Kubeastra

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Your clusters are talking. This assistant helps you listen.

An AI-powered Kubernetes troubleshooting assistant that lets teams investigate, diagnose, and resolve cluster issues through natural language — via a chat-based web UI or directly inside your IDE (Cursor / Claude Desktop / VS Code via MCP).

Combines live kubectl access with pluggable LLM providers (Gemini, Ollama/local, more coming) for root-cause analysis that turns cryptic Kubernetes failures into clear answers and actionable fix commands.

See it in action

▶ Watch the 90-second demo — Kubeastra walking through 7 real Kubernetes failures (CrashLoopBackOff, OOMKilled, ImagePullBackOff, stuck PVC, unschedulable pod, namespace-wide health, runbook generation).

Want to reproduce it locally? make demo spins up a kind cluster pre-seeded with six broken workloads. See demo/README.md.

Why this exists

Every DevOps engineer has been here: a pod is crashlooping at 2 AM, and you're mentally chaining together kubectl get, kubectl describe, kubectl logs, cross-referencing events, checking resource limits, and Googling error messages — all while half asleep.

This tool handles that investigation loop for you:

• Ask in plain English — "Why is payment-service crashing in production?"
• Get root-cause analysis — not just logs, but AI-synthesized explanations of what's wrong and why
• Receive fix commands — ready to run, with safety confirmations for write operations
• Generate runbooks — so your team doesn't debug the same issue twice
• Stay on your own infra — run entirely locally with Ollama, no data leaves your cluster

Key Features

🔗 Connect Any Cluster in Seconds

Four ways to connect — pick what fits your setup:

Switch between clusters without restarting. Each session tracks its own connection.

🔍 34 Built-in Kubernetes Tools

Live cluster tools (28) — pod/deployment/service inspection, event streams, multi-namespace discovery, rollout status, kubeconfig context switching, log retrieval with previous-container support, resource-graph topology, deployment-level investigation, namespace-wide health analysis, and safe write operations (delete, scale, restart, patch — all gated behind confirm=true).

AI analysis tools (6) — error analysis with RAG-backed similarity search, curated fix playbooks for 11 error categories, AI-generated runbooks, cluster health reports, post-incident summarization.

🤖 Agentic ReAct Investigation

Unlike single-shot "ask → answer" tools, Kubeastra runs a multi-step ReAct loop — reasoning through complex failures autonomously:

You: Why is checkout-service down?

Agent reasoning:
  ✓ find_workload — searching across all namespaces
  ✓ investigate_pod — found CrashLoopBackOff in checkout-svc-7d4f9b
  ✓ get_pods — checking Redis dependency → ConnectionRefused
  ✓ describe_pod — Redis pod Pending: unbound PVC

Root cause: PersistentVolumeClaim redis-data is unbound,
preventing Redis from starting, which cascades to checkout-service.

Each reasoning step is visible in real-time via the Investigation Trail — no black box. The agent answers listing questions in a single step and complex debugging in 2-3 steps, with a 90-second wall-clock safety timeout.

🔧 One-Click Fix Execution

When the AI identifies a fix, you get a Review & Execute button with the exact commands:

• Only write operations are suggested (delete pod, rollout restart, scale, patch) — never diagnostic commands you've already seen
• Slide-to-confirm safety gate before any command runs
• Button disappears after execution — no accidental re-runs
• When no safe automated fix exists (e.g., "update your Helm values"), the card shows Manual Steps Required with numbered instructions instead

👥 Collaborative Sessions

• Shareable URLs — click Share to copy a session link (/chat/:sessionId). Anyone with the URL sees the full investigation history — including the root-cause card, fix commands, and evidence.
• Investigation timeline — every ReAct step (tool call, thought, observation) renders as a real-time timeline, not simulated placeholders.
• Session not found — invalid or expired shared links show a clear message instead of a blank page.
• One-click post-mortems — generate a structured post-mortem (summary, timeline, root cause, impact, resolution, action items) from any investigation session via the API.

🗺️ Visual Debugging Canvas

The resource graph is an interactive investigation surface, not just a topology diagram:

• Health-aware nodes — pods, services, deployments, and ingresses colored by health status with pulsing red glow for degraded resources
• Click-to-inspect — click any node to see full metadata in a detail panel
• Hover tooltips — quick metadata preview (phase, restarts, IP, ports, replicas)
• Edge labels — see relationships at a glance: "routes →", "selects →", "manages →"
• MiniMap + zoom/pan — navigate large cluster topologies with ease

💬 Two Ways to Use It

🔌 Pluggable LLM Providers

Pick your LLM — Google Gemini (default, free tier available) or Ollama (fully local — your cluster data never leaves your network). OpenAI and Anthropic Claude adapters coming next.

🛡️ Safety First

• Read-only by default — all kubectl commands are validated before execution
• Explicit confirmation required for write operations (delete, scale, restart, patch) via slide-to-confirm
• Full audit logging of every command executed
• RBAC-aware — respects your existing Kubernetes permissions
• Input validation — namespace/name/label-selector safety checks prevent injection
• Session isolation — temp kubeconfig files are scoped per session with 0600 permissions, sanitized session IDs prevent path traversal, cryptographic session tokens prevent URL guessing
• Command allowlist — the execute endpoint only accepts specific kubectl write prefixes; everything else is rejected

🚀 Deploy Anywhere

• Local dev — docker-compose one-liner
• Kind demo cluster — make demo spins up a broken cluster so you can see the tool work in 60 seconds
• Production Helm chart — deploy into the same clusters it monitors
• SSH multi-cluster — query any remote kubeadm cluster without copying kubeconfigs

Quick Start

Option 1: Try the demo (60 seconds, no cluster needed)

Prerequisites: Docker Desktop, kind, kubectl

git clone https://github.com/kubeastra/KubeAstra.git
cd KubeAstra
make demo

Spins up a local kind cluster with pre-broken workloads (CrashLoop, OOM, ImagePull, stuck PVC) and launches the web UI.

Open http://localhost:3300 and ask "what's broken in the demo namespace?".

The demo generates its own kubeconfig automatically — it does not touch your host's current kubectl context. See demo/README.md for full prerequisites and troubleshooting.

Option 2: Run locally against your own cluster

Prerequisites: a running Kubernetes cluster with kubectl access, and a Google Gemini API key (free tier) or Ollama running locally.

# 1. Configure the backend
cp ui/backend/.env.example ui/backend/.env
#    → set GEMINI_API_KEY (or LLM_PROVIDER=ollama) in .env

# 2. Start via docker-compose (kubeconfig mounted read-only)
cd ui
docker compose up --build

# 3. Open http://localhost:3300

Option 3: Use via MCP (Cursor / Claude Desktop)

cd mcp
./setup.sh        # creates venv, installs deps, writes MCP config entry

Edit mcp/.env:

GEMINI_API_KEY=your-key-here          # or LLM_PROVIDER=ollama
ALLOWED_NAMESPACES=prod,staging,default

Restart your IDE — all 34 tools appear as MCP tools.

Option 4: Deploy to Kubernetes via Helm

helm upgrade --install kubeastra helm/kubeastra \
  --namespace kubeastra --create-namespace \
  --set backend.image.repository=ghcr.io/your-org/kubeastra-backend \
  --set frontend.image.repository=ghcr.io/your-org/kubeastra-frontend \
  --set secrets.geminiApiKey="YOUR_KEY" \
  --set secrets.kubeconfig="$(cat ~/.kube/config | base64 | tr -d '\n')"

How It Works

1. Connect your cluster — auto-detect your local kubeconfig, upload one, or enter SSH credentials. The connection is scoped to your session.
2. Ask a question — "Why are pods in checkout-service not starting?"
3. ReAct investigation — the LLM reasons step-by-step: picks a tool → executes it → observes the result → decides the next action. This continues autonomously (up to 6 iterations, 90-second timeout) until it has enough context to answer. Falls back to a keyword router if no LLM key is set.
4. Auto-discovery — if you don't specify a namespace, find_workload searches across all namespaces. Large clusters (thousands of pods) are handled efficiently via text-format parsing instead of JSON.
5. Live investigation — executes read-only kubectl commands against your cluster. Cluster connection flags are injected per-session automatically.
6. AI synthesis — returns a severity-rated root-cause card with metrics, evidence, and either one-click fix commands or manual steps.
7. Persistence — every message, tool call, and result saved to SQLite so you can pick up where you left off.
8. Share and collaborate — share the session URL with your team. They see the full investigation including the root-cause card, fix commands, and evidence.

Example Interactions

Quick listing — answered in one tool call, ~5 seconds:

You: what pods are in the jenkins namespace?
Astra: Here are the pods in the jenkins namespace.
       ┌─────────────────────────┬──────────┬───────┬──────────┐
       │ Name                    │ Status   │ Ready │ Restarts │
       ├─────────────────────────┼──────────┼───────┼──────────┤
       │ jenkins-0               │ Running  │ 2/2   │ 1        │
       │ avatar-agent-1s7k7      │ Pending  │ 0/0   │ 0        │
       └─────────────────────────┴──────────┴───────┴──────────┘

Deep investigation — multi-step ReAct, ~15 seconds:

You: why is mongo arbiter pod in crashloop?

Investigation Trail: 3/3 tools
  ✓ kubectl → pod status retrieved
  ✓ events  → events scanned
  ✓ ai      → analysis complete

┌─ CrashLoopBackOff ─────────────────────── CRITICAL ─┐
│ mongodb-arbiter-0 · infrastructure                   │
│                                                      │
│ The MongoDB arbiter pod is failing to start because  │
│ the designated primary host (mongodb-0) is not       │
│ available. The arbiter's setup process times out.    │
│                                                      │
│ ┌──────────┐ ┌──────────────────┐ ┌───────┐        │
│ │   360    │ │ CrashLoopBackOff │ │ False │        │
│ │ RESTARTS │ │      STATUS      │ │ READY │        │
│ └──────────┘ └──────────────────┘ └───────┘        │
│                                                      │
│ [Review & Execute Fix]                               │
└──────────────────────────────────────────────────────┘

Fix command: kubectl delete pod mongodb-arbiter-0 -n infrastructure

Configuration

All settings are read from environment variables (or .env):

Repository Layout

kubeastra/
├── ui/
│   ├── frontend/                # Next.js chat UI
│   │   ├── app/chat/            # Chat page + /chat/:sessionId share routes
│   │   │   ├── page-client.tsx  # Main chat component (ReAct rendering, result cards)
│   │   │   └── [sessionId]/     # Shareable session page
│   │   └── components/          # ClusterConnect, ResourceGraph, RootCauseCard, etc.
│   ├── backend/                 # FastAPI app + SQLite persistence
│   │   ├── routers/
│   │   │   ├── chat.py          # Chat flow, tool dispatch, fix execution
│   │   │   ├── cluster.py       # Cluster connection management (4 modes)
│   │   │   └── sessions.py      # History, SSH targets, post-mortem API
│   │   ├── react.py             # ReAct loop orchestrator
│   │   └── db.py                # SQLite with cluster_connections table
│   └── docker-compose.yml
├── mcp/
│   ├── mcp_server/              # MCP server (stdio + HTTP transports)
│   ├── k8s/                     # kubectl wrappers, SSH runner, validators
│   ├── ai_tools/                # Error analysis, fix playbooks, runbooks
│   ├── services/                # LLM providers, Weaviate, embeddings
│   └── config/settings.py
├── helm/kubeastra/              # Helm chart
├── demo/                        # Kind + broken workloads for `make demo`
└── docs/                        # Public documentation

Roadmap

• Gemini + Ollama (local) LLM support
• Demo mode with kind cluster
• Approval flow for write operations
• Deployment-level investigation (investigate_workload)
• Namespace-wide health analysis (analyze_namespace)
• Agentic ReAct investigation loop (multi-step tool calling)
• Shareable session URLs + investigation timeline
• Auto-generated post-mortems from investigation sessions
• Visual debugging canvas (interactive resource graph with health glow, click-to-inspect, tooltips, MiniMap)
• Multi-modal cluster connection (auto-detect, kubeconfig upload, SSH, in-cluster)
• One-click fix execution with safety guards and slide-to-confirm
• Manual steps fallback when no automated fix is available
• Large cluster support (text-format parsing for all-namespaces queries)
• Session security hardening (path traversal prevention, cryptographic session IDs, command allowlists)
• Team playbook engine — investigation templates that codify debugging patterns
• Alert-driven auto-investigation (PagerDuty / OpsGenie / Alertmanager webhooks)
• OpenAI + Anthropic Claude adapters
• Prometheus / Loki / Tempo observability integrations
• "What changed?" view — recent deployments, ConfigMap/Secret mutations
• Real-time collaborative sessions (WebSocket sync + presence indicators)
• Slack bot integration (alert → investigation → findings in channel)
• CNCF Sandbox submission

Contributing

Contributions are welcome — especially the items at the top of the roadmap. See CONTRIBUTING.md for local setup, project layout, and how to add a new tool, and CODE_OF_CONDUCT.md for community guidelines.

Looking for a starter task? Check the good first issue label.

License

Apache 2.0 — see LICENSE for details.