Flipkart Reviews Scraper — Ratings, Verified Buyers

Flipkart Reviews Scraper is an Apify Actor for analysts, builders, and AI agents that need structured review data from a specific Flipkart product URL or product-reviews URL. It accepts one or more product links that include an itm... id and a pid= parameter, then returns one record per review plus an optional product_summary record per product. Each review record can include rating, title, full review text, reviewer name and location, verified-purchase flag, review date, helpful and unhelpful votes, the reviewed variant, review images, and source URLs. The dataset also includes the product rating summary when enabled, and the Actor is usable through Apify MCP.

Best fit and connected workflows

Use this Actor when your workflow starts with a known Flipkart product and needs review-level detail in a structured dataset.

It fits naturally when you want to:

• collect review text and metadata for sentiment analysis, tagging, or summarization
• compare review patterns across product variants
• combine product details from an upstream listing scraper with review engagement data
• feed an AI agent that needs Flipkart review records through Apify MCP or the Apify API
• build a review archive from multiple specific product links

For connected product context, pair it with Flipkart Product Scraper. A practical workflow is to scrape the parent product first, then pass the related product link into this Actor to gather review engagement data for the same item.

Practical scenario

An ecommerce analyst, Priya, starts with a Flipkart product page for a smartphone. She pastes the product URL into productUrls and keeps the summary setting on. The dataset returns review rating, title, body, author, location, isVerifiedPurchase, and reviewDate, plus a product summary with averageRating and ratingDistribution. Priya uses the helpful-vote counts and review text to identify recurring themes, then sends the dataset into a reporting workflow for weekly product monitoring.

What you get

Review fields

Field	Description	Example
recordType	Record type identifying review rows	review
productKey	Flipkart product id (pid) the record belongs to	MOBH2Z9HJYYQBFGD
productUrl	Canonical Flipkart product page URL	https://www.flipkart.com/.../p/...
reviewsUrl	Product-reviews page URL used for scraping	https://www.flipkart.com/.../product-reviews/...
reviewId	Flipkart review id when exposed by the layout	null
rating	Star rating from 1 to 5 for the review	5
title	Flipkart review title or sentiment tag, such as "Value-for-money"	Value-for-money
body	Full review text, expanded past the "READ MORE" or "more" toggle	The phone feels solid and the display is sharp.
author	Reviewer name	Asha
location	Reviewer city or region when shown	Bengaluru
isVerifiedPurchase	Marked true when the review shows "Certified Buyer" or "Verified Purchase"	true
reviewDate	Flipkart's displayed date, either absolute or relative	Aug, 2024
helpfulCount	Helpful votes shown on the review	12
unhelpfulCount	Unhelpful votes shown on the review	1
variant	The product variant the review refers to	Color Blue, Storage 128 GB
images	Flipkart CDN image URLs attached to the review	["https://rukminim2.flixcart.com/..."]
sortOrder	Sort mode used for the record set	MOST_HELPFUL
page	Source page number	1
scrapedAt	ISO-8601 UTC capture timestamp	2026-06-21T00:00:00.000Z

Product summary fields

When includeProductSummary is enabled, the dataset also includes one recordType: "product_summary" row per product with:

Field	Description	Example
totalRatings	Total ratings shown for the product	1842
totalReviews	Total review count shown for the product	312
averageRating	Average rating derived from the star distribution	4.4
ratingDistribution	Counts per star, with keys 5 through 1	{"5":1200,"4":412,"3":104,"2":63,"1":63}

Input

Input fields

Field	Type	Default	Notes
productUrls	array[string]	required	Flipkart product or product-reviews URLs. Each needs an itm... id and a pid= parameter.
maxReviews	integer	100	Per-product cap, 1-1000. Ten reviews are paginated per page.
sortBy	string	MOST_HELPFUL	Also MOST_RECENT, POSITIVE_FIRST, NEGATIVE_FIRST. Controls ordering served by Flipkart before paging.
includeProductSummary	boolean	true	Emits one product_summary record per product when enabled.
proxyConfiguration	object	Apify proxy default	Residential India proxy settings.
customProxyUrls	array[string]	[]	Optional residential or mobile India proxy URLs.
maxConcurrency	integer	3	Number of review pages fetched in parallel.

Valid input example

{
  "productUrls": [
    "https://www.flipkart.com/samsung-galaxy-m35-5g-thunder-grey-128-gb/p/itm24a2a82cf1782?pid=MOBH2Z9HJYYQBFGD"
  ],
  "maxReviews": 50,
  "sortBy": "MOST_RECENT",
  "includeProductSummary": true,
  "proxyConfiguration": {
    "useApifyProxy": true
  },
  "customProxyUrls": [],
  "maxConcurrency": 3
}

Output

Results are written to the Apify dataset named in the Actor output. The dataset contains all scraped review records and, when enabled, one product_summary record per product. You can read the dataset as JSON, CSV, Excel, HTML, or RSS through the Apify platform.

Output fields

Field	Type	Meaning	Example
recordType	string	review for an individual review, product_summary for the aggregate product record	review
productKey	string	Flipkart product id (pid) the record belongs to	MOBH2Z9HJYYQBFGD
productUrl	string	Canonical Flipkart product page URL	https://www.flipkart.com/.../p/...
reviewsUrl	string	Product-reviews page URL used for scraping	https://www.flipkart.com/.../product-reviews/...
reviewId	string	Flipkart review id when exposed by the layout	null
rating	integer	Star rating 1-5	5
title	string	Review title or sentiment tag	Value-for-money
body	string	Full review text expanded past the toggle	The phone feels solid and the display is sharp.
author	string	Reviewer name	Asha
location	string	Reviewer location when shown	Bengaluru
isVerifiedPurchase	boolean	Verified purchase marker	true
reviewDate	string	Flipkart's displayed date	Aug, 2024
helpfulCount	integer	Helpful votes	12
unhelpfulCount	integer	Unhelpful votes	1
variant	string	Reviewed variant	Color Thunder Grey, Storage 128 GB
images	array[string]	Review image URLs	["https://rukminim2.flixcart.com/image/..."]
sortOrder	string	Sort mode used for the records	MOST_HELPFUL
page	integer	Source page number	1
totalRatings	integer	Product summary only	1842
totalReviews	integer	Product summary only	312
averageRating	number	Product summary only	4.4
ratingDistribution	object	Product summary only, star counts from 5 to 1	{"5":1200,"4":412,"3":104,"2":63,"1":63}
scrapedAt	string	ISO-8601 UTC capture timestamp	2026-06-21T00:00:00.000Z

Illustrative output record

{
  "recordType": "review",
  "productKey": "MOBH2Z9HJYYQBFGD",
  "productUrl": "https://www.flipkart.com/samsung-galaxy-m35-5g-thunder-grey-128-gb/p/itm24a2a82cf1782?pid=MOBH2Z9HJYYQBFGD",
  "reviewsUrl": "https://www.flipkart.com/samsung-galaxy-m35-5g-thunder-grey-128-gb/product-reviews/itm24a2a82cf1782?pid=MOBH2Z9HJYYQBFGD",
  "reviewId": null,
  "rating": 5,
  "title": "Value-for-money",
  "body": "The phone feels solid and the display is sharp. Battery backup is good for daily use.",
  "author": "Asha",
  "location": "Bengaluru",
  "isVerifiedPurchase": true,
  "reviewDate": "Aug, 2024",
  "helpfulCount": 12,
  "unhelpfulCount": 1,
  "variant": "Color Thunder Grey, Storage 128 GB",
  "images": [
    "https://rukminim2.flixcart.com/image/..."
  ],
  "sortOrder": "MOST_HELPFUL",
  "page": 1,
  "scrapedAt": "2026-06-21T00:00:00.000Z"
}

How it works

The Actor uses a Chromium browser with PlaywrightCrawler, Apify Residential proxy pinned to India, a fingerprint pool, and a session pool with cookie persistence. It is built to handle Flipkart's rotating obfuscated CSS classes by anchoring extraction on the stable buyer-label text and reading review fields positionally. The same extractor works across the classic web review front-end and the React-Native-Web review front-end.

Reviews are paginated with &page=N, and the Actor de-duplicates across pages. It also expands truncated review bodies before extraction. Image, media, and font bytes are skipped during download so the crawler keeps focus on page content and review image URLs.

Pricing

This Actor uses Pay per event plus Apify platform usage. Open the live Pricing tab for the current event pricing and platform usage details.

• Actor start: charged once per run
• Review scraped: charged for each review written to the dataset

The primary event is Review scraped. The product summary record is included in the dataset and is not charged as a review event.

For example, if you scrape one hundred reviews, you are billed for one hundred review events, plus the run start event. Use maxReviews to shape the number of review events per product.

Use with AI agents (MCP)

This Actor is available as an Apify Actor usable through Apify MCP. It is designed for tools that need structured Flipkart review retrieval from a specific product link.

Tool description: Fetch Flipkart review records and optional product summary data for one or more product URLs, then return a dataset that an agent or downstream automation can read.

Actor identity: khadinakbar/flipkart-reviews-scraper

"Scrape this Flipkart product URL and return the review dataset with rating, title, body, author, location, verified purchase status, review date, helpful votes, images, and product summary when available. Use the canonical product or product-reviews URL only."

Output interpretation for agents

• review records are the individual review facts
• product_summary records provide aggregate rating context for the same product
• productUrl and reviewsUrl identify the source pages used
• scrapedAt helps agents distinguish the capture time from the review date shown on Flipkart

Provenance, scope, pagination, and cost guidance

The output is sourced from public Flipkart review pages only. Pagination is handled automatically, with about ten reviews per page. maxReviews is a per-product cap, so agents can use it to bound dataset size and spend. sortBy changes the order in which Flipkart serves reviews, while includeProductSummary controls whether the per-product aggregate record is added.

Example with the Apify API

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
  token: process.env.APIFY_TOKEN,
});

const run = await client.actor('khadinakbar/flipkart-reviews-scraper').call({
  productUrls: [
    'https://www.flipkart.com/samsung-galaxy-m35-5g-thunder-grey-128-gb/p/itm24a2a82cf1782?pid=MOBH2Z9HJYYQBFGD',
  ],
  maxReviews: 10,
  sortBy: 'MOST_HELPFUL',
  includeProductSummary: true,
});

const datasetId = run.defaultDatasetId;
const { items } = await client.dataset(datasetId).listItems();

console.log(items);

Best results and outcome guidance

• Use specific product or product-reviews URLs with both itm... and pid= present.
• Set maxReviews per product to keep the dataset focused on the review depth you need.
• Use MOST_RECENT when you want the newest public review sequence, and MOST_HELPFUL when you want high-engagement reviews first.
• Keep the summary enabled when you want both review-level detail and a per-product aggregate in the same dataset.
• For multi-product jobs, keep each URL tied to one product so the output remains easy to group by productKey.
• If you are routing outputs into a downstream AI step, read recordType first so the agent can separate review rows from summary rows.

Design note

I found that the live dataset contract exposes recordType with exactly two values: review and product_summary. That makes the output shape straightforward to route in downstream code because each record clearly signals whether it is a single review or the per-product aggregate.

FAQ

Can I use a search results URL or category page?

This Actor is routed for specific Flipkart product or product-reviews URLs. Search and category discovery belongs in an upstream workflow, then you can pass the selected product links here.

Which Actor pairs well before this one?

Flipkart Product Scraper is the natural upstream step when you want the parent listing context first, then the related review data from this Actor.

What does maxReviews control?

It sets the maximum number of reviews collected per product URL, with pagination handled automatically.

Does sortBy change which reviews exist?

It changes the order Flipkart serves before paging. It is an ordering control, not a filter on the review set.

Can I use this with another Flipkart scraper?

Yes. A common workflow is to use Flipkart Product Scraper for product context, then this Actor for review-level engagement data.

What if I only need the aggregate rating data?

Keep includeProductSummary enabled and read the product_summary record for each product. If you need only individual reviews, you can disable the summary output.

Related Actors

• Flipkart Product Scraper - Use this upstream to collect the parent product context, then pass the product link into this Actor for review-level engagement data.

Responsible use

Use this Actor in ways that respect Flipkart's terms, applicable law, and the privacy of reviewers. The dataset can contain reviewer names, locations, and public review text, so handle it with appropriate care in your own systems.