The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Zomato Restaurant Scraper City, Cuisine, Contact listing page.
📦 Open source · MIT: github.com/AnshumanAtrey/zomato-restaurant-scraper
Scrape restaurant listings from Zomato by city and cuisine. Get structured records with name, phone, address, cuisines, rating, and cost for two — no Zomato account required.
Built for B2B sales teams, food-tech researchers, ghost-kitchen operators, lead-gen agencies, and competitor intelligence.
Typical 50-restaurant scan completes in 3-7 minutes.
Each restaurant is pushed as one dataset record:
| Field | Example |
|---|---|
name | Truffles |
url | https://www.zomato.com/bangalore/truffles-st-marks-road-central-bangalore/info |
slug | truffles-st-marks-road-central-bangalore |
phone | +91 80 4111 2400 |
address | 22, St Marks Road, Bangalore |
cuisines | American, Burger, Continental |
rating | 4.5 |
costForTwo | ₹800 |
city | bangalore |
area | Central Bangalore |
timestamp | 2026-05-22T09:43:18Z |
A final summary record reports restaurantsScraped, restaurantsFound, failures, duration, and sourceUrls.
Pick Mumbai, Best restaurants, maxRestaurants: 200. Export the CSV. You now have 200 verified phone numbers + addresses for outreach.
Pick Hyderabad, All restaurants, Cuisine: Biryani, maxRestaurants: 100.
Use the Custom city field under "Advanced overrides". Type Tirunelveli, Erode, Coorg, Manali, Madurai, Varanasi — auto-converted to the right Zomato URL.
Use the Custom cuisine field. Momos, Kebab, Hyderabadi, Awadhi all work.
Paste it into Custom URL under "Advanced overrides". Overrides every other dropdown.
Best restaurants (curated top picks), All restaurants, Dinner spots, or Breakfast spotsQuick — restaurant URLs + names only (fast, cheap, useful for bulk URL lists)Standard — full data: phone, address, rating, cost (default — recommended for B2B leads)Deep — full data with extra retries for blocked pages (slowest, highest yield)/info and /order URL patterns)/{city}/restaurants as a fallbackUses Apify Residential Proxy to avoid rate-limit issues.
$0.005 per restaurant record. A typical 50-restaurant scan costs ~$0.26.
| Scan size | Approx cost |
|---|---|
| 20 restaurants | $0.10 |
| 50 restaurants | $0.25 |
| 100 restaurants | $0.50 |
| 500 restaurants | $2.50 |
| 1000 restaurants | $5.00 |
No — it scrapes restaurant listings (name, phone, address, etc.), not menus. For menu data, use a different actor that hits individual restaurant pages with menu rendering.
Zomato organizes by cuisine category, not by individual dish. Specific dishes like dahi-puri don't have their own URL on Zomato. Use a broader category like Street Food or North Indian instead.
Zomato has no state-level URLs. Pick a specific city instead — e.g., for Tamil Nadu use Chennai, Coimbatore, or custom-city Madurai/Tirunelveli.
Yes. Use the Custom city field with the city name — Erode, Coorg, Manali, Varanasi, etc. The actor auto-slugifies and auto-falls-back to /{city}/restaurants if /best-restaurants doesn't exist for that smaller city.
Most likely causes:
Standard depthDubai, Abu Dhabi, Sharjah (UAE), Doha (Qatar), Muscat (Oman), Kuala Lumpur (Malaysia), Singapore, Manila, Colombo, Lisbon are in the dropdown. Other Zomato international cities (e.g., Sydney, Auckland) work via the Custom city field.
Pulled live from Zomato at run time. Phone numbers and addresses are exactly what Zomato displays today. Ratings may lag slightly behind Zomato's internal scoring.
Scrapes only publicly visible data on Zomato. You are responsible for complying with Zomato's Terms of Service, GDPR/DPDP, and local laws. Don't use phone numbers for mass cold-calling without consent.
Plus one summary record per run:
Enrich your restaurant lead lists with deeper intel:
Built by Anshuman Atrey using Apify SDK, Crawlee, Puppeteer, and Cheerio.
This scraper accesses publicly available data on Zomato. You are responsible for complying with Zomato's Terms of Service and applicable laws in your jurisdiction. Use the data responsibly — avoid mass cold-calling without consent.