Steam Reviews Scraper

Export public Steam game reviews with text, positive or negative recommendations, votes, language, purchase flags, timestamps and available author/playtime metadata. Steam Reviews Scraper accepts Steam app IDs or store URLs and saves one dataset row per usable review.

Use the results for player-feedback research, comparisons between games, localization analysis or your own review dashboard. No Steam login or Steam API key is required. Author details depend on what Steam returns.

Quick start

1. Open the Actor input and enter a Steam app ID such as 730.
2. Choose a small review limit and your language, then click Start.
3. When the run finishes, open its Dataset to view or export the reviews.

{
  "appIdsOrUrls": ["730"],
  "maxReviewsPerApp": 20,
  "language": "english",
  "filter": "recent"
}

Prefer a video guide? This short Apify walkthrough shows you how to set up and run an Actor. It uses a different Actor, but the setup steps are the same.

Ready-to-run examples

Choose an example to start with a small, preconfigured input. Each saves up to five English reviews for one game.

• Monitor Counter-Strike 2 Recent Reviews — Collect the latest reviews for player-feedback research. Repeat runs and comparisons belong to your workflow.
• Analyze Dota 2 Negative Reviews — Collect negative recommendations with available author and playtime context.
• Track No Man’s Sky Updated Reviews — Collect recently updated reviews to study feedback after game updates.

What you get

• Review content: Text, language, recommendation status and creation/update timestamps.
• Engagement: Helpful votes, funny votes, weighted vote score, comment count and available reactions.
• Player context: Public author identifiers, profile URLs, review counts and playtime when available.
• Review context: Purchase/free-copy flags, early-access flags and Steam's query summary.
• Focused collection: Language, sentiment, purchase type and feed-order filters, plus continuation from a returned cursor.

Who is it for?

User	Useful workflow
Game studios and publishers	Identify recurring complaints and praise after an update.
Market researchers	Compare public player feedback across games.
Localization teams	Collect reviews in a selected Steam language.
Data analysts	Build review datasets for spreadsheets, NLP or dashboards.

The Actor collects reviews. Sentiment summaries, change detection and cross-run comparisons are steps you add afterward.

Input settings

Setting	JSON key	Accepted values and behavior
Steam app IDs or URLs	appIdsOrUrls	Required list of app IDs or Steam store URLs. Repeated app IDs are processed once.
Maximum reviews per app	maxReviewsPerApp	1–10,000. Form default/prefill is 20. The runtime fallback for raw input without this field is 50. Set it explicitly for reproducible runs.
Language	language	Steam language code such as english, german, schinese or all. Default english.
Review sentiment	reviewType	all, positive or negative. Default all.
Purchase type	purchaseType	all, steam or non_steam_purchase. Default all.
Include author metadata	includeAuthor	Default true. Set false to leave author and playtime fields null.
Review order/filter	filter	recent, updated or all. Default recent. Uses Steam's feed ordering, not custom sorting.
Pagination cursor	cursor	Default *. Supply a returned nextCursor to continue the same app and filters.
Proxy configuration	proxyConfiguration	Optional existing Apify-managed or custom proxy settings. Default/prefill disables Apify Proxy. Omission uses direct access. No automatic proxy fallback.

Use a separate continuation input for each app. One supplied cursor applies to every app in that run. Keep the app and filters unchanged when continuing.

Input recipe: negative reviews in another language

{
  "appIdsOrUrls": ["https://store.steampowered.com/app/730/"],
  "maxReviewsPerApp": 20,
  "language": "german",
  "reviewType": "negative",
  "includeAuthor": false
}

Output data

Each row represents a review with a recommendationId. Fields that Steam does not supply are null. When includeAuthor is false, author and playtime fields are null.

This shortened synthetic shape-only example illustrates field types, not an observed review:

{
  "appId": "730",
  "appUrl": "https://store.steampowered.com/app/730/",
  "recommendationId": "example-review-id",
  "review": "Example review text, not a real player quotation.",
  "language": "english",
  "votedUp": true,
  "votesUp": 0,
  "authorPersonaName": null,
  "playtimeAtReviewMinutes": null,
  "nextCursor": null
}

Fields	Meaning
appId, appUrl, recommendationId	Game identifier, store page and review identifier.
review, language, votedUp	Review text, language and recommendation flag.
votesUp, votesFunny, weightedVoteScore, commentCount	Review engagement and score.
steamPurchase, receivedForFree, refunded	Available purchase/free-copy/refund flags.
writtenDuringEarlyAccess, primarilySteamDeck	Available early-access and Steam Deck flags.
timestampCreated, timestampUpdated	ISO creation/update timestamps.
timestampCreatedUnix, timestampUpdatedUnix	Original Unix timestamps in seconds.
appReleaseDate, appReleaseDateUnix	Release timestamp when supplied.
authorSteamId, authorPersonaName, authorProfileUrl	Available public author identifiers, name and profile URL.
authorNumGamesOwned, authorNumReviews	Available author game/review counts.
playtimeForeverMinutes, playtimeLastTwoWeeksMinutes, playtimeAtReviewMinutes	Available playtime, always in minutes.
lastPlayed, lastPlayedUnix	Available last-played timestamp as ISO and Unix seconds.
querySummaryNumReviews, querySummaryReviewScore, querySummaryReviewScoreDesc	Steam's page/query review count and score summary.
querySummaryTotalPositive, querySummaryTotalNegative, querySummaryTotalReviews	Steam's reported totals, not the number saved by this run.
filter, requestedLanguage, reviewType, purchaseType	Settings used to collect the row.
nextCursor	Continuation value for the fetched page, or null.
reactions	Reaction list, legacy reaction-count object or null.
scrapedAt	Collection timestamp.

The RUN_SUMMARY key-value record reports the run status and, for each processed app, its saved count and stopping reason. A run stopped near its time limit can also save PENDING_APPS. These are storage records, not review rows.

Download your results

Open your run's dataset, choose Export and select JSON, CSV or Excel. JSON keeps nested fields together. CSV and Excel are useful when you want to review the records in a spreadsheet.

See Apify's download and export guide for the available formats and field options.

Pricing

This Actor uses pay-per-event pricing. The start event is a one-time run charge after app input validation, including valid runs that return no matching reviews.

The result event is charged for each review saved to the dataset. Empty results and summary records do not create review-result charges. Saved reviews remain available if a later request or billing step fails.

Set maxReviewsPerApp and an Apify maximum charge to bound collection. See the live Pricing tab for current rates and discounts.

Tips for best results

• Start with a small limit. Check language and review fields before collecting a larger batch.
• Set your language deliberately. Use english for an English-only dataset or all for broader language coverage.
• Choose the right feed. Use recent for new reviews and updated for recently edited reviews.
• Keep author metadata when useful. Available playtime and review counts can help segment feedback, but missing data stays null.
• Continue one game at a time. Reuse its nextCursor with the same filters and store review IDs to manage overlap across runs.

Limits and caveats

• Only public Steam reviews are collected. Private profiles, private libraries and account-only data are not supported.
• Game-name search, date-range filters, developer replies and AI-generated sentiment summaries are not input/output features of this Actor.
• Steam controls review visibility and feed order. Counts and votes can change between runs. A requested maximum is a cap, not a guaranteed result count.
• Author names, games owned, release dates and other optional fields may be absent. No separate profile enrichment is performed.
• Valid queries with no matching reviews succeed with an empty dataset and truthful summary reasons. Unexpected responses, essential storage or billing failures still fail the run. Already saved reviews remain available.
• Runtime and result-budget limits can stop a batch before all apps are processed. Check RUN_SUMMARY before treating a batch as complete.

Automation and integrations

Save your settings for next time

Once you have an input that works for you, save it as an Apify task. This keeps your settings together so you can use them again without filling in the form from scratch.

Run on a schedule

Need fresh results regularly? You can schedule the Actor or a saved task to run at a time that suits you.

Each scheduled run collects the data again. If you want to compare results or highlight changes, add that step in your own workflow.

Send results to your other tools

Use n8n, Make or Zapier to connect this scraper to the rest of your workflow. For example, you could pass its results to an AI assistant or add records to a spreadsheet. Follow the Apify integration guides for your chosen tool.

For a custom connection, an Apify webhook can notify your app when a run finishes or fails. Your app can then fetch the results or handle the failure.

API usage

Want to collect this data from your own app or automation? The examples below send input to the scraper and show how to collect its results.

The Node.js and Python examples wait for the run to finish before reading its results. The cURL example starts the run and returns its details. Wait for that run to finish, then use its defaultDatasetId to fetch the results.

For setup instructions, see Apify's JavaScript client guide, Python client guide and Actor runs API reference.

cURL

curl -X POST 'https://api.apify.com/v2/acts/fetch_cat~steam-reviews-scraper/runs' \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"appIdsOrUrls":["730"],"maxReviewsPerApp":20,"language":"english"}'

# After the returned run finishes, replace DATASET_ID with its defaultDatasetId.
curl 'https://api.apify.com/v2/datasets/DATASET_ID/items?format=json' \
  -H "Authorization: Bearer $APIFY_TOKEN"

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: 20,
  language: 'english',
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("fetch_cat/steam-reviews-scraper").call(run_input={
    "appIdsOrUrls": ["730"],
    "maxReviewsPerApp": 20,
    "language": "english",
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)

MCP and AI agents

You can ask your AI assistant to collect data with Steam Reviews Scraper and help you work with the results. The scraper returns the data, and your assistant can then summarize it, compare records or prepare notes.

Connect your assistant through the Apify MCP server. MCP gives your assistant a way to find this scraper, run it and read the results. Follow the official setup and sign-in guide for your app.

Easier setup with an Apify plugin

If you use a supported coding assistant, the official Apify plugin can set up the connection for you. It connects your assistant to Apify and includes instructions for common tasks. Follow the guide for your particular app.

Use Apify AI in Console

Prefer to keep everything in Apify? Open Apify AI in Console and ask it to find Fetch Cat's Steam Reviews Scraper. You can describe the data you want and ask questions about the results.

Apify AI is currently in beta. It asks for confirmation before starting an Actor, and normal run charges apply.

Choose your assistant

• Claude and Claude Code (Anthropic): Apify setup guide.
• ChatGPT and Codex (OpenAI): Apify setup guides.
• Grok Bot (xAI): Apify plugin guide.
• Gemini CLI (Google): MCP server setup guide. Use the server URL below with the HTTP configuration supported by Gemini CLI.

Connect just this scraper

To keep your setup focused on this scraper, add this server configuration to a compatible MCP client:

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

Sign in to Apify when your client asks. If your client uses a Bearer token instead, follow its setup guide and keep the token in its secure configuration. Configuration formats vary by app, so use the linked guide if your client needs a different format.

Use Apify's general server

Already connected to the general Apify MCP server at https://mcp.apify.com? You can use it to find this scraper without adding another server. Ask your assistant to use Steam Reviews Scraper by Fetch Cat, or give it the identifier fetch_cat/steam-reviews-scraper.

Your assistant can check the available settings and retrieve the results from the returned run. If the run is still working, it should follow that run rather than start it again. Both connection options use Apify's hosted service.

Example requests

• “Get 20 recent English reviews for Counter-Strike 2 and summarize recurring complaints from the returned text.”
• “Collect 20 negative English reviews for Dota 2 and group the feedback by theme.”
• “Get 20 recently updated English reviews for No Man’s Sky and prepare a spreadsheet of recommendations, votes and available playtime.”

Normal Apify charges apply. Check the live Pricing tab before asking your assistant to run a larger batch.

Pay with x402

If your AI agent has a funded wallet, it can pay for Apify usage through x402. This experimental option lets an agent use Apify without a conventional account.

Apify AGI provides a prepaid API token with a spending cap after payment. Your agent can use that token to run this scraper through the Apify API or an authenticated MCP connection.

Some MCP setups also support paying for each request directly. That is a different payment option, so check the official guide for its availability.

Follow the official guide for wallet setup, supported payment options and current terms. Apify handles the payments. Keep wallet keys and payment tokens in your agent's secure configuration.

Other ways for agents to pay

Apify also supports MPP for prepaid access through AGI and Skyfire for eligible Actors. If your agent already uses one of these, see the agentic payment guide to choose the right setup.

FAQ

Can I collect reviews for any Steam game?

Use a public Steam app ID or store URL where Steam exposes reviews. A game can have no reviews matching your chosen filters.

Can I collect negative reviews only?

Yes. Set reviewType to negative. Choose positive for positive recommendations or all for both.

Does it require a Steam login?

No. It uses public review data without Steam credentials. Your Apify account or API client still needs its normal authorization.

Can I export a Steam reviews dataset to CSV or Excel?

Yes. Export the run's dataset in Apify Console, or retrieve JSON through the API. Playtime fields use minutes, not hours.

Is this a Steam reviews API alternative?

It provides structured public review collection through Apify's API and MCP. It is not an official Valve product and does not provide private Steam account data.

Can I monitor changes automatically?

You can schedule repeated runs on Apify. Each run collects the feed again. Your own workflow must compare review IDs, timestamps and text to detect changes.

Why did my run return no reviews?

The app may have no matching reviews for your language, sentiment or purchase filter. Check RUN_SUMMARY and try a broader valid filter. Empty success does not imply that a target was deleted or private.

Related Actors

• Reddit Scraper — Collect public discussions for broader player-feedback research.
• YouTube Search Results Scraper — Find videos about games and releases.
• YouTube Comments Scraper — Collect audience feedback on game videos.
• Google Autocomplete Scraper — Explore search suggestions about games.
• Trustpilot Reviews Scraper — Collect reviews from another public review source.

Support and feedback

For custom additions or integrations, email hello@fetchcat.dev.

Found an error or have a feature request? Open an issue with the run URL, a non-sensitive input example and what you expected.

Need help sharing it? See Apify’s guide to sharing a run when reporting an issue. Sharing a run can give the developer access to its input, logs and results, so use a run that contains no sensitive data. Please leave out API tokens, passwords, cookies, private documents and personal data. Use a public sample URL or a redacted example instead.

Tried this scraper? We'd love to hear how it worked for you. Leave a review to help others choose the right tool and help us improve it. Your feedback means a lot to us.

Made with care for curious people, AI agents and the possibilities in data. ♥