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Airline Customer Service Agent

This is a simple chatbot designed to assist airline customers with common queries. Here the agents are also used as tools to help the bot answer questions more effectively. Using AgentOps we can track the flow of the conversation and the agents used. This is useful for debugging and understanding how the bot is performing.

Prerequisites

Before running this notebook, you’ll need:
  1. AgentOps Account: Create a free account at app.agentops.ai
  2. AgentOps API Key: Obtain your API key from your AgentOps dashboard
  3. OpenAI API Key: Get your API key from platform.openai.com
Make sure to set these as environment variables or create a .env file in your project root with:
AGENTOPS_API_KEY=your_agentops_api_key_here
OPENAI_API_KEY=your_openai_api_key_here

Installation

pip install -q agentops openai-agents pydotenv
# Set the API keys for your AgentOps and OpenAI accounts.
import os
from dotenv import load_dotenv

load_dotenv()

os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY", "your_api_key_here")
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "your_openai_api_key_here")
from __future__ import annotations as _annotations  # noqa: F404

import random
import uuid

from pydantic import BaseModel
import agentops

from agents import (  # noqa: E402
    Agent,
    HandoffOutputItem,
    ItemHelpers,
    MessageOutputItem,
    RunContextWrapper,
    Runner,
    ToolCallItem,
    ToolCallOutputItem,
    TResponseInputItem,
    function_tool,
    handoff,
    trace,
)
from agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX  # noqa: E402
agentops.init(tags=["customer-service-agent", "openai-agents", "agentops-example"])
tracer = agentops.start_trace(trace_name="Customer Service Agent")
# Context model for the airline agent
class AirlineAgentContext(BaseModel):
    passenger_name: str | None = None
    confirmation_number: str | None = None
    seat_number: str | None = None
    flight_number: str | None = None
# Tools for the airline agent
@function_tool(name_override="faq_lookup_tool", description_override="Lookup frequently asked questions.")
async def faq_lookup_tool(question: str) -> str:
    if "bag" in question or "baggage" in question:
        return (
            "You are allowed to bring one bag on the plane. "
            "It must be under 50 pounds and 22 inches x 14 inches x 9 inches."
        )
    elif "seats" in question or "plane" in question:
        return (
            "There are 120 seats on the plane. "
            "There are 22 business class seats and 98 economy seats. "
            "Exit rows are rows 4 and 16. "
            "Rows 5-8 are Economy Plus, with extra legroom. "
        )
    elif "wifi" in question:
        return "We have free wifi on the plane, join Airline-Wifi"
    return "I'm sorry, I don't know the answer to that question."


@function_tool
async def update_seat(context: RunContextWrapper[AirlineAgentContext], confirmation_number: str, new_seat: str) -> str:
    """
    Update the seat for a given confirmation number.

    Args:
        confirmation_number: The confirmation number for the flight.
        new_seat: The new seat to update to.
    """
    # Update the context based on the customer's input
    context.context.confirmation_number = confirmation_number
    context.context.seat_number = new_seat
    # Ensure that the flight number has been set by the incoming handoff
    assert context.context.flight_number is not None, "Flight number is required"
    return f"Updated seat to {new_seat} for confirmation number {confirmation_number}"


### HOOKS


async def on_seat_booking_handoff(context: RunContextWrapper[AirlineAgentContext]) -> None:
    flight_number = f"FLT-{random.randint(100, 999)}"
    context.context.flight_number = flight_number


### AGENTS

faq_agent = Agent[AirlineAgentContext](
    name="FAQ Agent",
    handoff_description="A helpful agent that can answer questions about the airline.",
    instructions=f"""{RECOMMENDED_PROMPT_PREFIX}
    You are an FAQ agent. If you are speaking to a customer, you probably were transferred to from the triage agent.
    Use the following routine to support the customer.
    # Routine
    1. Identify the last question asked by the customer.
    2. Use the faq lookup tool to answer the question. Do not rely on your own knowledge.
    3. If you cannot answer the question, transfer back to the triage agent.""",
    tools=[faq_lookup_tool],
)

seat_booking_agent = Agent[AirlineAgentContext](
    name="Seat Booking Agent",
    handoff_description="A helpful agent that can update a seat on a flight.",
    instructions=f"""{RECOMMENDED_PROMPT_PREFIX}
    You are a seat booking agent. If you are speaking to a customer, you probably were transferred to from the triage agent.
    Use the following routine to support the customer.
    # Routine
    1. Ask for their confirmation number.
    2. Ask the customer what their desired seat number is.
    3. Use the update seat tool to update the seat on the flight.
    If the customer asks a question that is not related to the routine, transfer back to the triage agent. """,
    tools=[update_seat],
)

triage_agent = Agent[AirlineAgentContext](
    name="Triage Agent",
    handoff_description="A triage agent that can delegate a customer's request to the appropriate agent.",
    instructions=(
        f"{RECOMMENDED_PROMPT_PREFIX} "
        "You are a helpful triaging agent. You can use your tools to delegate questions to other appropriate agents."
    ),
    handoffs=[
        faq_agent,
        handoff(agent=seat_booking_agent, on_handoff=on_seat_booking_handoff),
    ],
)
faq_agent.handoffs.append(triage_agent)
seat_booking_agent.handoffs.append(triage_agent)
async def main():
    current_agent: Agent[AirlineAgentContext] = triage_agent
    input_items: list[TResponseInputItem] = []
    context = AirlineAgentContext()

    # Normally, each input from the user would be an API request to your app, and you can wrap the request in a trace()
    # Here, we'll just use a random UUID for the conversation ID
    conversation_id = uuid.uuid4().hex[:16]

    while True:
        user_input = input("Enter your message: ")
        with trace("Customer service", group_id=conversation_id):
            input_items.append({"content": user_input, "role": "user"})
            result = await Runner.run(current_agent, input_items, context=context)

            for new_item in result.new_items:
                agent_name = new_item.agent.name
                if isinstance(new_item, MessageOutputItem):
                    print(f"{agent_name}: {ItemHelpers.text_message_output(new_item)}")
                elif isinstance(new_item, HandoffOutputItem):
                    print(f"Handed off from {new_item.source_agent.name} to {new_item.target_agent.name}")
                elif isinstance(new_item, ToolCallItem):
                    print(f"{agent_name}: Calling a tool")
                elif isinstance(new_item, ToolCallOutputItem):
                    print(f"{agent_name}: Tool call output: {new_item.output}")
                else:
                    print(f"{agent_name}: Skipping item: {new_item.__class__.__name__}")
            input_items = result.to_input_list()
            current_agent = result.last_agent
await main()
agentops.end_trace(tracer, end_state="Success")

Conclusion

AgentOps makes observability effortless - simply import the library and all your interactions are automatically tracked, visualized, and analyzed. This enables you to:
  • Monitor tool performance across different use cases
  • Optimize costs by understanding tool usage patterns
  • Debug tool integration issues quickly
  • Scale your AI applications with confidence in tool reliability
Visit app.agentops.ai to explore your tool usage sessions and gain deeper insights into your AI application’s tool interactions.