# LinkedIn People Profile Search Cookie-less 🍪 ✅ (`datamagnet/linkedin-people-profile-search-cookie-less`) Actor Search for people profiles by keyword and optional filters like company, title, school, and location. Get a clean list of matching profiles with key public details in one place. - **URL**: https://apify.com/datamagnet/linkedin-people-profile-search-cookie-less.md - **Developed by:** [Datamagnet](https://apify.com/datamagnet) (community) - **Categories:** Lead generation, Jobs, Automation - **Stats:** 25 total users, 14 monthly users, 95.4% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## People Profile Search scraper Find LinkedIn people profiles by keyword and optional filters with this People Profile Search scraper. It helps recruiters, sales teams, founders, and researchers quickly discover relevant LinkedIn people data without manual searching. Use it to collect people profiles by company, title, school, and location, then review the results in a clean, easy-to-read format. It is built for fast lead generation, talent discovery, and market research when you need a focused list of matching profiles. ### Key Features - Extract people profiles from LinkedIn-style search results based on the keywords you provide. - Collect matching profiles by company, first name, last name, school, title, and location. - Return a simple list of people profiles with the most useful public details in one place. - Handle result loading automatically so you only choose how many profiles you want. - Save time by reducing manual searching and copy-pasting from profile pages. - Support targeted prospecting for recruiting, sales outreach, and research workflows. - Provide clear progress updates and friendly error messages for a smoother run. ### Use Cases #### Lead Generation Sales teams use this actor to find people profiles that match a target role, company, or location. For example, you can search by job title keyword like “CEO” or “Head of Marketing” and then review each person’s name, headline, company, and location to build a focused outreach list. #### Recruiting and Talent Sourcing Recruiters can search for candidates by skill-related keywords, current company, school, or location. The returned profile details help them quickly identify whether someone looks like a fit before spending time on manual profile review. #### Market Research Analysts and founders can use the actor to understand who is active in a specific industry, region, or professional niche. By reviewing names, titles, companies, schools, and locations, they can map talent clusters and identify where expertise is concentrated. #### Partnership and Business Development Business development teams can search for decision-makers at specific companies or people with relevant titles. The profile list makes it easier to spot potential partners, sponsors, or strategic contacts and prioritize who to contact first. #### Alumni and Community Outreach Universities, bootcamps, and community managers can search by school name keyword or school ID to find alumni and members. This helps them build outreach lists for events, mentorship programs, fundraising, and community engagement. ### Input | Field | Type | Required | Description | Example | |---|---|---:|---|---| | geo | string | No | Filter results by one or more location IDs to focus on a specific area. | 103644278,101165590 | | start | string | No | Set where the search should begin when loading more results. | 0 | | company | string | No | Show people associated with a specific company. | Microsoft | | keywords | string | Yes | Main search words used to find matching people profiles. | golang | | lastName | string | No | Narrow results to people with a specific last name. | Nadella | | schoolId | string | No | Filter results to people linked to a specific school. | 12345 | | firstName | string | No | Narrow results to people with a specific first name. | Satya | | max_results | integer | No | Choose the maximum number of profiles you want returned. | 10 | | keywordTitle | string | No | Search for people by job title or role. | CEO | | keywordSchool | string | No | Search for people by school name or school-related keyword. | Harvard | ### Output | Field | Type | Description | |---|---|---| | searchSummary | string | A short summary of what was searched and how the results were filtered. | | totalResultsReturned | integer | The number of people profiles returned in the final result set. | | results | array | A list of matching people profiles. Each item contains the public details found for that person. | | results[].name | string | The person’s name as shown in the search results. | | results[].headline | string | A short professional summary or title that helps identify the person. | | results[].company | string | The company the person is associated with, if available. | | results[].location | string | The person’s location, if available. | | results[].school | string | The school or education detail shown in the profile, if available. | | results[].profileUrl | string | A link or reference that helps identify the profile. | | results[].additionalDetails | object | Any other public profile details returned with the search result. | ### Sample Output ```json { "searchSummary": "Search for golang people with company and title filters applied.", "totalResultsReturned": 1, "results": [ { "name": "Satya Nadella", "headline": "Chairman and CEO at Microsoft", "company": "Microsoft", "location": "Seattle, Washington, United States", "school": "University of Wisconsin-Milwaukee", "profileUrl": "https://www.linkedin.com/in/satya-nadella/", "additionalDetails": { "industry": "Technology", "connections": "Public profile details available" } } ] } ```` ### How It Works 1. You enter a search keyword and, if needed, add filters like company, title, school, name, or location. 2. The actor searches for matching people profiles and loads more results automatically until it reaches your chosen limit. 3. It gathers the most useful public profile details into a clean list. 4. You review the results and use them for outreach, recruiting, research, or analysis. 5. If you want more or fewer results, you simply adjust the input and run it again. ### Getting Started Getting started is simple and does not require coding knowledge. Click **Try for free**, fill in your search keyword and any optional filters, then click **Run**. The actor will return a clean list of matching people profiles that you can review right away. Your results are available in **JSON, CSV, or Excel**, making it easy to share with your team or import into your workflow. ### Frequently Asked Questions #### Do I need technical skills? No. This actor is designed for business users, recruiters, marketers, and researchers who want results without dealing with complicated setup. Just enter your search terms, choose how many results you want, and run it. #### How fast does it run? Most searches finish quickly, especially when you use focused keywords and filters. Larger result requests may take a bit longer because the actor keeps loading more matches until it reaches your selected limit. #### What format is the output? You can export the results in JSON, CSV, or Excel. That makes it easy to review the data yourself, share it with teammates, or move it into spreadsheets and CRM tools. #### Is this legal to use? You should always use the results in a way that respects applicable laws, platform rules, and your organization’s policies. This actor is intended to help you organize publicly available profile information for legitimate business use. #### Can I schedule it to run automatically? Yes. You can run it whenever you need fresh results, and it can also be used as part of a recurring workflow if you want regular updates. This is useful for ongoing lead generation, hiring pipelines, and market tracking. # Actor input Schema ## `geo` (type: `string`): Filter results by location ID. Use one or more location IDs separated by commas. ## `start` (type: `string`): Pagination offset. Use 0 for the first page, then 100, 200, 300, and so on. ## `company` (type: `string`): Filter results by company name. ## `keywords` (type: `string`): Main search keywords to find people. ## `lastName` (type: `string`): Filter results by a person's last name. ## `schoolId` (type: `string`): Filter results by school ID. ## `firstName` (type: `string`): Filter results by a person's first name. ## `max_results` (type: `integer`): Maximum number of results to return. ## `keywordTitle` (type: `string`): Search by job title keyword. ## `keywordSchool` (type: `string`): Search by school name keyword. ## Actor input object example ```json { "geo": "103644278,101165590", "start": "0", "keywords": "golang", "max_results": 10 } ``` # 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": "golang" }; // Run the Actor and wait for it to finish const run = await client.actor("datamagnet/linkedin-people-profile-search-cookie-less").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": "golang" } # Run the Actor and wait for it to finish run = client.actor("datamagnet/linkedin-people-profile-search-cookie-less").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": "golang" }' | apify call datamagnet/linkedin-people-profile-search-cookie-less --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datamagnet/linkedin-people-profile-search-cookie-less", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn People Profile Search Cookie-less 🍪 ✅", "description": "Search for people profiles by keyword and optional filters like company, title, school, and location. Get a clean list of matching profiles with key public details in one place.", "version": "0.0", "x-build-id": "z5k4iyRzN87XM0h1c" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datamagnet~linkedin-people-profile-search-cookie-less/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datamagnet-linkedin-people-profile-search-cookie-less", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datamagnet~linkedin-people-profile-search-cookie-less/runs": { "post": { "operationId": "runs-sync-datamagnet-linkedin-people-profile-search-cookie-less", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datamagnet~linkedin-people-profile-search-cookie-less/run-sync": { "post": { "operationId": "run-sync-datamagnet-linkedin-people-profile-search-cookie-less", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "geo": { "title": "Location ID", "type": "string", "description": "Filter results by location ID. Use one or more location IDs separated by commas." }, "start": { "title": "Start", "type": "string", "description": "Pagination offset. Use 0 for the first page, then 100, 200, 300, and so on." }, "company": { "title": "Company", "type": "string", "description": "Filter results by company name." }, "keywords": { "title": "Keywords", "type": "string", "description": "Main search keywords to find people." }, "lastName": { "title": "Last name", "type": "string", "description": "Filter results by a person's last name." }, "schoolId": { "title": "School ID", "type": "string", "description": "Filter results by school ID." }, "firstName": { "title": "First name", "type": "string", "description": "Filter results by a person's first name." }, "max_results": { "title": "Max results", "type": "integer", "description": "Maximum number of results to return.", "default": 10 }, "keywordTitle": { "title": "Job title keyword", "type": "string", "description": "Search by job title keyword." }, "keywordSchool": { "title": "School keyword", "type": "string", "description": "Search by school name keyword." } } }, "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 } } } } } } } } } } ```