# Luma Email Scraper (`scraperoka/luma-email-scraper`) Actor 📧 Luma Email Scraper extracts verified emails from Luma profiles and pages fast. ✅ Clean results, export-ready. 🚀 Perfect for B2B lead generation, sales outreach, and networking—save time and boost conversion rates. - **URL**: https://apify.com/scraperoka/luma-email-scraper.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ### Luma Email Scraper 🎯 Manually collecting email addresses from Luma profiles takes hours and quickly turns into spreadsheet chaos. **Luma Email Scraper** automatically scrapes emails from Luma bios using your keywords and email-domain filters, so you can build targeted lists fast. This **Luma lead scraper** is ideal for marketers, recruiters, and growth teams who want Luma contact data extraction at scale—often yielding thousands of records in minutes with the right `maxEmails` and focused keywords. --- ### What You Get: Sample Output Here's a sample record from a single run: ```json { "network": "Luma.com", "keyword": "dj event", "title": "DJ Event Organizer", "description": "Book for collaborations: bookings@gmail.com", "url": "https://luma.com/some-event-page", "email": "bookings@gmail.com" } ```` | Field | Type | What It Tells You | |---|---|---| | `network` | string | Confirms the source network used for the contact search (`Luma.com`) | | `keyword` | string | The keyword that led to the email finding (useful for reporting and campaign matching) | | `title` | string | The title text associated with the result where the email was found | | `description` | string | The surrounding text that contained the email address (helpful for context and auditing) | | `url` | string | The page URL tied to the found email (good for validation and traceability) | | `email` | string | The extracted email address you can add to outreach workflows | | `error_message` | string | Error details if something goes wrong (captured via run-level behavior rather than per-email fields) | | `status` | string | Run/request status for operational visibility in logs and run completion outcomes | Export your dataset as JSON, CSV, or Excel — straight from the Apify dashboard. *** ### Why Luma Email Scraper? There are a lot of ways to pull data from Luma—here's what sets **Luma Email Scraper** (a Luma lead scraper and email scraping tool) apart for real-world outreach and list building. #### Keyword + domain filtering for better targeting You control the search intent with `keywords` and restrict matches with `customDomains`, so you’re more likely to harvest emails from the domains you actually want (for example, business mailboxes like `@gmail.com`). #### Resilient scraping with built-in stopping logic The actor includes pagination controls and a `maxEmails` stop condition, so it can keep collecting until your cap is reached, while also preventing runaway runtimes. #### Clean, integration-ready output Every discovered contact is pushed as a structured record with consistent fields like `email`, `url`, and the originating `keyword`, making it easy to export, deduplicate downstream, and load into a CRM. #### Proxy support for reliable bulk harvesting You can configure proxy settings via `proxyConfiguration`, and choose an `engine` that changes the scraping approach for speed vs. reliability—useful when you run larger email list building tool jobs. *** ### Configuring Your Run Drop this into your `input.json` to get started: ```json { "keywords": ["dj event", "music venue"], "location": "Berlin", "platform": "Luma", "customDomains": ["@gmail.com"], "maxEmails": 50, "engine": "legacy", "proxyConfiguration": { "useApifyProxy": true } } ``` | Parameter | Required | What It Does | |---|---|---| | `keywords` | ✅ | A list of keywords the actor uses to find Luma bios/posts related to your topic | | `location` | ⬜ | Adds a location filter to narrow the results the actor considers | | `platform` | ⬜ | Selects the platform to scrape (currently only `Luma`) | | `customDomains` | ⬜ | Limits extracted emails to only the domains you specify (e.g. `@gmail.com`) | | `maxEmails` | ⬜ | Caps how many emails the actor will collect before stopping | | `engine` | ⬜ | Chooses the scraping approach: `cost-effective` or `legacy` | | `proxyConfiguration` | ⬜ | Configure proxies for this Actor run | | ↳ `proxy support` | ⬜ | When set, routes requests through Apify Proxy for better reliability | *** ### Core Capabilities #### Email discovery from Luma bios using your keywords **Luma Email Scraper** finds emails from Luma bios and posts that relate to the keywords you provide, making it useful as an extract emails from Luma workflow for lead sourcing. #### Domain-restricted harvesting for cleaner prospect lists By combining `keywords` with `customDomains`, the scraper focuses on the email patterns that match your targeting strategy—ideal for a prospection email scraper style workflow and cleaner email list building tool results. #### Pagination + `maxEmails` for predictable run outcomes The scraper paginates through results and stops when `maxEmails` is reached. Setting a realistic cap helps you control scraping time while still exploring enough pages to collect a useful volume. #### Real-time dataset writing during the run Each discovered email is pushed to the output dataset as soon as it’s found, so you don’t have to wait for the entire job to finish to start exporting or validating results. #### Engine and proxy controls for higher reliability at scale If you’re running Luma contact data extraction at higher volume, the `engine` option and `proxyConfiguration` help you adapt for speed and reliability across different runs. Luma Email Scraper is built for fast, structured email harvesting from publicly available Luma sources. *** ### Who Gets the Most Out of This **Sales Development Representatives**\ Use **Luma Email Scraper** to turn Luma lead discovery into an outreach-ready contact list. By tuning `keywords` to your niche and `customDomains` to your desired email types, you can reduce noise while collecting emails tied to relevant Luma postings. **Marketers and Growth Teams**\ Build segmented email audiences for campaigns by running Luma contact scraper jobs per theme (for example, event types or organizer niches). Export the results as JSON/CSV/Excel and feed them into your outreach and reporting stack. **Freelance Researchers & Data Analysts**\ If you need a reproducible dataset for a study or internal analysis, the actor’s structured output fields (`email`, `url`, `keyword`, `description`) make it easier to trace findings back to their source and compare batches by keyword. **Automation & Data Engineering Specialists**\ Develop repeatable pipelines by triggering the actor and then consuming the dataset output in downstream systems. This makes it a practical email scraping tool for scheduled runs and programmatic export. *** ### Step-by-Step: How to Use It No coding needed. Here's how to run Luma Email Scraper from start to finish: 1. **Open the actor on Apify** — go to [console.apify.com](https://console.apify.com) and find **Luma Email Scraper**. 2. **Enter your inputs** — set `keywords` (required), then optionally `location`, `customDomains`, and `maxEmails` based on your targeting and desired volume. 3. **Configure proxy settings** — set `proxyConfiguration` and choose an `engine` (`cost-effective` or `legacy`) depending on whether you prioritize speed or reliability. 4. **Hit Run and watch the live log** — monitor progress and ensure emails are being pushed to the dataset. 5. **View results in the dataset tab** — each record includes fields like `email` and the source `url`. 6. **Export as JSON, CSV, or Excel** — download directly from Apify when you’re satisfied with the collected set. The whole process takes under 5 minutes to set up. *** ### Integrations & Export Options Once your data is collected, **Luma Email Scraper** plugs directly into your existing workflow. You can export your results in common formats from the Apify dataset tab (JSON, CSV, Excel), which is perfect for building lists, enrichment steps, and dashboards. For automation and scale, you can use Apify’s platform integrations and API access to move results into your tools. For deeper details, refer to Apify’s official documentation at https://apify.com/docs/api. *** ### Pricing & Free Trial **Luma Email Scraper** runs on the Apify platform, which offers a **free tier** — no credit card required to get started. You’ll typically use Apify platform resources on a pay-as-you-go basis (billed per Actor compute unit), while the free tier covers initial testing for several runs. For teams running frequent or large extraction tasks, check Apify’s subscription plans on the pricing page and scale when you need more throughput. Start for free at [apify.com](https://apify.com) and scale when you're ready. *** ### Reliability & Performance | What We Handle | How | |---|---| | Targeted filtering | Uses your `keywords` and `customDomains` to focus email harvesting on relevant bios | | Run caps | Stops when `maxEmails` is reached to keep runs predictable | | Retry and fallback behavior | Includes resilience logic to handle pages that return no results or face blocks | | Data freshness during runs | Pushes records as they’re found so you get usable partial output quickly | | Network reliability | Supports proxy configuration and engine choice to improve bulk scraping stability | **Limitations:** This actor works only with publicly available content from Luma. If emails are not present in bios/posts that match your filters, you may see fewer results than expected. For enterprise-scale runs, contact us to discuss custom configurations. *** ### Frequently Asked Questions #### Is there a free plan or trial? Yes. Apify provides a free tier so you can run Luma Email Scraper and validate your results before scaling up. #### Do I need to log in to Luma to use this? No. This actor scrapes emails from publicly available Luma content using your provided keywords and email-domain filters. #### How accurate is the data? Accuracy depends on what the Luma bio or post actually contains. The actor extracts email addresses that match your `customDomains`, and includes the source `url` and surrounding `description` for traceability. #### How many results can I get per run? You can control the maximum collected emails with `maxEmails`. Keep in mind that setting a higher limit allows for more potential results but doesn’t guarantee reaching that number. #### How often is the data updated / how fresh is it? Data freshness depends on when you run the actor. Each run collects the current publicly available information that matches your inputs at the time of execution. #### Is this legal? Does it comply with GDPR / CCPA? This actor is designed to work with **publicly available data**. It’s still your responsibility to comply with GDPR, CCPA, and relevant platform terms, as well as applicable laws regarding contacting individuals. #### Can I export results to Google Sheets or Excel? Yes. You can export from the Apify dataset tab in JSON, CSV, or Excel format, and you can connect exports into tools using Apify’s integrations. #### Can I run this on a schedule automatically? Yes. You can set up scheduled runs using Apify scheduling features so Luma email scraper runs automatically based on your workflow needs. #### Can I access this via API? Yes. You can access results programmatically via the Apify API—useful for building automated email list building tool pipelines. #### What happens if the actor hits an error? If requests fail or results are blocked, the actor includes resilience logic and can skip or stop based on pagination conditions and the `maxEmails` cap. You can monitor behavior via the actor logs and the run’s dataset outputs. *** ### Need Help or Have a Request? Got a question about Luma Email Scraper or want a new feature added? Reach out at . We’re happy to hear improvement ideas like webhook notifications on completion or additional export-friendly fields. We actively maintain this actor based on user feedback. *** ### Disclaimer & Responsible Use *Luma Email Scraper is the fastest, most reliable way to build targeted email lists from Luma — start your free run today.* **publicly available data** is used, and the actor does not access private accounts, login-gated content, or password-protected pages. It’s your responsibility to follow GDPR, CCPA, platform ToS, and applicable local regulations. For data removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `keywords` (type: `array`): A list of keywords to search for. ## `location` (type: `string`): Location to filter search results. ## `platform` (type: `string`): Select platform. ## `customDomains` (type: `array`): List of custom email domains ## `maxEmails` (type: `integer`): Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time. ## `engine` (type: `string`): Choose scraping engine. 🚀 Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. 🔧 Legacy: Uses GOOGLE\_SERP proxy with traditional selectors - more reliable but slower and more expensive. ## `proxyConfiguration` (type: `object`): Configure proxies for this Actor. ## Actor input object example ```json { "keywords": [ "dj event" ], "location": "", "platform": "Luma", "customDomains": [ "@gmail.com" ], "maxEmails": 20, "engine": "legacy" } ``` # 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": [ "dj event" ], "location": "", "customDomains": [ "@gmail.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/luma-email-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 = { "keywords": ["dj event"], "location": "", "customDomains": ["@gmail.com"], } # Run the Actor and wait for it to finish run = client.actor("scraperoka/luma-email-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 '{ "keywords": [ "dj event" ], "location": "", "customDomains": [ "@gmail.com" ] }' | apify call scraperoka/luma-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/luma-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Luma Email Scraper", "description": "📧 Luma Email Scraper extracts verified emails from Luma profiles and pages fast. ✅ Clean results, export-ready. 🚀 Perfect for B2B lead generation, sales outreach, and networking—save time and boost conversion rates.", "version": "0.0", "x-build-id": "QlKQreaFMMuFvGkuM" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~luma-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-luma-email-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~luma-email-scraper/runs": { "post": { "operationId": "runs-sync-scraperoka-luma-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~luma-email-scraper/run-sync": { "post": { "operationId": "run-sync-scraperoka-luma-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "A list of keywords to search for.", "default": [ "dj event" ], "items": { "type": "string" } }, "location": { "title": "Location", "type": "string", "description": "Location to filter search results.", "default": "" }, "platform": { "title": "Platform", "enum": [ "Luma" ], "type": "string", "description": "Select platform.", "default": "Luma" }, "customDomains": { "title": "Custom Email Domains", "type": "array", "description": "List of custom email domains", "default": [ "@gmail.com" ], "items": { "type": "string" } }, "maxEmails": { "title": "Max Emails", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time.", "default": 20 }, "engine": { "title": "Engine", "enum": [ "cost-effective", "legacy" ], "type": "string", "description": "Choose scraping engine. 🚀 Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. 🔧 Legacy: Uses GOOGLE_SERP proxy with traditional selectors - more reliable but slower and more expensive.", "default": "legacy" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Configure proxies for this Actor." } } }, "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 } } } } } } } } } } ```