Steam App Review Scraper

Steam App Review Scraper pulls structured data from Steam and returns it as a clean dataset you can export as JSON, CSV or Excel. It calls Steam's public appreviews endpoint and returns one row per review with the full text, recommend flag, helpfulness votes, author profile, playtime and language, ideal for product, sentiment and competitor research.

No API key, no login. Click Start and it works with the prefilled input.

What does Steam App Review Scraper do?

It queries Steam and returns one row per record, with only the fields that matter. Runs on the Apify platform, so you get an API endpoint, scheduling, monitoring and integrations (Zapier, Make, Google Sheets, webhooks) without maintaining any infrastructure.

Why use it?

• Steam data without the plumbing: no proxy setup, no pagination code, no scraping maintenance.
• Schedule it to monitor changes daily or weekly and trigger alerts on new records.
• Feed it straight into a spreadsheet, database, or an AI agent via the Apify API or MCP server.
• Runs in seconds and returns a typed dataset that is ready for analysis.

How to use it

1. Click Try for free / Start.
2. Leave the default input to see a sample run, or type your own values (see Input below).
3. Press Start and wait a few seconds.
4. Open the Output tab and download as JSON, CSV, Excel or HTML.

Input

Field	Type	Default	Description
appId	string	"730"	Steam app id, e.g. 730. You can paste the store or community URL and the id is extracted.
language	string	"all"	Review language filter. One of: 'all', 'english', 'german', 'french', 'spanish', 'schinese', 'japanese', 'korean', 'russian', 'portuguese', 'polish', 'italian', 'turkish'.
reviewType	string	"all"	Only positive reviews, only negative, or all. One of: 'all', 'positive', 'negative'.
maxResults	integer	50	Maximum number of reviews to return.

Example input:

{
  "appId": "730",
  "maxResults": 5
}

Output

One item per record. Sample:

{
  "review_id": "235370094",
  "app_id": "730",
  "author": "Bidon [TUC]",
  "author_id": "76561199699692447",
  "profile_url": "https://steamcommunity.com/id/example/",
  "recommended": true
}

You can download the dataset in various formats such as JSON, HTML, CSV, or Excel.

Data fields

Field	Type	Meaning
review_id	string	Review ID
app_id	string	App ID
author	string	Author
author_id	string	Author Steam ID
profile_url	string	Profile URL
recommended	boolean	Recommended
review	string	Review
helpful_votes	integer	Helpful Votes
funny_votes	integer	Funny Votes
comment_count	integer	Comments
playtime_hours	number	Playtime (hours)
language	string	Language
created	string	Created (UTC)
review_url	string	Review URL
app_url	string	App URL

How much does it cost to run?

This Actor uses pay-per-event pricing: you pay per result item pushed to the dataset, and nothing for an empty run. Each run is a few seconds of compute, so typical costs are fractions of a cent per run, and the Apify free tier covers plenty of testing.

Tips and advanced options

• Raise maxResults for a bigger pull; the Actor pages through the source for you.
• Schedule it (e.g. every morning) and combine with a webhook to get only the new records.
• Add the Actor to an AI agent through the Apify MCP server for natural-language queries.

FAQ, disclaimers and support

• Is this legal? It uses publicly available data from Steam. You are responsible for how you use the data and for complying with the source's terms.
• It returned nothing? Use a Steam app id such as 730 (Counter-Strike 2) or 570 (Dota 2).
• Something broken? Open an issue on the Actor's Issues tab. Data sources change and get fixed quickly.
• Need a custom variant (extra fields, a different source, a scheduled report)? Get in touch through the Issues tab.