# Linkedin User Posts (`benjarapi/linkedin-user-posts`) Actor

Scrape LinkedIn posts data for a given LinkedIn person profile including post text, post media attachments, reactions count, comments count, author basic info etc.

- **URL**: https://apify.com/benjarapi/linkedin-user-posts.md
- **Developed by:** [Benjar Scraping API](https://apify.com/benjarapi) (community)
- **Categories:** Social media, Lead generation
- **Stats:** 109 total users, 8 monthly users, 100.0% runs succeeded, 2 bookmarks
- **User rating**: 5.00 out of 5 stars

## Pricing

$3.00 / 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/actors/running/actors-in-store.md#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 Post Scraper — Extract Any User's Posts @ $3/1000 posts

Scrape LinkedIn posts from any user profile in bulk. Extract full post text, reactions, comments, reposts, media attachments, and author details — all structured and ready for analysis.

### Why Use This LinkedIn Post Scraper?

- **Bulk extraction** — Collect thousands of LinkedIn posts from any profile in a single run
- **Rich structured data** — Get post text, timestamps, engagement stats (likes, comments, reposts), author info, and attached media
- **Flexible input** — Pass a full LinkedIn profile URL, just a username, or a LinkedIn 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 |
|-------|------|----------|---------|-------------|
| `profile` | string | Yes | — | LinkedIn profile URL, username, or URN (e.g. `https://www.linkedin.com/in/satyanadella`) |
| `maxPosts` | integer | No | `100` | Maximum number of posts to scrape |

### Usage Examples

#### Scrape LinkedIn posts from a profile URL

```json
{
    "profile": "https://www.linkedin.com/in/satyanadella",
    "maxPosts": 50
}
```

#### Scrape LinkedIn posts using just a username

```json
{
    "profile": "satyanadella",
    "maxPosts": 200
}
```

### Output — LinkedIn Post Data Structure

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

| Field | Type | Description |
|-------|------|-------------|
| `urn` | object | LinkedIn URNs for the post (`activity_urn`, `share_urn`, `ugcPost_urn`) |
| `full_urn` | string | Full LinkedIn activity URN identifier |
| `posted_at` | object | Post date and time — includes `date` (ISO format), `relative` (human-readable), and `timestamp` (Unix ms) |
| `text` | string | Full text content of the LinkedIn post |
| `url` | string | Direct permalink to the post on LinkedIn |
| `post_type` | string | Type of post (e.g. `regular`) |
| `author` | object | Author details — `first_name`, `last_name`, `headline`, `username`, `profile_url`, `profile_picture` |
| `stats` | object | Engagement metrics — `total_reactions`, `like`, `support`, `love`, `insight`, `celebrate`, `funny`, `comments`, `reposts` |
| `media` | object | null | Attached media — `type` (`video`, `image`, etc.), `url`, `thumbnail`. `null` if no media is attached |

#### Sample Output

```json
[
    {
        "urn": {
            "activity_urn": "7420485585376620544",
            "share_urn": null,
            "ugcPost_urn": "7420485424273416192"
        },
        "full_urn": "urn:li:activity:7420485585376620544",
        "posted_at": {
            "date": "2026-01-29 11:54:39",
            "relative": "1 week ago • Visible to anyone on or off LinkedIn",
            "timestamp": 1769687679069
        },
        "text": "So much of dev work happens in the context of a larger team, and now you can bring all of that work context into the GitHub Copilot CLI with Work IQ.\n\nTry it out: https://lnkd.in/gGPd-FWr",
        "url": "https://www.linkedin.com/posts/satyanadella_so-much-of-dev-work-happens-in-the-context-activity-7420485585376620544-vudJ",
        "post_type": "regular",
        "author": {
            "first_name": "Satya",
            "last_name": "Nadella",
            "headline": "Chairman and CEO at Microsoft",
            "username": "satyanadella",
            "profile_url": "https://www.linkedin.com/in/satyanadella",
            "profile_picture": "https://media.licdn.com/dms/image/..."
        },
        "stats": {
            "total_reactions": 1749,
            "like": 1518,
            "support": 13,
            "love": 50,
            "insight": 83,
            "celebrate": 82,
            "funny": 3,
            "comments": 154,
            "reposts": 174
        },
        "media": {
            "type": "video",
            "url": "https://dms.licdn.com/playlist/vid/...",
            "thumbnail": "https://media.licdn.com/dms/image/..."
        }
    }
]
```

### Use Cases for LinkedIn Post Data

- **LinkedIn content analysis** — Analyze posting frequency, topics, and content strategy of industry leaders or competitors
- **Engagement benchmarking** — Compare LinkedIn reaction counts, comment rates, and repost ratios across profiles to benchmark social media performance
- **Lead generation** — Identify prospects who actively post about specific topics or industries on LinkedIn
- **Social listening & trend tracking** — Monitor what key people in your industry are talking about and spot emerging trends early
- **Sentiment analysis** — Feed LinkedIn post text into NLP pipelines to gauge sentiment around brands, products, or topics
- **Influencer research** — Evaluate potential LinkedIn influencer partners by analyzing their content output and audience engagement
- **Competitive intelligence** — Track how executives and companies communicate product launches, strategy shifts, or hiring activity on LinkedIn
- **AI & ML training data** — Build datasets of professional content for fine-tuning language models on industry-specific topics

# Actor input Schema

## `profile` (type: `string`):

profile URL, username, or URN of the person to scrape (eg: "https://www.linkedin.com/in/satyanadella")

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

Maximum number of posts to collect

## Actor input object example

```json
{
  "profile": "https://www.linkedin.com/in/satyanadella",
  "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 = {
    "profile": "https://www.linkedin.com/in/satyanadella",
    "maxPosts": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("benjarapi/linkedin-user-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 = {
    "profile": "https://www.linkedin.com/in/satyanadella",
    "maxPosts": 100,
}

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

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,benjarapi/linkedin-user-posts"
        }
    }
}

```

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/vyWtXDqJ3xKyA5ayO/builds/ByMHzzp6YZH5rneD1/openapi.json