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LinkedIn Posts Scraper
Extract all posts from any LinkedIn profile or company page — 40+ data points including author and engagement data.
Trusted worldwide by the best
Export posts from LinkedIn — right now
Extract
Feed any LinkedIn person or company profile URL and extract every post with 40+ data points — author details, full post text, engagement metrics, reaction breakdowns, top comments, hashtags, mentions, media URLs and repost info.
Schedule
Automate LinkedIn data collection on daily, weekly, or monthly schedules.
Export
Export all post data to Google Sheets, email, or S3 — fully automated.
Built for Agents
Run it from your stack.
Or hand it to your agent.
LinkedIn Posts Scraper ships with a Python SDK, a one-line CLI and a native MCP server your agents can plug into directly — no rate limits to dodge, no bot mitigation to bypass, no parsing errors to chase down.
# install once (Python 3.10+)
pip install lobstrio
# check your token
lobstr whoami
# find the scraper
lobstr crawlers search "linkedin"
# one call — launch, wait, download
lobstr go linkedin-posts-scraper \
"https://www.linkedin.com/in/gaspard-lezin/" \
-o results.csv41 data attributes per post
Every scrape returns structured data you can export as CSV, JSON, JSONL, XLSX, or via API.
Unique LinkedIn activity URN of the post
Full text content of the post
ISO datetime when the post was published
Unix timestamp in milliseconds when the post was published
Human-readable relative time since the post was published (e.g. 2w, 3mo, 1y)
Whether this post is a repost/share of another post
Type of the post: text, image, video, article, document, poll, etc.
Array of all image URLs attached to the post
Fast
8 posts per minute
Lightning-fast performance
Solid
99.95% task success rate
Dependable, reliable data every time
Cost-competitive
$0.5 per 1000 posts
Top affordability worldwide
Built to run.
Most scrapers break in silence. This one doesn't — and we show it, openly.
incident-free runs · last 90 days
Tracked from every run. Incidents stay on the record even after they're fixed.