# Newegg Product Scraper (`kawsar/newegg-product-scraper`) Actor
Newegg Products Scraper that extracts prices, ratings, availability, and specs from search results and product pages so price trackers, resellers, and market researchers get clean structured data without writing custom scripts.
- **URL**: https://apify.com/kawsar/newegg-product-scraper.md
- **Developed by:** [Kawsar](https://apify.com/kawsar) (community)
- **Categories:** E-commerce, Automation, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $2.90 / 1,000 results
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## Newegg Products Scraper: Extract Product Prices, Ratings, and Specs at Scale
Newegg Products Scraper pulls structured product data from Newegg.com. Give it a keyword like "RTX 4070", a category URL, or a list of product pages and it returns prices, ratings, availability, specs, and more for every matching item. Pagination is handled automatically.
### Use cases
- Track GPU and CPU prices daily to catch deals before they disappear
- See how competing sellers price and position similar products
- Detect when out-of-stock items come back and act on it quickly
- Pull product titles and tags from top-ranking Newegg search results for keyword research
- Compare review volumes and ratings across brands and categories
- Find underpriced products for resale or arbitrage
### Input
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `keywords` | string | | Search term to query on Newegg, e.g. "RTX 4070" |
| `categoryUrl` | string | | Newegg category URL to scrape |
| `productUrls` | string[] | | Individual Newegg product page URLs |
| `maxItems` | integer | 100 | Maximum number of products to extract |
| `maxPages` | integer | 5 | Maximum result pages to follow per input |
| `requestTimeoutSecs` | integer | 30 | Per-request timeout in seconds |
You can combine all three input types in a single run. Results from each are merged into one dataset.
#### Example input
```json
{
"keywords": "RTX 4070",
"maxItems": 50,
"maxPages": 3
}
````
### Output
Each record in the dataset contains:
```json
{
"itemNumber": "N82E16814137774",
"productTitle": "ASUS DUAL GeForce RTX 4070 SUPER OC Edition 12GB GDDR6X",
"brand": "ASUS",
"productUrl": "https://www.newegg.com/p/N82E16814137774",
"imageUrl": "https://c1.neweggimages.com/ProductImageCompressAll1280/14-137-774-V01.jpg",
"price": 529.99,
"originalPrice": 599.99,
"discountPercent": 11.7,
"rating": 4.7,
"reviewCount": 312,
"availability": "In Stock",
"seller": "Newegg",
"shipping": "Free Shipping",
"category": "Computer Hardware > Video Cards & Video Devices > Desktop Graphics Cards",
"specs": {
"GPU": "GeForce RTX 4070 SUPER",
"Memory Size": "12GB",
"Memory Type": "GDDR6X",
"Core Clock": "2505 MHz"
},
"scrapedAt": "2026-04-27T09:15:00Z"
}
```
| Field | Type | Description |
|-------|------|-------------|
| `itemNumber` | string | Newegg's internal item number (SKU) |
| `productTitle` | string | Full product name as listed on Newegg |
| `brand` | string | Manufacturer or brand name |
| `productUrl` | string | Direct link to the product page |
| `imageUrl` | string | URL of the primary product image |
| `price` | number | Current selling price in USD |
| `originalPrice` | number | Original price before any discount |
| `discountPercent` | number | Percentage discount from original price |
| `rating` | number | Average star rating (0-5) |
| `reviewCount` | integer | Total number of customer reviews |
| `availability` | string | Stock status, e.g. "In Stock" or "OUT OF STOCK" |
| `seller` | string | Seller name (Newegg or third-party) |
| `shipping` | string | Shipping cost or "Free Shipping" |
| `category` | string | Breadcrumb category path |
| `specs` | object | Technical specifications from the specs tab |
| `scrapedAt` | string | ISO 8601 UTC timestamp |
### How it works
1. Takes keywords, a category URL, or product URLs as input
2. For search and category inputs, it follows pagination up to `maxPages` pages
3. For each listing, it parses price, rating, availability, and image from the listing HTML
4. For product detail URLs, it also extracts the specs table, item number, and seller info
5. Results are saved to an Apify dataset as they arrive, so partial results are always kept
### FAQ
**Does this scraper work on Newegg search results?**
Yes. Provide a keyword in the `keywords` field and the actor queries Newegg search and follows pagination automatically.
**Can it extract product specifications?**
Yes, when given individual product page URLs. The specs table is captured as a structured key-value object under the `specs` field.
**What output formats are available?**
Results go to an Apify dataset. Export as JSON, CSV, Excel, or XML from the Apify console. You can also connect directly to Google Sheets, Make, or Zapier via Apify integrations.
**Can I run this on a schedule for price monitoring?**
Yes. Set up a scheduled run in Apify with your keywords and `maxItems` limit. The actor re-scrapes at whatever interval you set.
**What happens if a product page fails to load?**
The actor logs the error and records it in the dataset with an `error` field, then continues to the next item. One bad URL won't stop the whole run.
### Integrations
Connect Newegg Products Scraper with other tools using [Apify integrations](https://apify.com/integrations). Works with Make, Zapier, Slack, Google Sheets, Google Drive, Airbyte, and more. Use [webhooks](https://docs.apify.com/integrations/webhooks) to trigger notifications when new results are ready.
# Actor input Schema
## `keywords` (type: `array`):
One or more search terms to query on Newegg (e.g. "RTX 4070", "AMD Ryzen 9"). Each keyword is searched separately.
## `productUrls` (type: `array`):
Individual Newegg product page URLs to scrape. Use this to get full detail data including specs, seller info, and item numbers.
## `maxItems` (type: `integer`):
Maximum number of products to extract per run.
## `maxPages` (type: `integer`):
Maximum number of search or category result pages to follow per input.
## `requestTimeoutSecs` (type: `integer`):
Per-request timeout in seconds. Increase if you get timeout errors on slow connections.
## Actor input object example
```json
{
"keywords": [
"AMD Ryzen 9 7950X",
"RTX 4080"
],
"productUrls": [
"https://www.newegg.com/p/pl?d=Laptop"
],
"maxItems": 100,
"maxPages": 5,
"requestTimeoutSecs": 30
}
```
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"keywords": [
"RTX 4070"
],
"productUrls": [
"https://www.newegg.com/p/pl?d=Laptop"
]
};
// Run the Actor and wait for it to finish
const run = await client.actor("kawsar/newegg-product-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"keywords": ["RTX 4070"],
"productUrls": ["https://www.newegg.com/p/pl?d=Laptop"],
}
# Run the Actor and wait for it to finish
run = client.actor("kawsar/newegg-product-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"keywords": [
"RTX 4070"
],
"productUrls": [
"https://www.newegg.com/p/pl?d=Laptop"
]
}' |
apify call kawsar/newegg-product-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=kawsar/newegg-product-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Newegg Product Scraper",
"description": "Newegg Products Scraper that extracts prices, ratings, availability, and specs from search results and product pages so price trackers, resellers, and market researchers get clean structured data without writing custom scripts.",
"version": "0.0",
"x-build-id": "NxcvLXSu4Qqyi7RPB"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/kawsar~newegg-product-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-kawsar-newegg-product-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/kawsar~newegg-product-scraper/runs": {
"post": {
"operationId": "runs-sync-kawsar-newegg-product-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/kawsar~newegg-product-scraper/run-sync": {
"post": {
"operationId": "run-sync-kawsar-newegg-product-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"keywords": {
"title": "Search keywords",
"type": "array",
"description": "One or more search terms to query on Newegg (e.g. \"RTX 4070\", \"AMD Ryzen 9\"). Each keyword is searched separately.",
"items": {
"type": "string"
}
},
"productUrls": {
"title": "Search URLs",
"type": "array",
"description": "Individual Newegg product page URLs to scrape. Use this to get full detail data including specs, seller info, and item numbers.",
"items": {
"type": "string"
}
},
"maxItems": {
"title": "Max items",
"minimum": 1,
"maximum": 1000,
"type": "integer",
"description": "Maximum number of products to extract per run.",
"default": 100
},
"maxPages": {
"title": "Max pages",
"minimum": 1,
"maximum": 50,
"type": "integer",
"description": "Maximum number of search or category result pages to follow per input.",
"default": 5
},
"requestTimeoutSecs": {
"title": "Request timeout (seconds)",
"minimum": 5,
"maximum": 120,
"type": "integer",
"description": "Per-request timeout in seconds. Increase if you get timeout errors on slow connections.",
"default": 30
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```