# Ubereats Scraper (`sourabhbgp/ubereats-scraper`) Actor - **URL**: https://apify.com/sourabhbgp/ubereats-scraper.md - **Developed by:** [Sourabh Kumar](https://apify.com/sourabhbgp) (community) - **Categories:** E-commerce, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $2.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## UberEats Scraper — 100 Free | Restaurants, Menus & Prices Scrape UberEats restaurants, complete menus with prices, ratings, hours, and locations. Discover nearby restaurants or extract full store details. HTTP-only, fast, reliable. ### 💰 Pricing - **100 free results** — lifetime, no credit card needed - After that: **$2 per 1,000 results** (pay-per-event) - Discover mode: 1 result = 1 restaurant listing - Store mode: 1 result = 1 full store with complete menu | Results | Cost | |---------|------| | 100 | Free | | 1,000 | $2 | | 10,000 | $20 | | 100,000 | $200 | ### 🔍 What can you scrape? #### Discover Mode - Restaurant names, cuisine types, phone numbers - Full addresses with city, state, zip - Store URLs for deeper scraping #### Store Mode - Full restaurant details (name, cuisine, rating, reviews, price range) - Complete menu with sections, item names, descriptions, and USD prices - Opening hours, geo coordinates (lat/lng) - Images, phone numbers, addresses ### 📥 Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `urls` | string[] | `["https://www.ubereats.com/near-me"]` | Store URLs, location pages, or cuisine keywords | | `mode` | enum | auto-detect | Override: `discover`, `store` | | `maxResults` | integer | 50 | Max results. 0 = unlimited | #### Auto-Detection - `https://ubereats.com/store/...` → store - `https://ubereats.com/near-me` → discover - `pizza`, `chinese`, `sushi` → discover #### Example Input ```json { "urls": ["https://www.ubereats.com/store/jessies-breakfast/UAVHGNSBQCeqhKzEtis3SQ"], "mode": "store", "maxResults": 10 } ```` ### 📤 Output #### Store Record ```json { "type": "store", "name": "Jessie's Breakfast", "cuisine": ["Mexican", "Latin American"], "rating": 4.5, "reviewCount": 600, "priceRange": "$$", "address": "123 Main St", "city": "San Francisco", "state": "CA", "latitude": 37.7515, "longitude": -122.4182, "menuSections": 9, "menuItemCount": 91, "menu": [ { "section": "Mains", "name": "HUEVOS ALA MEXICANA", "description": "Tomatoes, onions, spinach eggs...", "price": 24.99, "currency": "USD" } ], "storeUrl": "https://www.ubereats.com/store/jessies-breakfast/UAVHGNSBQCeqhKzEtis3SQ" } ``` ### 💡 Use Cases - **Restaurant market research** — Compare menus and pricing across competitors - **Food delivery analytics** — Track restaurant availability and ratings by area - **Menu price monitoring** — Monitor price changes across restaurants - **Lead generation** — Build restaurant contact lists with phone and address - **Location intelligence** — Map restaurant density and cuisine distribution ### ⚡ Tips - Use cuisine keywords like "pizza", "chinese", "sushi" to discover restaurants by food type - Combine discover mode to find restaurants, then store mode for full menu extraction - The /near-me page returns results based on IP geolocation - Empty input defaults to /near-me discover mode (for Apify daily health checks) - Store mode includes complete menus with every item and price # Actor input Schema ## `urls` (type: `array`): UberEats store URLs, /near-me pages, or cuisine keywords (pizza, chinese, sushi). Mode is auto-detected. ## `mode` (type: `string`): Override auto-detection. Leave empty to auto-detect from URL. ## `maxResults` (type: `integer`): Maximum number of results to return. 0 = unlimited. ## Actor input object example ```json { "urls": [ "https://www.ubereats.com/near-me" ], "maxResults": 50 } ``` # Actor output Schema ## `results` (type: `string`): Scraped UberEats data. # 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 = { "urls": [ "https://www.ubereats.com/near-me" ] }; // Run the Actor and wait for it to finish const run = await client.actor("sourabhbgp/ubereats-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/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "urls": ["https://www.ubereats.com/near-me"] } # Run the Actor and wait for it to finish run = client.actor("sourabhbgp/ubereats-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "urls": [ "https://www.ubereats.com/near-me" ] }' | apify call sourabhbgp/ubereats-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=sourabhbgp/ubereats-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Ubereats Scraper", "version": "0.0", "x-build-id": "axHgyGYRjpxcpcG9X" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/sourabhbgp~ubereats-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-sourabhbgp-ubereats-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/sourabhbgp~ubereats-scraper/runs": { "post": { "operationId": "runs-sync-sourabhbgp-ubereats-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/sourabhbgp~ubereats-scraper/run-sync": { "post": { "operationId": "run-sync-sourabhbgp-ubereats-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": { "urls": { "title": "Store URLs, Location Pages, or Cuisine Keywords", "type": "array", "description": "UberEats store URLs, /near-me pages, or cuisine keywords (pizza, chinese, sushi). Mode is auto-detected.", "items": { "type": "string" } }, "mode": { "title": "Mode", "enum": [ "discover", "store" ], "type": "string", "description": "Override auto-detection. Leave empty to auto-detect from URL." }, "maxResults": { "title": "Max Results", "minimum": 0, "type": "integer", "description": "Maximum number of results to return. 0 = unlimited.", "default": 50 } } }, "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 } } } } } } } } } } ```