werf/kubedog

Library to watch and follow kubernetes resources in CI/CD deploy pipelines

Hosted by Cloud Native Computing Foundation · part of Linux Foundation

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werf/kubedog has 750+ stars and 50+ forks on GitHub, is written in Go, and was last pushed on Oct 9, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 14 minutes ago
Added to GitGenius on October 15th, 2026
Created on October 8th, 2018
Open Issues & Pull Requests: 34 (+0)
GitHub issues: Enabled
Number of forks: 52
Total Stargazers: 755 (+0)
Total Subscribers: 15 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 22.2 hours
Mean response time: 166.7 days
90th percentile: 1091.8 days
Tracked items: 8

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Issue Activity (beta)

Open issues: 7
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 898 days
Stale 30+ days: 7
Stale 90+ days: 5

Recent activity

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

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Detailed Description

Kubedog is a library for watching and following Kubernetes resources in CI/CD deploy pipelines.

The tool solves the problem of tracking resource readiness during deployments by providing programmatic access to Kubernetes resource state monitoring. It watches for resource readiness, presence, or absence, and collects Kubernetes Events along with logs from Pod containers. This approach allows deployment tools to make intelligent decisions about when a deployment has succeeded or failed based on actual cluster state rather than simple timeout-based checks.

Kubedog is suited for teams building deployment automation tools, CI/CD platforms, or Helm-based workflows that need reliable feedback on whether Kubernetes resources have reached their desired state. It works as an embedded library rather than a standalone tool, making it appropriate for projects that require programmatic control over deployment monitoring. The tool is actively used within the werf deployment framework and related projects, indicating it has been validated in production deployment scenarios.

The project maintains active community engagement through discussions and messaging channels. Development activity shows consistent attention to the codebase with regular updates and responsiveness to issues. The maintainers provide documentation and examples sufficient for integration into deployment pipelines.