Looking for a Tabby alternative? Meet Command Fleet

Tabby is a great self-hosted, open-source assistant for teams that want control. When you want to go from completions to whole-task agents — with a board, review, and deploys — Command Fleet keeps you local-first and adds the orchestration.

Command Fleet is a local-first, agent-agnostic AI coding agent orchestrator. Where Tabby is best known as an AI editor, Command Fleet runs Claude Code, Codex, and Gemini across a whole portfolio of projects — on a Kanban board, in isolated git worktrees, with a review gate and built-in deploys to six platforms. This guide is an honest, Tabby-versus-Command-Fleet comparison: what Tabby is genuinely great at, and the specific places a portfolio-scale, autonomous AI coding orchestrator goes further.

What Tabby does well

Tabby is self-hosted and open-source, giving privacy-conscious teams full control over where their completion model runs.

None of that goes away by choosing Command Fleet — and for hands-on work in a single project, Tabby may well stay open in another window. The point of this comparison runs the other direction: the specific capabilities you reach for once AI coding becomes a portfolio of projects to run rather than one file to edit.

When to consider a Tabby alternative

Tabby is a joy when you are heads-down in a single file or repository, and for hands-on, line-by-line coding it is hard to beat. You start weighing a Tabby alternative when the work changes shape: you are juggling several projects at once, you would rather hand a whole task to an AI coding agent and review the result than steer every keystroke, and you want to choose which model does the work. That is the gap Command Fleet fills — it sits one level up from the editor, turning agents into a fleet you direct from a board while you keep your editor for the hands-on parts.

Local-first by design

Command Fleet is local-first: your projects, data, and API keys never leave your machine, keys live in your operating system credential vault, and a per-project secrets vault is never included in any prompt. Your code — and your clients’ code — stays on your computer rather than uploaded to someone else’s servers, which turns confidentiality and NDAs into a one-sentence answer.

Local-first does not mean manual: an orchestrator on your own machine can plan a build, run agents in parallel, review, retry, and merge exactly like a cloud agent. And because everything is a git folder on disk, there is no lock-in — stop paying and you keep all of it.

A Kanban board, not a chat window

Agents are workers you assign cards to, and the Kanban board shows what is queued, running, waiting on your review, and merged across every project — so progress is a picture you read at a glance instead of a chat transcript you have to re-read. A board is a state machine; a chat is a transcript, and for running real work you want the picture.

A cross-project review queue means nothing falls through the cracks no matter how many agents run at once, and the home dashboard rolls the whole portfolio up so "what is the state of everything?" is one screen, not twelve.

An autonomous build loop

Describe an app to the workspace manager and the autonomous build loop scaffolds it from a stack pack, plans a dependency-aware task graph, runs the ready tasks in parallel across isolated worktrees, retries failures, merges finished branches, and can deploy. It is the difference between an assistant that edits the file you are in and an orchestrator that turns one sentence into a planned, built, reviewed, and shippable app.

Crucially, autonomy is not abdication: you set the verify gate and the retry cap, anything the loop cannot resolve lands in your review queue with its full history, and deploys wait for explicit credentials. The loop does the typing and the plumbing; the judgment calls stay yours.

Preview and deploy to six platforms, built in

Ship to Firebase, Vercel, Netlify, Cloudflare, Supabase, or Fly from the same app, with preview and deploy sharing one manifest so what you preview is exactly what goes live. Credential-gated deploys never fire with a missing secret, and a deploy you can reproduce is a deploy you can roll back.

Because the whole pipeline — scaffold, build, review, deploy — lives in one place, you go from prompt to production without stitching together separate tools for each stage. That end-to-end coverage is the part most AI coding tools stop short of.

Running a portfolio of AI coding agents

Most AI coding tools, Tabby included, are organized around one thing at a time — one file, one repo, one chat, one task. Command Fleet is organized around many. Each project gets its own Kanban board with To do, In progress, In review, and Done columns; a home dashboard rolls up how many workspaces, projects, and tasks you have, which AI agents are connected, your tasks-by-status, and what is waiting on review across the entire portfolio. You can even fan a single task out to two different agents, compare the diffs, and merge the better one. For anyone running more than one product, that portfolio view is the difference between feeling on top of the work and drowning in browser tabs — and it is the layer an editor or a single-task agent simply does not have.

Switching from Tabby to Command Fleet

1. Install Command Fleet and create a workspace — one per client or product line works well.
2. Add your projects by pointing Command Fleet at the same local git folders you already use, and set a setup script (such as pnpm install) so fresh worktrees build cleanly.
3. Connect your agents — your Claude Code, Codex, and Gemini subscriptions — then dispatch a first small task to see the in-app diff, the verify gate, and the one-click merge in action.

Because your projects are just git repositories on disk, there is nothing to export and nothing locked in: moving from Tabby is mostly a matter of opening the folders you already have and pressing Run.

Command Fleet vs Tabby at a glance

Who Command Fleet is for

Command Fleet tends to win over the same people who try Tabby and then realize they have outgrown working one project at a time: solo founders shipping a portfolio of apps who need real parallelism without losing the thread; agencies and freelancers who run a workspace per client and have to keep each client’s code confidential and cleanly separated; indie hackers who want an autonomous build loop on their own Claude, Codex, or Gemini subscriptions with no markup; and small teams who want isolated git worktrees, a review gate, and built-in deploys without standing up their own infrastructure. If any of those describe you, Command Fleet is well worth a look as a Tabby alternative.

Frequently asked questions

If Tabby is where you started, Command Fleet is where you go when AI coding becomes a fleet to run, not a file to edit.