# LinkedIn Post Scraper - $0.30/1k results (`thescrappa/linkedin-post-scraper`) Actor Scrape public LinkedIn posts and articles. Get content, author info, reactions, comments, and related articles. No LinkedIn login required. - **URL**: https://apify.com/thescrappa/linkedin-post-scraper.md - **Developed by:** [Scrappa](https://apify.com/thescrappa) (community) - **Categories:** Lead generation, Social media - **Stats:** 32 total users, 4 monthly users, 94.1% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing $0.30 / 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/platform/actors/running/actors-in-store#pay-per-event ## 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 Post Scraper Scrape public LinkedIn posts and articles without login. Extract content, author information, reactions, comments, and related articles from any public LinkedIn post or Pulse article. ### Features - **Post Content** - Full article text and images - **Author Info** - Name, headline, profile URL, and image - **Reactions** - Total reactions, likes, and comment counts - **Topics** - Article topics and tags - **Related Articles** - Recommended reading from LinkedIn - **Comments** - Post comments and engagement - **No Login Required** - Scrape public posts without authentication - **Cache Support** - Reduce costs with intelligent caching ### Input | Field | Type | Required | Description | |-------|------|----------|-------------| | `url` | string | Yes | LinkedIn post or article URL | | `use_cache` | boolean | No | Use cached results if available (default: true) | | `maximum_cache_age` | integer | No | Max cache age in seconds (default: 3600) | #### Supported URL Formats - LinkedIn Pulse articles: `https://www.linkedin.com/pulse/article-title-author-id/` - LinkedIn posts: `https://www.linkedin.com/posts/...` ### Output #### Dataset (Post Data) Each post is saved to the dataset with the following structure: ```json { "title": "The Future of Artificial Intelligence", "url": "https://www.linkedin.com/pulse/...", "date_published": "2024-01-15", "date_modified": "2024-01-16", "image": "https://media.licdn.com/...", "body": "Full article text...", "author_name": "John Doe", "author_url": "https://www.linkedin.com/in/johndoe", "author_image": "https://media.licdn.com/...", "author_headline": "AI Researcher | Tech Entrepreneur", "reactions_total": 1250, "reactions_likes": 1100, "comments_count": 150, "topics": ["Artificial Intelligence", "Technology", "Future"], "more_articles": [...], "comments": [...], "success": true } ```` #### Key-Value Store (Full Response) The complete response is saved to the `OUTPUT` key, including nested author object and reactions breakdown: ```json { "success": true, "title": "Article Title", "url": "https://www.linkedin.com/pulse/...", "date_published": "2024-01-15", "date_modified": "2024-01-16", "image": "https://media.licdn.com/...", "body": "Full article content...", "author": { "name": "John Doe", "url": "https://www.linkedin.com/in/johndoe", "image": "https://media.licdn.com/...", "headline": "AI Researcher | Tech Entrepreneur" }, "reactions": { "total": 1250, "likes": 1100, "comments": 150 }, "topics": ["Artificial Intelligence", "Technology"], "more_articles": [ { "title": "Related Article", "url": "https://...", "author": "Jane Smith" } ], "comments": [ { "author": "User Name", "text": "Great insights!", "date": "2024-01-15" } ] } ``` ### Example ```json { "url": "https://www.linkedin.com/pulse/future-artificial-intelligence-how-ai-reshaping-our-world-doe-1e/", "use_cache": true, "maximum_cache_age": 3600 } ``` ### Pricing $0.30 per 1,000 results. No additional API keys or LinkedIn login required. ### Support For issues or questions, contact us through Apify. # Actor input Schema ## `url` (type: `string`): URL of the LinkedIn post or article to scrape (e.g., https://linkedin.com/pulse/... or https://linkedin.com/posts/...) ## `use_cache` (type: `boolean`): Use cached results if available to reduce costs. When disabled, the actor omits the cache flag because Scrappa does not accept use\_cache=0. ## `maximum_cache_age` (type: `integer`): Maximum age of cached results in seconds. Must be at least 1. ## Actor input object example ```json { "url": "https://www.linkedin.com/posts/microsoft_worktrendindex-activity-7457369463198437376-3F4k", "use_cache": true, "maximum_cache_age": 3600 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "url": "https://www.linkedin.com/posts/microsoft_worktrendindex-activity-7457369463198437376-3F4k" }; // Run the Actor and wait for it to finish const run = await client.actor("thescrappa/linkedin-post-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "url": "https://www.linkedin.com/posts/microsoft_worktrendindex-activity-7457369463198437376-3F4k" } # Run the Actor and wait for it to finish run = client.actor("thescrappa/linkedin-post-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 '{ "url": "https://www.linkedin.com/posts/microsoft_worktrendindex-activity-7457369463198437376-3F4k" }' | apify call thescrappa/linkedin-post-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=thescrappa/linkedin-post-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post Scraper - $0.30/1k results", "description": "Scrape public LinkedIn posts and articles. Get content, author info, reactions, comments, and related articles. No LinkedIn login required.", "version": "1.0", "x-build-id": "9maAye3FN1H6lCEKc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/thescrappa~linkedin-post-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-thescrappa-linkedin-post-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/thescrappa~linkedin-post-scraper/runs": { "post": { "operationId": "runs-sync-thescrappa-linkedin-post-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/thescrappa~linkedin-post-scraper/run-sync": { "post": { "operationId": "run-sync-thescrappa-linkedin-post-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": [ "url" ], "properties": { "url": { "title": "LinkedIn Post URL", "type": "string", "description": "URL of the LinkedIn post or article to scrape (e.g., https://linkedin.com/pulse/... or https://linkedin.com/posts/...)" }, "use_cache": { "title": "Use Cache", "type": "boolean", "description": "Use cached results if available to reduce costs. When disabled, the actor omits the cache flag because Scrappa does not accept use_cache=0.", "default": true }, "maximum_cache_age": { "title": "Maximum Cache Age (seconds)", "minimum": 1, "type": "integer", "description": "Maximum age of cached results in seconds. Must be at least 1.", "default": 3600 } } }, "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 } } } } } } } } } } ```