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Showing content from https://python.langchain.com/docs/versions/migrating_memory/conversation_buffer_memory/ below:

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:

1. Using LangGraph persistence along with appropriate processing of the message history
2. 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.

%%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()

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

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

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={})]}

'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

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.

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


workflow = StateGraph(state_schema=MessagesState)


model = ChatOpenAI()



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



workflow.add_edge(START, "model")
workflow.add_node("model", call_model)



memory = MemorySaver()

app = workflow.compile(
    checkpointer=memory
)






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()


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()

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

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

Hello Bob! How can I assist you today?
================================ Human Message =================================

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

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

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.

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.

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."""
    
    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}"),
    ]
)


agent = create_tool_calling_agent(model, tools, prompt)

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


agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    memory=memory,  
)


print(agent_executor.invoke({"input": "hi! my name is bob what is my age?"}))
print()


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.'}

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.

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."""
    
    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,
)





thread_id = uuid.uuid4()
config = {"configurable": {"thread_id": thread_id}}



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()



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()

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

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

42 years old
================================== Ai Message ==================================

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

do you remember my name?
================================== Ai Message ==================================

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()

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

hi! do you remember my name?
================================== Ai Message ==================================

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

Explore persistence with LangGraph:

• LangGraph quickstart tutorial
• How to add persistence ("memory") to your graph
• How to manage conversation history
• How to add summary of the conversation history

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

• How to add message history

Working with message history:

• How to trim messages
• How to filter messages
• How to merge message runs

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