Monty Skill

Sandboxed Python (Monty) Tool

Execute Python in a deny-by-default sandbox powered by Pydantic Monty — a minimal Python interpreter written in Rust. Unlike the python_code tool (CPython exec with a restricted namespace), Monty enforces wall-clock and memory limits and grants zero host access unless you explicitly request it.

Prefer this tool when running code you don't fully trust, or when you need guaranteed time/memory bounds. Use python_code instead when you need libraries Monty doesn't support (e.g. random, collections) or language features it lacks (classes, generators).

How It Works

Connect the Monty Executor node to an agent's input-tools handle. The LLM calls the sandboxed_python tool with code (and optionally capabilities, timeout, max_memory_mb).

Schema Fields

Inputs & Output

• input_data — a dict of upstream node outputs is available as the variable input_data. Read with input_data.get('someNode', {}).
• Return a result by making it the last expression in your code (e.g. a final line that is just output or a dict literal).
• print(...) output is captured and returned as console_output.

Supported Python Subset

If you hit an unsupported feature, rewrite without it or switch to the python_code tool.

Capabilities (opt-in host access)

By default the sandbox has no filesystem, network, or environment access. Request only what the task needs via capabilities:

Requesting more than necessary is discouraged — each capability is a deliberate hole in the sandbox.

Examples

Basic calculation (no capabilities):

{
  "code": "total = sum(range(1, 11))\nprint(f'sum 1..10 = {total}')\ntotal"
}

Process upstream data:

{
  "code": "nums = input_data.get('start', {}).get('numbers', [1,2,3])\navg = sum(nums) / len(nums)\nprint(f'avg = {avg}')\n{'avg': avg, 'count': len(nums)}"
}

Use the curated stdlib:

{
  "code": "import math, json\nr = 5\narea = math.pi * r ** 2\njson.dumps({'radius': r, 'area': round(area, 2)})"
}

Fetch a URL (requires http_get):

{
  "code": "body = http_get('https://example.com')\nlen(body)",
  "capabilities": ["http_get"]
}

Read a workspace file (requires workspace_read):

{
  "code": "data = open('/workspace/input.txt').read()\nlen(data.splitlines())",
  "capabilities": ["workspace_read"]
}

Enforced timeout (this will be terminated, not hang):

{
  "code": "while True:\n    pass",
  "timeout": 1
}

Guidelines

1. Return via the last expression — don't rely on a magic output variable; the final expression's value is returned.
2. Use print() for debugging — captured as console_output.
3. Request minimal capabilities — start with none; add only what the task needs.
4. Keep limits sane — timeout and max_memory_mb are hard caps; raise them only when justified.
5. Unsupported feature? Rewrite without it, or fall back to the python_code tool.