Introduction

from pydantic import BaseModel
from awaithumans import await_human_sync


class RefundPayload(BaseModel):
    amount: int
    customer_id: str


class RefundDecision(BaseModel):
    approved: bool
    reason: str = ""


result = await_human_sync(
    task="Approve this $250 refund?",
    payload_schema=RefundPayload,
    payload=RefundPayload(amount=250, customer_id="cus_demo"),
    response_schema=RefundDecision,
    timeout_seconds=900,
)

if result.approved:
    print(f"refund approved: {result.reason}")

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Why

1. Judgment — when the right answer isn’t in any prompt, only a domain expert knows.
2. System uncertainty — when an upstream API just stopped responding and nobody knows whether the payment actually went through.
3. Embodiment — when the task needs hands in the physical world.

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What you get

• One function in Python and TypeScript: await_human() / awaitHuman().
• Channels: deliver tasks to humans via Slack, email, or a built-in web dashboard.
• Adapters for durable execution: Temporal, LangGraph. Workflows park while waiting; survive restarts.
• Verification: optional AI quality-check + NL parsing layer (Claude / OpenAI / Gemini / Azure).
• Routing: assign tasks to specific people, pools, or roles.
• Self-host in one command: awaithumans dev for development, docker compose up for production.
• Apache 2.0 across the SDK, server, dashboard, adapters, and channels. Free forever for self-hosted use, with an explicit patent grant.

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Five-minute test

pip install "awaithumans[server]"
awaithumans dev

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Where to next