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How to use BaseChatMessageHistory with LangGraph

Prerequisites

This guide assumes familiarity with the following concepts:

We recommend that new LangChain applications take advantage of the built-in LangGraph peristence to implement memory.

In some situations, users may need to keep using an existing persistence solution for chat message history.

Here, we will show how to use LangChain chat message histories (implementations of BaseChatMessageHistory) with LangGraph.

Set up​

%%capture --no-stderr
%pip install --upgrade --quiet langchain-anthropic langgraph
import os
from getpass import getpass

if "ANTHROPIC_API_KEY" not in os.environ:
    os.environ["ANTHROPIC_API_KEY"] = getpass()

ChatMessageHistory​

A message history needs to be parameterized by a conversation ID or maybe by the 2-tuple of (user ID, conversation ID).

Many of the LangChain chat message histories will have either a session_id or some namespace to allow keeping track of different conversations. Please refer to the specific implementations to check how it is parameterized.

The built-in InMemoryChatMessageHistory does not contains such a parameterization, so we'll create a dictionary to keep track of the message histories.

import uuid

from langchain_core.chat_history import InMemoryChatMessageHistory

chats_by_session_id = {}


def get_chat_history(session_id: str) -> InMemoryChatMessageHistory:
    chat_history = chats_by_session_id.get(session_id)
    if chat_history is None:
        chat_history = InMemoryChatMessageHistory()
        chats_by_session_id[session_id] = chat_history
    return chat_history

Use with LangGraph​

Next, we'll set up a basic chat bot using LangGraph. If you're not familiar with LangGraph, you should look at the following Quick Start Tutorial.

We'll create a LangGraph node for the chat model, and manually manage the conversation history, taking into account the conversation ID passed as part of the RunnableConfig.

The conversation ID can be passed as either part of the RunnableConfig (as we'll do here), or as part of the graph state.

import uuid

from langchain_anthropic import ChatAnthropic
from langchain_core.messages import BaseMessage, HumanMessage
from langchain_core.runnables import RunnableConfig
from langgraph.graph import START, MessagesState, StateGraph

# Define a new graph
builder = StateGraph(state_schema=MessagesState)

# Define a chat model
model = ChatAnthropic(model="claude-3-haiku-20240307")


# Define the function that calls the model
def call_model(state: MessagesState, config: RunnableConfig) -> list[BaseMessage]:
    # Make sure that config is populated with the session id
    if "configurable" not in config or "session_id" not in config["configurable"]:
        raise ValueError(
            "Make sure that the config includes the following information: {'configurable': {'session_id': 'some_value'}}"
        )
    # Fetch the history of messages and append to it any new messages.
    chat_history = get_chat_history(config["configurable"]["session_id"])
    messages = list(chat_history.messages) + state["messages"]
    ai_message = model.invoke(messages)
    # Finally, update the chat message history to include
    # the new input message from the user together with the
    # repsonse from the model.
    chat_history.add_messages(state["messages"] + [ai_message])
    return {"messages": ai_message}


# Define the two nodes we will cycle between
builder.add_edge(START, "model")
builder.add_node("model", call_model)

graph = builder.compile()

# Here, we'll create a unique session ID to identify the conversation
session_id = uuid.uuid4()
config = {"configurable": {"session_id": session_id}}

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

# Here, let's confirm that the AI remembers our name!
input_message = HumanMessage(content="what was my name?")
for event in graph.stream({"messages": [input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()
================================ Human Message =================================

hi! I'm bob
================================== Ai Message ==================================

Hello Bob! It's nice to meet you. I'm Claude, an AI assistant created by Anthropic. How are you doing today?
================================ Human Message =================================

what was my name?
================================== Ai Message ==================================

You introduced yourself as Bob when you said "hi! I'm bob".

This also supports streaming LLM content token by token if using langgraph >= 0.2.28.

from langchain_core.messages import AIMessageChunk

first = True

for msg, metadata in graph.stream(
    {"messages": input_message}, config, stream_mode="messages"
):
    if msg.content and not isinstance(msg, HumanMessage):
        print(msg.content, end="|", flush=True)
API Reference:AIMessageChunk
You| sai|d your| name was Bob.|

Using With RunnableWithMessageHistory​

This how-to guide used the messages and add_messages interface of BaseChatMessageHistory directly.

Alternatively, you can use RunnableWithMessageHistory, as LCEL can be used inside any LangGraph node.

To do that replace the following code:

def call_model(state: MessagesState, config: RunnableConfig) -> list[BaseMessage]:
    # Make sure that config is populated with the session id
    if "configurable" not in config or "session_id" not in config["configurable"]:
        raise ValueError(
            "You make sure that the config includes the following information: {'configurable': {'session_id': 'some_value'}}"
        )
    # Fetch the history of messages and append to it any new messages.
    chat_history = get_chat_history(config["configurable"]["session_id"])
    messages = list(chat_history.messages) + state["messages"]
    ai_message = model.invoke(messages)
    # Finally, update the chat message history to include
    # the new input message from the user together with the
    # repsonse from the model.
    chat_history.add_messages(state["messages"] + [ai_message])
    # hilight-end
    return {"messages": ai_message}

With the corresponding instance of RunnableWithMessageHistory defined in your current application.

runnable = RunnableWithMessageHistory(...) # From existing code

def call_model(state: MessagesState, config: RunnableConfig) -> list[BaseMessage]:
    # RunnableWithMessageHistory takes care of reading the message history
    # and updating it with the new human message and ai response.
    ai_message = runnable.invoke(state['messages'], config)
    return {
        "messages": ai_message
    }

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