Add and manage memory

AI applications need memory to share context across multiple interactions. In LangGraph, you can add two types of memory:

Add short-term memory as a part of your agent’s state to enable multi-turn conversations.
Add long-term memory to store user-specific or application-level data across sessions.

Add short-term memory

Short-term memory (thread-level persistence) enables agents to track multi-turn conversations. To add short-term memory:

# highlight-next-line
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph import StateGraph

# highlight-next-line
checkpointer = InMemorySaver()

builder = StateGraph(...)
# highlight-next-line
graph = builder.compile(checkpointer=checkpointer)

graph.invoke(
    {"messages": [{"role": "user", "content": "hi! i am Bob"}]},
    # highlight-next-line
    {"configurable": {"thread_id": "1"}},
)

Use in production

In production, use a checkpointer backed by a database:

from langgraph.checkpoint.postgres import PostgresSaver

DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"
# highlight-next-line
with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
    builder = StateGraph(...)
    # highlight-next-line
    graph = builder.compile(checkpointer=checkpointer)

Example: using Postgres checkpointer

pip install -U "psycopg[binary,pool]" langgraph langgraph-checkpoint-postgres

You need to call checkpointer.setup() the first time you’re using Postgres checkpointer

Sync
Async

from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
# highlight-next-line
from langgraph.checkpoint.postgres import PostgresSaver

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"
# highlight-next-line
with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
    # checkpointer.setup()

    def call_model(state: MessagesState):
        response = model.invoke(state["messages"])
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    # highlight-next-line
    graph = builder.compile(checkpointer=checkpointer)

    config = {
        "configurable": {
            # highlight-next-line
            "thread_id": "1"
        }
    }

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "hi! I'm bob"}]},
        # highlight-next-line
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "what's my name?"}]},
        # highlight-next-line
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

Example: using [MongoDB](https://pypi.org/project/langgraph-checkpoint-mongodb/) checkpointer

pip install -U pymongo langgraph langgraph-checkpoint-mongodb

Setup To use the MongoDB checkpointer, you will need a MongoDB cluster. Follow this guide to create a cluster if you don’t already have one.

Sync
Async

from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
# highlight-next-line
from langgraph.checkpoint.mongodb import MongoDBSaver

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "localhost:27017"
# highlight-next-line
with MongoDBSaver.from_conn_string(DB_URI) as checkpointer:

    def call_model(state: MessagesState):
        response = model.invoke(state["messages"])
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    # highlight-next-line
    graph = builder.compile(checkpointer=checkpointer)

    config = {
        "configurable": {
            # highlight-next-line
            "thread_id": "1"
        }
    }

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "hi! I'm bob"}]},
        # highlight-next-line
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "what's my name?"}]},
        # highlight-next-line
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

Example: using [Redis](https://pypi.org/project/langgraph-checkpoint-redis/) checkpointer

pip install -U langgraph langgraph-checkpoint-redis

You need to call checkpointer.setup() the first time you’re using Redis checkpointer

Sync
Async

from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
# highlight-next-line
from langgraph.checkpoint.redis import RedisSaver

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "redis://localhost:6379"
# highlight-next-line
with RedisSaver.from_conn_string(DB_URI) as checkpointer:
    # checkpointer.setup()

    def call_model(state: MessagesState):
        response = model.invoke(state["messages"])
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    # highlight-next-line
    graph = builder.compile(checkpointer=checkpointer)

    config = {
        "configurable": {
            # highlight-next-line
            "thread_id": "1"
        }
    }

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "hi! I'm bob"}]},
        # highlight-next-line
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "what's my name?"}]},
        # highlight-next-line
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

Use in subgraphs

If your graph contains subgraphs, you only need to provide the checkpointer when compiling the parent graph. LangGraph will automatically propagate the checkpointer to the child subgraphs.

from langgraph.graph import START, StateGraph
from langgraph.checkpoint.memory import InMemorySaver
from typing import TypedDict

class State(TypedDict):
    foo: str

# Subgraph

def subgraph_node_1(state: State):
    return {"foo": state["foo"] + "bar"}

subgraph_builder = StateGraph(State)
subgraph_builder.add_node(subgraph_node_1)
subgraph_builder.add_edge(START, "subgraph_node_1")
# highlight-next-line
subgraph = subgraph_builder.compile()

# Parent graph

builder = StateGraph(State)
# highlight-next-line
builder.add_node("node_1", subgraph)
builder.add_edge(START, "node_1")

checkpointer = InMemorySaver()
# highlight-next-line
graph = builder.compile(checkpointer=checkpointer)

If you want the subgraph to have its own memory, you can compile it with the appropriate checkpointer option. This is useful in multi-agent systems, if you want agents to keep track of their internal message histories.

subgraph_builder = StateGraph(...)
# highlight-next-line
subgraph = subgraph_builder.compile(checkpointer=True)

Read short-term memory in tools

LangGraph allows agents to access their short-term memory (state) inside the tools.

from typing import Annotated
from langgraph.prebuilt import InjectedState, create_react_agent

class CustomState(AgentState):
    # highlight-next-line
    user_id: str

def get_user_info(
    # highlight-next-line
    state: Annotated[CustomState, InjectedState]
) -> str:
    """Look up user info."""
    # highlight-next-line
    user_id = state["user_id"]
    return "User is John Smith" if user_id == "user_123" else "Unknown user"

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_user_info],
    # highlight-next-line
    state_schema=CustomState,
)

agent.invoke({
    "messages": "look up user information",
    # highlight-next-line
    "user_id": "user_123"
})

See the Context guide for more information.

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 typing import Annotated
from langchain_core.tools import InjectedToolCallId
from langchain_core.runnables import RunnableConfig
from langchain_core.messages import ToolMessage
from langgraph.prebuilt import InjectedState, create_react_agent
from langgraph.prebuilt.chat_agent_executor import AgentState
from langgraph.types import Command

class CustomState(AgentState):
    # highlight-next-line
    user_name: str

def update_user_info(
    tool_call_id: Annotated[str, InjectedToolCallId],
    config: RunnableConfig
) -> Command:
    """Look up and update user info."""
    user_id = config["configurable"].get("user_id")
    name = "John Smith" if user_id == "user_123" else "Unknown user"
    # highlight-next-line
    return Command(update={
        # highlight-next-line
        "user_name": name,
        # update the message history
        "messages": [
            ToolMessage(
                "Successfully looked up user information",
                tool_call_id=tool_call_id
            )
        ]
    })

def greet(
    # highlight-next-line
    state: Annotated[CustomState, InjectedState]
) -> str:
    """Use this to greet the user once you found their info."""
    user_name = state["user_name"]
    return f"Hello {user_name}!"

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[update_user_info, greet],
    # highlight-next-line
    state_schema=CustomState
)

agent.invoke(
    {"messages": [{"role": "user", "content": "greet the user"}]},
    # highlight-next-line
    config={"configurable": {"user_id": "user_123"}}
)

Add long-term memory

Use long-term memory to store user-specific or application-specific data across conversations.

# highlight-next-line
from langgraph.store.memory import InMemoryStore
from langgraph.graph import StateGraph

# highlight-next-line
store = InMemoryStore()

builder = StateGraph(...)
# highlight-next-line
graph = builder.compile(store=store)

Use in production

In production, use a store backed by a database:

from langgraph.store.postgres import PostgresStore

DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"
# highlight-next-line
with PostgresStore.from_conn_string(DB_URI) as store:
    builder = StateGraph(...)
    # highlight-next-line
    graph = builder.compile(store=store)

Example: using Postgres store

pip install -U "psycopg[binary,pool]" langgraph langgraph-checkpoint-postgres

You need to call store.setup() the first time you’re using Postgres store

Sync
Async

from langchain_core.runnables import RunnableConfig
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.postgres import PostgresSaver
# highlight-next-line
from langgraph.store.postgres import PostgresStore
from langgraph.store.base import BaseStore

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"

with (
    # highlight-next-line
    PostgresStore.from_conn_string(DB_URI) as store,
    PostgresSaver.from_conn_string(DB_URI) as checkpointer,
):
    # store.setup()
    # checkpointer.setup()

    def call_model(
        state: MessagesState,
        config: RunnableConfig,
        *,
        # highlight-next-line
        store: BaseStore,
    ):
        user_id = config["configurable"]["user_id"]
        namespace = ("memories", user_id)
        # highlight-next-line
        memories = store.search(namespace, query=str(state["messages"][-1].content))
        info = "\n".join([d.value["data"] for d in memories])
        system_msg = f"You are a helpful assistant talking to the user. User info: {info}"

        # Store new memories if the user asks the model to remember
        last_message = state["messages"][-1]
        if "remember" in last_message.content.lower():
            memory = "User name is Bob"
            # highlight-next-line
            store.put(namespace, str(uuid.uuid4()), {"data": memory})

        response = model.invoke(
            [{"role": "system", "content": system_msg}] + state["messages"]
        )
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    graph = builder.compile(
        checkpointer=checkpointer,
        # highlight-next-line
        store=store,
    )

    config = {
        "configurable": {
            # highlight-next-line
            "thread_id": "1",
            # highlight-next-line
            "user_id": "1",
        }
    }
    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
        # highlight-next-line
        config,
        stream_mode="values",
    ):
        chunk["messages"][-1].pretty_print()

    config = {
        "configurable": {
            # highlight-next-line
            "thread_id": "2",
            "user_id": "1",
        }
    }

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "what is my name?"}]},
        # highlight-next-line
        config,
        stream_mode="values",
    ):
        chunk["messages"][-1].pretty_print()

Example: using [Redis](https://pypi.org/project/langgraph-checkpoint-redis/) store

pip install -U langgraph langgraph-checkpoint-redis

You need to call store.setup() the first time you’re using Redis store

Sync
Async

from langchain_core.runnables import RunnableConfig
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.redis import RedisSaver
# highlight-next-line
from langgraph.store.redis import RedisStore
from langgraph.store.base import BaseStore

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "redis://localhost:6379"

with (
    # highlight-next-line
    RedisStore.from_conn_string(DB_URI) as store,
    RedisSaver.from_conn_string(DB_URI) as checkpointer,
):
    store.setup()
    checkpointer.setup()

    def call_model(
        state: MessagesState,
        config: RunnableConfig,
        *,
        # highlight-next-line
        store: BaseStore,
    ):
        user_id = config["configurable"]["user_id"]
        namespace = ("memories", user_id)
        # highlight-next-line
        memories = store.search(namespace, query=str(state["messages"][-1].content))
        info = "\n".join([d.value["data"] for d in memories])
        system_msg = f"You are a helpful assistant talking to the user. User info: {info}"

        # Store new memories if the user asks the model to remember
        last_message = state["messages"][-1]
        if "remember" in last_message.content.lower():
            memory = "User name is Bob"
            # highlight-next-line
            store.put(namespace, str(uuid.uuid4()), {"data": memory})

        response = model.invoke(
            [{"role": "system", "content": system_msg}] + state["messages"]
        )
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    graph = builder.compile(
        checkpointer=checkpointer,
        # highlight-next-line
        store=store,
    )

    config = {
        "configurable": {
            # highlight-next-line
            "thread_id": "1",
            # highlight-next-line
            "user_id": "1",
        }
    }
    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
        # highlight-next-line
        config,
        stream_mode="values",
    ):
        chunk["messages"][-1].pretty_print()

    config = {
        "configurable": {
            # highlight-next-line
            "thread_id": "2",
            "user_id": "1",
        }
    }

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "what is my name?"}]},
        # highlight-next-line
        config,
        stream_mode="values",
    ):
        chunk["messages"][-1].pretty_print()

Read long-term memory in tools

A tool the agent can use to look up user information

from langchain_core.runnables import RunnableConfig
from langgraph.config import get_store
from langgraph.prebuilt import create_react_agent
from langgraph.store.memory import InMemoryStore

# highlight-next-line
store = InMemoryStore() # (1)!

# highlight-next-line
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`
    # highlight-next-line
    store = get_store() # (6)!
    user_id = config["configurable"].get("user_id")
    # highlight-next-line
    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],
    # highlight-next-line
    store=store # (8)!
)

# Run the agent
agent.invoke(
    {"messages": [{"role": "user", "content": "look up user information"}]},
    # highlight-next-line
    config={"configurable": {"user_id": "user_123"}}
)

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.
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.
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.
A key within the namespace. This example uses a user ID for the key.
The data that we want to store for the given user.
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.
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.
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.

Write long-term memory from tools

Example of a tool that updates user information

from typing_extensions import TypedDict

from langgraph.config import get_store
from langchain_core.runnables import RunnableConfig
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`
    # highlight-next-line
    store = get_store() # (4)!
    user_id = config["configurable"].get("user_id")
    # highlight-next-line
    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],
    # highlight-next-line
    store=store
)

# Run the agent
agent.invoke(
    {"messages": [{"role": "user", "content": "My name is John Smith"}]},
    # highlight-next-line
    config={"configurable": {"user_id": "user_123"}} # (6)!
)

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

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.
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.
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.
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.
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.
The user_id is passed in the config. This is used to identify the user whose information is being updated.

Use semantic search

Enable semantic search in your graph’s memory store to let graph agents search for items in the store by semantic similarity.

from langchain.embeddings import init_embeddings
from langgraph.store.memory import InMemoryStore

# Create store with semantic search enabled
embeddings = init_embeddings("openai:text-embedding-3-small")
store = InMemoryStore(
    index={
        "embed": embeddings,
        "dims": 1536,
    }
)

store.put(("user_123", "memories"), "1", {"text": "I love pizza"})
store.put(("user_123", "memories"), "2", {"text": "I am a plumber"})

items = store.search(
    ("user_123", "memories"), query="I'm hungry", limit=1
)

Long-term memory with semantic search

from typing import Optional

from langchain.embeddings import init_embeddings
from langchain.chat_models import init_chat_model
from langgraph.store.base import BaseStore
from langgraph.store.memory import InMemoryStore
from langgraph.graph import START, MessagesState, StateGraph

llm = init_chat_model("openai:gpt-4o-mini")

# Create store with semantic search enabled
embeddings = init_embeddings("openai:text-embedding-3-small")
store = InMemoryStore(
    index={
        "embed": embeddings,
        "dims": 1536,
    }
)

store.put(("user_123", "memories"), "1", {"text": "I love pizza"})
store.put(("user_123", "memories"), "2", {"text": "I am a plumber"})

def chat(state, *, store: BaseStore):
    # Search based on user's last message
    items = store.search(
        ("user_123", "memories"), query=state["messages"][-1].content, limit=2
    )
    memories = "\n".join(item.value["text"] for item in items)
    memories = f"## Memories of user\n{memories}" if memories else ""
    response = llm.invoke(
        [
            {"role": "system", "content": f"You are a helpful assistant.\n{memories}"},
            *state["messages"],
        ]
    )
    return {"messages": [response]}


builder = StateGraph(MessagesState)
builder.add_node(chat)
builder.add_edge(START, "chat")
graph = builder.compile(store=store)

for message, metadata in graph.stream(
    input={"messages": [{"role": "user", "content": "I'm hungry"}]},
    stream_mode="messages",
):
    print(message.content, end="")

Manage short-term memory

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 from LangGraph state permanently
Summarize messages: Summarize earlier messages in the history and replace them with a summary
Manage checkpoints to store and retrieve message history
Custom strategies (e.g., message filtering, etc.)

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 maxTokens) to use for handling the boundary.

In an agent
In a workflow

To trim message history in an agent, use pre_model_hook with the trim_messages function:

# highlight-next-line
from langchain_core.messages.utils import (
    # highlight-next-line
    trim_messages,
    # highlight-next-line
    count_tokens_approximately
# highlight-next-line
)
from langgraph.prebuilt import create_react_agent

# This function will be called every time before the node that calls LLM
def pre_model_hook(state):
    trimmed_messages = trim_messages(
        state["messages"],
        strategy="last",
        token_counter=count_tokens_approximately,
        max_tokens=384,
        start_on="human",
        end_on=("human", "tool"),
    )
    # highlight-next-line
    return {"llm_input_messages": trimmed_messages}

checkpointer = InMemorySaver()
agent = create_react_agent(
    model,
    tools,
    # highlight-next-line
    pre_model_hook=pre_model_hook,
    checkpointer=checkpointer,
)

Full example: trim messages

# highlight-next-line
from langchain_core.messages.utils import (
    # highlight-next-line
    trim_messages,
    # highlight-next-line
    count_tokens_approximately
# highlight-next-line
)
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, START, MessagesState

model = init_chat_model("anthropic:claude-3-7-sonnet-latest")
summarization_model = model.bind(max_tokens=128)

def call_model(state: MessagesState):
    # highlight-next-line
    messages = trim_messages(
        state["messages"],
        strategy="last",
        token_counter=count_tokens_approximately,
        max_tokens=128,
        start_on="human",
        end_on=("human", "tool"),
    )
    response = model.invoke(messages)
    return {"messages": [response]}

checkpointer = InMemorySaver()
builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(checkpointer=checkpointer)

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

final_response["messages"][-1].pretty_print()

================================== Ai Message ==================================

Your name is Bob, as you mentioned when you first introduced yourself.

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, like MessagesState. To remove specific messages:

# highlight-next-line
from langchain_core.messages import RemoveMessage

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

To remove all messages:

# highlight-next-line
from langgraph.graph.message import REMOVE_ALL_MESSAGES

def delete_messages(state):
    # highlight-next-line
    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.

Full example: delete messages

# highlight-next-line
from langchain_core.messages import RemoveMessage

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

def call_model(state: MessagesState):
    response = model.invoke(state["messages"])
    return {"messages": response}

builder = StateGraph(MessagesState)
builder.add_sequence([call_model, delete_messages])
builder.add_edge(START, "call_model")

checkpointer = InMemorySaver()
app = builder.compile(checkpointer=checkpointer)

for event in app.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 app.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! How are you doing today? Is there anything I can help you with?')]
[('human', "hi! I'm bob"), ('ai', 'Hi Bob! How are you doing today? Is there anything I can help you with?'), ('human', "what's my name?")]
[('human', "hi! I'm bob"), ('ai', 'Hi Bob! How are you doing today? Is there anything I can help you with?'), ('human', "what's my name?"), ('ai', 'Your name is Bob.')]
[('human', "what's my name?"), ('ai', 'Your name is 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.

In an agent
In a workflow

To summarize message history in an agent, use pre_model_hook with a prebuilt SummarizationNode abstraction:

from langchain_anthropic import ChatAnthropic
from langmem.short_term import SummarizationNode, RunningSummary
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
    # highlight-next-line
    context: dict[str, RunningSummary]  # (2)!


checkpointer = InMemorySaver() # (3)!

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

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.
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.
The checkpointer is passed to the agent. This enables the agent to persist its state across invocations.
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.
The state_schema is set to the State class, which is the custom state that contains an extra context key.

Full example: summarize messages

from typing import Any, TypedDict

from langchain.chat_models import init_chat_model
from langchain_core.messages import AnyMessage
from langchain_core.messages.utils import count_tokens_approximately
from langgraph.graph import StateGraph, START, MessagesState
from langgraph.checkpoint.memory import InMemorySaver
# highlight-next-line
from langmem.short_term import SummarizationNode, RunningSummary

model = init_chat_model("anthropic:claude-3-7-sonnet-latest")
summarization_model = model.bind(max_tokens=128)

class State(MessagesState):
    # highlight-next-line
    context: dict[str, RunningSummary]  # (1)!

class LLMInputState(TypedDict):  # (2)!
    summarized_messages: list[AnyMessage]
    context: dict[str, RunningSummary]

# highlight-next-line
summarization_node = SummarizationNode(
    token_counter=count_tokens_approximately,
    model=summarization_model,
    max_tokens=256,
    max_tokens_before_summary=256,
    max_summary_tokens=128,
)

# highlight-next-line
def call_model(state: LLMInputState):  # (3)!
    response = model.invoke(state["summarized_messages"])
    return {"messages": [response]}

checkpointer = InMemorySaver()
builder = StateGraph(State)
builder.add_node(call_model)
# highlight-next-line
builder.add_node("summarize", summarization_node)
builder.add_edge(START, "summarize")
builder.add_edge("summarize", "call_model")
graph = builder.compile(checkpointer=checkpointer)

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

final_response["messages"][-1].pretty_print()
print("\nSummary:", final_response["context"]["running_summary"].summary)

We will keep track of our running summary in the context field

(expected by the SummarizationNode).

Define private state that will be used only for filtering

the inputs to call_model node.

We’re passing a private input state here to isolate the messages returned by the summarization node

================================== Ai Message ==================================

From our conversation, I can see that you introduced yourself as Bob. That's the name you shared with me when we began talking.

Summary: In this conversation, I was introduced to Bob, who then asked me to write a poem about cats. I composed a poem titled "The Mystery of Cats" that captured cats' graceful movements, independent nature, and their special relationship with humans. Bob then requested a similar poem about dogs, so I wrote "The Joy of Dogs," which highlighted dogs' loyalty, enthusiasm, and loving companionship. Both poems were written in a similar style but emphasized the distinct characteristics that make each pet special.

Manage checkpoints

You can view and delete the information stored by the checkpointer.

View thread state

Graph/Functional API
Checkpointer API

config = {
    "configurable": {
        # highlight-next-line
        "thread_id": "1",
        # optionally provide an ID for a specific checkpoint,
        # otherwise the latest checkpoint is shown
        # highlight-next-line
        # "checkpoint_id": "1f029ca3-1f5b-6704-8004-820c16b69a5a"

    }
}
# highlight-next-line
graph.get_state(config)

StateSnapshot(
    values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today?), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]}, next=(),
    config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}},
    metadata={
        'source': 'loop',
        'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}},
        'step': 4,
        'parents': {},
        'thread_id': '1'
    },
    created_at='2025-05-05T16:01:24.680462+00:00',
    parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
    tasks=(),
    interrupts=()
)

View the history of the thread

Graph/Functional API
Checkpointer API

config = {
    "configurable": {
        # highlight-next-line
        "thread_id": "1"
    }
}
# highlight-next-line
list(graph.get_state_history(config))

[
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]},
        next=(),
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}},
        metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}}, 'step': 4, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:24.680462+00:00',
        parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
        tasks=(),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?")]},
        next=('call_model',),
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
        metadata={'source': 'loop', 'writes': None, 'step': 3, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:23.863421+00:00',
        parent_config={...}
        tasks=(PregelTask(id='8ab4155e-6b15-b885-9ce5-bed69a2c305c', name='call_model', path=('__pregel_pull', 'call_model'), error=None, interrupts=(), state=None, result={'messages': AIMessage(content='Your name is Bob.')}),),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]},
        next=('__start__',),
        config={...},
        metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "what's my name?"}]}}, 'step': 2, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:23.863173+00:00',
        parent_config={...}
        tasks=(PregelTask(id='24ba39d6-6db1-4c9b-f4c5-682aeaf38dcd', name='__start__', path=('__pregel_pull', '__start__'), error=None, interrupts=(), state=None, result={'messages': [{'role': 'user', 'content': "what's my name?"}]}),),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]},
        next=(),
        config={...},
        metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')}}, 'step': 1, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:23.862295+00:00',
        parent_config={...}
        tasks=(),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob")]},
        next=('call_model',),
        config={...},
        metadata={'source': 'loop', 'writes': None, 'step': 0, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:22.278960+00:00',
        parent_config={...}
        tasks=(PregelTask(id='8cbd75e0-3720-b056-04f7-71ac805140a0', name='call_model', path=('__pregel_pull', 'call_model'), error=None, interrupts=(), state=None, result={'messages': AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')}),),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': []},
        next=('__start__',),
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-0870-6ce2-bfff-1f3f14c3e565'}},
        metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}}, 'step': -1, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:22.277497+00:00',
        parent_config=None,
        tasks=(PregelTask(id='d458367b-8265-812c-18e2-33001d199ce6', name='__start__', path=('__pregel_pull', '__start__'), error=None, interrupts=(), state=None, result={'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}),),
        interrupts=()
    )
]

Delete all checkpoints for a thread

thread_id = "1"
checkpointer.delete_thread(thread_id)

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.

Capabilities

Run and debug

LangGraph APIs

Add short-term memory

Use in production

Use in subgraphs

Read short-term memory in tools

Write short-term memory from tools

Add long-term memory

Use in production

Read long-term memory in tools

Write long-term memory from tools

Use semantic search

Manage short-term memory

Trim messages

Delete messages

Summarize messages

Manage checkpoints

View thread state

View the history of the thread

Delete all checkpoints for a thread

Prebuilt memory tools

Capabilities

Run and debug

LangGraph APIs

​Add short-term memory

​Use in production

​Use in subgraphs

​Read short-term memory in tools

​Write short-term memory from tools

​Add long-term memory

​Use in production

​Read long-term memory in tools

​Write long-term memory from tools

​Use semantic search

​Manage short-term memory

​Trim messages

​Delete messages

​Summarize messages

​Manage checkpoints

​View thread state

​View the history of the thread

​Delete all checkpoints for a thread

​Prebuilt memory tools

Add short-term memory

Use in production

Use in subgraphs

Read short-term memory in tools

Write short-term memory from tools

Add long-term memory

Use in production

Read long-term memory in tools

Write long-term memory from tools

Use semantic search

Manage short-term memory

Trim messages

Delete messages

Summarize messages

Manage checkpoints

View thread state

View the history of the thread

Delete all checkpoints for a thread

Prebuilt memory tools