Demo Adding Short Term Memory

In this lesson you’ll learn how to give a chatbot or other LLM-based application a minimal form of short-term memory (conversation history) by using a MessagesPlaceholder inside a chat prompt template. This approach ensures previous messages are included in the prompt so the model can respond with appropriate context.

Minimal example (no memory)

This example creates a ChatPromptTemplate without any history placeholder and then invokes the model twice. Because the previous exchange is not included in the prompt, the second invocation has no context about the first.

from langchain_core.prompts import ChatPromptTemplate
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"),
        ("human", "{input}"),
    ]
)
base_chain = prompt | model

# First invocation
base_chain.invoke({"ability": "math", "input": "What's a right-angled triangle?"})

Example output:

AIMessage(content='A triangle with one angle measuring 90 degrees.', response_metadata={...}, id='run-614688a6-...')

Now a follow-up question, still without passing any history:

# Follow-up invocation without history
base_chain.invoke({"ability": "math", "input": "What are the other types?"})

Because the model was not provided the prior exchange as part of the prompt, it typically asks for clarification:

AIMessage(content='Could you please provide more context or specify what you are referring to?', response_metadata={...}, id='run-875b6d0b-...')

This demonstrates the core problem: LLMs do not retain conversation context across separate calls unless you explicitly include that context in the prompt.

Adding a MessagesPlaceholder to carry short-term memory

To give the model access to prior turns, add a MessagesPlaceholder to the chat prompt template and pass a history list on invocation. At runtime the placeholder will be replaced with the provided messages.

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

Create a simple history list that represents the prior human/AI exchange, then invoke the chain while passing that history:

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.")
]

# Now the model receives the previous exchange as part of the prompt
base_chain.invoke({"ability": "math", "input": "What are the other types?", "history": history})

With the history included, the model can respond in context:

AIMessage(content='Other types of triangles include equilateral (all sides equal), isosceles (two sides equal), and scalene (no sides equal).', response_metadata={...}, id='run-ed7687bf-...')

The variable_name you assign to MessagesPlaceholder (for example, "history") is the key you must use when passing the list to invoke. The name can be anything, but the invocation dictionary key must match the placeholder’s variable_name.

Quick reference

Concept	Purpose	Example
Chat prompt template	Define the ordered set of messages the model sees	`ChatPromptTemplate.from_messages([...])`
MessagesPlaceholder	Placeholder for injected conversation turns at runtime	`MessagesPlaceholder(variable_name="history")`
History list format	Sequence of prior turns passed to the placeholder	`history = [("human", "Hi"), ("ai", "Hello")]`
Invocation dict	Values passed when running the chain (must include placeholder key)	`{"ability":"math","input":"...","history": history}`

Summary and next steps

Without explicit history included in the prompt, the model has no memory of prior invocations.
Adding a MessagesPlaceholder to your chat prompt template and providing a history list at invocation time gives your application short-term conversational memory.
Use this technique for multi-step workflows, follow-up questions, or any conversational app that needs access to preceding messages.

Introduction

Overview of LangChain

Key Components of LangChain

Interacting with LLMs

Tips, Tricks, and Resources

Introduction to LCEL

Adding Memory to LLM Apps

Performing Retrieval

Implementing Chains

Using Tools

Building Agents

Conclusion

Building Blocks of LLM Apps

Demo Adding Short Term Memory

Minimal example (no memory)

Adding a MessagesPlaceholder to carry short-term memory

Quick reference

Summary and next steps

Further reading

Watch Video

​Minimal example (no memory)

​Adding a MessagesPlaceholder to carry short-term memory

​Quick reference

​Summary and next steps

​Further reading

Watch Video

Minimal example (no memory)

Adding a MessagesPlaceholder to carry short-term memory

Quick reference

Summary and next steps

Further reading