Depop Real-Time Data

Live fashion resale intelligence from Depop — search, product details, seller shops, sold history, profiles, and trending — as clean structured JSON.

Built for resellers, market researchers, agencies, data teams, and AI workflows that need fresh Depop listing data without slow, heavy browser runs. Enable only the features you need, click Start, and stream results to your dataset, webhook, or LLM pipeline.

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Why teams use this Actor

	Depop Real-Time Data	Typical browser scraper
Speed	Fast per-listing collection	Often seconds per page
Memory	512 MB default	2–4 GB+
Setup	Checkbox UI, prefilled test input	Fragile & high-maintenance
Cost	Low compute	High
Output	Structured JSON, LLM-ready	Often messy HTML
Scale	Long runs stream row-by-row	Memory-heavy buffers
Delivery	Dataset + optional webhook	Export-only

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What you get

Every record includes featureType, country, url, and scrapedAt so you can filter, join, and pipe into any workflow.

Listing Search (featureType: "listing_search") — one product = one row

Field	Description
listingId, title, url	Identity and product link
price, originalPrice, currency, isOnSale	Pricing
brand, size, condition, color	Attributes
tags[], imageUrl, imageUrls[]	Tags and photos
sellerUsername, likes, postedAt	Seller and engagement
shippingCost, position	Shipping and rank
detailsFetched	false = search card only; true = full product fields merged into this same row

With “Fetch full listing details for search results” on, the Actor does not create a second listing_details row. It enriches the search row with:

Extra fields when detailsFetched: true	Description
fullDescription	Full listing text
shipping	Structured shipping (cost, methods, origin, …)
measurements	Parsed measurements when present in the description
comments, quantity, isAvailable	Engagement and stock
subCategory, pattern, lastUpdated	Extra catalog fields when available

Example: searchMaxResults: 20 + full details on → 20 dataset rows, not 40.

Listing Details (featureType: "listing_details")

Separate full-product rows only when you enable the Listing Details feature and pass specific listing IDs/slugs/URLs. Not used for search enrichment (that merges into listing_search).

Seller Listings (featureType: "seller_listings")

Shop inventory rows: price, brand, size, condition, images, sold flag when available.

Seller Profile (featureType: "seller_profile")

Username, display name, bio, verification, website/social links, and optional contact signals from the bio (email, phone, Instagram, WhatsApp cues).

Sold History (featureType: "sold_history")

Sold / completed listings for comps and sell-through research — price, brand, size, status, and timestamps when available.

Trending (featureType: "trending")

Discovery feed for niches like streetwear, vintage, footwear, y2k.

Scrape By URL (featureType: "scrape_by_url")

Paste any Depop product, search, shop, brand, or category URL — auto-detects type and returns structured rows.

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Features (checkbox UX)

Feature	Default	What it does
Listing Search	ON	Keyword search with brand, size, condition, price filters
Listing Details	off	Full product data for IDs / slugs / URLs
Seller Listings	off	Shop inventory for usernames
Seller Profile	off	Bio, verification, contact signals
Sold Item History	off	Sold listings for pricing comps
Scrape By URL	off	Any Depop URL
Trending / Category	off	Niche discovery feed

Full details from search: turn on Fetch full listing details for search results. Each product stays one row (listing_search) with full fields merged in (description, gallery, shipping, etc.). Volume is controlled by Max results per keyword.

Other options: extract seller contacts, include shipping fields, optional raw payload for advanced pipelines.

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Use cases

• Reseller pricing & comps — track similar listings and sold prices by brand/size
• Brand & inventory monitoring — watch keywords (Nike Dunk, Levi’s 501, Carhartt)
• Seller intelligence — shop size, sold history, bio contact signals
• Trend spotting — streetwear, vintage, y2k category feeds
• Market research — price distributions across US / UK / AU
• Agency reporting — client-ready resale datasets
• AI & LLM pipelines — JSON for RAG, scoring, alerts, and summaries
• Automation — schedules, webhooks, Zapier/Make/n8n, warehouses

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LLM & MCP integration

Output is structured JSON — ready for ChatGPT, Claude, Gemini, LangChain, LlamaIndex, and custom agents.

Recommended workflow

1. Run the Actor with the features you need (search is prefilled).
2. Fetch dataset items via the Apify API or export JSON/CSV.
3. Pass records to your LLM, or index them into a vector store.

Example: search row with full details merged

{
  "featureType": "listing_search",
  "country": "US",
  "title": "Levi's 501 Size 30 Straight-cut Vintage Jeans",
  "price": 15,
  "currency": "USD",
  "brand": "Levi's",
  "size": "30",
  "sellerUsername": "example_seller",
  "likes": 3,
  "position": 1,
  "detailsFetched": true,
  "fullDescription": "Levi's 501 Size 30 … #retro #levis",
  "shipping": { "cost": 3.99, "methods": ["USPS"], "shipsFrom": "US" },
  "isAvailable": true,
  "url": "https://www.depop.com/products/example-slug/",
  "scrapedAt": "2026-07-16T12:00:00.000Z"
}

Apify MCP (Model Context Protocol)

Use the Apify MCP server so AI assistants can:

• Run this Actor from natural language
• Read dataset results in chat
• Chain with other Actors (enrich → score → CRM)

User: "Find 25 newest vintage Levi's under $40 on Depop US and summarize pricing"
→ MCP runs Actor with searchKeywords=["vintage levis"], searchMaxPrice=40, searchSort=newest
→ MCP reads dataset items
→ LLM summarizes price bands and opportunities

API quick start

curl -X POST "https://api.apify.com/v2/acts/YOUR_ACTOR_ID/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "country": "US",
    "enableListingSearch": true,
    "searchKeywords": ["vintage jeans", "nike dunk"],
    "searchMaxResults": 20,
    "searchSort": "newest"
  }'

Dataset items: GET https://api.apify.com/v2/datasets/{datasetId}/items?format=json

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Input reference

Enable only what you need. Listing Search is on by default with sample keywords so you can run immediately.

Input	Type	Default	Description
Region
country	enum	US	US, UK, or AU
Listing Search
enableListingSearch	boolean	true	Keyword search
searchKeywords	string[]	["vintage jeans","nike dunk"]	Search terms
searchMaxResults	integer	20	Max per keyword (1–200)
searchSort	enum	newest	relevance, newest, price-low, price-high
searchBrand	string	—	Optional brand filter
searchSize	string	—	Optional size filter
searchCondition	enum	any	brand_new, used_like_new, used_good, used_fair
searchMinPrice / searchMaxPrice	integer	—	Price bounds
searchOnSale	boolean	false	Prefer discounted items
searchFetchFullDetails	boolean	false	Merge full product details into each search row (still 1 row per product)
Listing Details
enableListingDetails	boolean	false	Full product rows
listingSlugs	string[]	—	Slugs or IDs
listingUrls	string[]	—	Product URLs
Sellers
enableSellerListings	boolean	false	Shop inventory
enableSellerProfile	boolean	false	Profile + contacts
enableSoldHistory	boolean	false	Sold comps
sellerUsernames	string[]	—	Without @
sellerMaxListings	integer	30	Cap per shop
soldMaxItems	integer	30	Cap sold per seller
URL & Trending
enableScrapeByUrl	boolean	false	Any Depop URL
scrapeUrls	string[]	—	URLs to process
scrapeMaxPages	integer	3	Depth for list pages
enableTrending	boolean	false	Category feed
trendingCategory	string	streetwear	Niche keyword
trendingMaxItems	integer	30	Cap
Options & delivery
extractSellerContacts	boolean	true	Bio contact signals
includeRaw	boolean	false	Extended payload (larger)
maxItems	integer	—	Global row cap
webhookUrl	string	—	Optional real-time POST per row
webhookFormat	enum	json	json or slack
proxyConfiguration	object	Residential	Recommended

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Output & streaming

What to expect (row counts)

What you enable	Dataset rows
Listing Search, 20 results, full details off	20 × listing_search (detailsFetched: false)
Listing Search, 20 results, full details on	20 × listing_search (detailsFetched: true, richer fields on the same row)
Listing Details feature with 5 URLs	5 × listing_details
Seller Listings, max 30	Up to 30 × seller_listings per username

• Every row is written to the Apify Dataset as soon as it is ready — long runs do not hold the full result set in memory.
• Parallel workers process multiple keywords, sellers, product details, and URLs concurrently for faster runs.
• Spending limits — if you set a max cost per run (Apify Console / PPE), the Actor stops gracefully when that limit is reached so you never overspend.
• Filter the dataset by featureType.
• Dataset views: overview, search, details, seller_listings, seller_profile, sold_history, trending, scrape_by_url.
• Optional webhook: each record is still stored in the dataset and POSTed to your URL (CRM, Slack, Zapier, Make, custom API).

Webhook example (JSON body)

{
  "featureType": "listing_search",
  "title": "Nike Dunk Low Panda",
  "price": 85,
  "currency": "USD",
  "detailsFetched": true,
  "url": "https://www.depop.com/products/..."
}

Slack format sends a short formatted message instead of the full object.

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How to run

1. Open the Actor and keep the prefilled Listing Search keywords (or edit them).
2. Pick Region (US / UK / AU).
3. Use Residential proxy matching that region.
4. Click Start.
5. Export JSON/CSV, connect a webhook, or pull via API.

Pro tip: Schedule daily runs for price monitoring or trend tracking.

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Integrations

Full Apify support: API, schedules, webhooks, Google Sheets, Make, Zapier, n8n, CSV/JSON export.

Ideal next steps:

• Pipe dataset → Google Sheets for client reports
• Webhook → Slack channel for new “under $X” listings
• MCP / LLM → daily resale brief

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FAQ

Do I need code?
No — use the console UI. Developers can use the Apify API.

Which proxy?
Residential, matching your target region (US / UK / AU).

Multiple features in one run?
Yes — results are tagged with featureType.

Large runs?
Rows stream continuously, work runs in parallel, and your per-run spending limit is respected.

LLM / agents?
Yes — structured JSON + Apify MCP for agent-driven runs.

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Contact / custom projects

Need something tailored? I build custom scrapers, data pipelines, and web apps of any kind — marketplaces, lead gen, internal tools, dashboards, and full-stack products.

• Email: dubem115@gmail.com
• GitHub: https://github.com/DrunkCodes

Open to project work, integrations, and ongoing data infrastructure.