# LinkedIn Profile Activity Scraper (`iron-crawler/linkedin-profile-activity-scraper`) Actor Scrape recent posts, reposts, and activity from any LinkedIn profile. Returns full post text, engagement metrics, reaction breakdowns, media attachments, and author info. Paginated. No cookies or login required. Clean JSON output. - **URL**: https://apify.com/iron-crawler/linkedin-profile-activity-scraper.md - **Developed by:** [Iron Crawler](https://apify.com/iron-crawler) (community) - **Categories:** Social media, Lead generation - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 Profile Activity Scraper Extract the full post and activity history from any LinkedIn profile — post text, engagement metrics, reaction type breakdown, reposts, and media — without cookies, login, or a LinkedIn account. ### Activity Types Supported | Type | Description | |---|---| | `posts` | Original content published by the person | | `reposts` | Content they reshared (with or without commentary) | | `comments` | Posts they publicly commented on | | `reactions` | Posts they publicly reacted to | Set `activity_type` to any of the above, or run all types in sequence. ### Output Fields Per Activity Item - `activity_type` — Post / Repost / Comment / Reaction - `post_text` — full content text - `post_url` — canonical link - `created_at` — ISO 8601 timestamp - `author_name`, `author_headline`, `author_linkedin_url`, `author_type` - `num_likes`, `num_comments`, `num_shares` - `reaction_counts` — full breakdown by type - `media_attachments` — images, documents, videos - `is_repost`, `repost_commentary` - `shared_post` — original post details when applicable - `cursor` — pagination token (handled automatically) ### Input ```json { "profile_urls": ["https://www.linkedin.com/in/garyvaynerchuk/"], "activity_type": "posts", "max_results": 100 } ```` ### Use Cases - **Thought leader content analysis** — extract full post history to analyze writing patterns, topic focus, and engagement performance - **Audience intelligence** — the `comments` and `reactions` types reveal which posts a person engages with — useful for warm prospecting and network mapping - **Content research** — build a corpus of posts from a target industry's key voices - **Monitoring** — run on a schedule to track new posts from specific profiles ### Pricing $15 per 1,000 activity items. No subscription. ### FAQ **What's the difference between `posts` and `reposts`?** `posts` returns only original content. `reposts` returns reshared content. Setting `activity_type: all` returns both. **Does `comments` show posts this person commented on?** Yes — `comments` returns the posts they engaged with via comment, not their own posts. **How far back does the history go?** LinkedIn limits profile activity to roughly the most recent 200–300 items per activity type. The actor retrieves up to `max_results` items, paginating automatically. # Actor input Schema ## `linkedin_url` (type: `string`): The LinkedIn Profile Url to scraping ## `maxPages` (type: `integer`): Maximum number of pages to fetch (pagination handled automatically) ## Actor input object example ```json { "linkedin_url": "https://in.linkedin.com/in/mayankchaba", "maxPages": 1 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("iron-crawler/linkedin-profile-activity-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("iron-crawler/linkedin-profile-activity-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 iron-crawler/linkedin-profile-activity-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=iron-crawler/linkedin-profile-activity-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Profile Activity Scraper", "description": "Scrape recent posts, reposts, and activity from any LinkedIn profile. Returns full post text, engagement metrics, reaction breakdowns, media attachments, and author info. Paginated. No cookies or login required. Clean JSON output.\n", "version": "1.0", "x-build-id": "LRaqzqFckp7kxQLfU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/iron-crawler~linkedin-profile-activity-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-iron-crawler-linkedin-profile-activity-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/iron-crawler~linkedin-profile-activity-scraper/runs": { "post": { "operationId": "runs-sync-iron-crawler-linkedin-profile-activity-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/iron-crawler~linkedin-profile-activity-scraper/run-sync": { "post": { "operationId": "run-sync-iron-crawler-linkedin-profile-activity-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "linkedin_url" ], "properties": { "linkedin_url": { "title": "LinkedIn Profile Url", "type": "string", "description": "The LinkedIn Profile Url to scraping", "default": "https://in.linkedin.com/in/mayankchaba" }, "maxPages": { "title": "Maximum Pages", "minimum": 1, "maximum": 5, "type": "integer", "description": "Maximum number of pages to fetch (pagination handled automatically)", "default": 1 } } }, "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 } } } } } } } } } } ```