Agent 的状态与记忆：分清短期上下文、任务状态和长期存储

python -m pip install "langchain>=1,<2" "langgraph>=1,<2" "langchain-openai>=1,<2"

from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.tools import ToolRuntime, tool
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.store.memory import InMemoryStore


@dataclass(frozen=True)
class Context:
    user_id: str


@tool
def save_reply_style(style: str, runtime: ToolRuntime) -> str:
    """保存用户明确要求的回复风格。"""
    user_id = runtime.context.user_id
    runtime.store.put(("preferences", user_id), "reply_style", {"style": style[:30]})
    return "saved"


@tool
def get_reply_style(runtime: ToolRuntime) -> str:
    """读取当前用户保存的回复风格。"""
    item = runtime.store.get(("preferences", runtime.context.user_id), "reply_style")
    return item.value["style"] if item else "not_set"


agent = create_agent(
    model="provider-model-name",
    tools=[save_reply_style, get_reply_style],
    context_schema=Context,
    checkpointer=InMemorySaver(),
    store=InMemoryStore(),
)

config = {"configurable": {"thread_id": "u-17:conversation-1"}}
agent.invoke(
    {"messages": [{"role": "user", "content": "以后请用简洁风格回答。"}]},
    config,
    context=Context(user_id="u-17"),
)
result = agent.invoke(
    {"messages": [{"role": "user", "content": "我喜欢怎样的回复？"}]},
    config,
    context=Context(user_id="u-17"),
)
print(result["messages"][-1].text)