N8N No-Code Web Scraping: Build an AI-Powered Spider Without Writing Complex Code

Ever feel like you're drowning in manual data collection? You know the drill—copying competitor prices one by one, checking trending topics across dozens of sites, building lead lists from endless website pages. It's mind-numbing work that eats up hours you could spend actually running your business.

Here's the thing: web scraping doesn't have to mean hiring a developer or learning to code. Tools like n8n let you build visual workflows that handle repetitive tasks automatically. And when you combine that with AI-powered extraction, you're looking at something genuinely useful—scraping that describes what you want in plain English instead of wrestling with code that breaks every time a website changes its layout.

Think about what modern web scraping actually needs to handle. You've got sites blocking scrapers left and right, JavaScript rendering everywhere, and CSS selectors that become useless the moment a developer tweaks the site design. Traditional approaches require constant maintenance—like hiring someone to fix something that keeps breaking.

That's exactly why this walkthrough matters. We're building an intelligent link-crawling bot using n8n and an AI Web Scraping API that actually works. No servers to babysit, no fragile code to debug. Just a workflow that crawls pages, follows links, and extracts data without falling apart.

By the end, you'll have a functional spider that handles everything—from finding internal links to extracting H1 tags from multiple pages. The kind of thing that used to require weeks of development, built in an afternoon instead.

Getting Your Tools Ready: Two Quick Setups

Before jumping into workflow building, you need accounts on both platforms. Both offer free tiers that'll cover everything we're doing here.

Setting Up N8N (Free Tier Included)

Head to n8n.io and grab the "Get started for free" option. Once you're in, you'll see their workflow canvas—basically your visual programming space where you drag and drop nodes instead of writing functions.

Grabbing Your API Key (1000 Free Calls)

For handling tricky websites that block scrapers, we're using an API that deals with anti-bot systems like Cloudflare. If you're serious about collecting data from real websites and not just toy examples, you need something that can actually get past the defenses sites put up.

👉 Get reliable web scraping that handles bot detection, JavaScript rendering, and proxy rotation automatically

Sign up, navigate to your dashboard, and copy your API key. You'll need it in the next steps. And yeah, keep that key private—don't paste it in public repos or share it around.

Building the Workflow: Seven Steps to a Working Spider

Now we get to the actual construction. We're building a 4-node setup that handles everything from triggering the scrape to processing results. Nothing fancy, just solid functionality.

Creating a Fresh Workflow

From your n8n dashboard, hit "Start from scratch." You get a clean canvas for building our architecture.

Adding the Manual Trigger

Click the "+" button and search for "Manual Trigger." This node lets you start your workflow with a button click—perfect for testing because you control exactly when things run. No schedules or complications, just click and go.

Setting Up the First HTTP Request

Add an "HTTP Request" node and connect it to your Manual Trigger. This is where we call the scraping API.

Click on the node to configure it. Here's what you need:

Basic Settings:

• Method: GET

• URL: https://app.scrapingbee.com/api/v1/ai

Query Parameters (add these four):

1. api_key: [YOUR_API_KEY]

2. url: https://www.scrapingbee.com/blog/

3. ai_query: Extract the main H1 heading and find links to individual blog posts

4. ai_extract_rules: {"h1_heading":{"type":"string","description":"The main H1 heading from the blog page"},"blog_post_links":{"type":"list","description":"URLs that link to individual blog posts on this site"}}

Turn OFF Headers and Body in the settings.

Why this approach? Instead of CSS selectors that break constantly, we're using natural language. Tell the scraper what you want, and AI figures out how to get it. Much more resilient when sites update their design.

Adding the Code Node for Processing

Add a "Code" node after your HTTP Request. This processes the scraped data and prepares it for the next step. Paste this JavaScript:

javascript
const links = $input.first().json.blog_post_links;
const baseUrl = 'https://www.scrapingbee.com';

const blogLinks = links
.filter(link => link.startsWith('/blog/') && !link.includes('#'))
.map(link => ({ url: baseUrl + link }))
.slice(0, 5);

return blogLinks;

This code does three things: extracts blog post links from the response, filters to get actual blog posts (no anchors or external links), and converts relative URLs to full URLs. Simple but essential.

Adding the Second HTTP Request (The Spider)

Add another HTTP Request node after the Code node. This one "spiders" through individual pages.

Configure it similarly to the first, but with one key difference—it uses expressions to dynamically scrape each URL:

Basic Settings:

• Method: GET

• URL: https://app.scrapingbee.com/api/v1/ai

Query Parameters:

1. api_key: [YOUR_API_KEY]

2. url: {{ $json.url }}

3. ai_query: Extract the main H1 heading from this blog post

4. ai_extract_rules: {"h1_title":{"type":"string","description":"The main H1 heading of this blog post"}}

The {{ $json.url }} expression tells n8n to use the URL from each item the Code node outputs. That's what makes the spidering actually work.

Testing Your Spider

Your workflow should look like: Manual Trigger → HTTP Request → Code → HTTP Request

Click "Execute Workflow" and watch it run. Always test with small batches first—scraping 5 pages to start saves you hours of debugging later if something's misconfigured.

Checking the Results

After execution, click on each node to see what happened.

First HTTP Request: Should show the main H1 heading and an array of blog post links.

Second HTTP Request: Should show 5 separate results—one for each blog post with its H1 extracted.

If you see that, congratulations. You just built a spider that scraped a main page, extracted its H1, found internal links, automatically scraped 5 individual pages, and pulled each post's unique title. All without complex libraries, proxy management, JavaScript rendering setup, or error handling code.

What Comes Next: Real Applications

You've built something that traditionally requires weeks of development. But this is just the starting point.

The same n8n approach scales to real business use cases—monitoring competitor prices, tracking job listings, updating spreadsheets automatically, sending alerts when data changes. The visual workflow you just mastered handles complex scenarios without requiring you to become a developer.

👉 Stop wrestling with brittle scrapers and let AI handle data extraction while you focus on growing your business

Whether you're scraping data into Google Sheets, monitoring Amazon prices, extracting job postings, or setting up automated competitor analysis, the foundation is the same. Describe what you want, let the workflow handle the execution, and spend your time on work that actually moves your business forward instead of babysitting fragile code.

Wrapping Up: No-Code Scraping That Actually Works

Building automated data collection used to mean hiring developers, maintaining complex code, and dealing with constant breakage when websites updated. Not anymore.

The combination of visual workflow tools and AI-powered extraction changes the game completely. No brittle CSS selectors, no maintenance nightmares—just workflows that describe what you need and handle the rest automatically. That's the kind of setup that makes sense for anyone who needs data without becoming a full-time developer.