# Hacker News Scraper (`moving_beacon-owner1/my-actor-76`) Actor A production-ready Apify Actor that scrapes Hacker News stories, comments, user profiles, and search results using the official Firebase API and Algolia HN Search API. - **URL**: https://apify.com/moving\_beacon-owner1/my-actor-76.md - **Developed by:** [Jamshaid Arif](https://apify.com/moving_beacon-owner1) (community) - **Categories:** News, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 results 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 ## 🟧 Hacker News Scraper — Apify Actor (Python) A production-ready Apify Actor that scrapes **Hacker News** stories, comments, user profiles, and search results using the official Firebase API and Algolia HN Search API. ### Features | Mode | Description | |------|-------------| | **Top / New / Best** | Scrape front-page, newest, or highest-voted stories | | **Ask HN / Show HN** | Scrape community questions and project showcases | | **Jobs** | Scrape HN job postings | | **User Profile** | Scrape any user's karma, bio, and recent submissions | | **Search** | Full-text search via Algolia (by relevance or date) | | **Comments** | Optionally fetch top-level comments for each story | ### Input Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `scrape_type` | string | `"top"` | One of: `top`, `new`, `best`, `ask`, `show`, `job`, `user`, `search` | | `max_items` | integer | `30` | Max stories to scrape (1–500) | | `include_comments` | boolean | `false` | Fetch comments for each story | | `max_comments_per_story` | integer | `10` | Max top-level comments per story | | `min_score` | integer | `0` | Only include stories with ≥ this score | | `username` | string | `""` | HN username (required for `user` mode) | | `search_query` | string | `""` | Search query (required for `search` mode) | | `search_sort` | string | `"relevance"` | `relevance` or `date` | ### Example Input ```json { "scrape_type": "top", "max_items": 30, "include_comments": true, "max_comments_per_story": 5, "min_score": 50 } ```` ### Output Format Each story item in the dataset looks like: ```json { "id": 12345678, "rank": 1, "type": "story", "title": "Show HN: My Cool Project", "url": "https://example.com", "hn_url": "https://news.ycombinator.com/item?id=12345678", "score": 342, "author": "username", "comments_count": 87, "created_at": "2026-04-04T10:30:00+00:00", "time_ago": "3h ago", "text": "", "dead": false, "deleted": false, "scraped_at": "2026-04-04T14:00:00+00:00", "comments": [ { "id": 12345679, "story_id": 12345678, "type": "comment", "author": "commenter", "text": "Great project!", "created_at": "2026-04-04T11:00:00+00:00", "time_ago": "2h ago", "parent": 12345678, "children": [12345680, 12345681], "dead": false, "deleted": false } ] } ``` ### APIs Used - **HN Firebase API**: `https://hacker-news.firebaseio.com/v0/` — Official, no auth needed - **Algolia HN Search**: `https://hn.algolia.com/api/v1/` — Full-text search, no auth needed ### License ISC # Actor input Schema ## `scrape_type` (type: `string`): What type of content to scrape from Hacker News. ## `max_items` (type: `integer`): Maximum number of stories to scrape (1-500). ## `include_comments` (type: `boolean`): Whether to scrape comments for each story. This makes the scrape slower but provides richer data. ## `max_comments_per_story` (type: `integer`): Maximum number of top-level comments to fetch per story (only used if 'Include Comments' is enabled). ## `username` (type: `string`): HN username to scrape (only used when Scrape Type is 'User Profile'). ## `search_query` (type: `string`): Search query (only used when Scrape Type is 'Search'). Uses Algolia HN Search API. ## `search_sort` (type: `string`): Sort order for search results. ## `min_score` (type: `integer`): Only include stories with at least this many points (0 = no filter). ## `proxy_configuration` (type: `object`): Proxy configuration for the scraper. ## Actor input object example ```json { "scrape_type": "top", "max_items": 30, "include_comments": false, "max_comments_per_story": 10, "username": "", "search_query": "", "search_sort": "relevance", "min_score": 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 = { "scrape_type": "top", "max_items": 30 }; // Run the Actor and wait for it to finish const run = await client.actor("moving_beacon-owner1/my-actor-76").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 = { "scrape_type": "top", "max_items": 30, } # Run the Actor and wait for it to finish run = client.actor("moving_beacon-owner1/my-actor-76").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 '{ "scrape_type": "top", "max_items": 30 }' | apify call moving_beacon-owner1/my-actor-76 --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=moving_beacon-owner1/my-actor-76", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hacker News Scraper", "description": "A production-ready Apify Actor that scrapes Hacker News stories, comments, user profiles, and search results using the official Firebase API and Algolia HN Search API.", "version": "0.0", "x-build-id": "L9TFgWXZTokq4WNYz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/moving_beacon-owner1~my-actor-76/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-moving_beacon-owner1-my-actor-76", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/moving_beacon-owner1~my-actor-76/runs": { "post": { "operationId": "runs-sync-moving_beacon-owner1-my-actor-76", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/moving_beacon-owner1~my-actor-76/run-sync": { "post": { "operationId": "run-sync-moving_beacon-owner1-my-actor-76", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "scrape_type", "max_items" ], "properties": { "scrape_type": { "title": "Scrape Type", "enum": [ "top", "new", "best", "ask", "show", "job", "user", "search" ], "type": "string", "description": "What type of content to scrape from Hacker News.", "default": "top" }, "max_items": { "title": "Max Items", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of stories to scrape (1-500).", "default": 30 }, "include_comments": { "title": "Include Comments", "type": "boolean", "description": "Whether to scrape comments for each story. This makes the scrape slower but provides richer data.", "default": false }, "max_comments_per_story": { "title": "Max Comments Per Story", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of top-level comments to fetch per story (only used if 'Include Comments' is enabled).", "default": 10 }, "username": { "title": "Username", "type": "string", "description": "HN username to scrape (only used when Scrape Type is 'User Profile').", "default": "" }, "search_query": { "title": "Search Query", "type": "string", "description": "Search query (only used when Scrape Type is 'Search'). Uses Algolia HN Search API.", "default": "" }, "search_sort": { "title": "Search Sort", "enum": [ "relevance", "date" ], "type": "string", "description": "Sort order for search results.", "default": "relevance" }, "min_score": { "title": "Minimum Score", "minimum": 0, "type": "integer", "description": "Only include stories with at least this many points (0 = no filter).", "default": 0 }, "proxy_configuration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy configuration for the scraper." } } }, "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 } } } } } } } } } } ```