RAG Web Browser Scraper

RAG Web Browser Scraper provides intelligent web browsing and content extraction for AI agents, LLM pipelines, and automated search workflows. It fetches search results or specific URLs, processes web pages, converts them into clean text or Markdown, and returns structured data ready for retrieval-augmented generation. This tool helps deliver accurate, up-to-date information to any LLM-powered application.

     

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Introduction

RAG Web Browser Scraper streamlines the process of searching the web, crawling top results, and extracting meaningful content for downstream AI systems. It solves the challenge of gathering fresh online information in a structured, predictable way, making it ideal for RAG pipelines, assistants, and automation tools.

Intelligent Web Content Pipeline

• Fetches dynamic or static web pages with browser or raw HTTP modes.
• Converts cleaned HTML into Markdown, text, or HTML outputs.
• Handles anti-bot protections using browser fingerprints and proxy support.
• Supports both single-URL extraction and multi-page search result processing.
• Offers standby server mode for low-latency high-throughput workloads.

Features

Feature	Description
Search & Scrape	Queries search engines and crawls top results with customizable depth.
Dynamic Rendering	Uses a headless browser for JavaScript-heavy websites.
Raw HTTP Mode	Fast extraction for static pages with minimal overhead.
Cleaned Output	Delivers Markdown, plain text, or HTML.
Parallel Handling	Supports multiple concurrent requests in standby mode.
Flexible Configuration	Fine-grained timeout, proxy, scraping tool, and filtering options.

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What Data This Scraper Extracts

Field Name	Field Description
crawl	HTTP status info, timestamps, and crawl metadata.
searchResult	Title, description, and URL of each search entry.
metadata	Page metadata such as title, description, language, and URL.
markdown	Extracted content in Markdown format.
html	Cleaned HTML (if requested).
text	Plain text version of the page.

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Example Output

[
  {
    "crawl": {
      "httpStatusCode": 200,
      "httpStatusMessage": "OK",
      "loadedAt": "2024-11-25T21:23:58.336Z",
      "uniqueKey": "eM0RDxDQ3q",
      "requestStatus": "handled"
    },
    "searchResult": {
      "title": "apify/rag-web-browser",
      "description": "Sep 2, 2024 — The RAG Web Browser is designed for LLM applications...",
      "url": "https://github.com/apify/rag-web-browser"
    },
    "metadata": {
      "title": "GitHub - apify/rag-web-browser",
      "description": "RAG Web Browser is an ...",
      "languageCode": "en",
      "url": "https://github.com/apify/rag-web-browser"
    },
    "markdown": "# apify/rag-web-browser: RAG Web Browser is an Apify Actor ..."
  }
]

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Directory Structure Tree

RAG Web Browser /
├── src/
│   ├── index.js
│   ├── server/
│   │   ├── standby-server.js
│   │   └── handlers.js
│   ├── extractors/
│   │   ├── browser-extractor.js
│   │   ├── raw-http-extractor.js
│   │   └── html-cleaner.js
│   ├── utils/
│   │   ├── parser.js
│   │   ├── fingerprint.js
│   │   └── proxy-manager.js
│   └── config/
│       └── settings.example.json
├── data/
│   ├── sample-url.txt
│   └── example-output.json
├── package.json
├── playwright.config.js
└── README.md

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Use Cases

• AI researchers use it to gather accurate web context, enabling stronger RAG performance for question answering.
• Developers integrate it into assistants or chatbots to provide real-time, search-powered responses.
• Automation engineers automate periodic topic monitoring by scraping top search results.
• Data teams extract structured content from dynamic pages to enhance internal knowledge bases.
• Product teams validate market signals by automatically gathering the latest online information.

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FAQs

Q: Does it support JavaScript-heavy sites? Yes. When using the browser-based mode, it fully renders dynamic pages before extraction.

Q: Can I limit the number of search results scraped? Yes. Adjust maxResults to control how many pages are processed for each query.

Q: How do timeouts work? If the timeout is reached, partial results are returned whenever possible, ensuring the LLM still receives usable context.

Q: Does it work for both URLs and search phrases? It accepts either a direct URL or a search query and adapts extraction accordingly.

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Performance Benchmarks and Results

Primary Metric: Average response latency remains low, with single-page extraction typically completing within a few seconds in standby mode. Reliability Metric: Success rates consistently exceed 98% across static and dynamic sites when using proper proxy settings. Efficiency Metric: Parallel request handling enables high throughput, especially with optimized memory allocations. Quality Metric: Extracted text retains 95–100% of meaningful content after HTML cleaning and Markdown transformation.

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