A Beginner’s Guide to Getting Started with Nodes in LangGraph

If you’ve been following our LangGraph series so far, where we’ve toured the basics of LangGraph, peeked into agent state, reducers, add_reducers, graph messages, and even discussed MCP in LangGraph—you’re probably starting to see a pattern:

Everything in LangGraph feels modular… almost like LEGO blocks.

Well, today we finally meet the LEGO block itself: the Node.

Before we dive in, here’s something you’ll love:

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Think of nodes as the tiny brains of your graph. Each node is a single step, a decision point, a task, a “do-this-next” moment in your workflow. They just do one job really cleanly, and then pass the baton.

Whether you’re building a simple two-step agent, a multi-branching “choose-your-own-adventure” assistant, or a full-blown workflow that feels like it should have a boarding pass…

Nodes are the building blocks that make your agent predictable, traceable, and honestly… sane.

But here’s the best part: Once you understand what nodes are and how they behave, the rest of your LangGraph journey becomes way clearer.

By the end of this guide, you’ll be able to:

• spot a node in the wild,
• create your own nodes (yes, you),
• chain them together like a pro,
• and build simple flows that actually make sense.

What Exactly Is a Node in LangGraph?

A node is just a function or an LLM call that transforms your state and moves your graph forward.

Let’s break that down.

Nodes vs Edges vs State

• Node → *Where something happens*

  A function runs. An LLM thinks. A check is made.

  This is the action.

• Edge → *How you move to the next thing*

  It’s the arrow that says:

  “After this… go there.”

• State → *The backpack your agent carries around*

  Everything your workflow needs to remember — user messages, results, flags, decisions, whatever.

  Each node can update this backpack or use what’s already inside.

If you've read the earlier guide on agent state then you already know how state behaves. Nodes are simply the actors that read the state, use it, update it, and pass it along.

So What Makes LangGraph Nodes Special?

Well… they’re predictable.

Every node knows:

1. What comes in (state).
2. What it’s supposed to do (your logic).
3. What comes out (updated state).
4. Where to go next (edges).

This makes your workflow easier to debug, visualize, maintain, and way easier to extend as your agents get smarter.

Types of Nodes You’ll Encounter

Now that you know what a node is, let’s talk about the different types you’ll meet in LangGraph.

1. Callable Nodes (a.k.a. Your Normal Functions)

These are the simplest, most predictable nodes.

A callable node is basically:

“Hey LangGraph, when the workflow gets here, run this function.”

It could be formatting a message, fetching data, doing a calculation, or updating state in a very specific way.

Use these when:

You want clear, deterministic logic.

2. Agent Nodes (LLM Nodes)

These are the “thinkers” in your workflow — the nodes where the LLM gets to reason, respond, or decide what happens next.

Think of an LLM node as:

“You handle the thinking; I’ll handle the routing.”

An agent node usually:

• reads the current state (like messages),
• calls the LLM,
• returns the next message/state update.

If you’ve read my earlier article on graph messages, this is where that knowledge becomes super useful. LLM nodes depend heavily on message formatting and understanding how messages flow through the graph.

Use these when:

You want natural language reasoning or model-based decisions.

3. Conditional Nodes (Branching Logic)

These nodes decide which next node to jump to based on the state.

Imagine you're building a customer support bot. The conditional node might ask:

• “Is the user asking about billing?” → go to billing flow
• “Is the user asking about refunds?” → go to refund flow
• “Is the user confused?” → send a human-friendly clarification node

These nodes make your workflow smart — not everything has to be linear.

Use these when:

Your agent needs to choose between multiple paths.

4. Parallel Nodes (Fan-Out / Fan-In)

These are the “multitaskers”. They allow you to split your workflow into multiple branches that run side by side before merging the results.

For example, they can help you

• Analyze text
• Fetch related documents
• Generate summaries
• …and then combine everything

Think of parallel nodes as LangGraph saying:

“Why do this one by one when we can do them all at once?”

Use these when:

You want speed, efficiency, or multiple data sources combined.

---

A Quick Recap (Like a Cheat Sheet)

Node Type	What It Does	When to Use
Callable	Runs a normal Python function	Pure logic, utilities, transforms
Agent / LLM	Lets the model think or respond	Reasoning, writing, decisions
Conditional	Branches workflow based on state	Routing, decision trees
Parallel	Runs multiple branches at once	Multi-source workflows, speed

---

Once you understand the role of each, designing robust workflows becomes a breeze.

The Anatomy of a Node

Before we start creating nodes and connecting them, let’s zoom in and look at what actually makes up a node.

A node in LangGraph is made of three core ingredients:

1. Input (State Coming In)
2. Logic (What the node does)
3. Output (State Going Out)

…and then a little “map” that tells LangGraph where to go next.

Let’s break that down.

---

1. Input: What the Node Receives

Every time your workflow enters a node, LangGraph passes in the current state. It holds:

• user messages,
• previous results,
• flags,
• memory,
• whatever your agent needs to make decisions.

A node doesn’t need to use everything; it just pulls out the item(s) it cares about.

Think of it like opening your fridge — you're not using all the food, you’re just grabbing what you need for this step.

---

2. Logic: The Heart of the Node

The logic is the function, LLM call, or branching rule that defines what the node actually does. This could be, calling OpenAI or Anthropic, looking something up, updating messages, deciding which route to take, or merging results.

This logic is the “why” of the node — its purpose.

If you read my earlier pieces on reducers and state updates, you’ll notice that node logic often results in new data being produced, which your reducers then merge into the global state.

3. Output: What the Node Produces

After the logic runs, the node spits out something new — usually part of the state. LangGraph takes this result, merges it into the state (thanks to reducers), and carries the updated backpack forward to the next node.

4. Edges: The Next Step

This is the part beginners often overlook.

A node doesn’t live alone in the void — it needs edges to tell LangGraph:

• Where to go next?
• Which node should follow this one?
• Is this a normal transition or a conditional one?
• Are we branching or looping?

Think of edges as the arrows in a flowchart.

Without edges, even the smartest node has nowhere to send its results.

Nodes do the work, Edges define the journey.

Simple Diagram Showing How a Node Works in LangGraph

           ┌─────────────────────────┐
           │        STATE IN          │
           │   (Your agent's memory)  │
           └─────────────┬───────────┘
                         │
                         ▼
               ┌───────────────────┐
               │      NODE         │
               │  (One Step/Task)  │
               │                   │
               │ • Reads State     │
               │ • Runs Logic      │
               │ • Produces Output │
               └───────────┬───────┘
                           │
                           ▼
           ┌─────────────────────────┐
           │       STATE OUT         │
           │ (Updated by Reducers)   │
           └─────────────┬───────────┘
                         │
                         ▼
                ┌─────────────────┐
                │     NEXT NODE   │
                └─────────────────┘

And once you understand this anatomy, designing workflows becomes almost addictive.

Creating and Connecting Your First Nodes

Now that you understand what nodes are, it’s time to actually build them — and then connect them to create a real workflow.

Let’s walk through it step by step.

---

Step 1: Start With a Simple Function

Every node begins life as a small Python function.

def greet(state):
    name = state["name"]
    return {"message": f"Hello, {name}! Welcome to LangGraph."}

This function:

• reads something from state
• returns something new

Step 2: Turn That Function Into a Node

Next, we register it inside a graph:

from langgraph.graph import Graph

graph = Graph()
graph.add_node("greet_node", greet)

You now have your first node

LangGraph knows: “When the workflow hits greet_node, run the greet function.”

Step 3: Add Another Node (Optional)

Let’s add a second node that transforms the output:

def shout(state):
    message = state["message"]
    return {"shouted": message.upper()}

graph.add_node("shout_node", shout)

Step 4: Connect the Nodes Together

Nodes aren’t useful in isolation, they’re meant to flow.

Connecting them is just:

graph.add_edge("greet_node", "shout_node")

Now your workflow looks like:

[greet_node] → [shout_node]

LangGraph now understands:

“After greeting, jump to shouting.”

This is called a linear workflow, and it’s the foundation of everything else you’ll build.

Step 5: Run Your Mini-Workflow

Let’s try it out:

input_state = {"name": "Dami"}

result = graph.run(input_state)
print(result)

Sample output:

{
  "name": "Dami",
  "message": "Hello, Dami! Welcome to LangGraph.",
  "shouted": "HELLO, DAMI! WELCOME TO LANGGRAPH."
}

Congratulations — you just built a functioning LangGraph pipeline.

---

Connecting Nodes in Interesting Ways

Linear flows are great, but LangGraph becomes powerful when you start using different connection patterns. Let’s explore the essentials.

---

1. Linear Chains (The Classic Flow)

A → B → C → D

graph.add_edge("start", "format_node")
graph.add_edge("format_node", "llm_node")
graph.add_edge("llm_node", "save_node")
graph.add_edge("save_node", "end")

Perfect for simple pipelines.

2. Conditional Routing (Decision-Based Workflow)

Let’s say your agent detects user intent:

                   billing_flow
                 ↗
[intent_node] —──┤
                 ↘
                   refund_flow

In code:

graph.add_conditional_edges(
    "intent_node",
    {
        "billing": "billing_flow",
        "refund": "refund_flow",
        "other": "fallback_node",
    }
)

One node → multiple possible next steps.

3. Loops (Repeating a Step Until Ready)

Yes, you can loop:

graph.add_edge("refine_node", "refine_node")

Loops are super useful for:

• LLM refinement
• multi-turn reasoning
• repeated checking

4. Parallel Branching (Fan-Out + Merge)

Split work into multiple paths:

             ┌──→ branch_A
start_node ──┤
             └──→ branch_B

And merge later:

branch_A
        ↘
         merge_node
        ↗
branch_B

Code:

graph.add_edge("start_node", "branch_A")
graph.add_edge("start_node", "branch_B")

graph.add_edge("branch_A", "merge_node")
graph.add_edge("branch_B", "merge_node")

Perfect for tasks that can run simultaneously.

Why This Matters

Once you know how to create and connect nodes, you unlock the ability to design:

• chatbots with reasoning flows
• structured workflows
• agentic pipelines
• tool-calling systems
• data processing chains
• multi-agent multi-step applications
• anything that benefits from clarity and controlled execution

LangGraph doesn’t force you into one pattern, it hands you LEGO blocks and says: Build whatever you want — cleanly.

Running and Visualizing Your Node Chain

You’ve built your nodes. You’ve connected them. Now it’s time for the part everyone loves: running the graph and actually seeing how everything flows.

1. Running the Graph (The “Just Hit Play” Moment)

Once your graph is compiled, running it is as easy as:

runnable = graph.compile()

result = runnable.invoke({"name": "Dami"})
print(result)

Under the hood, LangGraph walks your flow step-by-step, updates the state after each node, and hands you the final result.

If you’ve chained multiple nodes, you’ll see all the accumulated state:

{
  "validated": True,
  "message": "Hello Dami! Welcome to LangGraph 🎉"
}

2. Visualizing Your Graph (Your Workflow, but as a Map)

LangGraph comes with built-in visualization, which is perfect when your graph starts growing beyond a few nodes.

Just call:

graph.display()

You’ll get a neat diagram showing:

• Each node in your flow
• How they connect
• The direction of execution

It’s like going from “I hope this works” to “Ah, now I see how everything fits.”

3. Why Visualization Matters (Especially for Beginners)

When you’re new to LangGraph, diagrams help you:

• Spot mistakes early (hello, missing edges 👀)
• Understand your flow at a glance
• Explain your graph to teammates—or future you
• Keep your mental model tight as the workflow grows

LangGraph’s visualization is especially helpful once you start mixing:

• Conditionals
• Tool nodes
• LLM calls
• Multiple branching paths

But even with a simple 2–3 node chain, seeing it makes a world of difference.

Wrapping Up: You’ve Just Built Your First LangGraph Node Flow

And that’s a solid win.

You came in knowing nodes were “a thing in LangGraph,” and you’re leaving with an understanding of what they are, how to create them, how to chain them, and how to visualize your workflow. That’s the core of almost every LangGraph project—whether you’re building a simple helper bot or a multi-agent system with branching logic and tools flying everywhere.

At this point in the LangGraph series, you should be feeling more confident about the building blocks:

• State (covered earlier in the series)
• Reducers (your quiet state-updating heroes)
• Messages & MCP (communication superpowers)
• And now—Nodes (the actions that make everything move)

Together, they form the foundation for more advanced patterns we’ll explore next.

If this clicked for you, amazing.

If it sparked new ideas, even better.

Keep experimenting, keep connecting nodes, and keep building smarter graphs.

Your next workflow is just a node away.