Give your local model its own Linux CLI computer. It gets a real isolated container it can run shell commands in, write and read files, install packages, manage processes, and run background tasks - all without touching your host system.
This plugin is still in beta version, it may be not that stable. This plugin is highly-inspired by the Claude's computer.
You'll need a container runtime. Install one based on your OS and make sure it's running before using the plugin.
Windows & macOS - Docker Desktop is the easiest option. Just install it and keep it open in the background.
Linux - Podman works great and doesn't require root. After installing, run these once:
# Ubuntu/Debian sudo apt install uidmap slirp4netns sudo usermod --add-subuids 100000-165535 --add-subgids 100000-165535 $USER
Then log out and back in. The plugin handles everything else automatically.
The easiest way:
Or from source:
On first use it pulls the Ubuntu image and sets up the container - this takes a minute. After that it's instant.
Click the gear icon next to the plugin to open settings.
Internet Access - off by default. Turn this on if you want the model to install packages with apt-get, pip, npm, etc. If you change this after the container is already running, just tell the model "rebuild the computer" and it will recreate with the new setting.
Persistence Mode - persistent by default, meaning files and installed packages survive across sessions. Switch to ephemeral if you want a clean slate every time LM Studio opens.
Base Image - Ubuntu 24.04 is recommended. Alpine is available if you want something ultra-lightweight, though some packages behave differently with its musl libc.
| Setting | Default | Range |
|---|---|---|
| CPU cores | 2 | 0–8 (0 = unlimited) |
| Memory | 1024 MB | 256–8192 MB |
| Disk | 4096 MB | 512–32768 MB |
Auto-Install Packages - choose what gets pre-installed when the container is first created. Options range from nothing to a full set including Python, Node.js, build tools, and networking utilities. Requires Internet Access to be on.
Shared Folder - mount a folder from your computer into the container at /mnt/shared. Useful for giving the model access to your files without going through the upload tool every time.
Port Forwards - expose ports from the container to your host. For example 8080:80 lets you open localhost:8080 in your browser to see whatever the model is running inside.
Command Timeout - how long a single command can run before it gets killed. Default is 30 seconds. Bump this up if you're doing long installs or builds.
The model has access to these tools automatically - no need to ask it to use them, it figures out what's needed on its own.
| Tool | Description |
|---|---|
| Execute | Run shell commands in a persistent session - cd, export, source, nvm use, conda activate all carry over between calls, just like a real terminal |
| ExecuteBackground | Start a long-running process in the background (servers, watchers, builds) and get a handle to check on it later |
| ReadProcessLogs | Read buffered stdout/stderr from a background process |
| Tool | Description |
|---|---|
| WriteFile | Create or overwrite a complete file |
| ReadFile | Read a file, optionally a specific line range to keep context short |
| StrReplace | Replace an exact unique string in a file - the preferred way to edit code without rewriting the whole thing |
| InsertLines | Insert lines at a specific position in a file |
| ListDirectory | Browse the filesystem |
| UploadFile | Copy a file from your computer into the container |
| DownloadFile | Copy a file from the container to your computer |
| Tool | Preserves data | Speed | Use when |
|---|---|---|---|
| ComputerStatus | - | Instant | Check system info, resource usage, running processes |
| ResetShell | V | Instant | Shell env is broken, want clean variables without touching files |
| RestartComputer | V | Fast | Container is sluggish, runaway process, want a fresh start |
| RebuildComputer | X | Slow | Network setting changed, environment is fully broken |
Run some code
"Write a Python script that generates a Fibonacci sequence up to 1000 and save it as fib.py"
Edit a file surgically
"Fix the bug in the handle_request function in server.py"
The model reads just that section of the file and replaces only the broken part instead of rewriting everything.
Set up an environment
"Set up a Node.js project with Express, create a basic REST API, and start the server"
Run a web server you can actually open
Set Port Forwards to 8080:8080 in settings, then:
"Build a simple dashboard and serve it on port 8080"
Work with your files Attach a CSV and say:
"Analyse this data and give me a summary with the key trends"
"No container found" error on startup
Your runtime isn't running. On Windows and Mac, make sure Docker Desktop is open. On Linux, check that docker or podman is installed.
Internet doesn't work inside the container Check that Internet Access is set to On in settings. If the container was already created before you toggled it, tell the model:
"Rebuild the computer"
It will recreate the container with internet enabled.
Container keeps failing on Linux Make sure you ran the setup commands from the requirements section. Then clear the old container and let it recreate:
Commands are timing out Increase Command Timeout in settings. The default 30 seconds isn't always enough for large package installs or slow builds.
Files disappeared after reopening LM Studio Check that Persistence Mode is set to Persistent. Ephemeral mode wipes the container on close - that's expected.
The shell session is persistent within a conversation - cd, export, nvm use, conda activate all carry over between commands, just like a real terminal.
When something goes wrong, every tool returns a short error and an actionable hint telling the model exactly what to do next - no wasted tool calls investigating the failure.
The container is fully isolated and can't access anything on your host unless you configure a shared folder or port forward.
The disk limit is only hard-enforced on XFS filesystems. On ext4 (most Linux installs) usage is tracked and reported correctly but not enforced at the OS level.
Changing the base image only takes effect on a fresh container - tell the model to rebuild after switching.
MIT
lms install khtsly/computer
git clone https://github.com/khtsly/lms-plugin-computer
cd lms-plugin-computer
bun run dev # or: npm run dev
podman rm -f lms-computer-main
Forked from khtsly/computer