# Github Email Scraper Fast Advanced And Cheapest (`scraperoka/github-email-scraper-fast-advanced-and-cheapest`) Actor 🚀 GitHub Email Scraper Fast Advanced & Cheapest extracts verified emails from GitHub profiles and repositories. ⚡ Fast, accurate, and cost‑effective for lead gen, outreach, and B2B sales research. 📈 Try it for smarter targeting! - **URL**: https://apify.com/scraperoka/github-email-scraper-fast-advanced-and-cheapest.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, 0 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 ### Github Email Scraper - Fast, Advanced and Cheapest 🚀 Manually hunting for contact emails inside GitHub repositories wastes hours you don’t have. **Github Email Scraper - Fast, Advanced and Cheapest** automatically extracts email addresses from publicly available GitHub data using your keywords and email-domain filters—ideal for marketers, recruiters, and growth teams. It’s a practical **Github Email Scraper** and **GitHub contacts email extractor** for building outreach lists at scale, with results you can collect in a single run. --- ### What You Get: Sample Output Here’s a sample record from a single run: ```json { "network": "Github.com", "keyword": "manager", "title": "No title", "description": "No data", "url": "https://github.com/example/repo", "email": "jane.doe@gmail.com" } ```` | Field | Type | What It Tells You | |---|---|---| | `network` | string | Confirms the source network as `Github.com` for consistent reporting | | `keyword` | string | The keyword that was used to drive the discovery for this record | | `title` | string | The result title captured during extraction (helpful for quick manual vetting) | | `description` | string | The surrounding text the scraper found when collecting the email (context for analysts) | | `url` | string | The link tied to the extracted email so you can verify source quickly | | `email` | string | The actual email address extracted from the GitHub-related publicly available content | | `error_message` | string | (If an error occurs) helps you understand why a record wasn’t captured as expected | | `status` | string | (If available) indicates success vs. failure at run/record level | Export your dataset as JSON, CSV, or Excel — straight from the Apify dashboard. *** ### Why Github Email Scraper - Fast, Advanced and Cheapest? There are a lot of ways to pull contact data from GitHub—here’s what sets Github Email Scraper - Fast, Advanced and Cheapest apart. #### Fast list-building with keyword + domain targeting This **GitHub email scraper** uses your `keywords` together with `customDomains` (like `@gmail.com`) to focus extraction on the email types you care about. You can broaden or narrow results depending on your outreach goals and deliver faster **email extraction from GitHub code**. #### Resilient scraping with progress that can be resumed The actor maintains progress (so it can resume instead of restarting from zero) while collecting unique emails. This makes it practical for longer runs and bulk email scraping from GitHub when you’re iterating on keywords and domains. #### Deduplicated, unique email collection Emails are tracked to avoid repeating the same address across results. If you’re building a clean prospect list, this **advanced GitHub email harvesting tool** helps keep your output focused on unique contacts. #### Cost control via a clear `maxEmails` limit You can cap the run using `maxEmails`, which stops collection once the limit is reached. This is especially useful when you need a **bulk email scraper GitHub** workflow but want predictable runtime and spend. *** ### Configuring Your Run Drop this into your `input.json` to get started: ```json { "keywords": ["manager", "founder"], "location": "San Francisco, CA", "customDomains": ["@gmail.com", "@yahoo.com"], "maxEmails": 50 } ``` | Parameter | Required | What It Does | |---|---|---| | `keywords` | ✅ | The list of keywords the actor uses to find relevant GitHub-related results | | `location` | ⬜ | Optional location text to filter results and help you focus on a region | | `customDomains` | ⬜ | Email domains (like `@gmail.com`) used to find matching email addresses | | `maxEmails` | ⬜ | Stops once it collects enough emails (higher values can take longer and may not always be fully reached) | *** ### Core Capabilities #### Email extraction from GitHub-related publicly available sources This **GitHub repository email finder** extracts email addresses from the publicly available content it encounters during the run. It’s designed specifically for “find emails in GitHub repositories” style workflows using your keyword targeting. #### Input flexibility for precise lead discovery You can provide multiple `keywords` and multiple `customDomains` in one run. That flexibility is what makes this **automated email scraping GitHub** approach work well for both general lead mining and more targeted **scrape business emails from GitHub** campaigns. #### Progress persistence for repeatable bulk runs The actor saves progress while running, including what emails have already been seen. If you pause and restart, you can pick up where you left off—useful for long keyword/domain combinations. #### Output completeness for downstream use Each collected record includes `keyword`, `url`, and the extracted `email`, plus extra context fields like `title` and `description`. That structure makes it easy to triage leads, filter by keyword/domain, and audit sources. #### Scale-friendly collection controls Use `maxEmails` to control how many emails you want to collect in a single run. This is a practical way to keep your **cheapest GitHub email scraper** workflow efficient while still enabling coverage. *** ### Who Gets the Most Out of This Here’s how different teams put Github Email Scraper - Fast, Advanced and Cheapest to work: **Growth teams & marketers** use it to build outreach lists by selecting high-intent `keywords` and narrowing to the `customDomains` that match their lead strategy. The result is faster discovery and less manual work than **advanced GitHub email harvesting tool** alternatives. **Recruiters & talent sourcers** run focused **GitHub contacts email extractor** jobs using role-like keywords and region text via `location`. They then export the dataset to quickly shortlist candidates for outreach. **B2B lead gen & sales development reps** use `customDomains` to target specific email providers and gather verified-looking contacts tied to a `url` for easy review. This supports **scrape business emails from GitHub** when you need more leads from code-adjacent sources. **Data analysts & researchers** value the dataset structure (`keyword`, `title`, `description`, `url`, `email`) because it’s built for filtering, auditing, and sampling. You can combine runs with different `keywords` to compare outcomes and improve **GitHub data mining email tool** results. **Automation developers** integrate the actor into pipelines using the Apify dataset output as a structured input for CRMs or internal systems, turning **bulk email scraper GitHub** collection into a repeatable process. *** ### Step-by-Step: How to Use It No coding needed. Here's how to run Github Email Scraper - Fast, Advanced and Cheapest from start to finish: 1. **Open the actor on Apify** — go to [console.apify.com](https://console.apify.com) and open the actor page for Github Email Scraper - Fast, Advanced and Cheapest. 2. **Enter your inputs** — set `keywords` (required), then optionally add `location`, `customDomains`, and `maxEmails`. 3. **Configure proxy settings** — enable proxy support via the run options/proxy configuration available in Apify for better reliability. 4. **Hit Run and watch the live log** — monitor progress and see emails being found and pushed to the dataset. 5. **View results in the dataset tab** — each extracted contact appears as a row in the dataset as it’s pushed. 6. **Export as JSON, CSV, or Excel** — download directly from Apify for sharing, importing, or analysis. The whole process takes under 5 minutes to set up. *** ### Integrations & Export Options Once your data is collected, Github Email Scraper - Fast, Advanced and Cheapest plugs directly into your existing workflow. You can export your dataset as **JSON**, **CSV**, or **Excel** from the Apify dataset tab. This makes it easy to import into spreadsheets, CRMs, or analyst tooling without custom parsing. For automation, you can connect your run to downstream systems using Apify’s API capabilities (for programmatic result retrieval), and standard Apify integration options such as scheduled runs and webhook-style triggers—so your GitHub email scraper pipeline can run continuously. *** ### Pricing & Free Trial Github Email Scraper - Fast, Advanced and Cheapest runs on the Apify platform, which offers a **free tier** — no credit card required to get started. You can use it for several test runs and then scale when your workflow needs more volume. For actual pricing details and how credits/CUs are calculated, check the pricing page on **apify.com** before running a large job. Start for free at [apify.com](https://apify.com) and scale when you’re ready. *** ### Reliability & Performance | What We Handle | How | |---|---| | Result control | Uses `maxEmails` to stop once enough emails are collected | | Deduplication | Tracks seen emails so you don’t repeatedly collect the same address | | Long runs | Saves progress so you can resume instead of restarting | | Run resilience | Includes handling for empty results and recovery behaviors to keep runs moving | | Dataset writing | Pushes extracted rows incrementally to the dataset | **Limitations:** Results depend on what contact info is publicly available in the sources encountered for your chosen `keywords` and `customDomains`. If there isn’t enough matching publicly available email content, you may collect fewer than your requested `maxEmails`. For enterprise-scale runs, contact us to discuss custom configurations. *** ### Frequently Asked Questions #### Is there a free plan or trial? Yes. Apify offers a free tier to get started without a credit card. You can run smaller tests first, then scale when you’re ready. #### Do I need to log in to GitHub to use this? No. This actor is designed to work with publicly available content only, so you don’t need login credentials to run it. #### How accurate is the data? The actor extracts email addresses that match your `customDomains` from publicly available sources it encounters during the run. Accuracy depends on what those sources actually contain. #### How many results can I get per run? You control this using `maxEmails`. Note that setting a higher limit can increase runtime, and there’s no guarantee the actor will reach the exact target number in every run. #### How often is the data updated / how fresh is it? Freshness depends on when you run the actor. Each run extracts emails from publicly available content at the time of execution. #### Is this legal? Does it comply with GDPR / CCPA? You should treat this as publicly sourced data extraction and ensure your usage complies with applicable privacy laws and platform terms. It’s your responsibility to follow GDPR, CCPA, and any relevant regulations. #### Can I export results to Google Sheets or Excel? Yes. You can export the Apify dataset as **JSON**, **CSV**, or **Excel**, which can then be imported into tools like spreadsheets. For direct workflow automation, you can also use Apify’s API and integration options. #### Can I run this on a schedule automatically? Yes. With Apify, you can schedule runs so your Github Email Scraper - Fast, Advanced and Cheapest jobs execute automatically according to your preferred cadence. #### Can I access this via API? Yes. You can trigger the actor via the Apify API and retrieve results programmatically from the dataset output. #### What happens if the actor hits an error? Errors during extraction/pushing are handled so the run can continue where possible, and extracted data is written incrementally to the dataset. Progress is also persisted, which helps reduce the impact of interruptions. *** ### Need Help or Have a Request? Got a question about Github Email Scraper - Fast, Advanced and Cheapest or want a new feature added? Reach out at . We welcome requests like batch CSV upload support and a webhook notification on completion. The actor is actively maintained based on user feedback. *** ### Disclaimer & Responsible Use *Github Email Scraper - Fast, Advanced and Cheapest is the fastest, most reliable way to extract GitHub email contacts at scale — start your free run today.* The actor collects **publicly available data**. It does not access private GitHub accounts, login-gated content, or password-protected pages. You are responsible for complying with GDPR, CCPA, platform Terms of Service, and any 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 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("scraperoka/github-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("scraperoka/github-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 scraperoka/github-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=scraperoka/github-email-scraper-fast-advanced-and-cheapest", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Github Email Scraper Fast Advanced And Cheapest", "description": "🚀 GitHub Email Scraper Fast Advanced & Cheapest extracts verified emails from GitHub profiles and repositories. ⚡ Fast, accurate, and cost‑effective for lead gen, outreach, and B2B sales research. 📈 Try it for smarter targeting!", "version": "1.0", "x-build-id": "LgauLZ0uYOsCTRHgQ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~github-email-scraper-fast-advanced-and-cheapest/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-github-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~github-email-scraper-fast-advanced-and-cheapest/runs": { "post": { "operationId": "runs-sync-scraperoka-github-email-scraper-fast-advanced-and-cheapest", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~github-email-scraper-fast-advanced-and-cheapest/run-sync": { "post": { "operationId": "run-sync-scraperoka-github-email-scraper-fast-advanced-and-cheapest", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 } } } } } } } } } } ```