software-mansion/TypeGPU

A modular and open-ended toolkit for WebGPU, with advanced type inference and the ability to write shaders in TypeScript

View on GitHub ↗Jump to charts ↓Open shareable report →

Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 1 hour ago
Added to GitGenius on September 21st, 2026
Created on February 17th, 2024
Open Issues & Pull Requests: 295 (+2)
GitHub issues: Enabled
Number of forks: 126
Total Stargazers: 3,231 (+0)
Total Subscribers: 9 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 23.5 hours
Mean response time: 31.6 days
90th percentile: 91.1 days
Tracked items: 775

How this project is maintained

Roughly one issue in four opened in the past year never receives a reply. Only 12% of open issues come from outside the core team — the tracker reads mainly as internal planning. Work labelled "bug" is answered fastest, typically in about 22 hours, while "documentation" waits about 8 days. 41% of tracked open issues have had no activity in three months. Only 58% of issues opened in the past year have been closed.

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 196
New in 7 days: 13
Closed in 7 days: 1
Avg open age: 191 days
Stale 30+ days: 161
Stale 90+ days: 93

Recent activity

Opened in 7 days: 12
Closed in 7 days: 1
Comments in 7 days: 12
Events in 7 days: 20

Top labels

  • enhancement (181)
  • bug (106)
  • documentation (56)
  • good first issue (34)
  • internal-dx (19)
  • exploration (18)
  • stability (17)
  • good first human issue (16)

Most active issues this week

Sign in to see which issues are moving.
Sign in

Detailed Description

TypeGPU is a modular toolkit for WebGPU that enables writing shaders in TypeScript with advanced type inference.

The toolkit addresses the friction of working with WebGPU by providing type-safe abstractions over common operations while preserving the ability to drop down to vanilla WebGPU at any point. It mirrors WGSL syntax in TypeScript, making it easier to learn WebGPU itself. The approach allows granular control: you can use TypeGPU's type-safe APIs together or in isolation, and integrate it into existing applications incrementally without full commitment to the framework.

TypeGPU suits developers building new WebGPU applications who want type safety and reduced boilerplate, as well as teams integrating GPU computing into existing codebases where partial adoption is valuable. It is particularly useful for library authors creating type-safe WebGPU utilities, as it solves common problems like data serialization, dynamic shader generation, and complex type inference. The toolkit can serve as an interoperability layer between specialized libraries, allowing them to pass typed values without copying data back to CPU memory. Unlike frameworks that impose architectural constraints, TypeGPU's modular design means you are not locked into its abstractions.

The project maintains active development with regular updates to its documentation and examples. The codebase demonstrates attention to developer experience through comprehensive guides covering getting started, fundamentals, and integration patterns. The team actively encourages library authors to build on TypeGPU and has established community channels for collaboration on ecosystem expansion.