跳至内容

快速入门

创建项目与虚拟环境

您只需要执行一次此操作。

mkdir my_project
cd my_project
python -m venv .venv

激活虚拟环境

每次启动新的终端会话时都要执行此操作。

source .venv/bin/activate

安装Agents SDK

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

设置OpenAI API密钥

如果您还没有，请按照这些说明创建一个OpenAI API密钥。

export OPENAI_API_KEY=sk-...

创建你的第一个智能体

智能体(Agents)通过指令、名称和可选配置(如model_config)来定义

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",
)

添加更多代理

可以以同样的方式定义其他代理。handoff_descriptions 为确定交接路由提供了额外的上下文

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",
)

定义您的交接流程

在每个代理上，您可以定义一组传出的交接选项清单，代理可以从这些选项中选择，以决定如何推进其任务。

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]
)

运行代理编排

让我们检查工作流是否正常运行，以及分类代理能否正确在两个专业代理之间进行路由。

from agents import Runner

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

添加防护栏

您可以定义自定义防护措施来运行在输入或输出上。

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,
    )

整合所有内容

让我们将所有内容整合起来，通过交接流程和输入防护机制来运行整个工作流。

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())

查看您的追踪记录

要查看代理运行期间发生的情况，请导航至OpenAI仪表板中的Trace查看器以查看代理运行的跟踪记录。

后续步骤

学习如何构建更复杂的代理流程：