# Buy Me A Coffee Email Scraper Fast Advanced And Cheapest (`solid-scraper/buy-me-a-coffee-email-scraper-fast-advanced-and-cheapest`) Actor 🚀 Find verified Buy Me A Coffee emails fast! ⚡ Advanced email scraper with targeted results, smarter filtering, and affordable pricing—perfect for outreach, sales, and agencies. 💼 Get leads quicker today! ✅ - **URL**: https://apify.com/solid-scraper/buy-me-a-coffee-email-scraper-fast-advanced-and-cheapest.md - **Developed by:** [SolidScraper](https://apify.com/solid-scraper) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 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 ### Buy Me A Coffee Email Scraper - Fast, Advanced and Cheapest 📬 **Buy Me A Coffee Email Scraper - Fast, Advanced and Cheapest** is an Apify actor that extracts email addresses from Buy Me A Coffee based on the keywords and email-domain filters you choose. If you’re searching for a **buy me a coffee email scraper**, a **buy me a coffee contact email scraper**, or a **buymeacoffee email extractor**, this tool helps you turn publicly available creator and lead information into usable outreach data—without manual copy-paste work. Whether you’re a marketer, data analyst, researcher, or growth specialist, you can use targeted keywords and domains to build a **buy me a coffee subscribers email list** faster at scale, saving you hours of manual work. --- ### What is Buy Me A Coffee Email Scraper - Fast, Advanced and Cheapest? **Buy Me A Coffee Email Scraper - Fast, Advanced and Cheapest** finds and extracts email addresses related to Buy Me A Coffee using the keywords you provide and filters them by the email domains you specify (for example, `@gmail.com`). It’s designed for users who want a **fast buy me a coffee email scraper** that still supports more advanced targeting through **keywords** and **custom email domains**—so you can build a **buy me a coffee email database** for outreach, research, or list building. --- ### Why choose Buy Me A Coffee Email Scraper - Fast, Advanced and Cheapest? | Feature | Benefit | | --- | --- | | ✅ **All-in-one email harvesting** | Extracts email addresses tied to your keywords and domain filters in one run | | ✅ **Targeting with keywords & custom domains** | Improves relevance by focusing on the email types you want (e.g. `@gmail.com`) | | ✅ **Resume support (progress persistence)** | Continues from where it left off if the run is interrupted | | ✅ **Resilient run behavior** | Includes stopping conditions to avoid wasteful runs and reduce unnecessary scraping time | | ✅ **Structured dataset output** | Saves results to a dedicated dataset with consistent fields you can analyze immediately | | ✅ **Scale-friendly control via maxEmails** | Helps you balance coverage vs. run cost by stopping once your email cap is reached | --- ### Key features - 🔎 **Keyword-based email discovery**: Use your own “keywords or queries” to guide what gets searched for - 🎯 **Custom email-domain filtering**: Add domains like `@gmail.com` and `@yahoo.com` to focus results on the addresses you care about - 🧭 **Checkpointing and resume**: Stores progress so you can pick up later without starting from scratch - ⏱️ **Stop-on-limit collection**: Enforces `maxEmails` to stop once enough unique emails are collected - 📌 **Deduplication of emails**: Avoids pushing the same email address multiple times during a run - 💾 **Incremental dataset saving**: Pushes each found lead immediately to the dataset - 🌐 **Structured lead records**: Outputs `keyword`, `title`, `description`, `url`, and `email` for each lead --- ### Input Provide input via an `input.json` file. Example structure: ```json { "keywords": ["manager", "founder"], "location": "London, UK", "customDomains": ["@gmail.com", "@yahoo.com"], "maxEmails": 20 } ```` #### Input Fields | Field | Required | Description | | --- | --- | --- | | `keywords` | Yes | A list of keywords or queries to search for. The actor uses each keyword to collect relevant Buy Me A Coffee leads and email contacts. | | `location` | No | Location to filter search results. Leave it empty (`""`) if you don’t want location-based narrowing. | | `customDomains` | No | List of custom email domains to filter extracted email addresses (for example: `@gmail.com`, `@yahoo.com`). Best results usually come from using domains that match your outreach strategy. | | `maxEmails` | No | Maximum number of emails to collect. The scraper stops once this limit is reached. Higher values may increase runtime and don’t guarantee that the exact number will be collected. | *** ### Output The actor pushes scraped leads into an Apify dataset in JSON format. Each pushed row includes the lead context and the extracted email. #### Sample Output Record ```json { "network": "Buy me a coffee.com", "keyword": "manager", "title": "No title", "description": "No data", "url": "No URL", "email": "example@gmail.com" } ``` #### Output Fields | Field | Type | Description | | --- | --- | --- | | `network` | string | The dataset “network” label used for these results (`Buy me a coffee.com`). | | `keyword` | string | The keyword from your input that was used for this lead. | | `title` | string | The title text extracted for the item that led to the email. | | `description` | string | The extracted description text where the email was found. | | `url` | string | The URL associated with the search result item. | | `email` | string | The extracted email address. | > The dataset view is titled **“Scraped Leads”** and is structured for table display with fields: `keyword`, `title`, `description`, `url`, `email`. *** ### How to use Buy Me A Coffee Email Scraper - Fast, Advanced and Cheapest (via Apify Console) 1. **Open Apify Console**\ Log in at [console.apify.com](https://console.apify.com) and open the **Actors** tab. 2. **Find the actor**\ Search for **Buy Me A Coffee Email Scraper - Fast, Advanced and Cheapest** in the Apify catalog. 3. **Add your input**\ In the **INPUT** section, fill in: - `keywords` (required) - optional: `location`, `customDomains`, `maxEmails` 4. **Set practical limits (recommended)**\ If you’re testing or building smaller lists, set `maxEmails` to something like `20`. For larger harvesting, increase it (up to `10000`). 5. **Start the run**\ Click **Run**. While it runs, you’ll see logs about progress and when it stops due to limits or consecutive low-yield pages. 6. **Monitor results in real time**\ As soon as emails are found, records are pushed incrementally to the dataset. 7. **Open the dataset output**\ After the actor completes, go to the **OUTPUT** tab and open the dataset (title: **Buy Me A Coffee Emails Dataset**, dataset view: **Scraped Leads**). 8. **Export your data**\ Export from Apify in your preferred format (e.g., JSON/CSV via the platform UI), then use it in your CRM, outreach workflows, or analysis pipeline. No coding required — get accurate results in minutes with this **buy me a coffee email scraper**. *** ### Advanced features & SEO optimization - 🧩 **Engineered-for-purpose keyword targeting**: Works as a **buy me a coffee email scraping software** built for “creator email” and outreach-lead style use cases, using your provided keywords and `customDomains`. - 🧠 **Input normalization behavior**: The actor supports `keywords` as a list (and if passed as a single string, it will treat it as a list internally). - 🔁 **Resume-friendly progress**: Keeps track of already seen emails and cursor state, so you can continue runs without losing what you already collected. - 🧾 **Deduplicated email collection**: Ensures each extracted email is only pushed once into your results set during the run. - 📊 **Result-volume control**: The `maxEmails` cap helps manage runtime and cost—useful when you’re trying to build a **buy me a coffee email database** efficiently. *** ### Best use cases - 🚀 **Marketers building outreach lists**: Quickly gather contact emails to start campaigns for Buy Me A Coffee creators and related leads, improving speed vs. manual searches. - 🧑‍💼 **Founder and team research**: Use role-focused keywords (for example, “manager” or “founder”) to find relevant contacts and assemble a **buy me a coffee contact email scraper** style dataset. - 🧪 **Lead validation & enrichment workflows**: Extract emails from publicly available sources and merge them into your CRM pipeline for faster data enrichment. - 📈 **Growth teams testing segments**: Run multiple batches with different `customDomains` to compare which email types produce the best outreach response rates. - 🕵️ **Researchers studying creator audiences**: Build a **buy me a coffee subscribers email list** for studies that require contact-data grounding (where legally appropriate). - 💻 **Developer and automation pipelines**: Feed dataset exports into internal systems to power automated outreach, deduplication, and reporting. - 🧾 **Analysts measuring list coverage**: Use the structured dataset fields (`keyword`, `url`, `email`, etc.) to analyze which keywords produce the most unique emails. *** ### Technical specifications - **Supported Input Formats** - ✅ `keywords` (array) - ✅ `location` (string) - ✅ `customDomains` (array of email domains like `@gmail.com`) - ✅ `maxEmails` (integer, between `1` and `10000`) - **Proxy Support** - ✅ Built-in proxy support for reliable scraping - ✅ Includes run resilience behavior (stopping conditions and continuation logic) - **Retry Mechanism** - ✅ Uses internal resilience logic to handle empty/low-yield pages and improve continuity - (Exact retry counts and timings are handled internally by the actor.) - **Dataset Structure** - ✅ Saves results as dataset rows with: `network`, `keyword`, `title`, `description`, `url`, `email` - **Rate Limits & Performance** - ⚙️ Runtime depends on the size of the keyword/domain search space and the `maxEmails` cap - 🧾 Bigger searches can take longer, and the actor can stop early if it detects repeated low-yield pages - **Limitations** - ❌ Results depend on publicly available information and whether email addresses are present - ❌ Does not guarantee it will reach the exact requested `maxEmails` value (the run stops based on collected progress and stop conditions) *** ### FAQ #### Is this a buy me a coffee email scraper that only extracts emails? ✅ Yes. **Buy Me A Coffee Email Scraper - Fast, Advanced and Cheapest** extracts email addresses from Buy Me A Coffee based on your provided `keywords` and `customDomains`, and it stores email contacts along with context fields like `keyword`, `title`, `description`, and `url`. #### What input do I need to start scraping buy me a coffee emails? You must provide `keywords`. Optional inputs include `location`, `customDomains`, and `maxEmails`. If you don’t set `location`, the actor runs without location filtering. If you don’t set `customDomains`, it uses the default domains defined in the actor input schema. #### Can I control how many emails the scraper collects? ✅ Yes. Use `maxEmails` to set the maximum number of emails to collect. The actor stops once this limit is reached, and higher values may increase runtime. #### What does the dataset output look like? The actor pushes records to the dataset in JSON rows. Each row includes `network`, `keyword`, `title`, `description`, `url`, and `email`, designed to be easy to review and export for later workflows like CRM import or list building. #### Does the actor resume if my run is interrupted? ✅ Yes. It persists progress (including already seen emails and a cursor) so you can continue without re-collecting the same emails during later runs. #### Do I need an API key to use this actor? No. You can run it directly in Apify Console by setting the input fields. If you use the Apify API, you still configure the same actor inputs (`keywords`, `location`, `customDomains`, `maxEmails`) as described above. #### Is it legal to use this tool for lead generation? The actor collects information from **publicly available sources**. Compliance with laws and platform terms (including GDPR/CCPA and spam regulations) is your responsibility. #### How can I request data removal? If you need data removal, contact . *** ### Support & feature requests Want to improve this **buy me a coffee email scraper** experience? Share ideas with us! - 💡 **Feature Requests**: Tell us what you want next—examples include better filtering options, extra output fields, or enhancements that make it easier to build a **buy me a coffee email database** for outreach. - 📧 **Contact**: Reach out at . Your feedback helps shape the roadmap for this **Buy Me A Coffee Email Scraper - Fast, Advanced and Cheapest** actor. *** *If you’re looking for the most comprehensive and SEO-optimized buy me a coffee email scraping workflow, this actor is built to get you to results fast.* # Actor input Schema ## `keywords` (type: `array`): A list of keywords or queries to search for. ## `location` (type: `string`): Location to filter search results. ## `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. ## Actor input object example ```json { "keywords": [ "manager", "founder" ], "location": "", "customDomains": [ "@gmail.com", "@yahoo.com" ], "maxEmails": 20 } ``` # 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": [ "manager", "founder" ], "location": "", "customDomains": [ "@gmail.com", "@yahoo.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("solid-scraper/buy-me-a-coffee-email-scraper-fast-advanced-and-cheapest").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": [ "manager", "founder", ], "location": "", "customDomains": [ "@gmail.com", "@yahoo.com", ], } # Run the Actor and wait for it to finish run = client.actor("solid-scraper/buy-me-a-coffee-email-scraper-fast-advanced-and-cheapest").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": [ "manager", "founder" ], "location": "", "customDomains": [ "@gmail.com", "@yahoo.com" ] }' | apify call solid-scraper/buy-me-a-coffee-email-scraper-fast-advanced-and-cheapest --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=solid-scraper/buy-me-a-coffee-email-scraper-fast-advanced-and-cheapest", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Buy Me A Coffee Email Scraper Fast Advanced And Cheapest", "description": "🚀 Find verified Buy Me A Coffee emails fast! ⚡ Advanced email scraper with targeted results, smarter filtering, and affordable pricing—perfect for outreach, sales, and agencies. 💼 Get leads quicker today! ✅", "version": "1.0", "x-build-id": "767ZdgDaCWSGNtAW7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/solid-scraper~buy-me-a-coffee-email-scraper-fast-advanced-and-cheapest/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-solid-scraper-buy-me-a-coffee-email-scraper-fast-advanced-and-cheapest", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/solid-scraper~buy-me-a-coffee-email-scraper-fast-advanced-and-cheapest/runs": { "post": { "operationId": "runs-sync-solid-scraper-buy-me-a-coffee-email-scraper-fast-advanced-and-cheapest", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/solid-scraper~buy-me-a-coffee-email-scraper-fast-advanced-and-cheapest/run-sync": { "post": { "operationId": "run-sync-solid-scraper-buy-me-a-coffee-email-scraper-fast-advanced-and-cheapest", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 or Queries", "type": "array", "description": "A list of keywords or queries to search for.", "default": [ "manager", "founder" ], "items": { "type": "string" } }, "location": { "title": "Location", "type": "string", "description": "Location to filter search results.", "default": "" }, "customDomains": { "title": "Enter Custom Email Domains (e.g. @gmail.com, @yahoo.com)", "type": "array", "description": "List of custom email domains", "default": [ "@gmail.com", "@yahoo.com" ], "items": { "type": "string" } }, "maxEmails": { "title": "Enter 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 } } }, "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 } } } } } } } } } } ```