JD.com Product Scraper and Price Tracker

Scrape JD.com product pages from SKU IDs or product URLs and return product, seller, price, availability and stock data with repeat-run price change tracking for monitoring and automation.

Why use this Actor

Teams tracking JD.com need stable SKU snapshots and actionable price-change signals, not repeated manual page checks or raw fields without decision context.

Key features

• Buyer-native inputs aligned to JD.com product scraper.
• Predictable structured output in the default Apify Dataset.
• Pay Per Event pricing tied to measurable result rows or reports.
• Optional MCP connector delivery for downstream automation and AI agents.

Quick start

Start with this working example and replace the target values with your own:

{
  "skuIds": [
    "100012043978"
  ],
  "targetPriceCny": 2000,
  "previousSnapshot": []
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• skuIds — JD SKU IDs: Numeric JD.com SKU IDs.
• productUrls — JD product URLs: Optional item.jd.com product URLs.
• targetPriceCny — Target price (CNY): Optional buy threshold used for agentAction.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.006000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Get a result in your first run

Paste this minimal tested input into the Apify Console. Change the query, SKU or records to match your own authorized target.

{
  "skuIds": [
    "100012043978"
  ],
  "targetPriceCny": 2000,
  "previousSnapshot": []
}

Evidence, not hype

On 28 October 2026, build latest successfully returned 1 dataset row(s) in run 8cgpgdD7BSN1tYywc. The test returned structured records from a public-source workflow; results may change when the source changes.

Supported scope: Public JD.com product page availability; some SKU prices and stock fields may not be visible.

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Scrape JD.com product pages from SKU IDs or product URLs and return product, seller, price, availability and stock data with repeat-run price change tracking for monitoring and automation.

Business use and first result

For: Teams monitoring known JD.com product SKUs, seller information and purchase thresholds.

Why run it: Collect public JD.com product records and keep prices, price visibility and availability together in a structured Dataset.

First result: Start with the one-SKU example below. Inspect sku, title, price, priceVisibility and agentAction. On later runs, pass an earlier Dataset as previousSnapshot to calculate changes. A missing or login-gated price is reported as unavailable, not invented.

Input scope: Enter SKU IDs or JD product URLs. If both lists are nonempty, the current runtime uses skuIds and does not merge the URLs.

Why use this Actor

JD.com product pages are useful for pricing and seller monitoring, but copying SKU data manually does not scale. This Actor fetches public JD product pages from SKU IDs or product URLs, normalizes the reusable fields, and can compare each run with a previous snapshot.

Key features

• Accepts JD.com SKU IDs and item.jd.com product URLs.
• Extracts product title, brand, shop, vendor/shop IDs, price when publicly available, availability and stock state.
• Keeps unavailable or login-gated prices explicit instead of inventing values.
• Calculates exact price deltas from an optional previous snapshot.
• Supports an optional target price and emits deterministic BUY_SIGNAL, WATCH_DROP, PRICE_CHECK_REQUIRED or MONITOR actions.
• Outputs structured Apify Dataset rows for API, Tasks, schedules, MCP and downstream automation.

Input

Provide skuIds or productUrls. If both lists are nonempty, skuIds takes precedence. Optional targetPriceCny sets a buy threshold. Optional previousSnapshot enables exact price-change comparison.

Output

Each row represents one JD.com product and includes SKU, canonical URL, title, brand, shop, vendor/shop IDs, price, reference price, price visibility/source, availability, stock state, scrape timestamp and monitoring fields when available.

Notes

JD may hide some price or stock information behind login, app-only flows or anti-bot controls. The Actor returns explicit visibility states rather than fabricated values.

Buyer-first quick start (November 2026)

This workflow is designed for one practical buyer question, with a small, reproducible first run and auditable Dataset output.

Example input

{
  "skuIds": [
    "100012043978"
  ],
  "targetPriceCny": 2000,
  "previousSnapshot": []
}

Source and limitations

The first run uses a public-source request or user-provided authorized URLs/documents. Upstream websites can block or throttle requests; not every result is assured to be available. Verify source URLs and timestamps before acting. If the source is inaccessible, a failed or limited result is not evidence of zero market demand.

Repeatable workflow

Use the default test to understand the output, verify record identifiers and field coverage, then schedule a Task with a bounded input. Compare real earlier snapshots only when available. Check the current Apify Store Pay Per Event rate and set Apify run charge limits for recurring use. Source evidence does not establish paying customers, successful outbound sales, or revenue.