Introduction

Contenox is a self-hostable runtime for building observable, deterministic AI workflows as explicit state machines.

Instead of wiring prompts together with ad-hoc Python glue, you define your AI behaviour as a task chain — a JSON graph of typed tasks, transitions, and tool calls. Every step is inspectable, replayable, and testable.

Three editions

Edition	Use case	Entry point
vibe CLI	Local AI agent on your machine	vibe run
Runtime API	Self-hosted REST backend for apps	Docker / go run
Enterprise (EE)	Multi-tenant, dashboard, RBAC	enterprise/

vibe is the flagship. This documentation focuses on it first. The runtime API and EE share the same chain / hook / task engine underneath.

How it works

User input
    │
    ▼
┌─────────────────────┐
│   Task Chain (JSON) │  ← you define this
│  task → task → …   │
└─────────────────────┘
    │
    ▼
Model (Ollama / OpenAI / vLLM / Gemini)
    │
    ├─ tool call? → Hook (local shell, remote API)
    │                    │
    └─ text reply ←──────┘

Each task has a handler (what it does), an optional LLM config (which model, which hooks), and a transition (where to go next). The chain engine drives the loop — the model doesn't.

Next steps

• Quickstart — install vibe and run your first chain in 5 minutes
• Core Concepts — chains, tasks, hooks, transitions explained
• Chains reference — build your own chains from scratch