How do you add conversation memory to an LLM application with LangChain?

LLMs are stateless — each API call is independent and the model has no memory of previous exchanges. Maintaining conversation context requires explicitly including past messages in the current prompt. LangChain provides memory abstractions that manage this history, automatically appending it to the messages sent to the LLM.

The most practical pattern in modern LangChain is to pass MessagesPlaceholder in the prompt template and maintain a list of messages externally. For longer conversations, the history must be trimmed or summarised to stay within the context window — raw storage of all messages eventually exceeds token limits.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(model='gpt-4o-mini', temperature=0.7)

prompt = ChatPromptTemplate.from_messages([
    SystemMessage(content='You are a helpful assistant.'),
    MessagesPlaceholder(variable_name='history'),  # slot for past messages
    ('human', '{input}'),
])

chain = prompt | llm | StrOutputParser()

# Maintain history externally
history = []

def chat(user_input: str) -> str:
    response = chain.invoke({'input': user_input, 'history': history})
    history.append(HumanMessage(content=user_input))
    history.append(AIMessage(content=response))
    return response

print(chat('My name is Alice.'))
print(chat('What is my name?'))   # correctly recalls 'Alice'

# Trim history to last N messages to avoid context overflow
from langchain_core.messages import trim_messages

def chat_with_trim(user_input: str, max_tokens: int = 4000) -> str:
    trimmed = trim_messages(
        history,
        max_tokens=max_tokens,
        token_counter=llm,
        strategy='last',   # keep most recent messages
        include_system=True,
    )
    response = chain.invoke({'input': user_input, 'history': trimmed})
    history.append(HumanMessage(content=user_input))
    history.append(AIMessage(content=response))
    return response