Woolworths Scraper

Scrape Woolworths Australian grocery products, prices, and category taxonomy from public pages and export Woolworths data to CSV, JSON, or Excel. This Woolworths API alternative turns repeatable inputs into structured results for Australian supermarket assortment and price monitoring.

Each dataset row represents a public result or a documented alternate record type. The output includes source identifiers and links where available, so you can verify a row, compare later runs, and distinguish missing source data from an omitted column.

What data can you extract from Woolworths?

The dataset schema defines every field below. Examples come from one successful published run; null means that field was not exposed for that particular record. Availability may differ by input mode, page, and record type.

Field	Description	Example
recordType	Product or category row.	category
id	Product stock code.	null
sku	Product stock code, repeated for catalog matching.	null
barcode	Product barcode when supplied.	null
name	Product display name.	null
brand	Product brand.	null
slug	Product URL slug.	null
sourceUrl	Direct page URL.	https://www.woolworths.com.au/shop/browse/specials
price	Regular price in Australian dollars; prior price during a special.	null
discountPrice	Current lower price during a special.	null
currentPrice	Current selling price in Australian dollars.	null
currency	Currency code.	null
isOnSpecial	Whether the source marks the product as on special.	null
savingsAmount	Source savings amount during a special.	null
unitPrice	Price per unit of measure.	null
unitPriceMeasure	Unit-price measure, such as 1L or 100G.	null
unitPriceText	Displayed unit-price text.	null
size	Pack size.	null
unit	Selling unit.	null
isAvailable	Availability flag from the source.	null
isInStock	Stock flag from the source.	null
isPurchasable	Purchase eligibility flag.	null
image	Primary product image URL.	null
images	Product image URLs.	null
description	Product description.	null
ingredients	Ingredient statement when supplied.	null
nutrition	Nutrition entries; richer values are available for item pages.	null
allergens	Allergen statement when supplied.	null
dietaryClaims	Dietary statements when supplied.	null
countryOfOrigin	Country-of-origin statement when supplied.	null
healthStarRating	Health star rating supplied by the source.	null
specifications	Selected public product specifications.	null
categoryId	Category node ID.	specialsgroup
categoryName	Category name.	Specials
categoryPath	Category names from top level to the current node.	["Specials"]
rating	Average product rating.	null
reviewCount	Number of product reviews.	null
isMarketProduct	Whether the product is sold in the marketplace.	null
seller	Marketplace seller when supplied.	null
categorySlug	Category URL slug.	specials
parentCategoryId	Parent category node ID.	null
categoryLevel	Category tree level.	1
productCount	Source product count for the category.	0
isSpecialCategory	Whether the category is a specials group.	true

Use the identifier and URL fields as your join keys before comparing snapshots. Fields that describe a page, search, category, author, or tournament establish where the record came from; keep them when you export a subset. Numeric values and boolean flags reflect the public page at collection time, not a permanent claim about the underlying item.

How to scrape Woolworths

1. Open the actor input form and choose a narrow public source: a search phrase, page URL, item URL, or ID supported by the input fields below.
2. Set the relevant per-source page or result limit and the total item limit. Start with a small sample to check which optional fields the public source exposes.
3. Run the actor. Its dataset contains one structured row per saved result; inspect the first rows and their source links for the chosen input.
4. Download the dataset as JSON, CSV, or Excel. Preserve identifiers when combining runs so repeated results can be deduplicated.

For this actor, operations return different record types: product rows have price and SKU fields, while category rows describe the browse tree. The latest successful published sample contains a category row, so the example below shows that record type.

Input

Use only the parameters relevant to your collection mode. An omitted optional filter uses the schema default shown here; an empty array, zero, and an omitted value can have different meanings, so keep intentional settings in your saved input.

| Parameter | Type | Default | Description | | --- | --- | --- | | operation | string | search | Choose a product search, category shelf, single product, or category directory. | | query | string | No default; example milk | Grocery keyword or brand for search. | | categoryId | string | None | Category node ID from a categories result, used for a category shelf. | | productId | string | None | Numeric stock code for a single product. | | itemId | string | None | Alternative name for productId. | | url | string | None | Product page URL, alternative to productId. | | page | integer | 1 | One-based starting page for search or category shelves. | | maxPages | integer | 1 | Maximum consecutive pages to collect from the starting page. Each page contains up to 36 products. | | limit | integer | 0 | Maximum dataset rows across the run. Zero means no row cap. | | proxyConfiguration | object | {"useApifyProxy":true} | Proxy settings for source requests; the platform proxy is used by default. |

Example input based on the published golden run (long URL lists are shortened):

{
  "operation": "search",
  "query": "milk",
  "page": 1,
  "maxPages": 1,
  "limit": 36
}

The example is a starting shape, not a guarantee of a particular result count. Source inventory and page accessibility change. When you need repeatable comparisons, save the exact input JSON with the run date and inspect the returned source or record-type field.

Output example

The following is one real item from a successful published dataset. Long text and media arrays are shortened for readability; the actual dataset keeps the original values and all schema fields.

{
  "recordType": "category",
  "categoryId": "specialsgroup",
  "categoryName": "Specials",
  "categorySlug": "specials",
  "categoryLevel": 1,
  "categoryPath": [
    "Specials"
  ],
  "productCount": 0,
  "isSpecialCategory": true,
  "sourceUrl": "https://www.woolworths.com.au/shop/browse/specials"
}

This row illustrates the observed output structure, including its identifiers and public links. Empty values elsewhere in the dataset should be interpreted field by field; a field shown in this example is not promised for every result.

Use cases

• Retail analysts can follow grocery price and promotion changes for selected products.
• Merchandising teams can map the public category tree and product placement.
• Brands can compare advertised pack sizes, unit prices, and special flags.
• Food researchers can collect public ingredient and nutrition fields where provided.

The strongest analyses keep source context. A field such as price, rating, engagement, or rank has meaning only with its associated item, query, date, and public URL. Keep the raw export and create a separate cleaned view for charts or alerts.

How much does it cost to scrape Woolworths?

The price is $1.00 per 1,000 saved results. Platform usage is included. The charge scales with output rows, so a restrictive filter or inaccessible page can produce fewer billable results than the requested maximum.

• 100 results: $0.10. This is useful for checking a small cohort and confirming which optional fields are present.
• 1,000 results: $1.00. This is the reference price for a larger export.
• 5,000 results: $5.00. Reaching this size may require multiple focused sources or scheduled runs, depending on public inventory and source caps.

Compute any other estimate as saved result count × $1.00 / 1,000. A maximum input is a ceiling, not a purchase of that many rows. For planning, use the actual saved-item count from an initial representative run.

Tips for best results

Choose search for a shopper phrase, category for a shelf, product for a known item, or navigation for taxonomy. Compare only product rows when studying prices; use unit-price fields for differently sized packs.

Collect a small baseline first, record the exact input and date, and inspect both a typical row and a sparse row. Expand by adding focused sources instead of assuming one broad input can reveal the full public inventory. When comparing two runs, match stable IDs or canonical URLs and use the same filters so changes reflect the source rather than a changed query.

Export the full JSON when nested arrays or objects matter. CSV and Excel are convenient for sorting and joins, but nested structures may need flattening before spreadsheet analysis. Keep numeric fields numeric and preserve source URLs as text; do not infer a zero from a null.

Limits

A category row has no product price by design. Product descriptions, ingredients, nutrition, ratings, and stock fields are populated only if the selected route exposes them. Pricing and availability can change.

Public pages can change, disappear, or expose different fields for different records. The schema is a list of possible output columns, not a promise that each column is filled in each row. The maxItems-style input limits cap saved results; they do not bypass source pagination, public visibility, or a site-specific result ceiling.

Treat a saved result as a snapshot. If a later run returns fewer rows, first compare the input, source access, and public inventory before concluding that the underlying market changed. If you need an audit trail, retain the source URL, stable identifier, and run timestamp with the export.

Use with AI agents (MCP)

An agent can supply the documented input JSON, run this actor, and work from its dataset. Ask it to keep source links and identify null values explicitly when summarizing results. Example prompts:

Run Woolworths Scraper for the sample input above. Return the first 20 results with their source URLs and the fields needed for Australian supermarket assortment and price monitoring. Mark unavailable fields as null.

Schedule a repeat Woolworths collection with the same filters. Compare records by stable ID or canonical URL and report only new, removed, or changed public values.

FAQ

Is it legal to scrape Woolworths?

This actor collects public data. Check the site’s terms, applicable law, and the rights of people whose data appears in your export. Follow GDPR and other privacy rules where relevant; do not use personal data for misuse, intrusive profiling, or unauthorized contact.

Do I need to configure proxies?

The input includes proxyConfiguration and defaults to Apify Proxy. Usually the default is enough to start. Availability can vary by page and region; changing network settings cannot make private or login-only information public.

How fast will a run finish?

Duration depends on the number of inputs, pages, detail requests, and source responses. A small sample is the best way to measure your workload. Increase the limits gradually, then use the observed run duration for scheduling and monitoring.

Can I schedule and monitor recurring runs?

Yes. Save the input and schedule recurring runs in Apify. Monitor run status, item count, and any missing-field changes; store the run date alongside exports when comparing snapshots.

Can I export to Google Sheets or Excel?

Yes. Download CSV or Excel from the dataset, or pass JSON to a spreadsheet integration. For nested fields, flatten the specific child values you need rather than losing the original JSON.

What if a field is empty?

An empty or null field means it was not available from that public source record in that run. Check the source URL, input mode, and record type. Do not replace missing prices, counts, dates, or flags with zero unless your own analysis has a documented rule for doing so.

Why does an output row have no price?

The actor can output category records. Check recordType; category rows describe navigation and do not represent priced products.

Integrations

Use the Apify API to start runs and retrieve the dataset, webhooks to react when a run finishes, or Zapier, Make, and n8n to route records into other systems. Google Sheets supports lightweight review, while scheduled runs provide repeat snapshots. Keep the raw JSON when downstream workflows need nested fields or exact null values.

Support

Open an issue on the Issues tab; we reply within 24h and add fields on request.