Memory

LangGraph supports two types of memory essential for building conversational agents:

• Short-term memory: Tracks the ongoing conversation by maintaining message history within a session.
• Long-term memory: Stores user-specific or application-level data across sessions.

This guide demonstrates how to use both memory types with agents in LangGraph. For a deeper understanding of memory concepts, refer to the LangGraph memory documentation.

Both short-term and long-term memory require persistent storage to maintain continuity across LLM interactions. In production environments, this data is typically stored in a database.

Terminology

In LangGraph:

• Short-term memory is also referred to as thread-level memory.
• Long-term memory is also called cross-thread memory.

A thread represents a sequence of related runs grouped by the same thread_id.

Short-term memory

Short-term memory enables agents to track multi-turn conversations. To use it, you must:

1. Provide a checkpointer when creating the agent. The checkpointer enables persistence of the agent's state.
2. Supply a thread_id in the config when running the agent. The thread_id is a unique identifier for the conversation session.

API Reference: create_react_agent | InMemorySaver

from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import InMemorySaver

checkpointer = InMemorySaver() # (1)!


def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"


agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_weather],
    checkpointer=checkpointer # (2)!
)

# Run the agent
config = {
    "configurable": {
        "thread_id": "1"  # (3)!
    }
}

sf_response = agent.invoke(
    {"messages": "what is the weather in sf"},
    config
)

# Continue the conversation using the same thread_id
ny_response = agent.invoke(
    {"messages": "what about new york?"},
    config # (4)!
)

1. The InMemorySaver is a checkpointer that stores the agent's state in memory. In a production setting, you would typically use a database or other persistent storage. Please review the checkpointer documentation for more options. If you're deploying with LangGraph Platform, the platform will provide a production-ready checkpointer for you.
2. The checkpointer is passed to the agent. This enables the agent to persist its state across invocations. Please note that
3. A unique thread_id is provided in the config. This ID is used to identify the conversation session. The value is controlled by the user and can be any string.
4. The agent will continue the conversation using the same thread_id. This will allow the agent to infer that the user is asking specifically about the weather in New York.

When the agent is invoked the second time with the same thread_id, the original message history from the first conversation is automatically included, allowing the agent to infer that the user is asking specifically about the weather in New York.

LangGraph Platform providers a production-ready checkpointer

If you're using LangGraph Platform, during deployment your checkpointer will be automatically configured to use a production-ready database.

Message history summarization

Message history can grow quickly and exceed the LLM's context window. A common solution is to maintain a running summary of the conversation. This allows the agent to keep track of the conversation without exceeding the LLM's context window.

Long conversations can exceed the LLM's context window. To handle this, you can summarize older messages by specifying a pre_model_hook, such as the prebuilt SummarizationNode:

API Reference: ChatAnthropic | count_tokens_approximately | create_react_agent | InMemorySaver

from langchain_anthropic import ChatAnthropic
from langmem.short_term import SummarizationNode
from langchain_core.messages.utils import count_tokens_approximately
from langgraph.prebuilt import create_react_agent
from langgraph.prebuilt.chat_agent_executor import AgentState
from langgraph.checkpoint.memory import InMemorySaver
from typing import Any

model = ChatAnthropic(model="claude-3-7-sonnet-latest")

summarization_node = SummarizationNode( # (1)!
    token_counter=count_tokens_approximately,
    model=model,
    max_tokens=384,
    max_summary_tokens=128,
    output_messages_key="llm_input_messages",
)

class State(AgentState):
    # NOTE: we're adding this key to keep track of previous summary information
    # to make sure we're not summarizing on every LLM call
    context: dict[str, Any]  # (2)!


checkpointer = InMemorySaver() # (3)!

agent = create_react_agent(
    model=model,
    tools=tools,
    pre_model_hook=summarization_node, # (4)!
    state_schema=State, # (5)!
    checkpointer=checkpointer,
)

1. The InMemorySaver is a checkpointer that stores the agent's state in memory. In a production setting, you would typically use a database or other persistent storage. Please review the checkpointer documentation for more options. If you're deploying with LangGraph Platform, the platform will provide a production-ready checkpointer for you.
2. The context key is added to the agent's state. The key contains book-keeping information for the summarization node. It is used to keep track of the last summary information and ensure that the agent doesn't summarize on every LLM call, which can be inefficient.
3. The checkpointer is passed to the agent. This enables the agent to persist its state across invocations.
4. The pre_model_hook is set to the SummarizationNode. This node will summarize the message history before sending it to the LLM. The summarization node will automatically handle the summarization process and update the agent's state with the new summary. You can replace this with a custom implementation if you prefer. Please see the create_react_agent API reference for more details.
5. The state_schema is set to the State class, which is the custom state that contains an extra context key.

To learn more about using pre_model_hook for managing message history, see this how-to guide

Long-term memory

Use long-term memory to store user-specific or application-specific data across conversations. This is useful for applications like chatbots, where you want to remember user preferences or other information.

To use long-term memory, you need to:

1. Configure a store to persist data across invocations.
2. Use the get_store function to access the store from within tools or prompts.

Reading

A tool the agent can use to look up user information

from langgraph.config import get_store
from langgraph.prebuilt import create_react_agent
from langgraph.store.memory import InMemoryStore

store = InMemoryStore() # (1)!

store.put(  # (2)!
    ("users",),  # (3)!
    "user_123",  # (4)!
    {
        "name": "John Smith",
        "language": "English",
    } # (5)!
)

def get_user_info(config: RunnableConfig) -> str:
    """Look up user info."""
    # Same as that provided to `create_react_agent`
    store = get_store() # (6)!
    user_id = config.get("configurable", {}).get("user_id")
    user_info = store.get(("users",), user_id) # (7)!
    return str(user_info.value) if user_info else "Unknown user"

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_user_info],
    store=store # (8)!
)

# Run the agent
agent.invoke(
    {"messages": "look up user information"},
    config={"configurable": {"user_id": "user_123"}}
)

1. The InMemoryStore is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the store documentation for more options. If you're deploying with LangGraph Platform, the platform will provide a production-ready store for you.
2. For this example, we write some sample data to the store using the put method. Please see the BaseStore.put API reference for more details.
3. The first argument is the namespace. This is used to group related data together. In this case, we are using the users namespace to group user data.
4. A key within the namespace. This example uses a user ID for the key.
5. The data that we want to store for the given user.
6. The get_store function is used to access the store. You can call it from anywhere in your code, including tools and prompts. This function returns the store that was passed to the agent when it was created.
7. The get method is used to retrieve data from the store. The first argument is the namespace, and the second argument is the key. This will return a StoreValue object, which contains the value and metadata about the value.
8. The store is passed to the agent. This enables the agent to access the store when running tools. You can also use the get_store function to access the store from anywhere in your code.

Writing

Example of a tool that updates user information

from typing import TypedDict

from langgraph.config import get_store
from langgraph.prebuilt import create_react_agent
from langgraph.store.memory import InMemoryStore

store = InMemoryStore() # (1)!

class UserInfo(TypedDict): # (2)!
    name: str

def save_user_info(user_info: UserInfo, config: RunnableConfig) -> str: # (3)!
    """Save user info."""
    # Same as that provided to `create_react_agent`
    store = get_store() # (4)!
    user_id = config.get("configurable", {}).get("user_id")
    store.put(("users",), user_id, user_info) # (5)!
    return "Successfully saved user info."

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[save_user_info],
    store=store
)

# Run the agent
agent.invoke(
    {"messages": "My name is John Smith"},
    config={"configurable": {"user_id": "user_123"}} # (6)!
)

# You can access the store directly to get the value
store.get(("users",), "user_123").value

1. The InMemoryStore is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the store documentation for more options. If you're deploying with LangGraph Platform, the platform will provide a production-ready store for you.
2. The UserInfo class is a TypedDict that defines the structure of the user information. The LLM will use this to format the response according to the schema.
3. The save_user_info function is a tool that allows an agent to update user information. This could be useful for a chat application where the user wants to update their profile information.
4. The get_store function is used to access the store. You can call it from anywhere in your code, including tools and prompts. This function returns the store that was passed to the agent when it was created.
5. The put method is used to store data in the store. The first argument is the namespace, and the second argument is the key. This will store the user information in the store.
6. The user_id is passed in the config. This is used to identify the user whose information is being updated.

Prebuilt memory tools

LangMem is a LangChain-maintained library that offers tools for managing long-term memories in your agent. See the LangMem documentation for usage examples.

Additional resources

• Memory in LangGraph

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