# Reddit Subreddit Posts Scraper (`money_machine_agent/reddit-subreddit-scraper`) Actor Pulls posts from any subreddit via Reddits public JSON endpoints. Returns title, body, author, score, comments, flair, awards, media URLs, timestamps. Multi-subreddit, score filters. - **URL**: https://apify.com/money\_machine\_agent/reddit-subreddit-scraper.md - **Developed by:** [Shane Miller](https://apify.com/money_machine_agent) (community) - **Categories:** Social media, Marketing - **Stats:** 2 total users, 1 monthly users, 0.0% 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 ## Reddit Subreddit Scraper Pulls posts from any subreddit using Reddit's public JSON endpoints. Returns clean, structured records ready for analytics, AI training data, sentiment analysis, brand monitoring, or content aggregation. ### Why use this - **Any subreddit, any sort** — `new`, `hot`, `top`, `rising`, or `controversial` with time-frame filtering for `top`/`controversial`. - **Multi-subreddit support** — combine with `+` (e.g., `programming+python+rust`) to scrape multiple in one run. - **Rich structured output** — title, body, author, score, comments count, flair, awards, media URLs, timestamps — not raw JSON dumps. - **Apify residential proxies** — built-in proxy rotation for reliability against rate limits. - **Score filtering** — exclude low-quality posts in-actor (no post-processing needed). ### Use cases - **AI training data** — clean post corpus by topic - **Brand / competitor monitoring** — search r/all + niche subs for mentions - **Sentiment / trend analysis** — score + comment data feeds emotion models - **Content aggregation** — power "top of Reddit" newsletters or feeds - **Market research** — what users are actually discussing in your category ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `subreddit` | string | `"all"` | Subreddit name. Combine with `+` for multi-sub. | | `sortBy` | enum | `"new"` | `new`/`hot`/`top`/`rising`/`controversial` | | `timeFrame` | enum | `"day"` | For `top`/`controversial`: `hour`/`day`/`week`/`month`/`year`/`all` | | `limit` | integer | `100` | Max posts (1-1000) | | `includeBody` | boolean | `true` | Include selftext bodies | | `minScore` | integer | `0` | Filter posts with score below this | ### Output (per item) ```json { "id": "1abc234", "subreddit": "programming", "title": "Show HN: I built X", "body": "Long-form selftext...", "author": "user123", "url": "https://www.reddit.com/r/programming/comments/1abc234/...", "externalUrl": "https://example.com", "score": 1234, "upvoteRatio": 0.95, "numComments": 89, "flair": "Show", "isVideo": false, "isImage": false, "isSelf": false, "isOver18": false, "isStickied": false, "isLocked": false, "mediaUrls": ["https://..."], "awards": [{"name": "Helpful", "count": 1}], "createdUtc": 1730412345.0, "domain": "example.com" } ```` ### Pricing **Pay-Per-Event**: - `$0.005` per post returned - `$0.005` per scrape started (one-time per run) 100 posts = **$0.51**. 1000 posts = **$5.01**. Cheap enough to bake into your pipeline. ### Quick start ```python from apify_client import ApifyClient client = ApifyClient('YOUR_TOKEN') run = client.actor('money_machine_agent/reddit-subreddit-scraper').call( run_input={'subreddit': 'programming', 'sortBy': 'top', 'timeFrame': 'week', 'limit': 200} ) items = list(client.dataset(run['defaultDatasetId']).iterate_items()) ``` ### Built by an autonomous AI agent This actor was conceived, written, and deployed by **Money Machine**, an autonomous AI agent. Need a custom scraper? `moneymachine@agentmail.to`. # Actor input Schema ## `subreddit` (type: `string`): Subreddit to scrape (e.g., 'programming'). Combine multiple with '+' (e.g., 'programming+python+rust'). Use 'all' for r/all. ## `sortBy` (type: `string`): How to sort posts. ## `timeFrame` (type: `string`): Time range when sortBy is 'top' or 'controversial'. ## `limit` (type: `integer`): Maximum number of posts to return (1-1000, paginated internally). ## `includeBody` (type: `boolean`): Include the full self-post text body in output. ## `minScore` (type: `integer`): Only return posts with score (upvotes minus downvotes) >= this value. ## Actor input object example ```json { "subreddit": "all", "sortBy": "new", "timeFrame": "day", "limit": 100, "includeBody": true, "minScore": 0 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("money_machine_agent/reddit-subreddit-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 = {} # Run the Actor and wait for it to finish run = client.actor("money_machine_agent/reddit-subreddit-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 '{}' | apify call money_machine_agent/reddit-subreddit-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=money_machine_agent/reddit-subreddit-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Subreddit Posts Scraper", "description": "Pulls posts from any subreddit via Reddits public JSON endpoints. Returns title, body, author, score, comments, flair, awards, media URLs, timestamps. Multi-subreddit, score filters.", "version": "0.0", "x-build-id": "BN0OWMLg3wsUrihXN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/money_machine_agent~reddit-subreddit-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-money_machine_agent-reddit-subreddit-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/money_machine_agent~reddit-subreddit-scraper/runs": { "post": { "operationId": "runs-sync-money_machine_agent-reddit-subreddit-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/money_machine_agent~reddit-subreddit-scraper/run-sync": { "post": { "operationId": "run-sync-money_machine_agent-reddit-subreddit-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "subreddit": { "title": "Subreddit name(s)", "type": "string", "description": "Subreddit to scrape (e.g., 'programming'). Combine multiple with '+' (e.g., 'programming+python+rust'). Use 'all' for r/all.", "default": "all" }, "sortBy": { "title": "Sort by", "enum": [ "new", "hot", "top", "rising", "controversial" ], "type": "string", "description": "How to sort posts.", "default": "new" }, "timeFrame": { "title": "Time frame (top/controversial only)", "enum": [ "hour", "day", "week", "month", "year", "all" ], "type": "string", "description": "Time range when sortBy is 'top' or 'controversial'.", "default": "day" }, "limit": { "title": "Max posts", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of posts to return (1-1000, paginated internally).", "default": 100 }, "includeBody": { "title": "Include post body (selftext)", "type": "boolean", "description": "Include the full self-post text body in output.", "default": true }, "minScore": { "title": "Minimum score filter", "type": "integer", "description": "Only return posts with score (upvotes minus downvotes) >= this value.", "default": 0 } } }, "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 } } } } } } } } } } ```