HyperBox

An MCP server that runs LLM-generated code in a disposable container, so your agent can test its own work before it touches your project.

Your agent writes code and wants to run it. By default that happens on your machine, against your files, with your credentials. Usually fine. Occasionally it is rm -rf, a global install that breaks another project, or a script that quietly talks to production.

HyperBox gives the agent somewhere else to run it:

create_sandbox()  →  run(code)  →  run(fixed code)  →  destroy_sandbox()

The agent gets real stdout, stderr and exit codes, so it can fix its code and try again somewhere that cannot hurt you — and only then touch your project.

What your agent gets

Four tools, and nothing else:

Plus a hyperbox://capabilities resource publishing the exact limits, so an agent can read them instead of discovering them by failing.

Five languages — python, javascript, bash, go and java — each on an official tagged image.

Declare dependencies at creation. packages=["requests"] installs while the sandbox may still reach the network, which is then cut off for good. run(libraries=[...]) still works and returns a deprecation field, but it has to reopen the network on an already-sealed sandbox.

Within one sandbox the filesystem and installed packages persist between runs; variables do not, because each run is a fresh process. Write what you need to keep to /work.

What is enforced

Set by the server, not negotiable by the model, and read back off the real container after creation — so a sandbox is never described as limited when it is not:

Declared dependencies are the one network exception: they install in a separate step that reattaches the network, runs a no-op program with the package list, and detaches again before your code runs.

Built on

• FastMCP — the MCP server layer (stdio, JSON-RPC).
• llm-sandbox — container session management, behind a Runtime protocol so the execution backend stays replaceable.
• Docker or Podman — whichever you have running. Both are supported and both pass the full acceptance suite.

Two runtime dependencies, no compiled extensions, one py3-none-any wheel for every platform.

Install

pip install hyperbox-mcp

Or, to get an isolated interpreter and a stable executable path — which is what an MCP client needs:

uv tool install hyperbox-mcp     # or: pipx install hyperbox-mcp

Requires Python 3.11+ and Docker or Podman running.

Check the machine and fetch the sandbox image once, so a multi-gigabyte download never happens inside a client request:

hyperbox doctor --pull

Use it: Cursor

Generate the config rather than typing it — the failure mode of getting a path wrong is silent, with no tools appearing and no error anywhere:

hyperbox config --format cursor > .vscode/mcp.json

That writes:

{
  "servers": {
    "hyperbox": {
      "command": "/Users/you/.local/bin/hyperbox",
      "args": [],
      "env": { "PATH": "/Users/you/.local/bin:/usr/local/bin:/usr/bin:/bin" }
    }
  }
}

Restart Cursor — MCP configs are read at launch. Then ask it to run something:

Use hyperbox to check whether this regex handles the empty string.

A typical exchange looks like:

create_sandbox(language="python", packages=["requests"])
  → {"sandbox_id": "6f5eaaefb938", ...}

run(sandbox_id="6f5eaaefb938", code="import re; print(re.match(r'^\\d+$', ''))")
  → {"stdout": "None\n", "exit_code": 0, "success": true}

destroy_sandbox(sandbox_id="6f5eaaefb938")
  → {"status": "destroyed"}

Other clients

hyperbox config --format json          # Claude Desktop, and most clients
hyperbox config --format antigravity   # Antigravity
hyperbox config --format yaml          # Continue-based clients

PATH in the generated config includes your container CLI's directory, because clients launch servers with a trimmed environment and Docker is frequently invisible otherwise.

Custom environments

Start sandboxes from a heavier image so you do not pay a package install every time:

hyperbox build data-science --dockerfile ./Dockerfile
hyperbox build torch --image pytorch/pytorch:latest     # or pull one
hyperbox envs

Your agent then asks for it by name: create_sandbox(environment="data-science"). A running server picks up a new environment without a restart.

Building is a CLI action on purpose. A build runs whatever the Dockerfile says — arbitrary commands, as root, with network, under none of a sandbox's limits — so an agent can use an environment but cannot create one.

Where the boundary sits

HyperBox is developer containment, not an isolation guarantee:

• Local containers share your host's kernel. No gVisor, no Firecracker, no VM boundary of its own.
• Not a multi-tenant boundary. Do not run untrusted third-party code as a service with it.
• Code runs as root inside the container. A non-root user breaks the execution backend; that root is confined by the container boundary, no-new-privileges, and the limits above.
• Dependencies come from the public index and are not vetted.

If you need a hard boundary for genuinely adversarial code, you want a VM or microVM sandbox, not a local container.

Commands

hyperbox                 Start the MCP server on stdio (default)
hyperbox doctor          Check this machine can run sandboxes
hyperbox config          Print a ready-to-paste MCP client config
hyperbox envs            List environments create_sandbox can use
hyperbox build <name>    Create an environment agents can select
                         (--dockerfile <path> or --image <ref>)
hyperbox logs            Show the server log

MIT licensed. Source, full documentation and issue tracker: github.com/Sanjay7089/hyperbox-mcp