# Hiring Demand & Workforce Intelligence Agent (`quanmatrix/hiring-demand-intelligence-agent`) Actor

Use this Actor to analyze hiring demand and workforce and return decision-ready structured signals. Turn LinkedIn, Indeed and other job listing datasets into hiring velocity, role demand, geography and company-expansion intelligence.

- **URL**: https://apify.com/quanmatrix/hiring-demand-intelligence-agent.md
- **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community)
- **Categories:** Jobs, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $7.00 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Hiring Demand & Workforce Intelligence Agent

Use this Actor to analyze hiring demand and workforce and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

Turn LinkedIn, Indeed and other job listing datasets into hiring velocity, role demand, geography and company-expansion intelligence.

### Why use this Actor

Raw job listings answer what is open. They do not directly answer which companies are accelerating hiring, which roles are rising, where demand is moving, or what changed since the last observation. This Actor converts supplied job rows into a compact intelligence layer for recruiting, workforce planning, competitive research, sales triggers and AI agents.

### Key features

- Accept current job rows inline or from an Apify Dataset.
- Accept an optional prior snapshot for period-over-period intelligence.
- Normalize common title, company, location, salary, seniority, URL and remote-work aliases.
- Produce deterministic actions and evidence instead of opaque prose.
- Work across LinkedIn, Indeed and other upstream public-job datasets.
- Run data-first at 256 MB without a browser or external LLM.

### Input

Provide current job listings inline through `currentItems` or reference an Apify Dataset with `currentDatasetId`. An optional previous snapshot can be supplied inline or through `previousDatasetId` to calculate period-over-period hiring movement. `maxItems` limits Dataset ingestion.

### Output

The default Dataset receives one structured intelligence report, also stored as `INTELLIGENCE_REPORT`. It contains observed counts, deltas and decision fields appropriate to this product mode.

### Example

Supply a current list of public job records plus the previous observation. The Actor normalizes the rows and reports the strongest hiring or market movement instead of returning another copy of the raw listings.

### Use cases

- Competitive hiring surveillance.
- Workforce and location planning.
- Recruiting-market research.
- Sales and investment trigger generation.
- Scheduled company monitoring.
- Agent-ready hiring intelligence.

### Pricing

Pay per completed intelligence report. The product deliberately prices the aggregated decision output rather than charging separately for every internal calculation.

### Limitations

The Actor analyzes the data supplied to it. It does not authenticate to private job accounts, infer actual employee headcount, or claim that every open listing represents a unique approved hire. Missing upstream fields remain missing rather than being invented.

### Reliability

All calculations are deterministic and reusable. If no current job rows are supplied, the run fails explicitly. This keeps scheduled monitoring honest, which is a surprisingly demanding standard for software.

### Data interpretation

A listing is evidence of recruiting activity, not proof that a role will be filled. For that reason the output labels observations as signals and keeps counts, deltas and source rows conceptually separate. Snapshot comparison is most useful when upstream collection scope is held stable between runs.

# Changelog

This Actor's version history is a separate document: https://apify.com/quanmatrix/hiring-demand-intelligence-agent/changelog.md

# Actor input Schema

## `currentItems` (type: `array`):

Current normalized or raw job listing rows. Common title/company/location/url aliases are detected automatically.

## `currentDatasetId` (type: `string`):

Optional Apify Dataset ID used when currentItems is not supplied.

## `previousItems` (type: `array`):

Optional previous snapshot rows for change and velocity intelligence.

## `previousDatasetId` (type: `string`):

Optional prior Dataset ID used instead of previousItems.

## `maxItems` (type: `integer`):

Maximum rows loaded from a Dataset source.

## `mcpConnectors` (type: `array`):

Optional MCP connectors authorized in your Apify account. Use them to send or write this Actor result to tools such as Slack, Notion, GitHub, Sentry, Supabase, or another compatible MCP service.

## `mcpToolName` (type: `string`):

Optional exact MCP tool name. Leave blank to let the selected MCP action preset discover a compatible tool automatically.

## `mcpToolArguments` (type: `object`):

JSON object passed to the selected MCP tool. String values may use {{actor\_title}}, {{result\_summary}}, or {{result\_json}} placeholders.

## `mcpFailOnError` (type: `boolean`):

When enabled, an MCP delivery error fails the Actor run. Disabled by default so data extraction and intelligence results remain available even if the external destination is unavailable.

## `mcpActionPreset` (type: `string`):

Choose a safe action pattern. AUTO\_SAFE\_WRITE discovers a compatible non-destructive write tool automatically; use a specific preset for Slack, GitHub, Notion, or database delivery.

## Actor input object example

```json
{
  "currentItems": [
    {
      "id": "sample-1",
      "title": "AI Engineer",
      "company": "Example Corp",
      "location": "Remote",
      "remote": true,
      "url": "https://example.com/jobs/sample-1"
    },
    {
      "id": "sample-2",
      "title": "Data Engineer",
      "company": "Example Corp",
      "location": "São Paulo",
      "remote": false,
      "url": "https://example.com/jobs/sample-2"
    }
  ],
  "maxItems": 20000,
  "mcpToolName": "",
  "mcpToolArguments": {},
  "mcpFailOnError": false,
  "mcpActionPreset": "AUTO_SAFE_WRITE"
}
```

# Actor output Schema

## `dataset` (type: `string`):

No description

## `report` (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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("quanmatrix/hiring-demand-intelligence-agent").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("quanmatrix/hiring-demand-intelligence-agent").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).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 quanmatrix/hiring-demand-intelligence-agent --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/hiring-demand-intelligence-agent"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/RPeequfUKyaHAuKn8/builds/bei4jCCqJPBwvECnN/openapi.json