# Linkedin B2b Email Scraper (`scraperoka/linkedin-b2b-email-scraper`) Actor 🔎 LinkedIn B2B Email Scraper extracts verified emails + company details from LinkedIn profiles and pages. 🚀 Boost lead gen, sales outreach & recruiting with accurate contact data—fast, scalable, and built for B2B workflows. 📈 - **URL**: https://apify.com/scraperoka/linkedin-b2b-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, 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 ### Linkedin B2b Email Scraper 🔍 **Linkedin B2b Email Scraper** helps you extract business email addresses from publicly available web data, so you can speed up B2B outreach and lead generation. If you’re looking for a *LinkedIn B2B email scraper*, an *automated LinkedIn email scraping tool*, or a *LinkedIn prospecting email scraper*, this actor is designed for exactly that—whether you’re a marketer, recruiter, or data enthusiast—saving you hours of manual work. --- ### Why choose Linkedin B2b Email Scraper? | Feature | Benefit | |---|---| | ✅ **Keyword-driven lead discovery** | Find relevant contacts using your **keywords** and a **country** focus | | ✅ **Scrape across multiple LinkedIn content types** | Use **Scrape From** to target **All, Profile, Company, Posts, or Pulse** | | ✅ **Email-type focus (B2B vs B2C)** | Extract emails aligned to your outreach goals using the **Email Type** choice | | ✅ **Reliability with built-in fallback logic** | Includes resilience mechanisms to improve results on large runs | | ✅ **Structured dataset output** | Produces a tabular dataset you can export and reuse in CRM or outreach workflows | | ✅ **Scales with limits you control** | Use **Max Emails** to manage scraping time and cost | --- ### Key features - 🔑 **Keyword-based targeting:** Enter one or more **keywords** to search for on LinkedIn. - 🌍 **Country focus:** Choose a **country** to guide what gets collected. - 🧩 **Flexible “Scrape From” coverage:** Select **All**, **Profile**, **Company**, **Posts**, or **Pulse** to match your use case (e.g., LinkedIn outreach email scraping, LinkedIn company email scraper). - 🛡️ **Resilient scraping engine options:** Pick between **Cost Effective (New)** and **Legacy** engines to fit your reliability vs. efficiency needs. - 🔄 **Resilience for large searches:** Works best when you set sensible **Max Emails** limits (large requests may take longer). - 📊 **Clean, structured email results:** Stores fields like **email**, **email_domain**, and the source metadata for easier analysis. - 💾 **Ready-to-export dataset:** Saves results to the **Linkedin Email Data** dataset as table-ready records. --- ### Input Provide input via an `input.json` file. Example structure: ```json { "keywords": ["developer", "founder"], "country": "United States", "scrapeFrom": "All", "engine": "legacy", "maxEmails": 20 } ```` #### Input Fields | Key | Required | Description | |---|---:|---| | `keywords` | ✅ | One or more keywords to search for on LinkedIn (for example: “fitness”, “marketing”). | | `country` | ✅ | The country to target for results. Choose from the provided country list (default is **United States**). | | `scrapeFrom` | ✅ | Where to scrape from: `All`, `Profile`, `Company`, `Posts`, or `Pulse`. Selecting `All` searches across every type. | | `engine` | ❌ | Choose the scraping engine: `cost-effective` (Cost Effective (New)) or `legacy` (Legacy). Default is `legacy`. | | `maxEmails` | ✅ | The maximum number of emails to collect (range: **1–10000**; default **20**). Higher values can take longer and aren’t guaranteed to yield more. | *** ### Output The actor saves the scraped results into the **Linkedin Email Data** dataset in JSON format with fields shown in the dataset views. Example output record: ```json [ { "keyword": "developer", "title": "Example Title", "url": "https://www.example.com/profile", "description": "Example description text", "email": "contact@example.com", "email_domain": "example.com", "scrape_from": "Profile", "country": "United States" } ] ``` #### Output Fields | Field | Required | Description | |---|---:|---| | `keyword` | ✅ | The keyword used for that result record. | | `title` | ✅ | The title associated with the scraped result. | | `url` | ✅ | The URL linked to the scraped profile or page (shown as **View Profile** in the dataset UI). | | `description` | ✅ | The description/snippet text associated with the result. | | `email` | ✅ | The extracted email address (if present for that result). | | `email_domain` | ✅ | The domain part of the extracted email (e.g., `example.com`). | | `scrape_from` | ✅ | Where the actor scraped from (matches your **Scrape From** selection like `Profile`, `Company`, `Posts`, `Pulse`, or `All`). | | `country` | ✅ | The country used for the run. | You can typically export this dataset from the Apify UI to formats like JSON/CSV for downstream processing in your LinkedIn B2B lead email finder or enrichment pipeline. *** ### How to use Linkedin B2b Email Scraper (via Apify Console) 1. **Open Apify Console**\ Log in at https://console.apify.com and open the Apify Actors page. 2. **Find the actor**\ Search for **Linkedin B2b Email Scraper** and open the actor details page. 3. **Configure your Input**\ In the **INPUT** panel, add: your `keywords`, choose a `country`, set `scrapeFrom`, and set `maxEmails`.\ Optionally choose `engine` between **Cost Effective (New)** and **Legacy**. 4. **Set the engine for your needs**\ If you prioritize efficiency, select **Cost Effective (New)** (`cost-effective`). If you prioritize the legacy approach, select **Legacy** (`legacy`). 5. **Run the actor**\ Click **Run**. For larger searches or higher `maxEmails`, runs may take longer. Plan for higher timeouts using Run Options when needed (default is **3600 s / 1 hr** per the actor description). 6. **Monitor progress and logs**\ Watch runtime logs for status updates and scraping progress. 7. **Open the dataset output**\ When the run completes, go to the **OUTPUT** section and open **Linkedin Email Data**. 8. **Export your leads**\ Use the dataset UI to export results and feed them into your CRM, outreach list, or data analysis workflow. No coding required—get accurate results in minutes with this Linkedin B2B email scraper. *** ### Advanced features & SEO optimization - 🚀 **Engine choice for different priorities:** `cost-effective` for faster/cheaper scraping behavior, or `legacy` for the traditional approach when you need it. - 🔄 **Designed for lead generation at scale:** Set `maxEmails` to control how much you collect while keeping run time predictable. - 🌐 **Content-type coverage:** With `scrapeFrom`, you can run LinkedIn email extractor workflows across profiles, companies, posts, and pulse pages. - 🧠 **Email-domain friendly output:** Each record includes `email` and `email_domain`, making it easier to segment campaigns and validate lead pools. *** ### Best use cases - 📈 **B2B lead generation:** Build targeted outreach lists by scraping emails associated with your niche keywords and country. - 🧑‍💼 **Sales development:** Use it as a LinkedIn sales email scraper to enrich prospect databases before outbound campaigns. - 🏢 **Company research:** Gather business emails for LinkedIn company email scraper use cases when you want to contact teams behind a brand. - ✉️ **Market research & segmentation:** Analyze `email_domain` patterns to compare lead sources by industry/region. - 🔍 **Data analysis pipelines:** Combine scraped email records with your internal datasets using `keyword`, `country`, and `scrape_from` for structured analysis. - 🤝 **Recruitment sourcing:** Find potential hiring and careers contacts when your keywords align with recruitment themes (LinkedIn B2B email harvesting from LinkedIn). *** ### Technical specifications - **Supported Input Formats** - ✅ Uses an Apify actor input schema with fields: `keywords`, `country`, `scrapeFrom`, `engine` (optional), `maxEmails` - **Proxy Support** - ✅ Built-in proxy support is available via the selected scraping engine approach - **Retry Mechanism** - ✅ Includes retries and fallbacks for resilience during scraping runs - **Dataset Structure** - ✅ Writes results to **Linkedin Email Data** dataset view fields: `keyword`, `title`, `url`, `description`, `email`, `email_domain`, `scrape_from`, `country` - **Rate Limits & Performance** - ⚠️ Larger searches and higher `maxEmails` may take longer (timeout guidance is available in Run Options) - **Limitations** - ❌ Results depend on what emails are publicly available in the sources the actor processes *** ### FAQ #### Does Linkedin B2b Email Scraper extract B2B emails only? ✅ The actor is designed around an **Email Type** concept described in the input documentation (B2B vs B2C). Your output records include `email` and `email_domain` fields for the collected results. #### What should I put in `keywords`? ✅ Enter one or more keywords to search for on LinkedIn (examples given include “fitness” and “marketing”). Use terms that match your target audience, product, or niche for best relevance. #### Can I control how many emails the actor collects? ✅ Yes. Use `maxEmails` to set an upper limit. The actor description notes that higher numbers help gather more but aren’t guaranteed, and large requests may take longer. #### What does `scrapeFrom` do? ✅ `scrapeFrom` controls where the actor looks for results. You can choose `All`, `Profile`, `Company`, `Posts`, or `Pulse`. #### Which engine should I choose: cost-effective or legacy? ✅ Choose **Cost Effective (New)** (`cost-effective`) if you want a more cost-efficient run. Choose **Legacy** (`legacy`) if you prefer the legacy engine option described as “more reliable but slower and more expensive” in the actor input description. #### Where are the results saved? ✅ Results are saved in the **Linkedin Email Data** dataset with fields such as `email`, `email_domain`, `scrape_from`, and `country`, which you can export from the Apify Console. #### Do I need any special login to run it? ✅ You run it like any other Apify actor from the Apify Console. The actor input is configured using the built-in form/JSON input. #### Is using scraped emails legal? ✅ You’re responsible for complying with applicable laws and regulations (for example GDPR/CCPA), spam rules, and platform terms. This actor works with **publicly available sources**. *** ### Support & feature requests If you want improvements to Linkedin B2b Email Scraper, tell us what would make it better for your LinkedIn B2B email extractor workflow. - 💡 **Feature Requests:** Examples include CSV export improvements, additional filtering options, or CRM-friendly output enhancements. - 📧 **Contact:** Reach out at Your feedback helps shape the roadmap for this Linkedin B2B email scraper. *** ### Closing CTA / Final thoughts *If you need an SEO-optimized Linkedin B2b Email Scraper for consistent lead prospecting, this actor’s structured output and keyword-based targeting make it a strong fit.*\ *Run it with the right `scrapeFrom` and `maxEmails` limits, then export your dataset for faster outreach.* *** ### Disclaimer **This actor accesses publicly accessible sources only.** It does not access private profiles, authenticated data, or password-protected content. You are responsible for ensuring your use complies with applicable laws and regulations (including GDPR/CCPA where relevant), spam regulations, and the platform’s terms of service. For data removal requests, contact . Please use this tool responsibly, ethically, and for legitimate purposes only. # Actor input Schema ## `keywords` (type: `array`): Enter one or more keywords to search for on Linkedin. ## `country` (type: `string`): Specify the country to target for Google search results. ## `scrapeFrom` (type: `string`): Choose one option — All, Profile, Company, Posts, or Pulse. ## `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. ## `maxEmails` (type: `integer`): Enter the maximum number of emails to collect. ## Actor input object example ```json { "keywords": [ "developer", "founder" ], "country": "United States", "scrapeFrom": "All", "engine": "legacy", "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": [ "developer", "founder" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/linkedin-b2b-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": [ "developer", "founder", ] } # Run the Actor and wait for it to finish run = client.actor("scraperoka/linkedin-b2b-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": [ "developer", "founder" ] }' | apify call scraperoka/linkedin-b2b-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/linkedin-b2b-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin B2b Email Scraper", "description": "🔎 LinkedIn B2B Email Scraper extracts verified emails + company details from LinkedIn profiles and pages. 🚀 Boost lead gen, sales outreach & recruiting with accurate contact data—fast, scalable, and built for B2B workflows. 📈", "version": "1.0", "x-build-id": "WAJebn9UzLJYeWhhW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~linkedin-b2b-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-linkedin-b2b-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~linkedin-b2b-email-scraper/runs": { "post": { "operationId": "runs-sync-scraperoka-linkedin-b2b-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~linkedin-b2b-email-scraper/run-sync": { "post": { "operationId": "run-sync-scraperoka-linkedin-b2b-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "country", "scrapeFrom", "maxEmails" ], "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "Enter one or more keywords to search for on Linkedin.", "items": { "type": "string" } }, "country": { "title": "Country", "enum": [ "United States", "United Kingdom", "Canada", "Australia", "Germany", "France", "India", "Japan", "Brazil", "Italy", "Spain", "Netherlands", "Sweden", "Norway", "Denmark", "Finland", "Poland", "Czech Republic", "Hungary", "Romania", "Bulgaria", "Croatia", "Slovenia", "Slovakia", "Estonia", "Latvia", "Lithuania", "Portugal", "Greece", "Cyprus", "Malta", "Luxembourg", "Ireland", "Belgium", "Austria", "Switzerland", "Liechtenstein", "Iceland", "South Korea", "China", "Mexico", "Argentina", "Chile", "Colombia", "Peru", "Venezuela", "Ecuador", "Uruguay", "Paraguay", "Bolivia", "Guyana", "Suriname", "French Guiana", "Falkland Islands (Malvinas)", "South Georgia and the South Sandwich Islands", "Bonaire, Sint Eustatius and Saba", "Curaçao", "Aruba", "Sint Maarten (Dutch part)", "Turks and Caicos Islands", "British Virgin Islands", "Anguilla", "Montserrat", "Antigua and Barbuda", "Barbados", "Dominica", "Grenada", "Saint Kitts and Nevis", "Saint Lucia", "Saint Vincent and the Grenadines", "Trinidad and Tobago", "Jamaica", "Bahamas", "Belize", "Costa Rica", "Guatemala", "Honduras", "Nicaragua", "Panama", "El Salvador", "Cuba", "Dominican Republic", "Haiti", "Puerto Rico", "U.S. Virgin Islands", "American Samoa", "Guam", "Northern Mariana Islands", "Saudi Arabia", "United Arab Emirates", "Bahrain", "Iraq", "Iran, Islamic Republic of", "Israel", "Jordan", "Kuwait", "Lebanon", "Oman", "Qatar", "Syrian Arab Republic", "Yemen", "Afghanistan", "Bangladesh", "Bhutan", "Maldives", "Nepal", "Pakistan", "Sri Lanka", "Myanmar", "Cambodia", "Lao People's Democratic Republic", "Thailand", "Viet Nam", "Malaysia", "Singapore", "Brunei Darussalam", "Philippines", "Indonesia", "Timor-Leste", "Papua New Guinea", "Fiji", "New Zealand", "Solomon Islands", "Vanuatu", "New Caledonia", "French Polynesia", "Wallis and Futuna", "Samoa", "Tonga", "Tuvalu", "Kiribati", "Nauru", "Federated States of Micronesia", "Marshall Islands", "Palau", "Cook Islands", "Niue", "Tokelau" ], "type": "string", "description": "Specify the country to target for Google search results.", "default": "United States" }, "scrapeFrom": { "title": "Scrape From", "enum": [ "All", "Profile", "Company", "Posts", "Pulse" ], "type": "string", "description": "Choose one option — All, Profile, Company, Posts, or Pulse.", "default": "All" }, "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" }, "maxEmails": { "title": "Max Emails", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Enter the maximum number of emails to collect.", "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 } } } } } } } } } } ```