Learn how Taiga and Worklenz differ in their key features, development activity, technology stack and community adoption, so you can decide which of these project management suites is best for you.
Last 30 days
Last commit
Repository age
License
Self-hosted
Repository

Last 30 days
Last commit
Repository age
Version
License
Repository

Both Taiga and Worklenz have their unique strengths and serve similar purposes effectively. Consider your specific needs regarding popularity, growth, activity, technology, maturity, licensing and features when making your decision.
Worklenz significantly outpaces Taiga in community adoption with 3,150 stars compared to 847 stars on GitHub. This 3.7x difference suggests Worklenz has a much larger and more active community. In terms of developer contributions, Worklenz has 360 forks, indicating moderate developer engagement.
Worklenz is growing faster, adding 36 stars in the last 30 days (+1.2%) against adding 4 stars for Taiga (+0.5%). Worklenz is both larger and pulling further ahead.
Both projects show recent activity, with Taiga last updated 20 days ago and Worklenz 40 minutes ago.
Both tools share common technology foundations, being built with CSS, Bash. However, they differ in their additional technology choices: Taiga uses Python, Django while Worklenz leverages JavaScript, Typescript, JSX, SCSS.
Taiga has been in development longer, starting 5 years ago, compared to Worklenz which began 2 years ago. This 3.1-year head start suggests Taiga may have more mature features and established processes.
The projects use different licenses: Taiga is licensed under MPL-2.0 while Worklenz uses AGPL-3.0. Consider the licensing requirements when choosing for your project.
Both tools serve similar use cases in Project Management Suites. However, they also have distinct specializations: Taiga also focuses on Agile Project Management while Worklenz extends into Task Management.
Taiga provides self-hosting options for complete data control and customization, while Worklenz may be primarily cloud-based or require different deployment approaches.
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs
vs