Steam Reviews Scraper

Extract public Steam game reviews into clean, analysis-ready datasets.

Steam Reviews Scraper helps studios, publishers, market researchers, agencies, and data teams monitor what players say about any public Steam game. Add Steam app IDs or store URLs, choose review filters, and export review text together with recommendation status, votes, playtime, purchase flags, language, timestamps, and author profile signals.

What does Steam Reviews Scraper do?

This actor collects public review records for Steam apps and saves one dataset item per review.

Use it to:

• 🎮 Track new reviews for your own game
• 🧭 Compare player sentiment across competitors
• 📈 Build dashboards for review volume and helpful votes
• 🌍 Analyze language mix across Steam communities
• ⏱️ Segment reviews by playtime at review time
• 💬 Export review text for NLP and sentiment workflows

Who is it for?

Game studios

Monitor launch feedback, patch reactions, and long-term player satisfaction.

Publishers

Compare portfolio titles, competitor games, and campaign impact across markets.

Market researchers

Create repeatable datasets for genre, pricing, reputation, and player behavior analysis.

Reputation-monitoring teams

Detect spikes in negative reviews, refund-related feedback, and language-specific issues.

Data analysts

Load normalized review rows into BI tools, notebooks, warehouses, or dashboards.

Why use this actor?

• ✅ Works with Steam app IDs and Steam app URLs
• ✅ Supports review filters and sentiment filters
• ✅ Captures review text and metadata in one row
• ✅ Includes playtime, author, purchase, vote, and timestamp fields
• ✅ Uses a low-cost HTTP workflow suitable for regular monitoring
• ✅ Exports to JSON, CSV, Excel, XML, RSS, and API endpoints through Apify

Typical use cases

• Daily review monitoring for a game launch
• Competitor reputation tracking by app ID
• Player feedback export before roadmap planning
• Negative-review analysis after a patch
• Language-specific sentiment reporting
• Dataset creation for ML/NLP labeling
• Helpful-vote and funny-vote analysis
• Early-access feedback monitoring

Input overview

The actor accepts one or more Steam app IDs or Steam app URLs.

Example app ID:

730

Example Steam URL:

https://store.steampowered.com/app/730/CounterStrike_2/

Input fields

Field	Type	Description
appIdsOrUrls	array	Steam app IDs or Steam store app URLs
maxReviewsPerApp	integer	Maximum reviews to save for each app
filter	string	Review ordering: recent, updated, or all
language	string	Steam language code, such as english or all
reviewType	string	all, positive, or negative
purchaseType	string	all, steam, or non_steam_purchase
cursor	string	Optional pagination cursor for continuation
includeAuthor	boolean	Include public author ID, profile URL, review counts, and playtime metadata
proxyConfiguration	object	Optional proxy settings

Example input

{
  "appIdsOrUrls": ["730", "https://store.steampowered.com/app/570/Dota_2/"],
  "maxReviewsPerApp": 50,
  "filter": "recent",
  "language": "english",
  "reviewType": "all",
  "purchaseType": "all",
  "cursor": "*",
  "includeAuthor": true,
  "proxyConfiguration": { "useApifyProxy": false }
}

Output overview

Each dataset item represents one Steam review.

Output data table

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	Review 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	When the item was collected

Example output item

{
  "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": "2026-06-27T10:30:00.000Z"
}

How much does it cost to scrape Steam reviews?

The actor uses pay-per-event pricing.

• Start event: $0.005 once per run.
• Review saved event (BRONZE): $0.0002 per saved review (about $0.20 per 1,000 reviews).
• Lower per-review prices are available on higher Apify tiers.

For most monitoring jobs, the cost scales with the number of reviews you request.

How to run

1. Open the actor on Apify.
2. Add one or more Steam app IDs or app URLs.
3. Set maxReviewsPerApp to the number of reviews you need.
4. Choose language and sentiment filters.
5. Click Start.
6. Export results from the Dataset tab.

Tips for best results

• Start with maxReviewsPerApp: 25 for a quick sample.
• Use filter: recent for monitoring new reviews.
• Use reviewType: negative to triage player complaints.
• Use language: all for global studies.
• Keep the returned nextCursor if you want to continue from a later page.
• Run separate jobs for different games when you need independent schedules.

Steam language examples

Common language values include:

• english
• german
• french
• spanish
• schinese
• japanese
• koreana
• russian
• all

Integrations

Use the dataset with:

• 📊 Google Sheets or Excel exports for quick analysis
• 🧠 NLP pipelines for topic extraction and sentiment labels
• 🗄️ BigQuery, Snowflake, or Postgres via Apify integrations
• 🔔 Slack or email alerts triggered by scheduled runs
• 📈 BI dashboards tracking review volume and recommendation ratio
• 🧪 QA workflows that compare reactions before and after game updates

API usage

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/steam-reviews-scraper').call({
  appIdsOrUrls: ['730'],
  maxReviewsPerApp: 25,
  filter: 'recent',
  language: 'english'
});

console.log(run.defaultDatasetId);

Python

from apify_client import ApifyClient
import os

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('fetch_cat/steam-reviews-scraper').call(run_input={
    'appIdsOrUrls': ['730'],
    'maxReviewsPerApp': 25,
    'filter': 'recent',
    'language': 'english',
})

print(run['defaultDatasetId'])

cURL

curl "https://api.apify.com/v2/acts/fetch_cat~steam-reviews-scraper/runs?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"appIdsOrUrls":["730"],"maxReviewsPerApp":25}'

MCP: use Steam reviews in AI tools

You can connect Apify actors to Claude Desktop, Claude Code, and other MCP clients.

Example MCP server URL:

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

Claude Code setup:

$claude mcp add apify-steam-reviews "https://mcp.apify.com/?tools=fetch_cat/steam-reviews-scraper"

Claude Desktop JSON setup:

{
  "mcpServers": {
    "apify-steam-reviews": {
      "url": "https://mcp.apify.com/?tools=fetch_cat/steam-reviews-scraper"
    }
  }
}

Example prompts:

• "Scrape the latest 50 negative Steam reviews for app 730 and summarize the top complaints."
• "Compare positive and negative review themes for this Steam game."
• "Extract recent English reviews and identify recurring performance issues."

Scheduling

Set up an Apify schedule to monitor reviews daily or weekly.

Good scheduled inputs:

• 25 recent reviews every day for launch monitoring
• 100 negative reviews after each patch
• Weekly competitor review samples across 5 app IDs

Data quality notes

Steam review text is user-generated, so it may contain profanity, emojis, links, formatting, or multiple languages. The actor preserves the review text as provided and adds normalized metadata fields for easier filtering.

Limits

• Only public Steam app reviews are supported.
• Private account-only data is not collected.
• Very large backfills should be split into multiple runs.
• Availability depends on Steam keeping the public review pages accessible.

FAQ

Can I scrape reviews for multiple Steam games?

Yes. Add multiple app IDs or Steam app URLs and set the per-app review limit.

Does this collect private Steam account data?

No. It only returns public review fields that are visible for Steam app reviews.

Troubleshooting

I got fewer reviews than requested. Why?

The selected app/filter/language combination may have fewer available reviews than your limit. Try language: all or broaden the sentiment and purchase filters.

My Steam URL was skipped. Why?

Use a URL containing /app/<id>/, or provide the numeric app ID directly.

Can I continue from a previous run?

Yes. Copy a nextCursor value from the dataset and use it as the cursor input in the next run.

Legality

This actor extracts publicly available Steam review information. Use the data responsibly, respect Steam's terms, avoid abusive request volumes, and do not use the results to identify or harass individual users.

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• Bluesky Profile Feed Scraper — monitor public social posts from Bluesky profiles.
• Google Flights Scraper — collect flight search results for travel market analysis.

Changelog

0.1

Initial version with app ID / URL input, review filters, language support, cursor pagination, and normalized review output.

Support

If you need help, open an issue on the actor page with your input, expected result, and run ID.