# Steam Store Scraper — Games, Reviews, Prices & Charts (`celebrated-quadraphonic/steam-store-scraper`) Actor

Scrape the Steam Store over plain HTTP: game search, full game details with pricing and user tags, paginated reviews with exact date windows and sentiment, plus top-seller and most-played feeds. No browser needed.

- **URL**: https://apify.com/celebrated-quadraphonic/steam-store-scraper.md
- **Developed by:** [XiaoZhi DataTools](https://apify.com/celebrated-quadraphonic) (community)
- **Categories:** Developer tools, Automation, E-commerce
- **Stats:** 2 total users, 1 monthly users, 33.3% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.50 / 1,000 game scrapeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Steam Store Scraper — Games, Reviews, Prices & Charts

Scrape the Steam Store over **plain HTTP** — no browser, no residential
proxies, no API keys. One Actor covering what used to need four separate
scrapers: **game search, full game details, user reviews, and live
charts/feeds**.

### What it does

**Find games**

- `searchQueries` — keyword search across the Steam catalog
  ("roguelike deckbuilder", "cozy farming sim", ...)
- `appIds` / `storeUrls` — direct look-up of games you already know
- `feeds` — live Steam feeds: **Top Sellers, New Releases, Coming Soon,
  Specials**, plus **Most Played** (Steam Charts concurrent-players
  ranking, keyless)

**Game details** (`includeGameDetails`)

- Pricing: current price, original price, discount %, currency
- Review summaries: recent + all-time score, score description
  ("Overwhelmingly Positive"), total reviews, positive %
- Genres, **user tags**, categories, developers, publishers
- Release date, Metacritic score, DLC list, achievements count
- Platforms, supported languages, **system requirements**
- Header/capsule images, screenshots, trailers, recommendations

**Reviews** (`includeReviews`)

- Cursor-paginated — from a handful to tens of thousands per game
- **Exact date windows** (`startDate` / `endDate`, YYYY-MM-DD)
- Spreadsheet-friendly `positive` / `negative` verdict
  (no more TRUE/FALSE mangling in Excel)
- **Playtime trio**: hours at review, total hours, last-two-weeks hours
- Language, review type (positive/negative), purchase type filters
- Helpful/funny votes, weighted score, comment count
- Steam purchase / received-for-free / Early Access / refunded /
  Steam Deck flags
- **Built-in sentiment analysis** (score + positive/neutral/negative)

**Monitor mode** (`newItemsOnly`)

- Only outputs games never seen in previous runs (per search query or
  feed). Run on a schedule to track new releases, chart entries, or
  price drops.

### Input

| Field | Description |
|---|---|
| `searchQueries` | Keywords to search (one per line) |
| `appIds` | Steam app IDs, e.g. `730` |
| `storeUrls` | `store.steampowered.com/app/<id>/...` URLs |
| `feeds` | `topSellers`, `newReleases`, `comingSoon`, `specials`, `mostPlayed` |
| `includeGameDetails` | Full metadata enrichment (default on) |
| `includeReviews` | Paginated user reviews (default off) |
| `reviewsPerGame` | Max reviews per game (default 100) |
| `reviewLanguage` / `reviewType` / `reviewFilter` / `purchaseType` | Review filters |
| `startDate` / `endDate` | Exact review date window |
| `includeSentiment` | Sentiment score + label per review (default on) |
| `maxGames` | Cap on games per run (default 100). Search queries are processed round-robin so every query gets coverage |
| `currency` | Country code for pricing, e.g. `US`, `GB`, `JP` |
| `storeLanguage` | Metadata language, e.g. `english` |
| `newItemsOnly` | Monitor mode via key-value-store state |
| `requestConcurrency` | Parallel requests (default 4, max 10) |

### Output

Two record types in the dataset:

**Game** — `appId`, `name`, `url`, `type`, `isFree`, `shortDescription`,
`price{currency, initial, final, initialFormatted, finalFormatted,
discountPercent}`, `reviewSummaryAll`, `reviewSummaryRecent`,
`genres[]`, `tags[]`, `categories[]`, `developers[]`, `publishers[]`,
`releaseDate`, `isComingSoon`, `metacritic{}`, `dlc[]`, `dlcCount`,
`achievementsTotal`, `platforms{}`, `languages[]`,
`systemRequirements{}`, `headerImage`, `capsuleImage`, `screenshots[]`,
`movies[]`, `recommendationsTotal`, `website`, `source`, `feedRank`,
`concurrentPlayers`, `scrapedAt`.

**Review** — `reviewId`, `appId`, `gameName`, `recommendation`
(`positive`/`negative`), `votedUp`, `reviewText`, `language`,
`createdAt`, `updatedAt`, `author{}`, `playtimeForever`,
`playtimeAtReview`, `playtimeLastTwoWeeks`, `votesUp`, `votesFunny`,
`weightedVoteScore`, `commentCount`, `steamPurchase`,
`receivedForFree`, `writtenDuringEarlyAccess`, `refunded`,
`primarilySteamDeck`, `sentiment{score, label}`, `url`, `scrapedAt`.

### Pricing

- First **20 results per run are free** (games + reviews combined).
- Then **$2.50 per 1,000 games** and **$1.00 per 1,000 reviews**
  (pay-per-event).

### Notes

- Data comes from Steam's public Store endpoints; no login required.
- Review counts and scores match what the Steam Store shows.
- Release dates are ISO-8601 where Steam gives an exact date; vague
  dates ("Q4 2025", "Coming soon") are kept in `releaseDateRaw`.
- Sentiment analysis is a fast deterministic lexicon model tuned for
  English game reviews — great for triage, not a replacement for a
  trained classifier on nuanced text.
- Exact date windows on games with millions of reviews scan back from
  the newest review, so very deep historical windows take a while;
  recent windows stop early and are fast.

### Changelog

- **0.2** — Feeds are now round-robin interleaved (quota shared fairly
  across all selected feeds instead of the first feed taking all);
  duplicates across queries/feeds no longer consume the `maxGames`
  quota (deeper results backfill); the `sentiment` key is omitted
  entirely when sentiment analysis is disabled.
- **0.1** — Initial release.

# Actor input Schema

## `appIds` (type: `array`):

Steam app IDs to scrape directly (e.g. 730 for Counter-Strike 2).

## `currency` (type: `string`):

Two-letter country code used for pricing (e.g. US, GB, DE, JP).

## `endDate` (type: `string`):

Only include reviews written on or before this date (YYYY-MM-DD).

## `feeds` (type: `array`):

Live Steam feeds to scrape: top sellers, new releases, coming soon, specials, and the most-played games chart (concurrent players).

## `includeGameDetails` (type: `boolean`):

Enrich each game with full metadata: pricing and discounts, review summaries, genres, user tags, categories, developers/publishers, release date, Metacritic, DLC, achievements, platforms, languages, system requirements, media and recommendations.

## `includeReviews` (type: `boolean`):

Scrape paginated user reviews for each game: full text, positive/negative verdict, playtime at review / total / last two weeks, helpful votes, purchase flags, and built-in sentiment analysis.

## `includeSentiment` (type: `boolean`):

Add a deterministic sentiment score and positive/neutral/negative label to every review.

## `maxGames` (type: `integer`):

Maximum number of games to process per run (across all modes).

## `newItemsOnly` (type: `boolean`):

Only output games never seen in previous runs (per search query / feed). State is kept in the key-value store — run on a schedule to monitor new releases, top-seller entries or new reviews.

## `proxyConfiguration` (type: `object`):

Optional. Steam's public endpoints work fine over plain HTTP; proxies are not needed for typical runs.

## `purchaseType` (type: `string`):

Only reviews from Steam purchases, non-Steam purchases, or all.

## `requestConcurrency` (type: `integer`):

How many requests run in parallel. Steam's public endpoints are rate-limited per IP; 4 is safe.

## `reviewFilter` (type: `string`):

Sort reviews by most recent, recently updated, or most helpful (all).

## `reviewLanguage` (type: `string`):

Filter reviews by language (e.g. english, german, french, schinese, japanese). Use "all" for every language.

## `reviewType` (type: `string`):

Only positive reviews, only negative reviews, or all.

## `reviewsPerGame` (type: `integer`):

Maximum number of reviews to scrape per game (cursor-paginated, up to 100 per request).

## `searchQueries` (type: `array`):

Keywords to search across the Steam catalog (e.g. "roguelike deckbuilder"). One query per line.

## `startDate` (type: `string`):

Only include reviews written on or after this date (YYYY-MM-DD). Enables exact date windows.

## `storeLanguage` (type: `string`):

Language for game metadata (e.g. english, german, french, schinese).

## `storeUrls` (type: `array`):

Steam store page URLs (https://store.steampowered.com/app/<id>/...). One per line.

## Actor input object example

```json
{
  "appIds": [
    440
  ],
  "currency": "US",
  "endDate": "",
  "feeds": [],
  "includeGameDetails": true,
  "includeReviews": false,
  "includeSentiment": true,
  "maxGames": 100,
  "newItemsOnly": false,
  "proxyConfiguration": {
    "useApifyProxy": false
  },
  "purchaseType": "all",
  "requestConcurrency": 4,
  "reviewFilter": "recent",
  "reviewLanguage": "all",
  "reviewType": "all",
  "reviewsPerGame": 100,
  "searchQueries": [],
  "startDate": "",
  "storeLanguage": "english",
  "storeUrls": []
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "appIds": [
        440
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("celebrated-quadraphonic/steam-store-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "appIds": [440] }

# Run the Actor and wait for it to finish
run = client.actor("celebrated-quadraphonic/steam-store-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "appIds": [
    440
  ]
}' |
apify call celebrated-quadraphonic/steam-store-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,celebrated-quadraphonic/steam-store-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/SeeMHDsxyFNwBk9BF/builds/656cpPUEdJxhXL8dr/openapi.json