openai-agents（OpenAI Agent 搜索）

OpenAI Agents SDK

Build multi-agent systems with tool definitions, handoffs, context management, and tracing.

Python Agent Flow

from agents import (
    Agent, Runner, RunContextWrapper,
    function_tool, handoff, trace,
)
from agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX
from pydantic import BaseModel

# Context shared across agents
class MyContext(BaseModel):
    user_name: str | None = None
    session_id: str | None = None

# Define tools with @function_tool
@function_tool(name_override="lookup_tool", description_override="Look up information.")
async def lookup_tool(question: str) -> str:
    return f"Answer to: {question}"

@function_tool
async def update_record(
    context: RunContextWrapper[MyContext], record_id: str, value: str
) -> str:
    """Update a record. Args: record_id, value."""
    context.context.session_id = record_id
    return f"Updated {record_id} to {value}"

# Define agents with handoffs
specialist = Agent[MyContext](
    name="Specialist",
    handoff_description="Handles specific tasks.",
    instructions=f"""{RECOMMENDED_PROMPT_PREFIX}
    You are a specialist agent. Use your tools to help the customer.""",
    tools=[update_record],
)

triage = Agent[MyContext](
    name="Triage",
    instructions=f"""{RECOMMENDED_PROMPT_PREFIX}
    You are a triage agent. Delegate to the appropriate specialist.""",
    handoffs=[specialist],
)

# Allow circular handoffs
specialist.handoffs.append(triage)

# Run the agent loop
async def main():
    current_agent = triage
    input_items = []
    context = MyContext()
    conversation_id = uuid.uuid4().hex[:16]

    while True:
        user_input = input("You: ")
        with trace("My workflow", group_id=conversation_id):
            input_items.append({"content": user_input, "role": "user"})
            result = await Runner.run(current_agent, input_items, context=context)

            for item in result.new_items:
                if isinstance(item, MessageOutputItem):
                    print(f"{item.agent.name}: {ItemHelpers.text_message_output(item)}")
                elif isinstance(item, HandoffOutputItem):
                    print(f"Handed off: {item.source_agent.name} -> {item.target_agent.name}")

            input_items = result.to_input_list()
            current_agent = result.last_agent

Go Agent Flow

Uses nlpodyssey/openai-agents-go:

import (
    "github.com/nlpodyssey/openai-agents-go/agents"
    "github.com/nlpodyssey/openai-agents-go/agents/extensions/handoff_prompt"
    "github.com/nlpodyssey/openai-agents-go/tracing"
)

// Tools
type LookupArgs struct {
    Question string `json:"question"`
}

func Lookup(_ context.Context, args LookupArgs) (string, error) {
    return "Answer to: " + args.Question, nil
}

var LookupTool = agents.NewFunctionTool("lookup", "Look up information.", Lookup)

// Agents
var (
    Specialist = agents.New("Specialist").
        WithHandoffDescription("Handles specific tasks.").
        WithInstructions(handoff_prompt.PromptWithHandoffInstructions(`...`)).
        WithTools(LookupTool).
        WithModel("gpt-4o")

    Triage = agents.New("Triage").
        WithInstructions(handoff_prompt.PromptWithHandoffInstructions(`...`)).
        WithAgentHandoffs(Specialist).
        WithModel("gpt-4o")
)

func init() {
    Specialist.AgentHandoffs = append(Specialist.AgentHandoffs, Triage)
}

// Run
result, err := agents.RunInputs(ctx, Triage, inputItems)

Key Patterns

• Context: Use a Pydantic BaseModel (Python) or context.Value (Go) for shared state across agents
• Handoffs: Agents delegate to each other; use on_handoff hooks for side effects
• Tracing: Wrap runs in trace() / tracing.RunTrace() with a group_id for conversation tracking
• Tools: Decorate with @function_tool; the SDK extracts args from the function signature
• Circular handoffs: Append handoffs after agent definition to avoid forward-reference issues

Webhook Validation

from fastapi import FastAPI, Request, Response
from openai import OpenAI, InvalidWebhookSignatureError

app = FastAPI()
client = OpenAI(webhook_secret=os.environ["OPENAI_WEBHOOK_SECRET"])

@app.post("/webhook")
async def webhook(request: Request):
    try:
        body = await request.body()
        headers = dict(request.headers)
        event = client.webhooks.unwrap(body, headers)

        if event.type == "response.completed":
            response = client.responses.retrieve(event.data.id)
            print("Response output:", response.output_text)

        return Response(status_code=200)
    except InvalidWebhookSignatureError:
        return Response(content="Invalid signature", status_code=400)