Interactive components

Install the Antigravity AG-UI adapter

pip install ag-ui-antigravity google-antigravity

Ask for the human with a frontend tool

The simplest route is an ordinary frontend tool. Register it in the browser with useHumanInTheLoop, or with useFrontendTool and an async handler that resolves once the user has answered. Each run carries that tool definition to the agent, the adapter rebuilds it as a custom Antigravity tool, and when the model calls it the call parks as an awaited coroutine — the SSE response for that run closes, and the adapter resolves the parked call from a later HTTP run with whatever the handler returned. The model receives that value as the tool's real return value, so there is no proxy tool and no fire-and-forget long-running-tool workaround.

Or pause a server tool with experimental_interrupt()

When the backend decides it needs the user, a Python server tool can pause itself on an AG-UI interrupt with the adapter's experimental experimental_interrupt() and carry on with the answer:

from ag_ui_antigravity import experimental_interrupt

async def schedule_meeting(topic: str, attendee: str) -> str:
    """Books a meeting once the user picks a time."""
    answer = await experimental_interrupt(
        "schedule_meeting",
        message=f"Pick a time for {topic}",
        metadata={"topic": topic, "attendee": attendee},
    )
    if not answer.resolved:
        return f"Not scheduled ({answer.status})."
    return f"Scheduled for {answer.payload['chosen_label']}."

The run ends with RUN_FINISHED carrying an interrupt outcome, which useInterrupt renders; the payload the user resolves it with comes back unchanged in answer.payload. answer.status is "resolved", "cancelled" or "abandoned" (the user sent a new message instead).

Name the tool in the agent's instructions

That leaves the agent with one job: knowing the tool exists and when to call it. A model that is never told about book_call will answer in prose and the picker will never mount.

from agents._common import build

HITL_IN_CHAT_PROMPT = (
    "You help users book an onboarding call with the sales team. "
    "When they ask to book a call, call the frontend-provided "
    "`book_call` tool with a short topic and the user's name. "
    "Keep any chat reply to one short sentence."
)


def hitl_in_chat_agent():
    return build(system_instructions=HITL_IN_CHAT_PROMPT)

The frontend tool is dispatched to the browser at most once per turn (deduplicate_tool_calls, on by default), so a model that re-issues the same call after the harness backgrounds it does not ask the user twice.