# LinkedIn Search Posts Scraper (`data_direct/linkedin-posts-scraper`) Actor Search LinkedIn posts by keyword with engagement metrics and content entities - **URL**: https://apify.com/data\_direct/linkedin-posts-scraper.md - **Developed by:** [Data Direct](https://apify.com/data_direct) (community) - **Categories:** Social media, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $6.00 / 1,000 runs 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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 Search Posts Scraper Search for LinkedIn posts by keyword. Returns post content, author information, engagement metrics (likes, comments, shares, reactions), and attached content entities (images, articles, videos, job listings). Includes a has_content_entities flag for repost detection. Results can be sorted by most recent or relevance. ### Features - Real-time LinkedIn data — results are fetched live at run time, never cached or stale - No login, cookies, or proxy configuration needed — just provide the input and run - Each result is pushed as a separate dataset item, exportable as JSON, CSV, or Excel - Fast: a typical run finishes in seconds ### Input | Field | Type | Required | Description | |---|---|---|---| | `query` | string | no | Search query (max 500 characters). Required unless at least one filter (e.g. author) is provided. | | `page` | integer | no | Page number for pagination | | `sort_by` | string | no | Sort order for results One of: most_recent, relevance. | | `author` | string | no | Filter to posts authored by a specific person. Accepts a profile URL, public slug (e.g. williamhgates), or member URN — resolved automatically. Comma-separate for multiple. | | `mentions_member` | string | no | Filter to posts that mention a specific person (profile URL, slug, or member URN). | | `from_company` | string | no | Filter to posts authored by a company page. Numeric LinkedIn company ID (from the Company Details endpoint). Comma-separate for multiple. | | `author_company` | string | no | Filter to posts written by people who work at a company. Numeric company ID. | | `mentions_company` | string | no | Filter to posts that mention a company. Numeric company ID. | | `author_title` | string | no | Filter by the author's job title as free text (e.g. CEO). | | `author_industry` | string | no | Filter by the author's industry. Numeric LinkedIn industry ID(s), comma-separated. Advanced. | ### Pricing This Actor is billed **per run** — a flat **$0.006 per run**. - One run returns all matching results for your input, and costs $0.006 — whether it comes back with one result or hundreds. - You’re only charged when a run succeeds. Failed runs are free. ### Example output ```json { "count": 20, "page": 1, "posts": [ { "article": null, "author": "Sina S. Amiri", "comments": 0, "date": "2026-04-04 21:13:18", "domain": "linkedin.com", "has_content_entities": false, "images": [], "job": null, "likes": 5, "reactions": { "interest": 1, "like": 4 }, "shares": 0, "snippet": "In many practices and DSOs, artificial intelligence isn\u2019t fixing bad processes.\n\nIt\u2019s scaling unnecessary complexity.\n\nAnd when complexity scales, inefficiencies don\u2019t just persist.\n\nThey compound into operational drag and financial leakage.\n\nFaster.\n\nLarger.\n\nHarder to unwind.\n\nOrganizations should simplify before they automate, and eliminate before they optimize.\n\nBecause if AI is doing the work, you should first ask why the work exists at all.\n\n#Dentistry #ArtificialIntelligence #Technology #Operations #Strategy", "source": "LinkedIn", "title": "@Sina S. Amiri on LinkedIn", "url": "https://www.linkedin.com/posts/betterdentistry_dentistry-artificialintelligence-technology-activity-7446303912871071744-Z5Ju?utm_source=social_share_send&utm_medium=member_desktop_web&rcm=ACoAAGaiQmMBKfdufODH2pNGp0lL6bF8EztcFWs", "urn": "urn:li:activity:7446303912871071744", "video": null }, { "article": null, "author": "Ponle Adeoye Esq.", "comments": 1, "date": "2026-04-04 21:12:53", "domain": "linkedin.com", "has_content_entities": false, "images": [], "job": null, "likes": 2, "reactions": { "like": 2 }, "shares": 0, "snippet": "AI is not replacing lawyers; it is empowering better lawyers.\n\nArtificial Intelligence (AI) is no longer a futuris ... (truncated) ```` ### About Maintained by **Data Direct** — a small suite of fast, reliable LinkedIn and web-data scrapers. Each actor returns clean, structured results with no login, cookies, or proxy setup required. Have a question or a custom data need? Open an issue on this actor. # Actor input Schema ## `query` (type: `string`): Search query (max 500 characters). Required unless at least one filter (e.g. author) is provided. ## `page` (type: `integer`): Page number for pagination ## `sort_by` (type: `string`): Sort order for results ## `author` (type: `string`): Filter to posts authored by a specific person. Accepts a profile URL, public slug (e.g. williamhgates), or member URN — resolved automatically. Comma-separate for multiple. ## `mentions_member` (type: `string`): Filter to posts that mention a specific person (profile URL, slug, or member URN). ## `from_company` (type: `string`): Filter to posts authored by a company page. Numeric LinkedIn company ID (from the Company Details endpoint). Comma-separate for multiple. ## `author_company` (type: `string`): Filter to posts written by people who work at a company. Numeric company ID. ## `mentions_company` (type: `string`): Filter to posts that mention a company. Numeric company ID. ## `author_title` (type: `string`): Filter by the author's job title as free text (e.g. CEO). ## `author_industry` (type: `string`): Filter by the author's industry. Numeric LinkedIn industry ID(s), comma-separated. Advanced. ## Actor input object example ```json { "query": "artificial intelligence", "page": 1, "sort_by": "most_recent" } ``` # 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 = { "query": "artificial intelligence" }; // Run the Actor and wait for it to finish const run = await client.actor("data_direct/linkedin-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 = { "query": "artificial intelligence" } # Run the Actor and wait for it to finish run = client.actor("data_direct/linkedin-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 '{ "query": "artificial intelligence" }' | apify call data_direct/linkedin-posts-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=data_direct/linkedin-posts-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Search Posts Scraper", "description": "Search LinkedIn posts by keyword with engagement metrics and content entities", "version": "1.0", "x-build-id": "qoczT6qZlEtc2a0sw" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/data_direct~linkedin-posts-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-data_direct-linkedin-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/data_direct~linkedin-posts-scraper/runs": { "post": { "operationId": "runs-sync-data_direct-linkedin-posts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/data_direct~linkedin-posts-scraper/run-sync": { "post": { "operationId": "run-sync-data_direct-linkedin-posts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "query": { "title": "Query", "type": "string", "description": "Search query (max 500 characters). Required unless at least one filter (e.g. author) is provided." }, "page": { "title": "Page", "type": "integer", "description": "Page number for pagination", "default": 1 }, "sort_by": { "title": "Sort by", "enum": [ "most_recent", "relevance" ], "type": "string", "description": "Sort order for results", "default": "most_recent" }, "author": { "title": "Author", "type": "string", "description": "Filter to posts authored by a specific person. Accepts a profile URL, public slug (e.g. williamhgates), or member URN — resolved automatically. Comma-separate for multiple." }, "mentions_member": { "title": "Mentions member", "type": "string", "description": "Filter to posts that mention a specific person (profile URL, slug, or member URN)." }, "from_company": { "title": "From company", "type": "string", "description": "Filter to posts authored by a company page. Numeric LinkedIn company ID (from the Company Details endpoint). Comma-separate for multiple." }, "author_company": { "title": "Author company", "type": "string", "description": "Filter to posts written by people who work at a company. Numeric company ID." }, "mentions_company": { "title": "Mentions company", "type": "string", "description": "Filter to posts that mention a company. Numeric company ID." }, "author_title": { "title": "Author title", "type": "string", "description": "Filter by the author's job title as free text (e.g. CEO)." }, "author_industry": { "title": "Author industry", "type": "string", "description": "Filter by the author's industry. Numeric LinkedIn industry ID(s), comma-separated. Advanced." } } }, "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 } } } } } } } } } } ```