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

The Problem With Getting Sheet Data In and Out of AgentQL

You have a Google Sheet full of URLs — competitor landing pages, company domains, career portals. You need structured data pulled from each one: prices, headlines, job titles, employee counts. The kind of data that lives on public web pages but doesn't come with a download button.

AgentQL is good at querying any website with natural language and returning clean JSON. But closing the loop between an AgentQL extraction and your spreadsheet takes more plumbing than it should. The default path is: run the query in AgentQL, copy the JSON output, parse it somewhere, paste the values into the right columns, repeat for the next URL.

Below are the four common ways teams handle this. Only the last one scales.

Method 1: Manual Copy-Paste

The default. You run an AgentQL query against a single URL, get back a JSON object with the fields you asked for, and then manually copy each value into the right cell. Column B gets the price. Column C gets the plan name. Column D gets the CTA text.

That works fine when your list has six URLs on it.

The moment it becomes 50 URLs — or 200 — the session looks like this: open the AgentQL playground, paste URL one, run the query, inspect the JSON, find the field, copy the value, switch tabs, find the row, paste into column B, go back, find the next field, copy, paste into column C. Repeat. For every row.

By URL eighteen you have made a mistake somewhere. By URL forty you have lost track of which rows you already did. The data is probably accurate but you will never be certain.

Method 2: Zapier or Make

Both platforms have AgentQL connector support. You can set up a scenario that triggers on a new row in your sheet, passes the URL from column A to AgentQL, and writes the response values back into the sheet.

Before you keep reading — do you know what a webhook trigger is? A JSON path expression? Field mapping between a dynamic API response and fixed spreadsheet columns? If those concepts aren't already part of your toolkit, this path is going to stall before it gets interesting. Method 4 is a better use of your next thirty minutes.

For those who are still here: the flow works. AgentQL returns a structured JSON object, the automation maps specific fields to specific columns, and you get consistent output. The challenge is everything upstream of that: choosing the right trigger, handling the response shape when AgentQL returns a nested object instead of a flat one, writing a path expression to extract the right value, and debugging what happened when row 43 returned an unexpected schema and the automation wrote nothing.

A trigger-per-row automation is also not the same as a bulk operation.

Sending 200 URLs through a Zap means 200 separate API calls, 200 trigger fires, and a task log that's effectively unreadable when something goes wrong partway through.

You probably just need the pricing data from that list of competitor sites. You probably have no idea how to build an automation that handles dynamic JSON field extraction at scale — and there's no particular reason you should. So you push this to whoever on your team builds these things, and now you're waiting on a Slack reply. If they reply at all before your deadline.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best option for repeatable spreadsheet ↔ web-scraping workflows was a category of add-ons that let you configure URL columns, field mappings, and saved templates. You pointed the tool at a column, defined the fields you wanted extracted, and ran it.

That was a real step up from manual copy-paste. Output was consistent, configs were reusable, and the team didn't have to rebuild the extraction logic from scratch each time.

But you were still responsible for the field mapping, the XPath or CSS selectors, the schedule, the conditional logic about which rows to include, and the column layout. The tool got the data through, but the structural thinking was still on you. And when a competitor redesigned their pricing page, your selector broke until someone went back in and fixed it.

This is the previous generation. It worked, but it asked a lot of the operator.

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 AgentQL integration it can query websites for you and write the results back into the right columns. No selector configuration, no automation glue, no copying JSON by hand. You just ask.

Example 1: Bulk-scrape competitor pricing from a URL list

Scrape the pricing table, plan names, and monthly prices from each URL in column A using AgentQL and write the results into columns B, C, and D

AgentQL queries each URL in sequence, extracts the pricing fields, and writes the values row by row. Column B gets the plan name, column C the monthly price, column D the CTA text — matched to the originating URL.

Example 2: Extract headline and subheadline from product pages

For each product URL in column A, extract the headline, subheadline, and primary CTA button text using AgentQL and fill in columns B through D

The pattern: instead of exporting data first and then cleaning it into a spreadsheet format, you ask for both the extraction and the placement in one prompt. SheetXAI handles the mapping inline.

Try It

Get the 7-day free trial of SheetXAI and open any Google Sheet with a column of URLs, then ask it to scrape and populate the columns next to them. The AgentQL integration is included in every SheetXAI plan.