A Beginner’s Guide to Getting Started with Graph Messages in LangGraph

If you’ve been following this LangGraph series, you’ve already learned how to build agents, manage their state, and use reducers to control how data flows through your graph. We’ve laid a foundation. But there’s one piece we haven’t zoomed in on yet, the actual messages moving through your graph.

Think about it: when you build a chatbot or any conversational agent, what really travels between nodes? It’s not just text, it’s structured data, context, and intent wrapped up as messages. These messages are what keep your graph alive, what make your AI remember what was said before, and what let it respond intelligently the next time around.

This is where Graph Messages come in. They’re the glue that connects everything, the courier that carries meaning from one node to another, the thread that ties state and logic together. Without understanding Graph Messages, you’re only scratching the surface of what LangGraph can do.

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

Learn LangChain in a clear, concise, and practical way.

Whether you’re just starting out or already building, Langcasts gives you guides, tips, hands-on walkthroughs, and in-depth classes to help you master every piece of the AI puzzle. No fluff, just actionable learning to get you building smarter and faster. Start your AI journey today at Langcasts.com.

In this guide, we’ll take a beginner-friendly look at how Graph Messages work inside LangGraph. You’ll see how they fit into the bigger picture you’ve already built in this series, and how mastering them will make your conversational agents smarter, more reliable, and a lot easier to reason about.

Ready to see how messages actually move through your LangGraph? Let’s break it down.

The Core Concept — What Are Graph Messages?

Before we get into the code, let’s strip the concept down to its essence.

A Graph Message in LangGraph is simply how information moves between nodes in your graph. Each node in your LangGraph setup does something, maybe it generates text, analyzes sentiment, or calls an API. But nodes don’t exist in isolation. They need a way to talk to each other. That’s what messages are for.

It might contain:

• The user’s input (HumanMessage)
• The model’s response (AIMessage)
• The current context or memory (ToolMessage)
• Or any other info that your next node needs to do its job (SystemMessage)

In LangGraph, these messages aren’t just free-floating text strings; they’re typed objects that keep your workflow organized and predictable. Each message carries both content and metadata, so your graph knows what kind of data it’s handling and where it came from.

Here’s a simple mental model:

“Nodes process. Edges are the routes. Messages move.”

Why does this matter? Because when you start building more advanced graphs, like chatbots that remember past turns, the way you handle and store these messages determines how well your system holds context over time. Mismanage messages, and your agent forgets its own history. Handle them right, and you get smooth, intelligent, multi-turn conversations. Now that we seem to have got the hang of Messages in Graph State, let's set up our LangGraph.

Setting Up Your First LangGraph Project

Now that you’ve got a handle on what Graph Messages are, let’s get your hands dirty with a simple example.

Before we start, make sure you’ve got LangGraph installed. If not, just run:

pip install langgraph

LangGraph projects usually follow a simple pattern:

1. Define your state — this is the shared data that moves through the graph.
2. Create your nodes — each node is just a Python function that does something with that state.
3. Connect the nodes with edges — these determine how the workflow moves from one node to the next.
4. Compile and run your graph — this “wires” everything together so LangGraph can handle message passing behind the scenes.

Here’s the smallest possible working example to see message flow in action:

from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END

# 1. Define the state
class GraphState(TypedDict):
    user_input: str
    response: str

# 2. Create nodes
def collect_input(state: GraphState):
    print("Node: collect_input")
    return {"user_input": "Hello, LangGraph!"}

def generate_response(state: GraphState):
    print("Node: generate_response")
    text = state["user_input"]
    return {"response": text + " Nice to meet you."}

# 3. Build and connect the graph
builder = StateGraph(GraphState)
builder.add_node("collect_input", collect_input)
builder.add_node("generate_response", generate_response)

builder.add_edge(START, "collect_input")
builder.add_edge("collect_input", "generate_response")
builder.add_edge("generate_response", END)

# 4. Compile and run
graph = builder.compile()
output = graph.invoke({})
print(output)

What’s happening here?

• The collect_input node sends out a message containing the user_input.
• That message travels along the edge to the generate_response node.
• The generate_response node reads the incoming message, updates the response, and sends its message forward.
• Once there are no more messages to deliver, the graph stops, and you get your result.

Even in this small demo, you can see how the data (or message) moves between nodes and evolves along the way.

Behind the scenes, LangGraph handles the message-passing logic automatically. You just define what each node should do and how they connect, and LangGraph does the rest.

Next, we’ll dive into how you can create and manage messages yourself, including how to store message history and keep your chatbot’s memory intact.

Creating and Passing Graph Messages

So far, we’ve seen messages move quietly behind the scenes, nodes sending and receiving state as the graph runs.

But when you’re building a chatbot or any agent that needs memory, you’ll often need to create, store, and manage those messages yourself.

That’s where LangGraph’s message system really shines.

Messages Are the Memory Backbone

Every turn in a conversation — user input, AI response, or system message — becomes part of the graph’s evolving state.

If you don’t manage those properly, your chatbot loses context with every new message.

LangGraph solves this neatly with two built-in tools:

1. add_messages reducer – for appending new messages to your state without overwriting old ones.
2. MessagesState helper class – a shortcut for setting up message storage in your graph automatically.

Using MessagesState — The Quick Way

If your project is all about managing chat history, the easiest path is to use MessagesState.

It sets up your graph state with a messages key and automatically configures the add_messages reducer for you.

Here’s a minimal example:

from langgraph.graph import StateGraph, START, END, MessagesState
from langgraph.reducers import add_messages

def chat_node(state: MessagesState):
    messages = state["messages"]
    last_message = messages[-1]["content"] if messages else "Hello!"
    reply = f"You said: {last_message}"
    return {"messages": [{"role": "assistant", "content": reply}]}

builder = StateGraph(MessagesState)
builder.add_node("chat", chat_node)
builder.add_edge(START, "chat")
builder.add_edge("chat", END)
graph = builder.compile()

result = graph.invoke({"messages": [{"role": "user", "content": "Hi!"}]})
print(result["messages"])

Here’s what’s going on:

• The user sends a message — LangGraph wraps it as part of the graph’s state.
• The node reads that message, builds a response, and returns a new message.
• The add_messages reducer kicks in automatically, appending the new message to the conversation history.

Now your chatbot remembers previous messages — no custom memory logic, no manual state juggling.

When You Need Custom State

If you want to include extra fields (like user data or conversation mode), you can define your own state and annotate it with the add_messages reducer.

from typing_extensions import TypedDict
from langgraph.reducers import add_messages

class ChatState(TypedDict):
    messages: list
    user_id: str

ChatState = add_messages(ChatState)

This lets you mix chat history with other useful data, and still keep message updates automatic and reliable.

Debugging and Inspecting Messages

When your graph starts to grow beyond a few nodes, things can get fuzzy fast.

Messages fly around, reducers run quietly, and before you know it, your chatbot starts “forgetting” things or producing strange responses.

That’s your cue to inspect what’s actually happening inside your graph.

Fortunately, LangGraph gives you a few simple ways to peek inside and trace message flow step by step.

1. Printing Message State (the Quick Way)

The easiest way to understand what’s going on is to just print the state your nodes receive and return.

Here’s an example:

def chat_node(state: MessagesState):
    print("\n--- Incoming State ---")
    print(state)

    messages = state["messages"]
    last_message = messages[-1]["content"] if messages else "Hello!"
    reply = f"You said: {last_message}"

    print("\n--- Outgoing State ---")
    print({"messages": [{"role": "assistant", "content": reply}]})

    return {"messages": [{"role": "assistant", "content": reply}]}

This might feel basic, but when you’re just starting out, it’s often the most revealing approach.

You can literally watch your graph evolve message by message.

2. Using graph.inspect()

LangGraph includes a handy inspection feature that lets you run your graph interactively and see the flow between nodes.

graph.inspect({"messages": [{"role": "user", "content": "Hi!"}]})

This displays how your graph processes input, which nodes get activated, and how messages change as they pass through the system.

It’s a quick sanity check when your state isn’t updating the way you expect.

3. Common Gotchas and Fixes

Here are a few message-related bugs you’ll probably run into — and how to fix them fast:

Problem	Likely Cause	Quick Fix
Your chatbot “forgets” the conversation	You overwrote messages instead of appending	Use the add_messages reducer
Graph stops after one node	Missing edge or wrong node name	Double-check your edge definitions
Messages are duplicated	Returning full state instead of partial update	Return only the fields you changed
Node never runs	No incoming message	Make sure a previous node actually returns something

4. Verify Message Flow Early

When your graph is still small, trace message flow early and often.

Print your state, run inspect(), and verify that each node gets the right data.

Catching issues at this stage saves you from debugging a tangle of nodes later.

---

At the end of the day, debugging in LangGraph is about visibility.

If you can see how messages move, you can reason about your graph.

Once you understand your message flow, fixing bugs and scaling up become much simpler.

---

If you’ve been following this LangGraph series from the start, you’ve now connected all the dots, from agents to state to reducers.

But here’s the key takeaway: Graph Messages are the lifeblood of it all.

They’re what connect your nodes, carry your state, and bring your AI workflows to life.

LangGraph’s message-passing system, powered by the add_messages reducer and the MessagesState helper, gives you a simple way to manage complexity.

If you haven’t already, watch the YouTube demo to see messages in action, then try adding them to your next LangGraph project.

Ready to start your AI journey? Don’t wait — join a class at Langcasts.com and start building smarter today.