koderover/zadig

Zadig: An AI-powered, cloud-native, distributed DevOps platform designed for developers

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koderover/zadig has 3k+ stars and 900+ forks on GitHub, is written in Go, and was last pushed on Oct 16, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 11 minutes ago
Added to GitGenius on September 21st, 2026
Created on May 12th, 2021
Open Issues & Pull Requests: 33 (+0)
GitHub issues: Enabled
Number of forks: 912
Total Stargazers: 3,253 (+0)
Total Subscribers: 44 (+0)

Repository Insights (GitGenius)

Median issue/PR response: N/A
Mean response time: 13.0 days
90th percentile: 40.8 hours
Tracked items: 68

Maintainer activity

5 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 18, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits. How this is counted.

Charts & Analytics

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Issue Activity (beta)

Open issues: 11
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 1,028 days
Stale 30+ days: 11
Stale 90+ days: 10

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 (20)
  • feature request (18)
  • question (13)
  • enhancement (6)
  • installation (6)
  • good first issue (1)

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Detailed Description

Zadig is a cloud-native DevOps platform that provides developers with integrated workflows, environment management, and AI-powered capabilities for continuous delivery.

The platform addresses the complexity of managing microservice deployments and release processes by automating workflow generation and enabling high-concurrency execution across multiple services. It operates on Kubernetes and incorporates AI large language models to enhance decision-making throughout the development lifecycle. Core capabilities include flexible workflow orchestration, multiple release strategy options, environment provisioning that generates multiple encapsulated environments from a single service configuration, and AI-driven features such as code review, release risk assessment, and task orchestration. The tool also provides efficiency diagnosis and environment inspection to identify bottlenecks and warn of environmental risks.

Teams managing microservice architectures with many services benefit most from the tool's ability to handle concurrent builds, tests, and deployments across multiple services simultaneously. The service-oriented environment model allows developers, QAs, and product managers to work with independent environments provisioned automatically from shared configuration, minimizing migration costs when adopting the platform. The README emphasizes that existing environments can be hosted with minimal effort through a one-click integration approach.

The project shows sustained development activity with regular updates and improvements across its core systems. The codebase demonstrates active maintenance with ongoing refinements to the platform's architecture and feature set. The project maintains comprehensive documentation and provides bootcamp resources for users learning the platform. Community engagement is supported through multiple channels for getting help and contributing to the project.