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

- **URL**: https://apify.com/webdatatools/steam-reviews-scraper.md
- **Developed by:** [Murat Uzun](https://apify.com/webdatatools) (community)
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$0.20 / 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.

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

### What does Steam Reviews Scraper do?

Collect public Steam reviews across multiple games for feedback analysis, reporting, or comparison. Get review text, recommendations, language, timestamps, helpful votes and playtime in a downloadable dataset.

Steam provides the underlying data for free. This Actor handles pagination, output limits and persistent duplicate suppression so you can continue collecting without receiving the same review again. No Steam API key or paid proxy is required.

### How to use it

1. Enter one to twenty Steam app IDs, such as `570` or `730`.
2. Choose a language and set the maximum number of reviews.
3. Set `maxReviewsPerGame` if you want to limit how much each game contributes.
4. Reuse the same `stateStoreName` to resume collection and suppress previously delivered reviews.
5. Download the reviews and check the OUTPUT coverage report for limits or errors.

```json
{
  "appIds": ["570", "730"],
  "language": "all",
  "maxReviews": 100,
  "maxReviewsPerGame": 50,
  "maxPagesPerGame": 3
}
```

### Input options

| Option | Behavior |
| --- | --- |
| `appIds` | One to twenty Steam app IDs. |
| `language` | Steam language code, such as `english`; defaults to `all`. |
| `maxReviews` | Maximum new reviews across all games per run: 1–1000, default 100. |
| `maxReviewsPerGame` | Maximum new reviews per game: 1–1000. When omitted, equals `maxReviews`. |
| `maxPagesPerGame` | Maximum pages per game per run: 1–20, default 3. |
| `pageSize` | Reviews requested per page: 1–100, default 100. |
| `stateStoreName` | Persistent deduplication and resume store; defaults to `steam-reviews-poc-ledger`. |

Games are processed in input order. A small global limit can leave later games untouched. Limits are ceilings, not promises of a particular number of results: empty games, upstream errors or previously collected reviews can reduce output.

### Output

Each review includes `appId`, `reviewId`, `text`, `language`, `recommended`, creation and update timestamps, helpful votes, playtime, a source URL and a content hash. Optional data may be missing. Download the dataset as JSON, CSV, Excel or HTML.

Synthetic example — illustrative data, not a live Steam review:

```json
{
  "appId": "570",
  "reviewId": "100",
  "text": "Strategic team play.",
  "language": "english",
  "recommended": true,
  "playtimeMinutes": 120,
  "votesHelpful": 3,
  "coverageComplete": false
}
```

The OUTPUT report includes per-game cursors, stopping reasons, duplicate and changed-review counts, and uncertain deliveries. Errors and empty results are not review rows. `fetchedAt` is the response fetch time, not a guarantee that Steam's data is fresh. `coverageComplete` remains false because collection is bounded and does not represent a complete snapshot taken at one instant.

### Pricing and cost example

USD/result: 0.0002

The listed rate is **$0.0002 per unique useful review delivered**. Consult the Apify Store Pricing tab for the currently applied price and platform charges. There is no separate Actor start, per-game or empty-result fee.

On November 4, 2026, our own two tests collected 100 reviews each, with cloud infrastructure costs of approximately **$0.012 per test run**. These observations are from our own tests only; they are not evidence of customer revenue, sales, profit or customer costs. Net profit margin has not been measured. Actual costs vary with workload, retries and storage use.

### Resume and duplicate suppression

Only one run may write to a given `stateStoreName` at a time. Concurrent writers are not supported. Different stores have separate review histories; changing or deleting a store loses duplicate suppression across runs.

Review IDs are remembered across runs and languages. An already reserved review is not automatically delivered again, even if its content changes. Content changes are counted rather than emitted as new reviews.

Resume continues from the saved cursor for each game and language. Partial pages retain their cursor so remaining reviews can be collected later. After an empty terminal page, the next run starts from the first page while retaining the review history. This is continuation of an existing traversal, not fresh daily tracking.

### Data and reliability limits

Responses are cached for ten minutes, and Steam may also return cached data. Cache expiry does not guarantee freshness. Rate limits can pause collection until a later run; upstream failures and incomplete coverage appear in OUTPUT.

To avoid automatic duplicate delivery, each review is reserved before it is written. If delivery or charging cannot be confirmed, its reservation stays pending and is not replayed automatically. This can omit a review after a crash or an uncertain write. Delivery, charging and state updates are not one transaction, so exactly-once delivery is not guaranteed.

State and cache storage grow with use. Long-term retention needs and large-scale costs have not been measured. Reviews reflect publicly available user submissions; availability, completeness and accuracy depend on Steam and its reviewers.

# Actor input Schema

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

Steam app IDs to collect reviews for, for example 570 or 730. Enter one to twenty IDs.

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

Steam review language code, such as english; all includes every language.

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

Maximum new reviews delivered across all games in this run. Games are processed in input order.

## `maxPagesPerGame` (type: `integer`):

Maximum upstream review pages fetched per game in this run. Resume continues from the saved cursor.

## `stateStoreName` (type: `string`):

Persistent review ledger and cursor store. Reuse this name for deduplication and resume; allow only one writer at a time.

## `maxReviewsPerGame` (type: `integer`):

Defaults to maxReviews when omitted; preserves the existing global-limit behavior.

## `pageSize` (type: `integer`):

Number of reviews requested per upstream page, from 1 to 100. Delivery is still bounded by the global and per-game limits.

## Actor input object example

```json
{
  "appIds": [
    "570"
  ],
  "language": "all",
  "maxReviews": 100,
  "maxPagesPerGame": 3,
  "stateStoreName": "steam-reviews-poc-ledger",
  "pageSize": 100
}
```

# Actor output Schema

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

No description

## `coverage` (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": [
        "570"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("webdatatools/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 = { "appIds": ["570"] }

# Run the Actor and wait for it to finish
run = client.actor("webdatatools/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 '{
  "appIds": [
    "570"
  ]
}' |
apify call webdatatools/steam-reviews-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,webdatatools/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/i070UHWnnZHElrKTk/builds/huxVyFXnoF7EHns4e/openapi.json