Why Go Is the Best Web Scraping Language for AI Agents
Goroutines give you thousands of concurrent scrapers on 2kb of memory each. No GIL, no garbage collection pauses, no runtime overhead. Here is why I use Go for all my scraping.
So everyone uses Python for web scraping right. Beautiful Soup, Scrapy, Selenium, it's what everyone reaches for. And I get it, Python is easy to write and there's a library for literally everything. But when you start building scrapers that AI agents actually depend on, scrapers that need to be fast, handle thousands of requests at once and not eat all your RAM, Python really starts to struggle.
I switched all my scraping to Go and it's been a massive difference. Not a small improvement. Massive.
It's just faster
Go compiles down to native machine code. Python is interpreted. So straight away you're looking at 10-30x faster execution. But for scraping the bottleneck is usually network stuff, waiting for responses to come back, and that's where it gets really interesting.
I've run the same scraping jobs in both languages on the same datasets. Go finishes in about 20 minutes what Python takes 40+ minutes to do. And when you've got an AI agent sitting there waiting for scraped data before it can make a decision, that's not a small thing right. The agent is literally blocked until the data comes back.
Goroutines changed everything for me
So in Python if you want to do things concurrently you're fighting the GIL the whole time. The Global Interpreter Lock means only one thread can actually run Python code at any given moment. You can use asyncio or multiprocessing to get around it but it always feels like a hack. It's never clean.
Go just does this natively. Goroutines are these incredibly lightweight threads that the Go runtime manages for you. You literally just write go scrape(url) and it runs concurrently. No thread pool setup, no executor configuration, no async/await chains everywhere. Just go and it goes.
But here's what really blew my mind when I first started using them. Each goroutine uses about 2kb of memory. A Python thread uses about 8MB. Think about that for a second. Where Python is struggling to run 100 concurrent scrapers without running out of memory, Go can comfortably run 10,000 on the same machine. When I need an agent to scrape a hundred pages at once to gather context for something, Go just does it without breaking a sweat. Python would fall over.
Memory just stays stable
I've had Python scrapers that slowly leak memory over long runs. You leave them going for a few hours and they're suddenly eating gigabytes. With Go I set a scraper running, come back hours later and it's using the same amount of memory it started with. The garbage collector is designed for low-latency stuff so you don't get those random pauses either.
If your agents need to scrape stuff in the background for hours at a time that stability is massive. You can't have your scraping infrastructure randomly dying because Python decided to eat all your RAM.
What I actually use
For static pages I use Colly. It's the main Go scraping framework and it's really good. Rate limiting built in, caching, parallel scraping with goroutines out of the box. For most scraping jobs it's all you need.
When I need to deal with JavaScript-heavy pages I use Rod. It controls a real Chrome browser through DevTools Protocol, similar to Puppeteer but in Go. And because Go handles concurrency so well you can spin up hundreds of browser instances and orchestrate them all without the code turning into spaghetti.
How this fits into my agent setup
So the way my workflows usually go is the agent decides what it needs to research, fires off the scraper to go get the data and then processes the results when they come back. With Python that scraping step was always the bottleneck. The agent just sits there waiting.
With Go the scraper fires off hundreds of goroutines, all the pages get fetched in parallel and the data comes back almost immediately. The agent barely has to wait. And because Go compiles to a single static binary I can build it once and deploy it everywhere. No virtual environments, no pip install, no dependency hell. On NixOS I just add the binary to my config and every machine in my fleet has it. Try doing that with a Python scraper that depends on Selenium and ChromeDriver and half of PyPI.
The code is not hard
People think Go is way harder to write than Python. For scraping it's really not. A basic Colly scraper is like five lines:
``
c := colly.NewCollector()
c.OnHTML("h1", func(e *colly.HTMLElement) {
fmt.Println(e.Text)
})
c.Visit("https://example.com")
``
To make it concurrent you wrap the visits in goroutines with a WaitGroup and you're done. You write what you mean and it runs fast. That's kind of the whole point of Go.
When Python is still fine
Look I'm not saying burn all your Python scrapers. If you need to quickly scrape 10 pages for a one-off thing then Python with requests and Beautiful Soup is totally fine. It's quicker to write for small jobs and the performance doesn't matter.
But if you're building scrapers that your AI agents actually rely on, stuff that needs to scale, run for long periods and handle thousands of connections without crashing, Go is where you want to be. I made the switch and I genuinely can't imagine going back. The difference in speed and reliability has been that big.