# LinkedIn Post Search (`arpitgandhi1934/linkedin-post-search`) Actor Live keyword search of LinkedIn posts → structured JSON (author, headline, profile, post URL, hashtags, media, attached job + apply URL). - **URL**: https://apify.com/arpitgandhi1934/linkedin-post-search.md - **Developed by:** [Arpit Gandhi](https://apify.com/arpitgandhi1934) (community) - **Categories:** Jobs, Automation, Agents - **Stats:** 3 total users, 1 monthly users, 16.7% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 1,000 posts 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 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 Search **Turn any keyword into a live feed of LinkedIn posts — as clean, structured JSON.** Search LinkedIn posts by keyword and instantly get ready-to-use data: who posted, their headline and profile, the post text and link, when it went up, hashtags, media — and when a post is a job opening, the role and a **direct apply link**. Fast, paginated, and exportable to JSON, CSV, or Excel. ### What you get for every post | Field | Example | |---|---| | `author` | Glen Hopkinson | | `profileUrl` | linkedin.com/in/glen-hopkinson-… | | `text` | "We're hiring a Claims Advisor Trainee in our Toronto office…" | | `postUrl` | direct link to the post | | `postedAt` | 2026-06-26T12:18Z | | `hashtags` | `#hiring` | | `media` | image URL (when present) | | `job` | `{ title, applyUrl }` — when the post is a hiring post | ### What you can do with it - **Recruiters & sourcers** — find people actively posting "we're hiring," each with a direct apply link. Build a who's-hiring lead list in seconds. - **Lead generation** — surface decision-makers posting about your space, and the buying signals that come with it. - **Job seekers & job boards** — pull fresh hiring posts by keyword and city, every one with an apply link. - **Sales & SDR teams** — catch trigger events (hiring, funding, launches) and reach out while they're hot. - **Brand & competitor monitoring** — track who's talking about a company, product, or topic. - **Content & trend research** — see what's being posted on any subject, newest first. ### How to use it 1. Enter a **keyword** — e.g. `data engineer hiring toronto`. 2. Choose how many posts you want, and optional filters. 3. Get a clean dataset back — download it, plug it into Make / Zapier / n8n / your CRM, or pull it straight from the API. #### Input | Field | What it does | |---|---| | **keyword** | The terms to search for | | **maxPosts** | How many posts to return (up to ~400 per search) | | **onlyWithJob** | Return only hiring posts that include a job + apply link | | **sortBy** | Relevance or most recent | #### Output A dataset of structured post records (fields above). Use it in a spreadsheet, your CRM, an automation, or your own app via the Apify API. ### Run it from your code ```` POST https://api.apify.com/v2/acts//run-sync-get-dataset-items?token=YOUR\_TOKEN { "keyword": "data engineer hiring toronto", "maxPosts": 100, "onlyWithJob": true } ```` Returns the posts array right in the response. ### Pricing Pay only for the results you receive — no subscription. Try a small run first to see the data quality. # Actor input Schema ## `keyword` (type: `string`): Search terms, e.g. "data engineer hiring toronto". ## `maxPosts` (type: `integer`): How many posts to return (LinkedIn caps a single search at ~400). ## `onlyWithJob` (type: `boolean`): Return only hiring posts that carry a structured job (title + direct apply URL). ## `delayMs` (type: `integer`): Delay between load-more calls. Higher = slower but safer on the account. ## Actor input object example ```json { "keyword": "hiring toronto", "maxPosts": 50, "onlyWithJob": false, "delayMs": 700 } ```` # 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": "hiring toronto" }; // Run the Actor and wait for it to finish const run = await client.actor("arpitgandhi1934/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": "hiring toronto" } # Run the Actor and wait for it to finish run = client.actor("arpitgandhi1934/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": "hiring toronto" }' | apify call arpitgandhi1934/linkedin-post-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=arpitgandhi1934/linkedin-post-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post Search", "description": "Live keyword search of LinkedIn posts → structured JSON (author, headline, profile, post URL, hashtags, media, attached job + apply URL).", "version": "0.1", "x-build-id": "XVeP0oK7F2c91g1Qs" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/arpitgandhi1934~linkedin-post-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-arpitgandhi1934-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/arpitgandhi1934~linkedin-post-search/runs": { "post": { "operationId": "runs-sync-arpitgandhi1934-linkedin-post-search", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/arpitgandhi1934~linkedin-post-search/run-sync": { "post": { "operationId": "run-sync-arpitgandhi1934-linkedin-post-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Keyword", "type": "string", "description": "Search terms, e.g. \"data engineer hiring toronto\"." }, "maxPosts": { "title": "Max posts", "minimum": 1, "maximum": 400, "type": "integer", "description": "How many posts to return (LinkedIn caps a single search at ~400).", "default": 50 }, "onlyWithJob": { "title": "Only posts with a job card", "type": "boolean", "description": "Return only hiring posts that carry a structured job (title + direct apply URL).", "default": false }, "delayMs": { "title": "Throttle (ms)", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Delay between load-more calls. Higher = slower but safer on the account.", "default": 700 } } }, "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 } } } } } } } } } } ```