Let’s break down the concepts of State, AgentState, TypeDict, and Message State in LangGraph, using a standard agent flow. This will help you understand how information moves and transforms within a LangGraph application.

🌐 What is LangGraph?

LangGraph is a framework for building stateful multi-step agents and conversational workflows on top of LangChain. It enables defining graph-based logic, where nodes represent steps (like calling a tool or LLM), and edges define transitions based on state.

💡 Core Concepts in LangGraph

1. 🧠 State

• The state in LangGraph is a data container that holds all the relevant information about a conversation/session.

• Think of it as a shared memory that gets updated at each node.

Example:

from langgraph.graph import StateGraph

state = {
    "messages": [],  # Chat history
    "tools": [],     # Tool calls and responses
    "step": 0        # Step tracker
}

You define a state as a TypedDict.

2. 🧱 TypeDict (TypedDict from typing)

• A Python way to define the structure of the state.

• It is used to validate and annotate what keys your state can contain.

Example:

from typing import TypedDict, List
from langchain_core.messages import BaseMessage

class AgentState(TypedDict):
    messages: List[BaseMessage]
    step: int

This tells LangGraph that your AgentState must contain a list of messages and an integer step.

3. 👤 AgentState

• This is a custom class (usually a TypedDict) that defines what your agent remembers and works with across steps.

• It is your operational state model for the agent’s context.

Example (continued):

class AgentState(TypedDict):
    messages: List[BaseMessage]
    step: int

You can expand this with other items like:

• tools_used: List[str]

• user_input: str

• final_output: str

4. 💬 Message State

• messages in the state usually refer to a list of LangChain BaseMessage objects.

• These track the full conversation (user, AI, tool messages).

• It’s important for context passing to LLMs and tools.

from langchain_core.messages import HumanMessage, AIMessage

messages = [
    HumanMessage(content="What's the weather?"),
    AIMessage(content="Where are you located?")
]

🔁 Standard Agent Flow in LangGraph

Here’s a simple LangGraph-based agent flow using a graph:

graph TD
    Start((Start)) --> Node1[Receive Input]
    Node1 --> Node2[Call LLM]
    Node2 --> Node3[Check for Tool Calls]
    Node3 -- Tool Call --> Node4[Call Tool]
    Node4 --> Node5[Update State]
    Node3 -- Final Answer --> End((End))
    Node5 --> Node2

🔄 Putting It All Together

Let’s sketch a minimal example:

from typing import TypedDict, List
from langchain_core.messages import BaseMessage, HumanMessage
from langgraph.graph import StateGraph, END

# Step 1: Define your State
class AgentState(TypedDict):
    messages: List[BaseMessage]
    step: int

# Step 2: Define your Node functions
def receive_input(state: AgentState) -> AgentState:
    user_msg = HumanMessage(content="Tell me a joke.")
    return {
        **state,
        "messages": state["messages"] + [user_msg],
        "step": state["step"] + 1
    }

def call_llm(state: AgentState) -> AgentState:
    # Dummy LLM reply
    from langchain_core.messages import AIMessage
    ai_msg = AIMessage(content="Why did the chicken cross the road? To get to the other side!")
    return {
        **state,
        "messages": state["messages"] + [ai_msg],
        "step": state["step"] + 1
    }

# Step 3: Create the Graph
builder = StateGraph(AgentState)
builder.add_node("input", receive_input)
builder.add_node("llm", call_llm)

# Define the flow
builder.set_entry_point("input")
builder.add_edge("input", "llm")
builder.add_edge("llm", END)

# Build and run the graph
graph = builder.compile()

initial_state: AgentState = {"messages": [], "step": 0}
final_state = graph.invoke(initial_state)

for msg in final_state["messages"]:
    print(f"[{msg.type}] {msg.content}")

✅ Summary (Color-Coded)