Two PhD students building the tools researchers deserve
We know what it's like to drown in papers, juggle six different apps, and lose hours to tooling instead of thinking. So we built Omnilib — a single, intelligent workspace where everything connects.
Our story
We met at CERN, where we both do our PhD research. Between particle physics experiments and late-night coding sessions, we realized something: the tools researchers use are fragmented, outdated, and built for a world before AI. We spent more time fighting our tools than doing actual science.
We started building Omnilib in late 2024 as a side project — a place where research papers, notes, code, and collaboration could finally live together. What began as a personal tool quickly grew into something bigger as colleagues started asking for access.
Recent breakthroughs in large language models convinced us that the research workspace of the future needed to be built now. Not just an AI chatbot bolted onto a text editor, but a truly intelligent environment that understands your entire project — your papers, your code, your notes — and helps you think better.
The people behind Omnilib
Andrea Protani
Co-Founder
PhD student at CERN and EPFL, working at the intersection of particle physics and machine learning. Passionate about building tools that make researchers' lives easier.
What drives us
Research-first
We build for researchers. Every feature is designed with the academic workflow in mind, from literature review to publication.
Open & transparent
Our roadmap is public, our community shapes the product. We believe the best tools are built in the open.
Built with care
We obsess over the details. Every interaction, every pixel, every millisecond matters when you're in the flow of deep work.
Ship fast, iterate
We believe in getting features into your hands quickly and improving them based on real feedback from real researchers.
What is Omnilib?
Omnilib is an AI-powered research workspace that unifies every stage of the research workflow into a single application. Instead of switching between a reference manager, a LaTeX editor, a coding environment, and a dozen browser tabs, researchers can search literature, write papers, run code, and map knowledge — all inside project-based workspaces that keep context together.
The platform connects to major academic databases — arXiv, Semantic Scholar, and PubMed — for deep literature search and paper discovery. A built-in LaTeX compiler lets you draft and typeset manuscripts without leaving the app. Interactive knowledge graphs visualize relationships between papers, concepts, and your own notes. Integrated Jupyter-style notebooks support Python and R for data analysis alongside your reading. And an OCR extraction engine pulls text, figures, and tables from scanned PDFs automatically.
Omnilib is designed for academic researchers, graduate students, engineers, and technical writers — anyone who works with complex information and needs more than a collection of disconnected tools. A multi-agent AI assistant runs across your entire workspace, answering questions with citations, generating literature review summaries, and suggesting related work based on the papers and notes you already have.
How Omnilib compares to a traditional research workflow
| Feature | Omnilib | Traditional Workflow |
|---|---|---|
| Literature Search | ✓ Unified search across arXiv, Semantic Scholar, and PubMed | Search each database separately in the browser |
| LaTeX Writing | ✓ Built-in compiler with live preview | Overleaf or local TeX distribution in a separate window |
| Knowledge Mapping | ✓ Interactive graphs linking papers, notes, and concepts | Manual organization in folders or spreadsheets |
| Code Notebooks | ✓ Jupyter-style notebooks alongside your reading | Separate Jupyter Lab or VS Code instance |
| AI Assistance | ✓ Multi-agent AI with full workspace context and citations | Generic chatbot with no access to your research |
| OCR / Extraction | ✓ Automatic text, figure, and table extraction from PDFs | Third-party OCR tool or manual copy-paste |
Frequently asked questions
What is Omnilib?
- Omnilib is an AI-powered research workspace that combines deep literature search across arXiv, Semantic Scholar, and PubMed with a built-in LaTeX compiler, interactive knowledge graphs, Jupyter notebooks, OCR extraction, and a multi-agent AI assistant — all unified in project-based workspaces.
Who is Omnilib for?
- Omnilib is designed for academic researchers, graduate students, engineers, and technical writers who need to search literature, write papers, run code, and organize research in one place.
Is Omnilib free to use?
- Omnilib offers a free tier with core features. Pro and Enterprise tiers are available for users who need unlimited AI assistance, priority support, and advanced features.
What platforms does Omnilib support?
- Omnilib is available as a native macOS desktop app (Apple Silicon and Intel) and as a web application accessible from any modern browser.
How does the AI agent work?
- The AI agent reads and indexes all papers, notes, and code in your workspace. It answers questions with citations, helps write LaTeX documents, generates literature review summaries, and suggests related papers based on your research direction.
Join us on this journey
We're building Omnilib in public and we'd love for you to be part of it. Follow our roadmap, share your ideas, and help us create the research workspace of the future.