jupyterlite/jupyterlite

Wasm powered Jupyter running in the browser 💡

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Summary Information

Updated 26 minutes ago
Added to GitGenius on March 24th, 2026
Created on March 27th, 2021
Open Issues & Pull Requests: 238 (+0)
GitHub issues: Enabled
Number of forks: 450
Total Stargazers: 4,907 (+0)
Total Subscribers: 44 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.6 days
Mean response time: 207.9 days
90th percentile: 872.0 days
Tracked items: 241

Maintainer activity

2 people did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 11, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits.

How this project is maintained

About 17% of issues opened in the past year have never received a reply. 73% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "bug" is answered fastest, typically in about 24 hours, while "documentation" waits about 3 weeks. Only 59% of issues opened in the past year have been closed. Three people close 75% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 135
New in 7 days: 0
Closed in 7 days: 1
Avg open age: 713 days
Stale 30+ days: 133
Stale 90+ days: 126

Recent activity

Opened in 7 days: 0
Closed in 7 days: 1
Comments in 7 days: 0
Events in 7 days: 1

Top labels

  • bug (167)
  • enhancement (109)
  • documentation (25)
  • maintenance (15)
  • extension idea (13)
  • kernel:pyodide (13)
  • performance (6)
  • help wanted (5)

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Detailed Description

JupyterLite is a JupyterLab distribution that runs entirely in the browser, built from the ground-up using JupyterLab components and extensions. It represents a significant shift in how Jupyter environments can be deployed and accessed, eliminating the need for users to install Python or start a Jupyter server on their local machine. Instead, users can access a fully functional interactive computing environment with a single click in their web browser.

The project is written primarily in TypeScript and is part of the Project Jupyter Frontiers subproject. It leverages WebAssembly technology to execute Python code directly in the browser, with support for multiple kernel implementations including Pyodide and Xeus Python, both of which run Python kernels in Web Workers. This architecture enables true client-side computing without requiring a dedicated application server or container orchestration infrastructure.

JupyterLite supports interactive visualization libraries such as altair, bqplot, ipywidgets, matplotlib, and plotly, allowing users to create rich visualizations entirely within the browser environment. The platform includes basic session and kernel management capabilities, enabling multiple kernels to run simultaneously. Users can view hosted notebooks and other files, then edit, save, and download them from the browser's IndexDB or localStorage, providing a seamless offline-capable experience.

The deployment model of JupyterLite is particularly distinctive. It is served via well-cacheable, static HTTP or HTTPS, requiring no dedicated application server. This makes it suitable for deployment on most static web hosts, and it can be embedded within larger applications. The platform offers fine-grained configurability of page settings and supports reuse of federated JupyterLab extensions, allowing developers to customize their JupyterLite instances extensively.

JupyterLite maintains compatibility with recent versions of JupyterLab and Jupyter Notebook, with version 0.7.0 bundling JupyterLab 4.5.0 and Notebook 7.5.0. The project actively supports only the last two releases. The platform works with both JupyterLab and Jupyter Notebook interfaces, providing users with choice in their preferred interface. While not all features available in JupyterLab and Classic Notebook are supported in JupyterLite, the project continues to expand its capabilities and provides comprehensive documentation for users and developers looking to build their own custom JupyterLite distributions.