短期记忆

概述

记忆是一个记住先前交互信息的系统。对于 AI 代理而言，记忆至关重要，因为它使它们能够记住先前的交互、从反馈中学习并适应用户偏好。随着代理处理具有众多用户交互的更复杂任务，这种能力对于效率和用户满意度都变得至关重要。

短期记忆让您的应用程序能够在单个线程或对话中记住先前的交互。

线程在会话中组织多个交互，类似于电子邮件在单个对话中分组消息的方式。

对话历史是短期记忆最常见的形式。长对话对当今的大语言模型（LLM）构成了挑战；完整的历史记录可能无法放入 LLM 的上下文窗口中，导致上下文丢失或错误。

即使您的模型支持完整的上下文长度，大多数 LLM 在长上下文下表现仍然不佳。它们会被过时或不相关的内容“分心”，同时伴随着响应时间变慢和成本增加。

聊天模型使用 messages 接受上下文，其中包括指令（系统消息）和输入（人类消息）。在聊天应用中，消息在人类输入和模型响应之间交替，导致消息列表随时间增长。由于上下文窗口有限，许多应用程序可以通过使用技术来移除或“遗忘”过时信息而受益。

需要在跨对话中记住信息？使用 长期记忆 在不同线程和会话中存储和回忆特定于用户或应用程序级别的数据。

用法

要向代理添加短期记忆（线程级持久性），您需要在创建代理时指定一个 checkpointer。

LangChain 的代理将短期记忆作为其代理状态的一部分进行管理。

通过将这些存储在图的状态中，代理可以访问给定对话的完整上下文，同时保持不同线程之间的分离。

状态使用 checkpointer 持久化到数据库（或内存），以便可以随时恢复线程。

当代理被调用或步骤（如工具调用）完成时，短期记忆会更新，并且状态在每个步骤开始时读取。

from langchain.agents import create_agent
from langgraph.checkpoint.memory import InMemorySaver  


agent = create_agent(
    "gpt-5",
    tools=[get_user_info],
    checkpointer=InMemorySaver(),
)

agent.invoke(
    {"messages": [{"role": "user", "content": "Hi! My name is Bob."}]},
    {"configurable": {"thread_id": "1"}},
)

生产环境

在生产环境中，使用由数据库支持的 checkpointer：

pip install langgraph-checkpoint-postgres

from langchain.agents import create_agent

from langgraph.checkpoint.postgres import PostgresSaver  


DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"
with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
    checkpointer.setup() # auto create tables in PostgreSQL
    agent = create_agent(
        "gpt-5",
        tools=[get_user_info],
        checkpointer=checkpointer,
    )

有关更多 checkpointer 选项，包括 SQLite、Postgres 和 Azure Cosmos DB，请参阅 Persistence 文档中的 checkpointer 库列表。

自定义代理记忆

默认情况下，代理使用 AgentState 管理短期记忆，特别是通过 messages 键进行对话历史。

您可以扩展 AgentState 以添加其他字段。自定义状态架构使用 state_schema 参数传递给 create_agent。

from langchain.agents import create_agent, AgentState
from langgraph.checkpoint.memory import InMemorySaver


class CustomAgentState(AgentState):
    user_id: str
    preferences: dict

agent = create_agent(
    "gpt-5",
    tools=[get_user_info],
    state_schema=CustomAgentState,
    checkpointer=InMemorySaver(),
)

# Custom state can be passed in invoke
result = agent.invoke(
    {
        "messages": [{"role": "user", "content": "Hello"}],
        "user_id": "user_123",
        "preferences": {"theme": "dark"}
    },
    {"configurable": {"thread_id": "1"}})

常见模式

启用 短期记忆 后，长对话可能会超过 LLM 的上下文窗口。常见的解决方案有：

修剪消息

移除前 N 条或后 N 条消息（在调用 LLM 之前）

删除消息

永久从 LangGraph 状态中删除消息

总结消息

总结历史记录中的早期消息并用摘要替换它们

自定义策略

自定义策略（例如，消息过滤等）

这允许代理跟踪对话而不会超过 LLM 的上下文窗口。

修剪消息

大多数 LLM 都有最大支持的上下文窗口（以令牌为单位表示）。

决定何时截断消息的一种方法是计算消息历史中的令牌数，并在接近该限制时截断。如果您正在使用 LangChain，可以使用修剪消息实用程序并指定要从列表中保留的令牌数量，以及用于处理边界的 strategy（例如，保留最后 max_tokens）。

要在代理中修剪消息历史，请使用 @before_model 中间件装饰器：

from langchain.messages import RemoveMessage
from langgraph.graph.message import REMOVE_ALL_MESSAGES
from langgraph.checkpoint.memory import InMemorySaver
from langchain.agents import create_agent, AgentState
from langchain.agents.middleware import before_model
from langgraph.runtime import Runtime
from langchain_core.runnables import RunnableConfig
from typing import Any


@before_model
def trim_messages(state: AgentState, runtime: Runtime) -> dict[str, Any] | None:
    """Keep only the last few messages to fit context window."""
    messages = state["messages"]

    if len(messages) <= 3:
        return None  # No changes needed

    first_msg = messages[0]
    recent_messages = messages[-3:] if len(messages) % 2 == 0 else messages[-4:]
    new_messages = [first_msg] + recent_messages

    return {
        "messages": [
            RemoveMessage(id=REMOVE_ALL_MESSAGES),
            *new_messages
        ]
    }

agent = create_agent(
    your_model_here,
    tools=your_tools_here,
    middleware=[trim_messages],
    checkpointer=InMemorySaver(),
)

config: RunnableConfig = {"configurable": {"thread_id": "1"}}

agent.invoke({"messages": "hi, my name is bob"}, config)
agent.invoke({"messages": "write a short poem about cats"}, config)
agent.invoke({"messages": "now do the same but for dogs"}, config)
final_response = agent.invoke({"messages": "what's my name?"}, config)

final_response["messages"][-1].pretty_print()
"""
================================== Ai Message ==================================

Your name is Bob. You told me that earlier.
If you'd like me to call you a nickname or use a different name, just say the word.
"""

删除消息

您可以从图状态中删除消息以管理消息历史。

当您想要删除特定消息或清除整个消息历史时，这很有用。

要从图状态中删除消息，您可以使用 RemoveMessage。

要使 RemoveMessage 正常工作，您需要使用带有 add_messages reducer 的状态键。

默认的 AgentState 提供了此功能。

要删除特定消息：

from langchain.messages import RemoveMessage  

def delete_messages(state):
    messages = state["messages"]
    if len(messages) > 2:
        # remove the earliest two messages
        return {"messages": [RemoveMessage(id=m.id) for m in messages[:2]]}

要删除所有消息：

from langgraph.graph.message import REMOVE_ALL_MESSAGES  

def delete_messages(state):
    return {"messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES)]}

删除消息时，确保生成的消息历史是有效的。检查您使用的 LLM 提供程序的局限性。例如：

• 某些提供程序期望消息历史以 user 消息开头
• 大多数提供程序要求带有工具调用的 assistant 消息后跟相应的 tool 结果消息。

from langchain.messages import RemoveMessage
from langchain.agents import create_agent, AgentState
from langchain.agents.middleware import after_model
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.runtime import Runtime
from langchain_core.runnables import RunnableConfig


@after_model
def delete_old_messages(state: AgentState, runtime: Runtime) -> dict | None:
    """Remove old messages to keep conversation manageable."""
    messages = state["messages"]
    if len(messages) > 2:
        # remove the earliest two messages
        return {"messages": [RemoveMessage(id=m.id) for m in messages[:2]]}
    return None


agent = create_agent(
    "gpt-5-nano",
    tools=[],
    system_prompt="Please be concise and to the point.",
    middleware=[delete_old_messages],
    checkpointer=InMemorySaver(),
)

config: RunnableConfig = {"configurable": {"thread_id": "1"}}

for event in agent.stream(
    {"messages": [{"role": "user", "content": "hi! I'm bob"}]},
    config,
    stream_mode="values",
):
    print([(message.type, message.content) for message in event["messages"]])

for event in agent.stream(
    {"messages": [{"role": "user", "content": "what's my name?"}]},
    config,
    stream_mode="values",
):
    print([(message.type, message.content) for message in event["messages"]])

[('human', "hi! I'm bob")]
[('human', "hi! I'm bob"), ('ai', 'Hi Bob! Nice to meet you. How can I help you today? I can answer questions, brainstorm ideas, draft text, explain things, or help with code.')]
[('human', "hi! I'm bob"), ('ai', 'Hi Bob! Nice to meet you. How can I help you today? I can answer questions, brainstorm ideas, draft text, explain things, or help with code.'), ('human', "what's my name?")]
[('human', "hi! I'm bob"), ('ai', 'Hi Bob! Nice to meet you. How can I help you today? I can answer questions, brainstorm ideas, draft text, explain things, or help with code.'), ('human', "what's my name?"), ('ai', 'Your name is Bob. How can I help you today, Bob?')]
[('human', "what's my name?"), ('ai', 'Your name is Bob. How can I help you today, Bob?')]

总结消息

如上所示，修剪或删除消息的问题在于，您可能会因清理消息队列而丢失信息。 因此，一些应用程序受益于使用聊天模型总结消息历史的更复杂方法。

要在代理中总结消息历史，请使用内置的 SummarizationMiddleware：

from langchain.agents import create_agent
from langchain.agents.middleware import SummarizationMiddleware
from langgraph.checkpoint.memory import InMemorySaver
from langchain_core.runnables import RunnableConfig


checkpointer = InMemorySaver()

agent = create_agent(
    model="gpt-4.1",
    tools=[],
    middleware=[
        SummarizationMiddleware(
            model="gpt-4.1-mini",
            trigger=("tokens", 4000),
            keep=("messages", 20)
        )
    ],
    checkpointer=checkpointer,
)

config: RunnableConfig = {"configurable": {"thread_id": "1"}}
agent.invoke({"messages": "hi, my name is bob"}, config)
agent.invoke({"messages": "write a short poem about cats"}, config)
agent.invoke({"messages": "now do the same but for dogs"}, config)
final_response = agent.invoke({"messages": "what's my name?"}, config)

final_response["messages"][-1].pretty_print()
"""
================================== Ai Message ==================================

Your name is Bob!
"""

有关更多配置选项，请参见 SummarizationMiddleware。

访问记忆

您可以通过多种方式访问和修改代理的短期记忆（状态）：

工具

在工具中读取短期记忆

使用 runtime 参数（类型为 ToolRuntime）在工具中访问短期记忆（状态）。

runtime 参数对工具签名隐藏（因此模型看不到它），但工具可以通过它访问状态。

from langchain.agents import create_agent, AgentState
from langchain.tools import tool, ToolRuntime


class CustomState(AgentState):
    user_id: str

@tool
def get_user_info(
    runtime: ToolRuntime
) -> str:
    """Look up user info."""
    user_id = runtime.state["user_id"]
    return "User is John Smith" if user_id == "user_123" else "Unknown user"

agent = create_agent(
    model="gpt-5-nano",
    tools=[get_user_info],
    state_schema=CustomState,
)

result = agent.invoke({
    "messages": "look up user information",
    "user_id": "user_123"
})
print(result["messages"][-1].content)
# > User is John Smith.

从工具写入短期记忆

为了在执行期间修改代理的短期记忆（状态），您可以直接从工具返回状态更新。

这对于持久化中间结果或使信息可供后续工具或提示使用非常有用。

from langchain.tools import tool, ToolRuntime
from langchain_core.runnables import RunnableConfig
from langchain.messages import ToolMessage
from langchain.agents import create_agent, AgentState
from langgraph.types import Command
from pydantic import BaseModel


class CustomState(AgentState):
    user_name: str

class CustomContext(BaseModel):
    user_id: str

@tool
def update_user_info(
    runtime: ToolRuntime[CustomContext, CustomState],
) -> Command:
    """Look up and update user info."""
    user_id = runtime.context.user_id
    name = "John Smith" if user_id == "user_123" else "Unknown user"
    return Command(update={
        "user_name": name,
        # update the message history
        "messages": [
            ToolMessage(
                "Successfully looked up user information",
                tool_call_id=runtime.tool_call_id
            )
        ]
    })

@tool
def greet(
    runtime: ToolRuntime[CustomContext, CustomState]
) -> str | Command:
    """Use this to greet the user once you found their info."""
    user_name = runtime.state.get("user_name", None)
    if user_name is None:
       return Command(update={
            "messages": [
                ToolMessage(
                    "Please call the 'update_user_info' tool it will get and update the user's name.",
                    tool_call_id=runtime.tool_call_id
                )
            ]
        })
    return f"Hello {user_name}!"

agent = create_agent(
    model="gpt-5-nano",
    tools=[update_user_info, greet],
    state_schema=CustomState,
    context_schema=CustomContext,
)

agent.invoke(
    {"messages": [{"role": "user", "content": "greet the user"}]},
    context=CustomContext(user_id="user_123"),
)

提示词

在中间件中访问短期记忆（状态），以根据对话历史或自定义状态字段创建动态提示词。

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


class CustomContext(TypedDict):
    user_name: str


def get_weather(city: str) -> str:
    """Get the weather in a city."""
    return f"The weather in {city} is always sunny!"


@dynamic_prompt
def dynamic_system_prompt(request: ModelRequest) -> str:
    user_name = request.runtime.context["user_name"]
    system_prompt = f"You are a helpful assistant. Address the user as {user_name}."
    return system_prompt


agent = create_agent(
    model="gpt-5-nano",
    tools=[get_weather],
    middleware=[dynamic_system_prompt],
    context_schema=CustomContext,
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]},
    context=CustomContext(user_name="John Smith"),
)
for msg in result["messages"]:
    msg.pretty_print()

Output

================================ Human Message =================================

What is the weather in SF?
================================== Ai Message ==================================
Tool Calls:
  get_weather (call_WFQlOGn4b2yoJrv7cih342FG)
 Call ID: call_WFQlOGn4b2yoJrv7cih342FG
  Args:
    city: San Francisco
================================= Tool Message =================================
Name: get_weather

The weather in San Francisco is always sunny!
================================== Ai Message ==================================

Hi John Smith, the weather in San Francisco is always sunny!

模型之前

在 @before_model 中间件中访问短期记忆（状态），以便在模型调用之前处理消息。

from langchain.messages import RemoveMessage
from langgraph.graph.message import REMOVE_ALL_MESSAGES
from langgraph.checkpoint.memory import InMemorySaver
from langchain.agents import create_agent, AgentState
from langchain.agents.middleware import before_model
from langchain_core.runnables import RunnableConfig
from langgraph.runtime import Runtime
from typing import Any


@before_model
def trim_messages(state: AgentState, runtime: Runtime) -> dict[str, Any] | None:
    """Keep only the last few messages to fit context window."""
    messages = state["messages"]

    if len(messages) <= 3:
        return None  # No changes needed

    first_msg = messages[0]
    recent_messages = messages[-3:] if len(messages) % 2 == 0 else messages[-4:]
    new_messages = [first_msg] + recent_messages

    return {
        "messages": [
            RemoveMessage(id=REMOVE_ALL_MESSAGES),
            *new_messages
        ]
    }


agent = create_agent(
    "gpt-5-nano",
    tools=[],
    middleware=[trim_messages],
    checkpointer=InMemorySaver()
)

config: RunnableConfig = {"configurable": {"thread_id": "1"}}

agent.invoke({"messages": "hi, my name is bob"}, config)
agent.invoke({"messages": "write a short poem about cats"}, config)
agent.invoke({"messages": "now do the same but for dogs"}, config)
final_response = agent.invoke({"messages": "what's my name?"}, config)

final_response["messages"][-1].pretty_print()
"""
================================== Ai Message ==================================

Your name is Bob. You told me that earlier.
If you'd like me to call you a nickname or use a different name, just say the word.
"""

模型之后

在 @after_model 中间件中访问短期记忆（状态），以便在模型调用之后处理消息。

from langchain.messages import RemoveMessage
from langgraph.checkpoint.memory import InMemorySaver
from langchain.agents import create_agent, AgentState
from langchain.agents.middleware import after_model
from langgraph.runtime import Runtime


@after_model
def validate_response(state: AgentState, runtime: Runtime) -> dict | None:
    """Remove messages containing sensitive words."""
    STOP_WORDS = ["password", "secret"]
    last_message = state["messages"][-1]
    if any(word in last_message.content for word in STOP_WORDS):
        return {"messages": [RemoveMessage(id=last_message.id)]}
    return None

agent = create_agent(
    model="gpt-5-nano",
    tools=[],
    middleware=[validate_response],
    checkpointer=InMemorySaver(),
)

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