PlatformOps AI

PlatformOps AI is an open-source, model-agnostic operations platform. The first release is a read-only Kubernetes MCP server that exposes structured operational evidence through safe tools.

Kubernetes is the first reference integration, not the product boundary. The architecture is designed to grow into observability, CI/CD, GitOps, source control, and approval-gated remediation while keeping credentials and infrastructure control outside the model.

Quick Start

Install the latest release from PyPI:

pip install platformops-ai

Then run:

platformops k8s nodes
platformops k8s investigate --namespace jenkins --allowed-namespaces jenkins

Local Development

python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
pytest

Run the Kubernetes MCP server with deterministic fake data:

PLATFORMOPS_K8S_PROVIDER=fake platformops-mcp-k8s

Use the direct CLI against a real Kubernetes context:

platformops k8s nodes
platformops k8s namespaces --allowed-namespaces default,kube-system
platformops k8s pods --namespace kube-system --allowed-namespaces default,kube-system

Use JSON output when you want the full evidence envelope:

platformops --output json k8s pods --namespace kube-system --allowed-namespaces kube-system

Run a more useful namespace investigation:

platformops k8s investigate \
  --namespace jenkins \
  --allowed-namespaces jenkins \
  --tail-lines 80

This collects pod status, namespace events, and bounded log excerpts for unhealthy pods.

For real Kubernetes API access, configure kubeconfig or in-cluster credentials:

PLATFORMOPS_K8S_PROVIDER=api \
PLATFORMOPS_K8S_ALLOWED_NAMESPACES=default \
platformops-mcp-k8s

Initial MCP Tools

• get_nodes()
• list_namespaces()
• list_pods(namespace=None)
• get_pod(namespace, name)
• list_events(namespace, pod_name=None)
• get_pod_logs(namespace, name, container=None, tail_lines=100)
• investigate_namespace(namespace, tail_lines=50)

All tools return structured evidence envelopes. The MCP server does not require an LLM API key.

Documentation

• Documentation index
• Architecture overview
• Roadmap
• ADR index
• Security policy
• Original open-source architecture and homelab validation plan
• Original learning project roadmap