# LinkedIn Profile Comments Scraper (No Cookies) (`wheaten_steelpan/linkedin-profile-comments-scraper`) Actor

Every comment a LinkedIn profile has made, with the full parent post attached: post content, author, reactions by type and engagement counts. No cookies or LinkedIn account needed. Time filters that really work. Never charged for a comment we did not return. Not affiliated with LinkedIn.

- **URL**: https://apify.com/wheaten\_steelpan/linkedin-profile-comments-scraper.md
- **Developed by:** [Vanja V](https://apify.com/wheaten_steelpan) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$5.00 / 1,000 comments

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/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

## LinkedIn Profile Comments Scraper (No Cookies)

Every comment a LinkedIn profile has made — with the **full post they commented on** attached to
each one. No cookies, no LinkedIn account, no browser session to keep alive.

You are never charged for a comment we did not return.

***

### What you get

Paste in a profile. Get back every comment that person has left on LinkedIn, newest first. Each
row carries the comment *and* its context:

- **The comment** — text, exact timestamp, direct link, whether it was edited
- **Reactions broken down by type** — likes, empathy, praise, appreciation, interest, with counts
- **The parent post in full** — its content, its author, its own like/comment/share counts, images,
  articles, polls, documents, reposts
- **Who commented** — name, headline, profile URL, profile picture
- **Replies to the comment** — up to 10 per comment

That parent post matters. Most comment scrapers hand you a line of text with a post ID and leave
you to fetch the post yourself. Here the context arrives with the comment.

### Example

![Example output: real comments by williamhgates, each with the post it was left on, reaction counts by type, and reply counts](https://api.apify.com/v2/key-value-stores/hz3kLBXPwFRdD6goJ/records/example-output.png)

*Real rows from a real run — nothing in this picture is mocked up.*

Input:

```json
{
  "profiles": ["williamhgates", "https://www.linkedin.com/in/satyanadella"],
  "postedWithin": "any"
}
```

One real output row, trimmed for length — everything shown is exactly as returned:

```json
{
  "id": "7507672989329154048",
  "commentary": "I've never been more hopeful about the advances in Alzheimer's research than I am today: https://gatesnot.es/3TEWxfa",
  "createdAt": "2026-09-21T05:33:17.895Z",
  "createdAtTimestamp": 1789968797895,
  "linkedinUrl": "https://www.linkedin.com/feed/update/urn:li:ugcPost:7507672954638336000?commentUrn=...",
  "edited": false,
  "engagement": {
    "likes": 65,
    "comments": 6,
    "shares": 0,
    "reactions": [
      { "type": "LIKE", "count": 48 },
      { "type": "EMPATHY", "count": 6 },
      { "type": "PRAISE", "count": 5 },
      { "type": "APPRECIATION", "count": 4 },
      { "type": "INTEREST", "count": 2 }
    ]
  },
  "actor": {
    "name": "Bill Gates",
    "position": "Chair, Gates Foundation and Founder, Breakthrough Energy",
    "linkedinUrl": "https://www.linkedin.com/in/williamhgates",
    "pictureUrl": "https://media.licdn.com/dms/image/..."
  },
  "postId": "7507672954638336000",
  "post": {
    "content": "As we mark World Alzheimer's Day, I'm encouraged by the remarkable research progress that's been made. From the development of blood-based diagnostics to the promise of antibody treatments, we're seeing …",
    "author": {
      "name": "Bill Gates",
      "info": "Chair, Gates Foundation and Founder, Breakthrough Energy",
      "linkedinUrl": "https://www.linkedin.com/in/williamhgates"
    },
    "postedAt": {
      "date": "2026-09-21T05:33:28.235Z",
      "postedAgoText": "12 hours ago • Visible to anyone on or off LinkedIn"
    },
    "engagement": { "likes": 1963, "comments": 233, "shares": 29 },
    "postImages": [ "…" ]
  },
  "replies": [ "… up to 10 replies to this comment …" ]
}
```

Export it as CSV, JSON or Excel from the **Export** button, or pull it from the API.

### Input

| Field | What it does |
|---|---|
| **LinkedIn profiles** | Usernames (`satyanadella`) or full URLs (`https://www.linkedin.com/in/satyanadella`). Both work. Comma-separated entries are split for you and duplicates are removed. Up to 100 per run. |
| **Only comments posted within** | Any time, last 24 hours, last week, last month, last 3 months, last 6 months, or last year. |
| **Max comments per profile** | Leave blank to get everything. Set a number only if you want to spend less. |

Nothing is required. Run it with the prefilled example to see the shape of the output.

#### The time filter actually filters

Worth stating plainly, because it is not true of every Actor in this category: if you select
**last year**, you get comments from the last year. Comments outside the window are dropped
before they reach your dataset, so you are never charged for them, and the run stops paging as
soon as it passes the end of your window — which makes a narrow window **cheaper**, not just
smaller.

### How do I know the pull was complete?

LinkedIn does not publish a comment total, so there is no number to check against. Instead, every
run writes a **SUMMARY** record saying, per profile, how many comments came back, how many pages
were read, and whether paging reached the end:

```json
{
  "commentsReturned": 166,
  "perProfile": [
    { "profile": "satyanadella", "comments": 166, "pages": 3, "complete": true, "stopped": "exhausted" }
  ],
  "incompleteProfiles": []
}
```

A profile reads `complete: true` when paging reached an empty page, or ran past the end of the
time window you chose. Anything else is listed under `incompleteProfiles` with the reason — a cap
you set, the run's time limit, or your maximum charge. The run's final message says so too.

### Pricing

**$0.005 per comment**, pay-per-event. Nothing else is charged.

- Comments filtered out by your time window are **not charged**
- Duplicate rows are dropped and **not charged**
- A failed run charges **nothing** — no key, no credit, provider outage: zero
- Set a **maximum charge** on the run and the Actor stops cleanly at that ceiling rather than
  overrunning it

Measured on real profiles: Satya Nadella has 166 comments, Bill Gates 73. Most single profiles
cost well under a dollar to pull completely.

### Speed and limits

Each page of comments takes roughly 2–5 seconds and returns 50–100 comments. A run has a 4-minute
budget; if it runs out with profiles still queued, the summary says which ones, and you can run
those again. Up to 100 profiles per run.

### Common uses

- Find everyone a prospect engages with, and what they say
- Track how a competitor's team shows up in comment sections
- Pull a person's own commentary as a body of text — their views, in their words
- Spot which posts in your industry are drawing the people you care about

### Notes

No LinkedIn account, cookies, or session tokens are needed — from you or from us. You never
supply credentials.

Only publicly visible comments are returned. Anything that requires being logged in, or that the
profile owner has restricted, is not available through this Actor.

***

**Unofficial.** This Actor is not affiliated with, endorsed by, or connected to LinkedIn
Corporation. "LinkedIn" is a trademark of LinkedIn Corporation, used here only to describe what
this Actor reads. You are responsible for using the data it returns in line with applicable law
and LinkedIn's terms.

# Actor input Schema

## `profiles` (type: `array`):

One or more LinkedIn profiles. Both spellings work: a username like satyanadella, or the full address like https://www.linkedin.com/in/satyanadella. Comma-separated entries are split for you, and duplicates are removed. No LinkedIn account or cookies are needed.

## `postedWithin` (type: `string`):

Limit the pull to recent comments. Every option here is real: comments outside the window are filtered out and never charged, and the pull stops early once it passes the window, so a narrow window costs you less. Leave it on "Any time" to get a profile's whole comment history.

## `maxCommentsPerProfile` (type: `integer`):

Leave blank to get EVERY comment a profile has made. That is the default and it is what this Actor is for. Set a number only if you want to spend less. For scale: measured on real profiles, Satya Nadella has 166 comments and Bill Gates has 73, so most profiles cost well under a dollar to pull completely.

## Actor input object example

```json
{
  "profiles": [
    "satyanadella",
    "https://www.linkedin.com/in/williamhgates"
  ],
  "postedWithin": "any"
}
```

# Actor output Schema

## `data` (type: `string`):

One row per comment: the comment text, when it was posted, its reactions broken down by type, and the full parent post it was left on. This is the product — download it as CSV, JSON or Excel from the Export button.

## `summary` (type: `string`):

Per profile: how many comments came back, how many pages were read, and whether the pull reached the end. LinkedIn does not publish a comment total, so this is how you check a pull was complete.

# 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 = {
    "profiles": [
        "satyanadella",
        "https://www.linkedin.com/in/williamhgates"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("wheaten_steelpan/linkedin-profile-comments-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "profiles": [
        "satyanadella",
        "https://www.linkedin.com/in/williamhgates",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("wheaten_steelpan/linkedin-profile-comments-scraper").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 '{
  "profiles": [
    "satyanadella",
    "https://www.linkedin.com/in/williamhgates"
  ]
}' |
apify call wheaten_steelpan/linkedin-profile-comments-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,wheaten_steelpan/linkedin-profile-comments-scraper"
        }
    }
}
```

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/TVI1olYxQziwN4xpv/builds/6Zk9FZeNS6nBEsDWA/openapi.json