Great AI agents do their best work as a team.

How is a Room different from several terminals?

Several terminals are useful when you want to run independent tasks yourself. A Room helps when the tasks belong to one piece of work: Orcheri carries the assignment, handoffs, decisions and results between named teammates, and a lead can coordinate review and revision before giving you one answer.

You keep the tools you already use. What changes is the work between them: you do not have to copy each finding to the next teammate or reconstruct the job tomorrow. What teammates share explains the boundary; their private provider sessions stay separate.

This is coordination, not a promise of parallel writes to one checkout. A project Room uses one folder, and Orcheri admits work that can change that folder one piece at a time within the same profile. It does not create a fleet of isolated worktrees or protect the folder from your own editor. Choose the project deliberately.

How does Orcheri compare with other kinds of AI tools?

Many good tools sit near Orcheri, and the right one depends on the job. These are the honest differences in kind, not a ranking.

• An agent inside your editor or terminal. Usually one agent from one maker at your side, though some tools also run their own subagents or several sessions. Orcheri keeps that agent as it is, native subagents included, and gives it teammates from other makers, a shared Room record and a lead that coordinates them.
• A multi-model chat app. Several models answer the same prompt side by side. In Orcheri each teammate is a full agent runtime with its own tools, files, permissions and session, and the lead turns their work into one answer rather than a column of replies.
• A parallel-agent manager. Many tools run several agents at once in separate worktrees or cloud sandboxes and leave the merging to you. Orcheri is about one piece of work moving between teammates — build, review, revise — with one folder and one record.
• An agent framework or workflow builder. These help developers write their own agents, prompts and pipelines. Orcheri uses no framework of its own between a model and its runtime: it runs finished agents you already have, through their official interfaces.
• An autonomous "AI company". Some products promise agents that run a business on their own. Orcheri keeps you as the Owner: work is bounded, Stop ends the work Orcheri is running, and the decisions that are yours come back to you.

Different tools, different strengths

Every AI agent brings its own models, its own habits and its own tools. One is quick to plan, another is thorough in the code, a third reads a finished piece of work with fresh eyes. People have always done their best work in teams for the same reason: the one who reviews did not write it, and the one who writes can hand over what they are not best placed to do.

Orcheri lets the AI agents you already use work that way. Claude Code, Codex, Cursor, Antigravity, OpenCode and GitHub Copilot CLI sit in one Room as named teammates, each with a job, each running as itself, with everything its own runtime already has.

A Room gives the team a place to work

A Room is where a project, a client or an idea lives. It holds the team, its conversations, a shared memory every teammate reads, the decisions you have settled, the files you shared and the results the team produced. Open it tomorrow or next week and the Room shows where you left off.

Teammates have names and jobs you chose — a reviewer, a builder, an architect, a researcher — because a colleague is someone with a role, not a session number. The same tool can join twice for two different jobs.

A lead runs the work

Choose a teammate that Orcheri shows as able to lead — Claude Code, Codex, Antigravity, OpenCode and GitHub Copilot CLI support this role, and so do compatible Cursor installations — and say what you want once. The lead decides who works and in what order, hands the work over inside the Room, reads what comes back, sends it for review and back for revision, and then writes the one answer you read. Every handoff behind that answer is kept, one click away, so you can see how it was reached.

In a Room with a project folder, a result the project contradicts goes back to the team instead of reaching you as finished. When work is finished, the answer says what was produced, how to open or run it, and what was checked.

You direct the team

You stay the Owner. The lead brings you the choices that are yours — a direction, a trade-off — with the options laid out and its recommendation. One autonomy setting decides how much the team does before asking: Ask me first, Work, ask when it matters or Keep going. Stop is there at any moment for the work Orcheri is running; work a tool has already handed to another app or service may carry on, as Trust and privacy explains.

Work that lasts

If Orcheri closes mid-task, the Room says where the work stopped and offers to continue it. The team keeps its memory and your decisions from one conversation to the next, so nobody starts from zero.

Orcheri is the room, not another voice in it. It is not a model, a chatbot or a router that picks one for you. It runs the AI agents you already have, under your own accounts, and owns what sits between them: the context, the handoffs, the decisions and the continuity.

Where it is today

Orcheri runs on Windows 11. Claude Code, Codex, Cursor, Google Antigravity, OpenCode and GitHub Copilot CLI join a Room today, through their official interfaces, under the accounts you already have; OpenAI's models take their seat through Codex. Teams in Orcheri plan, build, review and revise real work now, in code and in research, writing and planning.

The Private Alpha is free. Access is approved a group at a time, and the people in it are design partners: they use Orcheri on work they care about and tell us what to make better next.

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