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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.
Conversation history is the most common form of short-term memory. Long conversations pose a challenge to today’s LLMs; a full history may not fit inside an LLM’s context window, resulting in an context loss or errors. Even if your model supports the full context length, most LLMs still perform poorly over long contexts. They get “distracted” by stale or off-topic content, all while suffering from slower response times and higher costs. Chat models accept context using messages, which include instructions (a system message) and inputs (human messages). In chat applications, messages alternate between human inputs and model responses, resulting in a list of messages that grows longer over time. Because context windows are limited, many applications can benefit from using techniques to remove or “forget” stale information.

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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(  "gpt-5",  [get_user_info],  checkpointer=InMemorySaver(),  )  agent.invoke(  {"messages": [{"role": "user", "content": "Hi! My name is Bob."}]},  {"configurable": {"thread_id": "1"}},  )

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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(  "gpt-5",  [get_user_info],  checkpointer=checkpointer,   )

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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(  "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"}})

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Common patterns

With short-term memory enabled, long conversations can exceed the LLM’s context window. Common solutions are:

Trim messages

Remove first or last N messages (before calling LLM)

Delete messages

Delete messages from LangGraph state permanently

Summarize messages

Summarize earlier messages in the history and replace them with a summary

Custom strategies

Custom strategies (e.g., message filtering, etc.)
This allows the agent to keep track of the conversation without exceeding the LLM’s context window.

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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. """

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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]]}
To remove all messages: 
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(  "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?')]

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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="gpt-4o",  tools=[],  middleware=[  SummarizationMiddleware(  model="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! """
See SummarizationMiddleware for more configuration options.

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Access memory

You can access and modify the short-term memory (state) of an agent in several ways:

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Tools

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Read short-term memory in a tool

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="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.

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Write short-term memory from tools

To modify the agent’s short-term memory (state) during execution, you can return state updates directly from the tools. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts. 
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="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"), )

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Prompt

Access short term memory (state) in middleware to create dynamic prompts based on conversation history or custom state fields. 
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!

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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. """

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