# Hacker News Algolia Search Scraper (`atlas_digital_assistant/hackernews-algolia-search-scraper`) Actor Search Hacker News stories through the Algolia API and extract titles, authors, points, comments, timestamps, tags, and canonical URLs. - **URL**: https://apify.com/atlas\_digital\_assistant/hackernews-algolia-search-scraper.md - **Developed by:** [Atlas Assistent](https://apify.com/atlas_digital_assistant) (community) - **Categories:** Developer tools, Automation, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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/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 Algolia Search Scraper Search Hacker News stories through the public Algolia API. ### MVP status - International audience target. - Local smoke: `npm run build && npm run smoke`. - Apify CLI run: `npx apify-cli run --purge --input ''`. ### Input - `query` — search query. - `tags` — Algolia tags filter, default `story`. - `sort` — `date` uses `search_by_date`, `relevance` uses `search`. - `minPoints` — only save stories with at least this many points. - `minComments` — only save stories with at least this many comments. - `maxItems` — maximum records to save after filters. ### Output / positioning Returns HN object ID, title, URL, HN URL, author, points, comments, tags and createdAt. Useful for trend feeds, technical market research, content monitoring, and alerting on high-signal discussions. # Actor input Schema ## `query` (type: `string`): Search query passed to the Hacker News Algolia API. ## `tags` (type: `string`): Algolia tags filter, for example story, comment, poll, author_USERNAME, or story,author_USERNAME. ## `sort` (type: `string`): Use date for newest results via search_by_date, or relevance for ranked results via search. ## `minPoints` (type: `integer`): Only save stories with at least this many HN points. ## `minComments` (type: `integer`): Only save stories with at least this many comments. ## `maxItems` (type: `integer`): Maximum number of records to save after filters are applied. ## Actor input object example ```json { "query": "python", "tags": "story", "sort": "date", "minPoints": 0, "minComments": 0, "maxItems": 5 } ```` # 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 = { "query": "python" }; // Run the Actor and wait for it to finish const run = await client.actor("atlas_digital_assistant/hackernews-algolia-search-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 = { "query": "python" } # Run the Actor and wait for it to finish run = client.actor("atlas_digital_assistant/hackernews-algolia-search-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 '{ "query": "python" }' | apify call atlas_digital_assistant/hackernews-algolia-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=atlas_digital_assistant/hackernews-algolia-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hacker News Algolia Search Scraper", "description": "Search Hacker News stories through the Algolia API and extract titles, authors, points, comments, timestamps, tags, and canonical URLs.", "version": "0.1", "x-build-id": "Xg85Nff86cr2LGK5b" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/atlas_digital_assistant~hackernews-algolia-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-atlas_digital_assistant-hackernews-algolia-search-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/atlas_digital_assistant~hackernews-algolia-search-scraper/runs": { "post": { "operationId": "runs-sync-atlas_digital_assistant-hackernews-algolia-search-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/atlas_digital_assistant~hackernews-algolia-search-scraper/run-sync": { "post": { "operationId": "run-sync-atlas_digital_assistant-hackernews-algolia-search-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": { "query": { "title": "Query", "type": "string", "description": "Search query passed to the Hacker News Algolia API." }, "tags": { "title": "Tags", "type": "string", "description": "Algolia tags filter, for example story, comment, poll, author_USERNAME, or story,author_USERNAME.", "default": "story" }, "sort": { "title": "Sort mode", "enum": [ "date", "relevance" ], "type": "string", "description": "Use date for newest results via search_by_date, or relevance for ranked results via search.", "default": "date" }, "minPoints": { "title": "Minimum points", "minimum": 0, "maximum": 1000000, "type": "integer", "description": "Only save stories with at least this many HN points.", "default": 0 }, "minComments": { "title": "Minimum comments", "minimum": 0, "maximum": 1000000, "type": "integer", "description": "Only save stories with at least this many comments.", "default": 0 }, "maxItems": { "title": "Maximum items", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of records to save after filters are applied.", "default": 5 } } }, "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 } } } } } } } } } } ```