Here’s a clear,  explained from core concept → practical usage → mental model.

1️⃣ What is BaseMessage?

🔹 Definition

BaseMessage is the abstract parent class for all message types in LangChain.

It represents one unit of conversation passed to or from an LLM.

🔹 Think of it as:

📦 A generic envelope for a message

It does not represent who sent the message —
it only defines what a message must look like.

🔹 Core Responsibilities of BaseMessage

Every message in LangChain must have:

🔹 Simplified Structure

BaseMessage(
    content="text or structured data",
    additional_kwargs={}
)

⚠️ You never instantiate BaseMessage directly
It exists to enforce a contract for all message types.

🔹 Why BaseMessage Exists

LangChain needs a standard format so that:

• Different message types can be stored together

• Conversation history can be replayed

• LLMs can receive structured chat input

• Agents can reason over messages uniformly

2️⃣ What is HumanMessage?

🔹 Definition

HumanMessage is a concrete subclass of BaseMessage.

It represents input coming from the user.

🔹 Think of it as:

🧑 User talking to the AI

Whenever a user types:

“Explain transformers”

LangChain internally wraps it as:

HumanMessage(content="Explain transformers")

🔹 Key Properties

🔹 Example Usage

from langchain_core.messages import HumanMessage

msg = HumanMessage(content="What is RAG?")

This message can now be:

• Sent to an LLM

• Stored in memory

• Passed through an agent

• Used in a state graph

3️⃣ Relationship Between Them

BaseMessage
   ├── HumanMessage   (user input)
   ├── AIMessage      (LLM output)
   ├── SystemMessage  (instructions)
   └── ToolMessage    (tool responses)

📌 BaseMessage defines the rules
📌 HumanMessage follows those rules

4️⃣ Real-World Mental Model (Very Important)

Imagine a chat application backend:

• BaseMessage → 📄 Message schema

• HumanMessage → 👤 User sends message

• AIMessage → 🤖 Bot replies

• SystemMessage → ⚙️ App rules

• ToolMessage → 🔧 External service response

LangChain uses this model so agents and LLMs can:

• Reason step-by-step

• Maintain memory

• Route messages correctly

5️⃣ Why You Should Care (Agentic / RAG Context)

In Agentic RAG, LangGraph, and Memory systems:

• Messages are the state

• State is composed of BaseMessage objects

• Agents decide actions by reading message history

Example:

state["messages"].append(
    HumanMessage(content="Search latest RBI guidelines")
)

⚠️ If messages were not standardized → agents would break.

6️⃣ Interview Gold Line 🥇

“BaseMessage is the abstract contract for all conversational units in LangChain, while HumanMessage is a concrete implementation representing user input within that contract.”

7️⃣ One-Line Summary

BaseMessage defines what a message is; HumanMessage represents a message from the user.