# Booking.com Reviews Scraper (`khadinakbar/booking-reviews-scraper`) Actor Extract Booking.com guest reviews at scale — 24 structured fields per review (positive/negative text, score, traveler type, room, reviewer profile, owner response). Feed hotel URLs or a destination name, get ready-to-analyze JSON. MCP-ready for Claude, Cursor, ChatGPT, and AI agents. - **URL**: https://apify.com/khadinakbar/booking-reviews-scraper.md - **Developed by:** [Khadin Akbar](https://apify.com/khadinakbar) (community) - **Categories:** MCP servers, Travel, AI - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 review scrapeds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 🏨 Booking.com Reviews Scraper — Extract Hotel Reviews with Full Reviewer Profiles ### What does Booking.com Reviews Scraper do? Booking.com Reviews Scraper extracts complete guest review data from any Booking.com hotel, apartment, or accommodation listing. Give it a hotel URL or a destination name — it returns structured review records with **24 fields per review**, including review text (liked/disliked), guest scores, reviewer profiles, stay details, traveler type, and hotel owner responses. No login required. No API key needed. Works with both direct hotel URLs and destination search queries. ### Why use Booking.com Reviews Scraper? - **2x more data than competitors** — 24 fields per review including traveler type, owner response, reviewer loyalty level, and language filter that other scrapers don't expose - **Destination search built in** — give it "Paris" or "Bali beachfront" and it finds hotels, then scrapes reviews — no need to manually collect hotel URLs first - **25% cheaper** — $0.0015 per review vs. the leading alternative at $0.002 - **Reliable on Cloudflare** — uses a full browser with stealth mode and session rotation, not brittle HTTP scraping that gets blocked - **Smart cutoff date** — combined with "Newest First" sorting, the scraper stops at your cutoff date and doesn't waste compute retrieving older reviews ### What data does Booking.com Reviews Scraper extract? | Field | Type | Description | |---|---|---| | `hotel_name` | string | Hotel / property name | | `hotel_url` | string | Direct hotel page URL | | `hotel_id` | string | Booking.com internal property ID | | `hotel_rating` | number | Aggregate guest rating (0–10) | | `review_id` | string | Unique review identifier | | `review_title` | string | Review headline written by guest | | `review_positive` | string | "Liked" section — what guests praised | | `review_negative` | string | "Disliked" section — what guests criticized | | `review_score` | number | Individual review score (0–10) | | `review_score_label` | string | Score label (Superb, Very Good, etc.) | | `review_date` | string | Date review was published | | `date_of_stay` | string | Month + year guest actually stayed | | `length_of_stay` | integer | Number of nights at property | | `room_type` | string | Room category (e.g. Superior Double) | | `traveler_type` | string | Couple / Solo / Family / Business / Group | | `reviewer_name` | string | Guest display name | | `reviewer_country` | string | Guest country of origin | | `reviewer_review_count` | integer | Total reviews written by this guest | | `review_language` | string | ISO 639-1 language of the review text | | `helpful_votes` | integer | Number of "helpful" votes on the review | | `owner_response` | string | Hotel management response text | | `owner_response_date` | string | Date management posted their response | | `scraped_at` | string | ISO 8601 scrape timestamp | | `source_url` | string | Source hotel page URL | --- ### How to use Booking.com Reviews Scraper #### Mode 1: Direct hotel URLs 1. Go to a Booking.com hotel page and copy the URL 2. Paste it into **Hotel URLs** in the input form 3. Set your review limit and filters 4. Click **Start** **Example URLs:** ```` https://www.booking.com/hotel/cz/jeromehouse.en-gb.html https://www.booking.com/hotel/us/the-plaza.en-gb.html https://www.booking.com/hotel/fr/le-marais.en-gb.html ```` You can add multiple hotel URLs to scrape reviews from several properties in one run. #### Mode 2: Destination search 1. Type a destination in the **Search Query** field (e.g. `"Paris"`, `"Bali beachfront"`, `"New York Manhattan hotels"`) 2. Optionally set check-in/check-out dates to filter by availability 3. The scraper finds up to 30 hotels matching your query, then scrapes reviews from each #### Combining both modes You can provide both `startUrls` and `searchQuery` simultaneously — the scraper will process all hotel URLs from both inputs. --- ### API usage ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor('USERNAME/booking-reviews-scraper').call({ startUrls: [ { url: 'https://www.booking.com/hotel/cz/jeromehouse.en-gb.html' } ], maxReviewsPerHotel: 200, sortReviewsBy: 'f_recent_desc', filterByTravelerType: 'couple', }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ```` ```python from apify_client import ApifyClient client = ApifyClient("YOUR_API_TOKEN") run = client.actor("USERNAME/booking-reviews-scraper").call(run_input={ "startUrls": [ {"url": "https://www.booking.com/hotel/cz/jeromehouse.en-gb.html"} ], "maxReviewsPerHotel": 200, "sortReviewsBy": "f_recent_desc", "filterByTravelerType": "couple", }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ``` *** ### Review filter options #### Sort reviews by | Value | Description | |---|---| | `f_relevance` | Most relevant (Booking.com default) | | `f_recent_desc` | Newest first — best for cutoff date | | `f_recent_asc` | Oldest first | | `f_score_desc` | Highest score first | | `f_score_asc` | Lowest score first (find negative reviews) | #### Filter by score | Value | Description | |---|---| | `f_all_reviews` | All scores (default) | | `review_adj_superb` | Superb: 9+ | | `review_adj_good` | Good: 7–9 | | `review_adj_average_passable` | Passable: 5–7 | | `review_adj_poor` | Poor: 3–5 | #### Filter by traveler type `couple`, `solo_traveller`, `family_with_children`, `group_of_friends`, `business_traveler` #### Filter by language Use ISO 639-1 codes: `en`, `de`, `fr`, `es`, `it`, `nl`, `pt`, `ru`, `zh`, `ja`, `ko`, `ar`, etc. *** ### Use cases **Reputation monitoring** — Track what guests say about your property or competitors over time. Identify recurring complaints before they compound. **Competitive intelligence** — Scrape reviews from 10–30 competing hotels in a destination. Find the gaps between what they promise and what guests experience. **Sentiment analysis + AI pipelines** — Feed structured review data (positive/negative text, scores, traveler types) into LLM or NLP pipelines for automated sentiment classification. **Market research** — Understand what drives high scores vs. low scores in a specific hotel category or location. **Fake review detection** — Cross-reference reviewer\_review\_count, reviewer\_country, and reviewer\_name patterns to flag suspicious review clusters. **OTA content aggregation** — Build review dashboards that aggregate Booking.com guest feedback alongside TripAdvisor, Google, and Expedia reviews. *** ### Pricing This actor uses **pay-per-event (PPE)** pricing — you only pay for results you actually receive. | Event | Price | |---|---| | Actor start | $0.00005 (flat fee) | | Per review scraped | **$0.0015** | **Cost examples:** - 100 reviews from 1 hotel → ~$0.15 - 1,000 reviews from 10 hotels → ~$1.50 - 10,000 reviews → ~$15.00 You can set a spend cap in Actor → Settings → Budget to prevent unexpected costs during development. *** ### Tips for best results **Use "Newest First" + Cutoff Date** to efficiently track only new reviews since your last run. This is the most cost-efficient approach for ongoing monitoring. **Residential proxies** improve success rate on Cloudflare-protected pages. If you hit frequent blocks, switch from "Apify Proxy (datacenter)" to "Apify Proxy (residential)" in the proxy settings. **Large review counts (1000+)** work fine — the scraper paginates automatically in batches of 25. For very large hotels, expect runs of 5–20 minutes. **International reviews** — Use `filterByLanguage` to scrape only reviews in your target language, saving both time and cost. *** ### Output sample ```json { "hotel_name": "Jerome House Prague", "hotel_url": "https://www.booking.com/hotel/cz/jeromehouse.html", "hotel_id": "jeromehouse", "hotel_rating": 9.1, "review_id": "review_abc123", "review_title": "Absolutely perfect stay", "review_positive": "The breakfast was excellent and the staff went above and beyond. The location right in the old town was ideal for exploring Prague on foot.", "review_negative": "The room was slightly small but perfectly functional.", "review_score": 9.6, "review_score_label": "Superb", "review_date": "January 5, 2026", "date_of_stay": "December 2025", "length_of_stay": 3, "room_type": "Superior Double Room", "traveler_type": "Couple", "reviewer_name": "Sarah K.", "reviewer_country": "United Kingdom", "reviewer_review_count": 14, "review_language": "en", "helpful_votes": 6, "owner_response": "Thank you so much for your kind words! We're delighted you enjoyed your time with us in Prague.", "owner_response_date": "January 7, 2026", "scraped_at": "2026-04-13T10:30:00.000Z", "source_url": "https://www.booking.com/hotel/cz/jeromehouse.html" } ``` *** ### Integrations Booking.com Reviews Scraper integrates with the full Apify ecosystem. Connect it with Make, Zapier, Slack, Google Sheets, Google Drive, Airtable, or any tool via webhooks and the Apify API. Export scraped data as JSON, CSV, Excel, or XML from your dataset. *** ### Legal disclaimer This actor is intended for lawful data collection from publicly available sources. Users are responsible for compliance with applicable laws, Booking.com's Terms of Service, and data protection regulations (GDPR, CCPA, etc.). Do not use this actor to scrape personal data without a legitimate legal basis. Reviewer names and nationalities are publicly displayed on Booking.com listings. If you are unsure whether your use case is compliant, consult your legal team. # Actor input Schema ## `startUrls` (type: `array`): Use this field when the user provides direct Booking.com hotel/property page URLs (e.g. https://www.booking.com/hotel/us/the-plaza.html). Each item must be a booking.com /hotel/ URL. Do NOT use this when the user wants to search by destination — use searchQuery for that instead. ## `searchQuery` (type: `string`): Use this field when the user provides a city, region, neighborhood, or destination name (e.g. 'Paris', 'New York Manhattan', 'Bali beachfront'). The actor will search Booking.com for hotels matching this query and scrape their reviews. Do NOT use this when specific hotel URLs are provided — use startUrls for that. ## `checkIn` (type: `string`): Check-in date in YYYY-MM-DD format. Only used together with searchQuery to filter hotels by availability. Leave empty to search without date filter. ## `checkOut` (type: `string`): Check-out date in YYYY-MM-DD format. Only used together with searchQuery. Must be after checkIn. ## `maxReviewsPerHotel` (type: `integer`): Maximum number of reviews to scrape per hotel/property. Set to a high number (e.g. 9999) to scrape all available reviews. Higher values take proportionally longer and cost more. ## `maxHotelsFromSearch` (type: `integer`): Only used when searchQuery is provided. Caps how many hotels the destination search scrapes. Default is 5 — keeps typical run times under 5 minutes. Raise to 30 for exhaustive destination sweeps (will take proportionally longer). Ignored when only startUrls is provided. ## `sortReviewsBy` (type: `string`): Order in which reviews are retrieved from Booking.com. 'Most Relevant' is the default. Use 'Newest First' combined with cutoffDate to efficiently get only recent reviews without scraping the entire review history. ## `filterByScore` (type: `string`): Only retrieve reviews matching a specific score category. Use 'All scores' to include reviews of any rating. Useful when you only want to analyze negative feedback ('Poor') or excellent experiences ('Superb'). ## `filterByTravelerType` (type: `string`): Only scrape reviews from guests of a specific traveler type. Leave blank to get reviews from all traveler types. Useful for targeted analysis (e.g. business traveler feedback vs family feedback). ## `filterByLanguage` (type: `string`): Only retrieve reviews written in this language. Leave empty to scrape reviews in all languages. Use ISO 639-1 two-letter codes: en (English), de (German), fr (French), es (Spanish), it (Italian), nl (Dutch), pt (Portuguese), ru (Russian), zh (Chinese), ja (Japanese), ko (Korean), ar (Arabic), etc. ## `cutoffDate` (type: `string`): Stop scraping reviews that are older than this date (format: YYYY-MM-DD). Works most efficiently when combined with Sort='Newest First' — the actor stops as soon as it encounters a review older than this date, saving time and cost. Leave empty to scrape all reviews up to maxReviewsPerHotel. ## `proxyConfiguration` (type: `object`): Proxy settings for bypassing Cloudflare protection. Apify datacenter proxies are recommended as the default. Switch to residential proxies if you encounter persistent blocking (lower success rate). ## Actor input object example ```json { "startUrls": [ { "url": "https://www.booking.com/hotel/cz/jeromehouse.en-gb.html" } ], "maxReviewsPerHotel": 100, "maxHotelsFromSearch": 5, "sortReviewsBy": "f_relevance", "filterByScore": "f_all_reviews", "filterByTravelerType": "", "filterByLanguage": "", "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `results` (type: `string`): All scraped Booking.com review records as structured JSON. Each record includes review text (positive/negative), score, reviewer profile, stay details, room type, traveler type, and owner response. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "startUrls": [ { "url": "https://www.booking.com/hotel/cz/jeromehouse.en-gb.html" } ], "searchQuery": "", "checkIn": "", "checkOut": "", "maxReviewsPerHotel": 100, "maxHotelsFromSearch": 5, "sortReviewsBy": "f_relevance", "filterByScore": "f_all_reviews", "filterByTravelerType": "", "filterByLanguage": "", "cutoffDate": "", "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("khadinakbar/booking-reviews-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "startUrls": [{ "url": "https://www.booking.com/hotel/cz/jeromehouse.en-gb.html" }], "searchQuery": "", "checkIn": "", "checkOut": "", "maxReviewsPerHotel": 100, "maxHotelsFromSearch": 5, "sortReviewsBy": "f_relevance", "filterByScore": "f_all_reviews", "filterByTravelerType": "", "filterByLanguage": "", "cutoffDate": "", "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("khadinakbar/booking-reviews-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://www.booking.com/hotel/cz/jeromehouse.en-gb.html" } ], "searchQuery": "", "checkIn": "", "checkOut": "", "maxReviewsPerHotel": 100, "maxHotelsFromSearch": 5, "sortReviewsBy": "f_relevance", "filterByScore": "f_all_reviews", "filterByTravelerType": "", "filterByLanguage": "", "cutoffDate": "", "proxyConfiguration": { "useApifyProxy": true } }' | apify call khadinakbar/booking-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=khadinakbar/booking-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Booking.com Reviews Scraper", "description": "Extract Booking.com guest reviews at scale — 24 structured fields per review (positive/negative text, score, traveler type, room, reviewer profile, owner response). Feed hotel URLs or a destination name, get ready-to-analyze JSON. MCP-ready for Claude, Cursor, ChatGPT, and AI agents.", "version": "1.2", "x-build-id": "3EfYdq1jWtkwYMuOK" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/khadinakbar~booking-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-khadinakbar-booking-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/khadinakbar~booking-reviews-scraper/runs": { "post": { "operationId": "runs-sync-khadinakbar-booking-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/khadinakbar~booking-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-khadinakbar-booking-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "startUrls": { "title": "Hotel URLs", "type": "array", "description": "Use this field when the user provides direct Booking.com hotel/property page URLs (e.g. https://www.booking.com/hotel/us/the-plaza.html). Each item must be a booking.com /hotel/ URL. Do NOT use this when the user wants to search by destination — use searchQuery for that instead.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "searchQuery": { "title": "Search Query (destination)", "type": "string", "description": "Use this field when the user provides a city, region, neighborhood, or destination name (e.g. 'Paris', 'New York Manhattan', 'Bali beachfront'). The actor will search Booking.com for hotels matching this query and scrape their reviews. Do NOT use this when specific hotel URLs are provided — use startUrls for that." }, "checkIn": { "title": "Check-in Date (for search only)", "type": "string", "description": "Check-in date in YYYY-MM-DD format. Only used together with searchQuery to filter hotels by availability. Leave empty to search without date filter." }, "checkOut": { "title": "Check-out Date (for search only)", "type": "string", "description": "Check-out date in YYYY-MM-DD format. Only used together with searchQuery. Must be after checkIn." }, "maxReviewsPerHotel": { "title": "Max Reviews Per Hotel", "minimum": 1, "maximum": 99999, "type": "integer", "description": "Maximum number of reviews to scrape per hotel/property. Set to a high number (e.g. 9999) to scrape all available reviews. Higher values take proportionally longer and cost more.", "default": 100 }, "maxHotelsFromSearch": { "title": "Max Hotels From Search", "minimum": 1, "maximum": 30, "type": "integer", "description": "Only used when searchQuery is provided. Caps how many hotels the destination search scrapes. Default is 5 — keeps typical run times under 5 minutes. Raise to 30 for exhaustive destination sweeps (will take proportionally longer). Ignored when only startUrls is provided.", "default": 5 }, "sortReviewsBy": { "title": "Sort Reviews By", "enum": [ "f_relevance", "f_recent_desc", "f_recent_asc", "f_score_desc", "f_score_asc" ], "type": "string", "description": "Order in which reviews are retrieved from Booking.com. 'Most Relevant' is the default. Use 'Newest First' combined with cutoffDate to efficiently get only recent reviews without scraping the entire review history.", "default": "f_relevance" }, "filterByScore": { "title": "Filter By Score", "enum": [ "f_all_reviews", "review_adj_superb", "review_adj_good", "review_adj_average_passable", "review_adj_poor" ], "type": "string", "description": "Only retrieve reviews matching a specific score category. Use 'All scores' to include reviews of any rating. Useful when you only want to analyze negative feedback ('Poor') or excellent experiences ('Superb').", "default": "f_all_reviews" }, "filterByTravelerType": { "title": "Filter By Traveler Type", "enum": [ "", "couple", "solo_traveller", "family_with_children", "group_of_friends", "business_traveler" ], "type": "string", "description": "Only scrape reviews from guests of a specific traveler type. Leave blank to get reviews from all traveler types. Useful for targeted analysis (e.g. business traveler feedback vs family feedback).", "default": "" }, "filterByLanguage": { "title": "Filter By Review Language", "type": "string", "description": "Only retrieve reviews written in this language. Leave empty to scrape reviews in all languages. Use ISO 639-1 two-letter codes: en (English), de (German), fr (French), es (Spanish), it (Italian), nl (Dutch), pt (Portuguese), ru (Russian), zh (Chinese), ja (Japanese), ko (Korean), ar (Arabic), etc.", "default": "" }, "cutoffDate": { "title": "Cutoff Date (stop before this date)", "type": "string", "description": "Stop scraping reviews that are older than this date (format: YYYY-MM-DD). Works most efficiently when combined with Sort='Newest First' — the actor stops as soon as it encounters a review older than this date, saving time and cost. Leave empty to scrape all reviews up to maxReviewsPerHotel." }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for bypassing Cloudflare protection. Apify datacenter proxies are recommended as the default. Switch to residential proxies if you encounter persistent blocking (lower success rate).", "default": { "useApifyProxy": true } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```