A workbench for academic literature review

Open Paper is an AI-powered research assistant designed to streamline academic literature review. It enables researchers to upload, analyze, and interact with their entire library of PDF research papers using natural language queries. The platform supports contextual AI responses with verifiable citations, allowing users to maintain academic integrity while accelerating comprehension and synthesis of complex materials.
Targeted at PhD students, academics, and industry researchers, Open Paper integrates reading, annotation, and cross-paper analysis into a unified workbench. Its open-source foundation ensures transparency and reproducibility, aligning with rigorous academic standards. The tool is particularly valuable for managing large volumes of scholarly work efficiently and systematically.
Users begin by uploading PDFs of research papers to their personal library. Once processed, each paper is indexed for semantic search and AI interaction. The interface features a split-pane layout where the user can view the document on one side and engage in a chat with the AI assistant on the other, enabling real-time questioning without losing context.
The AI processes queries by analyzing the text within the selected paper or project, generating responses that are supported by specific citations. Each cited result includes page-level references, which users can click to verify the original context. Projects allow grouping of multiple papers, enabling queries across sources, automated data extraction using custom schemas, and generation of exportable CSV files for further analysis.
Open Paper addresses common research challenges such as time constraints, information overload, and citation management. By automating initial summarization and enabling rapid navigation through large corpora, it reduces the time required for literature reviews. Researchers can quickly identify relevant studies, compare findings across publications, and extract key metrics into structured formats.
The ability to generate audio overviews supports learning and review in non-desk settings. Smart annotations sync across devices and integrate with AI discussions, enhancing note-taking efficiency. As an open-source tool, Open Paper promotes trust through transparency—code, prompts, and models are inspectable, supporting reproducibility in scholarly workflows.