# Linkedin Post Search (`lisbuenas/linkedin-post-search`) Actor LinkedIn Post Scraper extracts public post data with advanced profile or company filtersโ€”no cookies or account required. Effortlessly track brand mentions, monitor industry trends, and gather audience insights in bulk. - **URL**: https://apify.com/lisbuenas/linkedin-post-search.md - **Developed by:** [Felipe Lisboa](https://apify.com/lisbuenas) (community) - **Categories:** Lead generation, Social media, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.30 / 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 Posts Search Scraper (No Cookies) Apify actor that searches LinkedIn posts by keyword and extracts post content, author details, reaction counts and media attachments. No account or cookies required โ€” don't risk your LinkedIn account by sharing session cookies. ### Features - **No account needed** โ€” scrapes public search results without cookies or login - Keyword-based post search - Post content and media attachments (images, videos, articles, documents) - Reaction, comment and repost counts - Canonical post URLs for sharing - Author details (name, headline, profile URL, picture) - Sorting by relevance or date posted - Date filters (past 24 hours / week / month) - Filtering by company, author's company, author's industry and author's job title - Automatic and manual pagination ### Input | Field | Type | Description | | --- | --- | --- | | `keyword` | string (required) | Keyword or phrase to search for. | | `sort_type` | string | `relevance` (default) or `date_posted`. | | `date_filter` | string | `past-24h`, `past-week` or `past-month`. | | `total_posts` | integer | Total posts to collect (default `20`). Pagination is automatic. | | `page_number` | integer | Page to start from for manual pagination (default `1`). Each page covers up to 50 posts. | | `company_urns` | string | Comma-separated company URNs; only posts published by these companies, e.g. `1441,1035`. | | `author_company_urns` | string | Comma-separated company URNs; only posts by authors working at these companies, e.g. `1035,1045`. | | `author_industry_urns` | string | Comma-separated industry URNs; only posts by authors in these industries, e.g. `43,4`. | | `author_job_title` | string | Only posts by authors with this job title, e.g. `software engineer`. | #### Where to find URNs Run a filtered search on LinkedIn and decode the URL. For example, in `https://www.linkedin.com/search/results/content/?fromOrganization=%5B%221441%22%2C%221035%22%5D&keywords=coding` the company URNs are `1441` (Google) and `1035` (Microsoft). #### Example input ```json { "keyword": "artificial intelligence", "sort_type": "date_posted", "total_posts": 50, "author_industry_urns": "4", "author_job_title": "software engineer" } ```` ### Output One dataset item per post: ```json { "urn": "7301882444501135360", "full_urn": "urn:li:activity:7301882444501135360", "url": "https://www.linkedin.com/feed/update/urn:li:activity:7301882444501135360/", "text": "Excited to share our latest work on...", "posted_at": { "relative": "2w", "date": "2026-05-29T00:00:00.000Z", "timestamp": 1779062400000 }, "author": { "name": "Jane Doe", "headline": "Software Engineer at Example Corp", "profile_url": "https://www.linkedin.com/in/janedoe", "profile_picture": "https://media.licdn.com/dms/image/..." }, "stats": { "total_reactions": 1234, "comments": 56, "reposts": 12 }, "media": { "type": "image", "url": null, "images": ["https://media.licdn.com/dms/image/..."] }, "page_number": 1, "from_keyword": "artificial intelligence" } ``` When the scraper cannot retrieve data for a given input it pushes an error item instead of silently skipping it, distinguishable by the `error` field (`NO_RESULTS`, `NO_MORE_RESULTS`, `LOGIN_WALL`, `INVALID_INPUT`). # Actor input Schema ## `keyword` (type: `string`): Keyword or phrase to search for, exactly as you would type it into the LinkedIn search bar. ## `sort_type` (type: `string`): How LinkedIn should sort the search results: most relevant posts or the most recent ones. ## `date_filter` (type: `string`): Only return posts published within the selected period. ## `total_posts` (type: `integer`): How many posts to collect in total. Pagination is handled automatically until this number is reached or LinkedIn runs out of results. ## `page_number` (type: `integer`): Page to start from, for manual pagination. Each page returns up to 50 posts: page 1 covers posts 1-50, page 2 covers 51-100, and so on. ## `company_urns` (type: `string`): Comma-separated list of company URNs. Only posts published by these companies are returned, e.g. "1441,1035" for Google and Microsoft. ## `author_company_urns` (type: `string`): Comma-separated list of company URNs. Only posts written by authors working at these companies are returned, e.g. "1035,1045" for Microsoft and Apple. ## `author_industry_urns` (type: `string`): Comma-separated list of industry URNs. Only posts written by authors working in these industries are returned, e.g. "43,4" for Financial Services and Software Development. ## `author_job_title` (type: `string`): Only posts written by authors with this job title are returned, e.g. "software engineer". ## `proxyConfiguration` (type: `object`): Select the proxies the scraper rotates through. Defaults to the cheap Apify datacenter pool; the actor relies on session rotation, cookie warmup and realistic headers to stay under the radar. ## Actor input object example ```json { "keyword": "artificial intelligence", "sort_type": "relevance", "total_posts": 20, "page_number": 1, "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `results` (type: `string`): Dataset containing LinkedIn posts matching the search keyword and filters. # 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 = { "keyword": "artificial intelligence", "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("lisbuenas/linkedin-post-search").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "keyword": "artificial intelligence", "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("lisbuenas/linkedin-post-search").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keyword": "artificial intelligence", "proxyConfiguration": { "useApifyProxy": true } }' | apify call lisbuenas/linkedin-post-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lisbuenas/linkedin-post-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin Post Search", "description": "LinkedIn Post Scraper extracts public post data with advanced profile or company filtersโ€”no cookies or account required. Effortlessly track brand mentions, monitor industry trends, and gather audience insights in bulk.", "version": "0.0", "x-build-id": "mG26fA6yks32wCJJy" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lisbuenas~linkedin-post-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lisbuenas-linkedin-post-search", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lisbuenas~linkedin-post-search/runs": { "post": { "operationId": "runs-sync-lisbuenas-linkedin-post-search", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lisbuenas~linkedin-post-search/run-sync": { "post": { "operationId": "run-sync-lisbuenas-linkedin-post-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keyword" ], "properties": { "keyword": { "title": "Search keyword", "type": "string", "description": "Keyword or phrase to search for, exactly as you would type it into the LinkedIn search bar." }, "sort_type": { "title": "Sort type", "enum": [ "relevance", "date_posted" ], "type": "string", "description": "How LinkedIn should sort the search results: most relevant posts or the most recent ones.", "default": "relevance" }, "date_filter": { "title": "Date filter", "enum": [ "past-24h", "past-week", "past-month" ], "type": "string", "description": "Only return posts published within the selected period." }, "total_posts": { "title": "Total posts to scrape", "minimum": 1, "type": "integer", "description": "How many posts to collect in total. Pagination is handled automatically until this number is reached or LinkedIn runs out of results.", "default": 20 }, "page_number": { "title": "Page number", "minimum": 1, "type": "integer", "description": "Page to start from, for manual pagination. Each page returns up to 50 posts: page 1 covers posts 1-50, page 2 covers 51-100, and so on.", "default": 1 }, "company_urns": { "title": "From companies (URNs)", "type": "string", "description": "Comma-separated list of company URNs. Only posts published by these companies are returned, e.g. \"1441,1035\" for Google and Microsoft." }, "author_company_urns": { "title": "Author's companies (URNs)", "type": "string", "description": "Comma-separated list of company URNs. Only posts written by authors working at these companies are returned, e.g. \"1035,1045\" for Microsoft and Apple." }, "author_industry_urns": { "title": "Author's industries (URNs)", "type": "string", "description": "Comma-separated list of industry URNs. Only posts written by authors working in these industries are returned, e.g. \"43,4\" for Financial Services and Software Development." }, "author_job_title": { "title": "Author's job title", "type": "string", "description": "Only posts written by authors with this job title are returned, e.g. \"software engineer\"." }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Select the proxies the scraper rotates through. Defaults to the cheap Apify datacenter pool; the actor relies on session rotation, cookie warmup and realistic headers to stay under the radar.", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```