运行时

from dataclasses import dataclass

from langchain_core.messages import AnyMessage
from langchain.agents import create_agent
from langgraph.runtime import get_runtime

@dataclass
class Context:
    user_name: str

agent = create_agent(
    model="openai:gpt-5-nano",
    tools=[...],
    context_schema=Context  
)

agent.invoke(
    {"messages": [{"role": "user", "content": "我叫什么名字？"}]},
    context=Context(user_name="John Smith")  
)

from typing import Any

from langchain_core.tools import tool

@tool
def fetch_user_email_preferences() -> str:
    runtime = get_runtime(Context)
    user_id = runtime.context.user_id

    preferences: str = "用户希望你写一封简洁礼貌的邮件。"
    if runtime.store:
        if memory := runtime.store.get(("users",), user_id):
            preferences = memory.value["preferences"]

    return preferences

from dataclasses import dataclass

from langchain_core.messages import AnyMessage
from langchain.agents import create_agent
from langgraph.runtime import get_runtime

@dataclass
class Context:
    user_name: str

def my_prompt(state: State) -> list[AnyMessage]:
    runtime = get_runtime(Context)
    system_msg = (
        "你是一个乐于助人的助手。"
        f"请称呼用户为 {runtime.context.user_name}。"
    )
    return [{"role": "system", "content": system_msg}] + state["messages"]

agent = create_agent(
    model="openai:gpt-5-nano",
    tools=[...],
    prompt=my_prompt,
    context_schema=Context
)

agent.invoke(
    {"messages": [{"role": "user", "content": "我叫什么名字？"}]},
    context=Context(user_name="John Smith")
)