# LinkedIn Posts Search Scraper (`powerai/linkedin-posts-search-scraper`) Actor

Extract posts from LinkedIn with detailed metadata including content, author details, engagement metrics, and direct URLs. Perfect for social media analysis, content research, and brand monitoring.

- **URL**: https://apify.com/powerai/linkedin-posts-search-scraper.md
- **Developed by:** [PowerAI](https://apify.com/powerai) (community)
- **Categories:** Jobs, Lead generation, Social media
- **Features:** Limited permissions
- **Stats:** 711 total users, 6 monthly users, 75.7% runs succeeded, 14 bookmarks
- **User rating**: 1.56 out of 5 stars

## Pricing

from $4.99 / 1,000 results

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?

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 Posts Search Scraper

Extract posts from LinkedIn with detailed metadata! This powerful tool helps you gather comprehensive information about LinkedIn posts, including content, author details, engagement metrics, and direct URLs.

### ✨ Key Features

- 📥 Search posts with flexible filters
- 📊 Get detailed post metadata including:
  - Post content and commentary
  - Author information
  - Engagement metrics (likes, comments, shares)
  - Article components
  - Social details
- 🔗 Generate direct URLs for:
  - Posts
  - Author profiles
- 📈 Support for large result sets with automatic pagination
- 📋 Clean and structured JSON output

### 💪 Why Choose This Actor?

- **Comprehensive**: Get all post details from LinkedIn
- **Flexible**: Multiple search filters and parameters
- **Efficient**: Automatic pagination for large result sets
- **Structured**: Clean JSON output for easy integration
- **Reliable**: Stable and consistent data extraction
- **User-Friendly**: Simple input parameters

### 🎯 Perfect For

- 📊 Social Media Analysts
- 📈 Content Researchers
- 📱 Marketing Professionals
- 🎯 Brand Monitors
- 🔍 Competitor Analysts
- 📋 Content Strategists

### 📈 Use Cases

1. **Content Research**: Analyze trending topics and content
2. **Competitor Analysis**: Monitor competitor's social media presence
3. **Content Discovery**: Find relevant posts and articles
4. **Engagement Analysis**: Study post performance metrics
5. **Brand Monitoring**: Track brand mentions and discussions

### 📝 Input Requirements

The actor accepts the following input parameters:

- `keyword` (required): Search keyword for post search (post search v2 API)
- `maxResults` (optional): Maximum number of posts to extract (default: 20, max: 5000)
- `sort_by` (optional): `relevance` or `date_posted`
- `date_posted` (optional): `past_month`, `past_week`, `past_24h`, or empty for no filter
- `content_type` (optional): `photos`, `videos`, `jobs`, `live_videos`, `documents`, `collaborative_articles`, or empty
- `from_member` (optional): Comma-separated member IDs to filter authors
- `from_company` (optional): Comma-separated company IDs to filter companies

#### Input Example

```json
{
  "keyword": "machine learning",
  "maxResults": 20,
  "sort_by": "relevance",
  "date_posted": "",
  "content_type": "",
  "from_member": "",
  "from_company": ""
}
```

#### Output Example

Each dataset record matches the post search v2 shape, plus a `scrapedAt` timestamp added by the actor:

```json
[
  {
    "id": "urn:li:activity:7324305403838693376",
    "url": "https://www.linkedin.com/posts/...",
    "title": "Post headline or preview text",
    "activity": {
      "num_likes": 16,
      "num_comments": 9,
      "num_shares": 1,
      "reaction_counts": [
        { "count": 12, "type": "LIKE" }
      ]
    },
    "created_at": "2025-05-03T05:34:47.000Z",
    "author": {
      "name": "Jane Doe",
      "description": "Software Engineer @ Example Corp",
      "url": "https://www.linkedin.com/in/...",
      "avatar": [
        {
          "width": 200,
          "height": 200,
          "url": "https://media.licdn.com/dms/image/...",
          "expires_at": 1752710400
        }
      ]
    },
    "scrapedAt": "2025-05-12T09:03:48.797Z"
  }
]
```

# Actor input Schema

## `keyword` (type: `string`):

Search keyword for LinkedIn posts (Fresh LinkedIn Scraper post search v2).

## `maxResults` (type: `integer`):

Maximum number of posts to return (pagination stops when this is reached or there are no more pages).

## `sort_by` (type: `string`):

Sort order: relevance or date_posted.

## `date_posted` (type: `string`):

Restrict to posts published within a time window. Leave empty for no date filter.

## `content_type` (type: `string`):

Filter by post content type. Leave empty for all types.

## `from_member` (type: `string`):

Comma-separated member identifiers to filter authors.

## `from_company` (type: `string`):

Comma-separated company identifiers to filter posts from those companies.

## Actor input object example

```json
{
  "keyword": "artificial intelligence",
  "maxResults": 10,
  "sort_by": "relevance",
  "date_posted": "",
  "content_type": ""
}
```

# 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 = {};

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

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("powerai/linkedin-posts-search-scraper").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 '{}' |
apify call powerai/linkedin-posts-search-scraper --silent --output-dataset

```

## MCP server setup

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

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/Sa8LUWgtparEklk5Y/builds/HNzz6vvKoo4lXMUfR/openapi.json