# LinkedIn Post & Comments Scraper ✅ No cookies (`practicaltools/apify-linkedin-post-scraper`) Actor A robust Actor that extracts structured data from public LinkedIn posts, including author, text, images, videos, date and reactions. - **URL**: https://apify.com/practicaltools/apify-linkedin-post-scraper.md - **Developed by:** [Practical Tools](https://apify.com/practicaltools) (community) - **Categories:** Automation, Developer tools, Social media - **Stats:** 219 total users, 7 monthly users, 100.0% runs succeeded, 5 bookmarks - **User rating**: 4.96 out of 5 stars ## Pricing from $4.00 / 1,000 successes This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 - Apify Actor A robust Apify Actor that extracts structured data from public LinkedIn posts, including author, text, images, videos, date, reactions, and comments. ### Features - 👤 **Author Extraction** - Captures the post author's name - 📝 **Text Extraction** - Extracts the main post content - 🖼️ **Image Extraction** - Collects all image URLs from the post - 🎥 **Video Support** - Extracts video and embedded video URLs - 📅 **Date Extraction** - Gets the post's published date - 👍 **Reactions** - Gathers reaction counts (like, celebrate, etc.) - 💬 **Comments** - Extracts all visible comments with author, text, and date - 🚀 **High Performance** - Concurrent processing with configurable limits - 🌐 **Proxy Support** - Built-in Apify Proxy integration ### Input The Actor accepts the following input parameters: ```json { "urls": [ "https://www.linkedin.com/posts/example_post_id" ], "maxConcurrency": 10, "proxyConfiguration": { "useApifyProxy": true } } ```` #### Parameters | Parameter | Type | Required | Description | |---------------------|--------|----------|---------------------------------------------| | `urls` | Array | ✅ | List of LinkedIn post URLs to scrape | | `maxConcurrency` | Int | ❌ | Max concurrent requests (default: 10) | | `proxyConfiguration`| Object | ❌ | Proxy configuration | ### Output The Actor outputs a simple JSON object for each post: ```json { "author": "Jane Doe", "text": "Excited to announce our new product launch!", "images": [ "https://media.licdn.com/dms/image/..." ], "videos": [ "https://www.youtube.com/embed/..." ], "date": "2d", "reactions": { "like": 12, "celebrate": 3, "support": 1 }, "comments": [ { "author": "John Smith", "text": "Congratulations!", "date": "1d" } ] } ``` ### Notes - Only public LinkedIn posts are supported (no authentication required). - Output fields may be empty if not present in the post. - Selectors may require updates if LinkedIn changes their UI. - Scraping LinkedIn may be subject to their terms of service. ### Usage 1. Deploy this Actor on the Apify platform or run locally. 2. Provide one or more public LinkedIn post URLs in the input. 3. Retrieve structured post data as output. # Actor input Schema ## `urls` (type: `array`): List of LinkedIn post URLs to scrape ## `proxyConfiguration` (type: `object`): Proxy settings for requests ## `maxConcurrency` (type: `integer`): Maximum number of concurrent requests per run ## `li_at` (type: `string`): If provided, will be used for authenticated scraping instead of the default session. ## `expandComments` (type: `boolean`): Whether to click 'See more comments' to expand the comment list. WARNING: This significantly increases scraping time. ## `maxComments` (type: `integer`): Maximum number of comments to extract if expansion is enabled. ## Actor input object example ```json { "urls": [ { "url": "https://www.linkedin.com/posts/tesla-motors_join-the-tesla-team-httpslnkding3mwe6z-activity-6736697001603403778-_MYv/" } ], "proxyConfiguration": { "useApifyProxy": false }, "maxConcurrency": 10, "expandComments": false, "maxComments": 10 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("practicaltools/apify-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 = {} # Run the Actor and wait for it to finish run = client.actor("practicaltools/apify-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 '{}' | apify call practicaltools/apify-linkedin-post-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=practicaltools/apify-linkedin-post-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post & Comments Scraper ✅ No cookies", "description": "A robust Actor that extracts structured data from public LinkedIn posts, including author, text, images, videos, date and reactions.", "version": "0.0", "x-build-id": "h0t3YKCrUKJ0RS9cq" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/practicaltools~apify-linkedin-post-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-practicaltools-apify-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/practicaltools~apify-linkedin-post-scraper/runs": { "post": { "operationId": "runs-sync-practicaltools-apify-linkedin-post-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/practicaltools~apify-linkedin-post-scraper/run-sync": { "post": { "operationId": "run-sync-practicaltools-apify-linkedin-post-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "urls" ], "properties": { "urls": { "title": "Post URLs (Required)", "type": "array", "description": "List of LinkedIn post URLs to scrape", "default": [ { "url": "https://www.linkedin.com/posts/tesla-motors_join-the-tesla-team-httpslnkding3mwe6z-activity-6736697001603403778-_MYv/" } ], "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Proxy settings for requests", "default": { "useApifyProxy": false } }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of concurrent requests per run", "default": 10 }, "li_at": { "title": "LinkedIn li_at session cookie (optional)", "type": "string", "description": "If provided, will be used for authenticated scraping instead of the default session." }, "expandComments": { "title": "Expand comments", "type": "boolean", "description": "Whether to click 'See more comments' to expand the comment list. WARNING: This significantly increases scraping time.", "default": false }, "maxComments": { "title": "Max comments", "minimum": 0, "type": "integer", "description": "Maximum number of comments to extract if expansion is enabled.", "default": 10 } } }, "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 } } } } } } } } } } ```