# LinkedIn People Search — Company, Title, Industry ✅ No Cookies (`neuralverge/neuralverge-linkedin-people-search`) Actor Search LinkedIn people with advanced filters — company, job title, seniority, function, industry, company size, location and experience. Returns profiles in blocks of 25, one row per person: name, title, company, tenure, location, summary and photo. No cookies or account required. - **URL**: https://apify.com/neuralverge/neuralverge-linkedin-people-search.md - **Developed by:** [NeuralVerge](https://apify.com/neuralverge) (community) - **Categories:** Automation, Developer tools, Lead generation - **Stats:** 13 total users, 8 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $100.00 / 1,000 search results blocks This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 People Search — Company, Title, Industry ✅ No Cookies Search **LinkedIn people** with advanced filters — **current & past company, current & past job title, seniority, function, industry, company size, location and experience**. Returns **up to 25 profiles per search**, one row per person, with name, title, company, tenure, location, summary and photo. Powered by the Neuralverge API. **No cookies or account required.** Ideal for recruiting, lead generation, sales prospecting, talent mapping and list building. ### ✨ Why this Actor - 🎯 **Advanced filters** — combine company, title, seniority, function, industry, size, location and experience to target exactly the people you want. - 🍪 **No cookies or account** — the search runs on the Neuralverge backend; you never share a LinkedIn login. - 👤 **Rich profile rows** — name, current title & company, tenure, location, summary, photo and the full `currentPositions` array, ready for a spreadsheet, CRM or AI pipeline. - 💳 **Block pricing** — $0.10 per block of up to 25 profiles; searches that return nothing are free. - ⚡ **Real-time & structured** — fresh results each run. ### 🔧 How it works 1. **Set your filters** — any combination of company, title, industry, location, etc. A single broad filter (e.g. current company) already returns results. 2. **Run the Actor.** The search happens on the Neuralverge backend. 3. **Get one dataset row per person** — up to 25 per search. Export to CSV, JSON or Excel, or pull it over the Apify API. > **Tip:** start with one or two filters. Very narrow filter combinations can return no results. ### Input Set any combination of the fields below (all optional). Each filter field accepts one or more values. | Field | Type | Notes | |---|---|---| | `searchQuery` | string | Optional free-text keyword. | | `currentCompany` / `pastCompany` | array | Current / previous employer(s). | | `currentJobTitleFilter` / `pastJobTitle` | array | Current / previous job title(s). | | `seniorityLevelFilter` | array | Seniority, e.g. `Senior`, `Director`, `VP`, `CXO`. | | `functionFilter` | array | Job function, e.g. `Sales`, `Engineering`. | | `industryIds` | array | LinkedIn industry slug(s). | | `companyHeadcountFilter` | array | Company size band(s). | | `yearsOfExperienceFilter` / `yearsAtCurrentCompanyFilter` | array | Experience / tenure ranges. | | `locations` | array | Location(s), e.g. `London`. | | `firstNames` / `lastNames` | array | Name filters. | | `scraperMode` | string | `short` (basic) or `full` (extended). | | `maxResults` | integer | 1-25 profiles per search. | ```json { "currentCompany": ["Google"], "scraperMode": "short", "maxResults": 5 } ```` ### What you'll receive One dataset row per person, with these fields: - **`fullName`**, **`firstName`**, **`lastName`**, **`linkedinUrl`**, **`id`** - **`title`**, **`company`**, **`companyLinkedinUrl`**, **`tenureAtCompany`** - **`location`**, **`summary`**, **`premium`**, **`openProfile`**, **`pictureUrl`** - **`currentPositions`** — the full array of the person's current roles. > **Nulls are normal.** LinkedIn doesn't expose every field for every profile. #### Example output (real run) Real dataset rows for the filter `currentCompany: ["Google"]` (`scraperMode: short`), two of the five profiles returned. ```json [ { "id": "ACwAAB7FMA4BFYUW0t-sJDpgQh1E1O1thW-vCbE", "linkedinUrl": "https://www.linkedin.com/in/ACwAAB7FMA4BFYUW0t-sJDpgQh1E1O1thW-vCbE", "firstName": "Genia", "lastName": "Xasis, PhD", "fullName": "Genia Xasis, PhD", "title": "Startups Mentor", "company": "Google", "companyLinkedinUrl": "https://www.linkedin.com/company/1441", "tenureAtCompany": "1y", "location": "Riyadh, Saudi Arabia", "summary": "I work at the intersection of capital, access, and execution in Saudi Arabia and the UAE...", "premium": true, "openProfile": false, "pictureUrl": "https://media.licdn.com/dms/image/v2/D4E03AQE2nALflOlYNw/profile-displayphoto-crop_800_800/B4EZzymfgXJAAI-/0/1773596703899?e=1786579200&v=beta&t=oqToDU12WVnSQNLaGHOdI8TagGZRUHvb3GiASbzd-Dc", "currentPositions": [ { "tenureAtPosition": { "numYears": 1 }, "companyName": "Google", "title": "Startups Mentor", "current": true, "tenureAtCompany": { "numYears": 1 }, "startedOn": { "month": 8, "year": 2025 }, "companyId": "1441", "companyLinkedinUrl": "https://www.linkedin.com/company/1441" } ] }, { "id": "ACwAAAAW6aoBZJOVwoABUyfa8IVBNN88-9z3KL0", "linkedinUrl": "https://www.linkedin.com/in/ACwAAAAW6aoBZJOVwoABUyfa8IVBNN88-9z3KL0", "firstName": "Emerson", "lastName": "M.", "fullName": "Emerson M.", "title": "Head of HR Brazil", "company": "Google", "companyLinkedinUrl": "https://www.linkedin.com/company/1441", "tenureAtCompany": "1y 4m", "location": "São Paulo, São Paulo, Brazil", "summary": "A Senior HR Executive and Consultant with more than 25 years of management and leadership experience...", "premium": false, "openProfile": false, "pictureUrl": "https://media.licdn.com/dms/image/v2/D4D03AQGBFvDB7wFyKw/profile-displayphoto-crop_800_800/B4DZybdFczJIAI-/0/1772134624861?e=1786579200&v=beta&t=azs_kA16tO7-_2flMWaMAUk3u71psyHaT0euCGt-gw4", "currentPositions": [ { "tenureAtPosition": { "numYears": 1, "numMonths": 4 }, "companyName": "Google", "title": "Head of HR Brazil", "current": true, "tenureAtCompany": { "numYears": 1, "numMonths": 4 }, "startedOn": { "month": 4, "year": 2025 }, "companyId": "1441", "companyLinkedinUrl": "https://www.linkedin.com/company/1441" } ] } ] ``` > Genuine output from a live run. The `summary` field is abbreviated here for readability — the > live run returns the full profile summary. ### Pricing Pay per block of results — **$0.10 per block of up to 25 profiles**. A search that returns 1-25 profiles costs $0.10; 26-50 costs $0.20, and so on. Searches that return nothing are **not charged**. | Event | Price | |---|---| | Results block (up to 25 profiles) | **$0.10** | Pricing is on top of your Apify platform usage. ### Integrations & API Results are stored in a standard Apify dataset — export as CSV, JSON, XML or Excel, or fetch on demand through the [Apify API](https://docs.apify.com/api). The Actor also plugs into Apify's integrations (Make, Zapier, n8n, webhooks) and can be called from any [MCP](https://mcp.apify.com/) client. ### FAQ **Do I need an account or cookies?** No. The search runs on the Neuralverge backend; you never share a LinkedIn login. **How many profiles per run?** Up to 25 per search. For more, run additional searches (e.g. change filters or narrow by location). **Why did I get no results?** Very narrow filter combinations can return nothing. Start with one or two broad filters and add more gradually. **How am I billed?** $0.10 per block of up to 25 profiles returned (26-50 = $0.20, etc.). Empty searches are free. *** **Disclaimer:** This Actor is an independent tool and is not affiliated with or endorsed by LinkedIn. Use the data in compliance with applicable laws and platform terms. # Actor input Schema ## `searchQuery` (type: `string`): Optional free-text keyword to match within people profiles. Leave empty to rely on the filters below. ## `currentCompany` (type: `array`): Filter by current employer, e.g. "Google", "Revolut". ## `currentJobTitleFilter` (type: `array`): Filter by current job title, e.g. "Recruiter", "Founder", "Head of Sales". ## `pastCompany` (type: `array`): Filter by a previous employer, e.g. "Meta", "Google". ## `pastJobTitle` (type: `array`): Filter by a previously-held job title, e.g. "Sales Manager". ## `locations` (type: `array`): Filter by location, e.g. "London", "Amsterdam". ## `seniorityLevelFilter` (type: `array`): Filter by seniority, e.g. "Senior", "Manager", "Director", "VP", "CXO". ## `functionFilter` (type: `array`): Filter by job function, e.g. "Sales", "Engineering", "Marketing". ## `industryIds` (type: `array`): Filter by LinkedIn industry slug(s), e.g. "technology\_information\_and\_media". ## `companyHeadcountFilter` (type: `array`): Filter by the person's company headcount band (LinkedIn range values). ## `yearsOfExperienceFilter` (type: `array`): Filter by total years of experience (LinkedIn range values). ## `yearsAtCurrentCompanyFilter` (type: `array`): Filter by tenure at the current company (LinkedIn range values). ## `firstNames` (type: `array`): Filter by one or more first names. ## `lastNames` (type: `array`): Filter by one or more last names. ## `scraperMode` (type: `string`): "short" returns a fast, basic profile per person (name, title, company, location, summary). Use "full" for extended profile data if enabled on your plan. ## `maxResults` (type: `integer`): Maximum number of profiles to return. Billed in blocks of 25: 1-25 = one block, 26-50 = two blocks, and so on. Default 25 (one block). ## Actor input object example ```json { "currentCompany": [ "Google" ], "scraperMode": "short", "maxResults": 25 } ``` # Actor output Schema ## `results` (type: `string`): Structured people results, one row per profile returned by the search. # 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 = { "currentCompany": [ "Google" ] }; // Run the Actor and wait for it to finish const run = await client.actor("neuralverge/neuralverge-linkedin-people-search").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 = { "currentCompany": ["Google"] } # Run the Actor and wait for it to finish run = client.actor("neuralverge/neuralverge-linkedin-people-search").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 '{ "currentCompany": [ "Google" ] }' | apify call neuralverge/neuralverge-linkedin-people-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=neuralverge/neuralverge-linkedin-people-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn People Search — Company, Title, Industry ✅ No Cookies", "description": "Search LinkedIn people with advanced filters — company, job title, seniority, function, industry, company size, location and experience. Returns profiles in blocks of 25, one row per person: name, title, company, tenure, location, summary and photo. No cookies or account required.", "version": "0.1", "x-build-id": "lD47d6addyGQOhU1g" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/neuralverge~neuralverge-linkedin-people-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-neuralverge-neuralverge-linkedin-people-search", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/neuralverge~neuralverge-linkedin-people-search/runs": { "post": { "operationId": "runs-sync-neuralverge-neuralverge-linkedin-people-search", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/neuralverge~neuralverge-linkedin-people-search/run-sync": { "post": { "operationId": "run-sync-neuralverge-neuralverge-linkedin-people-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchQuery": { "title": "Search query", "type": "string", "description": "Optional free-text keyword to match within people profiles. Leave empty to rely on the filters below." }, "currentCompany": { "title": "Current company", "type": "array", "description": "Filter by current employer, e.g. \"Google\", \"Revolut\".", "items": { "type": "string" } }, "currentJobTitleFilter": { "title": "Current job title", "type": "array", "description": "Filter by current job title, e.g. \"Recruiter\", \"Founder\", \"Head of Sales\".", "items": { "type": "string" } }, "pastCompany": { "title": "Past company", "type": "array", "description": "Filter by a previous employer, e.g. \"Meta\", \"Google\".", "items": { "type": "string" } }, "pastJobTitle": { "title": "Past job title", "type": "array", "description": "Filter by a previously-held job title, e.g. \"Sales Manager\".", "items": { "type": "string" } }, "locations": { "title": "Locations", "type": "array", "description": "Filter by location, e.g. \"London\", \"Amsterdam\".", "items": { "type": "string" } }, "seniorityLevelFilter": { "title": "Seniority level", "type": "array", "description": "Filter by seniority, e.g. \"Senior\", \"Manager\", \"Director\", \"VP\", \"CXO\".", "items": { "type": "string" } }, "functionFilter": { "title": "Function", "type": "array", "description": "Filter by job function, e.g. \"Sales\", \"Engineering\", \"Marketing\".", "items": { "type": "string" } }, "industryIds": { "title": "Industry", "type": "array", "description": "Filter by LinkedIn industry slug(s), e.g. \"technology_information_and_media\".", "items": { "type": "string" } }, "companyHeadcountFilter": { "title": "Company size", "type": "array", "description": "Filter by the person's company headcount band (LinkedIn range values).", "items": { "type": "string" } }, "yearsOfExperienceFilter": { "title": "Years of experience", "type": "array", "description": "Filter by total years of experience (LinkedIn range values).", "items": { "type": "string" } }, "yearsAtCurrentCompanyFilter": { "title": "Years at current company", "type": "array", "description": "Filter by tenure at the current company (LinkedIn range values).", "items": { "type": "string" } }, "firstNames": { "title": "First names", "type": "array", "description": "Filter by one or more first names.", "items": { "type": "string" } }, "lastNames": { "title": "Last names", "type": "array", "description": "Filter by one or more last names.", "items": { "type": "string" } }, "scraperMode": { "title": "Scraping mode", "enum": [ "short", "full" ], "type": "string", "description": "\"short\" returns a fast, basic profile per person (name, title, company, location, summary). Use \"full\" for extended profile data if enabled on your plan.", "default": "short" }, "maxResults": { "title": "Maximum items", "minimum": 1, "maximum": 250, "type": "integer", "description": "Maximum number of profiles to return. Billed in blocks of 25: 1-25 = one block, 26-50 = two blocks, and so on. Default 25 (one block).", "default": 25 } } }, "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 } } } } } } } } } } ```