Your models, your choice: plug in Claude, Codex, Ollama, or any OpenAI-compatible endpoint — or go fully local. The model layer is a choice, not a constraint.
Four interchangeable runtimes
| Runtime | Models | Where inference runs |
|---|---|---|
| Claude (default) | Anthropic models via Claude Code | Anthropic API |
| Codex | OpenAI models via Codex | OpenAI API |
| Ollama | Any Ollama model | Your machine or your LAN |
| OpenAI-compatible | vLLM, Together.ai, and similar | Any endpoint you configure |
From the companion's point of view, runtimes are interchangeable: same memory, same tools (files, shell, web, integrations), same skills, same permission gating, same collaboration features. Switching provider doesn't reset who the companion is.
Reuse the subscriptions you already pay for
No separate API billing required. tamag0 signs in to the accounts you already have:
- Claude — sign in with your Claude account (OAuth): usage runs on your Claude subscription. An Anthropic API key works too, if you prefer metered billing.
- Codex — sign in with ChatGPT: usage runs on your ChatGPT Pro or Plus subscription quota, not OpenAI API credits.
If your team already pays for Claude and ChatGPT, your companions run on those plans from day one — zero additional inference budget to negotiate before trying tamag0.
Fully local when you want it
With Ollama or a self-hosted OpenAI-compatible endpoint, inference never leaves your infrastructure — for companies that need complete control. Local endpoints can be keyless; cloud endpoints keep their keys in the OS keychain.
Per-agent choice and automatic failover
- Each companion can have its own runtime and model — a coding companion on Claude, a triage companion on a local model.
- A runtime priority order defines fallbacks: if a provider is unavailable (CLI not authenticated, endpoint down, rate-limited), execution falls back to the next available one instead of failing.
- Provider availability is resolved dynamically from what is actually installed and authenticated — the app never forces a default you don't have.
- Adding an Ollama provider auto-discovers its installed models; a company can also declare custom providers and models shared across its companions.
Right-sized execution
Model and effort are selected per task: routine work runs on lighter, faster settings; deep architecture or debugging work gets the strongest model and higher reasoning effort. Cost stays proportional to task complexity — and both onboarding installs (Claude and Codex) are set up out of the box so the choice stays open.
Related
- Performance — token compression and efficiency
- Security — key storage and isolation