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

The Problem With Getting Sheet Data In and Out of Linkup

You have a Google Sheet full of data — company names, blog topics, journalist contacts, market segments, competitor URLs. You need Linkup to search the web for each one and write the answers back, in a way that doesn't require you to run the API manually for every row.

Linkup is good at returning structured, sourced answers to natural language queries against live web content. But getting it to act on a column of 40 rows and write results into the sheet beside them is a different problem entirely. The default flow is: copy one value, call the API, paste the result, repeat.

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

Method 1: Manual Copy-Paste

The default. Open Linkup's interface or API docs, copy a topic from column A, run the search, copy the result, paste it into column B. Move to row 2. Repeat.

For a one-time lookup on three or four rows, this is fine. You get your answer, you move on.

But Linkup's value is in bulk enrichment — running the same research query across every row in a sheet. The moment you have 30 blog topics or 50 company names that need sourced summaries, the row-by-row paste routine becomes the majority of your afternoon. And the next time you refresh the sheet, you start over.

Method 2: Zapier or Make

Both automation platforms have Linkup connector options. You can wire up a trigger on a sheet row addition or a schedule, send the value to the Linkup API, and write the response back into the sheet.

Quick check before you read further — do you know what a webhook trigger is? A field mapping? An API response parser? If those don't mean anything to you right now, skip down to Method 3 or 4. This path will ask for all of that before you get a single result.

If you're still here: the automation works. You pick the trigger, authenticate to Linkup, map the input field, configure what the API should return, and map the output back into the right column. When it fires on a new row, it runs the search and writes the result. That part genuinely works.

The catch is what the structure doesn't do.

A row-trigger automation is not a bulk enrichment tool.

If you have 40 existing rows that need Linkup summaries today, the automation covers new rows going forward. Backfilling the existing 40 means either triggering manually row by row or building a separate batch flow.

And the row-by-row model means 40 API calls, 40 trigger fires, and a task history that becomes impossible to read when row 17 returns an unexpected format and the rest silently write partial data.

You probably just need the summaries for the topics in column A. You probably have no idea how to build a Zap that handles JSON parsing and partial-failure retry logic — and honestly, you shouldn't have to. So you send it to whoever on your team handles automations and wait for them to have time. If they haven't got to it by Thursday, you're back to copy-paste anyway.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best option for repeatable spreadsheet ↔ API workflows was a category of add-ons that let you manually configure column mappings and save query templates. You picked your range, tagged the input field, mapped the output columns, and ran it.

That was a genuine improvement over doing it row by row. The config was reusable, the output was consistent, the team wasn't reinventing the format each time.

But the thinking was still yours. You wrote the query template. You mapped which column held the input. You set the filter parameters. You decided what to do when a row came back empty. The tool moved data through, but you were the logic layer. And if column A got renamed or a new research question came up, you rebuilt the config.

This is the previous generation. It worked, but it put the operator in the middle of everything.

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're looking at, and through its built-in Linkup integration it can search the web for each row and write the results back — for you. No query templates, no column mapping config, no running the API by hand. You just ask.

Example 1: Enrich a column of topics with sourced summaries

For each topic in column A, search the web using Linkup and write a one-sentence sourced summary of the current landscape into column B, with the top source URL in column C.

SheetXAI runs a Linkup search for each row, extracts the summary and the primary citation, and writes them into columns B and C. The whole sheet is processed in a single pass.

Example 2: Look up funding data for a list of companies

For each company name in column A, use Linkup to search for their latest funding round and write the amount, date, and lead investor into columns B, C, and D.

The pattern: instead of researching each company separately and then organizing the output into columns, you describe the full enrichment job in one prompt. SheetXAI handles the per-row execution and the field placement inline.

Try It

Get the 7-day free trial of SheetXAI and open any Google Sheet with a column of topics, companies, or URLs, then ask it to enrich the sheet using Linkup. The Linkup integration is included in every SheetXAI plan.