fleet

Name: fleet
Author: sinolion

作者：sinolion版本 0.6.2i★ –i在你的电脑上运行

注册名称io.github.sinolion/fleet

安装

概览

作者提供的说明（英文原文）

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 其他

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
注册中心没有列出需要填写的密钥。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

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

在终端中运行这条命令

claude mcp add fleet -- uvx agents-fleet mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 sinolion 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

星标数多于此分类中其他 32455 个服务器的 0%
发布到注册中心的时间比此分类中其他 32455 个服务器的 81% 更新
基础检查结果等于或优于此分类中其他 32455 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

fleet 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.6.2）于8天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Spec Driven Development, Remote Desktop Commander, SSTracker free youth training

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者：formulahendry

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

在你的电脑上运行★ 440MIT14个月前更新

安装

Remote Desktop Commander

作者：desktop-commander

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

在云端运行★ 211昨天更新

安装

SSTracker free youth training

作者：sstracker.app

Free youth sports drills, practice plans and age-group answers for parents and coaches.

在云端运行今天更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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

只显示了开头部分；其余内容请在仓库中阅读