From first request to a working integration
Build with the web.
Start small, inspect real output, and make your next request repeatable. These guides cover the code and the checks that keep a useful extraction from becoming a misleading dataset.
Turn documentation pages into Markdown files ↗
Export a chosen list of documentation URLs to Markdown with a source manifest, bounded requests, and visible failures.
Extract product prices from a list of URLs ↗
A runnable Node.js recipe for extracting catalog titles, displayed prices and availability with site-specific selectors.
Detect changes on a SaaS pricing page ↗
Build a local pricing-page tracker that preserves good snapshots and reports changed content for review.
Web scraping with Python: a working API example ↗
Extract a webpage as Markdown with Python’s standard library. Handle HTTP errors, check extraction warnings, and keep your FetchRelay API key out of source code.
Web scraping with JavaScript and Node.js ↗
Use Node.js fetch to extract public webpages as Markdown or JSON fields. Includes a runnable request, timeout handling, and response-quality checks.
HTTP vs browser scraping: which mode should you use? ↗
Choose between HTTP, Automatic, and Browser rendering in FetchRelay. Learn how to diagnose missing content, redirects, bot challenges, and partial results.
Prefer a workflow builder? Connect FetchRelay to n8n. Need a reference? Open the API documentation.