langgraph/docs/docs/how-tos/cross-thread-persistence.ipynb

How to add cross-thread persistence to your graph

Prerequisites

This guide assumes familiarity with the following:

• Persistence
• Memory
• Chat Models

In the previous guide you learned how to persist graph state across multiple interactions on a single thread. LangGraph also allows you to persist data across multiple threads. For instance, you can store information about users (their names or preferences) in a shared memory and reuse them in the new conversational threads.

In this guide, we will show how to construct and use a graph that has a shared memory implemented using the Store interface.

Note

Support for the Store API that is used in this guide was added in LangGraph v0.2.32.

Support for index and query arguments of the Store API that is used in this guide was added in LangGraph v0.2.54.

Setup

First, let's install the required packages and set our API keys

In [1]:

%%capture --no-stderr
%pip install -U langchain_openai langgraph

In [2]:

import getpass
import os


def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")


_set_env("ANTHROPIC_API_KEY")

ANTHROPIC_API_KEY:  ········

!!! tip "Set up LangSmith for LangGraph development"

Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started [here](https://docs.smith.langchain.com)

Define store

In this example we will create a graph that will be able to retrieve information about a user's preferences. We will do so by defining an InMemoryStore - an object that can store data in memory and query that data. We will then pass the store object when compiling the graph. This allows each node in the graph to access the store: when you define node functions, you can define store keyword argument, and LangGraph will automatically pass the store object you compiled the graph with.

When storing objects using the Store interface you define two things:

• the namespace for the object, a tuple (similar to directories)
• the object key (similar to filenames)

In our example, we'll be using ("memories", <user_id>) as namespace and random UUID as key for each new memory.

Importantly, to determine the user, we will be passing user_id via the config keyword argument of the node function.

Let's first define an InMemoryStore already populated with some memories about the users.

In [3]:

from langgraph.store.memory import InMemoryStore
from langchain_openai import OpenAIEmbeddings

in_memory_store = InMemoryStore(
    index={
        "embed": OpenAIEmbeddings(model="text-embedding-3-small"),
        "dims": 1536,
    }
)

Create graph

In [4]:

import uuid
from typing import Annotated
from typing_extensions import TypedDict

from langchain_anthropic import ChatAnthropic
from langchain_core.runnables import RunnableConfig
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.memory import MemorySaver
from langgraph.store.base import BaseStore


model = ChatAnthropic(model="claude-3-5-sonnet-20240620")


# NOTE: we're passing the Store param to the node --
# this is the Store we compile the graph with
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(
        [{"type": "system", "content": system_msg}] + state["messages"]
    )
    return {"messages": response}


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

# NOTE: we're passing the store object here when compiling the graph
graph = builder.compile(checkpointer=MemorySaver(), store=in_memory_store)
# If you're using LangGraph Cloud or LangGraph Studio, you don't need to pass the store or checkpointer when compiling the graph, since it's done automatically.

Note

If you're using LangGraph Cloud or LangGraph Studio, you don't need to pass store when compiling the graph, since it's done automatically.

Run the graph!

Now let's specify a user ID in the config and tell the model our name:

In [5]:

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

================================ Human Message =================================

Hi! Remember: my name is Bob
================================== Ai Message ==================================

Hello Bob! It's nice to meet you. I'll remember that your name is Bob. How can I assist you today?

In [6]:

config = {"configurable": {"thread_id": "2", "user_id": "1"}}
input_message = {"type": "user", "content": "what is my name?"}
for chunk in graph.stream({"messages": [input_message]}, config, stream_mode="values"):
    chunk["messages"][-1].pretty_print()

================================ Human Message =================================

what is my name?
================================== Ai Message ==================================

Your name is Bob.

We can now inspect our in-memory store and verify that we have in fact saved the memories for the user:

In [7]:

for memory in in_memory_store.search(("memories", "1")):
    print(memory.value)

{'data': 'User name is Bob'}

Let's now run the graph for another user to verify that the memories about the first user are self contained:

In [8]:

config = {"configurable": {"thread_id": "3", "user_id": "2"}}
input_message = {"type": "user", "content": "what is my name?"}
for chunk in graph.stream({"messages": [input_message]}, config, stream_mode="values"):
    chunk["messages"][-1].pretty_print()

================================ Human Message =================================

what is my name?
================================== Ai Message ==================================

I apologize, but I don't have any information about your name. As an AI assistant, I don't have access to personal information about users unless it has been specifically shared in our conversation. If you'd like, you can tell me your name and I'll be happy to use it in our discussion.