# Leforem.be Scraper (`shahidirfan/leforem-be-scraper`) Actor Automate job data collection from Leforem.be. Get real-time access to Belgian job postings with complete details. Ideal for career tracking, recruiter databases, and competitive job market analysis. - **URL**: https://apify.com/shahidirfan/leforem-be-scraper.md - **Developed by:** [Shahid Irfan](https://apify.com/shahidirfan) (community) - **Categories:** Jobs, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Leforem.be Scraper Extract job listings from Leforem quickly and at scale. Collect structured vacancy data including title, company, location, contract details, publication metadata, and optional extended job information for analytics and automation. ### Why This Actor - API-based extraction for better stability and speed. - Optional detail enrichment per offer. - Automatic cleanup of empty and null fields before saving dataset items. - Search by keyword, location, and advanced Leforem filters. - Pagination control for predictable runs and costs. ### Use Cases #### Recruitment Monitoring Track fresh listings by keyword and location to keep talent pipelines updated. #### Labor Market Research Build datasets for trend analysis across roles, employers, and work conditions. #### Data Enrichment Pipelines Feed job intelligence into BI dashboards, internal tools, and matching systems. ### Input | Parameter | Type | Required | Default | Description | |---|---|---|---|---| | `keyword` | String | No | `developpeur` | Search keyword(s) | | `location` | String | No | `Liege` | Comma-separated city/region names | | `startUrl` | String | No | — | Optional search URL | | `operateur` | String | No | `ET` | Keyword operator (`ET` or `OU`) | | `dateDiffusionMin` | String | No | — | Minimum publication date (`YYYY-MM-DD`) | | `collectDetails` | Boolean | No | `true` | Include extended fields for each offer | | `results_wanted` | Integer | No | `20` | Maximum number of offers to save | | `max_pages` | Integer | No | `10` | Safety cap for pagination | | `maxConcurrency` | Integer | No | `5` | Parallel processing level | | `filtres` | Array | No | `[]` | Advanced filter list | | `filtresCodifies` | Array | No | `[]` | Advanced coded filters | | `metier` | Array | No | `[]` | Optional metier values | | `secteur` | Array | No | `[]` | Optional secteur values | | `priority` | Integer | No | `1` | Priority value | | `proxyConfiguration` | Object | No | `{ useApifyProxy: true }` | Proxy settings | Minimal example: ```json { "keyword": "developpeur", "location": "Liege", "results_wanted": 20, "max_pages": 3, "collectDetails": true } ```` ### Output Each dataset item contains standardized offer fields. | Field | Type | Description | |---|---|---| | `id` | Number | Offer identifier | | `numero` | String | Offer reference number | | `titre` | String | Job title | | `nomEmployeur` | String | Employer name | | `lieuxTravail` | Array | Work location(s) | | `typeContrat` | String | Contract type | | `regimeTravail` | String | Work schedule | | `publication` | String | Publication label | | `detailUrl` | String | Link to offer detail page | When `collectDetails` is enabled, additional rich fields such as requirements, competencies, application instructions, and employer details are included when available. Null and empty values are automatically removed from final output records. ### Sample Output ```json { "id": 1854024, "numero": "1854024", "titre": "Analyste-developpeur informatique (H/F/X)", "nomEmployeur": "Intercommunale namuroise de Services publics", "lieuxTravail": ["Naninne"], "typeContrat": "Duree indeterminee", "regimeTravail": "Temps plein", "publication": "Publie hier", "detailUrl": "https://www.leforem.be/recherche-offres/offre-detail/1854024" } ``` ### Local Run ```bash npm install npm start ``` The actor reads input from `INPUT.json` for local execution. ### Notes - For large runs, use proxy configuration and moderate concurrency. - Use smaller `results_wanted` values during testing, then scale gradually for production runs. # Actor input Schema ## `startUrl` (type: `string`): Optional Leforem search URL. Query parameters are used as additional filters. ## `keyword` (type: `string`): Job search keyword(s), for example: developpeur, comptable, infirmier. ## `location` (type: `string`): Comma-separated city/region names. The actor resolves them with Leforem localisation API. ## `collectDetails` (type: `boolean`): If enabled, actor calls the detail API for each offer id. ## `results_wanted` (type: `integer`): Maximum number of dataset items to save. ## `max_pages` (type: `integer`): Safety cap for API pagination. ## `proxyConfiguration` (type: `object`): Use Apify Proxy for reliability. ## Actor input object example ```json { "keyword": "developpeur", "location": "Liege", "collectDetails": true, "results_wanted": 20, "max_pages": 10, "proxyConfiguration": { "useApifyProxy": false } } ``` # Actor output Schema ## `overview` (type: `string`): No description # 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 = { "keyword": "developpeur", "location": "Liege" }; // Run the Actor and wait for it to finish const run = await client.actor("shahidirfan/leforem-be-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 = { "keyword": "developpeur", "location": "Liege", } # Run the Actor and wait for it to finish run = client.actor("shahidirfan/leforem-be-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 '{ "keyword": "developpeur", "location": "Liege" }' | apify call shahidirfan/leforem-be-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=shahidirfan/leforem-be-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Leforem.be Scraper", "description": "Automate job data collection from Leforem.be. Get real-time access to Belgian job postings with complete details. Ideal for career tracking, recruiter databases, and competitive job market analysis.", "version": "0.0", "x-build-id": "fCUQQJxOH3iMQsJh6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/shahidirfan~leforem-be-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-shahidirfan-leforem-be-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/shahidirfan~leforem-be-scraper/runs": { "post": { "operationId": "runs-sync-shahidirfan-leforem-be-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/shahidirfan~leforem-be-scraper/run-sync": { "post": { "operationId": "run-sync-shahidirfan-leforem-be-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": { "startUrl": { "title": "Start URL", "type": "string", "description": "Optional Leforem search URL. Query parameters are used as additional filters." }, "keyword": { "title": "Keyword", "type": "string", "description": "Job search keyword(s), for example: developpeur, comptable, infirmier." }, "location": { "title": "Location", "type": "string", "description": "Comma-separated city/region names. The actor resolves them with Leforem localisation API." }, "collectDetails": { "title": "Collect offer details", "type": "boolean", "description": "If enabled, actor calls the detail API for each offer id.", "default": true }, "results_wanted": { "title": "Maximum number of offers", "minimum": 1, "type": "integer", "description": "Maximum number of dataset items to save.", "default": 20 }, "max_pages": { "title": "Maximum pages", "minimum": 1, "type": "integer", "description": "Safety cap for API pagination.", "default": 10 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Use Apify Proxy for reliability.", "default": { "useApifyProxy": false } } } }, "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 } } } } } } } } } } ```