How to make your AI agent work in production — not just in demos. Web scraping, data extraction, and the architectural patterns that actually hold up.
The real numbers: self-built scraping infrastructure vs a managed API. Dev time, infrastructure, maintenance, and the hidden costs most teams miss — plus the volume crossover point where self-built actually makes sense.
Step-by-step: architecture, working Python and JavaScript code, and the four failure modes that sink every scraping agent in production. From zero to a working autonomous agent.
Firecrawl, ScrapingBee, Apify, Crawl4AI, Browserbase, and Shell City — compared on pricing, AI agent compatibility scores, reliability, and which one actually holds up in production.
LangChain, CrewAI, or custom — every AI agent eventually needs live web data. Here are 5 approaches, from raw HTTP to a managed multi-provider API, and what breaks at scale with each one.
Single-provider scraping is a production liability. Here's why aggregation-based routing is the only architecture that holds up when your agent hits real scale — and what it looks like in code.
Free: 1,000 pages. No credit card. Works with LangChain, CrewAI, AutoGPT.
# Scrape any URL → structured JSON curl -X POST https://shellcity.polsia.app/v1/scrape \ -H "Authorization: Bearer YOUR_KEY" \ -d '{"url": "https://example.com"}'