Build a Web Scraper

A production-grade scraping pipeline: headless browser, queues, retries, structured extraction. Use the LLM-friendly tools when the output goes to RAG; the classic ones when you need raw throughput.

Repos

Layers

Build time

A weekend

Outcome

See below

You will ship

A scraper that crawls thousands of pages reliably, stores clean output, and feeds your AI agent.

How to feed this to your AI agent Browse the full directory

LLM-grade scrapers

2 repos

bymendableai

firecrawl

One API, returns clean markdown ready for RAG. Self-host or use their cloud — same SDK.

byunclecode

crawl4ai

Python alternative when you need strict JSON-schema extraction with an embedded LLM.

Python-native LLM-friendly crawler. Strong at extracting structured data (JSON schemas) from messy HTML using an embedded LLM. Heavier setup…

Production crawlers

3 repos

byapify

crawlee

Node-native. Queues, retries, proxy rotation. The default for serious TS scraping.

Node-native crawler from the Apify team. Built-in queues, retries, proxy rotation, headless browser pool — production patterns out of the bo…

byscrapy

scrapy

Python's veteran. Battle-tested middleware + pipeline architecture.

The Python scraping veteran. Mature ecosystem, plugins for everything (caching, proxies, middlewares), and a years-honed pipeline architectu…

bygocolly

colly

Go option. Single binary, low memory, runs on a ₹500 VPS.

Go's answer to Scrapy. Built-in rate limiting, caching, parallelism, and storage backends. Compiles to a single binary which makes deploymen…

Browser automation

2 repos

bymicrosoft

playwright

JS-heavy sites, anti-bot bypass, multi-browser. Also your E2E test runner.

End-to-end browser testing. ~65k stars. Beat Cypress on speed, parallelism, and multi-browser support. Default for E2E testing in 2026.…

bypuppeteer

puppeteer

Chrome-only, thinner abstraction. Use when Playwright is too much.

Chrome-only browser automation from Google. Slightly more raw than Playwright with fewer batteries included, but lighter weight and battle-t…

3 more layers · 4 more repos · members only

Plain HTML parsing1 repo
Storage + queue1 repo
Feed to your AI agent2 repos

4 more curated repos · unlock full access · members only

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How to build build a web scraper with AI

The 4-step AI workflow

The AI agents are good at code. They're bad at deciding what stack to use. This bundle does the second part. You bring the agent.

1
Ideate with ChatGPT or Claude.ai (web)
Paste your idea: “I'm building build a web scraper. Help me sharpen the product spec — features, edge cases, MVP scope.” Iterate for 10-15 minutes until you have a clear one-page brief.
2
Pick your coding agent
For this kind of bundle, we recommend Claude Code — Sonnet 4.6/4.7 handles full-stack multi-file reasoning best. See the install guide → Cursor and Codex are also great; pick the one you already pay for.

Feed this bundle to the agent

Open Claude Code / Cursor / Codex in an empty folder, then paste:

I'm building build a web scraper. Use this bundle as the source of truth for the stack:
https://stackpicks.dev/build/web-scraper

Brief from my product spec:
[paste your brief from step 1]

Follow the bundle order strictly:
  1. LLM-grade scrapers
  2. Production crawlers
  3. Browser automation
  4. Plain HTML parsing
  ...

Stop and confirm with me after each layer.

4
Wire one layer at a time, commit between each
Don't let the agent install everything before the first git commit. One layer = one commit. Catches drift early, easy rollback.

Beyond the bundle

1Ship the boring version first. The bundle above is the maximalist list. For an MVP, start with 60% of these and add the rest when real users ask.
2Deploy early. Push to Railway / Vercel after layer 02 (auth) — not after layer 09. Production breaks differently than localhost.
3Read CLAUDE.md / .cursor/rules in this repo for the project conventions your AI agent should follow.
4Iterate on the take. If a repo here doesn't fit your specific use case, tell us — contact — and we'll add a better one within 60 minutes.