agent-framework-hyperlight

Alpha Hyperlight-backed CodeAct integrations for Microsoft Agent Framework.

Installation

pip install agent-framework-hyperlight --pre

This package depends on hyperlight-sandbox, the packaged Python guest, and the Wasm backend package on supported platforms. If the backend is not published for your current platform yet, execute_code will fail at runtime when it tries to create the sandbox.

Quick start

Context provider (recommended)

Use HyperlightCodeActProvider to automatically inject the execute_code tool and CodeAct instructions into every agent run. Tools registered on the provider are available inside the sandbox via call_tool(...) but are not exposed as direct agent tools.

from agent_framework import Agent, tool
from agent_framework_hyperlight import HyperlightCodeActProvider

@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 = HyperlightCodeActProvider(
    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 HyperlightExecuteCodeTool directly when you want full control over how the tool is added to the agent. This is useful when mixing sandbox tools with direct-only tools on the same agent.

from agent_framework import Agent, tool
from agent_framework_hyperlight import HyperlightExecuteCodeTool

@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 = HyperlightExecuteCodeTool(
    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 = HyperlightExecuteCodeTool(
    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 network access

Mount host directories into the sandbox and allow outbound HTTP to specific domains:

from agent_framework_hyperlight import HyperlightCodeActProvider, FileMount

codeact = HyperlightCodeActProvider(
    tools=[compute],
    file_mounts=[
        "/host/data",                                 # shorthand — same path in sandbox
        ("/host/models", "/sandbox/models"),           # explicit host → sandbox mapping
        FileMount("/host/config", "/sandbox/config"),  # named tuple
    ],
    allowed_domains=[
        "api.github.com",                             # all methods
        ("internal.api.example.com", "GET"),           # GET only
    ],
)

Notes

• This package is intentionally separate from agent-framework-core so CodeAct usage and installation remain optional.
• Alpha-package samples live under packages/hyperlight/samples/.
• file_mounts accepts a single string shorthand, an explicit (host_path, mount_path) pair, or a FileMount named tuple. The host-side path in the explicit forms may be a str or Path. Use the explicit two-value form when the host path differs from the sandbox path.
• allowed_domains accepts a single string target such as "github.com" to allow all backend-supported methods, an explicit (target, method_or_methods) tuple such as ("github.com", "GET"), or an AllowedDomain named tuple.
• Tools registered with the sandbox return their native Python value (dict, list, primitives, or custom objects) directly to the guest via the Hyperlight FFI. Any result_parser configured on a FunctionTool is intended for LLM-facing consumers and does not run on the sandbox path — apply formatting inside the tool function itself if you need it for in-sandbox consumers.