# Steam Reviews Scraper (`67-labs/steam-reviews-scraper`) Actor

Scrape Steam game reviews: recommend or not, text, date, hours played, helpful votes, language. CSV, JSON or Sheets.

- **URL**: https://apify.com/67-labs/steam-reviews-scraper.md
- **Developed by:** [Mokksh Bhatt](https://apify.com/67-labs) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 1,000 result (one review)s

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 Reviews Scraper — every review of any Steam game, with hours played and helpful votes

Get the public reviews of any game on Steam: recommended or not, review text, date, hours played, helpful votes and language. Give it Steam app numbers or store links, choose the sort order, the language and the review type, and set how many reviews you want. Export to CSV, JSON, Excel or Google Sheets. No login, no API key.

**Who uses this:** game studios and publishers reading player feedback, community managers, market analysts comparing games, researchers who need review data for sentiment work, AI agents that summarize what players say.

### What you get per review

| Field | What it is |
|---|---|
| `reviewId` | Steam's ID of the review |
| `appId`, `appName` | The game, for example `730` and `Counter-Strike 2` |
| `votedUp` | `true` if the player recommends the game, `false` if not |
| `text` | The review text |
| `language` | Language the review was written in, as Steam names it (`english`, `german`, `schinese`) |
| `date`, `updatedDate` | When the review was written, and when it was last edited (ISO time, UTC) |
| `votesUp`, `votesFunny`, `commentCount` | How many players found it helpful or funny, and how many comments it has |
| `playtimeAtReviewHours`, `playtimeTotalHours` | Hours the player had in the game when writing the review, and now |
| `steamPurchase`, `receivedForFree`, `earlyAccess` | `true` if bought on Steam, if the game was a free copy, and if it was written during early access |
| `url` | Link to the review on Steam |
| `scrapedAt` | When the row was collected |

All columns are flat, so the CSV opens cleanly in Google Sheets and Excel.

### Price

**$0.01 per run + $0.50 per 1,000 reviews.** That is two pay-per-event charges: `actor-start` ($0.01) once per run and `result` ($0.0005) per review. No compute fees on top. The free Apify plan ($5 per month credit) covers about 9,900 reviews a month at no cost. A run that Steam blocks before it returns any data is not charged.

### How to use

1. Open the Input tab and add games: the Steam app number (the digits in the store link, for example `730`) or the store link itself.
2. Set **Max reviews per game**. This is also your cost cap. Pick **Sort by**, **Review type** and **Language** if you need them.
3. Click Start. When it is done, open the Output tab and export the table as CSV, JSON or Excel, or send it to Google Sheets with Apify's Google Sheets integration.

### Input examples

The 100 newest English reviews of one game:

```json
{ "apps": ["730"], "maxReviews": 100 }
```

The most helpful negative reviews of a game, to see what players complain about:

```json
{
    "apps": ["https://store.steampowered.com/app/1245620/ELDEN_RING/"],
    "maxReviews": 250,
    "sort": "helpful",
    "reviewType": "negative"
}
```

Two games, reviews in every language:

```json
{ "apps": ["570", "1091500"], "maxReviews": 500, "language": "all" }
```

### Output example

```json
{
    "reviewId": "236147929",
    "appId": "730",
    "appName": "Counter-Strike 2",
    "language": "english",
    "votedUp": true,
    "text": "good",
    "date": "2026-09-25T13:06:58.000Z",
    "updatedDate": "2026-09-25T13:06:58.000Z",
    "votesUp": 0,
    "votesFunny": 0,
    "commentCount": 0,
    "playtimeAtReviewHours": 25.3,
    "playtimeTotalHours": 25.3,
    "steamPurchase": true,
    "receivedForFree": false,
    "earlyAccess": false,
    "url": "https://steamcommunity.com/profiles/76561198000000001/recommended/730/",
    "scrapedAt": "2026-09-26T07:30:00.000Z"
}
```

The link in this example is a placeholder. Real rows link to the reviewer's public Steam review page.

### Limits

- Only public reviews, exactly as Steam shows them. No login and no private data. Reviewer names are not collected.
- Steam returns up to 100 reviews per request. 1,000 reviews take about 10 requests.
- **Sort by newest** lists reviews by the day they were written. **Most helpful** follows Steam's own helpfulness ranking.
- The actor keeps to under 2 requests a second and retries with a pause. If a run fails with 403 or 429, turn on **Proxy**.

### FAQ

**Can I get every review of a game?** Yes, as far as Steam lists them. Set **Max reviews per game** high, for example 1,000,000.

**Can I watch new reviews every day?** Yes. Schedule the actor in Apify with `sort` set to `newest` and compare `reviewId` values between runs.

**How do I find the app number?** It is the number in the store link: store.steampowered.com/app/**730**/.

**Can an AI agent use it?** Yes. It works through Apify's API and MCP server. Give it a game and a `maxReviews` limit.

**Something is wrong. What now?** Open an issue on the Issues tab with the game and the input you used. Field requests are welcome.

### Changelog

- **0.1** First release: reviews with playtime and votes, sort, review type and language options, pay per event.

# Actor input Schema

## `apps` (type: `array`):

One entry per game: the Steam app number (730) or the store link (https://store.steampowered.com/app/730/).

## `maxReviews` (type: `integer`):

Stop after this many reviews for each game. You pay per review, so this is also your cost cap.

## `sort` (type: `string`):

Newest first, recently edited first, or most helpful first.

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

All reviews, only positive (recommended), or only negative (not recommended).

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

Steam language name: english, german, french, spanish, schinese, russian, brazilian and so on. Use "all" for every language.

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

Leave off unless Steam blocks Apify servers (errors 403 or 429). Then turn on Apify Proxy.

## Actor input object example

```json
{
  "apps": [
    "730",
    "https://store.steampowered.com/app/1245620/ELDEN_RING/"
  ],
  "maxReviews": 20,
  "sort": "newest",
  "reviewType": "all",
  "language": "english",
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `reviews` (type: `string`):

One row per review: reviewId, appId, appName, votedUp, text, date, playtime, votes, language, url.

## `reviewsCsv` (type: `string`):

Same rows as CSV for Google Sheets or Excel.

# 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 = {
    "apps": [
        "730"
    ],
    "maxReviews": 20
};

// Run the Actor and wait for it to finish
const run = await client.actor("67-labs/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 = {
    "apps": ["730"],
    "maxReviews": 20,
}

# Run the Actor and wait for it to finish
run = client.actor("67-labs/steam-reviews-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 '{
  "apps": [
    "730"
  ],
  "maxReviews": 20
}' |
apify call 67-labs/steam-reviews-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,67-labs/steam-reviews-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/JbpghEa1VYydKWW8P/builds/iN9hWrH9V67cWoIBU/openapi.json