# Steam Store Game Reviews Full History Scraper (`jungle_synthesizer/steam-store-game-reviews-full-history-scraper`) Actor Scrape the full review history for any Steam game. Extracts ratings, review text, playtime, and purchase context via Steam cursor-paginated JSON API. Supports language filters and exhausts all pages for titles with 100k+ reviews. Ideal for studios, sentiment analysis, and review-bomb detection. - **URL**: https://apify.com/jungle\_synthesizer/steam-store-game-reviews-full-history-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Games, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 Store Game Reviews Full History Scraper Scrape the complete review history for any Steam game using Steam's official cursor-paginated JSON API. Unlike competitors that return a single page, this actor exhausts the full cursor chain — retrieving every review for titles with hundreds of thousands of entries. ### What you get Each output record includes the full review body plus reviewer-quality signals ideal for brigade detection: - **Review content** — full text, recommended/not, language, timestamps - **Reviewer signals** — total games owned, prior review count, playtime at time of review (key for detecting low-effort reviews from new accounts) - **Engagement metrics** — helpful votes, funny votes, comment count, Steam-weighted helpfulness score - **Purchase context** — Steam purchase vs. external key, received for free, Early Access flag - **Per-game aggregate** — review score, score description (e.g. "Overwhelmingly Positive"), total positive, negative, and total reviews (joined onto every row for easy analysis) ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `appIds` | string[] | `["440"]` | Steam app IDs. Find the ID in the store URL: `store.steampowered.com/app/440/` → `440` | | `maxItems` | integer | `10` | Max reviews per game. Set to `0` to exhaust all reviews (may be slow for popular titles with 1M+ reviews) | | `language` | string | `all` | Language filter: `all`, `english`, `schinese`, `russian`, etc. | | `reviewType` | string | `all` | `all`, `positive`, or `negative` | | `purchaseType` | string | `all` | `all`, `steam`, or `non_steam_purchase` | | `dateRangeStart` | string | — | Optional ISO date `YYYY-MM-DD` — only include reviews created on or after | | `dateRangeEnd` | string | — | Optional ISO date `YYYY-MM-DD` — only include reviews created on or before | ### Output ```json { "steam_appid": 440, "recommendation_id": "226055761", "author_steamid": "76561198775042737", "author_num_games_owned": 42, "author_num_reviews": 4, "author_playtime_forever_min": 1240, "author_playtime_at_review_min": 92, "review_text": "Best game ever.", "voted_up": true, "votes_up": 15, "votes_funny": 2, "weighted_vote_score": 0.812, "comment_count": 0, "steam_purchase": true, "received_for_free": false, "written_during_early_access": false, "language": "english", "timestamp_created": "2026-05-21T10:05:11.000Z", "timestamp_updated": "2026-05-21T10:05:11.000Z", "game_review_score": 8, "game_review_score_desc": "Very Positive", "game_total_positive": 1119661, "game_total_negative": 111784, "game_total_reviews": 1231445, "source_url": "" } ```` ### Use cases - **Review-bomb detection** — filter by date range + `author_num_games_owned` / `author_playtime_at_review_min` to identify coordinated low-quality reviews - **Sentiment tracking over time** — combine date range filters with full history to build time-series sentiment charts - **Competitive research** — compare `game_review_score_desc` and positive/negative ratios across competing titles - **QA & community signals** — extract the most-voted reviews by `votes_up` for customer feedback analysis ### Rate limits & performance Steam's review API is unauthenticated and generous. The actor uses a 350ms inter-request delay and fetches 100 reviews per page. For a title with 100,000 reviews expect approximately 1,000 API calls; with `maxItems` set the run completes in seconds. ### Multiple games Pass multiple app IDs to process them in a single run: ```json { "appIds": ["440", "570", "730"], "maxItems": 500, "language": "english" } ``` Each app is processed sequentially; `maxItems` is applied per game. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `appIds` (type: `array`): List of Steam app IDs to scrape reviews for. Find the app ID in the Steam store URL, e.g. https://store.steampowered.com/app/440/ → 440. ## `maxItems` (type: `integer`): Maximum number of reviews to scrape per game. Set to 0 for all reviews (may take a long time for popular titles with 100k+ reviews). ## `language` (type: `string`): Filter reviews by language code (e.g. "english", "schinese", "all"). Defaults to "all" for all languages. ## `reviewType` (type: `string`): Which reviews to return: "all" (positive and negative), "positive", or "negative". Defaults to "all". ## `purchaseType` (type: `string`): Filter by purchase origin: "all" (all reviewers), "steam" (Steam purchases only), or "non\_steam\_purchase". Defaults to "all". ## `dateRangeStart` (type: `string`): Optional. Only include reviews created on or after this date (ISO format: YYYY-MM-DD). Leave blank for no filter. ## `dateRangeEnd` (type: `string`): Optional. Only include reviews created on or before this date (ISO format: YYYY-MM-DD). Leave blank for no filter. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "appIds": [ "440" ], "maxItems": 10, "language": "all", "reviewType": "all", "purchaseType": "all" } ``` # 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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "appIds": [ "440" ], "maxItems": 10, "language": "all", "reviewType": "all", "purchaseType": "all" }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/steam-store-game-reviews-full-history-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "appIds": ["440"], "maxItems": 10, "language": "all", "reviewType": "all", "purchaseType": "all", } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/steam-store-game-reviews-full-history-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "appIds": [ "440" ], "maxItems": 10, "language": "all", "reviewType": "all", "purchaseType": "all" }' | apify call jungle_synthesizer/steam-store-game-reviews-full-history-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/steam-store-game-reviews-full-history-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Steam Store Game Reviews Full History Scraper", "description": "Scrape the full review history for any Steam game. Extracts ratings, review text, playtime, and purchase context via Steam cursor-paginated JSON API. Supports language filters and exhausts all pages for titles with 100k+ reviews. Ideal for studios, sentiment analysis, and review-bomb detection.\n", "version": "0.1", "x-build-id": "1KfSwQcsuee0eWVcg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~steam-store-game-reviews-full-history-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-steam-store-game-reviews-full-history-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/jungle_synthesizer~steam-store-game-reviews-full-history-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-steam-store-game-reviews-full-history-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/jungle_synthesizer~steam-store-game-reviews-full-history-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-steam-store-game-reviews-full-history-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": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "appIds": { "title": "App IDs", "type": "array", "description": "List of Steam app IDs to scrape reviews for. Find the app ID in the Steam store URL, e.g. https://store.steampowered.com/app/440/ → 440.\n", "items": { "type": "string" } }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of reviews to scrape per game. Set to 0 for all reviews (may take a long time for popular titles with 100k+ reviews).\n" }, "language": { "title": "Language Filter", "type": "string", "description": "Filter reviews by language code (e.g. \"english\", \"schinese\", \"all\"). Defaults to \"all\" for all languages.\n" }, "reviewType": { "title": "Review Type", "type": "string", "description": "Which reviews to return: \"all\" (positive and negative), \"positive\", or \"negative\". Defaults to \"all\".\n" }, "purchaseType": { "title": "Purchase Type", "type": "string", "description": "Filter by purchase origin: \"all\" (all reviewers), \"steam\" (Steam purchases only), or \"non_steam_purchase\". Defaults to \"all\".\n" }, "dateRangeStart": { "title": "Date Range Start", "type": "string", "description": "Optional. Only include reviews created on or after this date (ISO format: YYYY-MM-DD). Leave blank for no filter.\n" }, "dateRangeEnd": { "title": "Date Range End", "type": "string", "description": "Optional. Only include reviews created on or before this date (ISO format: YYYY-MM-DD). Leave blank for no filter.\n" } } }, "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 } } } } } } } } } } ```