# Linkedin Lead Extractor (`allanjblythe/linkedin-lead-extractor`) Actor Extract qualified B2B leads, decision makers, and company intelligence from LinkedIn. Generate targeted prospect lists with contact information for sales teams, recruiters, and marketing agencies. Save 20+ hours weekly on manual prospecting. - **URL**: https://apify.com/allanjblythe/linkedin-lead-extractor.md - **Developed by:** [Allen Blythe](https://apify.com/allanjblythe) (community) - **Categories:** Lead generation, Automation, Open source - **Stats:** 176 total users, 9 monthly users, 100.0% runs succeeded, 3 bookmarks - **User rating**: 1.00 out of 5 stars ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 Lead Extractor - B2B Revenue Generator ### 🚀 Advanced B2B Lead Generation Extract qualified leads, decision makers, and company intelligence from LinkedIn. Perfect for sales teams, recruiters, and marketing agencies. ### 💡 Business Value - **Generate Qualified B2B Leads** automatically - **Identify Decision Makers** with contact information - **Save 20+ Hours Weekly** on manual prospecting - **Build Sales Pipelines** worth $10,000+ monthly - **Scale Recruitment Efforts** with targeted candidate sourcing ### 🛠️ Technical Implementation - Advanced LinkedIn data extraction - Decision maker identification - Email address discovery - Company intelligence gathering - Sales pipeline analytics ### 📥 Input Configuration ```json { "searchQueries": [ "CEO technology companies San Francisco", "Marketing Director e-commerce" ], "maxResults": 100, "extractEmails": true } # Actor input Schema ## `searchType` (type: `string`): Type of LinkedIn data to extract ## `searchQueries` (type: `array`): LinkedIn search queries (e.g., 'CEO tech companies San Francisco') ## `filters` (type: `object`): Filter results by location, industry, company size, etc. ## `maxResults` (type: `integer`): Maximum number of leads to extract ## `extractEmails` (type: `boolean`): Attempt to find professional email addresses ## `exportFormat` (type: `string`): Format for downloaded results ## Actor input object example ```json { "searchType": "people", "searchQueries": [ "CEO technology companies San Francisco", "Marketing Director e-commerce", "CTO startup London" ], "filters": { "locations": [ "San Francisco", "New York", "London" ], "industries": [ "Technology", "E-commerce" ], "companySizes": [ "51-200", "201-500", "501-1000" ], "experienceLevels": [ "Director", "VP", "C-Level" ] }, "maxResults": 100, "extractEmails": true, "exportFormat": "csv" } ```` # 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 = { "searchQueries": [ "CEO technology companies San Francisco", "Marketing Director e-commerce", "CTO startup London" ] }; // Run the Actor and wait for it to finish const run = await client.actor("allanjblythe/linkedin-lead-extractor").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 = { "searchQueries": [ "CEO technology companies San Francisco", "Marketing Director e-commerce", "CTO startup London", ] } # Run the Actor and wait for it to finish run = client.actor("allanjblythe/linkedin-lead-extractor").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 '{ "searchQueries": [ "CEO technology companies San Francisco", "Marketing Director e-commerce", "CTO startup London" ] }' | apify call allanjblythe/linkedin-lead-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=allanjblythe/linkedin-lead-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin Lead Extractor", "description": "Extract qualified B2B leads, decision makers, and company intelligence from LinkedIn. Generate targeted prospect lists with contact information for sales teams, recruiters, and marketing agencies. Save 20+ hours weekly on manual prospecting.", "version": "0.0", "x-build-id": "gpISdXgAKBAaYZzRP" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/allanjblythe~linkedin-lead-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-allanjblythe-linkedin-lead-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/allanjblythe~linkedin-lead-extractor/runs": { "post": { "operationId": "runs-sync-allanjblythe-linkedin-lead-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/allanjblythe~linkedin-lead-extractor/run-sync": { "post": { "operationId": "run-sync-allanjblythe-linkedin-lead-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searchQueries" ], "properties": { "searchType": { "title": "Search Type", "enum": [ "people", "companies", "jobs" ], "type": "string", "description": "Type of LinkedIn data to extract", "default": "people" }, "searchQueries": { "title": "Search Queries", "type": "array", "description": "LinkedIn search queries (e.g., 'CEO tech companies San Francisco')", "items": { "type": "string" } }, "filters": { "title": "Advanced Filters", "type": "object", "description": "Filter results by location, industry, company size, etc.", "default": { "locations": [ "San Francisco", "New York", "London" ], "industries": [ "Technology", "E-commerce" ], "companySizes": [ "51-200", "201-500", "501-1000" ], "experienceLevels": [ "Director", "VP", "C-Level" ] } }, "maxResults": { "title": "Maximum Results", "minimum": 10, "maximum": 1000, "type": "integer", "description": "Maximum number of leads to extract", "default": 100 }, "extractEmails": { "title": "Extract Email Addresses", "type": "boolean", "description": "Attempt to find professional email addresses", "default": true }, "exportFormat": { "title": "Export Format", "enum": [ "csv", "json", "excel" ], "type": "string", "description": "Format for downloaded results", "default": "csv" } } }, "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 } } } } } } } } } } ```