How to Connect Scrapfly to Excel (4 Methods Compared)

The Problem With Getting Sheet Data In and Out of Scrapfly

You have an Excel workbook 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 workbook to a Scrapfly API response and back into adjacent columns.

The typical Excel workflow looks like this: you export your URL list to CSV, drop it into a script or API client, run the scrape, download the results, parse the JSON, format the fields into rows, then import or paste them back into your workbook. That is survivable once. It becomes a recurring tax on your time every time the data needs refreshing.

Below are the four ways teams approach this. Only the last one removes the manual steps entirely.

Method 1: CSV Export and Re-Import

Most people who use Excel with an external API like Scrapfly go through CSV as the middle layer. Export the URL column, run the scrape against that file, get the results back as a CSV or JSON, massage them into the right column order, and import back into the workbook.

For a one-off project this is workable. The friction is acceptable when you only do it once.

The problem is that "one-off" rarely stays that way. Your manager asks for an updated competitive pricing snapshot next week, and the week after. The CSV shuffle is now a recurring calendar item, the workbook is always one CSV cycle behind, and you have gotten very good at a workflow that should not exist.

Method 2: Power Automate

Power Automate can call external APIs, including Scrapfly, via HTTP action steps. You can trigger a flow on a schedule, read URLs from an Excel table, call Scrapfly for each one, and write results back.

Before you go further — are you comfortable with HTTP action configuration in Power Automate? Do you know how to parse JSON output and map individual fields back into an Excel table row? Have you worked with dynamic content expressions before? If any of those are unfamiliar territory, skip ahead to Method 4. Power Automate is not the shortest path for non-technical users.

If you are still here — the plumbing works. Power Automate has enough primitives to build this: a scheduled trigger, an Excel list-rows step, an HTTP POST to Scrapfly, a parse-JSON step, and a write-back step. It takes some wiring, but the flow can run on a schedule once it is built.

The per-row execution model creates problems at scale.

A flow that processes one URL at a time means one run cycle per URL. A 50-row list means 50 separate HTTP calls within a single flow run. When Scrapfly returns a non-standard response for row 17 and the rest of the rows proceed without surfacing that failure, you end up with partial data and no obvious signal that something went wrong.

You probably just need the competitive pricing table updated before the Thursday business review. You probably have no idea how to configure a dynamic content expression to extract a price field from a scraped JSON response — and that is a perfectly reasonable place to be. So you either block a day to learn Power Automate's expression syntax, or you find someone on the team who knows it, which means the data sits stale until they get to it.

When you need conditional logic — only scrape URLs where column C says Active, join results against a product reference sheet, flag pages that returned 403s — you have moved beyond what a simple flow template can handle without significant customization.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the most practical option for repeatable Excel-to-API data pulls was a category of add-ins that let you configure an API connection, map response fields to workbook columns, and save that configuration for repeated runs. You set up the Scrapfly endpoint, tagged your columns, saved the template, and hit run whenever you needed fresh data.

That was a real improvement over CSV exports. Configs persisted, the process was repeatable, and the output landed in the right columns without reformatting.

But the add-in drew a hard boundary at judgment calls. Which rows get included? What happens when a page structure changes and your field mapping returns empty cells? How do you handle pages where the price loads via JavaScript after the initial HTML response? The add-in executed the call. Every decision about what to include, what to skip, and what to do when something breaks was still yours to make manually. A workbook restructure meant going back into the config to remap every column.

This was the right answer for a while. It asked a lot of the person operating it.

The Easy Way: Using SheetXAI in Excel

There is a different way entirely. SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook, 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 flow engineering, no CSV round-trips. 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 workbook — no API wrangling required.

Example 2: Crawl a site and export all discovered URLs for a technical 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 worksheet. No JSON parsing, no connector configuration, no waiting on a flow to finish.

Try It

Get the 7-day free trial of SheetXAI and open any Excel workbook 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.