Messages

Messages are the objects a chat model reads and writes. The four you use most are HumanMessage, AIMessage, SystemMessage and ToolMessage.

Last updated: 29 Sep, 2026 · LangChain 1.4

A conversation is a list of messages. Each one has a type (its role) and content. The model is given the list and returns the next message.

The LangChain section of the video separates two kinds of input. A plain string, such as model.invoke("what is langchain"), is a text prompt: fine for one request with no history. A list of message objects is a message prompt, which can tell the model how to behave and carry a conversation. The video's first list has a system message, "You are a poetry expert", and a human message asking for a poem on artificial intelligence; the poem comes back as an AI message. The video loads groq:qwen/qwen3-32b, since retired, so the code below uses the course model, loaded with init_chat_model, which Chat models covers:

from langchain.chat_models import init_chat_model
from langchain.messages import SystemMessage, HumanMessage, AIMessage

model = init_chat_model("groq:openai/gpt-oss-120b")   # the model from the setup lesson

messages = [
    SystemMessage("You are a poetry expert"),
    HumanMessage("Write a poem on artificial intelligence"),
]
response = model.invoke(messages)
print(response.content)

A graph passes the same kind of list from node to node. The support conversation below is built from the same classes and printed one message at a time.

The message-type imports

from langchain.messages import HumanMessage, AIMessage, SystemMessage, ToolMessage

A conversation as a list

Start by importing the two message types you need and building a conversation. A conversation is a plain Python list of message objects.

from langchain.messages import SystemMessage, HumanMessage

conversation = [
    SystemMessage("You are a support assistant."),   # your instructions to the model
    HumanMessage("I was charged twice."),            # the user's turn
]

Reading role and content

Walk the list and print each one. type(m).__name__ is the message's class name, which is its role, and m.content is its text.

for m in conversation:
    print(type(m).__name__, "->", m.content)   # role, then text

Printing the conversation

The two snippets together:

from langchain.messages import SystemMessage, HumanMessage

conversation = [
    SystemMessage("You are a support assistant."),
    HumanMessage("I was charged twice."),
]

for m in conversation:
    print(type(m).__name__, "->", m.content)

SystemMessage -> You are a support assistant.
HumanMessage -> I was charged twice.

What each message carries

• Each message is an object with a role (its class) and content.
• A SystemMessage sets instructions; a HumanMessage is the user's turn; the model replies with an AIMessage.
• A ToolMessage carries a tool's result back to the model and must set tool_call_id to match the call it answers.

The four message types

Where messages appear

• Every model call takes a list of messages and returns one.
• The conversation your agent remembers is a list of these objects.

Related

• Previous: Subgraphs
• Next: MessagesState and add_messages
• Reference: Messages