LangGraph: Unlock the Future of AI Application Development

Let’s assume you have two scenarios while cooking dinner. In one scenario, you have a messy kitchen, with utensils everywhere, spices misplaced, and ingredients scattered around—sounds stressful, doesn’t it? Now, in the second scenario, imagine carefully organized shelves, labeled spices, and all utensils neatly within reach. So, the second scenario sounds more inviting, peaceful, even enjoyable, right?

This analogy perfectly describes the difference in how LangGraph helps manage AI workflows. Developing AI applications can get chaotic — with multiple models, endless states, messy integrations, and tangled flows. LangGraph, however, helps to transform this chaos into a streamlined, enjoyable, and even delightful experience.

Ready to discover how? Let’s dive in! 🏊

📌 What Exactly is LangGraph?

LangGraph is a neatly organized kitchen for AI application development. It allows us to structure complex workflows — like integrating powerful AI models — in clearly defined steps called nodes, where each node has a single responsibility that will help developers manage complex AI integrations in an intuitive, visually organized graph structure.

Think of it as playing with Lego bricks. Each brick, in this case, node has a defined purpose and it effortlessly connects to other bricks, allowing us to easily build complex AI workflows. LangGraph brings this simplicity and organizes our AI coding workflow.

The Basic components of LangGraph are:

• Nodes: Python functions that implement the logic of agents, taking the current State as input and returning an updated state.
• Edges / Conditional Edges: Functions that implement fixed/conditional transitions to determine which Node to execute next based on the current state

Graphs in LangGraph are driven by:

• States: Shared data structures that evolve over time as Nodes execute and pass messages along Edges.
• Message Passing: Nodes send messages to activate other Nodes, facilitating the execution of workflows in discrete iterations or “super-steps”.

A Practical Step-by-Step Guide 🛠️💡

Now, let’s understand how exactly LangGraph simplifies AI-driven development:

Step 1: Clearly Defining Our AI Workflow 🎯

Assume we want to create a simple workflow where it will answer the simple math questions using the LLM, in this case GPT-4 model by OpenAI.

We have two clear, straightforward responsibilities:

• First, prepare the prompt properly
• Second to ask the model for the answer

Now, let’s build these responsibilities with clarity using LangGraph!

Step 2: Creating Simple Nodes (The Lego Bricks Model) 🧱

This is how we can clearly define tasks into nodes:

from dotenv import load_dotenv
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
from langgraph.graph import Graph

load_dotenv()  # Securely handle API keys and secrets

# Node 1: Prepare the prompt for GPT-4
def get_prompt(state: dict) -> dict:
    question = state["question"]
    prompt = question + "\nNOTE: Provide the answer only, do the calculation in your mind"
    return {"prompt": prompt}

# Node 2: Fetch clear answer from GPT-4
def llm_response(state: dict) -> dict:
    llm = ChatOpenAI(model="gpt-4-turbo", temperature=0)
    prompt = state["prompt"]
    chain = llm | StrOutputParser()
    answer = chain.invoke(prompt)
    return {"answer": answer}        

Each node has a single, easily understandable responsibility. This makes our code readable, maintainable, and beginner-friendly.

Step 3: Stitch Nodes with Ease 🌐

Now, let’s connect these defined nodes using the Graph() class provided by LangGraph. The structure will look like this:

workflow = Graph()

workflow.add_node("get_prompt", get_prompt)
workflow.add_node("response", llm_response)

workflow.set_entry_point("get_prompt")
workflow.add_edge("get_prompt", "response")
workflow.set_finish_point("response")        

See how easily the flow is defined? It almost looks like a perfectly organized recipe card!

Step 4: Running your LangGraph 🏃♂️✨

Now it's time to test our perfectly structured AI kitchen workflow:

app = workflow.compile()
result = app.invoke({"question": "What is 2 + 10?"})
print(result)        

And your output will look like this:

{'answer': '12'}        

Adding More Clarity with Typed States 🗂️🔍

So, just like having a well-labeled jar in our kitchen cabinets, TypedDict in LangGraph adds exceptional clarity. In this we defined all the keys that we will be using while calling each node.

In this case, we are going to take questions while invoking our Graph, prompt while calling second node from first and finally returning the answer.

from typing import TypedDict
from langgraph.graph import StateGraph

class GraphState(TypedDict):
    question: str
    prompt: str
    answer: str

def get_prompt(state: GraphState) -> GraphState:
    question = state["question"]
    prompt = question + "\nNOTE: Provide the answer only, do the calculation in your mind"
    return GraphState(prompt=prompt)

def llm_response(state: GraphState) -> GraphState:
    llm = ChatOpenAI(model="gpt-4-turbo", temperature=0)
    prompt = state["prompt"]
    chain = llm | StrOutputParser()
    answer = chain.invoke(prompt)
    return GraphState(answer=answer)

workflow = StateGraph(GraphState)
workflow.add_node("get_prompt", get_prompt)
workflow.add_node("response", llm_response)

workflow.set_entry_point("get_prompt")
workflow.add_edge("get_prompt", "response")
workflow.set_finish_point("response")

app = workflow.compile()
result = app.invoke({"question": "What is 2 + 10?"})
print(result)        

This returns an intuitive response, it will also show all the values we used in between, which will be very helpful during debugging.

{
  "question": "What is 2 + 10?",
  "prompt": "What is 2 + 10?\nNOTE: Provide the answer only, do the calculation in your mind",
  "answer": "12"
}        

Learning and Actionable Takeaways ✅🗒️

In this blog we have learnt how to:

• 🔑 Clearly defined workflow management (Nodes and Edges).
• 🔑 Simplified state handling using TypedDict.
• 🔑 Effortless integration with popular AI services (OpenAI).
• 🔑 Enhanced readability and maintainability (especially vital for growing teams).
• 🔑 Straightforward to start, delightful to use, powerful in every aspect.

Conclusion

Therefore, just like organizing our workspace can lead to better creativity, LangGraph gives us freedom, simplicity, and clarity while building AI workflows.

I would highly suggest you try LangGraph today. Pick any one existing workflow that you can rebuild using LangGraph, and share the difference it makes.

If you have any questions or need clarification, feel free to leave a comment on this blog or reach out to me on

Topmate: https://topmate.io/yash0307jain

You can read more blogs on Medium

Medium: https://medium.com/@yash0307jain

Thanks for reading, and I’ll see you next time!