LinkedIn Post Scraper: How to Extract Posts, Reactions, and Comments via API (2026)

A LinkedIn post scraper pulls the content and engagement of a post – its text, media, and reaction, comment, and repost counts – and, most usefully, the list of people who reacted and commented, each with their name, headline, and profile URL. There is no official LinkedIn API to read arbitrary post data, so every option is a scraper, and they differ a lot in reliability and account risk. This guide shows exactly what fields you can get, a real sample response, how to scrape one post and a person's or company's recent posts in code (Node and Python), and the part most guides skip: turning post engagers into a warm-lead list.

The short version. A post's content and its engager list – who reacted and who commented, with name, headline, and profile URL – are both extractable. Personal emails are not. Linked API reads known post URLs and a person's or company's recent posts (bounded by a limit and an optional since date), not an arbitrary keyword search of LinkedIn, and on big posts it samples engagement rather than dumping it whole. Chrome extensions and scripts run on your own account at machine speed and get it restricted; an API paces like a human and returns clean JSON.

What data is in a LinkedIn post

A post is really three records: the post itself, the reactions on it, and the comments on it. Here is what you get from each.

The reaction and comment records are the valuable part: each one carries the engager's profile URL – a durable key you can enrich or act on later. Note what is not here: no email, no phone. Post data is public-facing profile data, not contact data.

A real scraped post

The reactions and comments sections are opt-in – you ask for them, and you get typed JSON back instead of HTML to parse.

{
  "url": "https://www.linkedin.com/posts/jane-doe_growth-activity-7440011668937568257",
  "time": "2026-07-08T14:20:00Z",
  "type": "original",
  "repostText": null,
  "text": "We cut our onboarding drop-off by 40% this quarter. Three things that worked...",
  "images": ["https://media.linkedin.com/…/chart.png"],
  "hasVideo": false,
  "hasPoll": false,
  "reactionsCount": 214,
  "commentsCount": 37,
  "repostsCount": 9,
  "reactions": [
    {
      "engagerUrl": "https://www.linkedin.com/in/mark-lee",
      "engagerName": "Mark Lee",
      "engagerHeadline": "VP Sales at Northwind",
      "engagerType": "person",
      "type": "insightful"
    },
    {
      "engagerUrl": "https://www.linkedin.com/in/sara-kim",
      "engagerName": "Sara Kim",
      "engagerHeadline": "Head of RevOps at Acme",
      "engagerType": "person",
      "type": "celebrate"
    }
  ],
  "comments": [
    {
      "commenterUrl": "https://www.linkedin.com/in/david-ross",
      "commenterName": "David Ross",
      "commenterHeadline": "Growth Lead at Beacon",
      "text": "Which onboarding step moved the needle most?",
      "time": "2026-07-08T15:02:00Z",
      "isReply": false,
      "reactionsCount": 4,
      "repliesCount": 1
    }
  ]
}

Four ways to scrape LinkedIn posts, compared

There is no official post API, so the real choice is between four methods that trade off scale, output, and the risk to your account.

One scope note before the code: this account-based approach reads known posts – a specific post URL, or a person's or company's own timeline. It does not do an arbitrary post keyword search ("every post mentioning X"). For that job, a dedicated post-search scraper (Apify, Bright Data, as of 2026) is the better fit, with the usual scraping-risk and stale-versus-live tradeoffs. For the account-based model across all entities, see the LinkedIn scraper API guide; for vacancies rather than content posts, the LinkedIn jobs scraper. What follows is the live, account-based path for posts you can point to.

Scrape a single post and its engagers

Install the SDK:

# Node.js
npm install -S @linkedapi/node

# Python
pip install linkedapi

Then fetch a post by URL, asking for its reactions and comments:

import LinkedApi from '@linkedapi/node';

const linkedapi = new LinkedApi({
  linkedApiToken: process.env.LINKED_API_TOKEN,
  identificationToken: process.env.IDENTIFICATION_TOKEN,
});

const workflow = await linkedapi.fetchPost.execute({
  postUrl: 'https://www.linkedin.com/posts/jane-doe_growth-activity-7440011668937568257',
  retrieveReactions: true,
  retrieveComments: true,
  reactionsRetrievalConfig: { limit: 50 },
  commentsRetrievalConfig: { limit: 50, sort: 'mostRecent' },
});

const { data } = await linkedapi.fetchPost.result(workflow.workflowId);
console.log(data?.text, data?.reactionsCount, data?.reactions?.length);
// each reaction: { engagerUrl, engagerName, engagerHeadline, engagerType, type }

from linkedapi import LinkedApi, LinkedApiConfig, FetchPostParams

linkedapi = LinkedApi(
    LinkedApiConfig(
        linked_api_token="your-linked-api-token",
        identification_token="your-identification-token",
    )
)

workflow = linkedapi.fetch_post.execute(
    FetchPostParams(
        post_url="https://www.linkedin.com/posts/jane-doe_growth-activity-7440011668937568257",
        retrieve_reactions=True,
        retrieve_comments=True,
        reactions_retrieval_config={"limit": 50},
        comments_retrieval_config={"limit": 50, "sort": "mostRecent"},
    )
)

data = linkedapi.fetch_post.result(workflow.workflow_id).data
print(data.text, data.reactions_count, len(data.reactions or []))

The limit in each config is what keeps this honest on a viral post: you pull a bounded, recent slice of the engagement, not a guaranteed copy of every one of thousands of reactions.

Scrape a person's or company's recent posts

To watch what someone publishes, fetch the person (or company) with retrievePosts and a postsRetrievalConfig. The since date is the key to monitoring – pass your last run's timestamp and you get only what is new:

const workflow = await linkedapi.fetchPerson.execute({
  personUrl: 'https://www.linkedin.com/in/jane-doe',
  retrievePosts: true,
  postsRetrievalConfig: { limit: 20, since: '2026-06-01T00:00:00Z' },
});

const { data } = await linkedapi.fetchPerson.result(workflow.workflowId);
for (const post of data?.posts ?? []) {
  console.log(post.time, post.reactionsCount, post.url);
}
// swap fetchPerson for fetchCompany with a companyUrl to track a company's posts

from linkedapi import FetchPersonParams

workflow = linkedapi.fetch_person.execute(
    FetchPersonParams(
        person_url="https://www.linkedin.com/in/jane-doe",
        retrieve_posts=True,
        posts_retrieval_config={"limit": 20, "since": "2026-06-01T00:00:00Z"},
    )
)

data = linkedapi.fetch_person.result(workflow.workflow_id).data
for post in data.posts or []:
    print(post.time, post.reactions_count, post.url)

This returns a recent, bounded set of posts, not a full archive. Run it on a schedule (a nightly cron with since set to yesterday) and you have a monitor for a person's or company's new activity – the foundation for the use case below.

Turn post engagers into warm leads

Here is the part worth building for. A person who reacted to or commented on a relevant post has raised their hand. You already have their profile URL from the post – so the pipeline is short: read the post's engagers, optionally enrich each live, and act.

// You already fetched the post with retrieveReactions + retrieveComments above.
const leads = [
  ...(data?.reactions ?? []).map((r) => ({
    url: r.engagerUrl, name: r.engagerName, headline: r.engagerHeadline, signal: r.type,
  })),
  ...(data?.comments ?? []).map((c) => ({
    url: c.commenterUrl, name: c.commenterName, headline: c.commenterHeadline, signal: 'commented',
  })),
];

// From here: enrich one live with fetchPerson(url), or act with
// sendConnectionRequest / sendMessage - all on your own account.

Two honest limits keep this grounded. You get profile URLs and public headlines, not emails – so this builds a targeted list and a warm-intent signal, not a contact database. And it runs on your own account at a human pace, so it is built for steady, relevant lists, not for scraping every reactor on a 10,000-like post. For what to do with the list next, see our cold outreach guide; if your source list lives in Sales Navigator, see the Sales Navigator guide.

Can you get an engager's email?

No, and it is the same story as any profile scraper: emails and phone numbers are not shown on posts or profiles, so a scraper reading LinkedIn cannot return them. What you get is the engager's profile URL and headline. To attach a verified email, match that person against a separate, consented B2B enrichment provider – a different product from post scraping, and the data does not come off LinkedIn.

Is scraping LinkedIn posts legal, and will my account get banned?

Post content and public engagement are largely visible data, and courts have treated scraping genuinely public data more leniently than accessing private data – but public is not a free pass. LinkedIn's User Agreement restricts automated access to the platform, and personal data still falls under GDPR and CCPA. We break down the full four-layer legal picture in our guide to scraping LinkedIn.

The ban risk is highest exactly where most people start: a Chrome extension or headless script driving your own account too fast. Linked API takes the account-based path – it runs on your own authenticated account through a human-paced cloud browser, within built-in workflow limits – and returns maintained, structured JSON, so you are not rewriting selectors every time the feed changes. Pace like a human, stop on any warning, and keep contact data to consented sources. Read our safety model before you scale.

Frequently Asked Questions (FAQ)

Need LinkedIn post data and the people behind the engagement, without running an extension or a fragile script? Start with Linked API – read posts, reactions, and comments from your own account and get structured JSON back, through the API and SDKs, CLI, MCP, and skills.