LinkedIn Employee Scraper

Map company employees from LinkedIn records. Start with LinkedIn employee URLs; each returned employee keeps handles, names, headlines, roles, and employers.

What it returns

Each row keeps the LinkedIn source record beside the fields needed to use it. The opening set is requestedTarget, username, fullName, headline, currentTitle, currentCompany, location, country, profileUrl, workEmail, emailConfidence, and emailPattern. The complete schema is declared before the run, and dataset views keep related fields together without changing the underlying row.

Input

LinkedIn Employee Scraper accepts source URLs. Run controls stay in the same form.

Field	What it controls	Starting value
startUrls	Enter LinkedIn URLs or search terms, one per line.	["software engineer","https://www.linkedin.com/company/stripe","linkedin employee data"]
findWorkEmail	Choose whether to look for a work email for each matched person.	false
maxItems	Stop after this many dataset rows.	25
maxSeconds	Stop after this many seconds and keep completed rows.	180
maxConcurrency	Set the number of source requests that may run in parallel.	3
enrich	Choose the enrichment mode applied to returned records.	"off"

Example input

{
  "startUrls": [
    "software engineer",
    "https://www.linkedin.com/company/stripe",
    "linkedin employee data"
  ],
  "findWorkEmail": false,
  "maxItems": 3,
  "maxSeconds": 180
}

No field is required. Start with the filled example, then replace only the target values needed for the job. Run controls can stay at their starting values for the first collection.

Dataset fields

Field	Type
requestedTarget	string
username	string
fullName	string
headline	string
currentTitle	string
currentCompany	string
location	string
country	string
profileUrl	string
workEmail	string
emailConfidence	string
emailPattern	string
companyEmail	string
route	string
scrapedAt	string

Dataset views

Views are working surfaces for review and export. They select and order fields while leaving the stored row unchanged.

View	Opening fields
overview	username, fullName, currentTitle, workEmail, emailConfidence, emailPattern, companyEmail, and location
identity	username, fullName, and currentTitle
contact	username, fullName, requestedTarget, workEmail, emailConfidence, emailPattern, and companyEmail
location	username, fullName, requestedTarget, location, and country
timing	username, fullName, requestedTarget, and scrapedAt
provenance	requestedTarget, profileUrl, and route

Output and exports

Output	Type	Destination
results	string	{{links.apiDefaultDatasetUrl}}/items
json	string	{{links.apiDefaultDatasetUrl}}/items?clean=true&format=json
csv	string	{{links.apiDefaultDatasetUrl}}/items?clean=true&format=csv
excel	string	{{links.apiDefaultDatasetUrl}}/items?clean=true&format=xlsx
jsonl	string	{{links.apiDefaultDatasetUrl}}/items?clean=true&format=jsonl

Completed rows are available in the Apify dataset as JSON, CSV, Excel, and JSONL exports. The run output also carries the declared links above for API clients and automations.

Pricing

$0.7 per 1,000 linkedin jobs on the Free plan. Other Apify plans use the rates shown in the Pricing tab.

Console, API, schedules, and MCP

Runs can begin in Apify Console, from a saved task, or through the Actor API. A schedule can reuse the same input, and a run-finished webhook can pass the dataset or run ID to the next system.

POST https://api.apify.com/v2/acts/433vxZsfzpAv79vOk/runs
GET  https://api.apify.com/v2/datasets/{datasetId}/items

For MCP selection, use LinkedIn Employee Scraper. Its machine entry carries the same description, input field names, no-required-field contract, output types, dataset fields, views, and pricing facts as this document.

Saved tasks

Twenty saved-task products cover distinct lookup, comparison, research, operations, automation, and export jobs:

• LinkedIn company employee map: buyer-job; opens overview.
• Decision-makers by role and company: buyer-job; opens identity.
• Employee work emails matched to identity: buyer-job; opens contact.
• Employees grouped by country and location: buyer-job; opens location.
• Refresh a LinkedIn employee roster: buyer-job; opens overview.

Integrations

Use the dataset API from any HTTP client, export rows to a spreadsheet, or send the run ID through an Apify webhook. Saved tasks give schedules and automation tools a stable input without changing the Actor contract.

Related products

• LinkedIn Job Scraper (linkedin-job-scraper)
• LinkedIn Company Scraper (linkedin-company-scraper)
• LinkedIn Data Export Scraper (linkedin-data-export-scraper)
• Indeed Job Search Scraper (indeed-job-search-scraper)
• Indeed Job Export Scraper (indeed-job-export-scraper)

When a run needs attention

• No rows: Open the target in a browser, check spelling and source visibility, then retry the saved example before widening the input.
• A field is empty: Check the field beside its source URL. A missing source value stays empty instead of being replaced with a guess.
• A target fails: Keep successful targets in the dataset, then retry only the affected input.
• An automation cannot find results: Read the dataset ID from the run and request its items endpoint directly.

FAQ

What do I get back from one run?

One row per employee with 15 declared fields, opening on requestedTarget, username, fullName and headline. The schema is published before the run, so you know the shape before you spend anything.

Do I need a linkedin account or login?

No. The run works from the linkedin sources you supply in the input. Nothing is posted, changed or accessed on your behalf.

What does a run cost?

The current rate is shown on the Pricing tab and is charged per row you receive, so a run that finds nothing costs close to nothing. Cap the run with the item limit when you want a predictable ceiling.

Can I try it before committing budget?

Yes. Cap the run with the item limit in the input and inspect the first rows. The cap is enforced before charging, so a trial run stays a trial.

What do I put in the input?

The staged input is already usable: startUrls, findWorkEmail and maxItems. Replace the staged target with your own list when you are ready to run for real.

Are any fields required?

No field is required. Every input carries a working default, so the Actor can be started as-is and refined afterwards.

Can I limit how long a run takes?

Yes. The findWorkEmail input caps the run. Use it when you need a predictable cost and a predictable finish time.

How do I report a problem?

Open an issue on the Actor with the run ID, the input you used and the field or row that needs attention. The run ID lets the exact execution be inspected.

How is this different from LinkedIn Job Scraper?

LinkedIn Employee Scraper answers one job: Map company employees from LinkedIn records.. LinkedIn Job Scraper covers a different question on the same platform. Run both when you need both sides.

What is emailConfidence for?

It records how the row was resolved, so you can filter to the rows you trust instead of treating every row as equally certain.

How do I read the output without scrolling through JSON?

Open the overview view on the Output tab. 6 views ship with the Actor (overview, identity, contact, location, timing and provenance), each grouping the fields that belong to one question.

How do I get the data into my own tools?

Export the dataset as JSON, CSV, Excel or XML, call the dataset API directly, or attach a run-finished webhook and collect the dataset reference as soon as the run ends.

Can an agent or LLM call this?

Yes. LinkedIn Employee Scraper is exposed over MCP with the same description, no-required-field input contract and output types shown here, so an agent can select and call it without a human in the loop.

Why is a value empty on some rows?

linkedin does not expose every field on every employee. An absent value stays empty rather than being filled with a guess, so a row never invents a fact it did not receive.

A run returned fewer rows than I expected. Why?

The usual causes are a narrow source list, an item cap still set low, or a source that genuinely holds less than expected. Widen the input or raise the cap and run again.

Can I schedule this to run on its own?

Yes. Save the input as an Apify task and attach a schedule. Keep separate tasks when different teams need different targets or delivery paths.

Do I need to configure proxies?

No. Network access is handled inside the Actor and needs no proxy configuration from you.

How fresh is the data?

Every row is collected during the run you start, not served from a cache. Re-run the same input whenever you need the current state of a linkedin employee.

Can I use the results commercially?

The Actor collects publicly accessible linkedin information. You remain responsible for how you use it, including any privacy or contractual obligations that apply to your business.

How do I compare two runs?

Keep requestedTarget and username as your join key and diff the exports. The identity fields stay stable across runs, which is what makes a comparison meaningful.

What happens if a source fails mid-run?

The run continues through the remaining sources and finishes with what it collected. Partial results are still written to the dataset rather than discarded.

Support

Use the Actor issue form for product questions, broken source routes, schema mismatches, and feedback. Include the smallest input that reproduces the problem. That is enough to locate the run and its dataset without sharing an entire working list.

Use this Actor only for data you are allowed to collect. Follow source terms, privacy law, and your own retention policy.