Poshmark Scraper — Resale Listings by Category | $1.50/1K

Built for Poshmark resellers building inventory databases, fashion market researchers tracking category pricing, and dropshippers validating used clothing supply chains.

Pricing: Pay per listing — $1.50/1K. First 10 results free.

JSON-LD extraction (not brittle CSS selectors). No proxy. Search AND category modes.

Scrape Poshmark listings by category or search query. Extracts structured product data from Poshmark's embedded JSON-LD schema — no proxy required.

What you get

Each record contains:

Field	Description
name	Listing title
brand	Brand name
price	Asking price (USD)
currency	Currency code (always USD)
availability	InStock / OutOfStock / SoldOut
condition	Used / New
color	Color as listed
category	Full category path (e.g. Women > Jackets & Coats > Vests)
sku	Poshmark listing ID
image	Main product image URL
listingUrl	Direct link to the listing
description	Listing description (up to 2000 chars)
page	Catalog page number scraped from
parse_confidence	Data quality score 0.0–1.0
warnings	Machine-readable issue codes

How it works

1. Catalog page — fetches poshmark.com/category/<slug> or poshmark.com/search?query=<q> which contains an application/ld+json ItemList with ~48 listing URLs per page.
2. Listing detail — for each URL, fetches the listing page which contains an application/ld+json Product block with full structured data.
3. Paginates with ?page=N (category) or ?max_id=N (search) until maxItems reached.

~2 requests per item (catalog + detail). No proxy required — Poshmark's static pages work from Apify datacenter IPs.

Input

Field	Type	Description
category	string	Category slug, e.g. Women-Jackets_&_Coats, Men-Shirts, Kids-Dresses
searchQuery	string	Free-text search (overrides category when set)
maxItems	integer	Max listings to scrape (default 96, 0 = unlimited)

Category examples

Women-Jackets_&_Coats
Men-Shirts
Kids-Dresses
Women-Tops
Men-Pants_&_Chinos
Handbags
Shoes-Women-Boots

Competitive edge

• Structured JSON-LD extraction — more stable than CSS/XPath selectors
• parse_confidence on every record — instant data-quality visibility
• No proxy needed — zero buyer COGS on proxy
• Search + category — two access paths vs single-mode competitors

vs. competitors

Feature	This actor	epctex/poshmark-scraper
Data source	JSON-LD (structured)	HTML scraping
Category + search modes	Both	Usually 1
parse_confidence signal	Yes	No
Availability status (InStock/OutOfStock)	Yes	Rarely
Price	$1.50/1K	$3–5/1K

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/poshmark-listings

Pricing example

Volume	Cost
100 listings	$0.15
1,000 listings	$1.50
10,000 listings	$15.00

First 10 results are free. You pay only for listings successfully scraped (skipped/deleted listings are not charged).

FAQ

Do I need a proxy or Poshmark account? No. Poshmark's static listing pages work from Apify datacenter IPs — no proxy, no login needed.

What's the difference between category and searchQuery? category browses a specific Poshmark category path (e.g. Women-Jackets_&_Coats). searchQuery runs a free-text search. When both are set, searchQuery takes priority.

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

What if listings return empty or availability shows OutOfStock? Sold-out listings may return empty JSON-LD — they are skipped automatically. If a category returns no results, check the category slug format (examples in README above).

Pricing

Pay-per-event (PPE): charged per listing scraped — $1.50/1K. First 10 results free.

Limitations

• ~2 HTTP requests per listing (catalog discovery + detail fetch)
• Poshmark shows ~48 items per catalog page; search offset uses max_id
• Sold listings may return empty Product JSON-LD — skipped automatically
• No seller info in JSON-LD (would require HTML parsing)
• Seller details (rating, follower count) are not in JSON-LD and require a separate HTML parse — not included in this version.

Integrations

Built for resellers and fashion market researchers tracking used-clothing prices, brands, and inventory across categories — 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:

• Shopify Products Scraper
• Tiktok Shop Scraper
• Uber Eats Menu Scraper
• Vinted Marketplace
• Allegro Offers
• Amazon Products Scraper

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.