# Luma Events Scraper + New Event Monitor (`kenosis-labs/luma-events-scraper`) Actor Extract events from Luma (lu.ma) city pages with guest counts, ticket prices, host profiles and social links. Monitor mode delivers only newly published events on a schedule. - **URL**: https://apify.com/kenosis-labs/luma-events-scraper.md - **Developed by:** [Keeli Michael](https://apify.com/kenosis-labs) (community) - **Categories:** Lead generation, Social media - **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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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 ## Luma Events Scraper Extract events from Luma (lu.ma / luma.com) city pages into clean JSON or CSV: event name, date, venue city and coordinates, guest count, ticket pricing, the hosting calendar, and every host's profile with Instagram, LinkedIn and X links. Point it at "sf" or "nyc", get back the full upcoming event feed for that city in under a minute. ### What you get One record per event: ```json { "eventId": "evt-71eqD0JrBrCBHRk", "name": "The California Peptide Club IV", "url": "https://luma.com/californiapeptideclub4", "startAt": "2026-07-25T01:00:00.000Z", "timezone": "America/Los_Angeles", "city": "San Francisco", "neighborhood": "Clarendon Heights", "latitude": 37.7559, "longitude": -122.4523, "guestCount": 118, "ticketInfo": { "is_free": false, "require_approval": true }, "calendarName": "Personal", "calendarInstagram": null, "hosts": [ { "name": "Julius Ritter", "verified": false, "instagram": "jlsritter", "linkedin": "/in/juliusritter", "twitter": "JuliusYRitter" } ] } ```` ### Monitor new Luma events on a schedule Turn on **Monitor mode** (`onlyNewEvents`) and the scraper remembers every event it has delivered. Each following run returns only events published since the last run. Put it on a daily Apify Schedule and you have a hands-off new-event feed for any city, ready to push into Slack, Google Sheets, a CRM or a webhook through Apify integrations. You are only charged for events actually delivered. A monitor run that finds nothing new costs you nothing beyond the run start. ### Who uses this - Sales and sponsorship teams tracking AI, tech and startup events in their target cities, with host socials ready for outreach - Community and event teams watching what competing organizers are running and how full their events get - Newsletter and content curators auto-building "events this week" sections - Market researchers measuring event volume and topics by city over time ### Input | Field | Type | Description | |---|---|---| | `sources` | array | City slugs (`sf`, `nyc`, `london`), full city URLs, or single event URLs | | `maxEventsPerSource` | integer | Events to scan per source (default 200) | | `onlyNewEvents` | boolean | Monitor mode: deliver only events not seen in previous runs | | `proxy` | object | Optional. Runs without a proxy by default | ### Pricing Pay per event delivered. No subscription, no rental fee. A run that delivers nothing new costs nothing beyond the run start event. ### FAQ **Which cities work?** Any city with a Luma discover page: sf, nyc, la, london, singapore, tokyo, berlin, sydney and dozens more. Any `luma.com/` URL works as input. **Does it scrape attendee lists?** No. It extracts public event and host information only. Guest counts are included as numbers; personal attendee data is never touched. **Can I get results as CSV or Excel?** Yes. Every Apify dataset exports as JSON, CSV, Excel or XML from the run page or via API. **How fresh is the data?** Live. Every run reads Luma's current public pages at run time. **Can it feed my own app?** Yes. Call the actor through the Apify API or an MCP client and read the dataset programmatically. # Actor input Schema ## `sources` (type: `array`): Luma city slugs (sf, nyc, london...), full city URLs (https://luma.com/sf), or single event URLs. One entry per line. ## `maxEventsPerSource` (type: `integer`): Stop after this many events for each source. ## `onlyNewEvents` (type: `boolean`): When enabled, the actor remembers which events it has already seen and each run delivers only events that appeared since the previous run. Combine with an Apify Schedule for a hands-off new-event feed. You are only charged for delivered events. ## `proxy` (type: `object`): Proxy settings. Luma currently responds without a proxy for most regions; enable Apify Proxy if you see blocks. ## Actor input object example ```json { "sources": [ "sf", "nyc" ], "maxEventsPerSource": 200, "onlyNewEvents": false, "proxy": { "useApifyProxy": false } } ``` # 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 = { "sources": [ "sf", "nyc" ], "proxy": { "useApifyProxy": false } }; // Run the Actor and wait for it to finish const run = await client.actor("kenosis-labs/luma-events-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 = { "sources": [ "sf", "nyc", ], "proxy": { "useApifyProxy": False }, } # Run the Actor and wait for it to finish run = client.actor("kenosis-labs/luma-events-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 '{ "sources": [ "sf", "nyc" ], "proxy": { "useApifyProxy": false } }' | apify call kenosis-labs/luma-events-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=kenosis-labs/luma-events-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Luma Events Scraper + New Event Monitor", "description": "Extract events from Luma (lu.ma) city pages with guest counts, ticket prices, host profiles and social links. Monitor mode delivers only newly published events on a schedule.", "version": "0.1", "x-build-id": "8geKQy8rVCiSkvBpf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/kenosis-labs~luma-events-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-kenosis-labs-luma-events-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/kenosis-labs~luma-events-scraper/runs": { "post": { "operationId": "runs-sync-kenosis-labs-luma-events-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/kenosis-labs~luma-events-scraper/run-sync": { "post": { "operationId": "run-sync-kenosis-labs-luma-events-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": [ "sources" ], "properties": { "sources": { "title": "Cities or event URLs", "type": "array", "description": "Luma city slugs (sf, nyc, london...), full city URLs (https://luma.com/sf), or single event URLs. One entry per line.", "items": { "type": "string" } }, "maxEventsPerSource": { "title": "Max events per source", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Stop after this many events for each source.", "default": 200 }, "onlyNewEvents": { "title": "Monitor mode: only new events", "type": "boolean", "description": "When enabled, the actor remembers which events it has already seen and each run delivers only events that appeared since the previous run. Combine with an Apify Schedule for a hands-off new-event feed. You are only charged for delivered events.", "default": false }, "proxy": { "title": "Proxy", "type": "object", "description": "Proxy settings. Luma currently responds without a proxy for most regions; enable Apify Proxy if you see blocks." } } }, "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 } } } } } } } } } } ```