How to Scrape Poshmark in 2026 (API & Python)

Poshmark doesn't offer a general-purpose public API for reading its catalog, so the fastest reliable path in 2026 is a structured scraping API that returns normalized JSON for search results, listing detail, seller closets, and brand/category data — instead of parsing Poshmark's listing pages yourself. Poshmark sits in the same resale genre as StockX, but the shape is different: it's social-selling, not an order book, so the data worth collecting is closet reputation and community activity (likes, comments, followers) as much as price.

Why scrape Poshmark data?

• Resale-price research — track what a brand or item category actually sells for on a peer-to-peer resale platform, as a comp against retail or other marketplaces.
• Brand and trend tracking — pull listing counts and pricing by brand, or follow a curated trend (e.g., "Vintage Celine Handbags") to see what's circulating.
• Seller/closet monitoring — watch a specific closet's inventory, rating, and sold-item count over time for competitive or sourcing research.
• Market and category research — use the department/category taxonomy to size how many active listings exist in a given niche.
• AI pipelines and agents — feed normalized listing and brand data into a pricing or sourcing agent instead of screen-scraping HTML.

Is it legal to scrape Poshmark?

Option 1: DIY in Python (and why it breaks)

A naive scraper fetches a search results page or closet page and parses listing cards out of the rendered markup:

import requests
from bs4 import BeautifulSoup

resp = requests.get(
    "https://poshmark.com/search?query=nike+jacket",
    headers={"User-Agent": "Mozilla/5.0 (compatible; research-bot/1.0)"},
)
soup = BeautifulSoup(resp.text, "html.parser")
# Listing cards render from client-side state (React), so a plain HTML
# parse mostly returns an empty shell — you end up reverse-engineering
# the app's internal JSON endpoints instead

It demos once, then breaks:

• The ToS is explicit. Section 4(b) names scraping, harvesting, and automated collection directly, and Poshmark's robots.txt disallows /search, /listings, and /api for every crawler.
• Anti-bot defenses. Poshmark fronts its site with bot detection; datacenter IPs and headless requests get rate-limited or blocked quickly, especially on search and closet pages.
• Client-rendered markup. Listing grids, closet stats, and trend pages render from JavaScript state, not stable server HTML — a layout change silently breaks CSS-selector scraping.
• No sanctioned alternative. Unlike eBay or StockX, Poshmark doesn't run a developer portal at all, so there's no official-but-gated API to fall back on.

Option 2: No-code / ready-made tools

Browser extensions and point-and-click scrapers can export a single search page or closet snapshot, but tracking a brand's resale prices or a seller's inventory over time means re-pulling the same pages on a schedule — a job for a stable endpoint with stored history, not a one-off export.

Option 3: A structured Poshmark API

For a repeatable workflow, a Poshmark scraping API returns normalized JSON for search, listing detail, closets, brands, categories, and trends — no page parsing, no waiting on an app that doesn't exist. Search by keyword:

curl "https://api.crawlora.net/api/v1/poshmark/search?query=nike+jacket" \
  -H "x-api-key: $CRAWLORA_API_KEY"

{
  "code": 200,
  "msg": "OK",
  "data": {
    "total": 5000,
    "has_more": true,
    "next_max_id": "ENC_eyJtYXhfaWRzIjpbNDksMSwxXSwicGFnZV9udW0iOjIsInBhZ2VfZ3JvdXBfaWQiOiI2YTcyZTE3OTI1MzYyMTUzMDNhMmVmMWEifQ",
    "listings": [
      {
        "id": "6a6353cb88849a5f185280ea",
        "title": "Nike Jacket",
        "price": 19,
        "original_price": 98,
        "condition": "gu",
        "brand": "Nike",
        "category": "Jackets",
        "department": "Men",
        "size": "M",
        "colors": ["Black"],
        "status": "published",
        "cover_shot": "https://di2ponv0v5otw.cloudfront.net/posts/2026/07/24/x/m_x.jpg",
        "like_count": 12,
        "comment_count": 2,
        "seller_username": "maltimar",
        "url": "https://poshmark.com/listing/6a6353cb88849a5f185280ea"
      }
    ],
    "source_url": "https://poshmark.com/search?query=nike+jacket"
  }
}

query is required; department and max_id (cursor pagination, from next_max_id) narrow and page results. Pull full listing detail, a seller's closet, or a brand's active listings:

import requests

h = {"x-api-key": "YOUR_API_KEY"}
base = "https://api.crawlora.net/api/v1/poshmark"

hits = requests.get(f"{base}/search", headers=h, params={"query": "nike jacket"}).json()["data"]["listings"]
listing_id = hits[0]["id"]

listing = requests.get(f"{base}/listing/{listing_id}", headers=h).json()["data"]
closet = requests.get(f"{base}/closet/maltimar", headers=h).json()["data"]
brand = requests.get(f"{base}/brand/Nike", headers=h).json()["data"]

A listing detail call carries the full seller reputation object — closet rating breakdown, items sold, and orders shipped, alongside listing comments (real fields — check the docs):

{
  "code": 200,
  "msg": "OK",
  "data": {
    "id": "6a3fed1de9c77df92e2c0cc6",
    "title": "NWT Farm Rio Shorts",
    "price": 55,
    "original_price": 95,
    "condition": "nwt",
    "brand": "FARM Rio",
    "category": "Shorts",
    "department": "Women",
    "size": "S",
    "colors": ["Red", "Yellow"],
    "status": "published",
    "like_count": 111,
    "comment_count": 4,
    "seller": {
      "username": "maltimar",
      "full_name": "Mary Altimar",
      "followers": 9096,
      "items_sold_display": "500+",
      "rating_average": 4.93,
      "rating_count": 368,
      "rating_breakdown": {
        "five_star": 355,
        "four_star": 7,
        "three_star": 2,
        "two_star": 3,
        "one_star": 1
      },
      "orders_shipped": 522,
      "url": "https://poshmark.com/closet/maltimar"
    },
    "comments": [
      { "comment": "Hi can you tell me the width of the waist?", "creator_username": "muvlax20" }
    ],
    "source_url": "https://poshmark.com/listing/6a3fed1de9c77df92e2c0cc6"
  }
}

/poshmark/categories returns the full department/category tree (path values like Women-Shoes) to drive /poshmark/category/{path}, and /poshmark/trend/{id} pages a curated trend collection the same way search results page.

What you can collect

• Search results: title, price, original price, condition, brand, category, department, size, colors, like/comment counts, and listing URL
• Listing detail: description, cover shot and pictures, full seller reputation (rating average, rating breakdown, items sold, orders shipped), and visible comments
• Seller closets: profile plus paginated active listings for a given username
• Brand data: full brand directory (29,000+ names with aliases) and per-brand active listings
• Category taxonomy: department → category tree with path identifiers for browsing
• Trend collections: curated groupings (e.g., a designer or style trend) with their listings

Limitations

• No official API exists. Poshmark doesn't run a developer portal at all — there's no gated-but-sanctioned alternative to fall back on, unlike eBay or StockX.
• ToS and robots.txt both restrict scraping. Section 4(b) names scraping and harvesting directly, and robots.txt disallows /search, /listings, and /api for every crawler — this isn't an ambiguous case.
• Prices and stats are a snapshot. Poshmark items get repriced, bundled, or marked sold constantly, and closet rating/follower counts shift daily — timestamp what you store.
• Public data only. This covers public listing, closet, and brand pages — not private messages, offers, or a way to place an order on a buyer's behalf.
• Cursor pagination. List endpoints use an opaque max_id/next_max_id cursor rather than page numbers — carry it forward rather than trying to compute offsets.

Where this gets used

• Resale and sourcing research — compare a brand's Poshmark asking prices against retail or another resale marketplace.
• Closet and competitor monitoring — track a seller's inventory, pricing, and reputation growth over time.
• Trend and category sizing — measure how many active listings exist for a brand, category, or curated trend.

Sources

Start collecting

Try it first, free: run any public URL through the Free Web Scraper, or check whether a site blocks bots with the Anti-Bot Checker — no signup.

Test the search, listing, closet, and brand endpoints in the Playground, check the request and response schema in the API docs, and review credit costs on the pricing page. Poshmark is the social-selling counterpart to the live order-book resale data in how to scrape StockX and the fixed-price listings in how to scrape eBay — pair them for a fuller secondhand-market picture, or add the handmade/vintage angle from how to scrape Etsy. See also how to scrape Mercari for the general secondhand-marketplace version of the same closet-selling model and is web scraping legal.

Part of our how-to-scrape guide series — every platform we cover, in one index.