linkedin_scraper is a Python library that extracts profile, company, job, and post data from LinkedIn using web scraping.
The tool addresses the need to programmatically gather LinkedIn data by automating browser interaction through Playwright. It handles authentication via session files, then navigates LinkedIn pages to extract structured information about user profiles, company details, job listings, and company posts. The library returns all scraped data as Pydantic models, providing type safety and consistent data structures across different scraping operations.
Developers should adopt this tool if they need to extract LinkedIn data at scale and can manage the authentication workflow. It suits projects requiring bulk profile analysis, company research, or job market monitoring. The library emphasizes async/await patterns throughout, making it suitable for concurrent scraping operations. The README explicitly recommends rate limiting, session reuse, and headless mode for production use, and includes a disclaimer that users must comply with LinkedIn's Terms of Service.
The project underwent a complete architectural rewrite, replacing Selenium with Playwright and introducing async-first design with Pydantic models. Development activity shows active maintenance with structured error handling, progress tracking callbacks, and comprehensive documentation of breaking changes and migration paths. The codebase includes browser configuration options, session management features, and explicit best practices for responsible scraping.
