ai-python-serverless-execution

name	ai-python-serverless-execution
description	Use when building serverless AI SDK for Python endpoints, handling hook approvals, deferring hooks, or resuming runs across requests.
metadata	{"sdk-version":"0.2.1"}

ai-python-serverless-execution

Use this when working in a serverless setup, e.g. Vercel Fluid Compute.

The only major difference in serverless is processing tool approvals and other hooks. Since you can't keep the hook future alive, you need to stop the run, save messages, then start a later request with the hook resolution pre-registered.

Tool Approval

Mark approval-gated tools with require_approval=True:

@ai.tool(require_approval=True)
async def delete_file(path: str) -> str:
    return f"Deleted {path}"

First Request

When a deferred hook appears, send it to the client and call ai.defer_hook(...).

Keep draining the stream. Do not break after the first hook. This lets sibling tools finish or get marked deferred, and makes stream.messages complete.

deferred_hooks = []

async with agent.run(model, messages) as stream:
    async for event in stream:
        if (
            isinstance(event, ai.events.HookEvent)
            and event.hook.status == "pending"
        ):
            deferred_hooks.append(event.hook)
            ai.defer_hook(event.hook)

        yield event

saved_messages = [
    message.model_dump(mode="json")
    for message in stream.messages
]
save_messages(saved_messages)
save_deferred_hook_ids([hook.hook_id for hook in deferred_hooks])

Resume Request

Load the saved messages, pre-register hook resolutions, then call agent.run.

messages = [
    ai.messages.Message.model_validate(message)
    for message in load_messages()
]

for approval in approvals:
    ai.resolve_hook(
        approval.hook_id,
        ai.tools.ToolApproval(
            granted=approval.granted,
            reason=approval.reason,
        ),
    )

async with agent.run(model, messages) as stream:
    async for event in stream:
        yield event

save_messages([
    message.model_dump(mode="json")
    for message in stream.messages
])

Call ai.resolve_hook(...) before agent.run(...). Do not ask the model to make the tool call again.

Agent.run prepares saved interrupted messages for replay. Completed sibling tool results are reused, deferred hooks receive the pre-registered resolution, and replay-only events are hidden from the caller.

Rules

• Use normal agent.run(...); serverless resume usually does not need a custom loop.
• If you do write a custom loop, use context.resolve(...), ToolRunner, and context.add(...) so approvals and replay keep working.
• For custom hooks, pre-register with ai.resolve_hook(hook_id, data, payload=PayloadType).
• For AI SDK UI clients, use ai-python-ui-adapter for message conversion, approval responses, and SSE.