# LinkedIn Company Posts Scraper (`benjarapi/linkedin-company-posts`) Actor

Efficiently scrape LinkedIn company posts in bulk.

- **URL**: https://apify.com/benjarapi/linkedin-company-posts.md
- **Developed by:** [Benjar Scraping API](https://apify.com/benjarapi) (community)
- **Categories:** Social media, Automation, Developer tools
- **Stats:** 179 total users, 40 monthly users, 100.0% runs succeeded, 6 bookmarks
- **User rating**: 5.00 out of 5 stars

## Pricing

$2.00 / 1,000 posts

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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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 Posts Scraper — Extract Company Posts in Bulk

Scrape LinkedIn company page posts in bulk. Extract full post text, reactions, comments, reposts, media attachments, documents, and company author details — all structured and ready for analysis.

### Why Use This LinkedIn Company Posts Scraper?

- **Bulk extraction** — Collect thousands of LinkedIn posts from any company page in a single run
- **Rich structured data** — Get post text, timestamps, engagement stats (likes, comments, reposts), company info, attached media, and documents
- **Flexible input** — Pass a full LinkedIn company URL, just a universal name, or a company URN
- **Automatic pagination** — No manual scrolling or page handling; the scraper fetches all posts up to your specified limit
- **Export-ready** — Download results as JSON, CSV, Excel, or any format supported by Apify

### Input

| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `company` | string | Yes | — | LinkedIn company page URL, universal name, or URN (e.g. `https://www.linkedin.com/company/microsoft`, `microsoft`, or `1035`) |
| `maxPosts` | integer | No | `100` | Maximum number of posts to scrape |

### Usage Examples

#### Scrape company posts from a company URL

```json
{
    "company": "https://www.linkedin.com/company/microsoft",
    "maxPosts": 50
}
```

#### Scrape company posts using just a universal name

```json
{
    "company": "microsoft",
    "maxPosts": 200
}
```

### Output — LinkedIn Company Post Data Structure

Each run produces a dataset where every item is a LinkedIn company post with the following fields:

| Field | Type | Description |
|-------|------|-------------|
| `activity_urn` | string | LinkedIn activity URN identifier |
| `full_urn` | string | Full LinkedIn activity URN |
| `post_url` | string | Direct permalink to the post on LinkedIn |
| `text` | string | Full text content of the LinkedIn post |
| `posted_at` | object | Post date/time — `relative`, `is_edited`, `date` (UTC), and `timestamp` (Unix ms) |
| `post_language_code` | string | Detected language of the post (e.g. `en`, `tr`) |
| `post_type` | string | Type of post (`regular`, `repost`, `quote`) |
| `author` | object | Company details — `name`, `follower_count`, `company_url`, `logo_url` |
| `stats` | object | Engagement metrics — `total_reactions`, `like`, `support`, `love`, `celebrate`, `comments`, `reposts` (only non-zero fields included) |
| `media` | object | null | Attached media — `type` (`image`, `video`, `article`), `items` array. `null` if no media |
| `document` | object | null | Attached document/carousel — `title`, `total_page_count`, `transcribed_document_url`, `cover_images`. `null` if no document |
| `source_company` | string | The company identifier used to scrape this post |

#### Sample Output

```json
[
    {
        "activity_urn": "7426965716664307712",
        "full_urn": "urn:li:activity:7426965716664307712",
        "post_url": "https://www.linkedin.com/posts/microsoft_phonepe-is-leveraging-ai-...",
        "text": "The #BecomingFrontier journey of PhonePe is about building population-scale platforms...",
        "posted_at": {
            "relative": "47m",
            "is_edited": false,
            "date": "2026-02-10 12:31:12",
            "timestamp": 1770726672287
        },
        "post_language_code": "en",
        "post_type": "regular",
        "author": {
            "name": "Microsoft",
            "follower_count": 27535286,
            "company_url": "https://www.linkedin.com/company/microsoft/posts",
            "logo_url": "https://media.licdn.com/dms/image/v2/D560BAQH32RJQCl3dDQ/company-logo_400_400/..."
        },
        "stats": {
            "total_reactions": 18,
            "like": 18,
            "reposts": 3
        },
        "media": null,
        "document": {
            "title": "PhonePe is leveraging AI to strengthen digital payments...",
            "total_page_count": 8,
            "transcribed_document_url": "https://media.licdn.com/dms/document/...",
            "cover_images": [
                "https://media.licdn.com/dms/image/..."
            ]
        },
        "source_company": "microsoft"
    }
]
```

### Use Cases for LinkedIn Company Post Data

- **Competitive intelligence** — Monitor what competitors are posting and how their audience engages
- **Content strategy analysis** — Analyze posting frequency, topics, and content formats of industry leaders
- **Engagement benchmarking** — Compare reaction counts, comment rates, and repost ratios across companies
- **Brand monitoring** — Track how companies communicate product launches, strategy shifts, or hiring activity
- **Social listening & trend tracking** — Spot emerging trends by monitoring what top companies in your industry are discussing
- **Sentiment analysis** — Feed company post text into NLP pipelines to gauge sentiment around brands or products
- **AI & ML training data** — Build datasets of professional corporate content for fine-tuning language models

# Actor input Schema

## `company` (type: `string`):

Company page URL, universal name, or URN (e.g. "https://www.linkedin.com/company/microsoft", "microsoft", or "1035")

## `maxPosts` (type: `integer`):

Maximum number of posts to collect

## Actor input object example

```json
{
  "company": "https://www.linkedin.com/company/microsoft",
  "maxPosts": 100
}
```

# 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 = {
    "company": "https://www.linkedin.com/company/microsoft",
    "maxPosts": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("benjarapi/linkedin-company-posts").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 = {
    "company": "https://www.linkedin.com/company/microsoft",
    "maxPosts": 100,
}

# Run the Actor and wait for it to finish
run = client.actor("benjarapi/linkedin-company-posts").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 '{
  "company": "https://www.linkedin.com/company/microsoft",
  "maxPosts": 100
}' |
apify call benjarapi/linkedin-company-posts --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=benjarapi/linkedin-company-posts",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/gfUCdHdkPPX8GXcao/builds/rcounqf7NoAS2Swrf/openapi.json