# Product Hunt Launch Monitor — new launches by keyword (`davidbenittah/product-hunt-launch-monitor`) Actor Watch Product Hunt's featured launches on a schedule and get only the NEW products matching your keywords since the last run — title, tagline, link, launch date. Fresh startups are the warmest B2B leads. - **URL**: https://apify.com/davidbenittah/product-hunt-launch-monitor.md - **Developed by:** [David](https://apify.com/davidbenittah) (community) - **Categories:** Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $8.00 / 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 ## Product Hunt Launch Monitor — New Launches by Keyword Watch Product Hunt's featured launches on a schedule and get **only the NEW products matching your keywords** since your last run — title, tagline, Product Hunt link and launch date. A startup that launched **this week** is the warmest B2B lead there is: they have momentum, budget attention, and every tool decision still open. Agencies, dev-tool vendors, and service providers all know it — this Actor turns that into a deduplicated feed you can pipe into Slack, a Google Sheet, Make or Zapier. ### What it does 1. Reads Product Hunt's public featured-launches feed (about 50 fresh products at any time). 2. Keeps only launches whose title or tagline matches your keywords (leave empty to monitor everything). 3. Remembers every launch it has already seen (per keyword set, in a persistent state store) and outputs **only the NEW ones** on every later run — schedule it daily and you get a clean drip of fresh launches, never duplicates. ### Input example ```json { "keywords": ["ai agent", "analytics", "no-code"] } ```` The defaults work as-is. ### Output example ```json { "postId": "1188925", "title": "Astryx", "tagline": "A customizable, agent-ready open-source design system", "productUrl": "https://www.producthunt.com/products/astryx", "publishedAt": "2026-07-05T20:52:49-07:00", "matchedKeywords": ["agent"], "isNewSinceLastRun": true, "firstDetectedAt": "2026-07-06T16:00:00.000Z" } ``` A `SUMMARY` record in the run's key-value store gives the counts: launches in feed, matching, new, total known. ### How to use it - **Lead flow**: schedule daily with your niche keywords → each morning you get only the launches that appeared since yesterday. Reach out while they're in launch mode. - **Competitive radar**: put your category keywords in — you'll know the day a competitor launches. - **Trend pulse**: run keyword-free and diff the volume of launches over time per monitorId. ### Fair use & data - Reads only Product Hunt's **public** launch feed — no login, no page scraping. - **No personal data**: hunter/maker names are deliberately not collected; you get the product, not the person. - The feed exposes currently featured launches (~50); the monitor's memory is what turns it into a complete over-time stream. ### FAQ **Why keyword filtering instead of Product Hunt topics?** The public feed is global; keywords match against title + tagline, which in practice is how you'd scan the homepage yourself. Multi-word keywords are matched as phrases. **First run floods me.** That's the baseline (flagged `isBaseline: true`); disable `includeBaselineInOutput` to start silent. **How do I reset the memory?** Set a new `monitorId` — each ID keeps its own baseline. # Actor input Schema ## `keywords` (type: `array`): Case-insensitive keywords matched against each launch's title and tagline (e.g. 'ai agent', 'design', 'analytics'). Leave empty to monitor ALL featured launches. Hard maximum: 20. ## `includeBaselineInOutput` (type: `boolean`): The very first run saves a baseline of currently featured launches. When enabled, those are also pushed to the dataset (flagged isBaseline=true). Later runs always output only NEW launches. ## `monitorId` (type: `string`): Optional. Advanced: name this monitor to keep several baselines for the same keywords, or reset one by changing the ID. ## Actor input object example ```json { "keywords": [ "ai agent" ], "includeBaselineInOutput": 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 = { "keywords": [ "ai agent" ] }; // Run the Actor and wait for it to finish const run = await client.actor("davidbenittah/product-hunt-launch-monitor").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 = { "keywords": ["ai agent"] } # Run the Actor and wait for it to finish run = client.actor("davidbenittah/product-hunt-launch-monitor").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 '{ "keywords": [ "ai agent" ] }' | apify call davidbenittah/product-hunt-launch-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=davidbenittah/product-hunt-launch-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Product Hunt Launch Monitor — new launches by keyword", "description": "Watch Product Hunt's featured launches on a schedule and get only the NEW products matching your keywords since the last run — title, tagline, link, launch date. Fresh startups are the warmest B2B leads.", "version": "1.0", "x-build-id": "m9hHC35rUeOFumJtz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/davidbenittah~product-hunt-launch-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-davidbenittah-product-hunt-launch-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/davidbenittah~product-hunt-launch-monitor/runs": { "post": { "operationId": "runs-sync-davidbenittah-product-hunt-launch-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/davidbenittah~product-hunt-launch-monitor/run-sync": { "post": { "operationId": "run-sync-davidbenittah-product-hunt-launch-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "keywords": { "title": "Keywords to match", "type": "array", "description": "Case-insensitive keywords matched against each launch's title and tagline (e.g. 'ai agent', 'design', 'analytics'). Leave empty to monitor ALL featured launches. Hard maximum: 20.", "items": { "type": "string" } }, "includeBaselineInOutput": { "title": "Output the baseline on first run", "type": "boolean", "description": "The very first run saves a baseline of currently featured launches. When enabled, those are also pushed to the dataset (flagged isBaseline=true). Later runs always output only NEW launches.", "default": true }, "monitorId": { "title": "Custom monitor ID", "type": "string", "description": "Optional. Advanced: name this monitor to keep several baselines for the same keywords, or reset one by changing the ID." } } }, "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 } } } } } } } } } } ```