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Krkn

krkn-chaos.dev

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Spec score 8.5 #5 of 25 in Chaos Engineering Platforms

What it is

Krkn injects deliberate failures into Kubernetes clusters so teams can examine resilience under disruption. Its 20-plus scenarios cover pods, containers, nodes, networks, applications, resources, storage and time. The recommended krknctl command-line tool discovers, runs and coordinates scenarios; other choices include pre-built containers, a browser dashboard and a standalone Python program. Krkn evaluates Prometheus metrics against SLO pass/fail criteria and can review metrics during and after scenarios. Dashboard integrations include Elasticsearch and Grafana, and telemetry can be sent to S3 or Elasticsearch. Cerberus monitors Kubernetes and OpenShift health, providing a go/no-go signal, reports and a metrics API; it can also report failures in Slack. Krkn-AI uses evolutionary algorithms against user-defined objectives to identify experiments. Krkn says it saves recovery state and can restore a cluster, but some disruptive scenarios may leave the target unrecoverable. It is intended for resilience measurement, not security provision, and does not currently adhere to compliance standards. Requirements include Kubernetes or OpenShift 1.21+, cluster-admin kubeconfig access, and Docker 20.10+ or Podman 4.0+.

Spec sheet

Purpose
Krkn injects deliberate failures into Kubernetes clusters to test their resilience.Read 7 Oct 2026 · krkn-chaos.dev
Integrations
The documentation describes integrations with Prometheus, Elasticsearch, Grafana, Slack, S3, and CI/CD systems such as Jenkins and GitHub Actions.Read 7 Oct 2026 · krkn-chaos.dev
CLI
krknctl discovers, runs, and orchestrates chaos scenarios, and is the recommended way to run Krkn.Read 5 Oct 2026 · krkn-chaos.dev
Deployment options
Krkn offers a CLI, pre-built container images, a browser-based dashboard, and a standalone Python program.Read 5 Oct 2026 · krkn-chaos.dev
Install options
Installation options include a CLI, pre-built containers, a browser-based dashboard, and a standalone Python program.Read 7 Oct 2026 · krkn-chaos.dev
Krkn AI
Krkn-AI uses evolutionary algorithms and user-defined objectives such as SLOs and application health checks to identify impactful chaos experiments.Read 5 Oct 2026 · krkn-chaos.dev
Metrics and SLOs
Krkn can validate SLO pass/fail criteria with Prometheus PromQL queries and review metrics during and after chaos scenarios.Read 5 Oct 2026 · krkn-chaos.dev
Monitoring
Cerberus monitors Kubernetes and OpenShift health and exposes a go/no-go signal, reports, and a metrics API.Read 7 Oct 2026 · krkn-chaos.dev
Notable limits
krkn-hub runs one scenario type per execution; running multiple scenario types in one execution requires the standalone Krkn installation.Read 5 Oct 2026 · krkn-chaos.dev
Operating guidance
The installation guide recommends running Krkn outside the cluster against the Kubernetes or OpenShift API.Read 7 Oct 2026 · krkn-chaos.dev
Project status
The site identifies Krkn as a Cloud Native Computing Foundation Sandbox project.Read 7 Oct 2026 · krkn-chaos.dev
Requirements
The getting-started guide lists Kubernetes or OpenShift 1.21+, cluster-admin kubeconfig access, and Docker 20.10+ or Podman 4.0+ as requirements.Read 7 Oct 2026 · krkn-chaos.dev
Rollback
Krkn’s rollback system persists changes to the filesystem before a scenario so it can restore targeted Kubernetes objects or subsystems.Read 7 Oct 2026 · krkn-chaos.dev
Rollback limitation
The security self-assessment says some disruptive scenarios may leave the target system in a non-recoverable condition.Read 7 Oct 2026 · krkn-chaos.dev
Runtime requirement
The standalone Python installation requires a Python 3.9 environment and manual dependency management.Read 5 Oct 2026 · krkn-chaos.dev
Scenario coverage
The site lists 20+ chaos scenarios spanning pods, nodes, networks, applications, storage, and time.Read 7 Oct 2026 · krkn-chaos.dev
Scenarios
The site lists more than 20 chaos scenarios covering pods, containers, nodes, networks, applications, resources, storage, and time.Read 5 Oct 2026 · krkn-chaos.dev
Security documentation
The security self-assessment lists a software bill of materials for Krkn, krkn-lib, and krknctl, and says a security-insights.yml should be added to the roadmap.Read 5 Oct 2026 · krkn-chaos.dev
Security posture
Krkn says it is not a security provider and does not currently adhere to compliance standards.Read 7 Oct 2026 · krkn-chaos.dev
Security reporting
The project accepts vulnerability reports through GitHub’s vulnerability reporting tool.Read 7 Oct 2026 · krkn-chaos.dev
Security scope
Krkn's security self-assessment says Krkn is not a security provider; it is designed to measure application or cluster resilience through controlled disruptions.Read 5 Oct 2026 · krkn-chaos.dev
Slack integration
Cerberus supports reporting failures in Slack.Read 5 Oct 2026 · krkn-chaos.dev
SLO validation
Krkn can evaluate Prometheus metrics to automatically pass or fail a chaos run.Read 7 Oct 2026 · krkn-chaos.dev
Who it is for
The docs describe krknctl as suitable for users from first-time chaos engineers to teams building resilience testing workflows.Read 7 Oct 2026 · krkn-chaos.dev

Plans and prices

Krkn Free No pricing or usage limits stated on the opened pages Read 7 Oct 2026 · krkn-chaos.dev

Every Krkn plan and price →

Strong points and limits

Strong points

  • More than 20 disruption scenarios
  • Validates SLO criteria with Prometheus metrics
  • Offers CLI, containers, dashboard and Python options
  • Can save recovery state and restore clusters

Limits

  • Some disruptive runs may leave systems unrecoverable
  • Requires cluster-admin kubeconfig access
  • Standalone Python setup requires manual dependency management
  • Does not currently adhere to compliance standards

Who it suits

Krkn suits users from first-time chaos engineers to teams building resilience-testing workflows. It is aimed at Kubernetes and OpenShift environments where operators can provide the listed cluster access and runtime requirements.

Verdict

Krkn offers several ways to run disruption scenarios and evaluate their effects against metrics and SLOs. Its recovery features do not eliminate the stated risk of an unrecoverable target, so account for that when planning runs.

Where Specifiction places it

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