# LinkedIn Post Search Cookie-less 🍪 ✅ (`datamagnet/linkedin-post-search`) Actor Search LinkedIn-style posts by keyword and filters, then return clean, easy-to-read results. Get the most relevant post details surfaced in a simple format. - **URL**: https://apify.com/datamagnet/linkedin-post-search.md - **Developed by:** [Datamagnet](https://apify.com/datamagnet) (community) - **Categories:** Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 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 Post Searcher — LinkedIn Post Search Scraper Find LinkedIn-style posts by keyword and filters, then turn them into clean, easy-to-read results you can actually use. LinkedIn Post Searcher is built for marketers, recruiters, sales teams, researchers, and founders who want to search LinkedIn post content without wasting time scrolling manually. Use it as a LinkedIn post scraper, LinkedIn content extractor, or social listening data collector to quickly surface relevant posts, authors, companies, mentions, and posting dates. It is especially useful when you need fresh post data for outreach, trend tracking, competitor monitoring, or audience research. ### Key Features - **Extract** LinkedIn-style posts that match your keyword and return only the most relevant results. - **Collect** clean post details such as post text, author name, author title, company, industry, and content type. - **Filter** results by date posted, author title, company, industry, and mentions to narrow your search. - **Sort** posts by your preferred order so you can focus on the newest or most relevant content first. - **Scale** from a few results to larger searches while the actor keeps fetching more in the background. - **Surface** useful context like source member, source company, and mentioned people or organizations. - **Track** progress clearly so you know the search is running and when results are ready. ### Use Cases #### Market Research Product marketers and analysts use this LinkedIn post search scraper to find posts about a topic like cloud, AI, hiring, or cybersecurity. By reviewing post text, author title, author industry, and posted date, they can spot what people are talking about right now and identify emerging themes before they become mainstream. #### Lead Generation Sales teams can search for posts from specific member usernames, companies, or job titles to find people who are active and relevant. The returned author name, author company, and post content help reps personalize outreach based on what prospects are publicly discussing. #### Competitor Monitoring Founders and competitive intelligence teams can track posts from a competitor’s company or from authors associated with that company. With fields like from company, author company, mentions organization, and posted date, they can see how competitors position themselves, what campaigns they promote, and which topics they emphasize. #### Recruiting and Talent Sourcing Recruiters can search for posts by author title, author industry, or company to find professionals who are active in a target field. The post content and author details help recruiters identify subject-matter experts, understand what candidates care about, and start more relevant conversations. #### Social Listening Brand and communications teams can monitor mentions of their organization, executives, or partner companies in LinkedIn-style posts. By reviewing the post text, mentioned organizations, and source member, they can quickly understand sentiment, spot opportunities to engage, and respond to important conversations. #### Content Inspiration Content creators and thought leaders can search for posts around a keyword and review the most recent examples. Seeing the post text, content type, and sorting details makes it easier to discover angles, headlines, and topics that resonate with the audience. ### Input | Field | Type | Required | Description | Example | |---|---|---:|---|---| | sortBy | string | No | Choose how the results should be ordered. | | | keyword | string | Yes | The main word or phrase you want to search for in posts. | hello world | | datePosted | string | No | Limit results to posts from a chosen time period. | anyTime | | fromMember | string | No | Find posts shared from a specific member username. | | | authorTitle | string | No | Find posts written by people with a specific job title. | | | contentType | string | No | Filter by the kind of post content, such as text, image, or video. | | | fromCompany | string | No | Find posts shared from a specific company. | | | max_results | integer | No | The maximum number of posts you want returned. | 10 | | authorCompany | string | No | Find posts written by authors connected to a specific company. | | | authorIndustry | string | No | Find posts written by authors in a specific industry. | | | mentionsMember | string | No | Find posts that mention a specific member username. | | | mentionsOrganization | string | No | Find posts that mention a specific organization. | | ### Output | Field | Type | Description | |---|---|---| | postText | string | The main text of the post, so you can quickly understand the message and topic. | | authorName | string | The name of the person who wrote the post, when available. | | authorUsername | string | The author’s username or profile handle, when available. | | authorTitle | string | The author’s job title, which helps you understand their role and seniority. | | authorCompany | string | The company the author works for or is associated with. | | authorIndustry | string | The industry the author works in. | | contentType | string | The format of the post, such as text, image, article, or video. | | postedDate | string | When the post was published, so you can judge how recent it is. | | sourceMember | string | The member the post was shared from, if applicable. | | sourceCompany | string | The company the post was shared from, if applicable. | | mentionedMembers | array | People mentioned in the post, useful for relationship mapping and outreach. | | mentionedOrganizations | array | Organizations mentioned in the post, useful for brand tracking and research. | | sortDetails | string | Any sorting or relevance information that helps explain why the post appeared in the results. | ### Sample Output ```json { "postText": "Excited to share our latest thoughts on cloud adoption and how teams can move faster with better workflows.", "authorName": "Sarah Johnson", "authorUsername": "sarahjohnson", "authorTitle": "Senior Product Marketing Manager", "authorCompany": "TechNova", "authorIndustry": "Software Development", "contentType": "text", "postedDate": "2026-07-01", "sourceMember": "sarahjohnson", "sourceCompany": "TechNova", "mentionedMembers": ["alexlee"], "mentionedOrganizations": ["OpenAI"], "sortDetails": "Most recent match for keyword: cloud" } ```` ### How It Works 1. You enter a keyword and choose any filters that matter to your search. 2. The actor looks through LinkedIn-style posts and finds matches for your topic. 3. It keeps gathering results until it reaches your requested maximum. 4. It cleans up the information and presents the most useful post details in a readable format. 5. You review the results and use them for research, outreach, monitoring, or reporting. ### Getting Started Getting started is simple and does not require coding knowledge. Click **Try for free**, fill in your keyword and any optional filters, then click **Run**. The actor will search for matching LinkedIn-style posts and return the results in a clean format that is easy to review. You can download the output as JSON, CSV, or Excel, depending on how you want to use the data. ### Frequently Asked Questions #### Do I need technical skills? No. This actor is designed for business users who want results without dealing with complicated setup. Just enter a keyword, choose any filters you want, and run it. #### How fast does it run? Speed depends on how broad your search is and how many results you request. Smaller, focused searches usually finish quickly, while larger searches may take a bit longer because the actor keeps collecting more matches for you. #### What format is the output? You can work with the results in JSON, CSV, or Excel. That makes it easy to share with teammates, analyze in spreadsheets, or import into your reporting workflow. #### Is this legal to use? This tool is intended for lawful research, monitoring, and business use. You should always make sure your use complies with applicable laws, platform rules, and your organization’s policies. #### Can I schedule it to run automatically? Yes, you can set it up to run on a schedule if you want regular updates. That is useful for ongoing monitoring of keywords, competitors, mentions, or industry topics. # Actor input Schema ## `sortBy` (type: `string`): How to sort the results. ## `keyword` (type: `string`): The search term to find matching posts. ## `datePosted` (type: `string`): When the post was published. You can choose a preset time range or enter a specific date. ## `fromMember` (type: `string`): A member that the post was shared from. ## `authorTitle` (type: `string`): The job title of the post author. ## `contentType` (type: `string`): The type of content to look for, such as text, image, or video. ## `fromCompany` (type: `string`): A company that the post was shared from. ## `max_results` (type: `integer`): The maximum number of posts to return. ## `authorCompany` (type: `string`): The company the post author works for or is associated with. ## `authorIndustry` (type: `string`): The industry the post author works in. ## `mentionsMember` (type: `string`): A member mentioned in the post. ## `mentionsOrganization` (type: `string`): An organization mentioned in the post. ## Actor input object example ```json { "keyword": "hello world", "datePosted": "anyTime", "max_results": 10 } ``` # 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": "hello world" }; // Run the Actor and wait for it to finish const run = await client.actor("datamagnet/linkedin-post-search").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 = { "keyword": "hello world" } # Run the Actor and wait for it to finish run = client.actor("datamagnet/linkedin-post-search").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 '{ "keyword": "hello world" }' | apify call datamagnet/linkedin-post-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datamagnet/linkedin-post-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post Search Cookie-less 🍪 ✅", "description": "Search LinkedIn-style posts by keyword and filters, then return clean, easy-to-read results. Get the most relevant post details surfaced in a simple format.", "version": "0.0", "x-build-id": "v4FX9NPydhAYZiZ14" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datamagnet~linkedin-post-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datamagnet-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datamagnet~linkedin-post-search/runs": { "post": { "operationId": "runs-sync-datamagnet-linkedin-post-search", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datamagnet~linkedin-post-search/run-sync": { "post": { "operationId": "run-sync-datamagnet-linkedin-post-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "sortBy": { "title": "Sort by", "type": "string", "description": "How to sort the results." }, "keyword": { "title": "Keyword", "type": "string", "description": "The search term to find matching posts." }, "datePosted": { "title": "Date posted", "enum": [ "anyTime", "pastMonth", "pastWeek", "past24Hours" ], "type": "string", "description": "When the post was published. You can choose a preset time range or enter a specific date." }, "fromMember": { "title": "From member", "type": "string", "description": "A member that the post was shared from." }, "authorTitle": { "title": "Author title", "type": "string", "description": "The job title of the post author." }, "contentType": { "title": "Content type", "type": "string", "description": "The type of content to look for, such as text, image, or video." }, "fromCompany": { "title": "From company", "type": "string", "description": "A company that the post was shared from." }, "max_results": { "title": "Max results", "type": "integer", "description": "The maximum number of posts to return.", "default": 10 }, "authorCompany": { "title": "Author company", "type": "string", "description": "The company the post author works for or is associated with." }, "authorIndustry": { "title": "Author industry", "type": "string", "description": "The industry the post author works in." }, "mentionsMember": { "title": "Mentions member", "type": "string", "description": "A member mentioned in the post." }, "mentionsOrganization": { "title": "Mentions organization", "type": "string", "description": "An organization mentioned in the post." } } }, "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 } } } } } } } } } } ```