# Steam Reviews Scraper (`fetch_cat/steam-reviews-scraper`) Actor

Export public Steam game reviews with review text, sentiment, votes, playtime, language, purchase flags, timestamps, and author metadata.

- **URL**: https://apify.com/fetch\_cat/steam-reviews-scraper.md
- **Developed by:** [Hanna Nosova](https://apify.com/fetch_cat) (community)
- **Categories:** Games, Social media, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.12 / 1,000 review saveds

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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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 Reviews Scraper

Export public Steam game reviews with review text, sentiment, votes, playtime, language, purchase flags, timestamps, and author metadata.

Use this Actor when you need repeatable Steam review data for game research, player sentiment analysis, competitor monitoring, product feedback, localization analysis, or dashboards. Results can be downloaded as CSV, JSON, Excel, XML, RSS, or used through the Apify Dataset API.

### At a glance

- **Steam app input**: scrape reviews by Steam app ID or public Steam store URL.
- **Review text and sentiment**: save review text, recommendation status, language, votes, and comment counts.
- **Author and playtime data**: include public author IDs, profile URLs, review counts, playtime, and last-played fields when available.
- **Filtering controls**: choose review order, language, review type, purchase type, and cursor.
- **Continuation ready**: use `nextCursor` to continue review collection from previous runs.

### Ready-to-run examples

Use these saved Store examples as starting points. Open any example to prefill the Actor input, then adjust URLs, keywords, limits, or filters for your own run.

- **[Export Vampire Survivors Reviews](https://apify.com/fetch_cat/steam-reviews-scraper/examples/vampire-survivors-review-sample)**
- **[Monitor Project Zomboid Reviews](https://apify.com/fetch_cat/steam-reviews-scraper/examples/project-zomboid-review-monitor)**
- **[Export Raft Steam Reviews](https://apify.com/fetch_cat/steam-reviews-scraper/examples/raft-steam-reviews)**
- **[Build Subnautica Review Dataset](https://apify.com/fetch_cat/steam-reviews-scraper/examples/subnautica-review-dataset)**
- **[Scrape Sons of the Forest Reviews](https://apify.com/fetch_cat/steam-reviews-scraper/examples/sons-of-the-forest-reviews)**
- **[Export Valheim Positive Reviews](https://apify.com/fetch_cat/steam-reviews-scraper/examples/valheim-positive-reviews)**
- **[View all ready-to-run examples](https://apify.com/fetch_cat/steam-reviews-scraper/examples)** (50 examples)

### What can it do?

Steam Reviews Scraper turns public Steam review feeds into structured review rows.

- **Export Steam review data** with text, recommendation status, votes, language, purchase flags, timestamps, and app IDs.
- **Collect player context** such as public author profile URL, Steam ID, review counts, games owned, and playtime when returned by Steam.
- **Filter review feeds** by language, sentiment, purchase type, and review ordering.
- **Build game-review monitors** by scheduling recent-review runs for target apps.
- **Use it as a Steam reviews API workflow** for CSV, JSON, Excel, or direct Dataset API exports.

### Common workflows

- **Sentiment analysis**: classify positive and negative Steam reviews by language, game, or time period.
- **Game launch monitoring**: track recent reviews after updates, releases, discounts, or patches.
- **Competitor research**: compare player complaints and praise across similar games.
- **Localization analysis**: collect reviews by language to understand regional feedback.
- **Product feedback mining**: extract recurring issues, feature requests, and player language.
- **Review dashboards**: feed review rows into BI tools, warehouses, or spreadsheets.

### What data can you collect?

Each dataset row represents one public Steam review.

| Field | Description |
| --- | --- |
| `appId` | Steam app ID |
| `appUrl` | Steam app URL |
| `recommendationId` | Unique Steam review/recommendation ID |
| `review` | Review text |
| `language` | Review language |
| `votedUp` | Whether the player recommends the game |
| `votesUp` | Helpful vote count |
| `votesFunny` | Funny vote count |
| `weightedVoteScore` | Steam weighted vote score |
| `commentCount` | Number of review comments |
| `steamPurchase` | Whether the game was purchased on Steam |
| `receivedForFree` | Whether the reviewer marked it as received for free |
| `refunded` | Whether the review is associated with a refund flag |
| `writtenDuringEarlyAccess` | Early-access review flag |
| `primarilySteamDeck` | Steam Deck related flag when available |
| `timestampCreated` | Review creation date/time |
| `timestampUpdated` | Review update date/time |
| `authorSteamId` | Public author Steam ID |
| `authorPersonaName` | Public Steam persona name when returned |
| `authorProfileUrl` | Author profile URL |
| `playtimeForeverMinutes` | Total recorded playtime in minutes |
| `playtimeAtReviewMinutes` | Playtime at the time of review |
| `lastPlayed` | Last played timestamp |
| `querySummaryTotalReviews` | Total reviews reported by Steam for the query |
| `nextCursor` | Cursor that can be used for continuation |
| `scrapedAt` | Timestamp when the row was saved |

### Pricing

This Actor uses Apify pay-per-event pricing. The prices below come from the current Actor pricing configuration. Apify public plans map to Store discount tiers, so the table shows both the user-facing plan context and the pricing tier name. The final price shown in Apify depends on the user account plan and any custom agreement.

| Event | What is charged | Price |
| --- | --- | ---: |
| `start` | One-time fee per run | $0.005 |

| Event | What is charged | Free / no discount | Starter / Bronze | Scale / Silver | Business / Gold | Custom / Platinum | Custom / Diamond |
| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |
| `result` | Per Steam review saved to the dataset | $0.23 / 1,000 | $0.2 / 1,000 | $0.156 / 1,000 | $0.12 / 1,000 | $0.08 / 1,000 | $0.056 / 1,000 |

Apify may also charge platform usage for compute, storage, proxies, or data transfer outside this Actor pricing. Check the Actor run and the Apify Pricing tab for the exact cost shown to your account.

### Input configuration

| Setting | JSON key | Use it for | Example |
| --- | --- | --- | --- |
| Steam app IDs or URLs | `appIdsOrUrls` | Steam app IDs or full Steam app URLs. | `["730"]` |
| Maximum reviews per app | `maxReviewsPerApp` | Reviews saved for each Steam app. | `100` |
| Review order/filter | `filter` | Steam review feed ordering. | `recent` |
| Language | `language` | Steam language code or all languages. | `english` |
| Review sentiment | `reviewType` | Positive, negative, or all reviews. | `all` |
| Purchase type | `purchaseType` | Steam purchase status filter. | `all` |
| Pagination cursor | `cursor` | Continue from a previous run. | `*` |
| Include author metadata | `includeAuthor` | Save public author and playtime fields. | `true` |
| Proxy configuration | `proxyConfiguration` | Optional Apify Proxy settings. | `{"useApifyProxy":false}` |

### Example input

```json
{
  "appIdsOrUrls": ["730"],
  "maxReviewsPerApp": 100,
  "filter": "recent",
  "language": "english",
  "reviewType": "all",
  "purchaseType": "all",
  "cursor": "*",
  "includeAuthor": true,
  "proxyConfiguration": { "useApifyProxy": false }
}
```

### Example output

```json
{
  "appId": "730",
  "appUrl": "https://store.steampowered.com/app/730/",
  "recommendationId": "123456789",
  "review": "Great competitive game with a strong community.",
  "language": "english",
  "votedUp": true,
  "votesUp": 12,
  "votesFunny": 0,
  "weightedVoteScore": 0.72,
  "steamPurchase": true,
  "playtimeAtReviewMinutes": 2400,
  "authorProfileUrl": "https://steamcommunity.com/profiles/7656119...",
  "timestampCreated": "2026-07-03T10:30:00.000Z",
  "nextCursor": "AoIIPw..."
}
```

### Tips for best results

- **Use app IDs for clean inputs**: Steam app IDs such as `730` are more compact than full URLs.
- **Use `filter="recent"` for monitoring**: recent reviews are best for alerts and launch tracking.
- **Set language deliberately**: `english` is cleaner for English NLP; `all` is broader for global research.
- **Keep author metadata on for analysis**: public playtime and review counts can help segment feedback.
- **Store `nextCursor`**: use it when you need to continue collection later.

### Limits and caveats

- The Actor extracts publicly visible Steam review data only.
- It does not access private profiles, private libraries, account-only data, or non-public review information.
- Some author fields may be missing when Steam does not return them.
- Steam review counts, votes, and visibility can change after scraping.

### API usage

```bash
curl -X POST 'https://api.apify.com/v2/acts/fetch_cat~steam-reviews-scraper/runs?token=YOUR_APIFY_TOKEN' \
  -H 'Content-Type: application/json' \
  -d '{"appIdsOrUrls":["730"],"maxReviewsPerApp":50,"filter":"recent","language":"english"}'
```

### MCP and AI agents

This Actor can be used through the official Apify MCP server at `https://mcp.apify.com`.

For a focused single-Actor tool setup, use:

```text
https://mcp.apify.com?tools=fetch_cat/steam-reviews-scraper
```

Use the same JSON keys shown in the input configuration table, such as `appIdsOrUrls`, `maxReviewsPerApp`, `filter`, `language`, `reviewType`, `purchaseType`, and `includeAuthor`.

### FAQ

#### Can this scrape reviews for any Steam game?

It works with public Steam app IDs and store URLs where Steam exposes public reviews.

#### Can I collect negative reviews only?

Yes. Set `reviewType` to the negative option in the input UI or API.

#### Can I export to CSV or Excel?

Yes. Apify datasets can be downloaded as CSV, JSON, Excel, XML, RSS, HTML, or accessed through the API.

### Related scrapers

- [Reddit Scraper](https://apify.com/fetch_cat/reddit-scraper)
- [YouTube Search Results Scraper](https://apify.com/fetch_cat/youtube-search-results-scraper)
- [YouTube Comments Scraper](https://apify.com/fetch_cat/youtube-comments-scraper)
- [Google Autocomplete Scraper](https://apify.com/fetch_cat/google-autocomplete-scraper)

### Changelog

#### 0.1

- Initial public release with Steam app review scraping, sentiment filters, language filters, purchase filters, author metadata, cursor continuation, and API export.

### Support

If a run fails, returns no data, or a field looks wrong, open an issue from the Actor page.

Please include the Apify run ID or run URL, input JSON, one example public URL, query, or input item, what you expected, and what the dataset returned. Small reproducible inputs make parsing or site-layout issues much faster to fix.

### Privacy and data handling

This Actor runs with Apify limited permissions and only processes data needed for the documented run. It uses review lookup inputs and public review results to produce the output dataset and sends requests to public Steam Reviews pages/endpoints; results are stored in Apify run storage for your account. FetchCat does not use your inputs or outputs for advertising, does not use them for model training, and does not retain them outside the Apify run except for transient support debugging when you explicitly share run details. You are responsible for using the Actor lawfully, respecting the target site's terms, and avoiding unnecessary personal or sensitive data in inputs.

# Actor input Schema

## `appIdsOrUrls` (type: `array`):

Steam app IDs or full Steam app URLs. Examples: 730, 570, or https://store.steampowered.com/app/730/CounterStrike\_2/

## `maxReviewsPerApp` (type: `integer`):

How many reviews to save for each Steam app.

## `filter` (type: `string`):

Steam review feed filter. Recent is best for monitoring new reviews; all is useful for broad sampling.

## `language` (type: `string`):

Steam language code, such as english, german, spanish, schinese, or all.

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

Limit reviews by positive/negative recommendation or keep all reviews.

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

Limit reviews by Steam purchase status.

## `cursor` (type: `string`):

Optional Steam pagination cursor from a previous run. Leave empty to start from the beginning.

## `includeAuthor` (type: `boolean`):

Include public author Steam ID, profile URL, review counts, and playtime fields when Steam returns them.

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

Optional Apify proxy settings. Steam review API usually works without a proxy.

## Actor input object example

```json
{
  "appIdsOrUrls": [
    "730"
  ],
  "maxReviewsPerApp": 20,
  "filter": "recent",
  "language": "english",
  "reviewType": "all",
  "purchaseType": "all",
  "cursor": "*",
  "includeAuthor": true,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `overview` (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 = {
    "appIdsOrUrls": [
        "730"
    ],
    "maxReviewsPerApp": 20,
    "filter": "recent",
    "language": "english",
    "reviewType": "all",
    "purchaseType": "all",
    "cursor": "*",
    "includeAuthor": true,
    "proxyConfiguration": {
        "useApifyProxy": false
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("fetch_cat/steam-reviews-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 = {
    "appIdsOrUrls": ["730"],
    "maxReviewsPerApp": 20,
    "filter": "recent",
    "language": "english",
    "reviewType": "all",
    "purchaseType": "all",
    "cursor": "*",
    "includeAuthor": True,
    "proxyConfiguration": { "useApifyProxy": False },
}

# Run the Actor and wait for it to finish
run = client.actor("fetch_cat/steam-reviews-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 '{
  "appIdsOrUrls": [
    "730"
  ],
  "maxReviewsPerApp": 20,
  "filter": "recent",
  "language": "english",
  "reviewType": "all",
  "purchaseType": "all",
  "cursor": "*",
  "includeAuthor": true,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}' |
apify call fetch_cat/steam-reviews-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=fetch_cat/steam-reviews-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/e6zNDyPPxXG7kCvxE/builds/sSO3Kcz26PfEXEfRY/openapi.json