State

Memory shared across all nodes in the graph.

Overview

State is like a "notebook" that records everything the agent learns and decides. It is a shared data structure accessible to all nodes and edges in the graph.

Definition Methods

Using TypedDict

from typing import TypedDict

class State(TypedDict):
    messages: list[str]
    user_name: str
    count: int

Using Pydantic Model

from pydantic import BaseModel

class State(BaseModel):
    messages: list[str]
    user_name: str
    count: int = 0  # Default value

Reducer (Controlling Update Methods)

A function that specifies how each key is updated. If not specified, it defaults to value overwrite.

Addition (Adding to List)

from typing import Annotated
from operator import add

class State(TypedDict):
    messages: Annotated[list[str], add]  # Add to existing list
    count: int  # Overwrite

Custom Reducer

def concat_strings(existing: str, new: str) -> str:
    return existing + " " + new

class State(TypedDict):
    text: Annotated[str, concat_strings]

MessagesState (LLM Preset)

For LLM conversations, LangChain's MessagesState is convenient:

from langgraph.graph import MessagesState

# This is equivalent to:
class MessagesState(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]

The add_messages reducer:

Adds new messages
Updates existing messages (ID-based)
Supports OpenAI format shorthand

Important Principles

Store Raw Data: Format prompts within nodes
Clear Schema: Define types with TypedDict or Pydantic
Control with Reducer: Explicitly specify update methods

Example

from typing import Annotated, TypedDict
from operator import add

class AgentState(TypedDict):
    # Messages are added to the list
    messages: Annotated[list[str], add]

    # User information is overwritten
    user_id: str
    user_name: str

    # Counter is also overwritten
    iteration_count: int

01_core_concepts_node.md - How to use State in nodes
03_memory_management_overview.md - State persistence

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