# Hacker News Scraper (`seeb/hacker-news-scraper`) Actor Reliable Apify actor for scraping public Hacker News sections with HTML-only crawling. Extract rank, title, URL, points, author, age, and comment count in a clean dataset for trend tracking, research, content discovery, and automation. - **URL**: https://apify.com/seeb/hacker-news-scraper.md - **Developed by:** [Techionik](https://apify.com/seeb) (community) - **Categories:** News, Developer tools, Automation - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 dataset items 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ### Hacker News Scraper A fast, lightweight, and marketplace-ready Apify actor for scraping public Hacker News listings using plain HTML parsing. This actor is built specifically for Hacker News and uses CheerioCrawler instead of a full browser, making it efficient, low-cost, and reliable for structured data extraction. It collects one clean dataset item per post and supports the main Hacker News sections used for trending stories, discovery, monitoring, and research workflows. ### Features - Scrapes public Hacker News listing pages - Extracts one clean record per post - Supports multiple Hacker News sections - Automatically paginates until the requested result limit is reached - Uses HTML-only crawling for faster and cheaper runs - Keeps input simple and user-friendly ### Supported Page Types - front - newest - ask - show - jobs - best - active - classic ### Extracted Fields Each result may include the following fields: - pageType - rank - title - url - points - author - age - commentsCount ### Input This actor uses a very simple input format: - **pageType**: The Hacker News section to scrape - **maxResults**: The maximum number of posts to extract Example input: { "pageType": "best", "maxResults": 10 } ### Output The actor returns one dataset item per Hacker News post. Example output: { "pageType": "best", "rank": 1, "title": "Ghostty is leaving GitHub", "url": "https://mitchellh.com/writing/ghostty-leaving-github", "points": 3400, "author": "WadeGrimridge", "age": "1 day ago", "commentsCount": 1015 } ### Notes About the Data Some Hacker News sections do not always expose the same metadata. For example, on the **jobs** page, fields like `points`, `author`, or `commentsCount` may be missing on the page itself. In such cases, the actor returns default values such as `0` or `null` where appropriate. This is expected behavior and reflects the actual structure of Hacker News. ### Why Use This Actor Hacker News is mostly server-rendered HTML, which makes it a strong fit for a Cheerio-based scraper. Benefits of this actor include: - Faster execution than browser-based scrapers - Lower runtime and compute cost - Clean and structured output - Reliable extraction from major Hacker News sections - Good fit for automation, trend tracking, research, and content workflows ### Best Use Cases This actor is useful for: - Tracking trending Hacker News posts - Monitoring top stories by section - Startup and tech news aggregation - Research and content discovery workflows - Lightweight automation and data collection pipelines ### Technical Approach This actor is built with: - Apify - Crawlee CheerioCrawler - Plain HTML parsing - Automatic pagination handling Because it does not use Playwright or Puppeteer, it is more efficient for Hacker News than a full-browser solution. ### Scope This actor is designed specifically for **Hacker News**. It is not a universal news scraper and is not intended for arbitrary websites with different HTML structures. If you need broad website text extraction, a generic content scraper is a better fit. This actor is purpose-built for clean Hacker News post extraction. ### Summary If you need a simple, reliable, and cost-effective Hacker News scraper for Apify, this actor provides a clean structured output with minimal input and efficient HTML-only crawling. # Actor input Schema ## `pageType` (type: `string`): Choose which Hacker News section to scrape. ## `maxResults` (type: `integer`): Maximum number of Hacker News posts to extract in total. ## Actor input object example ```json { "pageType": "front", "maxResults": 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("seeb/hacker-news-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("seeb/hacker-news-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 seeb/hacker-news-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seeb/hacker-news-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hacker News Scraper", "description": "Reliable Apify actor for scraping public Hacker News sections with HTML-only crawling. Extract rank, title, URL, points, author, age, and comment count in a clean dataset for trend tracking, research, content discovery, and automation.", "version": "0.0", "x-build-id": "6fycOdVmRiEKyZTkz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seeb~hacker-news-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seeb-hacker-news-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seeb~hacker-news-scraper/runs": { "post": { "operationId": "runs-sync-seeb-hacker-news-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seeb~hacker-news-scraper/run-sync": { "post": { "operationId": "run-sync-seeb-hacker-news-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "pageType": { "title": "Page Type", "enum": [ "front", "newest", "ask", "show", "jobs", "best", "active", "classic" ], "type": "string", "description": "Choose which Hacker News section to scrape.", "default": "front" }, "maxResults": { "title": "Max Results", "minimum": 1, "type": "integer", "description": "Maximum number of Hacker News posts to extract in total.", "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 } } } } } } } } } } ```