k3d-io/k3d

Little helper to run CNCF's k3s in Docker

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Summary Information

Updated 10 minutes ago
Added to GitGenius on September 10th, 2026
Created on April 2nd, 2019
Open Issues & Pull Requests: 290 (+0)
GitHub issues: Enabled
Number of forks: 536
Total Stargazers: 6,552 (+0)
Total Subscribers: 54 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.0 days
Mean response time: 86.1 days
90th percentile: 348.6 days
Tracked items: 98

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 96% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "question" is answered fastest, typically in about 24 hours, while "enhancement" waits about 8 days. Only 3% of issues opened in the past year have been closed. Three people close 64% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 79
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 719 days
Stale 30+ days: 76
Stale 90+ days: 70

Recent activity

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

Top labels

  • bug (77)
  • enhancement (22)
  • question (16)
  • help wanted (4)
  • k3s (4)
  • not a bug (4)
  • priority/high (3)
  • docs (2)

Most active issues this week

Detailed Description

k3d is a utility that enables running k3s Kubernetes clusters inside Docker containers.

The tool addresses the need for lightweight, fast Kubernetes environments suitable for local development, testing, and CI/CD pipelines. Rather than requiring full virtual machines or complex cluster setups, k3d wraps the minimal k3s distribution in Docker, allowing developers to spin up and tear down Kubernetes clusters rapidly on any machine with Docker installed. This approach leverages k3s's reduced resource footprint while adding convenient orchestration around container lifecycle management.

Developers should choose k3d when they need quick, disposable Kubernetes environments for development work, testing applications locally, or running integration tests in CI systems. It suits projects where full production-grade cluster complexity is unnecessary and where fast iteration matters more than cluster scale. The tool is particularly valuable for teams already using Docker who want to avoid the overhead of managing separate Kubernetes infrastructure or virtual machines.

The project maintains active development with regular updates addressing both bug fixes and feature enhancements. Contributions flow steadily into the codebase, and the maintainers respond to issues and pull requests consistently. The project demonstrates sustained engagement with its user base through documentation improvements and tooling refinements.