LangChain v1.0Welcome to the new LangChain documentation! If you encounter any issues or have feedback, please open an issue so we can improve. Archived v0 documentation can be found here.See the release notes and migration guide for a complete list of changes and instructions on how to upgrade your code. Overview
Memory is a system that remembers information about previous interactions. For AI agents, memory is crucial because it lets them remember previous interactions, learn from feedback, and adapt to user preferences. As agents tackle more complex tasks with numerous user interactions, this capability becomes essential for both efficiency and user satisfaction.
Short term memory lets your application remember previous interactions within a single thread or conversation.
A thread organizes multiple interactions in a session, similar to the way email groups messages in a single conversation.
Usage
To add short-term memory (thread-level persistence) to an agent, you need to specify a checkpointer when creating an agent.
LangChain’s agent manages short-term memory as a part of your agent’s state.By storing these in the graph’s state, the agent can access the full context for a given conversation while maintaining separation between different threads.State is persisted to a database (or memory) using a checkpointer so the thread can be resumed at any time.Short-term memory updates when the agent is invoked or a step (like a tool call) is completed, and the state is read at the start of each step.
from langchain.agents import create_agent
from langgraph.checkpoint.memory import InMemorySaver
agent = create_agent(
"openai:gpt-5",
[get_user_info],
checkpointer=InMemorySaver(),
)
agent.invoke(
{"messages": [{"role": "user", "content": "Hi! My name is Bob."}]},
{"configurable": {"thread_id": "1"}},
)
In production
In production, use a checkpointer backed by a database:
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 PostgresSql
agent = create_agent(
"openai:gpt-5",
[get_user_info],
checkpointer=checkpointer,
)
Customizing agent memory
By default, agents use AgentState to manage short term memory, specifically the conversation history via a messages key.
You can extend AgentState to add additional fields. Custom state schemas are passed to create_agent using the state_schema parameter.
from langchain.agents import create_agent, AgentState
from langgraph.checkpoint.memory import InMemorySaver
class CustomAgentState(AgentState):
user_id: str
preferences: dict
agent = create_agent(
"openai:gpt-5",
[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"}})
Common patterns
With short-term memory enabled, long conversations can exceed the LLM’s context window. Common solutions are:
This allows the agent to keep track of the conversation without exceeding the LLM’s context window.
Trim messages
Most LLMs have a maximum supported context window (denominated in tokens).
One way to decide when to truncate messages is to count the tokens in the message history and truncate whenever it approaches that limit. If you’re using LangChain, you can use the trim messages utility and specify the number of tokens to keep from the list, as well as the strategy (e.g., keep the last max_tokens) to use for handling the boundary.
To trim message history in an agent, use the @before_model middleware decorator:
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(
model,
tools=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.
"""
Delete messages
You can delete messages from the graph state to manage the message history.
This is useful when you want to remove specific messages or clear the entire message history.
To delete messages from the graph state, you can use the RemoveMessage.
For RemoveMessage to work, you need to use a state key with add_messages reducer.
The default AgentState provides this.
To remove specific messages:
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]]}
from langgraph.graph.message import REMOVE_ALL_MESSAGES
def delete_messages(state):
return {"messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES)]}
When deleting messages, make sure that the resulting message history is valid. Check the limitations of the LLM provider you’re using. For example:
- Some providers expect message history to start with a
user message
- Most providers require
assistant messages with tool calls to be followed by corresponding tool result messages.
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(
"openai: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?')]
Summarize messages
The problem with trimming or removing messages, as shown above, is that you may lose information from culling of the message queue.
Because of this, some applications benefit from a more sophisticated approach of summarizing the message history using a chat model.
To summarize message history in an agent, use the built-in 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="openai:gpt-4o",
tools=[],
middleware=[
SummarizationMiddleware(
model="openai:gpt-4o-mini",
max_tokens_before_summary=4000, # Trigger summarization at 4000 tokens
messages_to_keep=20, # Keep last 20 messages after summary
)
],
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!
"""
SummarizationMiddleware for more configuration options.
Access memory
You can access and modify the short-term memory (state) of an agent in several ways:
Access short term memory (state) in a tool using the ToolRuntime parameter.
The tool_runtime parameter is hidden from the tool signature (so the model doesn’t see it), but the tool can access the state through it.
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="openai: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.
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:
"""Use this to greet the user once you found their info."""
user_name = runtime.state["user_name"]
return f"Hello {user_name}!"
agent = create_agent(
model="openai: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"),
)
Prompt
Access short term memory (state) in middleware to create dynamic prompts based on conversation history or custom state fields.
from langchain.messages import AnyMessage
from langchain.agents import create_agent, AgentState
from typing import TypedDict
class CustomContext(TypedDict):
user_name: str
from langchain.agents.middleware import dynamic_prompt, ModelRequest
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="openai: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()
================================ 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!
Before model
Access short term memory (state) in @before_model middleware to process messages before model calls.
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 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(
model,
tools=tools,
middleware=[trim_messages]
)
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.
"""
After model
Access short term memory (state) in @after_model middleware to process messages after model calls.
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="openai:gpt-5-nano",
tools=[],
middleware=[validate_response],
checkpointer=InMemorySaver(),
)
