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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Get started

Core components

Middleware

Frontend

Advanced usage

Agent development

Deploy with LangSmith

概述

用法

生产环境

自定义代理记忆

常见模式

修剪消息

删除消息

总结消息

自定义策略

修剪消息

删除消息

总结消息

访问记忆

工具

在工具中读取短期记忆

从工具写入短期记忆

提示词

模型之前

模型之后

Get started

Core components

Middleware

Frontend

Advanced usage

Agent development

Deploy with LangSmith

​概述

​用法

​生产环境

​自定义代理记忆

​常见模式

修剪消息

删除消息

总结消息

自定义策略

​修剪消息

​删除消息

​总结消息

​访问记忆

​工具

​在工具中读取短期记忆

​从工具写入短期记忆

​提示词

​模型之前

​模型之后

概述

用法

生产环境

自定义代理记忆

常见模式

修剪消息

删除消息

总结消息

访问记忆

工具

在工具中读取短期记忆

从工具写入短期记忆

提示词

模型之前

模型之后