How does state management work in LangGraph?

State in LangGraph is a TypedDict that is shared across all nodes in a graph run. Every time a node executes, it can return a partial update — a dict containing only the keys it wants to change. LangGraph merges the update into the current state using reducers.

The default reducer is last-write-wins: the node's returned value replaces the current value for that key. You can override this with Annotated[type, reducer_fn] where reducer_fn takes (current, update) and returns the new value:

from typing import TypedDict, Annotated
import operator

class GraphState(TypedDict):
    # Append-only: new messages are added to the list
    messages: Annotated[list, operator.add]

    # Last-write-wins: iteration count is replaced each time
    iteration_count: int

    # Custom reducer: keep the highest score seen so far
    best_score: Annotated[float, lambda a, b: max(a, b)]

State is immutable between node calls — nodes receive a snapshot and return updates; they do not mutate state in place. This design enables checkpointing (save the full state after each node), time-travel debugging (replay from any past state), and parallel node execution (each branch gets a copy of the state).

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