Chat Bot

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Objectives

1. Understand state persistence and message accumulation.
2. Build a looping dialogue agent that appends interactions.
3. Maintain contextual conversation state.

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Agent II: Memory Agent

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Goal

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Sample Input

1. {"messages": [HumanMessage(content="Hi, my name is Satish.")]}
2. {"messages": [HumanMessage(content="What is my name?")]} (including history)

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Sample Output

1. "Hello Satish! Nice to meet you."
2. "Your name is Satish."

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Plan

1. Define the AgentState schema containing messages (a list of messages, using both HumanMessage and AIMessage).
2. Create the process node function that invokes the LLM with the full message history and appends the result to messages.
3. Build the graph, compile it, and invoke it sequentially to see it remember previous details.

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Code Implementation

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1. Define the State Schema

from typing import TypedDict, Union
from langchain_core.messages import HumanMessage, AIMessage

class AgentState(TypedDict):
    messages: list[Union[HumanMessage, AIMessage]]

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2. Define the Node Function

from langchain_openai import ChatOpenAI

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

def process(state: AgentState) -> AgentState:
    response = llm.invoke(state["messages"])
    state["messages"].append(AIMessage(content=response.content))
    print(f"\nAI: {response.content}")
    return state

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3. Build and Compile the Graph

from langgraph.graph import StateGraph, START, END

graph = StateGraph(AgentState)
graph.add_node("chatbot", process)

graph.add_edge(START, "chatbot")
graph.add_edge("chatbot", END) 

app = graph.compile()

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4. Invoke the Agent

state = {"messages": []}
state["messages"].append(HumanMessage(content="Hi, my name is Satish."))
state = app.invoke(state)

state["messages"].append(HumanMessage(content="What is my name?"))
app.invoke(state)

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Exercise: Math Tutor Memory Agent 🧮

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Goal

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Sample Input

1. "Hi, I am Rahul. I need help with arithmetic."
2. "What is my name and what subject did I ask for help with?"

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Sample Output

1. "Hello Rahul! I'd be happy to help you with arithmetic. What is your first question?"
2. "Your name is Rahul, and you asked for help with arithmetic."

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Plan

1. Create a TypedDict for the list of conversation messages.
2. Write tutor_node that binds a math system message instruction, runs the LLM on the chat list, and appends the response.
3. Register the tutor node, set entry/finish points, compile the graph.
4. Test with sequential questions to confirm memory retention.

from typing import TypedDict, Union
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, START, END

# 1. State Schema
class AgentState(TypedDict):
    messages: list[Union[HumanMessage, AIMessage]]

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

# 2. Node Function
def tutor_node(state: AgentState) -> AgentState:
    sys = SystemMessage(content="You are an encouraging math tutor bot. Remember the user's name and preferences.")
    response = llm.invoke([sys] + state["messages"])
    state["messages"].append(AIMessage(content=response.content))
    print(f"\nTutor: {response.content}")
    return state

# 3. Create Graph
graph = StateGraph(AgentState)
graph.add_node("tutor", tutor_node)
graph.add_edge(START, "tutor")
graph.add_edge("tutor", END)
app = graph.compile()

# 4. Invoke sequentially
state = {"messages": []}
state["messages"].append(HumanMessage(content="Hi, I am Rahul. I need help with arithmetic."))
state = app.invoke(state)

state["messages"].append(HumanMessage(content="What is my name and what subject did I ask for help with?"))
app.invoke(state)

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Practice & Exercises