Steam Reviews Scraper

hipersoft/steam-reviews-scraper

Scrape user reviews and ratings for any game on Steam. Get the review text, recommended/not-recommended, helpful & funny votes, author playtime, purchase type, language, early-access flag and dates. One clean row per review as JSON, CSV or Excel.

Run this scraper →See the API call

Total users

Monthly users

Total runs

Per review scraped

$0.0008

Last rebuilt

Oct 26, 2026

Overview

Extract user reviews and ratings for any game on Steam. Paste one or more store links (or App IDs), choose your filters, and get one clean row per review — the review text, whether it's recommended, helpful and funny votes, the author's playtime, purchase type, language, early-access flag and dates. Export to JSON, CSV or Excel, or pull straight from the API.

Perfect for game market research, competitor analysis, community sentiment tracking, spotting bugs and feature requests, review monitoring around launches and updates, and building review datasets for dashboards or LLMs.

What you get

One flat, ready-to-use record per review:

Field	Description
`appId` / `appName`	Steam App ID and game title
`recommendationId`	Unique review ID
`steamId`	Reviewer's Steam ID
`language`	Review language
`votedUp`	`true` = Recommended, `false` = Not Recommended
`review`	Full review text
`votesUp` / `votesFunny`	"Helpful" and "Funny" vote counts
`weightedScore`	Steam's helpfulness score (0–1)
`commentCount`	Comments on the review
`playtimeForeverMin` / `playtimeAtReviewMin`	Author playtime, total and at review time (minutes)
`authorGamesOwned` / `authorReviews`	Reviewer's library size and review count
`steamPurchase` / `receivedForFree` / `earlyAccess` / `steamDeck`	Review context flags
`createdAt` / `updatedAt`	Posted and last-edited dates (ISO 8601)
`reviewUrl`	Link to the review
`collectedAt`	When the row was scraped

How to use

1. Add games

Paste Steam store links or numeric App IDs:

{
  "appUrls": [
    "https://store.steampowered.com/app/570/Dota_2/",
    "730"
  ],
  "filterBy": "recent",
  "maxItems": 500
}

2. Filter & order

Order reviews by — Most recent, Recently updated, or All (helpfulness).
Recommendation — all reviews, positive (recommended) only, or negative (not recommended) only.
Purchase type — all, players who bought on Steam, or key activations only.
Language — all, or a Steam language code such as english, spanish, german, french, schinese, russian, japanese, koreana, brazilian.
Max reviews — cap the total (0 = no limit).

Example output

{
  "appId": "570",
  "appName": "Dota 2",
  "recommendationId": "187654321",
  "language": "english",
  "votedUp": true,
  "review": "Best MOBA out there once it clicks.",
  "votesUp": 42,
  "votesFunny": 3,
  "weightedScore": 0.61,
  "playtimeForeverMin": 69765,
  "playtimeAtReviewMin": 41230,
  "steamPurchase": true,
  "earlyAccess": false,
  "createdAt": "2026-09-19T14:10:00.000Z",
  "reviewUrl": "https://steamcommunity.com/profiles/7656119.../recommended/570/",
  "collectedAt": "2026-09-20T03:15:00.000Z"
}

Common use cases

Game market research — measure sentiment, review volume and recommendation ratio across titles.
Competitor analysis — pull rivals' reviews to find complaints, praised features and churn drivers.
Launch & patch monitoring — track how each update lands by filtering recent and updated reviews.
QA & feedback — surface bug reports and feature requests straight from players.
Player insight — segment by playtime, purchase type and language.
Datasets — build clean, labelled review corpora for analysis or model training.

Frequently asked questions

Do I need any login, account or key? No. Just paste game links and run.

How many reviews can I get? Set Max reviews to whatever you need (0 = no limit) — the scraper pages through a game's entire review history.

Can I get only positive or only negative reviews? Yes — use the Recommendation filter.

Can I scrape reviews in one language only? Yes — set the Language field to a Steam language code.

What format is the data? A flat table you can export as JSON, CSV or Excel, or fetch from the Apify API.

Related scrapers

Building review intelligence across platforms? Pair this with our App Store reviews scraper and Google Play reviews scraper for mobile, all with the same clean, one-row-per-review output.

Input

Field	Type	Description
`appUrls` Games (Steam store URLs or App IDs)	array	One or more Steam games to scrape reviews for. Paste the store page URL (e.g. https://store.steampowered.com/app/570/Dota_2/) or a bare numeric App ID (570).
`filterBy` Order reviews by	string · recent, updated, all	Recent (newest first), Updated (recently edited first), or All (Steam's relevance/helpfulness order). Default: `"recent"`
`reviewType` Recommendation	string · all, positive, negative	Keep all reviews, only positive (recommended), or only negative (not recommended). Default: `"all"`
`purchaseType` Purchase type	string · all, steam, non_steam_purchase	All reviews, only reviews from players who bought the game on Steam, or only those who activated a key elsewhere. Default: `"all"`
`language` Language	string	Review language — use 'all' for every language, or a Steam language code such as english, spanish, german, french, schinese, russian, japanese, koreana, brazilian. Default: `"all"`
`maxItems` Max reviews	integer	Maximum number of reviews to collect across all games (0 = no limit; collect everything). Default: `200`

API

curl -X POST "https://api.apify.com/v2/acts/hipersoft~steam-reviews-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "appUrls": [
    "https://store.steampowered.com/app/570/Dota_2/"
  ]
}'

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("hipersoft/steam-reviews-scraper").call(run_input={
    "appUrls": [
        "https://store.steampowered.com/app/570/Dota_2/"
    ]
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('hipersoft/steam-reviews-scraper').call({
  "appUrls": [
    "https://store.steampowered.com/app/570/Dota_2/"
  ]
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Pricing

Event	Price	Paid plans from
Review scraped Charged for each Steam review returned.	$0.0008	$0.0004

You pay only for what the run returns. Set a maximum cost per run and it stops cleanly at that limit.

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