ResearchOS landing page showcasing the local-first AI research workspace.
Import, organize, and browse thousands of research papers in a unified local library.
Read, annotate, and interact with research papers using built-in AI assistance.
Organize active papers, pin important work, and manage research across multiple projects.
Interactive citation knowledge graph that visualizes relationships between papers, authors, and research topics.
Track literature review progress, reading status, topic distribution, and project insights from a single dashboard.

Project Overview

ResearchOS is an AI-powered, local-first research workspace built to simplify the modern research process. Researchers often juggle multiple disconnected tools for reading papers, taking notes, managing citations, organizing literature, and using AI assistants. This fragmented workflow creates unnecessary context switching and slows down scientific discovery.

ResearchOS brings these essential workflows together into one integrated application. It combines literature management, intelligent PDF reading, AI-powered paper understanding, citation analysis, knowledge graph visualization, research gap discovery, journal recommendations, and project organization in a unified workspace. By keeping research data local while integrating powerful AI capabilities, ResearchOS enables researchers to work more efficiently without compromising privacy or ownership of their work.

Our vision is to provide a single operating system for research that allows students, academics, and professionals to spend less time managing information and more time generating meaningful insights.

Inspiration

The idea for ResearchOS came from experiencing the fragmented nature of academic research firsthand. Reading papers, organizing notes, managing references, discovering related work, and using AI assistants often require switching between numerous applications and browser tabs. While AI has made individual research tasks easier, there is still no unified workspace where the entire research workflow exists in one place.

We wanted to build a platform that removes this friction by integrating every stage of the research process into a single, AI-native environment.

What it does

ResearchOS provides researchers with an end-to-end research workspace that includes:

Literature management and organization
Intelligent PDF reading and annotation
AI-assisted paper understanding
Citation analysis
Interactive knowledge graph visualization
Research gap discovery
Journal recommendation support
Research project organization
Local-first document management

Instead of treating these as separate tools, ResearchOS connects them into a seamless workflow that keeps knowledge organized and accessible throughout the research lifecycle.

How we built it

ResearchOS was built using a modern full-stack architecture designed for performance, scalability, and maintainability.

Frontend

Next.js
React
TypeScript
Tailwind CSS

Backend

Next.js API Routes
Prisma ORM
SQLite

AI & Research Infrastructure

OpenAlex API
GPT-5 via OpenAI
Local-first document storage
Knowledge graph generation
Intelligent metadata extraction

Development was accelerated using OpenAI Codex for architecture planning, implementation, refactoring, debugging, and rapid iteration. Throughout development, we focused on creating modular, production-ready components rather than isolated prototypes.

Challenges we faced

One of the biggest challenges was designing a research workflow that felt unified instead of simply combining multiple independent tools. Integrating literature management, AI-powered analysis, citation workflows, and visualization into a cohesive user experience required several iterations of both the architecture and interface.

Another challenge was balancing powerful AI features with a local-first approach. We wanted users to benefit from modern AI capabilities while maintaining ownership and control over their research data.

Finally, ensuring smooth interaction between multiple data sources, asynchronous AI processing, and responsive user interfaces required careful engineering throughout the project.

Accomplishments

We're particularly proud that ResearchOS evolved beyond a collection of utilities into a cohesive research platform.

Some highlights include:

A polished and modern user interface
Integrated literature management
AI-assisted research workflows
Interactive knowledge graph visualization
Citation intelligence
Local-first architecture
Modular and scalable codebase
Production-quality demonstration

Rather than solving a single research problem, ResearchOS demonstrates how AI can support the complete research lifecycle.

What we learned

Building ResearchOS reinforced the importance of designing software around real user workflows instead of individual features. We also learned how thoughtful integration of AI can significantly improve productivity without overwhelming users.

The project strengthened our understanding of modern full-stack development, AI-assisted software engineering, modular architecture, and designing products that balance usability, performance, and privacy.