Graph III: Sequential Graph

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Objectives

1. Create multiple nodes that sequentially process and update different parts of the state.
2. Connect nodes together using normal edges.
3. Invoke the graph and see how the state is transformed step-by-step.

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Graph III: Sequential Graph

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Goal

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Sample Input

{"name": "Chirag", "age": 20}

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Sample Output

{"name": "Chirag", "age": 20, "final": "Hi Chirag! You are 20 years old!"}

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Plan

1. Define the state schema (AgentState) with name, age, and final variables.
2. Define two node functions: first_node (to set greeting) and second_node (to append age description).
3. Build, connect sequentially using normal edges, and compile the graph.
4. Invoke the graph and view the final result.

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Code Implementation

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1. Define the State Schema

from typing import TypedDict

class AgentState(TypedDict):
    name: str
    age: str
    final: str

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2. Define the Nodes

def first_node(state: AgentState) -> AgentState:
    state["final"] = f"Hi {state['name']}!"
    return state

def second_node(state: AgentState) -> AgentState:
    state["final"] = state["final"] + f" You are {state['age']} years old!"
    return state

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3. Create and Compile the Graph

from langgraph.graph import StateGraph

graph = StateGraph(AgentState)

# Add both nodes
graph.add_node("first_node", first_node)
graph.add_node("second_node", second_node)

# Define the flow
graph.set_entry_point("first_node")
graph.add_edge("first_node", "second_node")
graph.set_finish_point("second_node")

app = graph.compile()

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4. Invoke the Graph

answers = app.invoke({"name": "Chirag", "age": 20})
print(answers["final"])
# Output: Hi Chirag! You are 20 years old!

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Exercise: Three-Node Sequential Agent 💪

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Goal

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Sample Input

{
    "name": "Deepa",
    "age": 31,
    "skills": ["Python", "Machine Learning", "LangGraph"]
}

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Sample Output

"Deepa, welcome to the system! You are 31 years old! You have skills in:
Python, Machine Learning, and LangGraph"

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Plan

1. Define an AgentState containing name (str), age (int), skills (list of strings), and result (str) fields.
2. Create greeting_node (to greet), age_node (to append age description), and skills_node (to join skills with commas and ‘and’, appending them to the result).
3. Build the graph, register all three nodes, connect them in order (greeting -> age -> skills), set entry/finish points, compile and run.

from typing import TypedDict
from langgraph.graph import StateGraph

# 1. State Schema
class AgentState(TypedDict):
    name: str
    age: int
    skills: list[str]
    result: str

# 2. Node Functions
def greeting_node(state: AgentState) -> AgentState:
    state["result"] = f"{state['name']}, welcome to the system!"
    return state

def age_node(state: AgentState) -> AgentState:
    state["result"] += f" You are {state['age']} years old!"
    return state

def skills_node(state: AgentState) -> AgentState:
    skills_str = ", ".join(state["skills"][:-1]) + ", and " + state["skills"][-1]
    state["result"] += f" You have skills in:\n{skills_str}"
    return state

# 3. Create Graph
graph = StateGraph(AgentState)
graph.add_node("greeting", greeting_node)
graph.add_node("age", age_node)
graph.add_node("skills", skills_node)

graph.set_entry_point("greeting")
graph.add_edge("greeting", "age")
graph.add_edge("age", "skills")
graph.set_finish_point("skills")

# 4. Compile and Run
app = graph.compile()
output = app.invoke({
    "name": "Deepa",
    "age": 31,
    "skills": ["Python", "Machine Learning", "LangGraph"]
})
print(output["result"])

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Practice & Exercises