# LinkedIn Company Employees (`ayeeyee/linkedin-company-employees`) Actor LinkedIn Company Employees returns normalized, review-ready API data with robust inputs, consistent outputs, and pay-per-result pricing. - **URL**: https://apify.com/ayeeyee/linkedin-company-employees.md - **Developed by:** [Virtual Footprint LLC](https://apify.com/ayeeyee) (community) - **Categories:** Lead generation, Open source - **Stats:** 1 total users, 0 monthly users, 60.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 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 Company Employees Scraper ![Apify](https://img.shields.io/badge/Apify-Actor-ff6b37?logo=apify) ![Version](https://img.shields.io/badge/version-2.0-green) ![Pricing](https://img.shields.io/badge/pricing-PAY_PER_EVENT-blue) ![Modes](https://img.shields.io/badge/modes-enrich|batch-purple) > Map company employees with role signals, seniority inference, and CRM-ready output. ### Why this actor is better This actor is designed to outperform competitors in reliability, data completeness, output quality, and pricing transparency. | Competitor issue | This actor advantage | |---|---| | Single data source | Multi-source: LinkedIn company pages (Playwright) + Employee discovery, Role inference, Company metadata | | No confidence scoring | 0-1 confidence score on every result | | No source attribution | Full source tracking on every dataset row | | Slow / blocking | Progressive dataset pushes, 8s timeouts, cache-first mode | | No enrichment | Built-in enrichment with confidence scoring | | Requires paid APIs | 100% open-source providers, no mandatory subscriptions | | No batch support | Queue-based batch mode with per-item error isolation | ### Architecture ```` ┌─────────────────────────────────────────────────────────────┐ │ LinkedIn Company Employees Scraper │ ├─────────────────────────────────────────────────────────────┤ │ Mode Router │ │ ├─ fast\_lookup (cache-first, <800ms) │ │ ├─ enrich (multi-provider, parallel, progressive push) │ │ └─ batch (queue-based, per-item isolation) │ ├─────────────────────────────────────────────────────────────┤ │ Provider Layer │ │ ├─ LinkedIn company pages (Playwright) │ │ └─ Enrichment: │ │ ├─ Employee discovery │ │ ├─ Role inference │ │ └─ Company metadata │ ├─────────────────────────────────────────────────────────────┤ │ Quality Layer │ │ ├─ Confidence scoring (0-1) │ │ ├─ Source attribution │ │ └─ Deduplication │ ├─────────────────────────────────────────────────────────────┤ │ Output Layer │ │ ├─ Apify Dataset (progressive push) │ │ ├─ Webhook delivery │ │ └─ Normalized schema with metadata │ └─────────────────────────────────────────────────────────────┘ ```` ### Enrichment Sources - ✅ Employee discovery - ✅ Role inference - ✅ Company metadata ### Modes #### `enrich` — Multi-provider enrichment (default) - Scrapes LinkedIn company pages (Playwright) - Enriches with: Employee discovery, Role inference, Company metadata - Pushes base result first, then enriched result (progressive) - 8s enrichment timeouts (fail-fast) - Best for: lead generation, CRM enrichment, analytics #### `batch` — Queue-based - Accepts large query lists - Processes each query through full enrichment - Per-item error isolation (one failed query doesn't kill the run) - Uses Apify Request Queue - Best for: bulk processing, market research, territory mapping ### Input | Field | Type | Required | Default | Description | |---|---|---|---|---| | `mode` | string | no | `enrich` | enrich, batch | | `queries` | array | yes | — | Search queries, profile URLs, or usernames | | `maxResults` | integer | no | `25` | Max results per query (1-1000) | | `webhookUrl` | string | no | `""` | Webhook for completion notification | #### Example input ```json { "mode": "enrich", "queries": ["https://linkedin.com/company/google"], "maxResults": 50 } ```` ### Output Every dataset row includes: | Field | Description | |---|---| | `query` | Input query used | | `name` / `title` | Primary result name | | `url` | Canonical URL | | `emails` | Extracted email addresses (if applicable) | | `phones` | Extracted phone numbers (if applicable) | | `socialMedia` | Social media profile links | | `techStack` | Detected technologies (if applicable) | | `metrics` | Engagement, rating, or count metrics | | `confidenceScore` | 0-1 score based on data completeness | | `sources` | Source attribution array | | `cacheStatus` | `hit`, `miss`, `partial`, or `bypass` | | `mode` | Execution mode used | | `extractedAt` | ISO timestamp | #### Example output ```json { "query": "https://linkedin.com/company/google", "name": "Example Result", "url": "https://example.com", "emails": ["contact@example.com"], "phones": ["+1 (555) 123-4567"], "socialMedia": ["facebook.com/example"], "techStack": ["React", "Google Analytics"], "metrics": {"rating": "4.5", "reviews": "1234"}, "confidenceScore": 0.85, "sources": ["primary_scraper", "website_scraper"], "cacheStatus": "miss", "mode": "enrich", "extractedAt": "2026-06-21T19:00:00Z" } ``` ### Monetization Pay-per-event pricing: | Actor Start | $0.00005 (one-time) | | Result | $0.0050 per result | | Email Found | $0.003 per email | | Batch Item Processed | $0.002 per query | ### Data Lifecycle Controls - **Purpose limitation:** Lead generation, business intelligence, market research - **Data minimization:** Collect only records required by `queries` and `maxResults` - **Transparency:** Input, output, pricing, and constraints documented - **Security:** No hardcoded API keys; secrets passed via Apify secure inputs - **Auditability:** Rows include `extractedAt`, `sources`, `mode`, and `cacheStatus` ### Compliance Use this actor for legitimate research, analytics, lead generation, or monitoring. Users are responsible for complying with applicable laws, source-site terms, and privacy obligations. ### Support - Apify Console: Check your actor runs - Issues: Report via Apify Store or actor page - Webhook: Set `webhookUrl` for completion notifications # Actor input Schema ## `mode` (type: `string`): fast\_lookup: cache-first. enrich: multi-provider enrichment. batch: queue-based. ## `queries` (type: `array`): Search queries, profile URLs, or usernames ## `maxResults` (type: `integer`): Maximum results per query (1-1000) ## `webhookUrl` (type: `string`): Optional webhook URL for completion notification ## Actor input object example ```json { "mode": "enrich", "queries": [ "https://linkedin.com/company/google" ], "maxResults": 25, "webhookUrl": "" } ``` # 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("ayeeyee/linkedin-company-employees").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("ayeeyee/linkedin-company-employees").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 ayeeyee/linkedin-company-employees --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ayeeyee/linkedin-company-employees", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Company Employees", "description": "LinkedIn Company Employees returns normalized, review-ready API data with robust inputs, consistent outputs, and pay-per-result pricing.", "version": "2.0", "x-build-id": "k6sspYr8MwgZ2yKqn" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ayeeyee~linkedin-company-employees/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ayeeyee-linkedin-company-employees", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ayeeyee~linkedin-company-employees/runs": { "post": { "operationId": "runs-sync-ayeeyee-linkedin-company-employees", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ayeeyee~linkedin-company-employees/run-sync": { "post": { "operationId": "run-sync-ayeeyee-linkedin-company-employees", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "queries" ], "properties": { "mode": { "title": "Execution Mode", "enum": [ "enrich", "batch" ], "type": "string", "description": "fast_lookup: cache-first. enrich: multi-provider enrichment. batch: queue-based.", "default": "enrich" }, "queries": { "title": "Search Queries", "type": "array", "description": "Search queries, profile URLs, or usernames", "default": [ "https://linkedin.com/company/google" ] }, "maxResults": { "title": "Max Results per Query", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum results per query (1-1000)", "default": 25 }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "Optional webhook URL for completion notification", "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 } } } } } } } } } } ```