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chore: import upstream snapshot with attribution
2026-07-13 13:39:25 +08:00

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# 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
```bash
pip install agent-framework-monty --pre
```
The package depends on [`pydantic-monty`](https://github.com/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(...)`.
```python
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).
```python
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:
```python
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:
```python
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`](https://github.com/pydantic/monty) 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](https://docs.astral.sh/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`.