How do you implement conversation memory?

The recommended modern approach for conversation memory uses RunnableWithMessageHistory which wraps an LCEL chain and automatically loads and saves message history per session ID from a configurable store — without any manual history tracking in application code.

from langchain_core.chat_history import BaseChatMessageHistory
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

# In-memory store (swap for Redis, DynamoDB etc. in production)
store = {}

def get_session_history(session_id: str) -> BaseChatMessageHistory:
    if session_id not in store:
        store[session_id] = ChatMessageHistory()
    return store[session_id]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    MessagesPlaceholder("history"),
    ("human", "{input}"),
])

chain = prompt | ChatOpenAI()

chain_with_memory = RunnableWithMessageHistory(
    chain,
    get_session_history,
    input_messages_key="input",
    history_messages_key="history",
)

# session_id identifies the conversation thread
chain_with_memory.invoke(
    {"input": "My name is Alice."},
    config={"configurable": {"session_id": "alice-123"}},
)
chain_with_memory.invoke(
    {"input": "What is my name?"},
    config={"configurable": {"session_id": "alice-123"}},
)

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