# Restaurant Leads Scraper - Email + POS/Booking Tech Stack (`oriented_wallpaper/restaurant-leads-scraper`) Actor Find restaurants by city and get contactable B2B leads enriched with their tech stack: POS (Toast, Square), reservations (OpenTable, Resy), online ordering (ChowNow, Olo) and delivery (DoorDash, Uber Eats). Includes name, address, phone, website, email & cuisine. No API key. - **URL**: https://apify.com/oriented\_wallpaper/restaurant-leads-scraper.md - **Developed by:** [Flash Scrape](https://apify.com/oriented_wallpaper) (community) - **Categories:** Lead generation, Travel, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.90 / 1,000 restaurant leads This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Restaurant Leads Scraper — find restaurants by POS, reservation & delivery tech stack Get **restaurant leads targetable by tech stack** — the only scraper that cross-references **POS (Toast, Square, Clover)**, **reservation systems (OpenTable, Resy, Tock)**, **online ordering (ChowNow, Olo)** and **delivery platforms (DoorDash, Uber Eats, Grubhub)** in one pass. No competitor does cross-platform stack detection. **$5 per 1,000 leads** — pay only for results you actually get. ### What does it do? This actor finds **restaurants by city** using OpenStreetMap, then **crawls each restaurant's own website** to extract a contact **email**, social profiles, and — the premium part — which **POS system**, **reservation platform**, **online-ordering tool**, and **delivery app** it runs. The output is a clean, deduped B2B **lead list segmented by tech stack**, so you can build a query like *"all Toast restaurants in Austin that are NOT on OpenTable"* without touching a spreadsheet formula. ### Why use it / who's it for - **Restaurant-tech & POS sales reps** (Toast, Square, Clover, Lightspeed, TouchBistro, Revel, SpotOn competitors) who need a list of prospects **not yet on their platform**, or already on a rival's — the classic "switch me" pitch. - **Reservation and online-ordering SaaS teams** (OpenTable, Resy, Tock, ChowNow, Olo, BentoBox, Popmenu) prospecting restaurants that have a website but no booking widget installed. - **Delivery platform BD teams** (DoorDash, Uber Eats, Grubhub) mapping which restaurants in a market are exclusive to a competitor and open to a second listing. - **Local marketing & web agencies** targeting restaurants with weak or missing tech stacks (no reservation system, no online ordering) as a "we can set this up for you" cold-email angle. - **Franchise and market researchers** who want a tech-adoption snapshot of a city's restaurant scene by cuisine. ### How to use it 1. Enter a **location** (city + region/country, e.g. `"Austin, Texas"`). 2. Optionally set a **cuisine filter** (e.g. `italian`, `sushi`, `mexican`) and include/exclude fast food or cafes. 3. Leave **crawlEmails** on to get emails + tech-stack detection (or turn it off for a faster listing-only run). 4. Optionally filter to **onlyWithWebsite** or **onlyWithEmail** to keep the list cold-email-ready. 5. Run it, then export the dataset to CSV, JSON, or Excel and filter by `pos_system`, `reservation_system`, `online_ordering`, or `delivery_platforms`. ### Output fields | Field | Description | |---|---| | `name` | Restaurant name | | `cuisine` | Cuisine tag (e.g. italian, pizza) | | `amenity` / `tourism` / `shop` | Raw OpenStreetMap category tags | | `address`, `city`, `state`, `postal_code` | Parsed street address | | `phone` | Phone number | | `website` | Restaurant's website URL | | `email` | Primary contact email (site or listing) | | `emails` | All emails found while crawling the site | | `facebook`, `instagram` | Social profile URLs | | `opening_hours` | OpenStreetMap opening hours string | | `latitude`, `longitude` | Coordinates | | `pos_system` | Detected POS platforms — Toast, Square, Clover, Lightspeed, TouchBistro, Revel, SpotOn | | `reservation_system` | Detected reservation platforms — OpenTable, Resy, Tock, Yelp Reservations, SevenRooms, Tablein | | `online_ordering` | Detected ordering platforms — ChowNow, Olo, Chowly, Slice, Toast Online Ordering, BentoBox, Popmenu | | `delivery_platforms` | Detected delivery platforms — DoorDash, Uber Eats, Grubhub, Seamless, Postmates | | `tech_signals` | Flattened list of every tech signal detected (all of the above combined) | | `has_email`, `has_phone`, `has_website` | Boolean flags for quick filtering | | `osm_type`, `osm_id`, `source` | Source record reference (OpenStreetMap) | ### Input example ```json { "location": "Austin, Texas", "cuisine": "italian", "includeFastFood": false, "includeCafes": false, "maxItems": 100, "crawlEmails": true, "onlyWithWebsite": true, "onlyWithEmail": false, "maxPagesPerSite": 3, "concurrency": 8 } ```` Only `location` is required — every other field has a sensible default. ### How much does it cost? This actor uses **pay-per-event pricing: $0.005 per result ($5 per 1,000 leads)**, effective 2026-07-16. You're charged only for restaurant leads actually returned — no subscription, no charge for empty runs. New Apify accounts get roughly **1,000 free results per month** on the platform's free tier, so you can pull a full mid-size city's restaurant list and test the tech-stack detection before paying anything. A 5,000-lead multi-city pull costs $25; a 20,000-lead national campaign costs $100. ### FAQ #### Is it legal to scrape restaurant contact and tech-stack data? Yes. This actor only reads **publicly available data**: OpenStreetMap listings and restaurants' own public websites (home, contact, about, menu pages). It doesn't log in, bypass paywalls, or access private data. Always follow applicable email-outreach laws (CAN-SPAM, GDPR, etc.) once you use the leads. #### How fresh is the data? Restaurant listings come from **live OpenStreetMap queries** at run time, and tech-stack signals are detected by **crawling each website live** during the run — so results reflect what's on the site today, not a cached snapshot. Re-run the actor periodically to catch restaurants that switch POS, reservation, or delivery providers. #### How accurate is the tech-stack detection? Detection is signal-based: the crawler looks for platform-specific script tags, embed domains, and links (e.g. `toasttab.com`, `opentable.com`, `doordash.com`) across a restaurant's site pages. It's highly accurate when a platform is embedded on the site, but restaurants using a tool only in the back office (with no public-facing widget) won't show a signal for it. #### Can I filter for restaurants NOT using a specific platform? Yes — this is the core use case. Run the actor for your target city, export to CSV/JSON, then filter rows where `pos_system` (or `reservation_system` / `delivery_platforms`) does **not** contain your competitor's name, or contains a rival's. That gives you a ready-to-pitch "switch" list. #### Do I need an API key or proxy? No. Discovery uses OpenStreetMap (Nominatim + Overpass) and enrichment crawls each restaurant's own public website — no API keys, no anti-bot bypass, and it runs fine on datacenter IPs. ### Other Flash Scrape lead tools - [Local Business Leads Scraper](https://apify.com/oriented_wallpaper/local-business-leads) — any local business category, by city, with website platform detection - [Google Maps Leads Scraper](https://apify.com/oriented_wallpaper/google-maps-leads-opener) — Google Maps business leads with AI-written cold-email openers - [Company & Domain Enricher](https://apify.com/oriented_wallpaper/company-domain-enricher) — turn a domain list into full company records with tech stack and socials - [Bulk Email Verifier](https://apify.com/oriented_wallpaper/email-verifier) — verify and score every email address before you send a campaign - [Hotel Host Leads Scraper](https://apify.com/oriented_wallpaper/hotel-host-leads-scraper) — hotel and hospitality leads with the same enrichment approach # Actor input Schema ## `location` (type: `string`): City and region/country to search, e.g. 'Austin, Texas', 'Lyon, France', 'Casablanca, Morocco'. ## `cuisine` (type: `string`): Optional cuisine to filter by, e.g. 'italian', 'pizza', 'sushi', 'mexican'. Leave empty for all. ## `includeFastFood` (type: `boolean`): Also include fast-food establishments. ## `includeCafes` (type: `boolean`): Also include cafes and coffee shops in the results (off = restaurants only). ## `maxItems` (type: `integer`): Maximum number of restaurants to return. ## `crawlEmails` (type: `boolean`): Visit each restaurant's own website to extract contact emails and detect its POS / reservation / ordering / delivery tech stack. Turn off for a faster, listing-only run. ## `onlyWithWebsite` (type: `boolean`): Drop restaurants that have no website (websites are required for email + tech-stack enrichment). ## `onlyWithEmail` (type: `boolean`): Keep only restaurants where an email was found (best for cold email). ## `maxPagesPerSite` (type: `integer`): How many pages (home, contact, about, menu...) to crawl per website while looking for an email. ## `concurrency` (type: `integer`): How many websites to crawl in parallel. ## Actor input object example ```json { "location": "Austin, Texas", "includeFastFood": false, "includeCafes": false, "maxItems": 100, "crawlEmails": true, "onlyWithWebsite": true, "onlyWithEmail": false, "maxPagesPerSite": 3, "concurrency": 8 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "location": "Austin, Texas", "onlyWithWebsite": true }; // Run the Actor and wait for it to finish const run = await client.actor("oriented_wallpaper/restaurant-leads-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 = { "location": "Austin, Texas", "onlyWithWebsite": True, } # Run the Actor and wait for it to finish run = client.actor("oriented_wallpaper/restaurant-leads-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 '{ "location": "Austin, Texas", "onlyWithWebsite": true }' | apify call oriented_wallpaper/restaurant-leads-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=oriented_wallpaper/restaurant-leads-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Restaurant Leads Scraper - Email + POS/Booking Tech Stack", "description": "Find restaurants by city and get contactable B2B leads enriched with their tech stack: POS (Toast, Square), reservations (OpenTable, Resy), online ordering (ChowNow, Olo) and delivery (DoorDash, Uber Eats). Includes name, address, phone, website, email & cuisine. No API key.", "version": "0.1", "x-build-id": "luqeApUYIV624ff0i" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/oriented_wallpaper~restaurant-leads-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-oriented_wallpaper-restaurant-leads-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/oriented_wallpaper~restaurant-leads-scraper/runs": { "post": { "operationId": "runs-sync-oriented_wallpaper-restaurant-leads-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/oriented_wallpaper~restaurant-leads-scraper/run-sync": { "post": { "operationId": "run-sync-oriented_wallpaper-restaurant-leads-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "location" ], "properties": { "location": { "title": "Location", "type": "string", "description": "City and region/country to search, e.g. 'Austin, Texas', 'Lyon, France', 'Casablanca, Morocco'." }, "cuisine": { "title": "Cuisine filter", "type": "string", "description": "Optional cuisine to filter by, e.g. 'italian', 'pizza', 'sushi', 'mexican'. Leave empty for all." }, "includeFastFood": { "title": "Include fast food", "type": "boolean", "description": "Also include fast-food establishments.", "default": false }, "includeCafes": { "title": "Include cafes", "type": "boolean", "description": "Also include cafes and coffee shops in the results (off = restaurants only).", "default": false }, "maxItems": { "title": "Max restaurants", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of restaurants to return.", "default": 100 }, "crawlEmails": { "title": "Crawl websites for email + tech stack", "type": "boolean", "description": "Visit each restaurant's own website to extract contact emails and detect its POS / reservation / ordering / delivery tech stack. Turn off for a faster, listing-only run.", "default": true }, "onlyWithWebsite": { "title": "Only places with a website", "type": "boolean", "description": "Drop restaurants that have no website (websites are required for email + tech-stack enrichment).", "default": false }, "onlyWithEmail": { "title": "Only places with an email", "type": "boolean", "description": "Keep only restaurants where an email was found (best for cold email).", "default": false }, "maxPagesPerSite": { "title": "Max pages per website", "minimum": 1, "maximum": 8, "type": "integer", "description": "How many pages (home, contact, about, menu...) to crawl per website while looking for an email.", "default": 3 }, "concurrency": { "title": "Concurrency", "minimum": 1, "maximum": 20, "type": "integer", "description": "How many websites to crawl in parallel.", "default": 8 } } }, "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 } } } } } } } } } } ```