# Linkedin Api (`tangible_omelette/linkedin-api`) Actor - **URL**: https://apify.com/tangible\_omelette/linkedin-api.md - **Developed by:** [scrapegoatz\_clerix](https://apify.com/tangible_omelette) (community) - **Categories:** Developer tools, Social media, Lead generation - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 full profile requests 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 Full Profile API Get comprehensive LinkedIn profile data through a single, powerful endpoint. Fetch **full profiles** including experience, education, skills, certifications, and much more โ€” all in one request. Designed for **developers, data teams, and automation workflows** that need structured LinkedIn data. --- ### ๐Ÿš€ What You Can Do - ๐Ÿ‘ค **Full Profile Data** โ€“ name, headline, about, location, profile picture, and more - ๐Ÿ’ผ **Experience** โ€“ complete work history with roles, companies, and dates - ๐ŸŽ“ **Education** โ€“ schools, degrees, and fields of study - ๐Ÿ› ๏ธ **Skills** โ€“ full skills list - ๐Ÿ“œ **Certifications** โ€“ professional certifications - ๐ŸŒ **Languages** โ€“ spoken languages - ๐Ÿ† **Awards** โ€“ honors and awards - ๐Ÿ“š **Publications & Projects** โ€“ research, projects, and publications - ๐Ÿค **Volunteering** โ€“ volunteer experience - โญ **Featured** โ€“ featured content on the profile - ๐Ÿ” **Two Run Modes** - **Batch Mode** โ€“ scheduled or one-off runs - **Standby Mode** โ€“ real-time HTTP API access --- ### ๐Ÿงฉ How It Works 1. Provide a **LinkedIn profile ID** (username or URL slug) 2. Run the actor (Batch mode) **or** call the HTTP endpoint (Standby mode) 3. Get the complete profile โ€” all sections in a single response 4. Results are stored automatically in datasets and displayed in table views --- ### โ–ถ๏ธ Usage #### Batch Mode (Standard) Run the actor with input JSON: ```json { "profileId": "satya-nadella" } ```` Best for: - Scheduled jobs - Bulk data collection - Dataset exports *** #### Standby Mode (HTTP API) When running in **Standby mode**, access the endpoint directly: ``` GET /api/v1/profile/{profileId}/fullProfile ``` Best for: - Backend services - Real-time enrichment - On-demand requests *** ### ๐Ÿงพ Input Parameters | Parameter | Type | Description | | ----------- | ------ | ------------------------------------ | | `profileId` | string | **Required.** LinkedIn profile username or URL slug | *** ### ๐Ÿ“Š Output The full profile response includes all of the following fields: | Field | Type | Description | | --------------------- | ------- | ---------------------------------- | | `firstName` | string | First name | | `middleName` | string | Middle name | | `lastName` | string | Last name | | `publicIdentifier` | string | LinkedIn username | | `urn` | string | Profile URN | | `url` | string | LinkedIn profile URL | | `headline` | string | Profile headline | | `about` | string | About section | | `hasPremium` | boolean | Premium subscriber | | `isInfluencer` | boolean | LinkedIn Influencer | | `isCreator` | boolean | Creator mode enabled | | `isOpenLink` | boolean | Open to InMail | | `isJobSeeker` | boolean | Open to work | | `isHiring` | boolean | Currently hiring | | `openProfileMessages` | boolean | Open profile messaging | | `linkedinId` | string | Numeric LinkedIn ID | | `connections` | number | Number of connections | | `followers` | number | Number of followers | | `location` | string | Location | | `profilePicture` | string | Profile picture URL | | `backgroundPicture` | string | Background picture URL | | `versionTag` | string | Profile version tag | | `roles` | array | Work experience / positions | | `education` | array | Education history | | `skills` | array | Skills list | | `languages` | array | Languages | | `certifications` | array | Professional certifications | | `courses` | array | Courses | | `projects` | array | Projects | | `publications` | array | Publications | | `awards` | array | Awards and honors | | `volunteering` | array | Volunteer experience | | `testScores` | array | Test scores | | `featured` | array | Featured content | *** ### ๐Ÿ“Œ Examples #### Get a Full Profile ```json { "profileId": "satya-nadella" } ``` #### Using a LinkedIn URL slug ```json { "profileId": "john-doe-123abc" } ``` *** ### ๐Ÿ’ฐ Pricing This actor uses **Pay-per-Event** pricing: - **$0.004 per request** โ€” you only pay for what you use - No monthly commitment - Works in both Batch and Standby modes *** ### โ— Error Handling Errors are returned in a consistent format: ```json { "success": false, "error": "Error message" } ``` Common errors: - **Missing profileId** โ€“ `profileId` is required - **HTTP 4xx** โ€“ invalid request or profile not found - **HTTP 5xx** โ€“ temporary backend issue, try again later *** ### ๐Ÿฅ Health Check ``` GET /health ``` Returns `200 OK` when the actor is running in Standby mode. *** # Actor input Schema ## `profileId` (type: `string`): LinkedIn profile ID (username or URL slug) ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("tangible_omelette/linkedin-api").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("tangible_omelette/linkedin-api").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 '{}' | apify call tangible_omelette/linkedin-api --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=tangible_omelette/linkedin-api", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin Api", "version": "1.0", "x-build-id": "KTVcjqkydra5QIhHc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/tangible_omelette~linkedin-api/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-tangible_omelette-linkedin-api", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/tangible_omelette~linkedin-api/runs": { "post": { "operationId": "runs-sync-tangible_omelette-linkedin-api", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/tangible_omelette~linkedin-api/run-sync": { "post": { "operationId": "run-sync-tangible_omelette-linkedin-api", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "profileId" ], "properties": { "profileId": { "title": "Profile ID", "type": "string", "description": "LinkedIn profile ID (username or URL slug)" } } }, "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 } } } } } } } } } } ```