kubeshop/testkube
☸️ The Open Testing Platform for AI-Driven Engineering Teams
What it solves
Testkube provides a centralized platform for defining, running, and analyzing automated tests directly within Kubernetes infrastructure. It eliminates the need to manage separate testing environments by allowing teams to execute any testing tool or script—including API, E2E, performance, and security tests—at scale within the cluster.
How it works
Testkube operates via an agent that runs in the Kubernetes cluster. This agent executes tests and aggregates all results, artifacts, logs, and resource metrics for centralized reporting. Users can trigger tests manually, via schedules, through CI/CD pipelines, Kubernetes events, a REST API, or an MCP server. The platform also offers AI agents and an MCP server for troubleshooting, analysis, and remediation.
Who it’s for
It is designed for AI-driven engineering teams and DevOps professionals who need to orchestrate automated testing across Kubernetes environments.
Highlights
- Tool Agnostic: Supports any existing testing tools, scripts, or executors (e.g., K6, Playwright, Maven, Gradle).
- Centralized Observability: Aggregates logs, artifacts, and resource metrics in one place for troubleshooting.
- Flexible Triggering: Tests can be launched via REST API, GitOps pipelines, or Kubernetes events.
- AI Integration: Includes native AI agents and an MCP server for failure investigation and remediation.
Related
- Project
vfarcic/dot-aiAn npm‑published Node library that adds LLM‑driven assistants to Kubernetes/DevOps workflows. It provides a CLI and a managed platform (MCP) for natural‑language cluster queries, deployment recommendations, automated remediation, policy‑driven prompt libraries, and semantic doc search, all packaged for easy installation on a Kubernetes cluster.
- Project
chenjigang4167/testhub_platformTestHub is an open‑source, AI‑enhanced test‑management system built with Django (backend) and Vue 3 (frontend). It covers requirement analysis, test‑case authoring, API/Web/Android automation, performance testing, and reporting, while offering LLM‑driven test generation and a chatbot assistant.
- Project
kagent-dev/kagentkagent is a CNCF‑incubated, open‑source framework that lets you define and run AI agents as native Kubernetes custom resources. Agents are described in YAML, can use any major LLM provider, run on Go/Python runtimes or custom back‑ends, and have built‑in tool integration, persistent sessions, and OpenTelemetry tracing. The system includes a controller, web UI, CLI, PostgreSQL storage, and a substrate that manages agent lifecycles, making AI workloads manageable with standard Kubernetes/GitOps workflows.
- Project
containers/kubernetes-mcp-serverA native Go-based MCP server that allows AI agents to directly interact with and manage Kubernetes and OpenShift clusters, replacing the need for manual CLI tool usage.
- Project
Flux159/mcp-server-kubernetesMCP Server Kubernetes is a Node‑based service that exposes a rich set of kubectl and Helm operations as MCP tools, letting AI assistants (Claude, Codex, VS Code, etc.) manage a Kubernetes cluster safely (including a read‑only mode) and optionally emit OpenTelemetry traces.