250 lines
8.7 KiB
Plaintext
250 lines
8.7 KiB
Plaintext
---
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title: 'OpenAI Agents'
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description: 'Airline Customer Service Agent'
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---
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{/* SOURCE_FILE: examples/openai_agents/customer_service_agent.ipynb */}
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_View Notebook on <a href={'https://github.com/AgentOps-AI/agentops/blob/main/examples/openai_agents/customer_service_agent.ipynb'} target={'_blank'}>Github</a>_
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# Airline Customer Service Agent
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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.
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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.
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## Prerequisites
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Before running this notebook, you'll need:
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1. **AgentOps Account**: Create a free account at [app.agentops.ai](https://app.agentops.ai)
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2. **AgentOps API Key**: Obtain your API key from your AgentOps dashboard
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3. **OpenAI API Key**: Get your API key from [platform.openai.com](https://platform.openai.com)
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Make sure to set these as environment variables or create a `.env` file in your project root with:
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```
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AGENTOPS_API_KEY=your_agentops_api_key_here
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OPENAI_API_KEY=your_openai_api_key_here
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```
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## Installation
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<CodeGroup>
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```bash pip
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pip install -q agentops openai-agents pydotenv
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```
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```bash poetry
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poetry add -q agentops openai-agents pydotenv
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```
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```bash uv
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uv pip install -q agentops openai-agents pydotenv
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```
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</CodeGroup>
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```python
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# Set the API keys for your AgentOps and OpenAI accounts.
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import os
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from dotenv import load_dotenv
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load_dotenv()
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os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY", "your_api_key_here")
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os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "your_openai_api_key_here")
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```
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```python
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from __future__ import annotations as _annotations # noqa: F404
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import random
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import uuid
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from pydantic import BaseModel
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import agentops
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from agents import ( # noqa: E402
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Agent,
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HandoffOutputItem,
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ItemHelpers,
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MessageOutputItem,
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RunContextWrapper,
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Runner,
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ToolCallItem,
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ToolCallOutputItem,
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TResponseInputItem,
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function_tool,
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handoff,
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trace,
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)
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from agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX # noqa: E402
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```
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```python
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agentops.init(tags=["customer-service-agent", "openai-agents", "agentops-example"])
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tracer = agentops.start_trace(trace_name="Customer Service Agent")
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```
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```python
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# Context model for the airline agent
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class AirlineAgentContext(BaseModel):
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passenger_name: str | None = None
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confirmation_number: str | None = None
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seat_number: str | None = None
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flight_number: str | None = None
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```
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```python
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# Tools for the airline agent
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@function_tool(name_override="faq_lookup_tool", description_override="Lookup frequently asked questions.")
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async def faq_lookup_tool(question: str) -> str:
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if "bag" in question or "baggage" in question:
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return (
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"You are allowed to bring one bag on the plane. "
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"It must be under 50 pounds and 22 inches x 14 inches x 9 inches."
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)
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elif "seats" in question or "plane" in question:
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return (
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"There are 120 seats on the plane. "
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"There are 22 business class seats and 98 economy seats. "
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"Exit rows are rows 4 and 16. "
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"Rows 5-8 are Economy Plus, with extra legroom. "
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)
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elif "wifi" in question:
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return "We have free wifi on the plane, join Airline-Wifi"
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return "I'm sorry, I don't know the answer to that question."
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@function_tool
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async def update_seat(context: RunContextWrapper[AirlineAgentContext], confirmation_number: str, new_seat: str) -> str:
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"""
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Update the seat for a given confirmation number.
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Args:
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confirmation_number: The confirmation number for the flight.
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new_seat: The new seat to update to.
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"""
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# Update the context based on the customer's input
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context.context.confirmation_number = confirmation_number
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context.context.seat_number = new_seat
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# Ensure that the flight number has been set by the incoming handoff
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assert context.context.flight_number is not None, "Flight number is required"
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return f"Updated seat to {new_seat} for confirmation number {confirmation_number}"
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### HOOKS
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async def on_seat_booking_handoff(context: RunContextWrapper[AirlineAgentContext]) -> None:
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flight_number = f"FLT-{random.randint(100, 999)}"
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context.context.flight_number = flight_number
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### AGENTS
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faq_agent = Agent[AirlineAgentContext](
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name="FAQ Agent",
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handoff_description="A helpful agent that can answer questions about the airline.",
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instructions=f"""{RECOMMENDED_PROMPT_PREFIX}
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You are an FAQ agent. If you are speaking to a customer, you probably were transferred to from the triage agent.
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Use the following routine to support the customer.
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# Routine
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1. Identify the last question asked by the customer.
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2. Use the faq lookup tool to answer the question. Do not rely on your own knowledge.
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3. If you cannot answer the question, transfer back to the triage agent.""",
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tools=[faq_lookup_tool],
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)
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seat_booking_agent = Agent[AirlineAgentContext](
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name="Seat Booking Agent",
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handoff_description="A helpful agent that can update a seat on a flight.",
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instructions=f"""{RECOMMENDED_PROMPT_PREFIX}
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You are a seat booking agent. If you are speaking to a customer, you probably were transferred to from the triage agent.
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Use the following routine to support the customer.
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# Routine
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1. Ask for their confirmation number.
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2. Ask the customer what their desired seat number is.
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3. Use the update seat tool to update the seat on the flight.
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If the customer asks a question that is not related to the routine, transfer back to the triage agent. """,
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tools=[update_seat],
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)
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triage_agent = Agent[AirlineAgentContext](
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name="Triage Agent",
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handoff_description="A triage agent that can delegate a customer's request to the appropriate agent.",
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instructions=(
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f"{RECOMMENDED_PROMPT_PREFIX} "
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"You are a helpful triaging agent. You can use your tools to delegate questions to other appropriate agents."
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),
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handoffs=[
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faq_agent,
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handoff(agent=seat_booking_agent, on_handoff=on_seat_booking_handoff),
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],
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)
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```
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```python
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faq_agent.handoffs.append(triage_agent)
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seat_booking_agent.handoffs.append(triage_agent)
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```
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```python
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async def main():
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current_agent: Agent[AirlineAgentContext] = triage_agent
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input_items: list[TResponseInputItem] = []
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context = AirlineAgentContext()
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# Normally, each input from the user would be an API request to your app, and you can wrap the request in a trace()
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# Here, we'll just use a random UUID for the conversation ID
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conversation_id = uuid.uuid4().hex[:16]
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while True:
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user_input = input("Enter your message: ")
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with trace("Customer service", group_id=conversation_id):
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input_items.append({"content": user_input, "role": "user"})
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result = await Runner.run(current_agent, input_items, context=context)
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for new_item in result.new_items:
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agent_name = new_item.agent.name
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if isinstance(new_item, MessageOutputItem):
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print(f"{agent_name}: {ItemHelpers.text_message_output(new_item)}")
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elif isinstance(new_item, HandoffOutputItem):
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print(f"Handed off from {new_item.source_agent.name} to {new_item.target_agent.name}")
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elif isinstance(new_item, ToolCallItem):
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print(f"{agent_name}: Calling a tool")
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elif isinstance(new_item, ToolCallOutputItem):
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print(f"{agent_name}: Tool call output: {new_item.output}")
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else:
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print(f"{agent_name}: Skipping item: {new_item.__class__.__name__}")
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input_items = result.to_input_list()
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current_agent = result.last_agent
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```
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```python
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await main()
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agentops.end_trace(tracer, end_state="Success")
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```
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## Conclusion
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**AgentOps makes observability effortless** - simply import the library and all your interactions are automatically tracked, visualized, and analyzed. This enables you to:
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- Monitor tool performance across different use cases
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- Optimize costs by understanding tool usage patterns
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- Debug tool integration issues quickly
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- Scale your AI applications with confidence in tool reliability
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Visit [app.agentops.ai](https://app.agentops.ai) to explore your tool usage sessions and gain deeper insights into your AI application's tool interactions.
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