Classify website changes

When Apify detects a change on a monitored page, have Hugging Face judge whether the change is significant before anyone is alerted.

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See all Apify + Hugging Face integrations or talk to a human

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How Knit builds this workflow

The Agent researches both APIs and wires the trigger on one side to the action on the other — no pre-built connector required. Here is the vocabulary it has to work with.

Apify Explore Apify →

Triggers

Scrape completed

Fires when a scrape, crawl or robot run finishes in Apify.

New result

Fires when Apify returns a new batch of extracted data.

Website change detected

Fires when Apify detects a change on a monitored page.

Actions

Scrape a URL

Extracts the content of a web page with Apify.

Crawl a website

Starts a crawl of a whole site in Apify.

Run a scraper

Runs a saved scraper, task or robot in Apify.

Hugging Face Explore Hugging Face →

Triggers

New result returned

Fires when Hugging Face returns a completion, prediction or other result.

Model run failed

Fires when a request to Hugging Face errors or times out.

Fine-tune job completed

Fires when a fine-tuning job in Hugging Face finishes.

Actions

Generate a completion

Sends a prompt to Hugging Face and returns the model's response.

Run a model on an input

Sends text, an image or other data to Hugging Face and returns the model's output.

Classify or extract data

Uses Hugging Face to classify or extract structured data from text or images.

Summarize each scraped page When a scrape completes in Apify, send the page content to Hugging Face and store the summary it returns. See details

Extract structured fields from results When Apify returns a new result, ask Hugging Face to pull out the fields you need, such as product name and price. See details

See all Apify + Hugging Face integrations → · Browse the full workflow library →

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FAQ

Common questions

Can Knit build “Classify website changes” between Apify and Hugging Face?

Yes — describe it in the box above and Knit's Integrations Agent researches Apify and Hugging Face's public API docs (or your own uploaded docs) and builds a working workflow, whether or not either app already has a pre-built connector.

How long does it take to build?

Minutes to a first working version, not weeks — you test it against real data before it goes anywhere near production.

Why send scraped pages to an AI model?

Scraped HTML and text are noisy. Passing each Apify result to Hugging Face lets you pull out prices, names or key points in a consistent shape instead of cleaning the data by hand.

Can the model decide which pages to scrape next?

Yes. You can have Hugging Face produce a list of URLs or search terms, then run a scrape in Apify for each one.