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In this tutorial, you’ll learn how to build a simple memory (conversation history) feature into a chatbot using LangChain. We’ll start with a stateless prompt, observe why follow-up questions break, then enhance our chain to carry context across messages for a more natural dialogue.

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1. Stateless Prompt: No Memory

First, let’s define a minimal chat chain without any history support: 
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai.chat_models import ChatOpenAI

model = ChatOpenAI()
prompt = ChatPromptTemplate.from_messages([
    ("system", "You’re an assistant with expertise in {ability}."),
    ("human", "{input}"),
])
base_chain = prompt | model
Invoke it twice—once to ask about right-angled triangles, then a follow-up without context: 
# First call: establishing the topic
response1 = base_chain.invoke({
    "ability": "math",
    "input":   "What’s a right-angled triangle?"
})
print(response1.content)
# Second call: follow-up without history
response2 = base_chain.invoke({
    "ability": "math",
    "input":   "What are the other types?"
})
print(response2.content)
# ➜ "Could you please clarify what you’re asking about?"
Without passing previous messages, the model has no context to answer follow-up questions.

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2. Adding a History Placeholder

To maintain context, inject a MessagesPlaceholder into your prompt template: 
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai.chat_models import ChatOpenAI

model = ChatOpenAI()
prompt = ChatPromptTemplate.from_messages([
    ("system", "You’re an assistant with expertise in {ability}."),
    MessagesPlaceholder(variable_name="history"),
    ("human", "{input}"),
])
base_chain = prompt | model

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2.1 Constructing the History

Build a simple list of (role, content) tuples that tracks the dialogue so far: 
history = [
    ("human", "What’s a right-angled triangle?"),
    ("ai",    "A right-angled triangle has one angle of 90 degrees, "
              "with the other two angles summing to 90 degrees."),
]

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2.2 Invoking with History

Pass the history on each request to provide context: 
response = base_chain.invoke({
    "ability": "math",
    "input":   "What are the other types?",
    "history": history
})
print(response.content)
# ➜ "Other types of triangles include equilateral (all sides equal), "
#     "isosceles (two sides equal), and scalene (no sides equal)."
By supplying the history key, the chain can reference previous turns and answer accurately.

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3. Putting It All Together

Here’s the complete, memory-enabled chatbot example: 
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai.chat_models import ChatOpenAI

model = ChatOpenAI()
prompt = ChatPromptTemplate.from_messages([
    ("system", "You’re an assistant who’s good at {ability}."),
    MessagesPlaceholder(variable_name="history"),
    ("human", "{input}"),
])
base_chain = prompt | model

history = [
    ("human", "What’s a right-angled triangle?"),
    ("ai",    "A right-angled triangle has one angle of 90 degrees, "
              "with the other two angles summing to 90 degrees.")
]

result = base_chain.invoke({
    "ability": "math",
    "input":   "What are the other types?",
    "history": history
})
print(result.content)

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4. Sending Configurable Parameters

You can also include adjustable parameters—like response length limits—directly in your system prompt: 
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai.chat_models import ChatOpenAI

model = ChatOpenAI()
prompt = ChatPromptTemplate.from_messages([
    ("system", "You’re an assistant who’s good at {ability}. Respond in 20 words or fewer."),
    MessagesPlaceholder(variable_name="history"),
    ("human", "{input}"),
])
base_chain = prompt | model

history = [
    ("human", "What’s a right-angled triangle?"),
    ("ai",    "A right-angled triangle has one angle of 90 degrees, "
              "with the other two angles summing to 90 degrees.")
]

response = base_chain.invoke({
    "ability": "math",
    "input":   "What are the other types?",
    "history": history
})
print(response.content)

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Comparison: Stateless vs. Memory-Enabled

Prompt TypeBehaviorUse Case
StatelessNo context recallOne-off Q&A
Memory-EnabledRemembers past messagesMulti-turn conversations, chatbots

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