# Event Organizer Leads — Emails & LinkedIn from Luma (`avinashchby/event-organizer-leads`) Actor Extract organizer contact info (name, email, LinkedIn, website) from Luma and Eventbrite event pages. Input a list of event URLs or a search query — output a lead list ready for cold outreach. No API key needed. - **URL**: https://apify.com/avinashchby/event-organizer-leads.md - **Developed by:** [Avinash](https://apify.com/avinashchby) (community) - **Categories:** Lead generation, Marketing, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $8.00 / 1,000 event processeds 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 ## Event Organizer Lead Extractor > **What this actor does:** Visits Luma and Eventbrite event pages and extracts organizer contact info — name, email, LinkedIn, website, Instagram, and Twitter. Turn a list of event URLs (or a keyword search) into an outreach-ready lead list. ### Features - **Two input modes**: URL list mode OR keyword search mode - Extracts: organizer name, email, LinkedIn URL, website, Instagram, Twitter - Supports **Luma** (lu.ma) and **Eventbrite** event pages - In search mode: auto-discovers event URLs then visits each page - No API key required - Output: structured JSON compatible with Apollo, Instantly, Smartlead, and HubSpot CSV import ### Use Cases - **Cold email outreach**: Find emails of event organizers in your niche - **Partnership development**: Identify companies running events in your space - **Sponsorship prospecting**: Discover organizers of conferences and summits to sponsor - **Community research**: Map who's organizing events in a specific city or industry - **Sales intelligence**: Build targeted lists of event-driven companies ### Input Parameters | Parameter | Type | Description | Default | |-----------|------|-------------|---------| | `eventUrls` | array | List of Luma or Eventbrite event URLs | — | | `query` | string | Search keyword (if no URLs provided) | — | | `source` | string | "luma", "eventbrite", or "both" (search mode) | "luma" | | `city` | string | City filter for search mode | — | | `maxResults` | number | Max leads to extract | 50 | ### Output Fields | Field | Description | |-------|-------------| | `eventName` | Event title | | `eventUrl` | Event page URL | | `eventDate` | ISO 8601 event date | | `organizerName` | Organizer or host name | | `organizerEmail` | Email address (if publicly listed) | | `organizerLinkedIn` | LinkedIn company or profile URL | | `organizerWebsite` | External website URL | | `organizerInstagram` | Instagram URL | | `organizerTwitter` | Twitter/X URL | | `source` | "luma" or "eventbrite" | | `scrapedAt` | Scrape timestamp | ### Example Input — URL Mode ```json { "eventUrls": [ "https://lu.ma/ai-founders-dinner", "https://www.eventbrite.com/e/startup-summit-2026-tickets-123456789" ], "maxResults": 50 } ```` ### Example Input — Search Mode ```json { "query": "AI startup networking", "source": "luma", "city": "San Francisco", "maxResults": 30 } ``` ### Example Output ```json { "eventName": "AI Founders Dinner SF", "eventUrl": "https://lu.ma/ai-founders-dinner", "eventDate": "2026-05-20T19:00:00.000Z", "organizerName": "Sarah Chen", "organizerEmail": "sarah@aistartups.com", "organizerLinkedIn": "https://linkedin.com/company/ai-startups-sf", "organizerWebsite": "https://aistartups.com", "source": "luma", "scrapedAt": "2026-04-23T10:00:00.000Z" } ``` ### Questions answered by this actor - Who organizes AI events in San Francisco and how do I contact them? - What are the emails of startup conference organizers in London? - Which companies run the most tech networking events in New York? - How do I find LinkedIn profiles of hackathon organizers in my city? ### Data Extraction Methodology - **Luma events**: Extracts from `__NEXT_DATA__` JSON embedded in the page (host profiles with LinkedIn, Instagram, Twitter handles, website URLs) - **Eventbrite events**: Parses JSON-LD schema.org markup + scans page text for email addresses and social links - **Email detection**: Regex scan of full page text — finds emails in descriptions, contact sections - **LinkedIn detection**: Pattern matching for linkedin.com/company/ and linkedin.com/in/ URLs ### Frequently Asked Questions **Is this legal?** This actor collects only publicly available information that organizers have voluntarily published on public event pages. **How accurate are emails?** Emails are extracted from event descriptions and organizer profiles where they've been intentionally shared. Accuracy depends on how much info the organizer made public. **Can I use this for cold email?** Always comply with CAN-SPAM, GDPR, and applicable regulations in your jurisdiction. **What if an organizer has no contact info?** The actor returns the event and organizer name even if no email or LinkedIn is found — partial data is still useful for research. # Actor input Schema ## `eventUrls` (type: `array`): List of Luma or Eventbrite event page URLs to extract organizer info from. If provided, 'query' and 'source' are ignored. ## `query` (type: `string`): Keywords to search for events (used when eventUrls is empty). E.g. 'AI startup San Francisco'. ## `source` (type: `string`): Which platform to search when using keyword search mode. ## `city` (type: `string`): City to filter events by (search mode only) ## `maxResults` (type: `integer`): Maximum number of organizer leads to extract. Default: 20. ## Actor input object example ```json { "query": "AI startup networking", "source": "luma", "city": "San Francisco", "maxResults": 20 } ``` # Actor output Schema ## `leads` (type: `string`): Extracted organizer leads with contact details for cold outreach and partnership prospecting. # 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": "AI startup networking", "city": "San Francisco" }; // Run the Actor and wait for it to finish const run = await client.actor("avinashchby/event-organizer-leads").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": "AI startup networking", "city": "San Francisco", } # Run the Actor and wait for it to finish run = client.actor("avinashchby/event-organizer-leads").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": "AI startup networking", "city": "San Francisco" }' | apify call avinashchby/event-organizer-leads --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=avinashchby/event-organizer-leads", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Event Organizer Leads — Emails & LinkedIn from Luma", "description": "Extract organizer contact info (name, email, LinkedIn, website) from Luma and Eventbrite event pages. Input a list of event URLs or a search query — output a lead list ready for cold outreach. No API key needed.", "version": "1.0", "x-build-id": "EIauFSX5x64afmtz9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/avinashchby~event-organizer-leads/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-avinashchby-event-organizer-leads", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/avinashchby~event-organizer-leads/runs": { "post": { "operationId": "runs-sync-avinashchby-event-organizer-leads", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/avinashchby~event-organizer-leads/run-sync": { "post": { "operationId": "run-sync-avinashchby-event-organizer-leads", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "eventUrls": { "title": "Event URLs", "type": "array", "description": "List of Luma or Eventbrite event page URLs to extract organizer info from. If provided, 'query' and 'source' are ignored.", "items": { "type": "string" } }, "query": { "title": "Search keyword", "type": "string", "description": "Keywords to search for events (used when eventUrls is empty). E.g. 'AI startup San Francisco'." }, "source": { "title": "Source platform", "enum": [ "luma", "eventbrite", "both" ], "type": "string", "description": "Which platform to search when using keyword search mode.", "default": "luma" }, "city": { "title": "City", "type": "string", "description": "City to filter events by (search mode only)" }, "maxResults": { "title": "Max leads", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum number of organizer leads to extract. Default: 20.", "default": 20 } } }, "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 } } } } } } } } } } ```