Flipkart Products Scraper

Collect public Flipkart search results and product pages as structured product rows for price comparisons, catalog research and marketplace analysis. This Flipkart products scraper returns available prices, ratings, images and product links. Access challenges and missing page data can limit results.

Quick start

1. Enter a search term or paste a Flipkart product URL.
2. Choose a small product limit and click Start.
3. Open the dataset and export JSON, CSV or Excel, or retrieve it through the API.

{
  "searchQueries": ["wireless earbuds"],
  "maxItems": 20,
  "pageLimit": 2,
  "includeProductDetails": false
}

Provide at least one search query or product URL. You can combine both, with at most 100 total entries. Results reflect the pages accessible during that run, not a complete Flipkart catalog.

Prefer a video guide? This short Apify walkthrough shows you how to set up and run an Actor. It uses a different Actor, but the setup steps are the same.

Ready-to-run examples

Choose a public example to see its settings before starting a run:

• Monitor Flipkart Wireless Earbuds Prices and Offers

What you get

Saved product rows contain available identifiers, source links, titles, price and rating fields, images and collection context. Direct-URL failures can also create separate error rows. Inspect status before treating a dataset row as a product, and leave optional values unknown when not exposed.

Who is it for?

• Retail researchers comparing displayed product prices and offer labels.
• Catalog teams collecting product identities and public metadata.
• Marketplace analysts reviewing bounded search or direct-product samples.
• Data teams comparing their own dated product exports.

How you can use the data

• Prepare an earbuds research sheet. Use the existing wireless-earbuds search with small product and page caps. Keep pid, productUrl, title, price, currency and scrapedAt together, then inspect source pages before comparing offers.
• Inspect a known product directly. Use the existing product-URL shape with a one-product cap. Direct URLs are always read as product pages, regardless of the optional search-enrichment setting.
• Choose optional details for the fields you need. Compare available offers, rating counts or category breadcrumbs after enrichment. Detail visits can replace listing values with missing values, brand can be inferred from the title and seller name remains unpopulated.
• Compare your own exports downstream. Join records using product identifiers or URLs and keep their source and collection time. Compare price observations in your own system rather than expecting built-in history or price alerts.

Input settings

Setting	API key	Purpose
Search queries	searchQueries	Search terms. Supply this, product URLs, or both.
Product URLs	productUrls	HTTPS Flipkart product-page URLs, entered as URL objects.
Maximum products	maxItems	Product-row limit across the run. Default 20.
Search pages	pageLimit	Pages per query. Default 2.
Search order	sortBy	Flipkart's requested search order. Default relevance.
Product details	includeProductDetails	Best-effort detail visits for search results. Default false.
Request timeout	requestTimeoutSecs	Per-request timeout. Default 30 seconds.
Proxy settings	proxyConfiguration	Connection settings. See field help for defaults and supported overrides.

Searches run before direct product URLs and share the product limit. The same product is saved once per run. Sorting requests Flipkart's order, not a guaranteed globally sorted dataset. Direct URLs are always read as product pages, regardless of includeProductDetails.

For direct product lookup, use this input shape with your chosen product URL:

{
  "productUrls": [
    { "url": "https://www.flipkart.com/realme-buds-t110-rma2306-ai-enc-calls-38-hours-playback-deep-bass-bluetooth/p/itma5620a39a816e?pid=ACCH4HY6GRGJYUU8" }
  ],
  "maxItems": 1
}

Output data

Each dataset item is a product row or a direct-URL error row. The following synthetic shape-only excerpt illustrates keys, not an observed product or current price:

{
  "inputType": "search",
  "status": "ok",
  "error": null,
  "title": "Example product",
  "productUrl": "https://www.flipkart.com/",
  "price": null,
  "currency": null,
  "sellerName": null,
  "offers": [],
  "warnings": []
}

Fields	Meaning
title, productUrl, pid, imageUrl	Available product identity and image.
price, originalPrice, currency, discount	Price data and labels. Missing values are null.
rating, ratingCount, reviewCount	Available rating/count data, not review text. Review counts may be available on product pages.
availability, brand, offers, categoryBreadcrumbs	Best-effort product information. Brand may be inferred from the title.
sellerName	Reserved field, currently always null. Seller extraction is not supported.
query, inputType, page, position, rankOnPage	Search context. Direct-URL rows have no search ranking.
status, error, warnings, sourceMethod	Row outcome and provenance. Inspect error rows separately from products.
sourceUrl, scrapedAt	Source page and extraction timestamp.

Product-page enrichment can replace listing values with missing values where the product page does not supply them. A status: "ok" row does not guarantee every field is populated.

The RUN_SUMMARY key-value record reports saved products, error rows, failed units, explicit empty searches, warnings and stopping flags. Check it alongside the dataset: some search failures produce warnings rather than dataset error rows. Automatic resume/checkpoint recovery is not supported.

Download your results

Open your run's dataset, choose Export and select JSON, CSV or Excel. JSON keeps nested fields together. CSV and Excel are useful when you want to review the records in a spreadsheet.

See Apify's download and export guide for the available formats and field options.

Pricing

Pay-per-event pricing uses a start event once when extraction begins and an item event for each extracted product row saved. Direct-URL error rows are saved without an item charge.

Use the product and page caps to bound work. The item cap limits products rather than additional error rows, and optional detail enrichment adds requests. See the live Pricing tab for current rates and discounts.

Tips for best results

• Start with one focused search and small caps. Inspect available fields before adding more sources.
• Separate product rows from errors. Review status, warnings and summary outcomes before counting records as products.
• Use direct-only input deliberately. Searches run first and share the saved-product cap, so omit search queries if you want only supplied product URLs.
• Enable search-detail visits only when useful. Direct product URLs are always read as product pages, and enrichment does not guarantee fuller values.
• Read the summary before repeating work. Some search failures create warnings instead of dataset error rows, and automatic resume is unsupported.

Limits and caveats

Flipkart can block requests or change its pages. Optional values can be missing, and an explicit no-results search can return an empty dataset. Other extraction problems can leave partial records and warnings. Completion alone does not establish data completeness.

Searches run before direct product URLs and share the whole-run product cap. Duplicate products are saved once. Requested sorting uses Flipkart's order rather than a globally sorted dataset, and direct URLs have no search ranking. Error rows are additional to the product cap.

Detail enrichment can replace listing values with missing values. An ok status does not guarantee every field, brand can be inferred from titles and seller name remains null. The Actor does not retrieve private data, review text, full specifications or seller profiles and is not affiliated with Flipkart.

RUN_SUMMARY reports saved products, error rows, failed units, explicit empty searches, warnings and stopping flags. Some search failures appear as warnings rather than dataset errors. Automatic resume or checkpoint recovery is not supported.

Automation and integrations

Save your settings for next time

Once you have an input that works for you, save it as an Apify task. This keeps your settings together so you can use them again without filling in the form from scratch.

Run on a schedule

Need fresh results regularly? You can schedule the Actor or a saved task to run at a time that suits you.

Each scheduled run collects the data again. If you want to compare results or highlight changes, add that step in your own workflow.

Send results to your other tools

Use n8n, Make or Zapier to connect this scraper to the rest of your workflow. For example, you could pass its results to an AI assistant or add records to a spreadsheet. Follow the Apify integration guides for your chosen tool.

For a custom connection, an Apify webhook can notify your app when a run finishes or fails. Your app can then fetch the results or handle the failure.

API usage

Want to collect this data from your own app or automation? The examples below send input to the scraper and show how to collect its results.

The Node.js and Python examples wait for the run to finish before reading its results. The cURL example starts the run and returns its details. Wait for that run to finish, then use its defaultDatasetId to fetch the results.

For setup instructions, see Apify's JavaScript client guide, Python client guide and Actor runs API reference.

cURL

curl -X POST 'https://api.apify.com/v2/acts/fetch_cat~flipkart-products-scraper/runs' \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  --data @- <<'JSON'
{
  "searchQueries": [
    "wireless earbuds"
  ],
  "maxItems": 20,
  "pageLimit": 2,
  "includeProductDetails": false
}
JSON

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/flipkart-products-scraper').call({
  "searchQueries": [
    "wireless earbuds"
  ],
  "maxItems": 20,
  "pageLimit": 2,
  "includeProductDetails": false
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('fetch_cat/flipkart-products-scraper').call(run_input={'searchQueries': ['wireless earbuds'], 'maxItems': 20, 'pageLimit': 2, 'includeProductDetails': False})
print(client.dataset(run['defaultDatasetId']).list_items().items)

MCP and AI agents

You can ask your AI assistant to collect data with Flipkart Products Scraper and help you work with the results. The scraper returns the data, and your assistant can then summarize it, compare records or prepare notes.

Connect your assistant through the Apify MCP server. MCP gives your assistant a way to find this scraper, run it and read the results. Follow the official setup and sign-in guide for your app.

Easier setup with an Apify plugin

If you use a supported coding assistant, the official Apify plugin can set up the connection for you. It connects your assistant to Apify and includes instructions for common tasks. Follow the guide for your particular app.

Use Apify AI in Console

Prefer to keep everything in Apify? Open Apify AI in Console and ask it to find Fetch Cat's Flipkart Products Scraper. You can describe the data you want and ask questions about the results.

Apify AI is currently in beta. It asks for confirmation before starting an Actor, and normal run charges apply.

Choose your assistant

• Claude and Claude Code (Anthropic): Apify setup guide.
• ChatGPT and Codex (OpenAI): Apify setup guides.
• Grok Bot (xAI): Apify plugin guide.
• Gemini CLI (Google): MCP server setup guide. Use the server URL below with the HTTP configuration supported by Gemini CLI.

Connect just this scraper

To keep your setup focused on this scraper, add this server configuration to a compatible MCP client:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=fetch_cat/flipkart-products-scraper"
    }
  }
}

Sign in to Apify when your client asks. If your client uses a Bearer token instead, follow its setup guide and keep the token in its secure configuration. Configuration formats vary by app, so use the linked guide if your client needs a different format.

Use Apify's general server

Already connected to the general Apify MCP server at https://mcp.apify.com? You can use it to find this scraper without adding another server. Ask your assistant to use Flipkart Products Scraper by Fetch Cat, or give it the identifier fetch_cat/flipkart-products-scraper.

Your assistant can check the available settings and retrieve the results from the returned run. If the run is still working, it should follow that run rather than start it again. Both connection options use Apify's hosted service.

Example requests

• “Use Fetch Cat’s Flipkart Products Scraper to collect up to 5 wireless-earbuds products from at most 1 search page, with detail enrichment off. Show product IDs, titles, source links and available prices with currency.”
• “Retrieve one product from the direct Flipkart product URL I provide, with search queries omitted. Show available ratings, price and offer fields, separating any uncharged error row from product data.”
• “Collect up to 3 products for my supplied Flipkart search term with details enabled and one search page. Keep identifiers and collection times, marking missing or replaced values before I compare the export with my own spreadsheet.”

Normal Apify charges apply. Check the live Pricing tab before asking your assistant to run a larger batch.

Pay with x402

If your AI agent has a funded wallet, it can pay for Apify usage through x402. This experimental option lets an agent use Apify without a conventional account.

Apify AGI provides a prepaid API token with a spending cap after payment. Your agent can use that token to run this scraper through the Apify API or an authenticated MCP connection.

Some MCP setups also support paying for each request directly. That is a different payment option, so check the official guide for its availability.

Follow the official guide for wallet setup, supported payment options and current terms. Apify handles the payments. Keep wallet keys and payment tokens in your agent's secure configuration.

Other ways for agents to pay

Apify also supports MPP for prepaid access through AGI and Skyfire for eligible Actors. If your agent already uses one of these, see the agentic payment guide to choose the right setup.

FAQ

Can I get a Flipkart products dataset through an API?

Yes. Run the Actor with the API examples above, then retrieve the default dataset. It is an independent scraping tool, not Flipkart's official API.

Can I export Flipkart product images and prices?

The dataset includes an image URL and price when available, and can be exported to CSV, Excel or JSON. It does not download image files.

Does it return price history or product authenticity checks?

No. Each run collects a current snapshot. It does not verify authenticity, track previous prices or compare exports automatically.

Why are some fields empty or some products missing?

Public pages may omit data or challenge requests. Check status, error, warnings and RUN_SUMMARY. Seller names are not extracted. Narrow the search, lower the limits and omit detail enrichment when unnecessary.

Related Actors

• Amazon Products Search Scraper — Research Amazon listings.
• Walmart Products Scraper — Compare another retail catalog.
• AliExpress Products Scraper — Research marketplace products.
• eBay Marketplace Scraper — Explore eBay listings.
• Shopify Products Scraper — Collect products from Shopify storefronts.

Support and feedback

For custom additions or integrations, email hello@fetchcat.dev.

Found an error or have a feature request? Open an issue with the run URL, a non-sensitive input example and what you expected.

Need help sharing it? See Apify’s guide to sharing a run when reporting an issue. Sharing a run can give the developer access to its input, logs and results, so use a run that contains no sensitive data. Please leave out API tokens, passwords, cookies, private documents and personal data. Use a public sample URL or a redacted example instead.

Tried this scraper? We'd love to hear how it worked for you. Leave a review to help others choose the right tool and help us improve it. Your feedback means a lot to us.

Made with care for curious people, AI agents and the possibilities in data. ♥