Apify

Run an Apify actor from inside the table and land its dataset as rows.

What it does

Apify is a library of thousands of ready-made scrapers (“actors”). This action executes one in the background and fetches its results when they are ready — so anything with an actor becomes a Sync GTM lead source, without code.

Apify is a SyncGTM native integration — it runs on SyncGTM credits, so there is no external Apify account or API key to connect.

Reach for it when the source you want does not have a built-in scraper in the Scrape group. Where a built-in exists, use it — it is configured and maintained for you.

How to use it

1. Find the actor on Apify and note its ID and the shape of its input
2. Open the table and click Table → Import → Apify
3. Paste the actor ID and its input JSON
4. Run it — the actor runs asynchronously, so results land when the run finishes
5. Map the dataset fields to columns and click Create Rows

Schedule Auto Import

Turn on Enable Auto Import in the import panel to re-run the actor on a frequency instead of only when you click it. Each run reuses the saved actor ID, input JSON and column mapping, and appends new dataset items to the same table.

• Run it manually once and confirm the mapping first — a schedule built on an unverified mapping repeats the same mistake every run
• Match the frequency to how fast the actor’s target site changes, and check the output after any gap in use — actors break when the site they scrape changes, and a broken schedule fails quietly
• Dedupe on whichever dataset field is the item’s stable ID
• Cap the result limit before you turn it on — every automatic run spends credits exactly as a manual one does
• Pair it with Auto Run so rows a scheduled run creates enrich themselves

Every schedule appears under Manage scheduled imports, where you can pause, edit, run now, or delete it.

Common use cases

• Scrape a source with no built-in action. A niche marketplace, a job board, a regional directory.
• Reuse a scraper you already run elsewhere. Same actor, same input, now feeding a table.
• Sweep a launch site. Product Hunt, Hacker News and similar all have maintained actors.
• Handle an odd page structure. An actor written for one site beats a generic scraper on it.

Best practices

• Test the actor on Apify first and copy the input JSON that worked — debugging input shape is much slower from inside a table
• Cap the actor’s result limit on the first run; some actors will happily return tens of thousands of items
• Keep the raw dataset column and create typed columns from the fields you need
• Expect an actor to break when its target site changes — re-run a small batch after any gap in use
• Check the target site’s terms and your local law before running at volume — see Web Scrapers

Where to next

• Apify integration — the same action as an enrichment column
• Parse JSON — break the raw dataset into usable fields
• Web Scrapers — the built-in scraper catalogue