🧠 FIRST: The Big Mental Model

LangGraph is a state machine where the STATE is made of messages.

Everything that happens in LangGraph is driven by:

state = {
    "messages": [BaseMessage, BaseMessage, ...]
}

📌 No messages → No reasoning → No agent

🧩 Core Components (Before Flow)

🔁 STEP-BY-STEP MESSAGE FLOW (Very Important)

① Human Starts the Graph 👤

HumanMessage("Explain RBI guidelines")

➡️ Added to state:

state.messages = [
   HumanMessage(...)
]

🧠 This is the trigger

② Entry Node Reads Messages 🟣

• Node receives state

• Reads entire message history

• Decides what to do next

last_message = state.messages[-1]

📌 Nodes never guess — they read messages.

③ LLM Node Responds 🤖

LLM sees:

[SystemMessage, HumanMessage, AIMessage, ...]

Produces:

AIMessage("Here are RBI guidelines...")

➡️ Appended to state:

state.messages.append(AIMessage)

🧠 State grows, nothing is overwritten

④ Conditional Edge Evaluates 🧭

LangGraph checks:

• Did AI ask to use a tool?

• Did AI finish?

• Is more reasoning needed?

Example:

if "search" in AIMessage.tool_calls:
    → Tool Node
else:
    → End

📌 Edges are logic gates, not code magic

⑤ Tool Node Adds ToolMessage 🔧

Tool executes → returns result:

ToolMessage("Search results...")

➡️ Appended to state:

state.messages.append(ToolMessage)

Now LLM has external knowledge.

⑥ LLM Re-Reads Everything 🔄

LLM now sees:

HumanMessage
AIMessage (tool call)
ToolMessage (result)

Produces refined:

AIMessage("Final structured answer")

🧠 This loop continues until END condition met

🔁 Visual Loop Summary

HumanMessage
   ↓
LLM Node → AIMessage
   ↓
Edge Decision
   ↓
Tool Node → ToolMessage
   ↓
LLM Node (again)
   ↓
Final AIMessage

📌 Messages = Memory + Reasoning + Control

🧠 WHY LangGraph Uses Messages as State

🚀 Agentic RAG Perspective

In Agentic RAG, messages carry:

• User intent

• Retrieved documents

• Tool outputs

• Intermediate reasoning

• Final answer

📌 Vector DB results → ToolMessage
📌 RAG context → AIMessage content

🥇 Interview Gold Line 

“In LangGraph, messages are the state. Nodes read messages, edges route based on messages, and agents reason by appending new messages.”

🧠 One-Line Mental Model

LangGraph is not a pipeline — it’s a conversation-driven state machine.