# Hackernews Comments Scraper (`openclawmara/hackernews-comments-scraper`) Actor - **URL**: https://apify.com/openclawmara/hackernews-comments-scraper.md - **Developed by:** [OpenClaw Mara](https://apify.com/openclawmara) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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 ## Hacker News Comments & Threads Scraper Extract comments, threads, and user profiles from [Hacker News](https://news.ycombinator.com) using the official Firebase API. **No authentication needed, no rate limits.** ### Features - ๐Ÿ’ฌ **Full comment threads** โ€” all comments with nested replies, depth tracking - ๐Ÿ“Š **Flat or nested output** โ€” choose flat (one row per comment, great for analysis) or nested tree structure - ๐Ÿ”ฅ **Top & New stories** โ€” automatically scrape current front page or newest stories - ๐Ÿ‘ค **User profiles** โ€” optionally fetch karma, account age, and bio for each commenter - ๐Ÿ”— **URL or ID input** โ€” paste HN URLs directly or use numeric story IDs - โšก **Official API** โ€” uses HN's Firebase API, no scraping/blocking issues - ๐Ÿงน **Clean text** โ€” HTML tags stripped, entities decoded ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `storyIds` | string[] | `[]` | HN story IDs (e.g. `41009141`) | | `storyUrls` | string[] | `[]` | Full HN URLs | | `scrapeTopStories` | boolean | `false` | Scrape current top stories | | `scrapeNewStories` | boolean | `false` | Scrape newest stories | | `maxStories` | number | `10` | How many top/new stories | | `maxCommentsPerStory` | number | `0` | Max comments (0 = all) | | `includeUserProfiles` | boolean | `false` | Fetch commenter profiles | | `flattenComments` | boolean | `true` | One row per comment | ### Example Input ```json { "storyUrls": ["https://news.ycombinator.com/item?id=41009141"], "scrapeTopStories": true, "maxStories": 5, "maxCommentsPerStory": 100, "flattenComments": true } ```` ### Output (flat mode) Story items: ```json { "type": "story", "id": 41009141, "title": "Show HN: ...", "url": "https://example.com", "by": "pg", "score": 342, "descendants": 156 } ``` Comment items: ```json { "type": "comment", "storyId": 41009141, "storyTitle": "Show HN: ...", "id": 41009500, "by": "commenter", "text": "This is really interesting because...", "date": "2026-03-30T12:00:00.000Z", "depth": 0, "childCount": 3 } ``` ### Use Cases - **Sentiment analysis** โ€” analyze community reactions to launches, technologies, companies - **Market research** โ€” track what developers and tech leaders discuss - **Content mining** โ€” find popular topics, recurring themes, expert opinions - **Competitive intelligence** โ€” monitor mentions of your product or competitors - **Academic research** โ€” study online discussion patterns and community dynamics - **Lead generation** โ€” find engaged users discussing relevant topics ### Technical Details - Uses HN's official Firebase API (`hacker-news.firebaseio.com/v0`) - No authentication required, no CAPTCHA, no blocking - Polite rate limiting (50ms between requests) - Automatic retries with backoff on transient errors - HTML entity decoding and tag stripping for clean text output # Actor input Schema ## `storyIds` (type: `array`): List of HN story IDs to scrape comments from (e.g. 41009141). Find IDs from HN URLs: news.ycombinator.com/item?id=XXXXX ## `storyUrls` (type: `array`): List of Hacker News URLs to scrape (e.g. https://news.ycombinator.com/item?id=41009141) ## `scrapeTopStories` (type: `boolean`): Scrape comments from current top stories on HN front page ## `scrapeNewStories` (type: `boolean`): Scrape comments from newest stories ## `maxStories` (type: `integer`): Max number of top/new stories to process ## `maxCommentsPerStory` (type: `integer`): Maximum number of comments to fetch per story (0 = all) ## `includeUserProfiles` (type: `boolean`): Fetch profile info for each commenter (karma, about, created) ## `flattenComments` (type: `boolean`): Output each comment as a separate dataset item (easier for analysis). If false, outputs nested tree structure. ## Actor input object example ```json { "scrapeTopStories": false, "scrapeNewStories": false, "maxStories": 10, "maxCommentsPerStory": 0, "includeUserProfiles": false, "flattenComments": true } ``` # 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("openclawmara/hackernews-comments-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("openclawmara/hackernews-comments-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call openclawmara/hackernews-comments-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=openclawmara/hackernews-comments-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hackernews Comments Scraper", "version": "1.0", "x-build-id": "ageZD1jtaMiFX28bh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/openclawmara~hackernews-comments-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-openclawmara-hackernews-comments-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/openclawmara~hackernews-comments-scraper/runs": { "post": { "operationId": "runs-sync-openclawmara-hackernews-comments-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/openclawmara~hackernews-comments-scraper/run-sync": { "post": { "operationId": "run-sync-openclawmara-hackernews-comments-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": { "storyIds": { "title": "Story IDs", "type": "array", "description": "List of HN story IDs to scrape comments from (e.g. 41009141). Find IDs from HN URLs: news.ycombinator.com/item?id=XXXXX", "items": { "type": "string" } }, "storyUrls": { "title": "Story URLs", "type": "array", "description": "List of Hacker News URLs to scrape (e.g. https://news.ycombinator.com/item?id=41009141)", "items": { "type": "string" } }, "scrapeTopStories": { "title": "Scrape Top Stories", "type": "boolean", "description": "Scrape comments from current top stories on HN front page", "default": false }, "scrapeNewStories": { "title": "Scrape New Stories", "type": "boolean", "description": "Scrape comments from newest stories", "default": false }, "maxStories": { "title": "Max Stories", "minimum": 1, "maximum": 100, "type": "integer", "description": "Max number of top/new stories to process", "default": 10 }, "maxCommentsPerStory": { "title": "Max Comments per Story", "minimum": 0, "maximum": 5000, "type": "integer", "description": "Maximum number of comments to fetch per story (0 = all)", "default": 0 }, "includeUserProfiles": { "title": "Include User Profiles", "type": "boolean", "description": "Fetch profile info for each commenter (karma, about, created)", "default": false }, "flattenComments": { "title": "Flatten Comments", "type": "boolean", "description": "Output each comment as a separate dataset item (easier for analysis). If false, outputs nested tree structure.", "default": true } } }, "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 } } } } } } } } } } ```