# Reddit Subreddit Monitor (`second_coming/reddit-subreddit-monitor`) Actor Monitor and scrape Reddit posts and comments from specified subreddits using the public Reddit JSON API. Features include keyword filtering, sentiment analysis, trending topic detection, and engagement metrics. - **URL**: https://apify.com/second\_coming/reddit-subreddit-monitor.md - **Developed by:** [Richard P](https://apify.com/second_coming) (community) - **Categories:** Social media, News, AI - **Stats:** 1 total users, 0 monthly users, 33.3% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 # Actor input Schema ## `subreddits` (type: `array`): List of subreddit names to monitor (without the r/ prefix) ## `keywords` (type: `array`): Optional keywords to filter posts. If empty, all posts are included. Posts must contain at least one keyword (case-insensitive). ## `sort` (type: `string`): How to sort subreddit posts ## `timePeriod` (type: `string`): Time period filter when using 'top' sort order ## `maxResults` (type: `integer`): Maximum number of posts to fetch per subreddit ## `includeComments` (type: `boolean`): Whether to fetch and include top comments for each post ## `maxComments` (type: `integer`): Maximum number of top-level comments to fetch per post (only used if Include Comments is enabled) ## `minScore` (type: `integer`): Only include posts with at least this many upvotes. Set to 0 to include all posts. ## `includeTrendingWords` (type: `boolean`): Whether to include trending word analysis in the output summary ## Actor input object example ```json { "subreddits": [ "technology", "programming", "MachineLearning" ], "keywords": [ "AI", "python", "machine learning" ], "sort": "hot", "timePeriod": "day", "maxResults": 50, "includeComments": true, "maxComments": 10, "minScore": 0, "includeTrendingWords": 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("second_coming/reddit-subreddit-monitor").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("second_coming/reddit-subreddit-monitor").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 second_coming/reddit-subreddit-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=second_coming/reddit-subreddit-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Subreddit Monitor", "description": "Monitor and scrape Reddit posts and comments from specified subreddits using the public Reddit JSON API. Features include keyword filtering, sentiment analysis, trending topic detection, and engagement metrics.", "version": "0.0", "x-build-id": "F44qNzzbvD1Skfvpr" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/second_coming~reddit-subreddit-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-second_coming-reddit-subreddit-monitor", "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/second_coming~reddit-subreddit-monitor/runs": { "post": { "operationId": "runs-sync-second_coming-reddit-subreddit-monitor", "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/second_coming~reddit-subreddit-monitor/run-sync": { "post": { "operationId": "run-sync-second_coming-reddit-subreddit-monitor", "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": [ "subreddits" ], "properties": { "subreddits": { "title": "Subreddits", "type": "array", "description": "List of subreddit names to monitor (without the r/ prefix)", "default": [ "technology", "programming" ], "items": { "type": "string" } }, "keywords": { "title": "Keywords", "type": "array", "description": "Optional keywords to filter posts. If empty, all posts are included. Posts must contain at least one keyword (case-insensitive).", "default": [], "items": { "type": "string" } }, "sort": { "title": "Sort Order", "enum": [ "hot", "new", "top", "rising" ], "type": "string", "description": "How to sort subreddit posts", "default": "hot" }, "timePeriod": { "title": "Time Period (for top sort only)", "enum": [ "hour", "day", "week", "month", "year", "all" ], "type": "string", "description": "Time period filter when using 'top' sort order", "default": "day" }, "maxResults": { "title": "Maximum Results Per Subreddit", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum number of posts to fetch per subreddit", "default": 50 }, "includeComments": { "title": "Include Comments", "type": "boolean", "description": "Whether to fetch and include top comments for each post", "default": true }, "maxComments": { "title": "Maximum Comments Per Post", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum number of top-level comments to fetch per post (only used if Include Comments is enabled)", "default": 10 }, "minScore": { "title": "Minimum Upvote Score", "minimum": 0, "type": "integer", "description": "Only include posts with at least this many upvotes. Set to 0 to include all posts.", "default": 0 }, "includeTrendingWords": { "title": "Include Trending Words", "type": "boolean", "description": "Whether to include trending word analysis in the output summary", "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 } } } } } } } } } } ```