How to Connect Scrapfly to Google Sheets (4 Methods Compared)

The Problem With Getting Sheet Data In and Out of Scrapfly

You have a Google Sheet full of URLs — competitor product pages, SaaS pricing sites, event listings, news articles. Scrapfly can render those pages with JavaScript, rotate proxies, bypass anti-bot measures, and return clean structured data. The problem is that there is no direct path from a column of URLs in your sheet to a Scrapfly API response and back into adjacent columns.

The default workflow looks like this: you copy URLs from your sheet into a script or API client, run the scrape job, download or parse the results, then paste the extracted fields back in. That is fine the first time. It becomes a two-hour task every time you need an update.

Below are the four ways teams handle this connection. Only the last one gets you there without code.

Method 1: Manual Copy-Paste

The manual flow is specific enough to be worth describing. You open your sheet, copy the URL list, paste it into your Scrapfly dashboard or a script, trigger the scrape, wait for the job to finish, parse the returned JSON, pull out the fields you care about (price, title, status code, whatever), format them into rows, then paste them back into your sheet — column by column.

For a one-off competitive check, that is tolerable. Fifty URLs, done once, probably under an hour.

The grind starts when someone asks for it again next Monday. And the Monday after. The copy-paste steps don't compress with repetition — they stay exactly as long, and your tolerance for doing them drops steadily. By the fourth run you are copying data in chunks so you don't lose your place, and the numbers are probably a day stale by the time they're back in the sheet.

Method 2: Zapier or Make

Both platforms can reach the Scrapfly API. You can build a trigger that fires when a new row appears in a sheet, pulls the URL from column A, sends it to Scrapfly, and writes the returned fields back into the same row.

A quick check before you keep reading: do you know what a webhook trigger looks like? Have you parsed a JSON response inside an automation platform before? Does "field mapping" sound familiar, or is that still fuzzy? If any of those feel like someone else's job, Method 3 or 4 will get you there faster. This path assumes you are comfortable building and debugging API-connected automations.

If you are still here — the flow works. Zapier and Make both have HTTP or webhook action steps that can call the Scrapfly API. You wire the URL from the sheet into the request body, parse the response, and map the fields you want back into the sheet.

The structural ceiling hits fast with scraping workflows.

A trigger-per-row automation means one API call per row. Fifty URLs means fifty separate trigger fires, fifty separate API calls, and a task history that gets messy when row 23 times out and the rest proceed silently without it.

You probably just need to scrape your competitor price list and get the numbers into the sheet. You probably have no idea how to write a JSON path expression to extract a price from a scraped DOM response — and why would you? So you either spend an afternoon learning it, or you push it to whoever on your team handles API integrations, and now you are waiting on Slack for a Zap that may or may not work by end of week.

Once you need to filter which URLs get scraped, join the results against another tab, or handle pages that return different HTML structures — you are past what a simple automation template can do cleanly.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best option for repeatable spreadsheet-to-API workflows was a category of add-ons that let you configure an API endpoint, map response fields to columns, and save that config for repeated runs. You pointed it at Scrapfly, mapped the fields you wanted, saved the template, and ran it on demand.

That was a genuine step forward. Configs were reusable, the team could hand them off, and the output was consistent across runs.

But you still owned everything that required judgment. Which URLs get included in this run? What do you do when a page structure changes and the field mapping breaks? How do you handle JavaScript-rendered prices that don't appear in the initial HTML? The tool moved the data. The thinking — the conditional logic, the exception handling, the field validation — was still entirely on you. And any structural change to your sheet sent you back into the config editor.

This generation of tool earned its place. But it drew a hard line at the edge of what template-based automation could do.

The Easy Way: Using SheetXAI in Google Sheets

There is a different way entirely. SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet, understands what you are looking at, and through its built-in Scrapfly integration it can scrape, crawl, and extract data for you. No template configuration, no automation glue, no copying URLs into an API client. You just ask.

Example 1: Scrape competitor prices from a URL list

For each URL in column A, use Scrapfly to scrape the page with JavaScript rendering enabled, extract the product price and stock status, and write them into columns B and C

SheetXAI calls Scrapfly for each URL, handles the JS rendering, parses out the price and availability fields, and writes the results back into the sheet — row by row, without you touching the API.

Example 2: Crawl a site and extract all page URLs for an SEO audit

Create a Scrapfly crawler for the website in cell A1 with a limit of 500 pages, retrieve all crawled contents, and write each page's URL and title into columns A and B of a new sheet called Crawl Results

SheetXAI kicks off the crawl, waits for completion, and populates a new tab — no API key juggling, no JSON parsing, no waiting on a Zap.

Try It

Get the 7-day free trial of SheetXAI and open any Google Sheet with a list of URLs or domains, then ask it to scrape, crawl, or extract data using Scrapfly. The Scrapfly integration is included in every SheetXAI plan.