litmuschaos/litmus

Litmus helps SREs and developers practice chaos engineering in a Cloud-native way. Chaos experiments are published at the ChaosHub...

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Summary Information

Updated 32 minutes ago
Added to GitGenius on September 12th, 2026
Created on March 15th, 2017
Open Issues & Pull Requests: 399 (+0)
GitHub issues: Enabled
Number of forks: 895
Total Stargazers: 5,609 (+0)
Total Subscribers: 84 (+0)

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Detailed Description

Litmus is an open source chaos engineering platform that enables teams to identify weaknesses and potential outages in infrastructure by inducing controlled chaos tests.

Litmus addresses the challenge of validating system resilience by providing a cloud-native approach to chaos engineering. It runs as a set of microservices on Kubernetes and uses custom resources to define chaos experiments and steady-state hypotheses. The platform separates concerns into a control plane, which provides centralized management through a chaos-center interface for constructing and scheduling workflows, and an execution plane made up of agents and operators that run experiments against target Kubernetes environments. At its core, Litmus defines three key custom resources: ChaosExperiment describes the fault configuration and permissions, ChaosEngine links application workloads to specific faults and allows tuning via probes for steady-state validation, and ChaosResult captures experiment outcomes and metrics. Experiments can be chained together in workflows and are sourced from a central hub where vendors and developers share reusable chaos definitions.

Teams should adopt Litmus if they need to systematically test Kubernetes application resilience in production-like environments. The platform suits organizations already running Kubernetes who want to practice chaos engineering without building custom tooling. The bring-your-own-chaos capability allows integration of third-party fault injection tools, making it flexible for teams with existing testing infrastructure. The centralized hub of community-contributed experiments reduces the effort needed to define new tests.

The project maintains active development across multiple areas of the codebase. Pull requests receive timely review and merging. Issues are regularly triaged and addressed. The maintainers engage consistently with community contributions and feedback. Documentation is kept current alongside feature development.