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Getting Started

Connect an LLM

Route model tool calls into an mvm sandbox with explicit boundaries.

An LLM should not execute generated code directly on the host. Route tool calls through a sandbox, validate the request, apply a timeout, and redact output before returning it to the model.

Minimal shape

model tool call
  -> app validates request
  -> mvm sandbox command
  -> app redacts stdout/stderr
  -> model receives bounded result

Minimal wrappers

Python
TypeScript

import json
import subprocess


def run_code(code: str) -> dict:
    if len(code) > 20_000:
        raise ValueError("tool input too large")

    proc = subprocess.run(
        ["mvmctl", "run", "--timeout", "10", "--", "python", "-c", code],
        check=False,
        text=True,
        capture_output=True,
    )
    return {
        "exit_code": proc.returncode,
        "stdout": proc.stdout[-8000:],
        "stderr": proc.stderr[-8000:],
    }


print(json.dumps(run_code("print(2 + 2)")))

import { spawnSync } from "node:child_process";

function runCode(code: string) {
  if (code.length > 20_000) {
    throw new Error("tool input too large");
  }

  const proc = spawnSync("mvmctl", ["run", "--timeout", "10", "--", "node", "-e", code], {
    encoding: "utf8",
  });

  return {
    exit_code: proc.status ?? 1,
    stdout: proc.stdout.slice(-8000),
    stderr: proc.stderr.slice(-8000),
  };
}

console.log(JSON.stringify(runCode("console.log(2 + 2)")));

For repeated calls, build and boot a named sandbox instead:

mvmctl init ./llm-tool --preset python
mvmctl machine build --flake ./llm-tool
mvmctl machine run --flake ./llm-tool --name llm-tool -d
mvmctl machine exec llm-tool -- python /work/tool.py

Security checklist

Validate the tool schema before invoking mvmctl.
Set a timeout.
Default to no network access unless the tool needs a named endpoint.
Do not pass secrets through command-line args.
Treat stdout and stderr as untrusted model-visible data.
Store receipts or audit identifiers with the model trace when available.

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