# Zomato Restaurant Scraper - India (`lulzasaur/zomato-scraper`) Actor Scrape restaurant data from Zomato India. Browse restaurants by city, filter by cuisine. Get names, addresses, ratings, reviews, cuisines, price range, phone, GPS, and hours. - **URL**: https://apify.com/lulzasaur/zomato-scraper.md - **Developed by:** [lulz bot](https://apify.com/lulzasaur) (community) - **Categories:** E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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 ## Zomato Restaurant Scraper Scrape restaurant data from [Zomato](https://www.zomato.com), India's leading food discovery platform. Extract ratings, reviews, cuisines, prices, addresses, phone numbers, GPS coordinates, and opening hours. **Note:** Zomato is India-only. US and other non-Indian cities redirect to a goodbye page. Use Indian city slugs only. ### Features - **City browsing** - Scrape restaurants from any Indian city - **Cuisine filter** - Filter by cuisine type (Chinese, Italian, North Indian, etc.) - **JSON-LD extraction** - Leverages structured data for reliable, rich output - **Detail pages** - Optional deep scrape for phone, hours, GPS, full address - **Pagination** - Automatically follows pages up to your result limit ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `city` | string | `mumbai` | Indian city slug (e.g. mumbai, delhi-ncr, bangalore, hyderabad, pune, kolkata, chennai) | | `cuisine` | string | - | Optional cuisine filter (e.g. chinese, italian, north-indian, biryani, pizza, cafe) | | `maxResults` | integer | `50` | Maximum number of restaurants to scrape | | `scrapeDetails` | boolean | `false` | Visit detail pages for richer data (phone, hours, GPS). Slower. | | `proxyConfiguration` | object | - | Optional proxy settings | ### Output Each result includes: | Field | Description | |-------|-------------| | `businessName` | Restaurant name | | `address` | Full address | | `city` | City name | | `latitude` | GPS latitude | | `longitude` | GPS longitude | | `rating` | Average rating (0-5 scale) | | `reviewCount` | Number of reviews/ratings | | `cuisines` | Array of cuisine types | | `priceRange` | Price range indicator | | `phone` | Phone number (detail pages) | | `openingHours` | Opening hours (detail pages) | | `menuUrl` | Link to menu page | | `imageUrl` | Restaurant image URL | | `url` | Zomato restaurant page URL | | `scrapedAt` | ISO timestamp of when the data was scraped | ### Usage Examples #### Browse Mumbai restaurants ```json { "city": "mumbai", "maxResults": 50 } ```` #### Chinese restaurants in Bangalore ```json { "city": "bangalore", "cuisine": "chinese", "maxResults": 30 } ``` #### Detailed scrape of Delhi restaurants ```json { "city": "delhi-ncr", "maxResults": 20, "scrapeDetails": true } ``` #### Cafes in Pune ```json { "city": "pune", "cuisine": "cafe", "maxResults": 100 } ``` ### Supported Cities Major Indian cities: mumbai, delhi-ncr, bangalore, hyderabad, pune, kolkata, chennai, ahmedabad, jaipur, lucknow, chandigarh, goa, kochi, indore, nagpur, vadodara, coimbatore, surat, visakhapatnam, and more. ### How It Works 1. **Listing pages**: Scrapes `zomato.com/{city}/restaurants` and extracts Restaurant schema from JSON-LD structured data. The ItemList schema contains 9+ restaurants per page. 2. **Detail pages**: When `scrapeDetails` is enabled, visits each restaurant page to extract the full Restaurant schema including phone, opening hours, GPS coordinates, and menu URL. 3. **JSON-LD priority**: Zomato embeds rich JSON-LD data (Restaurant schema with aggregateRating, geo, servesCuisine, etc.), which is the primary extraction method. 4. **Pagination**: Follows `?page=N` query parameters to scrape multiple listing pages. ### Notes - Zomato is India-only -- US and other non-Indian locations will not work - No anti-bot protection detected (standard HTML responses) - SSR via React Helmet ensures JSON-LD is available without JavaScript execution - Ratings are on a 0-5 scale - GPS coordinates and phone numbers require `scrapeDetails: true` # Actor input Schema ## `city` (type: `string`): Indian city to scrape restaurants from. Use the Zomato URL slug (e.g. mumbai, delhi-ncr, bangalore, hyderabad, pune, kolkata, chennai, ahmedabad, jaipur, lucknow, chandigarh, goa). ## `cuisine` (type: `string`): Optional cuisine type to filter by (e.g. chinese, italian, north-indian, south-indian, biryani, pizza, cafe). Leave empty for all cuisines. ## `maxResults` (type: `integer`): Maximum number of restaurants to scrape. ## `scrapeDetails` (type: `boolean`): Visit each restaurant's detail page for richer data (phone, hours, full address, GPS). Slower but more complete. ## `proxyConfiguration` (type: `object`): Optional proxy configuration for requests. ## Actor input object example ```json { "city": "mumbai", "maxResults": 50, "scrapeDetails": false } ``` # 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 = { "city": "mumbai" }; // Run the Actor and wait for it to finish const run = await client.actor("lulzasaur/zomato-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 = { "city": "mumbai" } # Run the Actor and wait for it to finish run = client.actor("lulzasaur/zomato-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 '{ "city": "mumbai" }' | apify call lulzasaur/zomato-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lulzasaur/zomato-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Zomato Restaurant Scraper - India", "description": "Scrape restaurant data from Zomato India. Browse restaurants by city, filter by cuisine. Get names, addresses, ratings, reviews, cuisines, price range, phone, GPS, and hours.", "version": "1.0", "x-build-id": "76olu3bgdkrQgngMS" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lulzasaur~zomato-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lulzasaur-zomato-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lulzasaur~zomato-scraper/runs": { "post": { "operationId": "runs-sync-lulzasaur-zomato-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lulzasaur~zomato-scraper/run-sync": { "post": { "operationId": "run-sync-lulzasaur-zomato-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "city": { "title": "City", "type": "string", "description": "Indian city to scrape restaurants from. Use the Zomato URL slug (e.g. mumbai, delhi-ncr, bangalore, hyderabad, pune, kolkata, chennai, ahmedabad, jaipur, lucknow, chandigarh, goa).", "default": "mumbai" }, "cuisine": { "title": "Cuisine Filter", "type": "string", "description": "Optional cuisine type to filter by (e.g. chinese, italian, north-indian, south-indian, biryani, pizza, cafe). Leave empty for all cuisines." }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of restaurants to scrape.", "default": 50 }, "scrapeDetails": { "title": "Scrape Detail Pages", "type": "boolean", "description": "Visit each restaurant's detail page for richer data (phone, hours, full address, GPS). Slower but more complete.", "default": false }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Optional proxy configuration for requests." } } }, "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 } } } } } } } } } } ```