One endpoint for every agent

Aggregate every model you can reach - your own servers, Anthropic, AWS Bedrock, Venice - behind one OpenAI-compatible endpoint, and serve it to Hermes, OpenCode, OpenClaw or any client that speaks the API.

Most agent frameworks ship as a program that wants an OpenAI-compatible endpoint and a model name. This platform is that endpoint - for every model you have access to, behind one key, with the character, tools and memory of a persona already attached.

Endpoints, then harnesses, then personas

An endpoint is a provider connection: a server on your own hardware that speaks the OpenAI protocol, Anthropic directly, Amazon Bedrock on your own AWS account, or Venice.ai with its hundred-plus models on one key. Save it, test it, and the platform asks the server what it serves and how much context it has loaded. A harness sits on an endpoint and fixes the model, the system prompt, the tools and the budgets. A persona sits on a harness and adds the voice, the skills and what it remembers. Every organization gets its own clone of each, on its own keys.

What an endpoint knows about its provider: every Venice model with context window, max output and capabilities, so the models your agents see are described before they are served.

Served to whatever you run

Point Hermes Agent, OpenCode, OpenClaw, Open WebUI, LibreChat or a plain OpenAI SDK at the personas API and list the models: what comes back is your roster, one entry per persona, each carrying its context window so the client can size its own history without a config entry. Ask for one by name and the model behind it answers with that persona's tools and memory. Repoint the harness at a different endpoint and every client keeps working, because they asked for the persona, not the model. Your Jarvis is there too, on a surface of its own: ask for jarvis, jarvis-fast or jarvis-deep and the agent from the voice room answers, with its memory, its skills and your work.

Built for agents, not only chat

An agent framework brings its own tools - a terminal, files, a browser, a skill library. Switch client tools on for a key and the persona sees them beside its own: it calls the agent's shell when the job is on the agent's machine, and the platform's web search when the answer is on the web. The call comes back as a real tool call the framework runs, never as a sentence describing one. A stateless client that replays its whole history every turn is recognized and continued server-side rather than re-read from the top, and a history that outgrows the model is folded to fit.

Guard rails the client does not have to build

Keys are minted per surface and carry only the holder's own visibility. Every plan has per-minute ceilings on requests, tokens and how fast one conversation may turn, so a looping agent is stopped in seconds. A model that starts repeating itself is cut off, a provider's per-request limits on tools and images are honoured before the request leaves, and usage is metered per endpoint, per harness and per persona so you can see which client spent what.

All features Talk to us

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.