Metadata-Version: 2.5
Name: platformops-ai
Version: 0.2.0
Summary: Read-only, model-agnostic MCP tools for evidence-grounded platform operations.
Project-URL: Homepage, https://github.com/FAHID-KHAN/platformops-ai
Project-URL: Repository, https://github.com/FAHID-KHAN/platformops-ai
Project-URL: Issues, https://github.com/FAHID-KHAN/platformops-ai/issues
Author: PlatformOps AI Contributors
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: aiops,kubernetes,mcp,platform-engineering,sre
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: System :: Monitoring
Classifier: Topic :: System :: Systems Administration
Requires-Python: >=3.11
Requires-Dist: kubernetes>=30.0.0
Requires-Dist: mcp>=1.0.0
Provides-Extra: dev
Requires-Dist: pytest-asyncio>=0.23.0; extra == 'dev'
Requires-Dist: pytest>=8.0.0; extra == 'dev'
Provides-Extra: kubernetes
Provides-Extra: mcp
Description-Content-Type: text/markdown

# 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:

```bash
pip install platformops-ai
```

Then run:

```bash
platformops k8s nodes
platformops k8s investigate --namespace jenkins --allowed-namespaces jenkins
platformops diagnose k8s --namespace jenkins --allowed-namespaces jenkins
```

## Local Development

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

Run the Kubernetes MCP server with deterministic fake data:

```bash
PLATFORMOPS_K8S_PROVIDER=fake platformops-mcp-k8s
```

Use the direct CLI against a real Kubernetes context:

```bash
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:

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

Run a more useful namespace investigation:

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

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

Generate a deterministic diagnosis report:

```bash
platformops diagnose k8s \
  --namespace jenkins \
  --allowed-namespaces jenkins \
  --tail-lines 80
```

The diagnosis report summarizes status, findings, evidence, recommended next actions, and current limitations without using an LLM.

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

```bash
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)`
- `diagnose_namespace(namespace, tail_lines=80)`

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

## Documentation

- [Documentation index](docs/README.md)
- [Architecture overview](docs/architecture/overview.md)
- [Roadmap](docs/roadmap.md)
- [ADR index](docs/adr/README.md)
- [Security policy](SECURITY.md)
- [Original open-source architecture and homelab validation plan](docs/architecture/source/open-source-architecture-and-homelab-validation.md)
- [Original learning project roadmap](docs/project/agentic-platform-engineering-learning-project.md)
