paperlib: an open-source academic paper manager with AI-powered summarization and semantic library search

What it solves

Paperlib addresses the difficulty of managing academic papers, particularly conference papers (like NeurIPS or ICLR) that often lack standard identifiers like DOIs, making their metadata hard to scrape and organize. It provides a streamlined alternative to traditional tools by focusing on accurate metadata extraction and a modern, uncluttered user interface.

How it works

It functions as a cross-platform library manager that allows users to import papers and use various scrapers to automatically retrieve accurate metadata. The tool includes organizational features like tags, folders, and smart filters, and integrates with writing workflows via a macOS spotlight-like plugin for easy reference copying. It is extensible, allowing users to add their own scrapers or plugins.

Who it’s for

Researchers and PhD students, especially those in computer science and other disciplines where conference publications are prevalent.

Highlights

• Advanced Metadata Scraping: Multiple scrapers tailored for various disciplines to ensure accurate paper information.
• LLM Integration: Extensions enable AI-powered paper summarization, automatic tagging, and natural language semantic search (e.g., "papers written by Geoffrey in 2024").
• Research Workflow Tools: Includes RSS feed subscriptions for new publications and the ability to locate and download PDFs from the web.
• Writing Integration: Supports MS Word and a quick-copy plugin for seamless citation during drafting.
• Cross-Platform: Available for macOS, Windows, and Linux with cloud sync support.