fleet

Name: fleet
Author: sinolion

by sinolionversion 0.6.2i★ –iRuns on your computer

Registry nameio.github.sinolion/fleet

Install

Overview

Description from the maker (original English)

Every machine you have, for your agent: free GPUs, jobs, served models, software, over SSH.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "fleet": {
      "command": "uvx",
      "args": [
        "agents-fleet",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add fleet -- uvx agents-fleet mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "fleet": {
      "command": "uvx",
      "args": [
        "agents-fleet",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "fleet": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "agents-fleet",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account sinolion, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32476 servers in this category
Published to the registry more recently than 81% of the other 32476 servers in this category
Basic checks equal to or better than 100% of the other 32476 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is fleet safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.6.2) reached the registry 8 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, zanadir

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Spec Driven Development

by formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago

Install

Remote Desktop Commander

by desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated yesterday

Install

zanadir

by MustacheCase

Find what a repository's CI is missing, and which security checks cannot fail the build.

Runs on your computerUpdated today

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

fleet — let your coding agent use every machine you have

**Claude Code, Codex and Gemini see one machine: the one they run on.

fleet shows them all of yours — every GPU and what is running on it, the models being served, the software installed, and where your models and data live.**

No account, no cloud service, nothing installed on the machines you add, no keys handed out: fleet logs in exactly as you do, with the SSH you already use.

https://github.com/user-attachments/assets/d35e0200-c2a2-44aa-8f60-a3b6e4addfb7

The problem

Ask your agent to train a model and it starts on your laptop — while a 4090 sits idle across the room and a rented A100 bills you by the hour. It cannot use what it cannot see.

Your agent is stuck on one machine. It has no idea your other boxes exist.
Finding a free GPU is manual. SSH into five hosts, run nvidia-smi, compare in your head.
It cannot see what is running, or what is already there — whose job is on that card, which box already serves Llama 3, where the datasets live — so it downloads and installs things twice.

Get started: ask your agent

Paste this into Claude Code, Codex, Gemini CLI, or any agent that can run commands, on the machine you work from:

Install fleet from https://github.com/sinolion/fleet

Agents: the steps are in INSTALL.md.

That is the whole setup. fleet is installed once on this machine, and every supported agent you have here learns it — not only the one you asked. From here on you say what you want in plain words, with no commands to remember.

Then add your machines

Tell the agent how you reach a machine — the SSH command you already use is enough — or let it look in your ~/.ssh/config and on your tailnet for machines you can already reach:

You: add my GPU server, ssh ubuntu@10.0.0.7

Agent: Added as gpu-box: 2× RTX 4090, both idle, 46 GB free. It's ready to use.

Each one takes a sentence, whatever the machine is:

The machine	What you tell the agent
a server you can already SSH into	"add `ssh ubuntu@10.0.0.7`" — fleet logs in as you do, with your key
one you reach with a particular key file	"add `ssh -i ~/.ssh/my_key ubuntu@1.2.3.4`" — or a `Host` from your `~/.ssh/config`
a machine on your tailnet	"find my machines" — it lists your ssh config hosts and tailnet machines, and adds the ones you pick
a new rental (vast.ai, RunPod, AutoDL)	"add `ssh -p 40001 root@1.2.3.4`, it costs $1.89/hr" — with the key on your provider account
one that only takes a password	"add it" — fleet offers `ssh-copy-id`, you type the password once into ssh, and your key goes on it
one that will not let you in	"why can't you reach the NAS?" — `fleet doctor` names the one thing to fix

Nothing is installed on the machines you add: they only need SSH, on Linux, macOS or Windows. fleet probes each one for its GPUs, memory and disks, and keeps that current.

Then let the agent pick

You describe the work; the agent finds where it fits. It checks what each machine has *and* what is free on it right now, so it will not send a job to a busy card:

You: train train.py on whatever has a free 24 GB card

Agent: rtx4090 has 23.1 GB free and an idle GPU; a100-spot is free too but costs

$1.89/hr. Starting on rtx4090, logging to train.log.

You say	How the agent finds it
"what's free right now?"	every machine checked, the free ones listed
"find me a box with a 24 GB card"	matched by what machines have — NVIDIA or AMD, VRAM, cores, RAM — not by name
"run the tests on the Linux box"	the machine that runs Linux, results brought back
"what's costing me money?"	idle paid rentals flagged, with their hourly price
"who is using the GPUs?"	every job on every card — user, VRAM, how long, the command
"is Llama 3 already served somewhere?"	every model server's models, and the URL or ssh tunnel to reach it
"which box has torch 2.5 and CUDA 12?"	each machine's CUDA or ROCm, Python environments and package versions
"download the dataset where datasets go"	the folder that machine says holds datasets, and the room left there

Keys and passwords never go into the conversation, and anything irreversible waits for your yes.

Only install fleet where you use it. The machines you add need nothing but SSH. Install fleet on another machine only if you also run agents there; it works the same everywhere, with nothing to join or sync. There, fleet ls --discover lists the machines in your ssh config and on your tailnet; the ones you added by hand are in one file you can share with your dotfiles.

An app that cannot run commands?

Desktop apps and editors get fleet in one click; it runs as an MCP server:

App	Install
Claude Desktop	open `fleet.mcpb` from the latest release
Cursor
VS Code

Plus plugins for Claude Code, Codex, Copilot CLI and Gemini CLI, and OpenCode, Amp, Windsurf, Cline, Zed, Qwen Code, Goose, Kiro, Hermes — 35+ agents in all: [every agent →](docs/guides/agents.md)

Built to be safe

Nothing to install on your machines. fleet probes with one script over one SSH connection — Linux, macOS or Windows. A NAS or a fresh rental works as it is. The few files it does leave there are listed in What fleet changes.
No keys handed out, no access managed. fleet connects exactly as you do — your ssh config, your keys and agent, Tailscale SSH, your rental provider's key — and writes no keys anywhere. Who may reach what stays where you already decide it. When a machine refuses, fleet doctor tells you the fix; fleet never makes it.
No passwords. fleet never asks for one or stores one, and the agent never sees a credential. Secrets in a GPU job's command line are blanked before anyone sees them.
The agent asks, not guesses. It is told to ask which machine you mean, to leave irreversible commands to you, and not to start work on a GPU someone else is using.
Nothing phones home. Apart from installing and updating itself, fleet talks to your machines and nothing else: no account, no telemetry, no update check. Found a flaw? SECURITY.md says how to report it privately.

Prefer the command line?

Everything the agent does is a plain fleet command. To install fleet yourself, run this on the machine you work from (this is also what your agent runs when you ask it):

curl -LsSf https://raw.githubusercontent.com/sinolion/fleet/main/install.sh | sh

Windows: powershell -ExecutionPolicy ByPass -c "irm https://raw.githubusercontent.com/sinolion/fleet/main/install.ps1 | iex"

Then drive it yourself:

fleet ls                            # every machine, with what is free right now

fleet ls --tag cuda --tag vram-24g # by capability: NVIDIA, a card of 24 GB or more fleet ls --tag rocm # every AMD machine fleet top # live view, like htop for the whole fleet fleet show gpu-box # one machine in detail: jobs, software, models, folders fleet ps # every job on every GPU, and which GPUs are idle fleet models # every model being served, and how to reach it fleet edit gpu-box --path models=/data/models # where that machine keeps its models fleet ssh gpu-box -- nvidia-smi # run something there fleet add "ssh ubuntu@10.0.0.7" # add a machine fleet ls --discover # machines in your ssh config and on your tailnet fleet doctor nas # why a machine will not let you in, and the fix

Where a machine keeps its models, datasets and media is written on the machine itself, in machine.yaml (fleet edit NAME --path models=/data/models). Whoever looks at that machine reads the same answer, and the folder sizes and free space come with every probe.

Rentals from vast.ai, RunPod or AutoDL show their price (fleet edit a100 --cost 1.89), the fleet's burn rate, and an alert when a rental sits idle. On Tailscale, ZeroTier or WireGuard? fleet just needs an address it can route to.

How fleet compares

fleet is about the machines you already have. It complements tools that launch new ones.

	`ssh` + `nvidia-smi`	nvitop / gpustat	all-smi / gpuwho	SkyPilot / dstack	fleet
All your machines in one view	✗	✗ one machine	✓	✓ the ones it manages	✓
Built for coding agents (skill / MCP)	✗	✗	✗	partly	✓
Nothing installed on target machines	✓	✗	gpuwho ✓, all-smi ✗	✗	✓
Jobs, served models, software, data folders	✗	jobs, one machine	jobs	✗	✓
NVIDIA, AMD and Apple GPUs	NVIDIA	NVIDIA	all-smi ✓	✓	✓
Launches new cloud VMs	✗	✗	✗	✓	✗
History, alerts sent to you	✗	✗	all-smi ✓	partly	✗ alerts when asked

When to use something else. To rent GPUs on demand at the lowest price, use SkyPilot or dstack: they launch and stop machines, and fleet does not. For weeks of metrics and paging on a large cluster, use DCGM with Prometheus and Grafana. For a live dashboard of many accelerator kinds (Gaudi, NPUs), use all-smi. fleet is for the machines you already have, and for letting your agent use them.

Why not just Tailscale, Ansible, or an `~/.ssh/config`? Use them with fleet, not instead of it

Only the beginning is shown; read the rest in the repo

fleet

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

zanadir

README from the maker

fleet — let your coding agent use every machine you have

The problem

Get started: ask your agent

Then add your machines

Then let the agent pick

An app that cannot run commands?

Built to be safe

Prefer the command line?

How fleet compares