https://github.com/joeyism/linkedin_scraper
A library that scrapes Linkedin for user data
https://github.com/joeyism/linkedin_scraper
chrome company driver firefox linkedin linkedin-profile linkedin-scraper linkedin-url profile scraper scrapes-linkedin users
Last synced: about 2 months ago
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A library that scrapes Linkedin for user data
- Host: GitHub
- URL: https://github.com/joeyism/linkedin_scraper
- Owner: joeyism
- License: gpl-3.0
- Created: 2017-10-27T20:58:54.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2025-05-23T16:45:59.000Z (about 1 year ago)
- Last Synced: 2025-12-02T20:54:56.608Z (6 months ago)
- Topics: chrome, company, driver, firefox, linkedin, linkedin-profile, linkedin-scraper, linkedin-url, profile, scraper, scrapes-linkedin, users
- Language: Python
- Homepage:
- Size: 4.02 MB
- Stars: 3,507
- Watchers: 48
- Forks: 827
- Open Issues: 130
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-github-projects - linkedin_scraper - A library that scrapes Linkedin for user data ⭐4,178 `Python` ⚡ (🔧 Utilities & Miscellaneous)
README
# LinkedIn Scraper
[](https://badge.fury.io/py/linkedin-scraper)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/Apache-2.0)
Async LinkedIn scraper built with Playwright for extracting profile, company, and job data from LinkedIn.
## ⚠️ Breaking Changes in v3.0.0
**Version 3.0.0 introduces breaking changes and is NOT backwards compatible with previous versions.**
### What Changed:
- **Playwright instead of Selenium** - Complete rewrite using Playwright for better performance and reliability
- **Async/await throughout** - All methods are now async and require `await`
- **New package structure** - Imports have changed (e.g., `from linkedin_scraper import PersonScraper`)
- **Updated data models** - Using Pydantic models instead of simple objects
- **Different API** - Method signatures and return types have changed
### Migration Guide:
**Before (v2.x with Selenium):**
```python
from linkedin_scraper import Person
person = Person("https://linkedin.com/in/username", driver=driver)
print(person.name)
```
**After (v3.0+ with Playwright):**
```python
import asyncio
from linkedin_scraper import BrowserManager, PersonScraper
async def main():
async with BrowserManager() as browser:
await browser.load_session("session.json")
scraper = PersonScraper(browser.page)
person = await scraper.scrape("https://linkedin.com/in/username")
print(person.name)
asyncio.run(main())
```
**If you need the old Selenium-based version:**
```bash
pip install linkedin-scraper==2.11.2
```
## Quick Testing
To test that this works, you can clone this repo, install dependencies with
```
git clone https://github.com/joeyism/linkedin_scraper.git
cd linkedin_scraper
pip3 install -e .
```
then run
```
python3 samples/create_session.py
python3 samples/scrape_company.py
python3 samples/scrape_person.py
```
and you will see the scraping in action.
---
## Features
- **Person Profiles** - Scrape comprehensive profile information
- Basic info (name, headline, location, about)
- Work experience with details
- Education history
- Skills and accomplishments
- **Company Pages** - Extract company information
- Company overview and details
- Industry and size
- Headquarters location
- **Company Posts** - Scrape posts from company pages
- Post content and text
- Reactions, comments, reposts counts
- Posted date and images
- **Job Listings** - Scrape job postings
- Job details and requirements
- Company information
- Application links
- **Async/Await** - Modern async Python with Playwright
- **Type Safety** - Full Pydantic models for all data
- **Progress Callbacks** - Track scraping progress
- **Session Management** - Reuse authenticated sessions
## Installation
```bash
pip install linkedin-scraper
```
### Install Playwright browsers:
```bash
playwright install chromium
```
## Quick Start
### Basic Usage
```python
import asyncio
from linkedin_scraper import BrowserManager, PersonScraper
async def main():
# Initialize browser
async with BrowserManager(headless=False) as browser:
# Load authenticated session
await browser.load_session("session.json")
# Create scraper
scraper = PersonScraper(browser.page)
# Scrape a profile
person = await scraper.scrape("https://linkedin.com/in/williamhgates/")
# Access data
print(f"Name: {person.name}")
print(f"Headline: {person.headline}")
print(f"Location: {person.location}")
print(f"Experiences: {len(person.experiences)}")
print(f"Education: {len(person.educations)}")
asyncio.run(main())
```
### Company Scraping
```python
from linkedin_scraper import CompanyScraper
async def scrape_company():
async with BrowserManager(headless=False) as browser:
await browser.load_session("session.json")
scraper = CompanyScraper(browser.page)
company = await scraper.scrape("https://linkedin.com/company/microsoft/")
print(f"Company: {company.name}")
print(f"Industry: {company.industry}")
print(f"Size: {company.company_size}")
print(f"About: {company.about_us[:200]}...")
asyncio.run(scrape_company())
```
### Job Scraping
```python
from linkedin_scraper import JobSearchScraper
async def search_jobs():
async with BrowserManager(headless=False) as browser:
await browser.load_session("session.json")
scraper = JobSearchScraper(browser.page)
jobs = await scraper.search(
keywords="Python Developer",
location="San Francisco",
limit=10
)
for job in jobs:
print(f"{job.title} at {job.company}")
print(f"Location: {job.location}")
print(f"Link: {job.linkedin_url}")
print("---")
asyncio.run(search_jobs())
```
### Company Posts Scraping
```python
from linkedin_scraper import BrowserManager, CompanyPostsScraper
async def scrape_company_posts():
async with BrowserManager(headless=False) as browser:
await browser.load_session("session.json")
scraper = CompanyPostsScraper(browser.page)
posts = await scraper.scrape(
"https://linkedin.com/company/microsoft/",
limit=10
)
for post in posts:
print(f"Posted: {post.posted_date}")
print(f"Text: {post.text[:200]}...")
print(f"Reactions: {post.reactions_count}")
print(f"Comments: {post.comments_count}")
print(f"URL: {post.linkedin_url}")
print("---")
asyncio.run(scrape_company_posts())
```
## Authentication
LinkedIn requires authentication. You need to create a session file first:
### Option 1: Manual Login Script
```python
from linkedin_scraper import BrowserManager, wait_for_manual_login
async def create_session():
async with BrowserManager(headless=False) as browser:
# Navigate to LinkedIn
await browser.page.goto("https://www.linkedin.com/login")
# Wait for manual login (opens browser)
print("Please log in to LinkedIn...")
await wait_for_manual_login(browser.page, timeout=300)
# Save session
await browser.save_session("session.json")
print("✓ Session saved!")
asyncio.run(create_session())
```
### Option 2: Programmatic Login
```python
from linkedin_scraper import BrowserManager, login_with_credentials
import os
async def login():
async with BrowserManager(headless=False) as browser:
# Login with credentials
await login_with_credentials(
browser.page,
username=os.getenv("LINKEDIN_EMAIL"),
password=os.getenv("LINKEDIN_PASSWORD")
)
# Save session for reuse
await browser.save_session("session.json")
asyncio.run(login())
```
## Progress Tracking
Track scraping progress with callbacks:
```python
from linkedin_scraper import ConsoleCallback, PersonScraper
async def scrape_with_progress():
callback = ConsoleCallback() # Prints progress to console
async with BrowserManager(headless=False) as browser:
await browser.load_session("session.json")
scraper = PersonScraper(browser.page, callback=callback)
person = await scraper.scrape("https://linkedin.com/in/williamhgates/")
asyncio.run(scrape_with_progress())
```
### Custom Callbacks
```python
from linkedin_scraper import ProgressCallback
class MyCallback(ProgressCallback):
async def on_start(self, scraper_type: str, url: str):
print(f"Starting {scraper_type} scraping: {url}")
async def on_progress(self, message: str, percent: int):
print(f"[{percent}%] {message}")
async def on_complete(self, scraper_type: str, url: str):
print(f"Completed {scraper_type}: {url}")
async def on_error(self, error: Exception):
print(f"Error: {error}")
```
## Data Models
All scraped data is returned as Pydantic models:
### Person
```python
class Person(BaseModel):
name: str
headline: Optional[str]
location: Optional[str]
about: Optional[str]
linkedin_url: str
experiences: List[Experience]
educations: List[Education]
skills: List[str]
accomplishments: Optional[Accomplishment]
```
### Company
```python
class Company(BaseModel):
name: str
industry: Optional[str]
company_size: Optional[str]
headquarters: Optional[str]
founded: Optional[str]
specialties: List[str]
about: Optional[str]
linkedin_url: str
```
### Job
```python
class Job(BaseModel):
title: str
company: str
location: Optional[str]
description: Optional[str]
employment_type: Optional[str]
seniority_level: Optional[str]
linkedin_url: str
```
### Post
```python
class Post(BaseModel):
linkedin_url: Optional[str]
urn: Optional[str]
text: Optional[str]
posted_date: Optional[str]
reactions_count: Optional[int]
comments_count: Optional[int]
reposts_count: Optional[int]
image_urls: List[str]
```
## Advanced Usage
### Browser Configuration
```python
browser = BrowserManager(
headless=False, # Show browser window
slow_mo=100, # Slow down operations (ms)
viewport={"width": 1920, "height": 1080},
user_agent="Custom User Agent"
)
```
### Error Handling
```python
from linkedin_scraper import (
AuthenticationError,
RateLimitError,
ProfileNotFoundError
)
try:
person = await scraper.scrape(url)
except AuthenticationError:
print("Not logged in - session expired")
except RateLimitError:
print("Rate limited by LinkedIn")
except ProfileNotFoundError:
print("Profile not found or private")
```
## Best Practices
1. **Rate Limiting** - Add delays between requests
```python
import asyncio
await asyncio.sleep(2) # 2 second delay
```
2. **Session Reuse** - Save and reuse sessions to avoid frequent logins
3. **Error Handling** - Always handle exceptions (rate limits, auth errors, etc.)
4. **Headless Mode** - Use `headless=False` during development, `True` for production
5. **Respect LinkedIn** - Don't scrape aggressively, respect rate limits
## Requirements
- Python 3.8+
- Playwright
- Pydantic 2.0+
- aiofiles
- python-dotenv (optional, for credentials)
## License
Apache License 2.0 - see [LICENSE](LICENSE) file for details.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## Disclaimer
This tool is for educational purposes only. Make sure to comply with LinkedIn's Terms of Service and use responsibly. The authors are not responsible for any misuse of this tool.
## Links
- [GitHub Repository](https://github.com/joeyism/linkedin_scraper)
- [Issue Tracker](https://github.com/joeyism/linkedin_scraper/issues)
- [PyPI Package](https://pypi.org/project/linkedin-scraper/)