Beta built-in tools for the Microsoft Agent Framework. A home for first-party Python tools that plug into any chat client's shell / function surface. The first tool is LocalShellTool.

pip install agent-framework-tools --pre

import asyncio
from agent_framework import Agent
from agent_framework.openai import OpenAIChatClient
from agent_framework.tools import LocalShellTool


async def main() -> None:
    client = OpenAIChatClient(model="gpt-5.4-nano")
    async with LocalShellTool() as shell:
        agent = Agent(
            client=client,
            instructions="You are a helpful assistant that can run shell commands.",
            tools=[client.get_shell_tool(func=shell.as_function())],
        )
        result = await agent.run("Print the current working directory.")
        print(result.text)


asyncio.run(main())

• Persistent (default): a single long-lived shell session. cd, export, and shell functions persist across tool invocations.
• Stateless (mode="stateless"): each command runs in a fresh subprocess.

LocalShellTool is not a sandbox. It runs commands directly on the host with the agent process's permissions. Human approval provides a check before agent-requested commands run, but does not isolate the shell. Production use needs separately enforced isolation and restricted permissions. For untrusted input use a sandboxed executor, such as DockerShellTool for shell commands — see also agent-framework-hyperlight.

Defenses (in priority order):

• Approval-in-the-loop — when an agent calls the tool returned by as_function() with approval_mode="always_require" (the default), commands require approval through a user_input_request before they run. Disabling this requires acknowledge_unsafe=True. Direct calls to run() do not request approval, regardless of the configured approval mode.
• Process-tree termination on timeout via psutil, so child processes (make, watchers, network tools) cannot survive the timeout.
• Output truncation to 64 KiB (head + tail with marker).
• Audit hook (on_command=…) for SIEM / append-only logs.
• Optional command-pattern filter via ShellPolicy(denylist=[...], allowlist=[...]). Empty by default. This is a UX pre-filter, not a security boundary — operators are expected to supply patterns that match their workload (and they can be defeated by trivial obfuscation such as \rm -rf /, ${RM:=rm} -rf /, python -c "…", encoded payloads, or PowerShell-native equivalents). Isolation must be enforced separately, for example by the sandbox tier (DockerShellTool); neither approval nor command filters isolate the shell. See tests/test_security.py for the documented residual risk surface.

Command-text filtering with ShellPolicy and required human approval:

from agent_framework.tools import LocalShellTool, ShellPolicy

shell = LocalShellTool(
    policy=ShellPolicy(allowlist=[r"^ls\b", r"^cat\b", r"^git status$"]),
    approval_mode="always_require",
)

Expose this tool to the agent through shell.as_function() and handle its approval requests with human review. Calling shell.run() directly does not request approval.

• Windows: pwsh -NoProfile -Command - (falls back to powershell.exe).
• Linux / macOS: /bin/bash --noprofile --norc (falls back to /bin/sh).
• Override via the shell= constructor argument or the AGENT_FRAMEWORK_SHELL environment variable.

A model talking to a PowerShell session will sometimes default to bash syntax (export FOO=bar, ls -la, > /dev/null) and vice versa. ShellEnvironmentProvider is an AIContextProvider that probes the live shell once per session — family, version, OS, working directory, and a configurable list of CLI tools (git, node, python, docker by default) — and injects a system-prompt block describing the shell idiom to use and the available CLIs.

from agent_framework.tools import (
    LocalShellTool,
    ShellEnvironmentProvider,
    ShellEnvironmentProviderOptions,
)

shell = LocalShellTool()
provider = ShellEnvironmentProvider(
    shell,
    ShellEnvironmentProviderOptions(probe_tools=("git", "uv", "node")),
)
agent = Agent(
    client=client,
    tools=[client.get_shell_tool(func=shell.as_function())],
    context_providers=[provider],
)

Probe failures from expected error types (timeouts, policy rejections, spawn failures) are recorded as None fields in the snapshot rather than raised; a missing CLI never fails the agent. A failed first probe does not poison the cache — the next call retries.

When commands originate from untrusted input (e.g. the model is acting on prompt-injected document content), prefer DockerShellTool. With the default isolation flags and a trusted container runtime, the container is the intended security boundary and approval gating becomes optional.

import asyncio
from agent_framework.tools import DockerShellTool


async def main() -> None:
    async with DockerShellTool(
        image="mcr.microsoft.com/azurelinux/base/core:3.0",
        approval_mode="never_require",  # container is the boundary
    ) as shell:
        result = await shell.run("uname -a && id")
        print(result.stdout)


asyncio.run(main())

Defaults applied to every container:

• --network none — no host or external network.
• --user 65534:65534 — runs as nobody:nogroup.
• --read-only root filesystem; only mounted host paths are writable.
• --cap-drop ALL and --security-opt no-new-privileges.
• --memory 512m, --pids-limit 256, ephemeral tmpfs /tmp.

To expose a host directory, pass host_workdir="/path" (mounted read-only by default; mount_readonly=False to allow writes). Swap the container runtime with docker_binary="podman".

agent-framework-hyperlight is a code sandbox (a single WASM guest loaded into a microVM, called via a hostcall ABI — there is no kernel, userland, or shell binary inside). It is the right tier for executing generated code. For sandboxing shell commands, the realistic tier is OCI, which DockerShellTool provides.