The missing open-source Kubernetes UI, with a built-in MCP server for your AI agent.
Single binary. No account required. Free forever.
🌐 radarhq.io · Docs · Benchmarks · Releases
Radar shows you what's broken in a Kubernetes cluster, why, and what changed: issues, topology, a timeline of events and resource changes, Helm, GitOps, traffic, audit, and upgrade impact. Your AI agent reads the same cluster data and relationships through Radar's MCP server, and Radar can run an AI investigation for you with the agent CLI you already use. It runs on your laptop as a kubectl plugin or desktop app, or in-cluster with Helm.
Table of contents
- Why Radar? · Debugging in the UI
- AI agents: MCP server and AI investigations — Connect your agent · AI investigations · Benchmarks
- Installation
- Usage
- Views — Issues · Topology · Resources · Image Filesystem · Timeline · Helm · Compare · TLS · GitOps · Traffic · Metrics · Capacity · Cost · Audit · Network path · Upgrade impact · RBAC · Auth
- Supported Resources & Integrations
- Keyboard Shortcuts
- Security
- Development · Contributing
Install and run in 30 seconds:
curl -fsSL https://get.radarhq.io | sh && kubectl radarOr brew install kubectl-radar. Windows, Krew, desktop app and Helm ↓
Then, with Radar running, connect your AI agent (read-only):
claude mcp add --transport http radar http://localhost:9280/mcp-readonlyCodex, Cursor, GitHub Copilot, other clients, and the desktop app ↓
- Nothing to install in your cluster — a single binary on your laptop that talks to the Kubernetes API with your kubeconfig. No agents, no CRDs, no dependencies
- Fast on big clusters — tested on tens of thousands of pods; watches the cluster through informers and pushes live updates to the browser over SSE
- Private by design — your cluster data stays on your machine. No account, no agents, no cloud sync. Usage reporting is off unless you opt in, and then sends only anonymous usage counts and coarse cluster facts: Kubernetes version, platform, node-count range, known integrations (what Radar sends). An AI agent you connect sends what it reads to its own model provider
- Airgapped-friendly — runs as a single binary against the Kubernetes API and works in locked-down environments with outbound egress blocked
- Works everywhere — GKE, EKS, AKS, minikube, kind, k3s, or any conformant cluster
- One model of the cluster, for you and your agent — the UI, the MCP server, and AI investigations read the same live data: relationships, health, events, and changes
- Ready for teams — deploy in-cluster with Helm for shared access, with SSO (OIDC or an auth proxy) and per-user Kubernetes RBAC in the open-source build
"Have Radar deployed at work. As far as Kubernetes dashboards go, this is one of the best." — u/TheRealNetroxen
Start from Issues, a ranked queue of what's broken right now. Open a failing resource to see it in context: its owners, Pods, Services, and config on the Topology, what changed and when on the Timeline (with diffs), and its events, logs, and YAML in the detail drawer. Helm, GitOps, and Traffic cover how it got deployed and who talks to it. When you want help, Investigate hands the same evidence to your agent (AI investigations). Radar records resource changes while it watches the cluster; history is in memory by default, and --timeline-storage=sqlite or postgres keeps it across restarts.
Issues for the incident the agent diagnoses below: a crash loop on checkout and a Service with no endpoints
Pointed at raw kubectl, an agent spends its context window on YAML and its time on round trips. Radar's built-in Model Context Protocol (MCP) server gives it what Radar already knows instead: how resources connect, what is unhealthy, deduplicated events, recent changes with diffs, and filtered logs. In our benchmarks, the same agent and model reached a diagnosis about 3× faster through Radar than through kubectl (details).
Claude Code on Radar's read-only endpoint, finding why a rollout failed (replay of a real session)
MCP is on by default and served on the same port as the UI: 9280 unless you set --port. The desktop app picks its own port: copy the URL from Radar's MCP dialog and change the final /mcp to /mcp-readonly. Start with the read-only endpoint:
# Claude Code
claude mcp add --transport http radar http://localhost:9280/mcp-readonly
# Codex
codex mcp add radar --url http://localhost:9280/mcp-readonlySetup for Cursor, VS Code (GitHub Copilot), Copilot CLI, OpenCode, Google Antigravity, Kiro, JetBrains, Cline, Devin, Claude Desktop, Zed, Gemini CLI, and any other MCP client is in the MCP guide. Its examples use /mcp; use /mcp-readonly instead to keep Radar's tools read-only. Your agent's other tools follow its own configuration.
- Context, not raw YAML — topology, health assessments, events, changes, and logs, minified so they don't burn the context window. The tools cover resources and topology, issues, events and changes, logs, Helm, audit, upgrade readiness, RBAC, cost, and Prometheus
- You pick the endpoint —
/mcp-readonlyhas no write tools. The full/mcpendpoint, also on by default, adds write tools (restart, scale, apply, rollback, GitOps sync, node drain) marked destructive so clients can ask before running them. On/mcp,diagnosecan also run an in-cluster reachability probe that creates short-lived pods. Calls use Radar's Kubernetes access: your kubeconfig locally, or each user's own RBAC when authentication is on.--mcp-read-onlyturns/mcpoff (and with it, applying a fix from an AI investigation);--no-mcpturns MCP and AI investigations off. Neither blocks ordinary UI writes - Secret values are withheld — Secret reads never include
.dataor.stringData. Environment variables, Helm values, and logs are scrubbed for known secret patterns, which can't catch everything, and the opt-inexec_podtool returns raw command output. See MCP security
Radar can also run the investigation for you, with the agent CLI you already have — Claude Code, Codex, Cursor, or OpenCode — on your machine and signed in with your own account. Radar needs no API key of its own. Start one from a resource's Investigate action, from an issue, by describing what you're seeing on the AI investigations page (where to start, since when, and optionally the resource it's about), or from a terminal against a running Radar:
radar diagnose deploy/checkout -n shop
radar diagnose deploy/checkout -n shop --json > conclusion.jsonEvery investigation ends on the same Findings page, with the evidence behind each claim. Radar's investigation tools can only read the cluster, and Radar applies a fix only when you approve it, from an investigation of the resource it changes. What else the agent can do depends on how Radar runs it. Radar safeguards (Claude Code and Codex) shuts off the agent's other MCP servers and its ability to write. Your agent setup (the only option for Cursor and OpenCode) runs the agent with its normal configuration, tools, and MCP servers, which Radar cannot constrain and which may be able to change your cluster. Cursor and OpenCode approve tool requests automatically in these runs, without asking you. See agents and isolation.
Investigations need a supported agent CLI on the machine running Radar, MCP on, and authentication off. In-cluster installs don't run them. See the AI investigations guide and agent CLI setup.
Three studies of an AI agent diagnosing live faults from SREGym (Microsoft and UIUC) on a 3-node EKS cluster, with the same judge scoring every tool. Times are averages to a submitted answer, right or wrong. These are our own benchmarks; compare numbers within a study, not across studies.
| Study | Faults | Result |
|---|---|---|
| Radar MCP vs raw kubectl — same agent and model | 50 | 64s vs 191s to a diagnosis (about 3× faster); 86% vs 60% diagnosed correctly within two minutes |
| Kubernetes MCP servers — Radar, containers/kubernetes-mcp-server, Flux159/mcp-server-kubernetes, Amazon EKS MCP Server, k8sgpt, raw kubectl | 25 | Radar took 57s, 2.3× faster than the next-fastest MCP server; on the 14 faults every tool got right, 33s vs 58s for the next-fastest |
| AI SRE tools — Claude Code + Radar MCP vs kubectl-ai, HolmesGPT, OpenSRE, kagent, AWS DevOps Agent | 50 | Claude Code + Radar MCP was the most accurate (44/50 correct), fastest (79s), and cheapest ($0.27 a fault) |
Per-scenario scores and timings are downloadable from the benchmark pages; the AI SRE study also includes costs.
Quick Install:
curl -fsSL https://get.radarhq.io | shHomebrew:
brew install kubectl-radarAlso available from our tap: brew install skyhook-io/tap/radar.
Then run: kubectl radar. Quick install, PowerShell, Homebrew, and Scoop also set up the radar shorthand. Krew and direct downloads use kubectl radar unless you add your own radar symlink.
More install options — Desktop App (macOS/Linux/Windows), Krew, Scoop, In-Cluster Helm
Krew (kubectl plugin manager):
kubectl krew install radarScoop (Windows):
scoop bucket add skyhook https://github.com/skyhook-io/scoop-bucket
scoop install radarPowerShell (Windows):
irm https://get.radarhq.io/install.ps1 | iexCustom install location (macOS/Linux):
curl -fsSL https://get.radarhq.io | INSTALL_DIR="$HOME/.local/bin" shQuick install writes to /usr/local/bin unless INSTALL_DIR says otherwise. The directory is created if missing, and needs to be on your PATH for radar and kubectl radar to resolve.
Direct download — GitHub Releases for macOS, Linux, or Windows.
Native desktop app — no terminal needed.
Homebrew (macOS):
brew install --cask skyhook-io/tap/radar-desktopDebian/Ubuntu — download the .deb from GitHub Releases, then:
sudo apt install ./radar-desktop_*.debFedora/RHEL — download the .rpm from GitHub Releases, then:
sudo rpm -i radar-desktop_*.rpmScoop (Windows):
scoop bucket add skyhook https://github.com/skyhook-io/scoop-bucket
scoop install radar-desktopWindows (direct download) — GitHub Releases.
Deploy to your cluster for shared team access:
helm repo add skyhook https://skyhook-io.github.io/helm-charts
helm install radar skyhook/radar -n radar --create-namespaceSee the In-Cluster Deployment Guide for Gateway API and ingress exposure, authentication, and RBAC configuration.
# Opens browser automatically
kubectl radar
# Quick install, PowerShell, Homebrew, and Scoop also set up the bare command
radarCommon flags
| Flag | Default | Description |
|---|---|---|
--kubeconfig |
~/.kube/config |
Path to primary kubeconfig file |
--namespace |
(context namespace) | Initial namespace filter (supports multi-select in the UI; also used as RBAC fallback for namespace-scoped users). Unset, Radar uses your saved pick, then the kubeconfig context's namespace, then all namespaces |
--port |
9280 |
Server port |
--listen-address |
127.0.0.1 |
HTTP listen IP address (IPv4 or IPv6), or localhost. Bind a specific local IP or use 0.0.0.0 for all interfaces. Non-loopback access needs authentication and network controls. |
--no-browser |
false |
Don't auto-open browser |
--timeline-storage |
memory |
Timeline storage backend: memory, sqlite, or postgres |
--disable-exec |
false |
Disable pod terminal, debug shells and pod file browsing |
--auth-mode |
none |
Authentication mode: none, proxy, or oidc (details) |
--no-mcp |
false |
Disable all MCP endpoints |
--mcp-read-only |
false |
Expose only read-only MCP tools; disable /mcp (env: RADAR_MCP_READ_ONLY) |
The Configuration Guide describes every startup flag in the table above and 27 more, plus tuning for slow or high-latency clusters, kubeconfig precedence, and context switching.
The other 27 flags
--kubeconfig-dir, --namespaces, --namespace-scope, --base-path, --allowed-hosts, --trusted-origins, --browser, --timeline-db, --timeline-max-size, --history-limit, --disable-helm-write, --disable-local-terminal, --debug-image, --list-page-size, --context-switch-timeout, --first-paint-backstop, --namespace-list-timeout, --max-scope-candidates, --prometheus-url, --prometheus-single-cluster, --prometheus-cluster-label, --prometheus-header, --prometheus-header-from-env, --beyla-job-selector, --opencost-currency, --mcp-catalog-stdio, --version
radar --help is authoritative for the installed version. radar cloud status inspects an in-cluster Radar Cloud installation without changing it (details).
One ranked queue of what's broken right now — crash loops, image pull failures, stuck rollouts, pods that can't be scheduled, dangling references, and failing Argo CD / Flux conditions — each with an Investigate action. See Issues.
Interactive graph showing how your Kubernetes resources are connected in real-time.
Topology View — Visualize resource relationships
- Two modes: Resources (full hierarchy) and Traffic (network flow path)
- Group by namespace, app label, or view ungrouped
- Filter by resource kind — click any node for full details
- Auto-layout powered by ELK.js, live updates via SSE
Table-based resource browser with smart columns per resource kind.
Resources View — Browse and filter all cluster resources
- Browse all resource types including CRDs
- Search by name, filter by status or problems (CrashLoopBackOff, ImagePullBackOff, etc.)
- Add custom columns from any label or annotation — sortable, filterable, and resizable
- Click any resource for YAML manifest, related resources, logs, and events
- Set regular or init-container images on Deployments, StatefulSets, DaemonSets, and Argo Rollouts, with live rollout progress in tables, drawers, workload views, and Applications
Inspect container image filesystems directly from the Pod view — no need to pull images locally or exec into containers.
Image Filesystem Viewer — Browse container image contents
- Browse, search, and download files from any container image in a Pod
- Works with public images (Docker Hub, Quay, GHCR) and private registries (GCR, ECR, ACR) using your cluster's ImagePullSecrets
See Image filesystem.
Unified timeline of Kubernetes events and resource changes.
Timeline View — Track cluster activity in real-time
- Filter by event type (all or warnings only)
- Resource change diffs showing what changed (replicas, images, etc.)
- Real-time updates as new events occur
Manage Helm releases deployed in your cluster — inspect values and rendered manifests, diff revisions, see why an upgrade or hook failed, then upgrade, roll back, or uninstall. Radar tracks available chart upgrades from your configured repos or your own OCI registries. See Helm Support for the detailed behavior and limits.
Helm View — Manage your Helm deployments
Diff any two Kubernetes resources of the same kind side-by-side — like comparing a staging Deployment to its production sibling, or two pods that should be identical but aren't. Server-assigned noise (managedFields, resourceVersion, last-applied) is stripped, and diff-only and spec-only modes cut the rest. See Compare.
Compare View — Side-by-side YAML diff with field-level highlighting
View TLS certificate details and expiry dates across all namespaces — catch expiring certificates before they cause outages.
- Parses TLS secrets to show certificate subject, issuer, and validity period
- Dashboard-level certificate expiry overview
- Available from the resource detail view for any TLS-type Secret
Monitor, diagnose, and manage FluxCD and ArgoCD resources in one place.
GitOps fleet view — Argo + Flux applications side-by-side with sync, health, source, destination, and lifecycle state
- Fleet view + per-app detail page for ArgoCD (
Application,ApplicationSet,AppProject) and FluxCD (GitRepository,OCIRepository,HelmRepository,Bucket,Kustomization,HelmRelease,Alert) - Diagnosis — field-level drift, recent events per resource, stuck-drift-loop detection, parsed operation failures, one-click remediation
- Sync, suspend, resume, reconcile, and rollback from the UI or through MCP
See the GitOps guide for the full feature matrix, RBAC requirements, demo cluster, and single-cluster scope notes.
Visualize live network traffic between services using Hubble, Caretta, Istio, or Beyla.
Traffic View — See how services communicate in real-time
- Auto-detects Hubble (Cilium), Istio, Caretta, or Grafana Beyla as traffic data sources, with a setup wizard to install one if none is detected
- Beyla needs its
networkfeature enabled, and per-port edges additionally needdst.portandtransportnamed inattributes.select— both are off by default, and Radar says so in the Traffic view rather than showing partial edges silently - Animated flow graph showing requests per second between services, filterable by namespace, protocol, or status code
- Focus on a workload to see only it and what it talks to; the focus is in the URL, so it can be shared
See Traffic for source-specific requirements and large-cluster behavior.
Open a Deployment, StatefulSet or DaemonSet's Metrics tab to investigate request rate, HTTP errors, latency, CPU, memory and throttling. Radar reads an existing Prometheus-compatible backend; resource charts do not require HTTP instrumentation, and request charts use supported Beyla or Istio observations.
See Workload metrics for screenshots, prerequisites, supported configurations and local multi-cluster setup limitations.
Read-only diagnosis for Karpenter-managed fleets — why is my pod pending, which NodePool could take it, why aren't my nodes joining, what is disruption doing to my fleet? Appears automatically when Karpenter NodePools are detected (RBAC-gated). Overview, NodePool detail, Demand, and Activity screens; every quantity carries its certainty (= ≥ ≤ ?), so unavailable is never shown as zero. See docs/capacity.md.
Track Kubernetes spending from OpenCost metri