Myntra Price Tracker

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Myntra Price Tracker is a ready-to-run Apify Actor for collecting current public Myntra India fashion search results as structured records, helping teams compare INR prices, discounts, sizes, ratings, visibility, and source evidence without manually copying product cards.

• Fashion-specific context: Capture brands, article types, colors, seasons, sizes, variants, promotions, and sponsored placement when Myntra exposes them.
• 58 normalized fields: Store product identity, price, discount, availability, rating, content, source, and processing context in one Dataset.
• Traceable search snapshots: Retain the query, original rank, product URL, and source URL beside every publishable record.
• API and agent delivery: Export through Apify, schedule repeat runs, call the Actor API, or connect it to an MCP client.

Run a minimum 10-result test with a clear fashion model or style before increasing volume. Programmatic users can start from the API tab.

What does Myntra Price Tracker do?

Myntra Price Tracker sends your keyword to public Myntra India search, reads organic and sponsored product entries in their marketplace order, enriches results from publicly accessible product pages when possible, removes duplicates, normalizes the records, and stores up to your chosen ceiling. It is intended for current fashion-market observations rather than automatic price history.

The Actor can return a brand, title, product ID, seller SKU, article category, breadcrumb context, color and size variants, current price, original price, discount, coupon text, rating, review count, stock signal, images, specifications, country of origin, warranty, product URL, search URL, query, rank, and processing timestamp. All 58 schema fields remain stable across rows, but optional values are null when Myntra does not publish a reliable value for that product.

Myntra merchandising is especially sensitive to style language, size availability, promotions, and sponsored placement. A broad search may combine footwear, apparel, accessories, gender segments, and variants that are not directly comparable. The Actor preserves marketplace context rather than inventing a clean catalog match.

Each run is a snapshot. Price, coupon, stock, available sizes, rating count, sponsored status, and order can change after collection. Keep processed_at, query, rank, and source links with the data if you intend to detect change.

How do I run Myntra Price Tracker?

Open Myntra Price Tracker on Apify, provide the three required fields, and start the run. This guide uses one specific scenario throughout so the input, output, and API examples are easy to compare:

{
  "keyword": "Nike Pegasus 41 running shoes",
  "country": "India",
  "max_results": 10
}

The schema minimum for max_results is 10. The value is an upper bound, not a guaranteed row count. A run can store fewer products when Myntra returns fewer public matches, entries repeat, pages are unavailable, or records lack the minimum publishable title and product URL.

Inspect the first Dataset before scheduling. Confirm the rows belong to the intended Nike Pegasus 41 running shoes search, check that price-bearing rows use INR, and compare gender, size, color, seller, promotion, and article type before treating two offers as equivalents. A similar title does not prove an identical fashion variant.

For history, schedule the Actor and retain each run separately. Calculate changes in your own database, warehouse, or spreadsheet using product IDs or carefully reviewed product URLs. The Actor does not merge old and new snapshots or label a price movement automatically.

What data does Myntra Price Tracker return?

The default Dataset contains 58 normalized fields. The groups below describe the contract without implying that every optional value exists on every product.

Data group	Dataset fields
Images and content	main_image, image_urls, title, brand, badges, category, breadcrumbs, description, features
Identity and fashion variants	product_id, sku, gtin, mpn, model, specifications, manufacturer, origin, energy_class, included, condition, release_date, variant_group, variants
Price and promotion	price, was_price, unit_price, currency, price_text, discount_pct, discount_text, promo_text, installment_text
Availability and demand	stock_status, stock_text, sales_text, buy_limit
Rating and seller	rating, review_count, seller, seller_url, seller_score
Fulfillment	shipping_text, shipping_cost, delivery_text, pickup_text, return_text, return_days, warranty_text
Marketplace context	platform, site, country, product_url, source_url, query, rank, sponsored
Processing context	processor, processed_at

This sample documents shape only; it is not a current Myntra quote:

{
  "title": "Nike Pegasus 41 Road Running Shoes",
  "brand": "Nike",
  "category": "Sports Shoes",
  "price": 11895,
  "was_price": 13995,
  "currency": "INR",
  "discount_pct": 15,
  "rating": 4.5,
  "variants": [
    {"name": "size", "value": "UK 8", "in_stock": true}
  ],
  "platform": "myntra",
  "country": "IN",
  "query": "Nike Pegasus 41 running shoes",
  "rank": 1,
  "sponsored": false,
  "product_url": "https://www.myntra.com/example-product/buy",
  "processed_at": "2026-07-21T12:00:00+00:00"
}

Use the output schema for the authoritative field list. Null is different from zero: for example, a null review count means the source did not supply a dependable count, not that the item has no reviews.

What inputs can I configure?

Input	Type	Required	Accepted behavior	Example
keyword	string	Yes	A non-empty fashion product, brand, style, category, or search phrase	Nike Pegasus 41 running shoes
country	string	Yes	The exact supported country value	India
max_results	integer	Yes	A result ceiling from 10 through 10,000	10

Required values are not replaced with hidden defaults. The country is normalized case-insensitively to India, while unsupported markets are rejected. max_results must be a true integer; booleans do not pass the runtime check. Extra properties are rejected by the public input schema.

Use fashion-aware queries. A model and product type give a cleaner initial test than a generic word such as shoes. If the goal is assortment analysis, broader terms are valid, but expect mixed brands, genders, materials, price bands, and sponsored products.

What platforms and markets does Myntra Price Tracker cover?

This Actor is dedicated to Myntra for India (IN) and uses myntra.com as its marketplace source. There is no public platform selector because the actor name and runtime resolve to Myntra only. It does not claim that the same run searches Flipkart, Amazon, or other fashion stores.

The official Myntra running-shoes catalog visibly demonstrates why fashion context matters: product cards can expose brand, article title, sizes, selling price, original price, discount, rating, and sponsored placement. Availability and merchandising can vary with time, so a fresh Actor run remains the operational source for your own snapshot.

This Actor reads public marketplace information. It does not authenticate as a shopper, access an order history, add items to a cart, or manage a seller catalog.

Why use Myntra Price Tracker?

The Actor makes Myntra search observations repeatable and reviewable. A fashion brand can compare visible prices and discounts while retaining rank and sponsored status. A catalog team can group results by brand, article type, color, gender, season, or size signal. A merchandising analyst can keep source links beside every observation instead of relying on screenshots.

Fashion matching requires more than title similarity. Nike Pegasus 41 rows may differ by men's or women's sizing, colorway, seller, offer, or stock. Keeping variants, specifications, promo_text, breadcrumbs, and original URLs reduces false price comparisons.

The stable Dataset schema also makes scheduled exports easier to consume. Optional fields can be sparse, but column names remain consistent, allowing downstream validation rules to flag missing context instead of silently changing shape.

Who is Myntra Price Tracker for?

• Fashion brands monitoring public price, discount, placement, and assortment signals on Myntra India.
• Ecommerce analysts comparing brands, article types, sizes, colors, ratings, and price bands.
• Retail and procurement teams collecting source-linked marketplace evidence for manual review.
• Catalog teams enriching product records with public fashion attributes and images.
• Data teams building scheduled snapshots, dashboards, or change-detection pipelines.
• AI-agent builders retrieving structured fashion search context through API or MCP.

It is not a buying assistant that completes checkout, a private account tool, or an automatic repricer. Important commercial decisions should be verified at the live product page.

How can I use Myntra Price Tracker through the API or MCP?

The Apify API tab generates token-aware examples for supported clients. The public Actor ID is yff3Hh7lpcCkZ92uT.

{
  "actorId": "yff3Hh7lpcCkZ92uT",
  "input": {
    "keyword": "Nike Pegasus 41 running shoes",
    "country": "India",
    "max_results": 10
  }
}

Use schedules for repeat snapshots and webhooks when another service should process a finished run. Retrieve items from the default Dataset and keep the processing timestamp and source fields.

For AI clients, follow the official Apify MCP integration guide. Apify supports a Streamable HTTP endpoint with OAuth and Actor selection through the tools=actor parameter. Pass the same explicit input object rather than assuming the client can omit required fields.

How much does Myntra Price Tracker cost?

Billing uses one Actor start event and one result event for each row stored in the default Dataset. Local pricing metadata verified on 2026-07-21 defines:

Apify tier	Actor start	Price per stored result
FREE	$0.01000	$0.00830
BRONZE	$0.01000	$0.00802
SILVER	$0.01000	$0.00775
GOLD	$0.01000	$0.00747
PLATINUM	$0.01000	$0.00747
DIAMOND	$0.01000	$0.00747

At FREE pricing, a run that stores 10 results costs 0.01000 + (10 × 0.00830) = $0.09300. Fewer stored rows mean fewer result events. Review the current pricing tab before budgeting because published prices may change.

How does Myntra Price Tracker compare with alternatives?

Option	Best fit	Important trade-off
Myntra Price Tracker	Public Myntra India fashion search snapshots with normalized variant and source context	One marketplace and current observations only
Product Price Tracker	Compatible multi-source product comparisons	Broader output can require more matching judgment
Manual Myntra browsing	A handful of visual checks where a person reviews every variant	Slow to repeat and difficult to audit at scale
Custom collection workflow	Teams that need bespoke matching, storage, and controls	You own maintenance, delivery, validation, and marketplace changes

Choose this Actor when the problem is repeatable public Myntra search collection. Choose manual review for a few high-value decisions, and retain it as a verification step even in automated workflows.

What are the limits and troubleshooting steps?

Symptom or limit	Explanation and next step
Empty Dataset	Confirm country is India; try a recognizable fashion model with max_results: 10.
Fewer rows than requested	The limit is a ceiling; public matches may be sparse, duplicated, removed, or incomplete.
Electronics or unrelated items dominate	Rewrite the keyword with a fashion article type, brand, gender, or model.
Prices differ between similar titles	Compare size, color, gender, seller, coupon, bundle, and stock context.
Optional values are null	Myntra did not expose a reliable value for that record; preserve null rather than inventing data.
Rank or sponsored status changes	Marketplace merchandising and advertising are time-dependent.
A product link stops working	Products can sell out, move, or be removed after collection.
Cost looks unexpected	Count stored rows and apply the start-plus-result-event formula.

Public pages and availability change. The Actor cannot guarantee a fixed count, field completeness, ranking, response time, or continuous source availability. Validate the schema-minimum run before scaling.

Frequently asked questions

Does the Actor create price history?

No. Schedule runs and retain successive Datasets to build history externally.

Does max_results: 10 guarantee ten fashion products?

No. It is the maximum to store, and only valid unique public records are published.

Why can variants be incomplete?

Myntra may expose size or color information differently across search and product pages. The Actor returns only dependable public values.

Are sample prices current?

No. Examples document the schema. Run the Actor and verify product URLs for current observations.

Can I compare rows solely by title?

No. Check product ID, gender, article type, size, color, seller, promotion, and URL.

How is billing counted?

One start event is charged per run, plus one result event for each stored Dataset row.

Can an AI assistant call the Actor?

Yes. Use the Actor API or Apify MCP server and provide all required inputs.

Related TrueFetch Actors

• Product Price Tracker for compatible multi-source shopping searches.
• Flipkart Price Tracker for general India marketplace products.
• Amazon Price Tracker for Amazon country storefronts.

Support and last updated

Open an Actor issue with the input, run ID, expected behavior, and a small redacted output sample. You may also use the TrueFetch group or support channel.

The public Store page checked on 2026-07-21 reported 2 total users, 1 monthly active user, 0 ratings, and 0 bookmarks, while still displaying an older published README. These dated signals do not prove current extraction quality; local runtime and schema evidence govern this revision.

Last updated: 2026-07-21.