Flipkart Products Scraper

Extract Flipkart product records from search queries, search/category URLs, and direct product URLs. No login or cookies needed.

This actor follows the Browse.sh Flipkart skill path: fetch the server-rendered search or product page, parse window.__INITIAL_STATE__, and extract product cards from PRODUCT_SUMMARY widgets. Search-card mode is the default because it is faster and cheaper than browser automation and already contains price, MRP, discount, rating, review count, key specs, images, product IDs, listing IDs, and product URLs.

Features

• Search query mode using https://www.flipkart.com/search?q=...
• Search/category URL mode that preserves existing Flipkart URL filters
• Direct product URL mode for /p/itm... pages
• Pagination, sort controls, max item limits, and request retries
• Post-filters for minimum price, maximum price, minimum rating, and sponsored-only exclusion
• Optional product detail enrichment for best-effort full specs, description, warranty, seller, offers, and rating histogram
• Optional bounded review samples from Flipkart product-review pages, including reviewer, rating, title, text, certified-buyer flag, helpful count, media, and rating summary
• Optional sellers comparison fetch with explicit ok, no_data, or failed status when Flipkart exposes or withholds seller records in server-rendered state
• Resolved Flipkart CDN image URLs from templated {@width} / {@height} / {@quality} URLs
• Review and sellers comparison URLs generated for downstream review/seller workflows
• Run-level duplicate suppression
• Smart Scrape mode to emit only new or changed products on scheduled runs
• Run artifacts in key-value store: SUMMARY, SEARCH_SPECS, and FAILED_TARGETS
• Optional debug HTML capture for blocked or malformed Flipkart responses

Example Input

{
  "searchQueries": ["laptop", "iphone 15"],
  "sortBy": "popularity",
  "maxItemsPerInput": 50,
  "maxPagesPerInput": 3,
  "minRating": 4,
  "includeDetails": false,
  "includeReviews": false,
  "includeSellers": false,
  "onlyNew": false,
  "proxyConfig": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Category URL Example

{
  "startUrls": [
    {
      "url": "https://www.flipkart.com/search?q=running%20shoes&sort=price_asc"
    }
  ],
  "maxItemsPerInput": 100,
  "organicOnly": true
}

Smart Scrape

Set onlyNew to true and keep the same cacheName for scheduled monitoring. The actor stores a material hash for each product/listing and only emits rows that are new or meaningfully changed.

{
  "searchQueries": ["gaming laptop"],
  "maxItemsPerInput": 100,
  "onlyNew": true,
  "cacheName": "flipkart-gaming-laptop-monitor"
}

Output

Each dataset item is a normalized Flipkart product row:

{
  "type": "product",
  "source": "flipkart",
  "status": "ok",
  "mode": "search",
  "query": "laptop",
  "page": 1,
  "sortBy": "popularity",
  "productId": "COMGZUYDFZXHWYWZ",
  "listingId": "LSTCOMGZUYDFZXHWYWZJ6V2KW",
  "url": "https://www.flipkart.com/samsung-galaxy-book4-.../p/itm...?pid=COMGZUYDFZXHWYWZ",
  "reviewUrl": "https://www.flipkart.com/samsung-galaxy-book4-.../product-reviews/itm...?pid=COMGZUYDFZXHWYWZ",
  "sellersUrl": "https://www.flipkart.com/sellers?pid=COMGZUYDFZXHWYWZ",
  "brand": "SAMSUNG",
  "title": "Samsung Galaxy Book4 Metal Intel Core i5...",
  "subtitle": "15.6 Inch, Gray, 1.55 Kg, With MS Office",
  "sellingPriceInr": 56990,
  "mrpInr": 78189,
  "discountPercent": 27,
  "currency": "INR",
  "rating": 4.4,
  "ratingBase": 5,
  "ratingCount": 18987,
  "reviewCount": 1791,
  "keySpecs": ["Intel Core i5 Processor", "8 GB RAM", "512 GB SSD"],
  "primaryImageUrl": "https://rukminim2.flixcart.com/image/416/416/...",
  "imageUrls": ["https://rukminim2.flixcart.com/image/416/416/..."],
  "isSponsored": false,
  "detailStatus": "not_requested",
  "reviewStatus": "not_requested",
  "sellerStatus": "not_requested",
  "scrapedAt": "2026-06-02T00:00:00.000Z"
}

Pricing Status

Pricing is not applied yet. Private hosted Apify validation is documented in PRICING_RESEARCH.md, and the recommended unapplied API payloads are preserved in pricing-payload.recommended.json and publication-payload.recommended.json. Before publication or PPE pricing, apply those payloads deliberately and run a paid public smoke that confirms charge events and dataset quality.

Expected event shape after validation:

• apify-actor-start: low one-time start event
• product-listing: primary event for each search/category/product row emitted
• item-detail: optional event for each successfully detail-enriched product
• product-review: optional event for each attached review sample
• seller-offer: optional event for each attached seller offer record

Limitations

• Flipkart may return a reCAPTCHA shell instead of the SSR payload. The actor records this as a failed target and can save debug HTML when saveDebugHtml is enabled.
• Local non-proxy requests are likely to be blocked. Hosted validation should test residential proxy settings before parser changes.
• Product detail extraction is best-effort because Flipkart product-page __INITIAL_STATE__ layouts vary by vertical.
• Review extraction is a bounded product-row add-on, not a standalone high-volume review actor replacement.
• Sellers comparison extraction is best-effort. Some Flipkart sellers pages return an initialized SSR reducer without seller records; these rows report sellerStatus: "no_data" instead of fabricating seller offers.
• AI custom-field extraction and AI product-quality scoring are intentionally out of scope for the first low-memory HTTP actor.