Pydantic AI

"""A Pydantic AI support agent on AgentOS. Its refund tool needs approval: Pydantic AI defers the call, AgentOS asks
a person, and the run continues with the decision, in whichever container answers the resume."""
import hashlib
import json

import agentos
from openai import AsyncOpenAI
from pydantic_ai import (Agent, DeferredToolRequests, DeferredToolResults, ModelMessagesTypeAdapter, ToolApproved,
                         ToolDenied)
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider

ORDERS = {  # stands in for your order system
    "A-1001": {"status": "delivered", "total": "49.90", "items": ["Desk lamp"], "delivered": "2026-10-02"},
    "A-1002": {"status": "in transit", "total": "129.00", "items": ["Office chair"], "delivered": None},
}


def build_agent(inv: agentos.Invocation, refunds: list[str]) -> Agent:
    """Built per invocation: the model client carries this run's token. Refunds it issues go into `refunds`."""
    model = OpenAIChatModel(
        inv.llm.model("main"),  # config.models.main, sent to the gateway as it is
        provider=OpenAIProvider(openai_client=AsyncOpenAI(**inv.llm.openai_kwargs(asynchronous=True), max_retries=0)),
    )
    agent = Agent(model, instructions=inv.config["prompts"]["system"],
                  output_type=[str, DeferredToolRequests])  # a call that needs approval ends the run for now

    @agent.tool_plain
    def lookup_order(order_id: str) -> str:
        """Look an order up by its id (like A-1001): its status, items, total and delivery date."""
        order = ORDERS.get(order_id.strip().upper())
        return json.dumps(order) if order else f"There is no order {order_id}."

    @agent.tool_plain(requires_approval=True)
    def issue_refund(order_id: str, amount: str, reason: str) -> str:
        """Refund `amount` (a decimal string like 49.90) on an order. A person approves every refund."""
        # Stands in for your payment provider; key the real call by run id so a retried run never pays twice.
        refund_id = "rf_" + hashlib.sha256(f"{inv.run_id}:{order_id}:{amount}".encode()).hexdigest()[:12]
        refunds.append(refund_id)
        return f"Refunded {amount} on {order_id}: {refund_id}"

    return agent


async def support(inv: agentos.Invocation):
    issued: list[str] = []
    agent = build_agent(inv, issued)
    try:
        if inv.resume is None:  # the start
            result = await agent.run(inv.input["message"])
        else:  # a person decided: continue the same conversation with their decisions
            history = ModelMessagesTypeAdapter.validate_python(await inv.state.run.aget("history"))
            pending = await inv.state.run.aget("pending")
            result = await agent.run(message_history=history, deferred_tool_results=decisions(inv.resume, pending))
    except Exception as error:
        if block := agentos.guardrail_block(error):
            raise block  # the run fails with blocked_by_guardrail and the guardrail's message
        raise

    if isinstance(result.output, DeferredToolRequests):  # the model wants to refund: ask a person
        await inv.state.run.aput("history", json.loads(ModelMessagesTypeAdapter.dump_json(result.all_messages())))
        items = []
        for call in result.output.approvals:  # one item per tool call waiting for approval
            args = call.args_as_dict()
            items.append({"id": call.tool_call_id, "title": f"Refund {args.get('amount')} on {args.get('order_id')}",
                          "data": args})
        await inv.state.run.aput("pending", [item["id"] for item in items])
        await inv.aask("Approve these refunds?", items, actions=["approve", "edit", "reject"])
    refunds = (await inv.state.run.aget("refunds", [])) + issued
    await inv.state.run.aput("refunds", refunds)
    return {"answer": result.output, "refunds": refunds}


def decisions(resume: agentos.Resume, pending: list[str]) -> DeferredToolResults:
    """The reviewer's decisions as Pydantic AI approvals: approve, edit (run with the edited arguments) or reject."""
    if resume.expired:  # nobody decided in time: deny every waiting call
        return DeferredToolResults(approvals={i: ToolDenied("Nobody approved this refund in time.") for i in pending})
    approvals = {}
    for item in resume.decided_items:
        if item.action == "approve":
            approvals[item.id] = True
        elif item.action == "edit":
            approvals[item.id] = ToolApproved(override_args=item.data)
        else:
            approvals[item.id] = ToolDenied(item.comment or "A person rejected this refund.")
    return DeferredToolResults(approvals=approvals)


agentos.serve({"support": support}, port=8080)

name: pydantic-ai-support
title: Support agent (Pydantic AI)
description: Answers order questions with a lookup tool; refunds go through a person first.
runtime: stateless
port: 8080
capabilities:
  support:
    description: Handles a customer's message. A refund the model wants to issue waits for a person's approval.
    input:
      type: object
      properties: {message: {type: string, minLength: 1}}
      required: [message]
    output:
      type: object
      properties:
        answer: {type: string}
        refunds: {type: array, items: {type: string}}
      required: [answer, refunds]
    timeout: 120s
config:
  models:
    main: anthropic/claude-sonnet-4-5    # a model from your Models page: <provider slug>/<model ID>
  prompts:
    system: >-
      You are a support agent for an online shop. Look orders up with lookup_order before you
      answer anything about an order. Use issue_refund only for delivered orders that arrived
      damaged. Answer in at most three sentences.

FROM python:3.13-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# the AgentOS SDK wheel, from the bundle's vendor/ folder
COPY vendor/ vendor/
RUN pip install --no-cache-dir vendor/*.whl
COPY app.py .
ENV PYDANTIC_AI_NO_BANNER=1
USER nobody
EXPOSE 8080
CMD ["python", "-u", "app.py"]

pydantic-ai-slim[openai]==2.62.0