# Steam Reviews Scraper (`foo121/steam-reviews-scraper`) Actor

Export Steam game reviews by App ID with full author detail — persona name, profile URL, avatar, games owned, and playtime at review — plus the per-game review-score summary. Public Steam API, no key, no proxy.

- **URL**: https://apify.com/foo121/steam-reviews-scraper.md
- **Developed by:** [ziv shay](https://apify.com/foo121) (community)
- **Categories:** E-commerce, Social media, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$1.00 / 1,000 result items

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?

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

**Scrape Steam game reviews by App ID** — full review text, sentiment, vote counts, and the complete author panel (persona name, profile URL, avatar, games owned, review count, and **playtime at the time of review**) — plus the per-game review-score summary. Powered by Steam's public review API: **no API key, no login, no proxy.**

### What you get (one review per result)

| Field | Description |
|---|---|
| `appId`, `gameName` | Steam App ID and resolved game name |
| `voteUp` | `true` = Recommended, `false` = Not Recommended |
| `review` | Full review text |
| `votesUp`, `votesFunny`, `weightedVoteScore`, `commentCount` | Community engagement signals |
| `language` | Review language |
| `steamPurchase`, `receivedForFree`, `earlyAccess`, `primarilySteamDeck` | Provenance flags |
| `createdAt`, `updatedAt` | ISO timestamps |
| `authorSteamId`, `authorName`, `authorProfileUrl`, `authorAvatar` | Author identity |
| `authorNumGamesOwned`, `authorNumReviews` | Author reputation |
| `authorPlaytimeForever`, `authorPlaytimeAtReview`, `authorPlaytimeLast2Weeks` | Playtime (minutes) — credibility signal |
| `reviewUrl` | Link back to the review |

### Example input

```json
{
  "appIds": ["570", "730"],
  "language": "english",
  "reviewType": "all",
  "filter": "recent",
  "maxItemsPerApp": 1000
}
```

### Example output (1 row)

```json
{
  "appId": "570",
  "gameName": "Dota 2",
  "voteUp": false,
  "review": "constantly crashing after the last patch",
  "votesUp": 3,
  "language": "english",
  "authorName": "Ben",
  "authorPlaytimeAtReview": 12480,
  "authorNumGamesOwned": 214,
  "createdAt": "2026-06-19T11:02:13.000Z",
  "reviewUrl": "https://steamcommunity.com/profiles/7656.../recommended/570/"
}
```

### Use cases

- **Game studios / publishers** — track player sentiment after a patch or launch.
- **Market researchers** — competitive reception analysis across titles.
- **Store-page / ASO** — mine common praise/complaints for copy and roadmap.
- **ML / sentiment datasets** — labelled reviews with playtime-weighted credibility.

### Why this one

Most Steam-review actors return the bare review text and a thumbs-up flag. This actor ships the **full author panel** — including `authorPlaytimeAtReview`, the single strongest signal of whether a review came from someone who actually played the game — alongside the per-game review-score summary, all from a **public source with no key and no proxy**. Multi-app batch in one run; cursor pagination handles long histories.

### How it works

Calls `store.steampowered.com/appreviews/<appid>?json=1` with cursor pagination, resolving the game name via the public `appdetails` endpoint. One review per dataset item (pay-per-result).

*Keywords: steam reviews scraper, steam review api, steam game reviews, steam sentiment, steam player feedback, steam store reviews export.*

# Actor input Schema

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

One or more numeric Steam App IDs to scrape reviews for (e.g. 570 = Dota 2, 730 = Counter-Strike 2, 1245620 = Elden Ring). Find the App ID in the store URL: store.steampowered.com/app/<APPID>/.

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

Review language filter. Use 'all' for every language, or a Steam language code like 'english', 'schinese', 'russian', 'german'.

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

Filter by sentiment.

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

Limit to Steam-purchased copies, non-Steam (key-activated) copies, or all.

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

'recent' = newest first, 'updated' = recently updated first, 'all' = Steam helpfulness ranking.

## `dayRange` (type: `integer`):

Only include reviews from the last N days (1-365). Leave 0 for no time limit. Only meaningful when sort = Most helpful.

## `maxItemsPerApp` (type: `integer`):

Maximum number of reviews to output per App ID.

## Actor input object example

```json
{
  "appIds": [
    "570"
  ],
  "language": "all",
  "reviewType": "all",
  "purchaseType": "all",
  "filter": "recent",
  "dayRange": 0,
  "maxItemsPerApp": 1000
}
```

# 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("foo121/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("foo121/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 foo121/steam-reviews-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,foo121/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/DQWkVKMwYRH997RM8/builds/Ja4STPUywJU1iy49a/openapi.json