# 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:** 104 total users, 14 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/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 — 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 '' with your token const client = new ApifyClient({ 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 '' with your token. client = ApifyClient("") # 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("💾 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 '{ "profile": "https://www.linkedin.com/in/satyanadella", "maxPosts": 100 }' | apify call benjarapi/linkedin-user-posts --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=benjarapi/linkedin-user-posts", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin User Posts", "description": "Scrape LinkedIn posts data for a given LinkedIn person profile including post text, post media attachments, reactions count, comments count, author basic info etc.", "version": "0.0", "x-build-id": "ByMHzzp6YZH5rneD1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/benjarapi~linkedin-user-posts/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-benjarapi-linkedin-user-posts", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/benjarapi~linkedin-user-posts/runs": { "post": { "operationId": "runs-sync-benjarapi-linkedin-user-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/benjarapi~linkedin-user-posts/run-sync": { "post": { "operationId": "run-sync-benjarapi-linkedin-user-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "profile" ], "properties": { "profile": { "title": "Profile URL or Username or URN of the LinkedIn profile to scrape", "type": "string", "description": "profile URL, username, or URN of the person to scrape (eg: \"https://www.linkedin.com/in/satyanadella\")" }, "maxPosts": { "title": "Maximum number of posts to collect", "type": "integer", "description": "Maximum number of posts to collect" } } }, "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 } } } } } } } } } } ```