \
  -H "Content-Type: application/json" \
  -d '{"queries": [{"text": "What is the transformer architecture?"}], "n_docs": 5}'

MASTER PROMPT

1-Click Visual Document Analysis & Extraction Prompt

Paste this prompt into Claude or GPT-4o alongside screenshot tiles of any complex table, dashboard, or financial report:

PIXEL_RAG_VISUAL_EXTRACTOR

Act as an expert Multimodal Document Intelligence and Visual Data Extraction Specialist utilizing the PixelRAG framework. Analyze the attached screenshot tiles:

1. SPATIAL & LAYOUT RECONSTRUCTION:
- Identify all tables, charts, callout boxes, and hierarchical headers visible in the image.
- Map out the exact columns, headers, and row data without dropping formatting.

2. VISUAL INSIGHT SYNTHESIS:
- Extract all key metrics, percentages, data trends, and footnotes.
- Explain what the visual diagram/infographic conveys that raw text would miss.

3. STRUCTURED DATA OUTPUT:
- Convert the extracted tabular data into a clean, copyable Markdown table and JSON object.

GITHUB REPO PixelRAG

View on GitHub ->

Visual document RAG and pixelbrowse framework from StarTrail-org / UC Berkeley.

Prerequisites: Python 3.10+, PyTorch, Playwright / Selenium

# 1. Clone the PixelRAG repository
git clone https://github.com/StarTrail-org/PixelRAG.git
cd PixelRAG

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run visual document retrieval pipeline
python pixelrag_demo.py --doc sample.pdf --query "Extract total ARR and EBITDA from table"

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