🔹 TypedDict — What is it?

✅ Meaning:

TypedDict is a feature from Python's typing module that allows you to define a dictionary with specific keys and types, just like a class. It's useful when you're working with structured dictionaries and want type checking and auto-completion in IDEs.

🧠 Example:

from typing import TypedDict

class Person(TypedDict):
    name: str
    age: int

p: Person = {"name": "Abhi", "age": 34}  # ✅ Valid

So, in your example:

class AgentState(TypedDict):
    messages: List[BaseMessage]
    intermediate_steps: List[str]

It means:

• AgentState is a dictionary that must contain:

  • a key "messages" with a list of BaseMessage items.

  • a key "intermediate_steps" with a list of strings.

🔹 BaseMessage — What is it?

✅ Meaning:

BaseMessage is an abstract base class from LangChain, specifically from langchain.schema. It represents a message exchanged in a conversation, and can be a:

• HumanMessage (user input)

• AIMessage (LLM response)

• SystemMessage (instructions)

• FunctionMessage (tool call outputs)

🧠 Example:

from langchain.schema import HumanMessage

msg = HumanMessage(content="Hello!")

So, when you define:

messages: List[BaseMessage]

You're saying the messages list can contain any combination of HumanMessage, AIMessage, etc., because they all inherit from BaseMessage.

🧩 Why This is Useful in LangGraph?

• TypedDict helps define and validate the AgentState structure.

• BaseMessage allows handling chat history in a structured way.

✅ Final Summary