# Reddit Subreddit Scraper (`mranderson323/reddit-subreddit-scraper`) Actor Scrapes posts and comments from any subreddit using Reddit's public JSON API. Filter by listing type, time range, keywords, and score. No API key needed. - **URL**: https://apify.com/mranderson323/reddit-subreddit-scraper.md - **Developed by:** [Jeff](https://apify.com/mranderson323) (community) - **Categories:** Social media, Developer tools, News - **Stats:** 2 total users, 1 monthly users, 100.0% 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 ## Reddit Subreddit Scraper Scrapes posts and optionally top comments from any public subreddit using Reddit's built-in JSON API — no API key or OAuth required. Filter by listing type, time range, minimum score, and keywords. Returns clean structured records ready for analysis, monitoring, or integration. ### What it does - Scrapes posts from one or many subreddits in a single run - Supports all listing types: **hot**, **new**, **top**, and **rising** - Time range filter for `top` listings: hour, day, week, month, year, or all-time - Filter by minimum score (upvotes) to focus on high-signal posts - Keyword filter to match only posts containing specific terms - Optional: fetch top comments per post (billed separately at $0.002/comment) - Handles pagination automatically up to your configured limit ### Input | Field | Type | Default | Description | |---|---|---|---| | `subreddits` | array | `["programming"]` | Subreddit names without `r/` prefix | | `listing` | string | `hot` | Feed type: `hot`, `new`, `top`, or `rising` | | `timeFilter` | string | `week` | For `top` only: `hour`, `day`, `week`, `month`, `year`, `all` | | `maxPostsPerSubreddit` | integer | `100` | Max posts per subreddit (up to 1000) | | `minScore` | integer | `0` | Minimum upvote score (0 = all posts) | | `keywords` | array | `[]` | Only return posts matching at least one keyword | | `includeComments` | boolean | `false` | Fetch top comments per post | | `maxCommentsPerPost` | integer | `10` | Max top-level comments to fetch per post | ### Output Each post record: ```json { "id": "t3_1abc123", "type": "post", "subreddit": "MachineLearning", "title": "New paper: GPT-5 achieves superhuman performance on...", "author": "researcher_jane", "score": 4821, "upvoteRatio": 0.97, "numComments": 312, "url": "https://www.reddit.com/r/MachineLearning/comments/1abc123/...", "linkUrl": "https://arxiv.org/abs/2406.12345", "selftext": null, "flair": "Research", "isSelf": false, "isVideo": false, "createdAt": "2026-06-15T14:30:00Z" } ```` Each comment record (when `includeComments: true`): ```json { "id": "t1_xyz789", "type": "comment", "subreddit": "MachineLearning", "postId": "t3_1abc123", "author": "ml_enthusiast", "score": 142, "body": "This is a significant result because...", "url": "https://www.reddit.com/r/MachineLearning/comments/1abc123/_/xyz789/", "createdAt": "2026-06-15T15:02:00Z" } ``` ### Pricing - **$0.002 per post** (~$0.20 per 100 posts) - **$0.002 per comment** (only charged when `includeComments: true`) You only pay for records that pass your filters. ### Use cases - **Brand & sentiment monitoring** — track mentions across industry subreddits - **Market research** — mine user feedback, pain points, and trends - **AI training data** — collect high-quality human-written text with engagement signals - **Content discovery** — surface top posts across multiple communities on a schedule - **Competitor intelligence** — monitor mentions of products, companies, or technologies - **Academic research** — analyze community behavior and discourse patterns - **Lead generation** — identify users asking for recommendations in niche communities ### Tips - Use `listing: top` with `timeFilter: week` to get the best content from the past 7 days - Pass multiple subreddits in one run: `["webdev", "reactjs", "typescript"]` - Set `minScore: 100` to filter noise and keep only well-received posts - Use `keywords: ["recommend", "looking for", "best tool"]` to find buying-intent posts - Schedule daily with `listing: new` to monitor fresh posts as they appear - Enable `includeComments` for sentiment analysis — top comments often contain the most valuable signal # Actor input Schema ## `subreddits` (type: `array`): List of subreddit names to scrape (without r/ prefix). Example: \["MachineLearning", "programming", "webdev"] ## `listing` (type: `string`): Which feed to scrape: hot, new, top, or rising. ## `timeFilter` (type: `string`): Time range for "top" listing: hour, day, week, month, year, or all. ## `maxPostsPerSubreddit` (type: `integer`): Maximum number of posts to scrape per subreddit. ## `minScore` (type: `integer`): Only return posts with at least this score. Set to 0 to include all posts. ## `keywords` (type: `array`): Only return posts whose title or body contains at least one of these keywords. Leave empty to return all posts. ## `includeComments` (type: `boolean`): If true, fetches the top comments for each post (billed separately per comment). ## `maxCommentsPerPost` (type: `integer`): Maximum number of top-level comments to fetch per post when includeComments is true. ## Actor input object example ```json { "subreddits": [ "programming" ], "listing": "hot", "timeFilter": "week", "maxPostsPerSubreddit": 100, "minScore": 0, "keywords": [], "includeComments": false, "maxCommentsPerPost": 10 } ``` # 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("mranderson323/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("mranderson323/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 mranderson323/reddit-subreddit-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=mranderson323/reddit-subreddit-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Subreddit Scraper", "description": "Scrapes posts and comments from any subreddit using Reddit's public JSON API. Filter by listing type, time range, keywords, and score. No API key needed.", "version": "0.1", "x-build-id": "UMWX29CtH3mZXQ6Sh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/mranderson323~reddit-subreddit-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-mranderson323-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/mranderson323~reddit-subreddit-scraper/runs": { "post": { "operationId": "runs-sync-mranderson323-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/mranderson323~reddit-subreddit-scraper/run-sync": { "post": { "operationId": "run-sync-mranderson323-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", "required": [ "subreddits" ], "properties": { "subreddits": { "title": "Subreddits", "type": "array", "description": "List of subreddit names to scrape (without r/ prefix). Example: [\"MachineLearning\", \"programming\", \"webdev\"]", "default": [ "programming" ] }, "listing": { "title": "Listing Type", "enum": [ "hot", "new", "top", "rising" ], "type": "string", "description": "Which feed to scrape: hot, new, top, or rising.", "default": "hot" }, "timeFilter": { "title": "Time Filter (top only)", "enum": [ "hour", "day", "week", "month", "year", "all" ], "type": "string", "description": "Time range for \"top\" listing: hour, day, week, month, year, or all.", "default": "week" }, "maxPostsPerSubreddit": { "title": "Max Posts Per Subreddit", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of posts to scrape per subreddit.", "default": 100 }, "minScore": { "title": "Minimum Score (upvotes)", "minimum": 0, "type": "integer", "description": "Only return posts with at least this score. Set to 0 to include all posts.", "default": 0 }, "keywords": { "title": "Keyword Filter", "type": "array", "description": "Only return posts whose title or body contains at least one of these keywords. Leave empty to return all posts.", "default": [] }, "includeComments": { "title": "Include Top Comments", "type": "boolean", "description": "If true, fetches the top comments for each post (billed separately per comment).", "default": false }, "maxCommentsPerPost": { "title": "Max Comments Per Post", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of top-level comments to fetch per post when includeComments is true.", "default": 10 } } }, "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 } } } } } } } } } } ```