# LinkedIn Company Employees Scraper (No Cookies) (`scrapemint/linkedin-company-employees-scraper`) Actor
Pull public employee insights from any LinkedIn company page without a cookie or login. Each row carries total headcount, distributions by location, role, skill, and school, plus any featured employees LinkedIn renders to anonymous visitors. JSON. Pay per company.
- **URL**: https://apify.com/scrapemint/linkedin-company-employees-scraper.md
- **Developed by:** [Ken M](https://apify.com/scrapemint) (community)
- **Categories:** Jobs, Social media, Other
- **Stats:** 15 total users, 6 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## 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 Company Employees Scraper (No Login Required)
Pull public employee insights from any LinkedIn company page. No cookies. No login. No Sales Navigator seat. Each row ships the total headcount, distributions of employees by location, role, top skill, and top school, plus any employee profile cards LinkedIn renders to anonymous visitors. Pay per company.
**Built for** B2B sales teams, recruiters, sourcers, agencies, and competitive intel pipelines that need fast workforce snapshots for prospecting, talent mapping, and account research.
**Keywords this actor ranks for:** linkedin company employees scraper, linkedin headcount tracker, linkedin people insights api, linkedin talent mapping, linkedin workforce intelligence, linkedin employee distribution, linkedin company size scraper, linkedin people search no login, sourcing linkedin employees, linkedin top skills by company, linkedin top schools by company, account research linkedin.
---
### Why this actor
| Other LinkedIn employee scrapers | **This actor** |
|---|---|
| Need your session cookie | Zero cookies, zero login |
| Risk your account on every run | Touches only public surfaces |
| Return raw HTML blobs | Distributions parsed into discrete rows with counts |
| Drop featured employee cards | Featured profile cards shipped with name, title, location, URL |
| Charge per profile inspected | Charge per company, get the whole snapshot |
---
### How it works
```mermaid
flowchart LR
A[Company URL] --> B[Public people page
no auth needed]
B --> C[Parse insight cards
locations roles skills schools]
B --> D[Parse featured employee cards]
C --> E[Normalize into discrete rows]
D --> E
E --> F[Push one row per company]
F --> G[(JSON CSV Excel API)]
B -.fallback.-> H[Landing page for headcount]
H --> E
````
The actor hits the public people page first. If LinkedIn gates that surface for your IP, it falls back to the company landing page and pulls total headcount and company name from public meta tags. No cookie passes through the actor at any stage.
***
### What you get per row
```mermaid
flowchart LR
R[Company row] --> R1[Identity
handle url companyName]
R --> R2[Headcount
totalEmployees]
R --> R3[Insights.locations
where employees live]
R --> R4[Insights.jobFunctions
what employees do]
R --> R5[Insights.skills
top skills]
R --> R6[Insights.schools
top schools]
R --> R7[Featured employees
name title location URL]
```
Pipe straight into a sourcing tool, a CRM enrichment job, or a talent map.
***
### Quick start
**Talent map a target account list**
```json
{
"companyUrls": [
"https://www.linkedin.com/company/microsoft/",
"https://www.linkedin.com/company/openai/",
"https://www.linkedin.com/company/anthropic/"
]
}
```
**Sales prospecting (skip insights to keep rows lean)**
```json
{
"companyUrls": ["https://www.linkedin.com/company/stripe/"],
"includeInsights": false,
"includeFeaturedEmployees": true
}
```
**Recruiter research (every distribution, every featured profile)**
```json
{
"companyUrls": [
"https://www.linkedin.com/company/perplexity-ai/",
"https://www.linkedin.com/company/mistralai/"
],
"includeInsights": true,
"includeFeaturedEmployees": true
}
```
***
### Sample output
```json
{
"handle": "openai",
"kind": "company",
"url": "https://www.linkedin.com/company/openai/people/",
"companyUrl": "https://www.linkedin.com/company/openai/",
"companyName": "OpenAI",
"totalEmployees": 3200,
"insights": {
"locations": [
{ "label": "San Francisco Bay Area", "count": 1820 },
{ "label": "New York City Metropolitan Area", "count": 240 },
{ "label": "London Area, United Kingdom", "count": 180 }
],
"jobFunctions": [
{ "label": "Engineering", "count": 1450 },
{ "label": "Research", "count": 520 },
{ "label": "Operations", "count": 310 }
],
"skills": [
{ "label": "Machine Learning", "count": 920 },
{ "label": "Python", "count": 880 },
{ "label": "Deep Learning", "count": 640 }
],
"schools": [
{ "label": "Stanford University", "count": 210 },
{ "label": "UC Berkeley", "count": 140 }
]
},
"featuredEmployees": [
{
"name": "Sam Altman",
"title": "CEO at OpenAI",
"location": "San Francisco, California",
"profileUrl": "https://www.linkedin.com/in/samaltman/",
"photoUrl": "https://media.licdn.com/dms/image/..."
}
],
"scrapedAt": "2026-05-09T10:00:00.000Z"
}
```
***
### Who uses this
| Role | Use case |
|---|---|
| B2B sales | Score target accounts by engineering vs sales headcount before outreach |
| Recruiter | Surface top schools and skills for sourcing lookalike candidates |
| Sourcer | Map talent footprint by city to plan local outreach campaigns |
| Founder | Track competitor hiring trends and team composition over time |
| Investor | Read engineering ratio, location split, and growth signals into a watchlist |
| ABM lead | Cluster accounts by skill and function for tiered outbound plays |
***
### Input reference
| Field | Type | What it does |
|---|---|---|
| `companyUrls` | string\[] | LinkedIn company, school, or showcase URLs. Required. |
| `includeInsights` | boolean | Keep the locations, jobFunctions, skills, and schools distributions. Default true. |
| `includeFeaturedEmployees` | boolean | Keep the featured employee cards array. Default true. |
| `concurrency` | integer | Companies processed in parallel. Six is the safe default. |
| `proxyConfiguration` | object | Apify proxy. Residential is required at any meaningful volume. |
***
### API call
```bash
curl -X POST \
"https://api.apify.com/v2/acts/YOUR_USER~linkedin-company-employees-scraper/runs?token=YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"companyUrls": [
"https://www.linkedin.com/company/microsoft/",
"https://www.linkedin.com/company/openai/"
]
}'
```
***
### Pricing
The first few companies per run are free so you can validate output before paying. After that, each company row is charged. No surprise add on charges.
***
### FAQ
#### Do I need a LinkedIn account or cookie?
No. The actor only touches LinkedIn's public people and landing pages. Your account is never touched.
#### Can I get the full employee list of a 50,000 person company?
No. The full employee list is gated behind a logged in session and is not exposed publicly. This actor returns the public distribution counts (locations, roles, skills, schools) plus whatever featured employee cards LinkedIn renders to anonymous visitors. For most companies that is a small set of profiles, not the whole roster.
#### What if the people page is gated for anonymous viewers?
The actor falls back to the company landing page and pulls total headcount and company name from JSON-LD and Open Graph tags. The insights and featured employee arrays may be empty in that path.
#### Can I scrape schools or showcase pages?
Yes. URLs in the form `linkedin.com/school/{handle}/` and `linkedin.com/showcase/{handle}/` are supported alongside `linkedin.com/company/{handle}/`.
#### How fresh is the data?
Each run hits the live people page, so headcount and distribution counts reflect what LinkedIn renders at scrape time. Schedule weekly runs to track hiring trends and team composition over time.
#### Is scraping LinkedIn allowed?
This actor reads HTML any anonymous web visitor can see. Respect LinkedIn's terms and rate limit sensibly. Do not redistribute personal data you have no lawful basis to process.
***
### Related actors
- **LinkedIn Company Profile Scraper (No Cookies)** — pull industry, headcount range, HQ, founded year, specialties, website per company
- **LinkedIn Profile Scraper (No Cookies)** — pull a single profile's experience, education, and skills
- **LinkedIn Hiring Tracker & Salary Intelligence** — parsed salary, tech stack, and seniority on every job row
- **Lead Enrichment Pipeline** — multi source enrichment for a list of company domains
- **Reddit Brand Monitor & Lead Finder** — subreddit mentions and high intent leads
# Actor input Schema
## `companyUrls` (type: `array`):
List of LinkedIn company URLs whose people insights you want to scrape. Examples: 'https://www.linkedin.com/company/microsoft/', 'https://www.linkedin.com/company/openai/'. School and showcase pages are also accepted.
## `includeInsights` (type: `boolean`):
Pull the public distributions LinkedIn renders for each company: where employees live, what they do, top skills, top schools. Useful for talent mapping and ABM segmentation.
## `includeFeaturedEmployees` (type: `boolean`):
Pull any employee cards LinkedIn renders to anonymous visitors. Counts vary per company. Each entry ships name, title, location when present, and profile URL.
## `concurrency` (type: `integer`):
Number of companies processed in parallel. Higher concurrency runs faster but increases proxy and detection risk. Six is a safe default.
## `proxyConfiguration` (type: `object`):
Apify proxy. Residential is required for LinkedIn at any meaningful volume.
## Actor input object example
```json
{
"companyUrls": [
"https://www.linkedin.com/company/microsoft/",
"https://www.linkedin.com/company/openai/"
],
"includeInsights": true,
"includeFeaturedEmployees": true,
"concurrency": 6,
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
```
# 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 = {
"companyUrls": [
"https://www.linkedin.com/company/microsoft/",
"https://www.linkedin.com/company/openai/"
],
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrapemint/linkedin-company-employees-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 = {
"companyUrls": [
"https://www.linkedin.com/company/microsoft/",
"https://www.linkedin.com/company/openai/",
],
"proxyConfiguration": {
"useApifyProxy": True,
"apifyProxyGroups": ["RESIDENTIAL"],
},
}
# Run the Actor and wait for it to finish
run = client.actor("scrapemint/linkedin-company-employees-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 '{
"companyUrls": [
"https://www.linkedin.com/company/microsoft/",
"https://www.linkedin.com/company/openai/"
],
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}' |
apify call scrapemint/linkedin-company-employees-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrapemint/linkedin-company-employees-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "LinkedIn Company Employees Scraper (No Cookies)",
"description": "Pull public employee insights from any LinkedIn company page without a cookie or login. Each row carries total headcount, distributions by location, role, skill, and school, plus any featured employees LinkedIn renders to anonymous visitors. JSON. Pay per company.",
"version": "0.1",
"x-build-id": "jABSTaYPX5ePiCSmo"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrapemint~linkedin-company-employees-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrapemint-linkedin-company-employees-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/scrapemint~linkedin-company-employees-scraper/runs": {
"post": {
"operationId": "runs-sync-scrapemint-linkedin-company-employees-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/scrapemint~linkedin-company-employees-scraper/run-sync": {
"post": {
"operationId": "run-sync-scrapemint-linkedin-company-employees-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": [
"companyUrls"
],
"properties": {
"companyUrls": {
"title": "Company URLs",
"type": "array",
"description": "List of LinkedIn company URLs whose people insights you want to scrape. Examples: 'https://www.linkedin.com/company/microsoft/', 'https://www.linkedin.com/company/openai/'. School and showcase pages are also accepted.",
"default": [],
"items": {
"type": "string"
}
},
"includeInsights": {
"title": "Include insights",
"type": "boolean",
"description": "Pull the public distributions LinkedIn renders for each company: where employees live, what they do, top skills, top schools. Useful for talent mapping and ABM segmentation.",
"default": true
},
"includeFeaturedEmployees": {
"title": "Include featured employees",
"type": "boolean",
"description": "Pull any employee cards LinkedIn renders to anonymous visitors. Counts vary per company. Each entry ships name, title, location when present, and profile URL.",
"default": true
},
"concurrency": {
"title": "Concurrency",
"minimum": 1,
"maximum": 20,
"type": "integer",
"description": "Number of companies processed in parallel. Higher concurrency runs faster but increases proxy and detection risk. Six is a safe default.",
"default": 6
},
"proxyConfiguration": {
"title": "Proxy configuration",
"type": "object",
"description": "Apify proxy. Residential is required for LinkedIn at any meaningful volume.",
"default": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```