LinkedIn Posts Scraper

Export known public LinkedIn posts from post URLs, activity URNs or numeric activity IDs. This LinkedIn post scraper returns one row per unique saved post with text, author details, publication time, available images, links and engagement snapshots—without LinkedIn cookies or an account.

It resolves posts you already know. It does not search for posts or collect a profile's history. Only content LinkedIn exposes publicly can be returned.

Quick start

1. Paste a public post URL into Post URLs, or an activity URN into Activity URNs.
2. Set Maximum posts to a small limit and run the Actor.
3. Open the dataset and export JSON, CSV or Excel. Optional comments remain nested in their post row.

{
  "postUrns": ["urn:li:activity:7247998907798978560"],
  "maxPosts": 1,
  "includeMedia": true
}

Prefer a video guide? This short Apify walkthrough shows you how to set up and run an Actor. It uses a different Actor, but the setup steps are the same.

Ready-to-run examples

Choose a public example to see its settings before starting a run:

• Get LinkedIn post details from activity URNs
• Extract LinkedIn post details from public URLs

What you get

Fields	Meaning and limitations
postId, activityUrn	Resolved numeric activity identity.
postUrl, canonicalUrl, sourceInput	Canonical post URL and original submitted target.
postType	article when article metadata is found. Otherwise post. Not a full attachment classifier.
text, textHtml, headline, publishedAt	Public text, available markup or plain-text fallback, headline and publication time. Image-only posts can have empty captions.
authorName, authorUrl, authorType	Available author attribution.
authorImageUrl, authorFollowerCount	Public author image and follower count when exposed. Otherwise null.
images, article	Available image metadata and article title/link/image. includeMedia controls images, not article metadata.
outboundLinks, hashtags, mentions, taggedCompanies	Links and entities exposed by the post text. Link coverage can include LinkedIn identity links.
likeCount, commentCount, reactionBreakdown	Available totals, not a reaction-type breakdown. Missing totals may appear as 0. reactionBreakdown is a total or null.
commentsPreview	Optional nested comments with identity, text, author, date and available aggregate reactions. No replies or guaranteed ranking.
sourceStatus, collectedAt	Basic complete/partial metadata status and collection time—not a guarantee that every optional field is available.
authorHeadline, videos, document, poll, repostCount, isRepost, repost	Reserved fields currently remain null, [] or false. Video, document, poll and repost details are not extracted.

Comment preview fields such as authorHeadline remain null, replyCount is 0, and isPinned, isEdited and isPostAuthor are false. These are not verified comment-state signals. The preview is a bounded sample, not all comments or a sorted list of the most popular comments.

The key-value store's RUN_SUMMARY records requested inputs, saved count, failures, remaining work and status. PENDING_WORK can contain unprocessed targets when the run deadline is reached. It is not resumed automatically.

Who is it for?

• Marketing analysts refreshing text and available engagement for known campaign posts.
• Employer-brand researchers comparing their own snapshots of specific public posts.
• Content analysts reviewing exposed text, images and linked articles with source context.
• Developers importing known-post records into their own reporting or analysis tools.

How you can use the data

• Inspect one known activity. Use the existing activity URN with a one-post cap and media enabled. Keep canonicalUrl, sourceInput, text and available images together. An image-only post can have an empty caption rather than a failed text field.
• Choose whether a comment preview is useful. Enable the optional preview with a small explicit cap only when a few exposed comments help your review. They stay nested in the saved post, with no replies or guarantee of popularity order or complete discussion.
• Reconcile a bounded known-post list. URLs and activity URNs can be combined, with URLs considered first. Retain resolved postId to join your own exports and inspect summary failures. The target cap applies before requests, so an unavailable target is not replaced by an input beyond the cap.
• Compare snapshots in your own destination. Join canonical activity identities and collection times across exports to review text or available totals. Missing engagement counts can look like zero. The Actor does not discover posts, collect full profile histories or maintain changes for you.

Input settings

Setting / API key	Default	Purpose
Post URLs · postUrls	No API default	Public post, share-form or feed/update URLs. The form prefills one example.
Activity URNs · postUrns	None	Activity URNs or numeric activity IDs.
Maximum posts · maxPosts	20	Up to 1–1000 unique normalized targets.
Include media · includeMedia	true	Include available images.
Include comments preview · includeCommentsPreview	false	Nest a small sample of available public comments.
Maximum preview comments · maxPreviewComments	5	Up to 1–10 comments per saved post.

Provide at least one value across postUrls and postUrns. Both lists can be combined. URLs are considered first. The limit selects targets before requests, so an unavailable target is not replaced by a later input beyond the limit. Repeated activity IDs are normalized once, and successfully resolved share links to an already saved post do not add another row.

Example inputs

Use the Quick start input as a small first run. Adjust the fields described above for your own data.

Output data

Illustrative shape only—not a newly collected record. This abbreviated example shows a textless image post. Timestamps, image URLs and public fields vary by post.

{
  "postId": "7247998907798978560",
  "activityUrn": "urn:li:activity:7247998907798978560",
  "canonicalUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7247998907798978560",
  "postType": "post",
  "text": "",
  "textHtml": "",
  "headline": "",
  "authorHeadline": null,
  "images": [{"url": "https://example.com/post-image.jpg", "type": "image"}],
  "videos": [],
  "document": null,
  "poll": null,
  "repostCount": null,
  "commentsPreview": [],
  "sourceStatus": "complete"
}

Download your results

Open your run's dataset, choose Export and select JSON, CSV or Excel. JSON keeps nested fields together. CSV and Excel are useful when you want to review the records in a spreadsheet.

See Apify's download and export guide for the available formats and field options.

Pricing

The start event is charged once after valid targets are normalized. The post event applies to each saved post row, and preview-comment applies to each comment nested in a saved post. No separate error or status rows are written to the dataset. A started run can still incur its setup fee when no post is saved.

Keep the post cap small and leave comments preview off when unnecessary. See the live Pricing tab for current rates and discounts.

Tips for best results

• Supply known public activity identities. This Actor resolves requested posts rather than searching for new ones.
• Use a small target cap deliberately. Unavailable targets are not replaced beyond the selected limit.
• Leave comments preview off unless needed. Available previews are small nested samples with their own charge event.
• Retain original and canonical post identity. Share-form links can resolve to a post already saved from another input.
• Inspect summary failures before interpreting missing posts. Public-looking targets may still be inaccessible without login.

Limits and caveats

Only LinkedIn posts exposed publicly without an account or cookies can be returned. Private or login-gated content is unavailable, and a failed request does not establish its cause. No connection setting is exposed. This is not an official LinkedIn API, post search or a profile-history collector.

Known URLs, activity URNs and numeric activity IDs are normalized with URLs first. The cap selects targets before collection. Repeated activity IDs and successfully resolved share links to already saved posts do not add rows. No replacement target beyond the cap is requested when an earlier target is unavailable.

Available images and article metadata are supported, but includeMedia controls images rather than article metadata. Videos, documents, polls, repost details and authorHeadline remain reserved empty fields. Image-only captions can be empty. Missing engagement totals can be zero, and reactionBreakdown is an aggregate total or null rather than a reaction-type breakdown.

Comments preview is an optional bounded sample, not full comments, replies, reactors or a guaranteed popularity ranking. Its authorHeadline remains null, replyCount zero and pinned, edited and post-author flags false rather than verified states. Complete or partial sourceStatus describes basic metadata rather than every optional field's availability.

Mixed batches preserve posts saved when another target fails. A run with no saved posts fails and reports why, without inventing empty post records. RUN_SUMMARY records failures, saved counts and remaining work. PENDING_WORK can list deadline-limited targets and is not resumed automatically. Compare later exports or handle pending work in your own workflow.

Automation and integrations

Save your settings for next time

Once you have an input that works for you, save it as an Apify task. This keeps your settings together so you can use them again without filling in the form from scratch.

Run on a schedule

Need fresh results regularly? You can schedule the Actor or a saved task to run at a time that suits you.

Each scheduled run collects the data again. If you want to compare results or highlight changes, add that step in your own workflow.

Send results to your other tools

Use n8n, Make or Zapier to connect this scraper to the rest of your workflow. For example, you could pass its results to an AI assistant or add records to a spreadsheet. Follow the Apify integration guides for your chosen tool.

For a custom connection, an Apify webhook can notify your app when a run finishes or fails. Your app can then fetch the results or handle the failure.

API usage

Want to collect this data from your own app or automation? The examples below send input to the scraper and show how to collect its results.

The Node.js and Python examples wait for the run to finish before reading its results. The cURL example starts the run and returns its details. Wait for that run to finish, then use its defaultDatasetId to fetch the results.

For setup instructions, see Apify's JavaScript client guide, Python client guide and Actor runs API reference.

cURL

curl -X POST 'https://api.apify.com/v2/acts/fetch_cat~linkedin-posts-scraper/runs' \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  --data @- <<'JSON'
{
  "postUrns": [
    "urn:li:activity:7247998907798978560"
  ],
  "maxPosts": 1,
  "includeMedia": true
}
JSON

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/linkedin-posts-scraper').call({
  "postUrns": [
    "urn:li:activity:7247998907798978560"
  ],
  "maxPosts": 1,
  "includeMedia": true
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('fetch_cat/linkedin-posts-scraper').call(run_input={'postUrns': ['urn:li:activity:7247998907798978560'], 'maxPosts': 1, 'includeMedia': True})
print(client.dataset(run['defaultDatasetId']).list_items().items)

LinkedIn post API usage notes

Use your Apify API token, not LinkedIn credentials. This cURL example starts a run. Retrieve its dataset using the returned defaultDatasetId after it finishes.

MCP and AI agents

You can ask your AI assistant to collect data with LinkedIn Posts Scraper and help you work with the results. The scraper returns the data, and your assistant can then summarize it, compare records or prepare notes.

Connect your assistant through the Apify MCP server. MCP gives your assistant a way to find this scraper, run it and read the results. Follow the official setup and sign-in guide for your app.

Easier setup with an Apify plugin

If you use a supported coding assistant, the official Apify plugin can set up the connection for you. It connects your assistant to Apify and includes instructions for common tasks. Follow the guide for your particular app.

Use Apify AI in Console

Prefer to keep everything in Apify? Open Apify AI in Console and ask it to find Fetch Cat's LinkedIn Posts Scraper. You can describe the data you want and ask questions about the results.

Apify AI is currently in beta. It asks for confirmation before starting an Actor, and normal run charges apply.

Choose your assistant

• Claude and Claude Code (Anthropic): Apify setup guide.
• ChatGPT and Codex (OpenAI): Apify setup guides.
• Grok Bot (xAI): Apify plugin guide.
• Gemini CLI (Google): MCP server setup guide. Use the server URL below with the HTTP configuration supported by Gemini CLI.

Connect just this scraper

To keep your setup focused on this scraper, add this server configuration to a compatible MCP client:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=fetch_cat/linkedin-posts-scraper"
    }
  }
}

Sign in to Apify when your client asks. If your client uses a Bearer token instead, follow its setup guide and keep the token in its secure configuration. Configuration formats vary by app, so use the linked guide if your client needs a different format.

Use Apify's general server

Already connected to the general Apify MCP server at https://mcp.apify.com? You can use it to find this scraper without adding another server. Ask your assistant to use LinkedIn Posts Scraper by Fetch Cat, or give it the identifier fetch_cat/linkedin-posts-scraper.

Your assistant can check the available settings and retrieve the results from the returned run. If the run is still working, it should follow that run rather than start it again. Both connection options use Apify's hosted service.

Example requests

• “Use Fetch Cat’s LinkedIn Posts Scraper to retrieve urn:li:activity:7247998907798978560 with a one-post cap, images enabled and comment preview off. Show its source link, text and available images without inventing a caption for an image-only post.”
• “Collect the known post urn:li:activity:7247998907798978560 with a one-post cap, images enabled and an exposed comment preview capped at 3. Keep comments nested and label the preview as a sample, without retrieving replies.”
• “Retrieve urn:li:activity:7247998907798978560 with a one-post cap, images off and comment preview off. Keep canonical identity, collection time and available engagement totals for my own snapshot comparison, marking missing totals as uncertain.”

Normal Apify charges apply. Check the live Pricing tab before asking your assistant to run a larger batch.

Pay with x402

If your AI agent has a funded wallet, it can pay for Apify usage through x402. This experimental option lets an agent use Apify without a conventional account.

Apify AGI provides a prepaid API token with a spending cap after payment. Your agent can use that token to run this scraper through the Apify API or an authenticated MCP connection.

Some MCP setups also support paying for each request directly. That is a different payment option, so check the official guide for its availability.

Follow the official guide for wallet setup, supported payment options and current terms. Apify handles the payments. Keep wallet keys and payment tokens in your agent's secure configuration.

Other ways for agents to pay

Apify also supports MPP for prepaid access through AGI and Skyfire for eligible Actors. If your agent already uses one of these, see the agentic payment guide to choose the right setup.

FAQ

Can I use this as a LinkedIn post API or export posts to CSV?

Yes. Call this Apify Actor through the API examples above and read its dataset, or export the dataset as CSV, Excel or JSON in Apify Console. This is not LinkedIn's official API.

Does it need LinkedIn cookies or a proxy setting?

No. It reads public post pages without LinkedIn login. No connection setting is exposed. An inaccessible post may still fail.

Can it search LinkedIn posts or collect every post from a company?

No. Supply known post URLs, activity URNs or numeric IDs. The related Company Posts Actor serves a different workflow.

Are all comments, media and engagement fields complete?

No. Comments preview is optional and bounded. Images and available article metadata are supported. Reserved video, document, poll and repost fields remain empty. Missing engagement totals can be zero, so do not interpret zero as a verified absence.

What should I do if a run returns no posts?

Check that the input contains a supported public post URL or activity identity. Inspect RUN_SUMMARY for failures and try a small known-public target. A public-looking URL can still be inaccessible to anonymous visitors. This version fails when nothing can be saved.

Related Actors

• LinkedIn Post Comments Scraper — collect public comment text and exposed replies from known LinkedIn post URLs or URNs.
• LinkedIn Company Posts Scraper — company-post collection.
• LinkedIn Company Profiles Scraper — company profile data.
• LinkedIn Jobs Scraper — job search and export.
• LinkedIn Ad Library Scraper — public advertising research.

Support and feedback

For custom additions or integrations, email hello@fetchcat.dev.

Found an error or have a feature request? Open an issue with the run URL, a non-sensitive input example and what you expected.

Need help sharing it? See Apify’s guide to sharing a run when reporting an issue. Sharing a run can give the developer access to its input, logs and results, so use a run that contains no sensitive data. Please leave out API tokens, passwords, cookies, private documents and personal data. Use a public sample URL or a redacted example instead.

Tried this scraper? We'd love to hear how it worked for you. Leave a review to help others choose the right tool and help us improve it. Your feedback means a lot to us.

Made with care for curious people, AI agents and the possibilities in data. ♥