Quickstart

Quickstart#

Create a project and virtual environment#

You’ll only need to do this once.

mkdir my_project
cd my_project
python -m venv .venv

Activate the virtual environment#

Do this every time you start a new terminal session.

source .venv/bin/activate

Install the Agents SDK#

pip install openai-agents # or `uv add openai-agents`, etc

Set an OpenAI API key#

If you don’t have one, follow these instructions to create an OpenAI API key.

export OPENAI_API_KEY=sk-...

Create your first agent#

Agents are defined with instructions, a name, and optional config (such as model_config)

from agents import Agent

agent = Agent(
    name="Math Tutor",
    instructions="You provide help with math problems. Explain your reasoning at each step and include examples",
)

Add a few more agents#

Additional agents can be defined in the same way. handoff_descriptions provide additional context for determining handoff routing

from agents import Agent

history_tutor_agent = Agent(
    name="History Tutor",
    handoff_description="Specialist agent for historical questions",
    instructions="You provide assistance with historical queries. Explain important events and context clearly.",
)

math_tutor_agent = Agent(
    name="Math Tutor",
    handoff_description="Specialist agent for math questions",
    instructions="You provide help with math problems. Explain your reasoning at each step and include examples",
)

Define your handoffs#

On each agent, you can define an inventory of outgoing handoff options that the agent can choose from to decide how to make progress on their task.

triage_agent = Agent(
    name="Triage Agent",
    instructions="You determine which agent to use based on the user's homework question",
    handoffs=[history_tutor_agent, math_tutor_agent]
)

Run the agent orchestration#

Let’s check that the workflow runs and the triage agent correctly routes between the two specialist agents.

from agents import Runner

async def main():
    result = await Runner.run(triage_agent, "What is the capital of France?")
    print(result.final_output)

Add a guardrail#

You can define custom guardrails to run on the input or output.

from agents import GuardrailFunctionOutput, Agent, Runner
from pydantic import BaseModel

class HomeworkOutput(BaseModel):
    is_homework: bool
    reasoning: str

guardrail_agent = Agent(
    name="Guardrail check",
    instructions="Check if the user is asking about homework.",
    output_type=HomeworkOutput,
)

async def homework_guardrail(ctx, agent, input_data):
    result = await Runner.run(guardrail_agent, input_data, context=ctx.context)
    final_output = result.final_output_as(HomeworkOutput)
    return GuardrailFunctionOutput(
        output_info=final_output,
        tripwire_triggered=not final_output.is_homework,
    )

Put it all together#

Let’s put it all together and run the entire workflow, using handoffs and the input guardrail.

from agents import Agent, InputGuardrail, GuardrailFunctionOutput, Runner
from pydantic import BaseModel
import asyncio

class HomeworkOutput(BaseModel):
    is_homework: bool
    reasoning: str

guardrail_agent = Agent(
    name="Guardrail check",
    instructions="Check if the user is asking about homework.",
    output_type=HomeworkOutput,
)

math_tutor_agent = Agent(
    name="Math Tutor",
    handoff_description="Specialist agent for math questions",
    instructions="You provide help with math problems. Explain your reasoning at each step and include examples",
)

history_tutor_agent = Agent(
    name="History Tutor",
    handoff_description="Specialist agent for historical questions",
    instructions="You provide assistance with historical queries. Explain important events and context clearly.",
)


async def homework_guardrail(ctx, agent, input_data):
    result = await Runner.run(guardrail_agent, input_data, context=ctx.context)
    final_output = result.final_output_as(HomeworkOutput)
    return GuardrailFunctionOutput(
        output_info=final_output,
        tripwire_triggered=not final_output.is_homework,
    )

triage_agent = Agent(
    name="Triage Agent",
    instructions="You determine which agent to use based on the user's homework question",
    handoffs=[history_tutor_agent, math_tutor_agent],
    input_guardrails=[
        InputGuardrail(guardrail_function=homework_guardrail),
    ],
)

async def main():
    result = await Runner.run(triage_agent, "who was the first president of the united states?")
    print(result.final_output)

    result = await Runner.run(triage_agent, "what is life")
    print(result.final_output)

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

View your traces#

To review what happened during your agent run, navigate to the Trace viewer in the OpenAI Dashboard to view traces of your agent runs.

Next steps#

Learn how to build more complex agentic flows:

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