Real Time Collaboration With Jupyter Notebooks Using Cocalc

Leo Migdal

-Nov 17, 2025, 4:20 AM

real time collaboration with jupyter notebooks using cocalc

Collaborative Calculation and Data Science Real-time collaboration for Jupyter Notebooks, Linux Terminals, LaTeX, and more, all in one place. CoCalc: Collaborative Calculations and Data Science William Stein launched the Sage Notebook in 2006, initially motivated by a talk at Sage Days 1 about a GUI for IPython. CoCalc is a continuation of this project using more modern technology. CoCalc includes a distinct full stack implementation of both the frontend and backend parts of Jupyter, built from scratch using React, Node.js, and Kubernetes.

Compatibility with Jupyter—both the published wire protocol and the general feel of UI—is a fundamental design goal. The core goal of CoCalc is to provide easy, safe, and beginner-friendly access to all open source mathematics and data science software, including SageMath, LaTeX, R, Anaconda, and a large number of Jupyter kernels. The motivation for fully supporting multiple simultaneous people editing Jupyter notebooks is that it makes it much easier for teachers to support their students, for students to support each other, and for the people... The same technology used to implement real-time collaboration also provides a complete history of all modifications of the notebook. With a granularity of about two seconds, people can follow exactly how a document evolved over time. Hence, there is no longer the fear of “messing everything up,” because it is easy to go back in time.

Additionally, professors can follow exactly how students solved a given problem. People can collaborate without having to learn Git or fiddle around with nbdiff. CoCalc is primarily a whole product designed to make life easier for teachers who want to use open source data science software in teaching beginners with minimal extra effort. College professors are very busy, so CoCalc is built as a single centralized service that is hosted in Google’s cloud and run by SageMath, Inc., rather than something professors or staff have to install... Moreover, a huge amount of software is preinstalled in the standard image that CoCalc projects use; this includes everything that anybody has ever requested since 2013 that the team could figure out how to... William explains how CoCalc relates to Project Jupyter and shares how he implemented real-time collaborative editing of Jupyter notebooks in CoCalc.

CoCalc tackles many of the same problems as JupyterLab and JupyterHub but with very different design constraints, motivations, and results. For example, real-time collaboration has been a core feature of CoCalc since it launched in 2013, whereas classical Jupyter does not have real-time collaboration support; on the other hand, drag-and-drop and a flexible plugin... Another subtle difference is that in CoCalc if you close your browser while a computation is running, then open it later, all your output will be there, whereas classical Jupyter discards that output (though... Implementing real-time collaboration in a web application involves many choices and trade-offs. CoCalc has had real-time collaboration support for five years. It is optimized for what users need, the sort of documents they edit, and the choices made for how to store data, both in the long and short term.

William Stein is a full professor of mathematics at the University of Washington (where he is currently on leave) and the CEO of SageMath, Inc., whose main product is CoCalc. William is the founder of the SageMath open source math software project. He also came up with the name Cython and launched that project. He has published three books and a few dozen papers on number theory. Jupyter Notebooks have become indispensable in physics research. They seamlessly combine code, equations (via LaTeX), visualizations, and narrative text, making them ideal for data analysis, simulations, theoretical derivations, and reproducible research.

However, collaborating on a single Jupyter Notebook with a colleague—whether for co-authoring a paper, troubleshooting code, or validating results—can be challenging. Emailing files back and forth leads to version chaos, and local notebook edits often conflict with each other. This blog post focuses on free, practical tools and workflows tailored to two-person physics research teams. We’ll break down step-by-step solutions, their pros and cons, and physics-specific considerations (e.g., handling large datasets, LaTeX equations, or GPU-intensive simulations). By the end, you’ll know exactly which tool to use to streamline collaboration and keep your research on track. Collaborating on such notebooks introduces unique challenges:

The solutions below address these pain points—let’s dive in. Overview: GitHub (with Git) is the gold standard for version control. It lets you track changes to your notebook, revert to old versions, and collaborate asynchronously. Ideal if you and your collaborator prefer working on local Jupyter Notebooks (e.g., with custom environments or sensitive data). As the official page of the Jupyter project states: A notebook is a shareable document that combines computer code, plain language descriptions, data, rich visualizations like 3D models, charts, graphs and figures, and interactive controls.

A notebook, along with an editor (like JupyterLab), provides a fast interactive environment for prototyping and explaining code, exploring and visualizing data, and sharing ideas with others. Nowadays a Jupyter Notebook is a de facto standard document format, typically stored in .ipynb files, which records interactive sessions with a kernel. It is made up of cells, which can either store one or more lines of code or formatted text. When you run a code cell, the active kernel session evaluates the piece of code in it and the resulting output is shown below. The combination of communicating back and forth with a kernel and adding descriptive text makes this form of document very attractive. CoCalc has its own implementation of the user interface to work with Jupyter Notebooks, which supports our unique features such as real-time collaboration, AI Assistant, and TimeTravel.

However, the underlying format of .ipynb files is exactly the same. You can bring your existing notebooks to CoCalc and resume working, or you can download your work from CoCalc to a local computer or another cloud platform, there is no vendor-lock-in! Most of the time you don’t have to think much about the Jupyter kernel which you are using, because your notebook already has the right kernel selected or your default kernel is picked automatically... But when you want or need to, you can choose the programming language and environment by selecting a kernel explicitly. See Jupyter Kernel Selection The course management interface gives you full control over distributing, collecting, grading and returning everyone's assignments.

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Real Time Collaboration With Jupyter Notebooks Using Cocalc

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