LinkedIn Post Search Scraper

LinkedIn Post Search Scraper helps you search public LinkedIn posts, feed updates, and articles by keyword and save the matching records to the Apify dataset. It accepts a search query as the required input, and can also use a date filter, result cap, provider-page cap, start cursor, comment toggle, and output mode.

Each dataset record represents one public post or article. The record can include post text, author name and profile URL, follower count when publicly exposed, likes, comments, engagement total, media URLs, public comments when enabled, and cursor metadata for continuing the search. The outcome is a structured set of public LinkedIn search results plus run summary data for downstream automation.

Best fit and connected workflows

This Actor fits workflows that start with a topic, company name, product category, exact quote, or other focused phrase and end with a list of public LinkedIn posts for review, enrichment, or monitoring.

Common workflows include:

• social listening around a brand, competitor, event, or category
• lead and account research based on public LinkedIn conversations
• AI-agent research pipelines that need recent public LinkedIn context
• follow-up enrichment after discovery, using a returned author profile or public post URL
• post-level analysis when you want text, engagement, and public comments in one record

For discovery-to-enrichment workflows, the verified downstream Actors are:

• Use LinkedIn Profile Scraper & Email Finder when you want to enrich a returned public LinkedIn profile or record
• Use LinkedIn Profile Posts Scraper when you want to explore a discovered author's public post history

Practical scenario

Maya is tracking public discussion around "AI agents" for a product launch. She starts with a focused query, sets datePosted to last-week, and keeps includeComments enabled so she can see public reactions.

The Actor returns a record with postUrl, description, authorName, likeCount, commentCount, engagementCount, and publishedAt. Maya sees a post from an industry author with strong engagement, reviews the public comments, and decides to pass the author's profile URL into a downstream enrichment workflow.

Input fields

Field	Type	Required	Purpose
query	string	Yes	Keyword or phrase to search for in public LinkedIn posts
datePosted	string	No	Freshness filter for Google-indexed LinkedIn results
maxResults	integer	No	Maximum number of post records to save and bill
maxProviderPages	integer	No	Safety cap for provider pagination
startCursor	string	No	Continue from a prior run cursor
includeComments	boolean	No	Include public comments in each record
outputMode	string	No	full or compact output shape

Focused input example

{
  "query": "ai agents",
  "datePosted": "last-week",
  "maxResults": 25,
  "maxProviderPages": 60,
  "includeComments": true,
  "outputMode": "full"
}

Output fields

Field	Type	Meaning
postUrl	string	Public LinkedIn URL for the post, feed update, or article
postId	string or null	Activity or article identifier when available
searchQuery	string	Input query used for the run
datePosted	string	Date filter used for the provider request
publishedAt	string or null	Public publication timestamp when available
description	string or null	Post text or article description
authorName	string or null	Public author name
authorUrl	string or null	Public LinkedIn author URL
authorFollowers	integer or null	Public follower count when exposed
authorImage	string or null	Public author image URL when exposed
likeCount	integer or null	Public like or reaction count
commentCount	integer or null	Public comment count
engagementCount	integer or null	Convenience total of likes plus comments when available
mediaUrl	string or null	Main media URL when exposed
imageUrl	string or null	Primary image URL when exposed
images	array	All public image URLs returned for the post
comments	array	Public comments returned when includeComments is enabled
position	integer	One-based position in the output order
pageNumber	integer	Provider page number that produced the record
sourceCursor	string or null	Cursor used to fetch the page
nextCursorAtFetch	string or null	Next cursor returned by that provider page
runId	string or null	Apify run ID that produced the record
source	string	Data source used for the record
scrapedAt	string	Timestamp when the Actor saved the record

Illustrative output record

{
  "postUrl": "https://www.linkedin.com/posts/aagupta_what-you-need-to-know-ai-agents-activity-7354600338621906944-RvXR",
  "postId": "7354600338621906944",
  "searchQuery": "ai agents",
  "datePosted": "last-week",
  "publishedAt": "2025-07-25T19:56:02.566Z",
  "description": "There's way too much hype about AI agents...",
  "authorName": "Aakash Gupta",
  "authorUrl": "https://www.linkedin.com/in/aagupta",
  "authorFollowers": 313422,
  "likeCount": 217,
  "commentCount": 25,
  "engagementCount": 242,
  "position": 1,
  "pageNumber": 1,
  "sourceCursor": "2",
  "nextCursorAtFetch": "3",
  "runId": "6neqzJ0WbGrfDOHb4",
  "source": "scrapecreators",
  "scrapedAt": "2026-06-08T00:00:00.000Z"
}

How it works

This Actor uses Google-indexed LinkedIn post results via ScrapeCreators, then extracts the visible public LinkedIn page details. It works with public posts, feed updates, and articles that are available through that upstream provider.

Implementation facts from the live contract:

• the source is scrapecreators
• the Actor reads SCRAPECREATORS_API_KEY from the environment
• the default run memory is 512 MB
• the default timeout is 1800 seconds
• the Actor supports pagination with cursors
• the dataset record includes page and cursor provenance when available
• public comments are included when includeComments is enabled
• outputMode supports full and compact

Pricing

This Actor uses Pay per event pricing plus Apify platform usage.

The primary charged event is LinkedIn post found, which is billed when one public LinkedIn post, feed update, or article is returned by keyword search and saved to the dataset. The live pricing tab in the Apify Console shows the current pricing details for this Actor, including the event model and any platform usage that applies to your run.

As a simple example, if a run saves twenty public posts, it charges twenty LinkedIn post found events. Platform usage is billed separately according to the Apify platform rules shown in the live Pricing tab.

Use with AI agents (MCP)

This Actor is available through Apify MCP as a tool for searching public LinkedIn posts by keyword and returning structured records with provenance.

Exact Actor identity: khadinakbar/linkedin-post-search-scraper

Tool description: Search public LinkedIn posts, feed updates, and articles by keyword, then return post text, author details, engagement counts, media URLs, optional public comments, and cursor metadata for continued retrieval.

Search public LinkedIn posts about "AI agents" from the last week, include public comments, return up to 10 results, and give me the next cursor if available. Summarize which posts have the highest engagement and preserve the source URL for each record.

Output interpretation for agents:

• results contains the dataset items
• summary contains run-level metadata such as saved count, pagination status, next cursor, and provider diagnostics
• runOutput and runSummary are useful for automation and orchestration
• postUrl, authorUrl, and comments support provenance-aware follow-up steps

Scope and provenance:

• the Actor searches public LinkedIn content only
• each record ties back to the public post URL and provider cursor context when exposed
• searchQuery, source, pageNumber, sourceCursor, and nextCursorAtFetch help agents track where the record came from

Pagination and cost guidance:

• use maxResults to set the save-and-bill cap
• use startCursor to continue from the last cursor returned by a previous run
• use maxProviderPages when you want to bound upstream pagination
• use includeComments only when public comments are relevant to the task, since smaller records are easier for downstream agents to process

Apify API example

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
  token: process.env.APIFY_TOKEN,
});

const actor = client.actor('khadinakbar/linkedin-post-search-scraper');

const run = await actor.call({
  query: 'ai agents',
  datePosted: 'last-week',
  maxResults: 10,
  includeComments: true,
  outputMode: 'compact',
});

const dataset = client.dataset(run.defaultDatasetId);
const { items } = await dataset.listItems();

console.log(items);

Best results and outcome guidance

Use a focused search phrase for cleaner matches, such as a company name, topic, product category, or exact quote. The input schema notes that Google-indexed results are available and depend on public indexing, so narrower queries usually produce clearer datasets.

For recent coverage, last-week and last-month are practical date filters. Use includeComments when public reactions matter to the workflow, and choose compact output when you want a smaller record shape for agents or tables. When continuing a search, pass the previous run's nextCursor into startCursor.

Continue the workflow

• Then use LinkedIn Posts Scraper to continue from LinkedIn Post Search Scraper discovery into content data for the selected records.
• Then use LinkedIn Profile Comments Scraper — Engagement Leads to continue from LinkedIn Post Search Scraper discovery into engagement data for the selected records.

Design note

I found that the live dataset contract includes both engagementCount and the underlying likeCount and commentCount, which makes it practical to support either quick ranking or deeper engagement analysis from the same record.

FAQ

Can I use this Actor to start from a keyword and then enrich specific people?

Yes. A common routing pattern is keyword search first, then use a returned public authorUrl or post URL in a downstream workflow such as the verified LinkedIn profile or posts Actors.

Which input should I use for recent public discussion?

datePosted with last-week or last-month is the most direct way to ask for recent public LinkedIn search results.

How do I continue a long search?

Read nextCursor from the run summary and pass it as startCursor in the next run.

When should I choose compact output?

Choose compact when your downstream step only needs the main post fields, author data, and engagement values.

What is the actor's source of data?

The live contract shows scrapecreators as the data source, and the input schema explains that the Actor uses Google-indexed LinkedIn post results from that provider.

Responsible use

Use this Actor for public web data that is visible without authentication. Review your use of the output against applicable laws, platform terms, privacy requirements, and your own governance policies before storing or sharing the data.