# Steam Game & Reviews Scraper (`seemuapps/steam-game-reviews-scraper`) Actor Extract Steam game details, pricing, player ratings, and full user reviews for any title by app ID or store URL. No login required. - **URL**: https://apify.com/seemuapps/steam-game-reviews-scraper.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Lead generation, Developer tools, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.70 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Steam Game & Reviews Scraper Extract Steam game details, pricing, ratings, and full user reviews for any title — by app ID or store URL, no login required. ### What you get **Game records** (one per app): - Name, type, app ID, store URL, release date, and "coming soon" flag - Price — currency, final price, formatted price, and current discount percent - Developers, publishers, genres, and feature categories (multiplayer, controller support, trading cards, etc.) - Platform availability (Windows / Mac / Linux), required age, and content descriptor notes - Metacritic score and link, total achievements, and total store recommendations - Header image and full-resolution screenshot URLs - Aggregate review summary: review score, score description (e.g. "Very Positive"), total reviews, total positive, total negative **Review records** (one per user review): - Reviewer SteamID, games owned, and review count - Playtime at review, playtime total, and playtime in the last two weeks - Recommendation (thumbs up/down), helpful votes, funny votes, and weighted helpfulness score - Full review text, language, created/updated timestamps, and comment count - Whether the copy was a Steam purchase or a non-Steam key, and whether it was written during early access - Direct link to the review Export everything to JSON, CSV, Excel, or Google Sheets straight from the Apify console. ### Use cases - **Game market research** — compare pricing, genres, and ratings across competing titles - **Sentiment analysis** — pull thousands of reviews to mine player sentiment and feature requests - **Competitor & launch benchmarking** — track review velocity and score for any game over time - **Indie publishing** — study what players praise and complain about in your niche - **Academic & data science** — build review datasets with playtime and helpfulness signals ### How to use 1. Enter one or more **Steam app IDs** (e.g. `730`) or **store page URLs** in **Steam app IDs or store URLs** 2. Toggle **Include user reviews** on or off 3. Set **Max reviews per app** (default 100; set `0` for unlimited) 4. Choose the **Review sort / filter**, **Review type**, **Purchase type**, and **Review language** as needed 5. Set the **Country** and **Store language** to control the pricing currency and the language of game text 6. Run the actor — results appear in the **Dataset** tab, one game or review per row Each row has a `recordType` field set to either `"game"` or `"review"` so you can split or filter the two shapes downstream. ### Fetching more reviews across runs The actor auto-paginates reviews within a single run up to your **Max reviews per app**. To pull more than one run's worth (or to resume after a timeout): > After the run finishes, open the **Key-value store** tab → copy the `NEXT_PAGE_ID` value → paste it into **Page ID** on your next run. If `NEXT_PAGE_ID` is `null`, you've fetched every review. `NEXT_PAGE_ID` is a JSON map of app ID to review cursor, so you can resume several games at once. ### Output format A game row: ```json { "recordType": "game", "appId": 730, "name": "Counter-Strike 2", "type": "game", "isFree": true, "priceFinalFormatted": "Free", "developers": ["Valve"], "publishers": ["Valve"], "genres": ["Action", "Free To Play"], "platformWindows": true, "platformLinux": true, "reviewScoreDesc": "Very Positive", "totalReviews": 5100487, "totalPositive": 4356788, "totalNegative": 743699, "storeUrl": "https://store.steampowered.com/app/730" } ```` A review row: ```json { "recordType": "review", "appId": 730, "recommendationId": "226710775", "authorSteamId": "76561199671665188", "playtimeForeverMinutes": 50021, "playtimeAtReviewMinutes": 50021, "language": "schinese", "review": "good", "votedUp": true, "votesUp": 0, "votesFunny": 0, "steamPurchase": true, "createdAt": "2026-...T...Z", "reviewUrl": "https://steamcommunity.com/profiles/76561199671665188/recommended/730/" } ``` ### Input options | Field | Type | Description | |-------|------|-------------| | Steam app IDs or store URLs | array | App IDs (`730`) or store URLs — required | | Include user reviews | boolean | Fetch reviews in addition to the game record — default `true` | | Max reviews per app | integer | Cap per game; `0` = unlimited — default 100 | | Review sort / filter | enum | `recent`, `updated`, or `all` — default `recent` | | Review type | enum | `all`, `positive`, `negative` — default `all` | | Purchase type | enum | `all`, `steam`, `non_steam_purchase` — default `all` | | Review language | string | Steam language code or `all` — default `all` | | Country | string | Two-letter code for pricing currency — default `us` | | Store language | string | Language of game name/description — default `english` | | Page ID | string | `NEXT_PAGE_ID` from a previous run to resume reviews | ### Notes - Use the **recent** or **updated** filter to paginate deep into a game's reviews. The **all** filter is helpfulness-ranked and best for a quick representative sample. - Region-locked, delisted, or non-store app IDs return no game data and are skipped with a warning. # Actor input Schema ## `appIds` (type: `array`): One or more Steam app IDs (e.g. 730) or store page URLs (e.g. https://store.steampowered.com/app/730/Counter-Strike\_2/). ## `includeReviews` (type: `boolean`): Fetch user reviews for each game in addition to the game details record. ## `maxReviewsPerApp` (type: `integer`): Maximum number of reviews to fetch per game. Set 0 for unlimited (paginates until exhausted or the run nears its timeout). ## `reviewFilter` (type: `string`): How Steam orders the reviews. 'recent' and 'updated' support deep pagination; 'all' is best for a quick helpfulness-ranked sample. ## `reviewType` (type: `string`): Limit reviews to positive (thumbs up), negative (thumbs down), or include all. ## `purchaseType` (type: `string`): Limit reviews by how the reviewer obtained the game: bought on Steam, activated a non-Steam key, or all. ## `reviewLanguage` (type: `string`): Steam language code to filter reviews (e.g. 'english', 'schinese', 'german') or 'all' for every language. ## `countryCode` (type: `string`): Two-letter country code controlling the currency and price shown in the game record (e.g. 'us', 'gb', 'de'). ## `language` (type: `string`): Steam store language for the game's name and description (e.g. 'english', 'german', 'french'). ## `pageId` (type: `string`): Paste the NEXT\_PAGE\_ID value from the previous run's Key-value store to resume fetching reviews where you left off. ## Actor input object example ```json { "appIds": [ "730", "570" ], "includeReviews": true, "maxReviewsPerApp": 100, "reviewFilter": "recent", "reviewType": "all", "purchaseType": "all", "reviewLanguage": "all", "countryCode": "us", "language": "english" } ``` # Actor output Schema ## `results` (type: `string`): recordType='game' rows carry name, type, price, genres, categories, platforms, metacritic, release date, and aggregate review counts. recordType='review' rows carry the author, playtime, votedUp, votes, text, timestamps, and review URL. ## `nextPageId` (type: `string`): NEXT\_PAGE\_ID record in the default key-value store. A JSON map of appId to review cursor for apps with more reviews to fetch; null when every app is exhausted. Paste into Page ID on the next run to resume. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "appIds": [ "730", "570" ] }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/steam-game-reviews-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "appIds": [ "730", "570", ] } # Run the Actor and wait for it to finish run = client.actor("seemuapps/steam-game-reviews-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "appIds": [ "730", "570" ] }' | apify call seemuapps/steam-game-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/steam-game-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Steam Game & Reviews Scraper", "description": "Extract Steam game details, pricing, player ratings, and full user reviews for any title by app ID or store URL. No login required.", "version": "1.0", "x-build-id": "Od0hQV7dzinpZ0fra" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~steam-game-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-steam-game-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~steam-game-reviews-scraper/runs": { "post": { "operationId": "runs-sync-seemuapps-steam-game-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~steam-game-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-seemuapps-steam-game-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "appIds" ], "properties": { "appIds": { "title": "Steam app IDs or store URLs", "type": "array", "description": "One or more Steam app IDs (e.g. 730) or store page URLs (e.g. https://store.steampowered.com/app/730/Counter-Strike_2/).", "items": { "type": "string" } }, "includeReviews": { "title": "Include user reviews", "type": "boolean", "description": "Fetch user reviews for each game in addition to the game details record.", "default": true }, "maxReviewsPerApp": { "title": "Max reviews per app", "minimum": 0, "type": "integer", "description": "Maximum number of reviews to fetch per game. Set 0 for unlimited (paginates until exhausted or the run nears its timeout).", "default": 100 }, "reviewFilter": { "title": "Review sort / filter", "enum": [ "recent", "updated", "all" ], "type": "string", "description": "How Steam orders the reviews. 'recent' and 'updated' support deep pagination; 'all' is best for a quick helpfulness-ranked sample.", "default": "recent" }, "reviewType": { "title": "Review type", "enum": [ "all", "positive", "negative" ], "type": "string", "description": "Limit reviews to positive (thumbs up), negative (thumbs down), or include all.", "default": "all" }, "purchaseType": { "title": "Purchase type", "enum": [ "all", "steam", "non_steam_purchase" ], "type": "string", "description": "Limit reviews by how the reviewer obtained the game: bought on Steam, activated a non-Steam key, or all.", "default": "all" }, "reviewLanguage": { "title": "Review language", "type": "string", "description": "Steam language code to filter reviews (e.g. 'english', 'schinese', 'german') or 'all' for every language.", "default": "all" }, "countryCode": { "title": "Country (for pricing)", "type": "string", "description": "Two-letter country code controlling the currency and price shown in the game record (e.g. 'us', 'gb', 'de').", "default": "us" }, "language": { "title": "Store language", "type": "string", "description": "Steam store language for the game's name and description (e.g. 'english', 'german', 'french').", "default": "english" }, "pageId": { "title": "Page ID (pagination)", "type": "string", "description": "Paste the NEXT_PAGE_ID value from the previous run's Key-value store to resume fetching reviews where you left off." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```