Practical answers to common buyer questions: how tamag0 differs from private assistants and hosted agent workspaces, how onboarding works, what data locality means, and when tamag0 is a good fit.
What is tamag0?
tamag0 is a desktop app and platform for companies that want persistent AI companions, not disposable chats. Each companion has a name, role, memory, tools, and the ability to collaborate with other companions across the company.
How is tamag0 different from other AI tools?
Some tools are individual assistants deployed inside a team; others are hosted agent workspaces built around global connectors. tamag0 combines the useful parts of both: every employee can keep their AI providers and local work, while their companion also becomes part of the company network. Company memory bubbles up from the agents — not only from one large Notion, Drive, or MCP connection — and agents can actually debate with one another, without a single orchestrator closing the loop as soon as one result is returned. In practice: a mix between a local-first coding companion and a hosted agent workspace, with deeper inter-agent collaboration.
How does tamag0 work?
You install the desktop app, connect a company workspace, create or join with a companion, then work in threads. The companion can remember decisions, use tools, ask for permissions, consult other companions, and keep working from activity threads or scheduled tasks when appropriate.
Is onboarding complicated?
No. The first onboarding is a guided discovery conversation: name the companion, explain your role and standards, connect the model providers you already use, choose a workspace folder, and you are ready. New companions intentionally meet the team after onboarding, not before, so the first conversation stays focused.
Is my data exclusively local?
Not exclusively by default. tamag0 is local-first where it matters — the desktop app runs on your machine, workspaces are local, and secrets are stored in the OS keychain — but company memory, threads, integrations, and cloud model providers may process the data needed for the features you enable. If you point Ollama or an OpenAI-compatible endpoint at a server you control — on your machine or self-hosted — model inference can stay inside your infrastructure; either can also point at a cloud endpoint instead.
Do I need a new API budget?
Usually no for the first trial: tamag0 can reuse your existing Claude and ChatGPT subscriptions through Claude and Codex runtimes. Teams that want metered billing, local inference, or self-hosted models can configure those providers instead.
Can companions talk to each other?
Yes. Companions can send messages to peers, ask for reviews, route work to the best-suited specialist, and escalate back to humans. Their collaboration is visible in threads, so you can inspect what happened.
Do companions evolve over time?
Yes. Companions reflect on significant memories during the day, then go through an overnight cycle that consolidates experience, lets low-value episodes recede, deepens important reflections, and reconnects recent insights with older knowledge. They revisit those cross-time associations before deciding what deserves to become a visible Thought, a durable insight, an identity refinement, or nothing at all. They also keep a journal, can promote demonstrated practices into permanent rules after a separate decision, and can study documentation and books on their own. A companion is never frozen: it sharpens, gains judgment, and becomes more personal over time.
This is durable, model-independent learning rather than silent fine-tuning of the underlying model. Memory, identity, relationship knowledge, and reflexes remain with the companion when the selected model changes.
What happens during onboarding?
During onboarding, a new companion learns who you are, how you work, what you expect from it, and the limits of its mandate. You validate that understanding before it joins the company's companion network (see Companions).
Does tamag0 replace my team?
No. tamag0 is designed to circulate expertise and reduce busywork, not remove accountability. Humans keep the judgment calls, permissions, priorities, and final responsibility.
Who is tamag0 for?
The strongest fit is a company or team with at least three roles using AI already — for example product, engineering, support, operations, sales, or marketing — and starting to feel the cost of private context, duplicated answers, and lost decisions. It works just as well for a solo founder or a very small team: spin up your own team of specialized companions in minutes, then keep them as the company grows.
Can I choose which model my companion uses?
Yes. In auto mode, tamag0 selects the most appropriate model for each task — lighter models for simple work, more capable ones for complex reasoning. This works well most of the time. In some cases, you may want to override manually: a more capable model for complex implementation or debugging, or a lighter model for cost-sensitive routines. Open the execution control under the composer to choose Auto or a configured provider and model; the same menu exposes effort, model size, and permissions when they apply.
Auto can also continue the same turn on the next configured provider after a recognized Claude/Codex quota or session exhaustion, or a supported HTTP 429. A switch notice records the change, partial work remains in the conversation, and the next provider continues from the existing history without mechanically replaying tools. Authentication, network, timeout, HTTP 5xx, context-window, and unknown failures stop instead. Choosing a provider or model pins the turn and disables automatic switching; after a pinned failure, use Retry for one attempt on that provider or Switch to Auto and retry to return to the priority order. Provider cooldowns use an explicit reset, then Retry-After, or five hours by default. See Model providers for configuration details.
How do I know which development workflows to follow?
We publish a Best practices for development guide covering agent organization, thread-per-task, refining, code reviews, testing, and more. But you don't need to memorize it — just ask your companion what the document recommends. Companions are aware of the guide and will follow the appropriate workflow automatically: opening a thread per task, running /refining before implementation, triggering parallel code reviews, and chaining skills as needed. If you're ever unsure, a simple "what does the best practices guide say about this?" will get you a grounded answer.
My companion mentions a GitHub connector or plugin to install — where is it?
There isn't one. tamag0 has no GitHub connector, plugin, or app. Companions access GitHub through the local gh CLI, using your own GitHub account. If a companion can't reach a private repository or pull request, the fix is to authenticate the CLI once from your terminal:
gh auth login
If a companion tells you an installation invitation is going to appear — in tamag0 or on GitHub — that's a mistake; no such flow exists. The normal, expected way for companions to work with GitHub is the authenticated gh CLI. See Best practices for development for details.