LangChain

"""A LangChain support agent on AgentOS. `create_agent` builds a LangGraph graph, so the refund tool asks a person
with LangGraph's `interrupt()`, and the SDK's LangGraph adapter turns that into an AgentOS approval."""
import hashlib
import json

import agentos
from agentos.adapters import langgraph
from langchain.agents import create_agent
from langchain.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.types import interrupt

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},
}


@tool
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}."


def refund_tool(run_id: str):
    @tool
    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."""
        # Everything before interrupt() runs again on the resume: keep it free of side effects.
        decision = interrupt({
            "intent": f"Refund {amount} on order {order_id}?",
            "items": [{"id": order_id, "title": f"{order_id} · {amount}",
                       "data": {"order_id": order_id, "amount": amount}}],
            "actions": ["approve", "edit", "reject"],
            "details": f"Reason given by the agent: {reason}",
        })
        item = decision["items"][0] if decision["items"] else None  # no items: the ask expired
        if item is None or item["action"] == "reject":
            return f"The refund was not approved. {(item or {}).get('comment') or ''}".strip()
        amount = item["data"]["amount"]  # as approved, or as the reviewer edited it
        # 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"{run_id}:{order_id}:{amount}".encode()).hexdigest()[:12]
        return f"Refunded {amount} on {order_id}: {refund_id}"

    return issue_refund


def support(inv: agentos.Invocation):
    # The model and the agent are built per invocation: the client carries this run's token.
    model = ChatOpenAI(
        model=inv.llm.model("main"),          # config.models.main, sent to the gateway as it is
        base_url=inv.llm.base_url,            # the AgentOS model gateway
        api_key=inv.llm.api_key,              # ignored by the gateway
        default_headers=inv.llm.headers,      # x-agentos-run-token
        http_client=inv.llm.httpx_client(),   # no redirects; the token never leaves the platform
        max_retries=0,                        # a guardrail block is a 400: don't retry it
    )
    agent = create_agent(model, tools=[lookup_order, refund_tool(inv.run_id)], system_prompt=inv.config["prompts"]["system"],
                         checkpointer=langgraph.checkpointer())  # kept in the run's state while a person decides
    try:
        state = langgraph.run(inv, agent, {"messages": [{"role": "user", "content": inv.input["message"]}]})
    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
    return {"answer": state["messages"][-1].text}


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

name: langchain-support
title: Support agent (LangChain)
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}}
      required: [answer]
    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, with its LangGraph adapter
COPY vendor/ vendor/
RUN pip install --no-cache-dir "$(ls vendor/*.whl)[langgraph]"
COPY app.py .
USER nobody
EXPOSE 8080
CMD ["python", "-u", "app.py"]

langchain==1.4.7
langchain-openai==1.7.3