# LinkedIn Keyword Search Scraper (`han-projects/good-linkedin-scraper`) Actor Search LinkedIn by keyword for posts, jobs, profiles, and companies. Optimized for finding timely posts to comment on for reach. - **URL**: https://apify.com/han-projects/good-linkedin-scraper.md - **Developed by:** [Han](https://apify.com/han-projects) (community) - **Categories:** Lead generation, Social media, Jobs - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 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. 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 Keyword Search Scraper Search LinkedIn by keyword across posts, jobs, profiles, and companies. The post search is optimized for finding timely conversations worth commenting on. ### Inputs - `keywords` — Array of keywords or phrases to search. Required. - `searchTypes` — Array containing one or more of `posts`, `jobs`, `profiles`, `companies`. Required; defaults to `posts`. - `maxResultsPerKeyword` — Maximum rows to output for each keyword and selected search type. Default `25`; range `1`-`100`. - `sortBy` — Post search sort order: `recent` or `relevance`. Default `recent`. - `postedWithin` — Post date filter: `anytime`, `past24h`, `pastWeek`, or `pastMonth`. Default `anytime`. ### Example input ```json { "keywords": ["AI agents", "Apify scraping"], "searchTypes": ["posts"], "maxResultsPerKeyword": 25, "sortBy": "recent", "postedWithin": "pastWeek" } ```` ### Output The actor writes one dataset row per scraped item or failed item. Important output fields include: `search_type`, `keyword`, `rank`, `result_url`, `detail_url`, `post_link`, `message`, `detail_text`, `job_description`, `profile_about`, `engagement`, `author_name`, `author_headline`, `author_profile_url`, `author_biodata`, `posted_at_text`, `comment_opportunity`, `title`, `subtitle`, `location`, `company_name`, `company_url`, `fetched_at`, `detail_error`, `error` Rows with a problem include an `error` field instead of failing the whole run when possible. ### Notes - Use full public URLs, including the platform hostname. # Actor input Schema ## `keywords` (type: `array`): Search keywords/phrases. Use buyer personas, topics, pain points, or company names to find posts worth commenting on for reach. ## `searchTypes` (type: `array`): LinkedIn areas to search. Posts are optimized for finding conversations to comment on; other types help identify authors, companies, jobs, and prospects. ## `maxResultsPerKeyword` (type: `integer`): Maximum number of rows to output for each keyword and selected search type. ## `sortBy` (type: `string`): LinkedIn post-search sort order. recent is best for timely comments; relevance can surface bigger threads. ## `postedWithin` (type: `string`): Optional LinkedIn post-search date filter. ## Actor input object example ```json { "keywords": [ "AI agents", "Apify scraping" ], "searchTypes": [ "posts" ], "maxResultsPerKeyword": 25, "sortBy": "recent", "postedWithin": "anytime" } ``` # Actor output Schema ## `results` (type: `string`): Default dataset with one item per post, job, profile, or company. Post rows include post\_link, message, engagement, author biodata, and comment\_opportunity fields. Jobs and profiles include detail\_text plus job\_description or profile\_about when available. # 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 = { "keywords": [ "AI agents", "Apify scraping" ] }; // Run the Actor and wait for it to finish const run = await client.actor("han-projects/good-linkedin-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 = { "keywords": [ "AI agents", "Apify scraping", ] } # Run the Actor and wait for it to finish run = client.actor("han-projects/good-linkedin-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 '{ "keywords": [ "AI agents", "Apify scraping" ] }' | apify call han-projects/good-linkedin-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=han-projects/good-linkedin-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Keyword Search Scraper", "description": "Search LinkedIn by keyword for posts, jobs, profiles, and companies. Optimized for finding timely posts to comment on for reach.", "version": "0.1", "x-build-id": "AP7paDJBhiYtJcEjr" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/han-projects~good-linkedin-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-han-projects-good-linkedin-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/han-projects~good-linkedin-scraper/runs": { "post": { "operationId": "runs-sync-han-projects-good-linkedin-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/han-projects~good-linkedin-scraper/run-sync": { "post": { "operationId": "run-sync-han-projects-good-linkedin-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords", "searchTypes" ], "properties": { "keywords": { "title": "Keywords", "minItems": 1, "uniqueItems": true, "type": "array", "description": "Search keywords/phrases. Use buyer personas, topics, pain points, or company names to find posts worth commenting on for reach.", "items": { "type": "string", "minLength": 1 } }, "searchTypes": { "title": "Search types", "minItems": 1, "uniqueItems": true, "type": "array", "description": "LinkedIn areas to search. Posts are optimized for finding conversations to comment on; other types help identify authors, companies, jobs, and prospects.", "items": { "type": "string", "enum": [ "posts", "jobs", "profiles", "companies" ], "enumTitles": [ "Posts", "Jobs", "Profiles", "Companies" ] }, "default": [ "posts" ] }, "maxResultsPerKeyword": { "title": "Results per keyword/type", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of rows to output for each keyword and selected search type.", "default": 25 }, "sortBy": { "title": "Post sort order", "enum": [ "recent", "relevance" ], "type": "string", "description": "LinkedIn post-search sort order. recent is best for timely comments; relevance can surface bigger threads.", "default": "recent" }, "postedWithin": { "title": "Post date filter", "enum": [ "anytime", "past24h", "pastWeek", "pastMonth" ], "type": "string", "description": "Optional LinkedIn post-search date filter.", "default": "anytime" } } }, "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 } } } } } } } } } } ```