kern

# Linux. Windows and macOS below.
curl -fsSL https://raw.githubusercontent.com/getkern/kern/main/install.sh | sh

---

What kern is

A rootless container runtime in one static binary, with no daemon. The same binary is the sandbox an agent's code runs in, called from Python, Node or any MCP client. kern calls a container a box.

• Real OCI images: pull, build, commit, push, save/load. A box starts in single-digit milliseconds.
• A sandbox for code your model wrote. It runs where it can't reach your files unless you hand them over, with no network unless you ask.
• No daemon, and root is never required. Six namespaces, a read-only or overlay root, a seccomp allowlist by default, cgroup v2 limits.
• Your docker-compose.yml, unchanged, or kern's own stack.toml.
• Limits without a sandbox. kern run puts the same caps on a plain process on the host. docs/RESOURCES.md
• The tools you expect: ps, logs, exec, stats, inspect, top, doctor.

Install

curl -fsSL https://raw.githubusercontent.com/getkern/kern/main/install.sh | sh

irm https://raw.githubusercontent.com/getkern/kern/main/install.ps1 | iex

brew install colima
colima start
colima ssh

Then, inside the VM:

curl -fsSL https://raw.githubusercontent.com/getkern/kern/main/install.sh | sh

Kern Sandbox goes in the VM too, with the code that calls it: pip install kern-sandbox there.

Then kern doctor checks the host and says what to fix. Ubuntu 23.10 and newer need one root step first: docs/INSTALL.md.

Quickstart

What it does	Command
A shell in a real OCI image	kern box dev --image alpine -it -- sh
A service, published on the host	kern box svc --image nginx:alpine -d -p 8080:80
Untrusted code, with the strict profile	kern box job --image python:3.12-slim --security-profile untrusted -- python3 -c "print('hi')"
What is running (--json too)	kern ps

--security-profile untrusted is the seccomp allowlist, --cap-drop ALL and --read-only in one flag. examples/ holds 94 runnable scripts, one per thing kern does.

Kern Sandbox: run an agent's code from Python or Node

In a terminal:

python3 -m venv .venv && . .venv/bin/activate

pip install -U kern-sandbox

Then in a Python file:

import kern_sandbox as kern

r = kern.run_code("print(sum(range(100)))")
print(r.stdout, r.fault)   # 4950  None

Your code runs in a container of its own, for one call or for a whole session, and a timeout or an out-of-memory comes back as a typed fault. On Linux, pip brings kern with it, and so does npm install kern-sandbox for Node.

These three and three more, ready to copy: the Python SDK, the same for Node.

MCP server for Claude Code, Cursor, Claude Desktop and LM Studio. The same block goes in claude_desktop_config.json, in Cursor's or LM Studio's mcp.json, or in .mcp.json at your project root for Claude Code:

{
  "mcpServers": {
    "kern": { "command": "uvx", "args": ["--from", "kern-sandbox", "kern-mcp"] }
  }
}

Every option: docs/MCP.md.

Run a whole stack: your docker-compose.yml, unchanged

# stack.toml - one table per service, keys spelled like the `kern box` flags
[box.cache]
image = "redis:7-alpine"

[box.web]
image      = "nginx:alpine"
ports      = ["8080:80"]
depends_on = ["cache"]

kern compose stack.toml up            # start it (or point it at your compose.yaml)
kern compose stack.toml ps            # what is running, and what each service publishes
kern compose stack.toml port web 80   # the host address serving a port

Each service gets its own network namespace and they reach each other by name. docs/DOCKER-COMPAT.md

Speed and footprint

one isolated /bin/true	kern is
one container, against docker run --rm	80x faster
200 at once, against docker run --rm	130x faster
one container, against rootless runc	3.6x faster