# LinkedIn Profile Posts Scraper (`capable_cauldron/linkedin-profile-posts-scraper`) Actor Scrape LinkedIn profile posts with automatic pagination. No cookies needed. Get rich post data including engagement metrics, content categories, hashtags, mentioned companies/profiles, and computed fields not available in other actors. - **URL**: https://apify.com/capable\_cauldron/linkedin-profile-posts-scraper.md - **Developed by:** [Capable Cauldron](https://apify.com/capable_cauldron) (community) - **Categories:** Social media, Automation, Lead generation - **Stats:** 9 total users, 7 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $1.00 / 1,000 post scrapeds 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 Posts Scraper [No Cookies] Scrape LinkedIn posts data for any profile — including post content, full engagement metrics, media attachments, mentioned entities, and computed enrichment fields not available in other actors. ### 💰 Pricing **$3.00 / 1,000 results** This Actor uses Pay-Per-Event pricing. You only pay for what you get: | Event | Price | | ----------------- | ------ | | Post scraped | $0.003 | | Profile processed | $0.01 | Scraping 100 posts from 1 profile costs **$0.31**. The `maxDatasetItems` parameter provides a hard cost ceiling. ### ✨ Key Features - **No account needed** — No cookies, no login, no risk to your LinkedIn account - **Automatic pagination** — Set how many posts you want, the actor handles the rest - **55 enriched fields per post** — More data than any other LinkedIn posts actor: - Total engagement pre-calculated (reactions + comments + reposts) - Content category classified automatically (text, image, video, article, document, carousel, poll) - Hashtags extracted and counted - Mentioned companies and profiles as structured objects - Individual reaction breakdowns (like, love, celebrate, support, insight, funny, curious) - ISO-8601 dates derived from timestamps for clean sorting - Word and character counts for content analysis - **Multiple profiles per run** — Scrape one or hundreds of profiles in a single run - **Cost control** — Hard cap on dataset items to prevent runaway costs ### 📖 Simple Usage Simply provide one or more LinkedIn profile usernames or URLs. The username is the last part of a LinkedIn profile URL (e.g., `satyanadella` from `linkedin.com/in/satyanadella`). Set `maxPostsPerProfile` to control how many posts you want per profile. The actor automatically paginates — no manual token handling needed. ### 📊 Output Structure Each post in the dataset contains 55 fields: ```json { "activity_urn": "7470793400858984449", "share_urn": "7470793399273537536", "full_urn": "urn:li:activity:7470793400858984449", "post_url": "https://www.linkedin.com/posts/adamselipsky_achieving-success-with-ai-the-official", "post_type": "repost", "is_repost": true, "text": "My conversations with customers today are less about whether to adopt AI...", "content_category": "article", "word_count": 157, "char_count": 990, "hashtags": ["AI", "CloudComputing"], "hashtag_count": 2, "media_type": "image", "media_url": "https://media.licdn.com/dms/image/...", "media_thumbnail": "https://media.licdn.com/dms/image/...", "has_article": true, "article_url": "https://blogs.microsoft.com/blog/2026/06/16/achieving-success-with-ai/", "article_title": "Achieving success with AI - The Official Microsoft Blog", "article_subtitle": "blogs.microsoft.com", "has_document": false, "document_title": "", "document_page_count": null, "author_name": "Adam Selipsky", "author_headline": "CEO of Helix Digital Infrastructure. Former CEO at Amazon Web Services.", "author_username": "adamselipsky", "author_profile_url": "https://www.linkedin.com/in/adamselipsky", "author_profile_picture": "https://media.licdn.com/dms/image/...", "total_reactions": 2173, "reaction_like": 1882, "reaction_love": 44, "reaction_celebrate": 204, "reaction_support": 12, "reaction_insight": 30, "reaction_funny": 1, "reaction_curious": 0, "num_comments": 187, "num_reposts": 55, "total_engagement": 2415, "mentioned_companies": [ { "name": "Amazon Web Services (AWS)", "urn": "https://www.linkedin.com/company/amazon-web-services/" } ], "mentioned_profiles": [], "posted_date": "2026-06-11 11:05:47", "posted_relative": "1w", "posted_timestamp": 1781175947394, "posted_date_iso": "2026-06-11T11:05:47.394000+00:00", "profile_username": "adamselipsky", "scraped_at": "2026-06-17T15:30:00.000000+00:00" } ```` #### Dataset Views Three pre-built views for quick analysis in the Apify Console: - **Overview** — Profile, author, date, text, category, URL - **Engagement Metrics** — All reaction types, comments, reposts, totals - **Content Analysis** — Category, word count, hashtags, mentions, media type ### 🔧 Input | Field | Type | Required | Default | Description | | -------------------- | ------- | -------- | ------- | ------------------------------------------------------------------------------------------- | | `usernames` | Array | Yes | — | LinkedIn usernames, member IDs, or profile URLs (e.g., `["satyanadella", "williamhgates"]`) | | `maxPostsPerProfile` | Integer | No | 100 | Max posts to scrape per profile (1–2,000) | | `maxDatasetItems` | Integer | No | 10,000 | Hard cap on total dataset items across all profiles | | `maxRetries` | Integer | No | 3 | Retry attempts for failed requests | #### Input example ```json { "usernames": ["satyanadella", "williamhgates"], "maxPostsPerProfile": 100, "maxDatasetItems": 10000 } ``` ### 🔌 API You can run this Actor programmatically using the Apify API. #### JavaScript ```javascript import { ApifyClient } from "apify-client"; const client = new ApifyClient({ token: "" }); const run = await client.actor("YOUR_ACTOR_ID").call({ usernames: ["satyanadella"], maxPostsPerProfile: 100, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ``` #### Python ```python from apify_client import ApifyClient client = ApifyClient("") run = client.actor("YOUR_ACTOR_ID").call(run_input={ "usernames": ["satyanadella"], "maxPostsPerProfile": 100 }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ``` #### CLI ```bash echo '{"usernames": ["satyanadella"], "maxPostsPerProfile": 100}' | apify call YOUR_ACTOR_ID --silent --output-dataset ``` *** **Note:** This Actor is an independent tool for extracting publicly available social media post data. It is not affiliated with or endorsed by any social media platform. # Actor input Schema ## `usernames` (type: `array`): LinkedIn usernames, member IDs, or profile URLs. Accepted formats: - Vanity username: satyanadella - Profile URL: https://www.linkedin.com/in/satyanadella/ - Member ID: ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ - Sales Navigator: linkedin.com/sales/people/ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ,name,qU7\_ Pagination is handled automatically. ## `maxPostsPerProfile` (type: `integer`): Maximum number of posts to scrape per profile. The actor automatically handles pagination — no need to manually pass pagination tokens. ## `maxDatasetItems` (type: `integer`): Hard cap on the total number of items pushed to the dataset across all profiles. Prevents runaway costs on large runs. ## `maxRetries` (type: `integer`): How many times to retry a failed API request before giving up. Applies exponential backoff on retries. ## Actor input object example ```json { "usernames": [ "satyanadella" ], "maxPostsPerProfile": 100, "maxDatasetItems": 10000, "maxRetries": 3 } ``` # Actor output Schema ## `posts` (type: `string`): All scraped posts from LinkedIn profiles, including post text, engagement metrics, author details, content categories, hashtags, mentioned entities, and computed fields. ## `summary` (type: `string`): Summary of the scraping run including profiles processed, total posts scraped, and pagination tokens. # 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 = { "usernames": [ "satyanadella" ] }; // Run the Actor and wait for it to finish const run = await client.actor("capable_cauldron/linkedin-profile-posts-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 = { "usernames": ["satyanadella"] } # Run the Actor and wait for it to finish run = client.actor("capable_cauldron/linkedin-profile-posts-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 '{ "usernames": [ "satyanadella" ] }' | apify call capable_cauldron/linkedin-profile-posts-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=capable_cauldron/linkedin-profile-posts-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Profile Posts Scraper", "description": "Scrape LinkedIn profile posts with automatic pagination. No cookies needed. Get rich post data including engagement metrics, content categories, hashtags, mentioned companies/profiles, and computed fields not available in other actors.", "version": "2.0", "x-build-id": "CmJT25triDYROcew0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/capable_cauldron~linkedin-profile-posts-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-capable_cauldron-linkedin-profile-posts-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/capable_cauldron~linkedin-profile-posts-scraper/runs": { "post": { "operationId": "runs-sync-capable_cauldron-linkedin-profile-posts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/capable_cauldron~linkedin-profile-posts-scraper/run-sync": { "post": { "operationId": "run-sync-capable_cauldron-linkedin-profile-posts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "usernames" ], "properties": { "usernames": { "title": "LinkedIn Usernames or Profile URLs", "type": "array", "description": "LinkedIn usernames, member IDs, or profile URLs.\n\nAccepted formats:\n- Vanity username: satyanadella\n- Profile URL: https://www.linkedin.com/in/satyanadella/\n- Member ID: ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ\n- Sales Navigator: linkedin.com/sales/people/ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ,name,qU7_\n\nPagination is handled automatically.", "items": { "type": "string" } }, "maxPostsPerProfile": { "title": "Max Posts Per Profile", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Maximum number of posts to scrape per profile. The actor automatically handles pagination — no need to manually pass pagination tokens.", "default": 100 }, "maxDatasetItems": { "title": "Max Total Dataset Items", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Hard cap on the total number of items pushed to the dataset across all profiles. Prevents runaway costs on large runs.", "default": 10000 }, "maxRetries": { "title": "Max Retries Per Request", "minimum": 0, "maximum": 10, "type": "integer", "description": "How many times to retry a failed API request before giving up. Applies exponential backoff on retries.", "default": 3 } } }, "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 } } } } } } } } } } ```