In the world of Generative AI, Large Language Models (LLMs) are getting smarter—but they still lack structure, memory, and the ability to act over time like a human would. That’s where agentic workflows come in.

What is Agentic AI?

Agentic AI is an AI that can think and act like an assistant by remembering information, making decisions, and performing multiple steps to complete a task — instead of just answering one question at a time.

Example:
Imagine you ask an AI to “Plan my day.” A simple AI might just list some suggestions. But an agentic AI will:

1. Check your calendar for meetings.

2. Find the weather forecast.

3. Suggest the best time to work out.

4. Book a restaurant reservation if you want.

5. Remind you about important tasks.

It “acts” by chaining these steps together intelligently, making it much more helpful.

What is LangGraph?

LangGraph is an open-source framework designed to build AI agents that can:

• Maintain memory between steps

• Take decisions based on past steps

• Follow structured paths (like a flowchart)

• Work asynchronously and robustly

It’s built on top of LangChain, and designed for long-running tasks, tool-using agents, and decision-making flows.

Key Concepts in LangGraph

1. State:
   A Python class (often a TypedDict or Pydantic model) that holds all the information passed across nodes.

2. Node:
   Each step in your workflow. For example, a classifier node, or a chatbot response node.

3. Edge:
   Connects two nodes based on logic or outcomes.

4. Graph:
   The complete workflow combining multiple nodes and paths.

A Simple Chatbot Agent

from langchain_openai import OpenAI
from pydantic import BaseModel, Field
from langgraph.graph import StateGraph, START, END
from openai import OpenAI
from dotenv import load_dotenv
import os

load_dotenv()
client = OpenAI()


# Define the state model
class State(BaseModel):
    query:str = Field(description = "User's query")
    llm_response : str =  Field(default=None)


# Define the chat function(Node)
def chat(state:State):
    query = state.query
    SYSTEM_PROMPT = """
    you are a helpful assistant."""

    response = client.chat.completions.create(
        model = "gpt-4.1-mini",
        messages = [
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": query}
        ]
    )
    state.llm_response = response.choices[0].message.content
    return state

# Create a state graph
graph_builder = StateGraph(State)

# Add nodes and edges to the graph
graph_builder.add_node("chatbot",chat)
graph_builder.add_edge(START,"chatbot")
graph_builder.add_edge("chatbot",END)

# Compile the graph
graph = graph_builder.compile()

def main():

    query = input("Enter your query: ")
    state = State(query = query)


    # Invoke the graph with the state
    graph_response = graph.invoke(state)

    # Print the response from the graph
    print("Graph_Result",graph_response)


main()

Visual representation of the chat agent

What Can You Build with LangGraph?

Honestly, there are so many cool possibilities, like:

• A customer support bot that actually remembers previous chats and uses FAQs or tools to help out better.

• An educational tutor that understands the question, gives hints, and helps you improve step by step.

• A research assistant that knows when to dig deeper, summarize info, or just give you the straight answer.

• Something that scans job applications, figures out the best fits, and scores candidates automatically.

The more I learn about LangGraph, the more I’m excited to build stuff like this!

Final Thoughts

LangGraph is a powerful way to turn your LLM from a passive responder into an interactive, intelligent agent. Whether you're building a chatbot, a document assistant, or a full-fledged workflow automation tool, LangGraph gives you the structure you need.

I'm currently exploring what exciting things I can build with this. Got ideas or want to collaborate? Let’s connect! 🚀