Grailed Scraper — Streetwear Resale + Sold Prices | from $1.50/1K | No Proxy

Built for streetwear resellers doing arbitrage between Grailed, StockX, and eBay — and for brand analysts tracking which designers are trending in the secondary market.

Pricing: Two-tier PPE — $1.50/1K (active listings), $2.50/1K (sold listings with confirmed sold_price). First 10 results free.

No proxy. No login. Self-healing Algolia key rotation.

Search Grailed listings by keyword, designer, category, or size via the public Algolia search API. Returns clean structured data including price history, condition, designers, seller score, and sold status. No proxy required. Pay per result.

Features

• Search any keyword — brands, styles, items, designer names
• 24 normalized fields — price, price_drops, condition, designers, category, size, color, sold, seller_username, seller_score, seller_reviews, cover_photo, listing_url, created_at, and more
• parse_confidence field — quality indicator in every record (detects drift early)
• No proxy needed — Grailed uses a public Algolia search key by design
• Key-rotation fallback — if the hardcoded Algolia key rotates, the actor fetches a fresh key from the Grailed homepage automatically and retries
• Client-side filters — designerFilter, categoryFilter, sizeFilter for targeted collection

How it works

Grailed's search is powered by Algolia and uses a public, intentionally exposed search-only API key embedded in window.PUBLIC_CONFIG on every Grailed page load. This is by design — the same key powers the site's search UI. The Algolia endpoint (*.algolia.net) has no Cloudflare protection.

Key-rotation durability

The hardcoded Algolia key is extracted once:

app_id:  MNRWEFSS2Q
api_key: c89dbaddf15fe70e1941a109bf7c2a3d  (as of 2026-05-31)
index:   Listing_production

If this key ever rotates and returns HTTP 403, the actor automatically:

1. Fetches https://www.grailed.com to extract the fresh key via publicSearchKey from window.PUBLIC_CONFIG
2. Retries the search with the fresh credentials

This makes the actor self-healing against key rotations without any manual update.

Input

Field	Type	Default	Description
searchQuery	string	required	Keyword(s) to search (e.g. "nike vintage jacket")
maxItems	integer	100	Max listings to return (1–10,000)
designerFilter	string	""	Filter by designer name (partial, case-insensitive)
categoryFilter	string	""	Filter by category (partial, case-insensitive)
sizeFilter	string	""	Filter by size (exact, case-insensitive)

Output fields

Field	Type	Description
listing_id	int	Grailed numeric listing ID
title	string	Listing title
price	float	Current asking price (USD)
price_drops	list	Price drop history (descending)
condition	string	"new" / "new_with_tags" / "used"
designers	string	Comma-separated designer names
category	string	Top-level category (e.g. "outerwear")
category_path	string	Full category path (e.g. "outerwear.bombers")
size	string	Size string (e.g. "xl", "32")
color	string	Primary color
sold	bool	True if sold
sold_price	float	Final sale price (null if not sold)
seller_username	string	Grailed seller username
seller_score	float	Seller rating average (0–5)
seller_reviews	int	Number of seller reviews
cover_photo	string	Cover photo image URL
location	string	Seller location
marketplace	string	"grailed" / "hbx" / etc.
strata	string	"hype" / "luxury" / "sartorial" / "basic"
listing_url	string	Canonical Grailed listing URL
created_at	string	ISO 8601 UTC listing creation time
scraped_at	string	ISO 8601 UTC when this run started
parse_confidence	float	Data quality score 0–1
warnings	list	Quality warning codes (e.g. ["price_missing"])

Sample output record

{
  "listing_id": 97807015,
  "title": "Nike x Off White Virgil Abloh Air Max 90 Brand New",
  "price": 400.0,
  "price_drops": [],
  "condition": "new",
  "designers": "Nike",
  "category": "footwear",
  "category_path": "footwear.lowtop_sneakers",
  "size": "10",
  "color": "white",
  "sold": false,
  "sold_price": null,
  "seller_username": "luxcollector",
  "seller_score": 4.993,
  "seller_reviews": 146,
  "cover_photo": "https://media-assets.grailed.com/prd/listing/temp/9d13f47b437549a69008a0baa5885421",
  "location": "United States",
  "marketplace": "grailed",
  "strata": "hype",
  "listing_url": "https://www.grailed.com/listings/97807015",
  "created_at": "2026-05-31T15:47:24.368Z",
  "scraped_at": "2026-05-31T15:48:53Z",
  "parse_confidence": 1.0,
  "warnings": []
}

Use cases

• Market research — track prices and sold data for specific brands or items
• Price monitoring — collect price_drops history to find deals
• Resale arbitrage — compare Grailed prices vs other platforms
• Trend analysis — track which categories and designers are trending (strata field)
• Seller research — filter by seller_score for high-quality sources

Technical notes

• Grailed uses Algolia's Listing_production index with ~250k+ active listings
• Results are sorted by price_updated_at (default Algolia sort for this index)
• No login or proxy required — this is Grailed's own public search infrastructure
• The actor fetches 48 listings per page (Algolia max for this index)

Resale arbitrage example

Compare sold_price vs price to find underpriced listings:

• sold_price: 320 + price: 200 → $120 spread on a Jordan 1
• Use strata: "hype" filter to target the most liquid resale segment

vs. competitors

Feature	This actor	epctex/grailed-scraper (HTML)
Data source	Official Algolia API (public)	HTML scraping
price_drops history	Yes	No
sold_price (comp data)	Yes	Rarely
strata field (hype/luxury)	Yes	No
Key rotation fallback	Self-healing	Hard-coded (breaks on key change)
Price	$1.50/1K active, $2.50/1K sold	$3–5/1K flat

parse_confidence trust signal

Every record ships a parse_confidence score (0.0–1.0). Detects Algolia schema drift early.

Use with AI agents (MCP)

This actor is tagged MCP_SERVERS — compatible with Claude, GPT-4o, and other MCP-aware agents:

https://mcp.apify.com/?tools=bovi/grailed-listings

Pricing example

Volume	Mode	Cost
100 listings	Active	$0.15
1,000 listings	Active	$1.50
100 listings	Sold (with sold_price)	$0.25
1,000 listings	Sold (with sold_price)	$2.50

First 10 results are free. Active vs sold routing happens automatically — sold items with a confirmed sold_price use the $2.50/1K rate.

FAQ

Do I need a proxy or login? No. Grailed search runs on a public Algolia endpoint — no proxy, no login, no credentials required.

What if the Algolia key changes? The actor self-heals: if the hardcoded key returns a 403, it fetches a fresh key from the Grailed homepage automatically and retries. No manual update needed.

What output formats are available? JSON (default), CSV, and Excel — via the Apify dataset export or API.

What if search returns empty? Try a broader keyword. Algolia's Grailed index has ~250k+ active listings. Misspellings and very niche terms return empty — use partial brand names (e.g. "Nike" not "Nike Off-White Collab Limited").

Pricing

Two-tier pay-per-result:

• $1.50/1K — active listings (price, condition, designer, size)
• $2.50/1K — sold listings with confirmed sold_price (comp data for arbitrage)

No proxy costs. No COGS.

Integrations

Built for streetwear resellers running arbitrage across Grailed, StockX, and eBay, plus analysts tracking secondary-market trends — the JSON/dataset output drops into the tools you already run, no glue code:

• n8n / Make / Zapier — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: n8n, Make, Zapier.
• Webhooks — fire your own endpoint the moment a run finishes, to push results straight into your pipeline (docs).
• MCP server — expose this actor as a tool to Claude, Cursor, or any MCP client so an AI agent can pull this data mid-conversation (guide).
• API & SDKs — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all Apify integrations.

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• Kleinanzeigen Listings Scraper
• Leboncoin Listings
• Marktplaats Listings Scraper
• Mercadolibre Scraper
• Olx Listings
• Poshmark Listings

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.