Graphs Over Chains: My First Steps with LangGraph (Part - 1)

Introduction

I've just started learning LangGraph, and I’m excited to document my journey here on this blog. My goal is to explore what LangGraph can do, understand its concepts, and share the insights I gain along the way.

So, what exactly is LangGraph, and why does it matter?

What is LangGraph?

LangGraph builds on top of LangChain, but it introduces a powerful idea: graphs of AI interactions instead of simple chains. In a typical LangChain project, you pass a prompt through a chain of models or tools, and that's it, one-way. LangGraph, on the other hand, lets you create graphs where:

• Nodes represent steps (like LLM calls, tool invocations, or control flow logic).

• Edges represent transitions between nodes, possibly based on conditions.

• You can loop back, retry steps, or branch into different flows.

And hence this enables you to design more stateful, multi-turn, and complex AI applications.

Core concepts in LangGraph

Let’s briefly introduce some of the key concepts I’ve learned so far:

1. Graph

   A Graph in LangGraph is like the blueprint of your AI workflow. It defines how nodes connect and how the flow progresses. You can think of it as a state machine where each node represents a state or an action.

   The image you see above, is entirely known as a graph.

2. Node

   A node is an atomic step in the graph, it represents many things for example:

   • A call to an LLM

   • A function that processes data

   • A decision-making step that branches the flow

3. Edges

   Edges define how you move from one node to another. Edges can be static (always go to the next node) or dynamic (conditionally branch based on logic or the current state).

   

4. State Management

   LangGraph introduces the idea of a shared state object that persists across steps in your graph.

   • This is typically a dictionary-like object that stores variables, intermediate results, or metadata.

   • Every node can read from and write to this state, allowing information to flow across the graph.

5. Conditional logic

   Unlike simple pipelines, LangGraph lets you define conditional flows:

   • You can have if-else branches inside the graph.

   • You can implement retry mechanisms or loops.

This is crucial for handling real-world tasks like error handling or iterative refinement.

Why I’m learning LangGraph

My earlier experiences with LangChain taught me the limitations of linear pipelines. When building more agentic workflows, like multi-turn chatbots or decision-making systems, I often ran into rigid structures. I believe that learning LangGraph will give me an edge in building next-generation AI applications that are smarter and more interactive.

Difference between LangChain & LangGraph (Chains vs Graphs)

Feature	LangChain	LangGraph
Workflow Structure	Linear pipelines (Chains)	Graphs (Nodes + Edges, like a flowchart)
State Management	Global input/output, less structured	Explicit AgentState shared across nodes
Learning Curve	Easier for quick prototypes	Steeper but more powerful for advanced tasks
Use Cases	Simple, single-step or sequential tasks	Complex, multi-step workflows (chatbots, tools, APIs)

Conclusion

This is just the start of my LangGraph journey. I’m keeping this blog raw and honest—sharing both what clicks and what confuses me. If you're also exploring LangGraph or have ideas to share, drop a comment below. Let’s learn together!

References and Resources

1. LangGraph Documentation

2. LangGraph GitHub Repository

3. LangChain Youtube