# LinkedIn Company & Employee Directory (`trusted_tech_spot/linkedin-company-employee-finder`) Actor

Extracts company profiles, employee directories, job titles, and predicted corporate email patterns from LinkedIn companies without requiring browser cookies or account login.

- **URL**: https://apify.com/trusted\_tech\_spot/linkedin-company-employee-finder.md
- **Developed by:** [David Sandor](https://apify.com/trusted_tech_spot) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.70 / 1,000 extracted linkedin company & employee directories

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## LinkedIn Company & Employee Directory Intelligence (No Cookies) 🚀

Extracts company profiles, employee directories, job titles, and predicted corporate email patterns from LinkedIn companies without requiring browser cookies or account login.

### 🌟 20+ Enterprise Enhancements (v2.0)

- **RAG & LLM Ready**: Pre-computed OpenAI token counts and chunked embeddings.
- **Smart Keyword Filters**: Include or exclude records by targeted keyword lists.
- **Sentiment Scoring**: Built-in lexical sentiment rating on text contents.
- **Noise & Tracking Scrubber**: Removes tracking query parameters and boilerplate banners.
- **Pay-Per-Event (PPE)**: Ultra-cost-effective pricing per extracted item.
- **Zero Cold Start**: Sub-second execution with automated fallback guarantees.

### 💻 Integration Examples

#### Node.js (Apify Client)

```javascript
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('linkedin-company-employee-finder').call({
    // Pass customized inputs here
    enableRagEnrichment: true
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log('Extracted Items:', items);
```

#### Python

```python
from apify_client import ApifyClient

client = ApifyClient('YOUR_APIFY_TOKEN')
run = client.actor('linkedin-company-employee-finder').call(run_input={ 'enableRagEnrichment': True })
for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item)
```

### 📄 Output Schema

Returns structured JSON, token counts, and RAG vector chunks.

# Actor input Schema

## `companyNameOrUrl` (type: `string`):

LinkedIn company name, domain, or URL (e.g., "stripe" or "https://www.linkedin.com/company/stripe")

## `maxEmployees` (type: `integer`):

Maximum number of key employee profiles to discover

## `includeKeywords` (type: `string`):

Filter results to only include items containing these comma-separated keywords

## `excludeKeywords` (type: `string`):

Filter out results containing these comma-separated keywords

## `enableRagEnrichment` (type: `boolean`):

Automatically compute OpenAI token counts and generate semantic vector chunks for RAG pipelines

## `outputFormat` (type: `string`):

Format results for preferred downstream consumer (e.g. JSON, Markdown, Flat)

## Actor input object example

```json
{
  "companyNameOrUrl": "https://www.linkedin.com/company/stripe",
  "maxEmployees": 5,
  "includeKeywords": "",
  "excludeKeywords": "",
  "enableRagEnrichment": true,
  "outputFormat": "JSON"
}
```

# Actor output Schema

## `dataset` (type: `string`):

Structured output and extracted data from LinkedIn Company & Employee Directory Intelligence (No Cookies)

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "companyNameOrUrl": "https://www.linkedin.com/company/stripe",
    "maxEmployees": 5,
    "includeKeywords": "",
    "excludeKeywords": "",
    "enableRagEnrichment": true,
    "outputFormat": "JSON"
};

// Run the Actor and wait for it to finish
const run = await client.actor("trusted_tech_spot/linkedin-company-employee-finder").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "companyNameOrUrl": "https://www.linkedin.com/company/stripe",
    "maxEmployees": 5,
    "includeKeywords": "",
    "excludeKeywords": "",
    "enableRagEnrichment": True,
    "outputFormat": "JSON",
}

# Run the Actor and wait for it to finish
run = client.actor("trusted_tech_spot/linkedin-company-employee-finder").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "companyNameOrUrl": "https://www.linkedin.com/company/stripe",
  "maxEmployees": 5,
  "includeKeywords": "",
  "excludeKeywords": "",
  "enableRagEnrichment": true,
  "outputFormat": "JSON"
}' |
apify call trusted_tech_spot/linkedin-company-employee-finder --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,trusted_tech_spot/linkedin-company-employee-finder"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/wWxds5u1hUXtPIptl/builds/mLcFxpHA1Tg69iPxm/openapi.json