Steam Review Monitor: New Reviews Since Last Run

Get the Steam reviews that are new since your last run, for any game, by app ID. Each row is one review with the recommendation (thumbs up or down), the reviewer's playtime when they wrote it, the review language, the helpful and funny votes and the review text. Turn on monitoring and run it on a schedule: you only get, and only pay for, reviews posted since the previous run, and a quiet day is free. No login, no API key, no proxy. Reviewer names, Steam IDs and profile links are never returned.

Who uses it: studios and publishers watching sentiment after a patch or sale, community managers who want a review-bomb alert (set Recommendation to thumbs down), and market researchers tracking how a competitor's game is received.

Related actors: for a game's price, genres and release data by app ID use Steam App Lookup. This actor is reviews only.

How to use

1. In the Apify Console. Open the actor page and click Start — the appIds field is already pre-filled with a working example. Results land in the run's dataset as soon as each item is found.
2. Via the API. Call it directly with a POST request — no Console needed once you have an API token:

   curl "https://api.apify.com/v2/acts/accountable_eel~steam-review-monitor/run-sync-get-dataset-items?token=<YOUR_TOKEN>" \
     -X POST \
     -H "Content-Type: application/json" \
     -d '{"appIds":["730"]}'
   

3. On a schedule. Save this actor as an Apify Task with the input you want, then add a Schedule (hourly, daily, weekly) so it runs on its own — no server of your own required.

Input

{
  "appIds": [
    "730"
  ]
}

One per line: the number in the game's store URL (730 for store.steampowered.com/app/730/) or the full store URL. Reads Steam's public review feed with no login and no key. Reviewer names and profile links are never returned. Accepted formats: 730, 570, https://store.steampowered.com/app/1245620/.

Apps are read one after another. Each app returns its newest reviews first (up to Reviews per app, 1 to 1,000). Language, Recommendation and the standard "only keep rows containing" words narrow the reviews before anything is remembered, so a review you filtered out is never counted as seen. With monitoring on, the run stops reading as soon as it reaches a review it already knows, so a quiet run is a single request per app. If more reviews arrive between two runs than Reviews per app, the run reports truncated: true; raise the cap or schedule more often.

The first monitoring run for an app returns its newest reviews and remembers them; from the next run on you only get reviews posted since. An unknown app ID comes back as a found: false row. A game with no reviews for your filters comes back as one row with reviewCount: 0. Neither is charged.

Output

One row per review (the game's name, overall rating and all-time totals repeat on each row so a downstream sheet needs no join), for example:

query	found	status	appId	appName	overallRating	totalReviews	totalPositive	totalNegative	positivePercent	monitorStatus	truncated	reviewCount	reviews	reviewId	recommended	reviewText	language	playtimeAtReviewHours	playtimeTotalHours	helpfulVotes	funnyVotes	weightedVoteScore	commentCount	steamPurchase	receivedForFree	writtenDuringEarlyAccess	primarilySteamDeck	createdAt	updatedAt	storeUrl	sourceUrl	isNew	firstSeenAt	scrapedAt
730	true	OK							<positive share (%)>				<all reviews (full list)>					<playtime when reviewed (h)>	<playtime total (h)>													<is this review new?>		1970-01-01T00:00:00.000Z

A miss comes back as a row with "found": false and is never charged. totalReviews, totalPositive and totalNegative are null when Steam does not send them for the filter you chose, never 0; playtimeAtReviewHours is null on older reviews that predate that field. weightedVoteScore is Steam's 0-1 helpfulness weight.

Example runs

Paste any of these into the Input tab and press Start. All three return rows with no key, no token and no account of your own beyond an Apify login.

The latest 20 reviews of Counter-Strike 2, every language.

{
  "appIds": ["730"],
  "maxReviews": 20
}

Thumbs-down English reviews of two games, newest 50 each (a review-bomb check).

{
  "appIds": ["730", "570"],
  "language": "english",
  "reviewType": "negative",
  "maxReviews": 50
}

A daily Steam review alert: only reviews posted since the last run.

{
  "appIds": ["https://store.steampowered.com/app/1245620/"],
  "maxReviews": 200,
  "onlyNewSinceLastRun": true,
  "deltaName": "elden-ring-daily"
}

Pricing

• Review returned: $1.5 per 1,000 reviews

Plus a $0.005 start fee per run. Each event above is billed independently, only when it actually returns data — misses (found:false), games with no matching reviews and monitoring runs with nothing new are never charged.

Use it from Clay, n8n, Make, or an AI agent

This actor runs synchronously over plain HTTP — call it directly from a script, a workflow tool, or an AI agent, no Apify Console needed once you have an API token.

curl "https://api.apify.com/v2/acts/accountable_eel~steam-review-monitor/run-sync-get-dataset-items?token=<YOUR_TOKEN>" \
  -X POST \
  -H "Content-Type: application/json" \
  -d '{"appIds":["730"]}'

n8n. Add an HTTP Request node: Method POST, URL https://api.apify.com/v2/acts/accountable_eel~steam-review-monitor/run-sync-get-dataset-items?token=<YOUR_TOKEN>, Body Content Type JSON, JSON Body {"appIds":["730"]} (swap in an expression from an earlier node for a real value).

Clay. Add an "HTTP API" column: Method POST, URL https://api.apify.com/v2/acts/accountable_eel~steam-review-monitor/run-sync-get-dataset-items?token=<YOUR_TOKEN>, Body {"appIds":["{{app ID}}"]}, mapping the row's app ID into the appIds array.

MCP. In Claude, Cursor, or any MCP client with the Apify MCP server, ask for "steam reviews monitor by app id" — the agent will find and run this actor.