# Reddit Subreddit Posts Scraper. No Login (`seemuapps/reddit-subreddit-posts-scraper`) Actor Scrape posts from any public subreddit. Title, author, score, comment count, body text, link, flair, and timestamp. Filter by hot, new, top, rising, or controversial. No login. - **URL**: https://apify.com/seemuapps/reddit-subreddit-posts-scraper.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Social media, AI, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.50 / 1,000 reddit subreddit posts 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 Posts Scraper — No Login Scrape posts from any public subreddit — title, author, score, comment count, body text, link, flair, and timestamp. Filter by Hot, New, Top, Rising, or Controversial. No login, no cookies. ### What you get - Post ID, title, full body text (and HTML), author, and permalink - Engagement: score, upvote ratio, comment count, awards count - Listing sort: Hot, New, Top, Rising, Controversial — with Top/Controversial supporting Past Hour through All Time - Flair, post hint, domain, thumbnail, NSFW / spoiler / stickied / locked flags - Cursor-based pagination — fetch unlimited posts across multiple runs - Direct export to JSON, CSV, Excel, or Google Sheets ### Use cases - Track a community's most-discussed topics for content research - Build a dataset for sentiment, trend, or topic analysis - Monitor mentions of a brand, product, or competitor across subreddits - Archive a subreddit's top posts for historical research - Feed downstream LLM pipelines with current Reddit content ### How to use 1. Enter a **Subreddit** name (e.g. `programming`, `r/programming`, or a full URL) 2. Choose a **Sort** — Hot, New, Top, Rising, or Controversial 3. If using Top or Controversial, pick a **Time Filter** (Past Day, Past Week, etc.) 4. Set **Max Posts** (default 100; 0 for unlimited) 5. Run the actor — one post per row in the **Dataset** tab 6. To fetch the next page, open the **Key-value store** tab → copy the `NEXT_PAGE_ID` value → paste it into **Page ID** on your next run ### Output format One post per dataset row — perfect for direct CSV, Excel, or Google Sheets export: ```json { "id": "1abc234", "name": "t3_1abc234", "subreddit": "programming", "title": "Show HN: My new project", "author": "exampleuser", "permalink": "https://www.reddit.com/r/programming/comments/1abc234/show_hn_my_new_project/", "url": "https://example.com/project", "isSelf": false, "selftext": "", "score": 1234, "upvoteRatio": 0.94, "numComments": 87, "createdUtc": 1762900000, "createdAt": "2026-05-12T10:00:00.000Z", "linkFlairText": "Show HN", "postHint": "link", "domain": "example.com", "over18": false, "stickied": false, "locked": false, "isVideo": false } ```` ### Pagination If the listing has more posts than **Max Posts** allows, the actor saves a resume cursor to the default **Key-value store** under the key `NEXT_PAGE_ID`. 1. Open the **Key-value store** tab on the run page 2. Copy the value of `NEXT_PAGE_ID` 3. Start a new run and paste it into **Page ID** When `NEXT_PAGE_ID` is `null`, the listing has been fully scraped. ### Input options | Field | Type | Description | |-------|------|-------------| | Subreddit | string | Subreddit name, with or without `r/` (required) | | Sort | enum | Hot, New, Top, Rising, Controversial — default Hot | | Time Filter | enum | Past Hour through All Time — only used by Top and Controversial | | Max Posts | integer | Cap per run — default 100, 0 for unlimited | | Page ID | string | `NEXT_PAGE_ID` from the previous run, to resume pagination | # Actor input Schema ## `subreddit` (type: `string`): Subreddit name (e.g. 'programming'), with or without leading r/. Full URLs (https://www.reddit.com/r/programming) are also accepted. ## `sort` (type: `string`): Listing sort. 'Top' and 'Controversial' use the Time Filter window; the others ignore it. ## `timeFilter` (type: `string`): Time window for 'Top' and 'Controversial' sorts. Ignored for other sorts. ## `maxItems` (type: `integer`): Maximum posts to return. 0 = no cap (paginates until the listing is exhausted or the run times out). ## `pageId` (type: `string`): Optional. Paste the NEXT\_PAGE\_ID from the previous run's Key-value store to resume paginating from where you left off. ## Actor input object example ```json { "subreddit": "programming", "sort": "hot", "timeFilter": "day", "maxItems": 100 } ``` # Actor output Schema ## `results` (type: `string`): One row per post: id, title, author, subreddit, permalink, url, selftext, score, upvoteRatio, numComments, createdUtc, createdAt, flair, isSelf, isVideo, over18, etc. ## `nextPageId` (type: `string`): NEXT\_PAGE\_ID record in the default key-value store. Paste into Page ID on the next run to resume; null when the listing is exhausted. # 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 = { "subreddit": "programming" }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/reddit-subreddit-posts-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 = { "subreddit": "programming" } # Run the Actor and wait for it to finish run = client.actor("seemuapps/reddit-subreddit-posts-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 '{ "subreddit": "programming" }' | apify call seemuapps/reddit-subreddit-posts-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/reddit-subreddit-posts-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Subreddit Posts Scraper. No Login", "description": "Scrape posts from any public subreddit. Title, author, score, comment count, body text, link, flair, and timestamp. Filter by hot, new, top, rising, or controversial. No login.", "version": "1.0", "x-build-id": "lXqs1kjk7xoA6EXhY" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~reddit-subreddit-posts-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-reddit-subreddit-posts-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~reddit-subreddit-posts-scraper/runs": { "post": { "operationId": "runs-sync-seemuapps-reddit-subreddit-posts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~reddit-subreddit-posts-scraper/run-sync": { "post": { "operationId": "run-sync-seemuapps-reddit-subreddit-posts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "subreddit" ], "properties": { "subreddit": { "title": "Subreddit", "type": "string", "description": "Subreddit name (e.g. 'programming'), with or without leading r/. Full URLs (https://www.reddit.com/r/programming) are also accepted." }, "sort": { "title": "Sort", "enum": [ "hot", "new", "top", "rising", "controversial" ], "type": "string", "description": "Listing sort. 'Top' and 'Controversial' use the Time Filter window; the others ignore it.", "default": "hot" }, "timeFilter": { "title": "Time Filter", "enum": [ "hour", "day", "week", "month", "year", "all" ], "type": "string", "description": "Time window for 'Top' and 'Controversial' sorts. Ignored for other sorts.", "default": "day" }, "maxItems": { "title": "Max Posts", "minimum": 0, "type": "integer", "description": "Maximum posts to return. 0 = no cap (paginates until the listing is exhausted or the run times out).", "default": 100 }, "pageId": { "title": "Page ID (pagination)", "type": "string", "description": "Optional. Paste the NEXT_PAGE_ID from the previous run's Key-value store to resume paginating from where you left off." } } }, "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 } } } } } } } } } } ```