Use Crawlora's Adidas API endpoints to extract supported public Adidas data as structured JSON. This documentation covers 7 active endpoints including Get an Adidas product, Get Adidas review topics for a product, Get Adidas product reviews, Search or browse Adidas products, and related APIs. Developers can use these endpoints for monitoring, enrichment, research dashboards, internal data pipelines, and agent-native workflows without maintaining platform-specific scraping code. Each endpoint page documents authentication, request parameters such as product_id, model_number, store_id, lat, cURL examples, response examples, response schemas, errors, credit cost, and Playground testing. Send requests to the Crawlora API with an x-api-key header, review usage and plan limits on pricing, and test safe sample requests in Playground before moving the workflow into production.
Choose an endpoint, send an authenticated request with the x-api-key header, and inspect the normalized JSON response. The examples below use the public Crawlora API base URL and the same endpoint catalog used by Playground.
Base URL
https://api.crawlora.net/api/v1
Auth header
x-api-key: $CRAWLORA_API_KEY
Primary endpoint
GET /adidas/search
Credit cost
3 credits/request
curl -X GET "https://api.crawlora.net/api/v1/adidas/search?query=coffee&page=1" \
-H "x-api-key: $CRAWLORA_API_KEY"
These endpoint cards are generated from the active Crawlora endpoint catalog, including method, path, auth mode, credit cost, parameter summary, docs, and Playground links.
GET/adidas/productapiKey3 credits/request
Get an Adidas product
Returns normalized product-detail data for one Adidas SKU: name, brand, category, description, pricing (current/standard/sale), images, and every purchasable size variant. product_id is the Adidas SKU (e.g. JI0397), taken from a search result's products[].id field or the trailing segment of an Adidas product page URL. An unknown product_id returns a not-found error.
Returns the topics an Adidas product model's customer reviews can be filtered by -- the "filter by topic" chips the product page shows, commonly satisfaction, comfort, color, purchase, fit, appearance, quality and style. Feed a topics[].topic value back to /adidas/product/reviews as its topic parameter to return only reviews about that aspect. The topic vocabulary is per model, not a fixed list: a shoe exposes fit and comfort topics that an accessory does not, so read it per model rather than hard-coding it. model_number is the Adidas model number (e.g. SAMBAU2312) -- NOT the SKU: take it from an adidas_search result's products[].model_number field. A model with no reviews, including a well-formed but unrecognized model_number, returns an empty topics list rather than an error. Note the label field is a display form of topic, not translated text: Adidas returns the same English values for every locale on this route.
Returns one page of customer reviews for an Adidas product model, plus the model's rating summary: overall rating, star histogram, percentage of reviewers who recommend it, per-attribute averages (Size, Width, Comfort, Quality with their own scale labels), and Adidas's AI-generated review digest when one exists. Each review carries the rating, headline, body, author nickname, purchased colorway, helpful/not-helpful vote counts, badges, customer photos, and submission time. model_number is the Adidas model number (e.g. SAMBAU2312) -- NOT the SKU: take it from an adidas_search result's products[].model_number field, which is a different value from products[].id. Reviews are returned 10 per page. Reviews are scoped to review text written in the requested locale's language, and most of the US catalog's reviews are English, so a non-English locale commonly returns rating statistics and a localized summary with an empty reviews list. A model with no reviews -- including a well-formed but unrecognized model_number -- returns an empty reviews list rather than an error, because Adidas answers 200 with a zero count rather than 404.
Searches Adidas.com product listings by keyword, or browses a category listing by taxonomy slug, with real pagination and sort options. Exactly one of query or category is required. Returns normalized product summaries (title, price, rating, images, color variants) plus facet filter groups, sort options, and (for category browse) a breadcrumb trail. Keyword search is best-effort relevance, not a guaranteed match: an obscure keyword returns whatever Adidas's own search index surfaces. A genuinely empty keyword search returns an empty product list, and requesting a page beyond the available result pages (or an unknown category) returns a not-found error. Category values are the path segment after /us/ in an Adidas category URL (e.g. women-athletic_sneakers); they can also be read from the url fields of a search/category response's own filters and breadcrumbs. Facets are applied by composing them into the category slug rather than by a separate parameter: each filters[].values[].slug is a token you splice into the category value (e.g. category=women-black-athletic_sneakers applies the Color=Black facet, and category=women-athletic_sneakers-prime applies Shipping=PRIME). Use filters[].key (the facet's stable name, e.g. searchcolor) rather than filters[].id, which is an opaque per-deployment UUID that cannot be used to build a request. Note the token's position within the slug varies by facet, so compose from a slug you have seen rather than assuming a fixed order.
Returns normalized detail for one Adidas retail store: name, status, phone, description, full address, coordinates, opening hours, and in-store services (e.g. Click and Collect, Free Wi-Fi). store_id is the numeric Adidas store id, taken from an adidas-stores response's stores[].id field. An unknown store_id returns a not-found error.
Returns Adidas physical retail stores nearest to a coordinate, sourced from Adidas's own store-finder API: name, address, phone, coordinates, distance in miles, opening hours, and in-store feature flags. lat and lng are both required. Adidas's upstream ignores a caller-supplied radius and returns the nearest ~20 stores ordered by distance. A location with no stores returns an empty list rather than an error.
Returns the top matching products for a partial query, the same search-as-you-type preview Adidas's own search box shows. Adidas has no separate term-autocomplete index, so each suggestion is a matching product (id, title, url, image, price) rather than a completed search phrase. Best-effort relevance: an obscure query returns whatever Adidas's own search surfaces.