# Zomato Restaurant Scraper - Listings, Ratings & Prices (`fascinating_lentil/zomato-restaurant-scraper`) Actor Scrape public Zomato restaurant listings with names, cuisines, ratings, review counts, pricing, locality, images, and restaurant URLs. Useful for restaurant market research and local food data monitoring. - **URL**: https://apify.com/fascinating\_lentil/zomato-restaurant-scraper.md - **Developed by:** [Md Jakaria Mirza](https://apify.com/fascinating_lentil) (community) - **Categories:** Other, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 restaurant scrapeds 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 - Listings, Ratings & Prices Scrape public Zomato restaurant listings by city and keyword. This Actor collects restaurant names, cuisines, ratings, review counts, pricing signals, locality, address snippets, images, and Zomato restaurant URLs. It is designed for restaurant market research, food delivery category analysis, local competitor monitoring, cuisine discovery, and location-level hospitality research. The scraper supports multiple keywords in one run, deduplicates restaurants by URL, scrolls Zomato result pages, handles missing fields with `null`, and saves clean records to the Apify Dataset. It uses Playwright with session rotation, retries, random delays, and optional residential proxy configuration for more reliable regional access. This Actor extracts public business listing facts only. It does not collect emails, personal profiles, private accounts, or non-public customer data. ### Use Cases - Restaurant market research by city, locality, cuisine, or category. - Food delivery competitor tracking across visible restaurant listings. - Pricing and rating analysis for restaurant discovery products. - Local hospitality research for sales, operations, and expansion teams. - Building restaurant datasets for dashboards, maps, and BI workflows. ### Input ```json { "city": "Mumbai", "searchQueries": ["restaurants", "pizza", "biryani"], "categories": ["cafe"], "minRating": 3.5, "maxResults": 50, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "IN" } } ```` ### Output ```json { "source": "zomato", "searchQuery": "restaurants", "city": "Mumbai", "position": 1, "restaurantId": "the-bombay-canteen-lower-parel", "restaurantName": "The Bombay Canteen", "cuisines": ["Modern Indian", "Desserts"], "rating": 4.5, "reviewCount": 4200, "costForTwo": "₹2,000 for two", "priceRange": null, "locality": "Lower Parel", "address": "Lower Parel, Mumbai", "deliveryTime": null, "imageUrl": "https://...", "restaurantUrl": "https://www.zomato.com/mumbai/the-bombay-canteen-lower-parel", "scrapedAt": "2026-06-13T12:00:00.000Z" } ``` ### How to Scrape Zomato Restaurants 1. Enter the city, for example `Mumbai`, `Delhi NCR`, `Bengaluru`, `Pune`, or `Hyderabad`. 2. Add one or more search keywords such as `restaurants`, `pizza`, `biryani`, `cafe`, or `fine dining`. 3. Set `maxResults` to control how many unique restaurants are saved. 4. Optionally set `minRating` or cuisine/text filters. 5. Run the Actor and export the dataset as JSON, CSV, Excel, or via API. ### Pricing | Event | Price | When charged | | --- | ---: | --- | | `restaurant-scraped` | `$0.003` | Once per clean restaurant record saved to the dataset | The Actor charges only after a restaurant record is successfully saved. ### Notes - Zomato pages can vary by city, query, and region. - Some fields may be `null` when Zomato does not show them on the listing page. - India residential proxies are recommended for stable regional access. - For very large runs, use multiple focused keywords rather than one broad query. ### Responsible Use Use this Actor only for lawful purposes and in compliance with Zomato's terms, robots.txt, applicable privacy laws, and local regulations. Do not use it to collect, store, or resell personal data without a lawful basis. This Actor is intended for public restaurant listing research and market analytics. # Actor input Schema ## `city` (type: `string`): Zomato city to search, for example Mumbai, Delhi NCR, Bengaluru, Pune, Hyderabad, or Chennai. ## `searchQueries` (type: `array`): Restaurant or cuisine keywords. Use restaurants for the general city listing. ## `categories` (type: `array`): Optional filters such as biryani, pizza, cafe, chinese, or dessert. Matching is case-insensitive. ## `minRating` (type: `number`): Only save restaurants at or above this rating when rating is available. ## `maxResults` (type: `integer`): Maximum unique restaurant records to save across all keywords. ## `proxyConfiguration` (type: `object`): Residential India proxies are recommended for reliable regional access. ## Actor input object example ```json { "city": "Mumbai", "searchQueries": [ "restaurants" ], "categories": [], "minRating": 0, "maxResults": 50, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "IN" } } ``` # 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 = { "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "IN" } }; // Run the Actor and wait for it to finish const run = await client.actor("fascinating_lentil/zomato-restaurant-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 = { "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "IN", } } # Run the Actor and wait for it to finish run = client.actor("fascinating_lentil/zomato-restaurant-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 '{ "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "IN" } }' | apify call fascinating_lentil/zomato-restaurant-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fascinating_lentil/zomato-restaurant-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Zomato Restaurant Scraper - Listings, Ratings & Prices", "description": "Scrape public Zomato restaurant listings with names, cuisines, ratings, review counts, pricing, locality, images, and restaurant URLs. Useful for restaurant market research and local food data monitoring.", "version": "1.0", "x-build-id": "FRFlNx2Zr5hlbJHGr" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fascinating_lentil~zomato-restaurant-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fascinating_lentil-zomato-restaurant-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/fascinating_lentil~zomato-restaurant-scraper/runs": { "post": { "operationId": "runs-sync-fascinating_lentil-zomato-restaurant-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/fascinating_lentil~zomato-restaurant-scraper/run-sync": { "post": { "operationId": "run-sync-fascinating_lentil-zomato-restaurant-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Zomato city to search, for example Mumbai, Delhi NCR, Bengaluru, Pune, Hyderabad, or Chennai.", "default": "Mumbai" }, "searchQueries": { "title": "Search keywords", "type": "array", "description": "Restaurant or cuisine keywords. Use restaurants for the general city listing.", "default": [ "restaurants" ], "items": { "type": "string" } }, "categories": { "title": "Cuisine / text filters", "type": "array", "description": "Optional filters such as biryani, pizza, cafe, chinese, or dessert. Matching is case-insensitive.", "default": [], "items": { "type": "string" } }, "minRating": { "title": "Minimum rating", "minimum": 0, "maximum": 5, "type": "number", "description": "Only save restaurants at or above this rating when rating is available.", "default": 0 }, "maxResults": { "title": "Maximum restaurants", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum unique restaurant records to save across all keywords.", "default": 50 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Residential India proxies are recommended for reliable regional access.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "IN" } } } }, "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 } } } } } } } } } } ```