Agentic AI Application Architecture

4.6 Message Abstractions

1 min

LangChain Message Abstractions

The application uses LangChain message abstractions rather than one long prompt string.

python 
from langchain_core.messages import (
    AIMessage,
    BaseMessage,
    HumanMessage,
    SystemMessage,
)

This is important because each node can separate:

For example:

python 
messages = [
    SystemMessage(content="You are a flight booking specialist. Provide concise options."),
    HumanMessage(content=step),
]
result = llm.invoke(messages)

Knowledge Check

How would you define system, human, and AI messages?

Click here to see the answer

In LangChain and LangGraph, messages are typically categorized by who is speaking and what role they play in guiding the conversation:

  • System message: Sets the rules and context for the AI’s behavior. It defines instructions, constraints, tone, and goals that guide how the model should respond throughout the interaction.
  • Human message: Input from the user. It contains questions, requests, or information that the AI should respond to.
  • AI message: The model’s response. It represents the assistant’s generated output based on the system instructions and human input.
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