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

​
Add short-term memory

Short-term memory (thread-level persistence) enables agents to track multi-turn conversations. To add short-term memory: 
from langgraph.checkpoint.memory import InMemorySaver  
from langgraph.graph import StateGraph

checkpointer = InMemorySaver()  

builder = StateGraph(...)
graph = builder.compile(checkpointer=checkpointer)  

graph.invoke(
    {"messages": [{"role": "user", "content": "hi! i am Bob"}]},
    {"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"
with PostgresSaver.from_conn_string(DB_URI) as checkpointer:  
    builder = StateGraph(...)
    graph = builder.compile(checkpointer=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
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.postgres import PostgresSaver  

model = init_chat_model(model="claude-haiku-4-5-20251001")

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

    graph = builder.compile(checkpointer=checkpointer)  

    config = {
        "configurable": {
            "thread_id": "1"
        }
    }

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

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

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.
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.mongodb import MongoDBSaver  

model = init_chat_model(model="claude-haiku-4-5-20251001")

DB_URI = "localhost:27017"
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")

    graph = builder.compile(checkpointer=checkpointer)  

    config = {
        "configurable": {
            "thread_id": "1"
        }
    }

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

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

pip install -U langgraph langgraph-checkpoint-redis
You need to call checkpointer.setup() the first time you’re using Redis checkpointer.
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.redis import RedisSaver  

model = init_chat_model(model="claude-haiku-4-5-20251001")

DB_URI = "redis://localhost:6379"
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")

    graph = builder.compile(checkpointer=checkpointer)  

    config = {
        "configurable": {
            "thread_id": "1"
        }
    }

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

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "what's my name?"}]},
        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")
subgraph = subgraph_builder.compile()  

# Parent graph

builder = StateGraph(State)
builder.add_node("node_1", subgraph)  
builder.add_edge(START, "node_1")

checkpointer = InMemorySaver()
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(...)
subgraph = subgraph_builder.compile(checkpointer=True)

​
Add long-term memory

Use long-term memory to store user-specific or application-specific data across conversations. 
from langgraph.store.memory import InMemoryStore  
from langgraph.graph import StateGraph

store = InMemoryStore()  

builder = StateGraph(...)
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"
with PostgresStore.from_conn_string(DB_URI) as store:  
    builder = StateGraph(...)
    graph = builder.compile(store=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
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
from langgraph.store.postgres import PostgresStore  
from langgraph.store.base import BaseStore

model = init_chat_model(model="claude-haiku-4-5-20251001")

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

with (
    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,
        *,
        store: BaseStore,  
    ):
        user_id = config["configurable"]["user_id"]
        namespace = ("memories", user_id)
        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"
            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,
        store=store,  
    )

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

    config = {
        "configurable": {
            "thread_id": "2",  
            "user_id": "1",
        }
    }

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

pip install -U langgraph langgraph-checkpoint-redis
You need to call store.setup() the first time you’re using Redis store.
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
from langgraph.store.redis import RedisStore  
from langgraph.store.base import BaseStore

model = init_chat_model(model="claude-haiku-4-5-20251001")

DB_URI = "redis://localhost:6379"

with (
    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,
        *,
        store: BaseStore,  
    ):
        user_id = config["configurable"]["user_id"]
        namespace = ("memories", user_id)
        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"
            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,
        store=store,  
    )

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

    config = {
        "configurable": {
            "thread_id": "2",  
            "user_id": "1",
        }
    }

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

​
Manage short-term memory

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, use the trim_messages function: 
from langchain_core.messages.utils import (  
    trim_messages,  
    count_tokens_approximately  
)  

def call_model(state: MessagesState):
    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]}

builder = StateGraph(MessagesState)
builder.add_node(call_model)
...

from langchain_core.messages.utils import (
    trim_messages,  
    count_tokens_approximately  
)
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, START, MessagesState

model = init_chat_model("claude-sonnet-4-5-20250929")
summarization_model = model.bind(max_tokens=128)

def call_model(state: MessagesState):
    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: 
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  

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]]}  

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. Prompting and orchestration logic can be used to summarize the message history. For example, in LangGraph you can extend the MessagesState to include a summary key: 
from langgraph.graph import MessagesState
class State(MessagesState):
    summary: str
Then, you can generate a summary of the chat history, using any existing summary as context for the next summary. This summarize_conversation node can be called after some number of messages have accumulated in the messages state key. 
def summarize_conversation(state: State):

    # First, we get any existing summary
    summary = state.get("summary", "")

    # Create our summarization prompt
    if summary:

        # A summary already exists
        summary_message = (
            f"This is a summary of the conversation to date: {summary}\n\n"
            "Extend the summary by taking into account the new messages above:"
        )

    else:
        summary_message = "Create a summary of the conversation above:"

    # Add prompt to our history
    messages = state["messages"] + [HumanMessage(content=summary_message)]
    response = model.invoke(messages)

    # Delete all but the 2 most recent messages
    delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]
    return {"summary": response.content, "messages": delete_messages}

from typing import Any, TypedDict

from langchain.chat_models import init_chat_model
from langchain.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
from langmem.short_term import SummarizationNode, RunningSummary  

model = init_chat_model("claude-sonnet-4-5-20250929")
summarization_model = model.bind(max_tokens=128)

class State(MessagesState):
    context: dict[str, RunningSummary]  

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

summarization_node = SummarizationNode(  
    token_counter=count_tokens_approximately,
    model=summarization_model,
    max_tokens=256,
    max_tokens_before_summary=256,
    max_summary_tokens=128,
)

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

checkpointer = InMemorySaver()
builder = StateGraph(State)
builder.add_node(call_model)
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)
  1. We will keep track of our running summary in the context field
(expected by the SummarizationNode).
  1. Define private state that will be used only for filtering
the inputs to call_model node.
  1. 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

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

    }
}
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

config = {
    "configurable": {
        "thread_id": "1"
    }
}
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.

​
Database management

If you are using any database-backed persistence implementation (such as Postgres or Redis) to store short and/or long-term memory, you will need to run migrations to set up the required schema before you can use it with your database. By convention, most database-specific libraries define a setup() method on the checkpointer or store instance that runs the required migrations. However, you should check with your specific implementation of BaseCheckpointSaver or BaseStore to confirm the exact method name and usage. We recommend running migrations as a dedicated deployment step, or you can ensure they’re run as part of server startup.
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