# LinkedIn Job Details Scraper (`thescrappa/linkedin-job-details-scraper`) Actor Scrape structured public LinkedIn job posting details from one or more job URLs. No LinkedIn login required. - **URL**: https://apify.com/thescrappa/linkedin-job-details-scraper.md - **Developed by:** [Scrappa](https://apify.com/thescrappa) (community) - **Categories:** Lead generation, Marketing, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $0.30 / 1,000 job 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 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 Job Details Scraper Extract structured public LinkedIn job posting details from one or more LinkedIn job URLs through Scrappa. No LinkedIn login, cookies, or browser automation required. ### What It Returns - Job title, company, location, employment type, seniority, and posted date when present - Applicant count and apply URL when LinkedIn exposes them - Original input URL, normalized LinkedIn job URL, cache metadata, and the raw Scrappa response fields - One dataset item per requested job URL, including recoverable per-job failure rows ### Input Use `urls` for normal batch usage. The legacy `url` field is still supported for one-off runs. ```json { "urls": [ "https://www.linkedin.com/jobs/view/1234567890/", "https://www.linkedin.com/jobs/view/software-engineer-at-example-2345678901/?trk=public_jobs_topcard-title" ], "use_cache": true, "maximum_cache_age": 2592000 } ```` | Field | Type | Required | Description | |-------|------|----------|-------------| | `urls` | string\[] | No | Recommended batch list of LinkedIn job posting URLs. | | `url` | string | No | Backward-compatible single LinkedIn job URL. | | `use_cache` | boolean | No | Use cached Scrappa data when available. Defaults to `true`. | | `maximum_cache_age` | integer | No | Maximum cache age in seconds. Defaults to 30 days. | At least one URL must be supplied through `urls` or `url`. ### Output Each LinkedIn job URL produces one dataset item. ```json { "success": true, "title": "Software Engineer", "company": "Example Corp", "location": "New York, NY", "employment_type": "Full-time", "seniority_level": "Mid-Senior level", "posted_date": "2026-06-01", "applicants": "23 applicants", "apply_url": "https://www.linkedin.com/jobs/view/1234567890/", "url": "https://www.linkedin.com/jobs/view/1234567890", "input_url": "https://www.linkedin.com/jobs/view/1234567890/?trk=public_jobs_topcard-title", "normalized_url": "https://www.linkedin.com/jobs/view/1234567890" } ``` Recoverable misses are saved as dataset rows instead of failing the entire batch: ```json { "success": false, "input_url": "https://www.linkedin.com/jobs/view/missing-job", "normalized_url": "https://www.linkedin.com/jobs/view/missing-job", "url": "https://www.linkedin.com/jobs/view/missing-job", "message": "Job not found", "status_code": 404, "error_type": "scrappa_api_error" } ``` ### Batch Workflow This actor is designed for the common workflow where LinkedIn Jobs Search returns job URLs and a second step enriches those URLs with posting details. Submit many job URLs in a single run so Apify run startup and storage overhead are shared across results. ### Direct API For higher-volume or direct API access, use Scrappa's LinkedIn Job endpoint at `https://scrappa.co/api`. ### Support For issues or questions, contact us through Apify. # Actor input Schema ## `urls` (type: `array`): Recommended. Process many LinkedIn job URLs in one Apify run so run startup and storage overhead are shared across results. ## `url` (type: `string`): Backward-compatible single job URL. Prefer URLs for normal usage, especially when processing more than one job. ## `use_cache` (type: `boolean`): Use cached data if available to reduce costs and speed up requests. ## `maximum_cache_age` (type: `integer`): Maximum age of cached data in seconds (default: 2592000 = 30 days). Must be at least 1 second. Only used if use\_cache is enabled. ## Actor input object example ```json { "urls": [ "https://www.linkedin.com/jobs/view/1234567890/", "https://www.linkedin.com/jobs/view/software-engineer-at-example-2345678901/" ], "url": "https://www.linkedin.com/jobs/view/1234567890/", "use_cache": true, "maximum_cache_age": 2592000 } ``` # 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 = { "urls": [ "https://www.linkedin.com/jobs/view/1234567890/", "https://www.linkedin.com/jobs/view/software-engineer-at-example-2345678901/" ], "url": "https://www.linkedin.com/jobs/view/1234567890/" }; // Run the Actor and wait for it to finish const run = await client.actor("thescrappa/linkedin-job-details-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 = { "urls": [ "https://www.linkedin.com/jobs/view/1234567890/", "https://www.linkedin.com/jobs/view/software-engineer-at-example-2345678901/", ], "url": "https://www.linkedin.com/jobs/view/1234567890/", } # Run the Actor and wait for it to finish run = client.actor("thescrappa/linkedin-job-details-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 '{ "urls": [ "https://www.linkedin.com/jobs/view/1234567890/", "https://www.linkedin.com/jobs/view/software-engineer-at-example-2345678901/" ], "url": "https://www.linkedin.com/jobs/view/1234567890/" }' | apify call thescrappa/linkedin-job-details-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=thescrappa/linkedin-job-details-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Job Details Scraper", "description": "Scrape structured public LinkedIn job posting details from one or more job URLs. No LinkedIn login required.", "version": "1.0", "x-build-id": "bV2m6YcMsCnompDFY" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/thescrappa~linkedin-job-details-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-thescrappa-linkedin-job-details-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/thescrappa~linkedin-job-details-scraper/runs": { "post": { "operationId": "runs-sync-thescrappa-linkedin-job-details-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/thescrappa~linkedin-job-details-scraper/run-sync": { "post": { "operationId": "run-sync-thescrappa-linkedin-job-details-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", "properties": { "urls": { "title": "LinkedIn Job URLs", "minItems": 1, "type": "array", "description": "Recommended. Process many LinkedIn job URLs in one Apify run so run startup and storage overhead are shared across results.", "items": { "type": "string", "minLength": 1, "pattern": "^(https?://)?((www|m|[a-z]{2,3})\\.)?linkedin\\.com/jobs/view/[^\\s?#]+" } }, "url": { "title": "Legacy Single LinkedIn Job URL", "pattern": "^(https?://)?((www|m|[a-z]{2,3})\\.)?linkedin\\.com/jobs/view/[^\\s?#]+", "minLength": 1, "type": "string", "description": "Backward-compatible single job URL. Prefer URLs for normal usage, especially when processing more than one job." }, "use_cache": { "title": "Use Cache", "type": "boolean", "description": "Use cached data if available to reduce costs and speed up requests.", "default": true }, "maximum_cache_age": { "title": "Maximum Cache Age", "minimum": 1, "type": "integer", "description": "Maximum age of cached data in seconds (default: 2592000 = 30 days). Must be at least 1 second. Only used if use_cache is enabled.", "default": 2592000 } } }, "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 } } } } } } } } } } ```