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OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. The SDK provides a comprehensive set of tools for creating, managing, and monitoring agent-based applications.

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Core Concepts

  • Agents: LLMs configured with instructions, tools, guardrails, and handoffs
  • Handoffs: Allow agents to transfer control to other agents for specific tasks
  • Guardrails: Configurable safety checks for input and output validation
  • Tracing: Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows
1

Install the AgentOps SDK

pip install agentops
2

Install OpenAI Agents SDK

pip install openai-agents
This will be updated to a PyPI link when the package is officially released.
3

Add 2 lines of code

Make sure to call agentops.init before calling any openai, cohere, crew, etc models.
import agentops
agentops.init(<INSERT YOUR API KEY HERE>)
Set your API key as an .env variable for easy access.
AGENTOPS_API_KEY=<YOUR API KEY>
OPENAI_API_KEY=<YOUR OPENAI API KEY>
Read more about environment variables in Advanced Configuration
4

Run your agents

Execute your program and visit app.agentops.ai/drilldown to observe your Agents! 🕵️
After your run, AgentOps prints a clickable url to console linking directly to your session in the Dashboard

Clickable link to session

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Hello World Example

from agents import Agent, Runner
import agentops

# Initialize AgentOps
agentops.init()

agent = Agent(name="Assistant", instructions="You are a helpful assistant")

result = Runner.run_sync(agent, "Write a haiku about recursion in programming.")
print(result.final_output)

# Output:
# Code within the code,
# Functions calling themselves,
# Infinite loop's dance.

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Handoffs Example

from agents import Agent, Runner
import asyncio
import agentops

# Initialize AgentOps
agentops.init()

spanish_agent = Agent(
    name="Spanish agent",
    instructions="You only speak Spanish.",
)

english_agent = Agent(
    name="English agent",
    instructions="You only speak English",
)

triage_agent = Agent(
    name="Triage agent",
    instructions="Handoff to the appropriate agent based on the language of the request.",
    handoffs=[spanish_agent, english_agent],
)


async def main():
    result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?")
    print(result.final_output)
    # ¡Hola! Estoy bien, gracias por preguntar. ¿Y tú, cómo estás?


if __name__ == "__main__":
    asyncio.run(main())

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Functions Example

import asyncio
from agents import Agent, Runner, function_tool
import agentops

# Initialize AgentOps
agentops.init()

@function_tool
def get_weather(city: str) -> str:
    return f"The weather in {city} is sunny."


agent = Agent(
    name="Hello world",
    instructions="You are a helpful agent.",
    tools=[get_weather],
)


async def main():
    result = await Runner.run(agent, input="What's the weather in Tokyo?")
    print(result.final_output)
    # The weather in Tokyo is sunny.


if __name__ == "__main__":
    asyncio.run(main())

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The Agent Loop

When you call Runner.run(), the SDK runs a loop until it gets a final output:
  1. The LLM is called using the model and settings on the agent, along with the message history.
  2. The LLM returns a response, which may include tool calls.
  3. If the response has a final output, the loop ends and returns it.
  4. If the response has a handoff, the agent is set to the new agent and the loop continues from step 1.
  5. Tool calls are processed (if any) and tool response messages are appended. Then the loop continues from step 1.
You can use the max_turns parameter to limit the number of loop executions.

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Final Output

Final output is the last thing the agent produces in the loop:
  • If you set an output_type on the agent, the final output is when the LLM returns something of that type using structured outputs.
  • If there’s no output_type (i.e., plain text responses), then the first LLM response without any tool calls or handoffs is considered the final output.

Basic Agent

Multi-Agent

Tool Usage