![US Ateliernewyork Scraper avatar](https://images.apifyusercontent.com/9sJ0r1vcGERd0fQ6bbymiJQbutOYYt52nlVYRNnoq0s/rs:fill:250:250/cb:1/aHR0cHM6Ly9hcGlmeS1pbWFnZS11cGxvYWRzLXByb2QuczMudXMtZWFzdC0xLmFtYXpvbmF3cy5jb20vWWJxWXV1bGNucWk5MUVpRDEvQkJ5Z2JsUE9tM1FtMnBCdlQtYXRlbGllci1sb2dvLnBuZw.webp)
US Ateliernewyork Scraper
$9.99/month
![US Ateliernewyork Scraper](https://images.apifyusercontent.com/9sJ0r1vcGERd0fQ6bbymiJQbutOYYt52nlVYRNnoq0s/rs:fill:250:250/cb:1/aHR0cHM6Ly9hcGlmeS1pbWFnZS11cGxvYWRzLXByb2QuczMudXMtZWFzdC0xLmFtYXpvbmF3cy5jb20vWWJxWXV1bGNucWk5MUVpRDEvQkJ5Z2JsUE9tM1FtMnBCdlQtYXRlbGllci1sb2dvLnBuZw.webp)
US Ateliernewyork Scraper
$9.99/month
This actor is intended to extract data from ateliernewyork.com
You can access the US Ateliernewyork Scraper programmatically from your own applications by using the Apify API. You can choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.
1{
2 "openapi": "3.0.1",
3 "info": {
4 "version": "0.0",
5 "x-build-id": "FdbdjOnOZhITIhqm4"
6 },
7 "servers": [
8 {
9 "url": "https://api.apify.com/v2"
10 }
11 ],
12 "paths": {
13 "/acts/styleindexamerica~us-ateliernewyork-scraper/run-sync-get-dataset-items": {
14 "post": {
15 "operationId": "run-sync-get-dataset-items-styleindexamerica-us-ateliernewyork-scraper",
16 "x-openai-isConsequential": false,
17 "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
18 "tags": [
19 "Run Actor"
20 ],
21 "requestBody": {
22 "required": true,
23 "content": {
24 "application/json": {
25 "schema": {
26 "$ref": "#/components/schemas/inputSchema"
27 }
28 }
29 }
30 },
31 "parameters": [
32 {
33 "name": "token",
34 "in": "query",
35 "required": true,
36 "schema": {
37 "type": "string"
38 },
39 "description": "Enter your Apify token here"
40 }
41 ],
42 "responses": {
43 "200": {
44 "description": "OK"
45 }
46 }
47 }
48 },
49 "/acts/styleindexamerica~us-ateliernewyork-scraper/runs": {
50 "post": {
51 "operationId": "runs-sync-styleindexamerica-us-ateliernewyork-scraper",
52 "x-openai-isConsequential": false,
53 "summary": "Executes an Actor and returns information about the initiated run in response.",
54 "tags": [
55 "Run Actor"
56 ],
57 "requestBody": {
58 "required": true,
59 "content": {
60 "application/json": {
61 "schema": {
62 "$ref": "#/components/schemas/inputSchema"
63 }
64 }
65 }
66 },
67 "parameters": [
68 {
69 "name": "token",
70 "in": "query",
71 "required": true,
72 "schema": {
73 "type": "string"
74 },
75 "description": "Enter your Apify token here"
76 }
77 ],
78 "responses": {
79 "200": {
80 "description": "OK",
81 "content": {
82 "application/json": {
83 "schema": {
84 "$ref": "#/components/schemas/runsResponseSchema"
85 }
86 }
87 }
88 }
89 }
90 }
91 },
92 "/acts/styleindexamerica~us-ateliernewyork-scraper/run-sync": {
93 "post": {
94 "operationId": "run-sync-styleindexamerica-us-ateliernewyork-scraper",
95 "x-openai-isConsequential": false,
96 "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
97 "tags": [
98 "Run Actor"
99 ],
100 "requestBody": {
101 "required": true,
102 "content": {
103 "application/json": {
104 "schema": {
105 "$ref": "#/components/schemas/inputSchema"
106 }
107 }
108 }
109 },
110 "parameters": [
111 {
112 "name": "token",
113 "in": "query",
114 "required": true,
115 "schema": {
116 "type": "string"
117 },
118 "description": "Enter your Apify token here"
119 }
120 ],
121 "responses": {
122 "200": {
123 "description": "OK"
124 }
125 }
126 }
127 }
128 },
129 "components": {
130 "schemas": {
131 "inputSchema": {
132 "type": "object",
133 "properties": {
134 "productUrls": {
135 "title": "Product URLs",
136 "type": "array",
137 "description": "This is a list of product URLs.",
138 "items": {
139 "type": "string"
140 }
141 },
142 "listingUrls": {
143 "title": "Category / Brand URLs",
144 "type": "array",
145 "description": "This is a list of category URLs",
146 "items": {
147 "type": "string"
148 }
149 },
150 "keywords": {
151 "title": "Keywords",
152 "type": "array",
153 "description": "This is a list of keyword to search products based on.",
154 "items": {
155 "type": "string"
156 }
157 },
158 "startPageNumber": {
159 "title": "Start Page",
160 "minimum": 0,
161 "type": "integer",
162 "description": "Page number to start scraping products."
163 },
164 "finalPageNumber": {
165 "title": "Final Page",
166 "minimum": 0,
167 "type": "integer",
168 "description": "Page number to start scraping products."
169 },
170 "minPrice": {
171 "title": "Minimum Price",
172 "minimum": 0,
173 "type": "integer",
174 "description": "Minimum price of scrapped products."
175 },
176 "maxPrice": {
177 "title": "Maximum Price",
178 "minimum": 0,
179 "type": "integer",
180 "description": "Maximum price of scrapped products."
181 },
182 "minDiscountRate": {
183 "title": "Minimum Discount Percentage",
184 "minimum": 0,
185 "type": "integer",
186 "description": "Minimum discount percentage of scrapped products."
187 }
188 }
189 },
190 "runsResponseSchema": {
191 "type": "object",
192 "properties": {
193 "data": {
194 "type": "object",
195 "properties": {
196 "id": {
197 "type": "string"
198 },
199 "actId": {
200 "type": "string"
201 },
202 "userId": {
203 "type": "string"
204 },
205 "startedAt": {
206 "type": "string",
207 "format": "date-time",
208 "example": "2025-01-08T00:00:00.000Z"
209 },
210 "finishedAt": {
211 "type": "string",
212 "format": "date-time",
213 "example": "2025-01-08T00:00:00.000Z"
214 },
215 "status": {
216 "type": "string",
217 "example": "READY"
218 },
219 "meta": {
220 "type": "object",
221 "properties": {
222 "origin": {
223 "type": "string",
224 "example": "API"
225 },
226 "userAgent": {
227 "type": "string"
228 }
229 }
230 },
231 "stats": {
232 "type": "object",
233 "properties": {
234 "inputBodyLen": {
235 "type": "integer",
236 "example": 2000
237 },
238 "rebootCount": {
239 "type": "integer",
240 "example": 0
241 },
242 "restartCount": {
243 "type": "integer",
244 "example": 0
245 },
246 "resurrectCount": {
247 "type": "integer",
248 "example": 0
249 },
250 "computeUnits": {
251 "type": "integer",
252 "example": 0
253 }
254 }
255 },
256 "options": {
257 "type": "object",
258 "properties": {
259 "build": {
260 "type": "string",
261 "example": "latest"
262 },
263 "timeoutSecs": {
264 "type": "integer",
265 "example": 300
266 },
267 "memoryMbytes": {
268 "type": "integer",
269 "example": 1024
270 },
271 "diskMbytes": {
272 "type": "integer",
273 "example": 2048
274 }
275 }
276 },
277 "buildId": {
278 "type": "string"
279 },
280 "defaultKeyValueStoreId": {
281 "type": "string"
282 },
283 "defaultDatasetId": {
284 "type": "string"
285 },
286 "defaultRequestQueueId": {
287 "type": "string"
288 },
289 "buildNumber": {
290 "type": "string",
291 "example": "1.0.0"
292 },
293 "containerUrl": {
294 "type": "string"
295 },
296 "usage": {
297 "type": "object",
298 "properties": {
299 "ACTOR_COMPUTE_UNITS": {
300 "type": "integer",
301 "example": 0
302 },
303 "DATASET_READS": {
304 "type": "integer",
305 "example": 0
306 },
307 "DATASET_WRITES": {
308 "type": "integer",
309 "example": 0
310 },
311 "KEY_VALUE_STORE_READS": {
312 "type": "integer",
313 "example": 0
314 },
315 "KEY_VALUE_STORE_WRITES": {
316 "type": "integer",
317 "example": 1
318 },
319 "KEY_VALUE_STORE_LISTS": {
320 "type": "integer",
321 "example": 0
322 },
323 "REQUEST_QUEUE_READS": {
324 "type": "integer",
325 "example": 0
326 },
327 "REQUEST_QUEUE_WRITES": {
328 "type": "integer",
329 "example": 0
330 },
331 "DATA_TRANSFER_INTERNAL_GBYTES": {
332 "type": "integer",
333 "example": 0
334 },
335 "DATA_TRANSFER_EXTERNAL_GBYTES": {
336 "type": "integer",
337 "example": 0
338 },
339 "PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
340 "type": "integer",
341 "example": 0
342 },
343 "PROXY_SERPS": {
344 "type": "integer",
345 "example": 0
346 }
347 }
348 },
349 "usageTotalUsd": {
350 "type": "number",
351 "example": 0.00005
352 },
353 "usageUsd": {
354 "type": "object",
355 "properties": {
356 "ACTOR_COMPUTE_UNITS": {
357 "type": "integer",
358 "example": 0
359 },
360 "DATASET_READS": {
361 "type": "integer",
362 "example": 0
363 },
364 "DATASET_WRITES": {
365 "type": "integer",
366 "example": 0
367 },
368 "KEY_VALUE_STORE_READS": {
369 "type": "integer",
370 "example": 0
371 },
372 "KEY_VALUE_STORE_WRITES": {
373 "type": "number",
374 "example": 0.00005
375 },
376 "KEY_VALUE_STORE_LISTS": {
377 "type": "integer",
378 "example": 0
379 },
380 "REQUEST_QUEUE_READS": {
381 "type": "integer",
382 "example": 0
383 },
384 "REQUEST_QUEUE_WRITES": {
385 "type": "integer",
386 "example": 0
387 },
388 "DATA_TRANSFER_INTERNAL_GBYTES": {
389 "type": "integer",
390 "example": 0
391 },
392 "DATA_TRANSFER_EXTERNAL_GBYTES": {
393 "type": "integer",
394 "example": 0
395 },
396 "PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
397 "type": "integer",
398 "example": 0
399 },
400 "PROXY_SERPS": {
401 "type": "integer",
402 "example": 0
403 }
404 }
405 }
406 }
407 }
408 }
409 }
410 }
411 }
412}
US Ateliernewyork Scraper OpenAPI definition
OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.
OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.
By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.
You can download the OpenAPI definitions for US Ateliernewyork Scraper from the options below:
If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.
You can also check out our other API clients:
Actor Metrics
1 monthly user
-
0 No stars yet
>99% runs succeeded
Created in Jul 2024
Modified 4 months ago