# Hacker News Scraper - Stories, Comments & Search (`legend006/hackernews-scraper`) Actor Scrape Hacker News stories, comments, polls, jobs, and Ask/Show HN posts. Search by keyword and date range, pull a user's full activity, or fetch any HN list (front page, new, best, ask, show, job). No auth required. Built for AI/ML datasets, tech trend research, and news monitoring. - **URL**: https://apify.com/legend006/hackernews-scraper.md - **Developed by:** [NIJ KANANI](https://apify.com/legend006) (community) - **Categories:** News, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.30 / 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 Scrape **Hacker News** stories, comments, polls, jobs, and Ask/Show HN posts. Search by keyword + date range, pull full HN lists (front page, new, best, ask, show, jobs), or fetch any user's complete activity. > ⚡ Uses HN's official Algolia + Firebase APIs. No login, no rate-limit nightmares, no scraping HTML. --- ### ✨ What you can do - 🔎 **Keyword search** with date range, tag filter, min-points threshold, and sort by relevance/date/points - 📋 **Pull any HN list** — top, new, best, Ask HN, Show HN, jobs - 👤 **Get any user's full activity** — stories AND comments, by username - 💬 **Optionally fetch full comment trees** — flattened with parent IDs (great for AI training) --- ### 🚀 Quick start 1. Click **Try for free** 2. Pick mode: `search`, `list`, or `user` 3. Enter targets 4. Click **Start** --- ### 📥 Input examples #### Search for "claude" stories from the last month with 50+ points ```json { "mode": "search", "searchQueries": ["claude"], "tags": "story", "sortBy": "points", "since": "2026-04-01", "minPoints": 50, "maxItems": 500, "includeComments": true } ```` #### Pull current front page ```json { "mode": "list", "listType": "topstories", "maxItems": 30, "includeComments": true, "maxCommentsPerStory": 50 } ``` #### Get pg's recent activity ```json { "mode": "user", "users": ["pg", "dang"], "maxItems": 100 } ``` *** ### 📤 Output (per item) ```json { "type": "story", "id": 12345678, "title": "Show HN: A new way to scrape data", "text": null, "author": "username", "points": 234, "numComments": 87, "createdAt": "2026-04-15T12:34:56.000Z", "url": "https://example.com/article", "hnUrl": "https://news.ycombinator.com/item?id=12345678", "tags": ["story", "front_page"], "comments": [ { "type": "comment", "id": 12345679, "text": "Great post!", "author": "commenter", "points": 12, "createdAt": "2026-04-15T13:00:00.000Z", "parentId": 12345678 } ] } ``` *** ### 🎯 Use cases | Who | Why | |---|---| | 🤖 AI / LLM teams | High-signal tech-discussion training data, filterable by topic and quality (points threshold) | | 📰 Tech journalists | Track what's trending in dev/startup community | | 🧑‍💻 Engineers | Dataset of "Show HN" launches, open-source releases, new tools | | 📊 VCs / scouts | Monitor early-stage signals across founders posting on HN | | 📈 SEO researchers | Track tech keywords surfacing in HN search trends | *** ### ⚙️ Tech notes - Search uses HN's [Algolia API](https://hn.algolia.com/api) — fast, fielded, supports complex queries - Lists & item details use HN's [Firebase API](https://github.com/HackerNews/API) — official, real-time - No auth required, no API key needed, no rate-limit (within reason) - Comments are fetched recursively from Firebase for accuracy and full trees *** ### ❓ FAQ **Will I get rate-limited?** HN is generous — the Algolia API is unmetered for reasonable use. Comment-tree scraping at large scale is slower because each item is a separate Firebase call. **Are deleted/dead items included?** No — they're filtered automatically. **Can I scrape historical data?** Yes. Use `since` and `until` in search mode to pull any historical date range. HN's archive goes back to 2007. **Schedule it?** Yes — set up an Apify Schedule for daily/hourly trend tracking. # Actor input Schema ## `mode` (type: `string`): Pick what to scrape. 'search' = keyword search via Algolia. 'list' = front page, new, best, ask, show, or job. 'user' = a user's stories and comments. ## `searchQueries` (type: `array`): Keywords or phrases. Used when mode = search. ## `tags` (type: `string`): Algolia tags filter. Examples: 'story', 'comment', 'poll', 'show\_hn', 'ask\_hn', 'front\_page'. Combine with commas (AND) or use parentheses for OR. Default 'story' returns stories only. ## `sortBy` (type: `string`): Sort search results. ## `since` (type: `string`): Lower bound for created\_at. Format YYYY-MM-DD. Search mode only. ## `until` (type: `string`): Upper bound for created\_at. Format YYYY-MM-DD. Search mode only. ## `minPoints` (type: `integer`): Skip stories below this score. Useful to filter quality. Search mode only. ## `listType` (type: `string`): Used when mode = list. ## `users` (type: `array`): Used when mode = user. Plain usernames (no '@' or 'u/'). ## `maxItems` (type: `integer`): Maximum items to return per query / list / user. ## `includeComments` (type: `boolean`): Fetch full comment trees for each story (slower, much richer data — great for AI training). ## `maxCommentsPerStory` (type: `integer`): Cap on comments per story when includeComments is on. ## Actor input object example ```json { "mode": "search", "searchQueries": [ "claude" ], "tags": "story", "sortBy": "relevance", "minPoints": 0, "listType": "topstories", "users": [], "maxItems": 200, "includeComments": false, "maxCommentsPerStory": 100 } ``` # 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("legend006/hackernews-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("legend006/hackernews-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 legend006/hackernews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=legend006/hackernews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hacker News Scraper - Stories, Comments & Search", "description": "Scrape Hacker News stories, comments, polls, jobs, and Ask/Show HN posts. Search by keyword and date range, pull a user's full activity, or fetch any HN list (front page, new, best, ask, show, job). No auth required. Built for AI/ML datasets, tech trend research, and news monitoring.", "version": "0.1", "x-build-id": "zjrEnfS5QJeNe67rB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/legend006~hackernews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-legend006-hackernews-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/legend006~hackernews-scraper/runs": { "post": { "operationId": "runs-sync-legend006-hackernews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/legend006~hackernews-scraper/run-sync": { "post": { "operationId": "run-sync-legend006-hackernews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "mode" ], "properties": { "mode": { "title": "Scrape mode", "enum": [ "search", "list", "user" ], "type": "string", "description": "Pick what to scrape. 'search' = keyword search via Algolia. 'list' = front page, new, best, ask, show, or job. 'user' = a user's stories and comments.", "default": "search" }, "searchQueries": { "title": "Search queries", "type": "array", "description": "Keywords or phrases. Used when mode = search.", "default": [ "claude" ], "items": { "type": "string" } }, "tags": { "title": "Filter by tags (search mode)", "type": "string", "description": "Algolia tags filter. Examples: 'story', 'comment', 'poll', 'show_hn', 'ask_hn', 'front_page'. Combine with commas (AND) or use parentheses for OR. Default 'story' returns stories only.", "default": "story" }, "sortBy": { "title": "Sort by", "enum": [ "relevance", "date", "points" ], "type": "string", "description": "Sort search results.", "default": "relevance" }, "since": { "title": "Since (ISO date or relative)", "type": "string", "description": "Lower bound for created_at. Format YYYY-MM-DD. Search mode only." }, "until": { "title": "Until (ISO date)", "type": "string", "description": "Upper bound for created_at. Format YYYY-MM-DD. Search mode only." }, "minPoints": { "title": "Minimum points", "minimum": 0, "type": "integer", "description": "Skip stories below this score. Useful to filter quality. Search mode only.", "default": 0 }, "listType": { "title": "List type", "enum": [ "topstories", "newstories", "beststories", "askstories", "showstories", "jobstories" ], "type": "string", "description": "Used when mode = list.", "default": "topstories" }, "users": { "title": "Hacker News usernames", "type": "array", "description": "Used when mode = user. Plain usernames (no '@' or 'u/').", "default": [], "items": { "type": "string" } }, "maxItems": { "title": "Max items per target", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum items to return per query / list / user.", "default": 200 }, "includeComments": { "title": "Include comments", "type": "boolean", "description": "Fetch full comment trees for each story (slower, much richer data — great for AI training).", "default": false }, "maxCommentsPerStory": { "title": "Max comments per story", "minimum": 0, "maximum": 5000, "type": "integer", "description": "Cap on comments per story when includeComments is on.", "default": 100 } } }, "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 } } } } } } } } } } ```