# Steam Store Scraper 🎮 (price, reviews, metadata) (`tagadanar/steam-scraper`) Actor
Scrape Steam store pages for price, discount, developer, genres, platforms, Metacritic score, release date and review scores by search term or app ID, plus unlimited individual reviews with language and sentiment filters. No login, no browser, no API key needed.
- **URL**: https://apify.com/tagadanar/steam-scraper.md
- **Developed by:** [Tagada Data](https://apify.com/tagadanar) (community)
- **Categories:** E-commerce, AI, Automation
- **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $0.70 / 1,000 app founds
This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.
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
## Steam Store Scraper 🎮 (price, reviews and metadata)
**Scrape the Steam store into clean JSON: game metadata, pricing, and unlimited reviews.** Give a search term or an app ID and this actor returns the full store record, including **price & discount, developer, publisher, genres, platforms, Metacritic, release date and a review-score summary**, plus, on demand, **every review of a game** via cursor pagination. It runs straight off Steam's public JSON endpoints, so there's **no login, no browser, no API key** to worry about.
Built for **game market research, price & discount monitoring, review sentiment analysis, competitive tracking and AI agents** (REST API + MCP).
### Why this Steam scraper
Most Steam actors either scrape metadata *or* reviews, and cap reviews at a few dozen. This one does **both in one actor** and removes the cap: you can download **all reviews of a game** (hundreds of thousands of them) as individual dataset items, with the filters review analysis actually needs.
- 🔁 **Unlimited reviews**: pagination runs on cursors, so you can pull up to 1,000,000 reviews per app, each saved as its own `type: "review"` record.
- 🎛️ You can filter reviews by language, positive or negative, purchase type, date range and sort order.
- 🏷️ Full metadata and pricing come together too, covering regional price, discount, Metacritic, genres, platforms and release date.
- 💸 **Cheap and no browser required**: app metadata runs at **$1/1,000**, reviews at **$0.40/1,000**.
### What you can do with it
- For **review sentiment analysis**, pull every review for a game (text, votes, helpfulness, author playtime) and feed it into NLP or an LLM.
- Use it for **price and discount monitoring** by tracking a list of app IDs across regions and catching sales as they happen.
- **Game market research** gets easier too: genres, developers, Metacritic scores and review scores across a search term or a whole catalog.
- **Competitive tracking**: compare titles by review score, recommendation counts and pricing.
- Or simply **feed AI agents** structured Steam data over REST or MCP.
### What you get
- ✅ **Pricing**: current and original price, discount percentage and currency, in any country region you pick.
- ✅ Catalog data covering developer, publisher, genres, categories, platforms, release date and Metacritic.
- ✅ A review pulse: aggregate score (things like *Overwhelmingly Positive*), positive/negative/total counts, and positive percentage.
- ✅ **Unlimited reviews** (optional): every review of a game via cursor pagination, with full text, votes, helpfulness score, author playtime and stats, and timestamps. Filter by language, positive/negative, purchase type and date range.
- ✅ Search or go straight by ID: discover apps by term, or enrich exact app IDs and store URLs.
- ✅ Filters for type, free-only, minimum reviews and maximum price.
### Quick start
Search and keep only well-reviewed games:
```json
{
"searchTerms": ["roguelike", "city builder"],
"types": ["game"],
"minReviews": 500,
"includeReviewSummary": true,
"maxResults": 200
}
````
Download **all reviews** of specific games (each review = one dataset item):
```json
{
"appIds": ["400", "620"],
"maxReviewsPerApp": 1000000,
"reviewLanguage": "english"
}
```
### Output (per app)
```json
{
"source": "Steam",
"appId": "400",
"name": "Portal",
"type": "game",
"url": "https://store.steampowered.com/app/400",
"isFree": false,
"currency": "USD",
"priceInitial": 9.99,
"priceFinal": 1.99,
"discountPercent": 80,
"developers": ["Valve"],
"publishers": ["Valve"],
"genres": ["Action"],
"platforms": { "windows": true, "mac": false, "linux": true },
"metacriticScore": 90,
"releaseDate": "Oct 10, 2007",
"reviewScoreDesc": "Overwhelmingly Positive",
"totalReviews": 196159,
"totalPositive": 193260,
"positivePercent": 99,
"recommendations": 170784,
"scrapedAt": "2026-07-05T20:10:00.000Z"
}
```
### Pricing
Pay per event, no subscription:
| Event | Price | When |
|---|---|---|
| App found | **$1.00 / 1,000** | Per app record returned |
| Review scraped | **$0.40 / 1,000** | Per individual review item (only when `maxReviewsPerApp` > 0) |
Apify platform usage (compute) is billed separately at platform rates. No proxy is required.
### FAQ
**How do I download ALL reviews of a Steam game?** Set `maxReviewsPerApp` high (up to 1,000,000) and reviews stream out as separate `type: "review"` items at $0.40/1,000. A 100k-review dump costs about $40 in events. The aggregate score summary is on the app record by default at no extra event cost.
**Can I filter reviews?** Yes, by `reviewLanguage`, `reviewType` (positive/negative) and `reviewDayRange` (e.g. only the last 30 days).
**How are apps discovered?** `searchTerms` use Steam's store search; you can also pass exact `appIds` or store URLs.
**Which region's price?** Set `country` (e.g. `fr`, `de`) and the price and currency will follow it.
**How do I export the data?** Use the Apify dataset export (CSV, Excel, JSON) or the API `.../dataset/items?format=csv`.
**Is it reliable?** These are Steam's own public JSON endpoints. The actor stays polite (low concurrency) and retries on rate limits, and delisted or region-locked apps are skipped rather than failing the run.
**Can I use it with AI agents?** Yes, both REST API and **MCP** are supported.
***
### Something missing?
If you need an extra field, another source, or a different output, open an issue on this Actor and describe it. I read every request and small additions usually ship within days. More data and lead generation Actors are on [my profile](https://apify.com/tagadanar).
*Keywords: steam scraper, steam store scraper, steam api, steam reviews scraper, download steam reviews, steam price monitoring, steam sentiment analysis, metacritic, game market research, steam appdetails, appreviews, MCP.*
# Actor input Schema
## `searchTerms` (type: `array`):
Store search terms (e.g. roguelike, portal, city builder). Each match is enriched with its full store record.
## `appIds` (type: `array`):
Specific Steam app IDs to scrape, e.g. 400 (Portal), 620 (Portal 2).
## `startUrls` (type: `array`):
Steam store page URLs — the app ID is read from each, e.g. https://store.steampowered.com/app/400/Portal/.
## `country` (type: `string`):
Two-letter country code for pricing/currency (e.g. us, fr, de).
## `language` (type: `string`):
Store language code (e.g. en, fr).
## `includeReviewSummary` (type: `boolean`):
Add the aggregate review score (e.g. Overwhelmingly Positive), positive/negative/total counts and positive %. One lightweight request per app.
## `maxReviewsPerApp` (type: `integer`):
Scrape up to this many individual reviews per app as separate dataset items (type: "review" — full text, votes, playtime, author stats). Set it high to download ALL reviews of a game; cursor pagination handles hundreds of thousands. 0 = summary only.
## `reviewLanguage` (type: `string`):
Steam language code (e.g. english, french, schinese) or all.
## `reviewType` (type: `string`):
Keep only positive or only negative reviews.
## `reviewDayRange` (type: `integer`):
Only reviews from the last N days — e.g. 30 for last-month sentiment. 0 = all time.
## `types` (type: `array`):
Filter by store item type. Empty = all.
## `minReviews` (type: `integer`):
Keep only apps with at least this many reviews (needs review summary on).
## `maxPrice` (type: `integer`):
Keep only apps priced at or below this (in the region's currency units, e.g. dollars).
## `maxResults` (type: `integer`):
Stop after this many app records.
## `proxyConfiguration` (type: `object`):
Steam's public endpoints work without a proxy. Enable Apify Proxy for large runs to avoid rate limits.
## Actor input object example
```json
{
"searchTerms": [
"roguelike"
],
"appIds": [],
"startUrls": [],
"country": "us",
"language": "en",
"includeReviewSummary": true,
"maxReviewsPerApp": 0,
"reviewLanguage": "all",
"reviewType": "all",
"reviewDayRange": 0,
"types": [],
"maxResults": 20,
"proxyConfiguration": {
"useApifyProxy": false
}
}
```
# Actor output Schema
## `apps` (type: `string`):
One item per app in the default dataset.
# 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 = {
"searchTerms": [
"roguelike"
],
"maxResults": 20
};
// Run the Actor and wait for it to finish
const run = await client.actor("tagadanar/steam-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 = {
"searchTerms": ["roguelike"],
"maxResults": 20,
}
# Run the Actor and wait for it to finish
run = client.actor("tagadanar/steam-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 '{
"searchTerms": [
"roguelike"
],
"maxResults": 20
}' |
apify call tagadanar/steam-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=tagadanar/steam-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Steam Store Scraper 🎮 (price, reviews, metadata)",
"description": "Scrape Steam store pages for price, discount, developer, genres, platforms, Metacritic score, release date and review scores by search term or app ID, plus unlimited individual reviews with language and sentiment filters. No login, no browser, no API key needed.",
"version": "0.1",
"x-build-id": "FhPmIpaHhOYLmPNmU"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/tagadanar~steam-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-tagadanar-steam-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/tagadanar~steam-scraper/runs": {
"post": {
"operationId": "runs-sync-tagadanar-steam-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/tagadanar~steam-scraper/run-sync": {
"post": {
"operationId": "run-sync-tagadanar-steam-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"searchTerms": {
"title": "Search terms",
"type": "array",
"description": "Store search terms (e.g. roguelike, portal, city builder). Each match is enriched with its full store record.",
"items": {
"type": "string"
}
},
"appIds": {
"title": "App IDs",
"type": "array",
"description": "Specific Steam app IDs to scrape, e.g. 400 (Portal), 620 (Portal 2).",
"default": [],
"items": {
"type": "string"
}
},
"startUrls": {
"title": "Store URLs",
"type": "array",
"description": "Steam store page URLs — the app ID is read from each, e.g. https://store.steampowered.com/app/400/Portal/.",
"default": [],
"items": {
"type": "string"
}
},
"country": {
"title": "Country (price region)",
"type": "string",
"description": "Two-letter country code for pricing/currency (e.g. us, fr, de).",
"default": "us"
},
"language": {
"title": "Language",
"type": "string",
"description": "Store language code (e.g. en, fr).",
"default": "en"
},
"includeReviewSummary": {
"title": "Include review score summary",
"type": "boolean",
"description": "Add the aggregate review score (e.g. Overwhelmingly Positive), positive/negative/total counts and positive %. One lightweight request per app.",
"default": true
},
"maxReviewsPerApp": {
"title": "Reviews per app",
"minimum": 0,
"maximum": 1000000,
"type": "integer",
"description": "Scrape up to this many individual reviews per app as separate dataset items (type: \"review\" — full text, votes, playtime, author stats). Set it high to download ALL reviews of a game; cursor pagination handles hundreds of thousands. 0 = summary only.",
"default": 0
},
"reviewLanguage": {
"title": "Review language",
"type": "string",
"description": "Steam language code (e.g. english, french, schinese) or all.",
"default": "all"
},
"reviewType": {
"title": "Review type",
"enum": [
"all",
"positive",
"negative"
],
"type": "string",
"description": "Keep only positive or only negative reviews.",
"default": "all"
},
"reviewDayRange": {
"title": "Review look-back (days)",
"minimum": 0,
"maximum": 365,
"type": "integer",
"description": "Only reviews from the last N days — e.g. 30 for last-month sentiment. 0 = all time.",
"default": 0
},
"types": {
"title": "Keep only these types",
"type": "array",
"description": "Filter by store item type. Empty = all.",
"items": {
"type": "string",
"enum": [
"game",
"dlc",
"demo",
"music",
"video",
"hardware",
"mod",
"application"
],
"enumTitles": [
"Game",
"DLC",
"Demo",
"Music",
"Video",
"Hardware",
"Mod",
"Application"
]
},
"default": []
},
"minReviews": {
"title": "Minimum total reviews",
"minimum": 0,
"type": "integer",
"description": "Keep only apps with at least this many reviews (needs review summary on)."
},
"maxPrice": {
"title": "Maximum price",
"type": "integer",
"description": "Keep only apps priced at or below this (in the region's currency units, e.g. dollars)."
},
"maxResults": {
"title": "Max apps",
"minimum": 1,
"maximum": 20000,
"type": "integer",
"description": "Stop after this many app records.",
"default": 500
},
"proxyConfiguration": {
"title": "Proxy (optional)",
"type": "object",
"description": "Steam's public endpoints work without a proxy. Enable Apify Proxy for large runs to avoid rate limits.",
"default": {
"useApifyProxy": false
}
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```