# i n Scraper ⭐ (`jupri/linkedin`) Actor πŸ’Ό Scrape Linkedin.com - **URL**: https://apify.com/jupri/linkedin.md - **Developed by:** [cat](https://apify.com/jupri) (community) - **Categories:** Jobs, Social media - **Stats:** 16 total users, 0 monthly users, 100.0% runs succeeded, 2 bookmarks - **User rating**: No ratings yet ## Pricing $30.00/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 ## Welcome to LinkedIn Scraper ![don't be sad readme is here](https://raw.githubusercontent.com/JupriGH/resources/main/cats/catframe.png) ### About LinkedIn.com **LinkedIn** ([/lΙͺΕ‹ktˈΙͺn/](https://en.wikipedia.org/wiki/Help:IPA/English "Help:IPA/English")) is a business and employment-focused [social media platform](https://en.wikipedia.org/wiki/Social_media_platform "Social media platform") that works through websites and mobile apps. It was launched on May 5, 2003 by [Reid Hoffman](https://en.wikipedia.org/wiki/Reid_Hoffman "Reid Hoffman") and [Eric Ly](https://en.wikipedia.org/wiki/Eric_Ly "Eric Ly").[[6]](https://en.wikipedia.org/wiki/LinkedIn#cite_note-about-6) Since December 2016, LinkedIn has been a wholly owned subsidiary of [Microsoft](https://en.wikipedia.org/wiki/Microsoft "Microsoft").[[7]](https://en.wikipedia.org/wiki/LinkedIn#cite_note-7) The platform is primarily used for professional networking and career development, and allows jobseekers to post their [CVs](https://en.wikipedia.org/wiki/Curriculum_vitae "Curriculum vitae") and employers to post jobs. From 2015, most of the company's revenue came from [selling access to information about its members](https://en.wikipedia.org/wiki/Information_broker "Information broker") to recruiters and sales professionals.[[8]](https://en.wikipedia.org/wiki/LinkedIn#cite_note-8) LinkedIn has more than 1 billion registered members from over 200 countries and territories.[[9]](https://en.wikipedia.org/wiki/LinkedIn#cite_note-9) LinkedIn allows members (both workers and employers) to create profiles and connect with each other in an online social network which may represent real-world professional relationships. Members can invite anyone (whether an existing member or not) to become a connection. LinkedIn can also be used to organize offline events, join groups, write articles, publish job postings, post photos and videos, and more.[[10]](https://en.wikipedia.org/wiki/LinkedIn#cite_note-Help.linkedin.com-10) ### πŸ“ŽAbout This Actor ### πŸ“ŽRequired `COOKIES` #### _πŸ‘‰ Google Chrome_ 1. Login to **Linkein.com** 2. Open Chrome Developer Tools *(Ctrl + Shift + I)* 3. Open **Application** Tab 4. On left panel, go to: `Storage > Cookies > https://www.linkedin.com` 5. Find cookie named **`li_at`** 6. Copy & paste in: **`☒️ Advanced Options > HTTP Cookies`** #### πŸ‘‰ Example `INPUT` with `Cookies` ```yaml { "query": [ ... ], "limit": 10, "dev_custom_cookies": [ { "key": "li_at", "value": "XXXXXXXXXXXXXXXXXXXXXX" } ] } ```` ### πŸ“ŽTutorial #### πŸ‘‰ Basic Usage #### πŸ‘‰ LinkedIn Query Language (LQL) ```html [ : | # | @ ] [ | | | ] [ /
] ``` #### πŸ‘‰ Possible `Query` Values | Format | Examples | Description | |-|-|-| | **@<*USER*>** | `@williamhgates` | ***User Data*** | | `/info` | `@williamhgates/info` | User info | | `/posts` | `@williamhgates/posts` | User posts | | `/comments` | `@williamhgates/comments` | User comments | | `/reactions` | `@williamhgates/reactions` | User reactions | | `/images` | `@williamhgates/images` | User images | | `/videos` | `@williamhgates/videos` | User videos | | `/documents` | `@williamhgates/documents` | User documents | | `/articles` | `@williamhgates/articles` | User articles | | `/newsletter` | `@williamhgates/newsletter` | User newsletters | | **https:<*URL*>** | `https://www.linkedin.com/...` | ***Start URL*** | ### πŸ“ŽInput Examples ### πŸ“ŽOutput Examples ### πŸ“ŽSupport # Actor input Schema ## `query` (type: `array`): πŸ’‘ Search Terms, Query or URL ## `limit` (type: `integer`): πŸ’‘ Number of results (per-query) ## `dev_proxy_config` (type: `object`): πŸ’‘ Supported protocol:

HTTP(S), SOCKS5
{http|socks5}://{user:pass}@{hostname|ip-address}:port

Example: socks5://example.com:9000 ## `dev_custom_headers` (type: `array`): πŸ’‘ Additional HTTP Headers ## `dev_custom_cookies` (type: `array`): πŸ’‘ Additional HTTP Cookies ## `dev_transform_fields` (type: `array`): πŸ’‘ Transform the resulting output. Select only needed fields.

For nested object use DOT. For example: 
address.streetAddress

For nested array use NUMBER (index of array element starting from index=0). For example: 
images.0.url
## `dev_dataset_name` (type: `string`): πŸ’‘ Save results into custom named Dataset, use mask to customize dataset name

{ACTOR} = actor name
{DATE} = date (YYYYMMDD)
{TIME} = time (HHMMSS)

This masks can be used to autogenerate Dataset Name.

example: data-{DATE}
Depending on today date the dataset name will be: data-20230603

default: data-{ACTOR}-{DATE}-{TIME} ## `dev_dataset_clear` (type: `boolean`): Clear Dataset before insert/update. ## `dev_no_strip` (type: `boolean`): πŸ’‘ Keep/Save empty values (NULL, FALSE, empty ARRAY, empty OBJECT, empty STRING) ## Actor input object example ```json { "query": [ "https://www.linkedin.com/in/williamhgates/", "@williamhgates/videos" ], "limit": 5, "dev_custom_cookies": [ { "key": "li_at", "value": "" } ] } ``` # 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": [ "https://www.linkedin.com/in/williamhgates/", "@williamhgates/videos" ], "limit": 5, "dev_custom_cookies": [ { "key": "li_at", "value": "" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("jupri/linkedin").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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": [ "https://www.linkedin.com/in/williamhgates/", "@williamhgates/videos", ], "limit": 5, "dev_custom_cookies": [{ "key": "li_at", "value": "", }], } # Run the Actor and wait for it to finish run = client.actor("jupri/linkedin").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "query": [ "https://www.linkedin.com/in/williamhgates/", "@williamhgates/videos" ], "limit": 5, "dev_custom_cookies": [ { "key": "li_at", "value": "" } ] }' | apify call jupri/linkedin --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jupri/linkedin", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "i n Scraper ⭐", "description": "πŸ’Ό Scrape Linkedin.com", "version": "0.0", "x-build-id": "EVqD74pPCukRccXE4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jupri~linkedin/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jupri-linkedin", "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/jupri~linkedin/runs": { "post": { "operationId": "runs-sync-jupri-linkedin", "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/jupri~linkedin/run-sync": { "post": { "operationId": "run-sync-jupri-linkedin", "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": "array", "description": "πŸ’‘ Search Terms, Query or URL", "items": { "type": "string" } }, "limit": { "title": "♾️ Limit", "type": "integer", "description": "πŸ’‘ Number of results (per-query)" }, "dev_proxy_config": { "title": "🌐 PROXY NETWORKING", "type": "object", "description": "πŸ’‘ Supported protocol:

HTTP(S), SOCKS5
{http|socks5}://{user:pass}@{hostname|ip-address}:port

Example: socks5://example.com:9000" }, "dev_custom_headers": { "title": "πŸ“œ HTTP HEADERS", "type": "array", "description": "πŸ’‘ Additional HTTP Headers", "items": { "type": "object", "required": [ "key", "value" ], "properties": { "key": { "type": "string", "title": "Key" }, "value": { "type": "string", "title": "Value" } } } }, "dev_custom_cookies": { "title": "🍰 HTTP COOKIES", "type": "array", "description": "πŸ’‘ Additional HTTP Cookies", "items": { "type": "object", "required": [ "key", "value" ], "properties": { "key": { "type": "string", "title": "Key" }, "value": { "type": "string", "title": "Value" } } } }, "dev_transform_fields": { "title": "♻️ CUSTOM FIELD", "type": "array", "description": "πŸ’‘ Transform the resulting output. Select only needed fields.

For nested object use DOT. For example: 
address.streetAddress

For nested array use NUMBER (index of array element starting from index=0). For example: 
images.0.url
", "items": { "type": "object", "required": [ "key", "value" ], "properties": { "key": { "type": "string", "title": "Key" }, "value": { "type": "string", "title": "Value" } } } }, "dev_dataset_name": { "title": "πŸ“ CUSTOM STORAGE", "type": "string", "description": "πŸ’‘ Save results into custom named Dataset, use mask to customize dataset name

{ACTOR} = actor name
{DATE} = date (YYYYMMDD)
{TIME} = time (HHMMSS)

This masks can be used to autogenerate Dataset Name.

example: data-{DATE}
Depending on today date the dataset name will be: data-20230603

default: data-{ACTOR}-{DATE}-{TIME}" }, "dev_dataset_clear": { "title": "Clear Storage", "type": "boolean", "description": "Clear Dataset before insert/update." }, "dev_no_strip": { "title": "Disable data cleansing", "type": "boolean", "description": "πŸ’‘ Keep/Save empty values (NULL, FALSE, empty ARRAY, empty OBJECT, empty STRING)" } } }, "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 } } } } } } } } } } ```