에이전트

from langchain.agents import create_agent

agent = create_agent(
    "openai:gpt-5",
    tools=tools
)

from langchain.agents import create_agent
from langchain_openai import ChatOpenAI

model = ChatOpenAI(
    model="gpt-5",
    temperature=0.1,
    max_tokens=1000,
    timeout=30
    # ... (other params)
)
agent = create_agent(model, tools=tools)

from langchain.tools import tool
from langchain.agents import create_agent


@tool
def search(query: str) -> str:
    """정보를 검색합니다."""
    return f"Results for: {query}"

@tool
def get_weather(location: str) -> str:
    """위치의 날씨 정보를 가져옵니다."""
    return f"Weather in {location}: Sunny, 72°F"

agent = create_agent(model, tools=[search, get_weather])

from langchain.agents import create_agent
from langchain.agents.middleware import wrap_tool_call
from langchain_core.messages import ToolMessage


@wrap_tool_call
def handle_tool_errors(request, handler):
    """사용자 정의 메시지로 도구 실행 오류를 처리합니다."""
    try:
        return handler(request)
    except Exception as e:
        # 모델에 사용자 정의 오류 메시지를 반환합니다
        return ToolMessage(
            content=f"Tool error: Please check your input and try again. ({str(e)})",
            tool_call_id=request.tool_call["id"]
        )

agent = create_agent(
    model="openai:gpt-4o",
    tools=[search, get_weather],
    middleware=[handle_tool_errors]
)

agent = create_agent(
    model,
    tools,
    system_prompt="You are a helpful assistant. Be concise and accurate."
)

from typing import TypedDict

from langchain.agents import create_agent
from langchain.agents.middleware import dynamic_prompt, ModelRequest


class Context(TypedDict):
    user_role: str

@dynamic_prompt
def user_role_prompt(request: ModelRequest) -> str:
    """사용자 역할에 따라 시스템 프롬프트를 생성합니다."""
    user_role = request.runtime.context.get("user_role", "user")
    base_prompt = "You are a helpful assistant."

    if user_role == "expert":
        return f"{base_prompt} Provide detailed technical responses."
    elif user_role == "beginner":
        return f"{base_prompt} Explain concepts simply and avoid jargon."

    return base_prompt

agent = create_agent(
    model="openai:gpt-4o",
    tools=[web_search],
    middleware=[user_role_prompt],
    context_schema=Context
)

# 시스템 프롬프트는 컨텍스트에 따라 동적으로 설정됩니다
result = agent.invoke(
    {"messages": [{"role": "user", "content": "Explain machine learning"}]},
    context={"user_role": "expert"}
)

from pydantic import BaseModel
from langchain.agents import create_agent
from langchain.agents.structured_output import ToolStrategy


class ContactInfo(BaseModel):
    name: str
    email: str
    phone: str

agent = create_agent(
    model="openai:gpt-4o-mini",
    tools=[search_tool],
    response_format=ToolStrategy(ContactInfo)
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "Extract contact info from: John Doe, [email protected], (555) 123-4567"}]
})

result["structured_response"]
# ContactInfo(name='John Doe', email='[email protected]', phone='(555) 123-4567')

from langchain.agents.structured_output import ProviderStrategy

agent = create_agent(
    model="openai:gpt-4o",
    response_format=ProviderStrategy(ContactInfo)
)