How to Scrape Poshmark Listings and Closet Data (2026)

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Poshmark publishes millions of secondhand listings across fashion, sneakers, and home goods — and if you need to scrape Poshmark at scale, the good news is that a large portion of that data is exposed through both the web and an unofficial mobile API that returns clean JSON. the bad news is that Poshmark runs Cloudflare and fingerprints browser sessions aggressively, so naive requests will get you blocked within minutes. this guide covers the practical architecture for pulling listing data, closet pages, and sold history reliably in 2026.

What Data You Can Actually Get

Poshmark exposes three distinct data surfaces, each with different access patterns:

search results — listing cards with price, brand, size, condition, and seller handle, paginated via query params
individual listing pages — full description, original price, listing date, like count, share count, and offer eligibility
closet/seller pages — all active listings for a given user, follower count, sold count, and avg response time
sold listings — visible on seller closets under the “Sold” filter, includes sale price and sale date

Sold listings are the most valuable for resale pricing intelligence and arbitrage. if you’re building a price model for streetwear, the sold feed is ground truth. for a similar pattern on another platform, How to Scrape StockX Sneaker Pricing and Volume Data (2026) covers how to extract confirmed transaction prices from a more API-hostile target.

The Mobile API Approach (Recommended)

Poshmark’s iOS and Android apps talk to api.posh.mn with JSON responses. this endpoint is not officially documented but has been stable for years. you authenticate with a device fingerprint and receive a JWT, then hit search and closet endpoints directly. the responses are cleaner than parsing HTML and don’t require JavaScript execution.

Key endpoints:

GET https://api.posh.mn/api/v2/searches?category_v2=...&size=...&max_id=... — paginated search
GET https://api.posh.mn/api/v2/user_closets/{username} — closet listing feed
GET https://api.posh.mn/api/v2/pm_app_listings/{listing_id} — single listing detail

here’s a minimal Python snippet using httpx with a spoofed mobile UA:

import httpx
import time

HEADERS = {
    "User-Agent": "Poshmark/240 CFNetwork/1492.0.1 Darwin/23.3.0",
    "Accept": "application/json",
    "X-Poshmark-Device-Id": "abc123-your-spoofed-device-id",
}

def fetch_closet(username: str, proxies: dict) -> list:
    url = f"https://api.posh.mn/api/v2/user_closets/{username}"
    all_listings = []
    max_id = None

    while True:
        params = {"max_id": max_id} if max_id else {}
        resp = httpx.get(url, headers=HEADERS, params=params, proxies=proxies, timeout=15)
        resp.raise_for_status()
        data = resp.json()
        listings = data.get("data", {}).get("listings", [])
        if not listings:
            break
        all_listings.extend(listings)
        max_id = data.get("data", {}).get("next_cursor")
        if not max_id:
            break
        time.sleep(1.2)

    return all_listings

the X-Poshmark-Device-Id header needs to be consistent per session. rotating it on every request triggers bot detection. rotate the proxy, not the device ID.

Anti-Bot Landscape in 2026

Poshmark sits behind Cloudflare and applies TLS fingerprinting to API requests. a stock httpx or requests session with a residential IP will pass most checks, but you need to match the TLS hello that iOS sends. use curl_cffi or tls-client in Python to spoof the correct TLS fingerprint.

Approach	Success Rate	Speed	Cost
`requests` + datacenter proxy	~10% (blocked fast)	fast	$
`httpx` + residential proxy	~60-70%	fast	$$
`curl_cffi` + residential proxy	~85-90%	fast	$$
Playwright headless + residential	~90%+	slow (5-10x)	$$$
Scraping API (e.g. Oxylabs, Brightdata)	~95%+	medium	$$$$

Playwright is a fallback for listing detail pages that require JS render, not for bulk closet scraping. it’s too slow and expensive to run headfully at scale. How to Scrape Mercari Marketplace Product Data (2026) faces a similar tradeoff — Mercari’s bot detection is comparable to Poshmark’s and the mobile API approach wins there too.

Proxy and Rate-Limit Strategy

Poshmark rate-limits by IP, not by account. residential US proxies are mandatory — datacenter ranges are almost fully blocked. aim for one request per 1-2 seconds per proxy IP, with a pool of at least 20-50 IPs for any sustained crawl.

Follow this rotation pattern:

assign one proxy IP per seller closet you’re crawling (sticky session per target)
rotate to a fresh IP after completing each closet or after any 429 response
back off 30-60 seconds on a 403 before retrying with a new IP
log cf-ray headers on failures — they indicate Cloudflare challenge pages, not server errors

for sneaker-specific sellers, cross-referencing Poshmark sold data with data from How to Scrape GOAT and Flight Club Sneaker Marketplace Data (2026) gives you a multi-venue price curve that’s far more useful than any single-source feed.

Parsing and Structuring the Output

the mobile API returns nested JSON. the fields worth extracting per listing:

id, title, brand, size, color
price_amount.val (current price, in cents)
original_price_amount.val (listed retail)
like_count, comment_count
seller.username, seller.rating_count, seller.avg_response_time_minutes
status — available, sold, or not_for_sale
created_at, updated_at

sold listings carry status: "sold" and sold_at in the seller closet feed. the search endpoint only surfaces available items, so if you need sold history for a specific seller, you must hit their closet page directly with the ?availability=sold param.

if you’re comparing scraping patterns across verticals — fashion, sneakers, auto — How to Scrape Cars.com Vehicle Listings and Dealer Data (2026) is a good reference for handling paginated JSON feeds with location-aware filtering, a pattern that maps cleanly to Poshmark’s category + size search params. for luxury resale and authentication-gated listings, How to Scrape Grailed and Stadium Goods Sneaker Data (2026) covers a platform with similar closet-page structures and GraphQL-based search.

Bottom Line

the mobile API at api.posh.mn is your fastest path to clean Poshmark data — use curl_cffi to match iOS TLS fingerprints, stick with residential US proxies, and paginate closet feeds via cursor rather than offset. browser automation is a last resort, not a default. dataresearchtools.com covers this class of marketplace scraping in depth — if your target shifts, the same proxy and fingerprinting principles apply across nearly every resale platform.