Wire it into your stack

Documented, versioned surfaces with live reference pages you can test in the browser. A key per surface to name, switch off or delete, and per-minute ceilings that stop a looping agent.

An integration you cannot inspect is one you cannot trust. Every surface here is documented, versioned and testable from inside the app with your own credentials.

Reference pages you can actually use

Each surface ships an OpenAPI schema and a live reference page. Authorise with a real key, expand an endpoint, send the request, read the response - the same request your code will make, against your own data, before you commit to anything. There is a ReDoc rendering alongside it for reading rather than poking.

The personas API reference: how a key is minted, what it may reach, a quick start you can run as written, and the Open WebUI settings.

One key, one job

Keys are minted per surface. A key issued for personas is refused by the Jarvis or teams endpoint even when the person holding it is entitled to all three, so revoking one has a blast radius you can predict rather than discover.

Name them for what they are - the client, the script, the machine - and the list tells you when each was last used. Switch one off while you investigate something and switch it back on; the token survives. Delete it and it does not.

Rate limits that protect you from your own automation

Every plan carries per-minute ceilings on requests and tokens, plus a limit on how fast any single conversation may turn. That last one is the useful one: an agent that loops is the ordinary way a month's budget disappears in an afternoon, and it is caught in seconds rather than at the invoice.

Three surfaces, and why each exists

The personas API is chat with the colleagues you built: every persona is a model in the list, and any OpenAI client, from Open WebUI to an agent framework, can talk to it. The Jarvis API is your own agent, as one model in three speeds, carrying its memory of you, the skills it has learned and your meetings and missions. The teams API runs a whole multi-agent crew as one call, for work that needs several roles in conversation rather than one voice answering.

There is deliberately no fourth for customers. Raw model-configuration access stays with platform staff: it would give you nothing the personas surface does not, on the same models, minus the voice, memory and skills that make an endpoint worth naming.

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More of what it does

Jarvis, the expert at your fingertips

Ask Jarvis in plain words and it does the work in every module on your plan: reschedule a routine, copy a team, draft an article. It asks before anything changes, naming exactly what.

Jarvis remembers, learns and follows through

Jarvis remembers you, your goals and your decisions, learns after each conversation, and lists every change for you to undo. It keeps how you like things done and checks in when something waits on you.

Jarvis shows its work, and you act on it

While it talks, Jarvis puts what it found on screen: figures, charts, tables, maps, video, your day. Many cards take an answer too: tap a choice, tick a checklist, fill in a form.

Jarvis, where your people already are

Reach Jarvis on Slack, Teams, WhatsApp, Telegram, a phone or email, and run your home and workflows through it, each on your own account with limits you set. Webhooks work both ways.

Missions: long work, with a team and a swarm

Give Jarvis a goal too big for one answer and it works it in the background: a plan, a budget, a team and a swarm, and an independent check before it calls the work done.

The whole platform, on Jarvis's desk

Ask for the thing itself and Jarvis puts its page on the desk: an article, a chapter, a knowledge graph turning in 3D, a dashboard. Each card comes back as you left it.