Simple Bot

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Objectives

1. Define the State schema for storing message history.
2. Initialize ChatOpenAI and invoke it from a graph node.
3. Build a stateless agent graph with a single processor node.
4. Compile and run the agent.

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Agent I: Simple Agent Bot

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Goal

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Sample Input

{"messages": [HumanMessage(content="Tell me a quick 1-line joke.")]}

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Sample Output

"Why don't skeletons fight each other? They don't have the guts."

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Plan

1. Define the state schema (AgentState) containing messages (a list of messages).
2. Define the node function process that invokes the LLM with the messages.
3. Build the graph by adding the node and connecting START to it and it to END.
4. Compile and invoke the agent.

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Code Implementation

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1. Define the State Schema

from typing import TypedDict
from langchain_core.messages import HumanMessage

class AgentState(TypedDict):
    messages: list[HumanMessage]

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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"])
    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("process", process)

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

agent = graph.compile()

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4. Invoke the Agent

user_msg = [HumanMessage(content="Tell me a quick 1-line joke.")]
agent.invoke({"messages": user_msg})

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Exercise: Pirate Speak Translator Agent 🏴‍☠️

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Goal

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Sample Input

{"messages": [HumanMessage(content="Hello, how are you doing today?")]}

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Sample Output

"Ahoy, matey! How be ye farin' this fine day?"

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Plan

1. Reuse or create an AgentState containing the message list.
2. Write a node function pirate_node that inserts a SystemMessage specifying the pirate persona ahead of the user messages, invokes the LLM, and prints the pirate response.
3. Initialize the StateGraph, register the pirate node, hook it to START/END, and compile.
4. Test the compiled agent with a human message and verify the pirate speak output.

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

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

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

# 2. Node Function
def pirate_node(state: AgentState) -> AgentState:
    sys_msg = SystemMessage(content="You are a pirate. Translate the user's input into dramatic pirate speak.")
    response = llm.invoke([sys_msg] + state["messages"])
    print(f"\nPirate AI: {response.content}")
    return state

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

# 4. Invoke
app.invoke({"messages": [HumanMessage(content="Hello, how are you doing today?")]})

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Practice & Exercises