# ⚡️ Linkedin Jobs Scraper 🔥 (`oza-dev/linkedin-jobs-scraper`) Actor LinkedIn Job Scraper is a powerful and reliable automation tool Designed to extract job listings directly from LinkedIn Jobs using advanced search logic and filters. It helps recruiters, job boards, analysts, and HR professionals gather high-quality job data at scale — with over 98% success rate - **URL**: https://apify.com/oza-dev/linkedin-jobs-scraper.md - **Developed by:** [Oza Dev](https://apify.com/oza-dev) (community) - **Categories:** Jobs - **Stats:** 37 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $20.00/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 ## LinkedIn Jobs Scraper (Apify Actor) Ce scraper permet d'extraire des offres d'emploi de LinkedIn sans avoir besoin de se connecter. Il est conçu pour être déployé en tant qu'Actor sur la plateforme Apify. ### Fonctionnalités - **Recherche par mots-clés** : Trouvez des postes spécifiques (ex: "Data Scientist"). - **Localisation flexible** : Filtrez par ville, pays ou région. - **Filtres avancés** : - Date de publication (24h, semaine, mois). - Type de travail (Sur site, Remote, Hybride). - Niveau d'expérience (Stagiaire, Junior, Senior, etc.). - **Pagination automatique** : Extrayez autant d'offres que nécessaire (jusqu'à 1000). - **Exportation facile** : Résultats disponibles en JSON, CSV, Excel, etc. via Apify. ### Paramètres d'entrée | Champ | Type | Description | Par défaut | |-------|------|-------------|------------| | `title` | String | Titre du poste recherché | "Python Developer" | | `location` | String | Lieu de la recherche | "United States" | | `rows` | Integer | Nombre d'offres à extraire | 50 | | `publishedAt` | Enum | Période de publication | "Any time" | | `workType` | Enum | Type de travail (Remote, etc.) | "Any" | | `experienceLevel` | Enum | Niveau d'expérience requis | "Any" | ### Données extraites Pour chaque offre, le scraper récupère : - Le titre du poste - Le nom de l'entreprise - La localisation - Le lien direct vers l'offre - La date de publication - La source (LinkedIn) ### Comment l'utiliser sur Apify 1. Créez un nouvel Actor sur [Apify](https://apify.com). 2. Copiez le contenu de `main.py`, `INPUT_SCHEMA.json`, `requirements.txt` et `Dockerfile`. 3. Déployez et lancez l'Actor avec vos paramètres. ### Notes techniques Ce scraper utilise l'API publique de LinkedIn (`jobs-guest`), ce qui évite les problèmes de blocage liés aux comptes personnels. Pour des volumes importants, l'utilisation de **proxies résidentiels** Apify est fortement recommandée. # Actor input Schema ## `title` (type: `string`): The name of the job (e.g. Web developer). ## `location` (type: `string`): The location of the job (e.g. Paris, New York). ## `rows` (type: `integer`): How many rows do you want to display? ## `publishedAt` (type: `string`): Select time range for job postings. ## `workType` (type: `string`): Select work type to display. ## `experienceLevel` (type: `string`): Select the experience levels to be displayed. ## Actor input object example ```json { "title": "Python Developer", "location": "United States", "rows": 50, "publishedAt": "", "workType": "", "experienceLevel": "" } ```` # 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("oza-dev/linkedin-jobs-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 = {} # Run the Actor and wait for it to finish run = client.actor("oza-dev/linkedin-jobs-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 '{}' | apify call oza-dev/linkedin-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=oza-dev/linkedin-jobs-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "⚡️ Linkedin Jobs Scraper 🔥", "description": "LinkedIn Job Scraper is a powerful and reliable automation tool Designed to extract job listings directly from LinkedIn Jobs using advanced search logic and filters. It helps recruiters, job boards, analysts, and HR professionals gather high-quality job data at scale — with over 98% success rate", "version": "0.0", "x-build-id": "z8c7axaybpAqcTnNT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/oza-dev~linkedin-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-oza-dev-linkedin-jobs-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/oza-dev~linkedin-jobs-scraper/runs": { "post": { "operationId": "runs-sync-oza-dev-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/oza-dev~linkedin-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-oza-dev-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "title", "location" ], "properties": { "title": { "title": "Job title", "type": "string", "description": "The name of the job (e.g. Web developer).", "default": "Python Developer" }, "location": { "title": "Job location", "type": "string", "description": "The location of the job (e.g. Paris, New York).", "default": "United States" }, "rows": { "title": "Total rows", "minimum": 1, "maximum": 1000, "type": "integer", "description": "How many rows do you want to display?", "default": 50 }, "publishedAt": { "title": "Published at", "enum": [ "", "r2592000", "r604800", "r86400" ], "type": "string", "description": "Select time range for job postings.", "default": "" }, "workType": { "title": "On-site/Remote", "enum": [ "", "1", "2", "3" ], "type": "string", "description": "Select work type to display.", "default": "" }, "experienceLevel": { "title": "Experience Level", "enum": [ "", "1", "2", "3", "4", "5", "6" ], "type": "string", "description": "Select the experience levels to be displayed.", "default": "" } } }, "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 } } } } } } } } } } ```