agent-framework/python/packages/monty

agent-framework-monty

Monty-backed CodeAct integrations for Microsoft Agent Framework.

Warning

This package is in alpha. APIs may change without notice. It is not part of agent-framework[all] yet; install it explicitly with --pre.

Installation

pip install agent-framework-monty --pre

The package depends on pydantic-monty, a Rust-based Python interpreter, so it runs on Linux, macOS, and Windows wherever Monty wheels are published — no hypervisor or WASM backend required.

Quick start

Context provider (recommended)

Use MontyCodeActProvider to automatically inject the execute_code tool and CodeAct instructions into every agent run. Tools registered on the provider are available inside the Monty interpreter as typed async functions (e.g. await compute(operation="add", a=1, b=2)), and as a fallback through call_tool(...).

from agent_framework import Agent, tool
from agent_framework_monty import MontyCodeActProvider


@tool
def compute(operation: str, a: float, b: float) -> float:
    """Perform a math operation."""
    ops = {"add": a + b, "subtract": a - b, "multiply": a * b, "divide": a / b}
    return ops[operation]


codeact = MontyCodeActProvider(
    tools=[compute],
    approval_mode="never_require",
)

agent = Agent(
    client=client,
    name="CodeActAgent",
    instructions="You are a helpful assistant.",
    context_providers=[codeact],
)

result = await agent.run("Multiply 6 by 7 using execute_code.")

Standalone tool

Use MontyExecuteCodeTool directly when you want full control over how the tool is added to the agent (e.g. when mixing sandbox tools with direct-only tools on the same agent).

from agent_framework import Agent, tool
from agent_framework_monty import MontyExecuteCodeTool


@tool
def send_email(to: str, subject: str, body: str) -> str:
    """Send an email (direct-only, not available inside the sandbox)."""
    return f"Email sent to {to}"


execute_code = MontyExecuteCodeTool(
    tools=[compute],
    approval_mode="never_require",
)

agent = Agent(
    client=client,
    name="MixedToolsAgent",
    instructions="You are a helpful assistant.",
    tools=[send_email, execute_code],
)

Manual static wiring

For fixed configurations where provider lifecycle overhead is unnecessary, build the CodeAct instructions once and pass them to the agent at construction time:

execute_code = MontyExecuteCodeTool(
    tools=[compute],
    approval_mode="never_require",
)

codeact_instructions = execute_code.build_instructions(tools_visible_to_model=False)

agent = Agent(
    client=client,
    name="StaticWiringAgent",
    instructions=f"You are a helpful assistant.\n\n{codeact_instructions}",
    tools=[execute_code],
)

File mounts and resource limits

Mount host directories into the sandbox and cap execution resources:

from agent_framework_monty import FileMount, MontyCodeActProvider

codeact = MontyCodeActProvider(
    tools=[compute],
    workspace_root="/host/workspace",       # auto-mounted at /input (read-write)
    file_mounts=[
        "/host/data",                                                # shorthand: same path on both sides
        ("/host/models", "/sandbox/models"),                          # explicit (host, mount_path)
        FileMount(                                                    # full control
            host_path="/host/cache",
            mount_path="/sandbox/cache",
            mode="overlay",                # "read-only" | "read-write" | "overlay"
            write_bytes_limit=10 * 1024 * 1024,
        ),
    ],
    resource_limits={                       # Monty ResourceLimits TypedDict
        "max_duration_secs": 5.0,
        "max_memory": 64 * 1024 * 1024,
    },
)

• workspace_root mirrors the Hyperlight default: the directory is mounted at /input in read-write mode.
• file_mounts accepts a string shorthand, a (host_path, mount_path) tuple, or a FileMount named tuple (with optional mode and write_bytes_limit).
• Files written by the sandbox to any read-write mount are scanned after each execute_code call and returned as Content.from_data(...) attachments (with a path annotation in additional_properties), mirroring Hyperlight's /output flow.
• overlay mounts buffer writes in memory (nothing leaks to the host and nothing is captured). read-only mounts reject writes.
• resource_limits is forwarded straight to Monty's ResourceLimits TypedDict (max_allocations, max_duration_secs, max_memory, gc_interval, max_recursion_depth).

DSL inside execute_code

The model generates Python code that runs inside Monty's Rust-based interpreter. Available primitives:

Primitive	Behavior
await tool_name(**kwargs)	Direct typed call to a registered host tool. Argument types are checked before execution.
await call_tool("name", **kwargs)	Generic fallback that dispatches by tool name. Not type-checked.
asyncio.gather(...)	Fans out concurrent tool calls.
print(...)	Captured and surfaced as text in the tool result.

Notes

• MontyCodeActProvider and MontyExecuteCodeTool mirror the API surface of the agent-framework-hyperlight counterparts where the underlying runtime supports it.
• Monty interprets a subset of Python (a Rust-based interpreter). Most control flow, common stdlib modules (sys, os, typing, asyncio, re, datetime, json), and async functions are supported, but exotic features may not be available. OS-level access (filesystem, network, subprocess) is rejected with PermissionError by default; mount host directories with workspace_root / file_mounts to grant scoped filesystem access.
• Code is type-checked against tool signatures via ty before execution, so wrong argument types surface as a clear error before any host tool runs.
• The alpha package is not part of agent-framework[all] yet, so it must be installed explicitly. Once promoted to beta it will be reachable via the lazy-loading namespace agent_framework.monty.