Migrating off ConversationBufferMemory or ConversationStringBufferMemory

ConversationBufferMemory and ConversationStringBufferMemory were used to keep track of a conversation between a human and an ai asstistant without any additional processing.

note

The ConversationStringBufferMemory is equivalent to ConversationBufferMemory but was targeting LLMs that were not chat models.

The methods for handling conversation history using existing modern primitives are:

Using LangGraph persistence along with appropriate processing of the message history
Using LCEL with RunnableWithMessageHistory combined with appropriate processing of the message history.

Most users will find LangGraph persistence both easier to use and configure than the equivalent LCEL, especially for more complex use cases.

Set up

%%capture --no-stderr
%pip install --upgrade --quiet langchain-openai langchain

import os
from getpass import getpass

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

Usage with LLMChain / ConversationChain

This section shows how to migrate off ConversationBufferMemory or ConversationStringBufferMemory that's used together with either an LLMChain or a ConversationChain.

Legacy

Below is example usage of ConversationBufferMemory with an LLMChain or an equivalent ConversationChain.

Details

from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain_core.messages import SystemMessage
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    MessagesPlaceholder,
)
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate(
    [
        MessagesPlaceholder(variable_name="chat_history"),
        HumanMessagePromptTemplate.from_template("{text}"),
    ]
)

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

legacy_chain = LLMChain(
    llm=ChatOpenAI(),
    prompt=prompt,
    memory=memory,
)

legacy_result = legacy_chain.invoke({"text": "my name is bob"})
print(legacy_result)

legacy_result = legacy_chain.invoke({"text": "what was my name"})

{'text': 'Hello Bob! How can I assist you today?', 'chat_history': [HumanMessage(content='my name is bob', additional_kwargs={}, response_metadata={}), AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={}, response_metadata={})]}

legacy_result["text"]

'Your name is Bob. How can I assist you today, Bob?'

note

Note that there is no support for separating conversation threads in a single memory object

LangGraph

The example below shows how to use LangGraph to implement a ConversationChain or LLMChain with ConversationBufferMemory.

This example assumes that you're already somewhat familiar with LangGraph. If you're not, then please see the LangGraph Quickstart Guide for more details.

LangGraph offers a lot of additional functionality (e.g., time-travel and interrupts) and will work well for other more complex (and realistic) architectures.

Details

import uuid

from IPython.display import Image, display
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import START, MessagesState, StateGraph

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

# Define a chat model
model = ChatOpenAI()


# Define the function that calls the model
def call_model(state: MessagesState):
    response = model.invoke(state["messages"])
    # We return a list, because this will get added to the existing list
    return {"messages": response}


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


# Adding memory is straight forward in langgraph!
memory = MemorySaver()

app = workflow.compile(
    checkpointer=memory
)


# The thread id is a unique key that identifies
# this particular conversation.
# We'll just generate a random uuid here.
# This enables a single application to manage conversations among multiple users.
thread_id = uuid.uuid4()
config = {"configurable": {"thread_id": thread_id}}


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

API Reference:HumanMessage | MemorySaver | StateGraph

================================[1m Human Message [0m=================================

hi! I'm bob
==================================[1m Ai Message [0m==================================

Hello Bob! How can I assist you today?
================================[1m Human Message [0m=================================

what was my name?
==================================[1m Ai Message [0m==================================

Your name is Bob. How can I help you today, Bob?

LCEL RunnableWithMessageHistory

Alternatively, if you have a simple chain, you can wrap the chat model of the chain within a RunnableWithMessageHistory.

Please refer to the following migration guide for more information.

Usage with a pre-built agent

This example shows usage of an Agent Executor with a pre-built agent constructed using the create_tool_calling_agent function.

If you are using one of the old LangChain pre-built agents, you should be able to replace that code with the new langgraph pre-built agent which leverages native tool calling capabilities of chat models and will likely work better out of the box.

Legacy Usage

Details

from langchain import hub
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain.memory import ConversationBufferMemory
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI

model = ChatOpenAI(temperature=0)


@tool
def get_user_age(name: str) -> str:
    """Use this tool to find the user's age."""
    # This is a placeholder for the actual implementation
    if "bob" in name.lower():
        return "42 years old"
    return "41 years old"


tools = [get_user_age]

prompt = ChatPromptTemplate.from_messages(
    [
        ("placeholder", "{chat_history}"),
        ("human", "{input}"),
        ("placeholder", "{agent_scratchpad}"),
    ]
)

# Construct the Tools agent
agent = create_tool_calling_agent(model, tools, prompt)
# Instantiate memory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

# Create an agent
agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    memory=memory,  # Pass the memory to the executor
)

# Verify that the agent can use tools
print(agent_executor.invoke({"input": "hi! my name is bob what is my age?"}))
print()
# Verify that the agent has access to conversation history.
# The agent should be able to answer that the user's name is bob.
print(agent_executor.invoke({"input": "do you remember my name?"}))

{'input': 'hi! my name is bob what is my age?', 'chat_history': [HumanMessage(content='hi! my name is bob what is my age?', additional_kwargs={}, response_metadata={}), AIMessage(content='Bob, you are 42 years old.', additional_kwargs={}, response_metadata={})], 'output': 'Bob, you are 42 years old.'}

{'input': 'do you remember my name?', 'chat_history': [HumanMessage(content='hi! my name is bob what is my age?', additional_kwargs={}, response_metadata={}), AIMessage(content='Bob, you are 42 years old.', additional_kwargs={}, response_metadata={}), HumanMessage(content='do you remember my name?', additional_kwargs={}, response_metadata={}), AIMessage(content='Yes, your name is Bob.', additional_kwargs={}, response_metadata={})], 'output': 'Yes, your name is Bob.'}

LangGraph

You can follow the standard LangChain tutorial for building an agent an in depth explanation of how this works.

This example is shown here explicitly to make it easier for users to compare the legacy implementation vs. the corresponding langgraph implementation.

This example shows how to add memory to the pre-built react agent in langgraph.

For more details, please see the how to add memory to the prebuilt ReAct agent guide in langgraph.

Details

import uuid

from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
from langgraph.prebuilt import create_react_agent


@tool
def get_user_age(name: str) -> str:
    """Use this tool to find the user's age."""
    # This is a placeholder for the actual implementation
    if "bob" in name.lower():
        return "42 years old"
    return "41 years old"


memory = MemorySaver()
model = ChatOpenAI()
app = create_react_agent(
    model,
    tools=[get_user_age],
    checkpointer=memory,
)

# The thread id is a unique key that identifies
# this particular conversation.
# We'll just generate a random uuid here.
# This enables a single application to manage conversations among multiple users.
thread_id = uuid.uuid4()
config = {"configurable": {"thread_id": thread_id}}

# Tell the AI that our name is Bob, and ask it to use a tool to confirm
# that it's capable of working like an agent.
input_message = HumanMessage(content="hi! I'm bob. What is my age?")

for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()

# Confirm that the chat bot has access to previous conversation
# and can respond to the user saying that the user's name is Bob.
input_message = HumanMessage(content="do you remember my name?")

for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()

API Reference:HumanMessage | tool | ChatOpenAI | MemorySaver | create_react_agent

================================[1m Human Message [0m=================================

hi! I'm bob. What is my age?
==================================[1m Ai Message [0m==================================
Tool Calls:
  get_user_age (call_oEDwEbIDNdokwqhAV6Azn47c)
 Call ID: call_oEDwEbIDNdokwqhAV6Azn47c
  Args:
    name: bob
=================================[1m Tool Message [0m=================================
Name: get_user_age

42 years old
==================================[1m Ai Message [0m==================================

Bob, you are 42 years old! If you need any more assistance or information, feel free to ask.
================================[1m Human Message [0m=================================

do you remember my name?
==================================[1m Ai Message [0m==================================

Yes, your name is Bob. If you have any other questions or need assistance, feel free to ask!

If we use a different thread ID, it'll start a new conversation and the bot will not know our name!

config = {"configurable": {"thread_id": "123456789"}}

input_message = HumanMessage(content="hi! do you remember my name?")

for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()

================================[1m Human Message [0m=================================

hi! do you remember my name?
==================================[1m Ai Message [0m==================================

Hello! Yes, I remember your name. It's great to see you again! How can I assist you today?

Next steps

Explore persistence with LangGraph:

Add persistence with simple LCEL (favor langgraph for more complex use cases):

How to add message history

Working with message history:

Set up​

Usage with LLMChain / ConversationChain​

Legacy​

LangGraph​

LCEL RunnableWithMessageHistory​

Usage with a pre-built agent​

Legacy Usage​

LangGraph​

Next steps​

Was this page helpful?

Set up

Usage with LLMChain / ConversationChain

Legacy

LangGraph

LCEL RunnableWithMessageHistory

Usage with a pre-built agent

Legacy Usage

LangGraph

Next steps