How to use Seek Jobs Scraper

Step by step: in the Apify Store, over the API, from JavaScript or Python, in n8n, and from AI agents through MCP or LangChain.

$1.80 per 1,000 jobs (live Apify price on 2026-10-31).

1. In the Apify Store (no code)

1. Open arcadium/seek-jobs and sign in to Apify (a free account works).
2. Fill in the input form. The fields below are the ones the example uses.
3. Set a maximum cost per run in the run options if you want a hard cap.
4. Start the run. Rows appear in the Output tab as they arrive.
5. Export the dataset as JSON, CSV or Excel, or connect an integration.

Example input:

{
  "keywords": [
    "data analyst"
  ],
  "locations": [
    "Sydney NSW"
  ],
  "startUrls": [],
  "maxJobsPerSearch": 50
}

2. Over the Apify API

The synchronous endpoint runs the actor and returns its rows in one call (for runs up to about five minutes; use the asynchronous run endpoint for longer ones).

curl -X POST "https://api.apify.com/v2/acts/arcadium~seek-jobs/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"keywords":["data analyst"],"locations":["Sydney NSW"],"startUrls":[],"maxJobsPerSearch":50}'

3. From JavaScript

// npm install apify-client
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('arcadium/seek-jobs').call({
  "keywords": [
    "data analyst"
  ],
  "locations": [
    "Sydney NSW"
  ],
  "startUrls": [],
  "maxJobsPerSearch": 50
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

4. From Python

# pip install apify-client
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("arcadium/seek-jobs").call(run_input={
    "keywords": ["data analyst"],
    "locations": ["Sydney NSW"],
    "startUrls": [],
    "maxJobsPerSearch": 50,
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)

5. In n8n

Paste this node into a workflow (it is a plain HTTP Request node). Create a Header Auth credential with name Authorization and value Bearer <APIFY_TOKEN>; each returned row becomes one item for the next node.

{
  "nodes": [
    {
      "name": "Run arcadium/seek-jobs",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4,
      "position": [
        0,
        0
      ],
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/arcadium~seek-jobs/run-sync-get-dataset-items",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "{\"keywords\":[\"data analyst\"],\"locations\":[\"Sydney NSW\"],\"startUrls\":[],\"maxJobsPerSearch\":50}",
        "options": {
          "timeout": 300000
        }
      }
    }
  ],
  "connections": {}
}

6. From an AI assistant (MCP)

Add Apify’s MCP server to Claude Desktop, Cursor, VS Code or any MCP client, limited to this actor:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=arcadium/seek-jobs",
      "headers": {
        "Authorization": "Bearer <APIFY_TOKEN>"
      }
    }
  }
}

More on the MCP page.

7. From LangChain

# pip install langchain-apify   (reads APIFY_API_TOKEN from the environment)
from langchain_apify import ApifyActorsTool

tool = ApifyActorsTool("arcadium/seek-jobs")
rows = tool.invoke({"run_input": {
    "keywords": ["data analyst"],
    "locations": ["Sydney NSW"],
    "startUrls": [],
    "maxJobsPerSearch": 50,
}})
# or give `tool` to an agent: create_react_agent(llm, [tool])

Sample output

One row, from the example in the actor’s README. Long text is shortened and lists are cut to two items.

{
  "rowType": "job",
  "platform": "seek",
  "jobId": "95426731",
  "url": "https://www.seek.com.au/job/95426731",
  "title": "Data Analysis Officer",
  "companyName": "Macquarie University",
  "advertiserName": "Macquarie University",
  "advertiserId": "28800385",
  "companyProfileUrl": "https://au.seek.com/companies/macquarie-university-432440",
  "location": "Sydney NSW",
  "area": "Ryde & Macquarie Park",
  "suburb": "North Ryde",
  "state": "New South Wales",
  "postcode": "2113",
  "country": "AU",
  "classification": "Information & Communication Technology",
  "subClassification": "Other",
  "workTypes": [
    "Full time"
  ],
  "workArrangements": [
    "Hybrid"
  ],
  "salaryLabel": null,
  "salary": null,
  "listedAt": "2026-10-22T03:18:06Z",
  "listedAgo": "8h ago",
  "expiresAt": "2026-11-21T03:18:04.000Z",
  "teaser": "We are hiring an enthusiastic Data Analysis Officer to join our team!",
  "bulletPoints": [],
  "description": "$102,136 - 112,741 per annum (HEW Level 6), plus 17% employer's superannuation and annual leave loading\nFull-time, 16-month fixed-term contract ...",
  "normalizedTitle": "Data Analytics Officer",
  "applyOnSeek": false,
  "featured": false,
  "companyWebsite": "http://mq.edu.au/",
  "companySize": "1,001-5,000 employees",
  "companyIndustry": "Education, Training & Professional Development",
  "companyRating": 3.4,
  "companyReviewCount": 85,
  "detailsIncluded": true,
  "source": "data analyst in Sydney NSW (AU)",
  "sourceType": "search",
  "searchKeyword": "data analyst",
  "searchLocation": "Sydney NSW 2000",
  "position": 11,
  "scrapedAt": "2026-10-22T12:00:00.000Z"
}

Schedule it

In Apify Console, create a Task from your input and add a Schedule (for example daily). Each run writes a new dataset; integrations can push the rows to Google Sheets, Slack, a webhook or your database.