# Steam Reviews Scraper (`sync-network/steam-reviews-scraper`) Actor Scrape user reviews from the Steam Store for any game by app ID or name. Extracts playtime, ratings, review text, and full game metadata. No proxy or authentication needed. - **URL**: https://apify.com/sync-network/steam-reviews-scraper.md - **Developed by:** [Alam](https://apify.com/sync-network) (community) - **Categories:** Developer tools, Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 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 Reviews Scraper Extract user reviews from any game or app on Steam. Get detailed review data including review text, ratings, playtime statistics, author profiles, and reaction data. ### Features - **Search by game name** — just type the game name and the actor finds it automatically - **Bulk scraping** — scrape reviews from multiple games in a single run - **Rich filters** — filter by language, review type (positive/negative), purchase type, and sort order - **Game metadata** — optionally include game details like genre, price, developer, and platforms in each review - **Playtime data** — see how many hours the reviewer played before and after writing their review ### Input | Parameter | Type | Default | Description | |---|---|---|---| | **App IDs** | Array of numbers | `[730]` | Steam app IDs to scrape (find the ID in the Steam store URL) | | **Game Names** | Array of strings | `[]` | Game names to search for (auto-resolves to app IDs) | | **Max Reviews** | Number | `100` | Maximum reviews per app (0 = unlimited) | | **Language** | Select | `english` | Filter reviews by language | | **Review Type** | Select | `all` | All, positive only, or negative only | | **Purchase Type** | Select | `all` | All, Steam purchases, or non-Steam | | **Sort By** | Select | `recent` | Most recent, recently updated, or all | | **Include App Info** | Boolean | `true` | Include game metadata in results | ### Output Each review includes: - Review text, rating (Positive/Negative), creation and update dates - Author name, profile URL, number of reviews, games owned - Playtime data — total hours, hours at review time, recent hours - Votes — upvotes, funny votes, comment count - Flags — Steam purchase, received for free, refunded, early access, Steam Deck - Reactions data - Game metadata (when enabled) — genre, developer, publisher, price, platforms, release date ### Use Cases - **Sentiment analysis** — analyze player opinions across different games and time periods - **Game market research** — understand what players like and dislike about competing games - **Review monitoring** — track new reviews for your game over time - **Playtime analysis** — correlate review sentiment with playtime data - **Competitive intelligence** — compare review scores and sentiment across similar games ### Tips - Use **0** for Max Reviews to collect all available reviews (may take longer for popular games) - Combine **Game Names** and **App IDs** in a single run for efficiency - Use **Review Type: Negative** to focus on complaints and issues - Sort by **All (Helpfulness)** to get the most impactful reviews first # Actor input Schema ## `appIds` (type: `array`): List of Steam app IDs to scrape reviews from (e.g. 730 for CS2, 570 for Dota 2). You can find the app ID in the Steam store URL. ## `appNames` (type: `array`): List of game names to search on Steam. The actor will automatically find the correct app ID. Useful when you don't know the exact app ID. ## `maxReviews` (type: `integer`): Maximum number of reviews to collect per app. Use 0 for unlimited (collects all available reviews). ## `language` (type: `string`): Filter reviews by language ## `reviewType` (type: `string`): Filter by positive or negative reviews, or get all ## `purchaseType` (type: `string`): Filter by how the user obtained the game ## `sortBy` (type: `string`): How to sort reviews ## `includeAppInfo` (type: `boolean`): Include game details (name, genres, price, developers) in each review record ## Actor input object example ```json { "appIds": [ "730" ], "appNames": [], "maxReviews": 100, "language": "english", "reviewType": "all", "purchaseType": "all", "sortBy": "recent", "includeAppInfo": true } ```` # Actor output Schema ## `results` (type: `string`): No description # 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" ], "appNames": [] }; // Run the Actor and wait for it to finish const run = await client.actor("sync-network/steam-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"], "appNames": [], } # Run the Actor and wait for it to finish run = client.actor("sync-network/steam-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" ], "appNames": [] }' | apify call sync-network/steam-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=sync-network/steam-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Steam Reviews Scraper", "description": "Scrape user reviews from the Steam Store for any game by app ID or name. Extracts playtime, ratings, review text, and full game metadata. No proxy or authentication needed.", "version": "0.0", "x-build-id": "o4qz3gDzIvTjmTTeM" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/sync-network~steam-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-sync-network-steam-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/sync-network~steam-reviews-scraper/runs": { "post": { "operationId": "runs-sync-sync-network-steam-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/sync-network~steam-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-sync-network-steam-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "appIds": { "title": "Steam App IDs", "type": "array", "description": "List of Steam app IDs to scrape reviews from (e.g. 730 for CS2, 570 for Dota 2). You can find the app ID in the Steam store URL.", "items": { "type": "string" } }, "appNames": { "title": "Game Names (auto-resolve)", "type": "array", "description": "List of game names to search on Steam. The actor will automatically find the correct app ID. Useful when you don't know the exact app ID.", "items": { "type": "string" } }, "maxReviews": { "title": "Maximum Reviews per App", "minimum": 0, "maximum": 100000, "type": "integer", "description": "Maximum number of reviews to collect per app. Use 0 for unlimited (collects all available reviews).", "default": 100 }, "language": { "title": "Review Language", "enum": [ "all", "english", "spanish", "german", "french", "portuguese", "brazilian", "russian", "simplified_chinese", "traditional_chinese", "japanese", "korean", "polish", "turkish", "italian", "czech", "danish", "dutch", "finnish", "hungarian", "norwegian", "romanian", "swedish", "thai", "vietnamese", "indonesian" ], "type": "string", "description": "Filter reviews by language", "default": "english" }, "reviewType": { "title": "Review Type", "enum": [ "all", "positive", "negative" ], "type": "string", "description": "Filter by positive or negative reviews, or get all", "default": "all" }, "purchaseType": { "title": "Purchase Type", "enum": [ "all", "steam", "non_steam" ], "type": "string", "description": "Filter by how the user obtained the game", "default": "all" }, "sortBy": { "title": "Sort Order", "enum": [ "recent", "updated", "all" ], "type": "string", "description": "How to sort reviews", "default": "recent" }, "includeAppInfo": { "title": "Include App Metadata", "type": "boolean", "description": "Include game details (name, genres, price, developers) in each review record", "default": true } } }, "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 } } } } } } } } } } ```