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Migrating from ConversationalChain

ConversationChain incorporated a memory of previous messages to sustain a stateful conversation.

Some advantages of switching to the Langgraph implementation are:

  • Innate support for threads/separate sessions. To make this work with ConversationChain, you'd need to instantiate a separate memory class outside the chain.
  • More explicit parameters. ConversationChain contains a hidden default prompt, which can cause confusion.
  • Streaming support. ConversationChain only supports streaming via callbacks.

Langgraph's checkpointing system supports multiple threads or sessions, which can be specified via the "thread_id" key in its configuration parameters.

%pip install --upgrade --quiet langchain langchain-openai
import os
from getpass import getpass

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

Legacy​

Details
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

template = """
You are a pirate. Answer the following questions as best you can.
Chat history: {history}
Question: {input}
"""

prompt = ChatPromptTemplate.from_template(template)

memory = ConversationBufferMemory()

chain = ConversationChain(
    llm=ChatOpenAI(),
    memory=memory,
    prompt=prompt,
)

chain({"input": "I'm Bob, how are you?"})
{'input': "I'm Bob, how are you?",
 'history': '',
 'response': "Arrr matey, I be a pirate sailin' the high seas. What be yer business with me?"}
chain({"input": "What is my name?"})
{'input': 'What is my name?',
 'history': "Human: I'm Bob, how are you?\nAI: Arrr matey, I be a pirate sailin' the high seas. What be yer business with me?",
 'response': 'Your name be Bob, matey.'}

Langgraph​

Details
import uuid

from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import START, MessagesState, StateGraph

model = ChatOpenAI(model="gpt-4o-mini")

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


# Define the function that calls the model
def call_model(state: MessagesState):
    response = model.invoke(state["messages"])
    return {"messages": response}


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

# Add memory
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.
thread_id = uuid.uuid4()
config = {"configurable": {"thread_id": thread_id}}
query = "I'm Bob, how are you?"

input_messages = [
    {
        "role": "system",
        "content": "You are a pirate. Answer the following questions as best you can.",
    },
    {"role": "user", "content": query},
]
for event in app.stream({"messages": input_messages}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()
================================[1m Human Message [0m=================================

I'm Bob, how are you?
==================================[1m Ai Message [0m==================================

Ahoy, Bob! I be feelin' as lively as a ship in full sail! How be ye on this fine day?
query = "What is my name?"

input_messages = [{"role": "user", "content": query}]
for event in app.stream({"messages": input_messages}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()
================================[1m Human Message [0m=================================

What is my name?
==================================[1m Ai Message [0m==================================

Ye be callin' yerself Bob, I reckon! A fine name for a swashbuckler like yerself!

Next steps​

See this tutorial for a more end-to-end guide on building with RunnableWithMessageHistory.

Check out the LCEL conceptual docs for more background information.

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