Agentic AI Application Architecture
4.2 Shared State
Shared State in LangGraph #
The most important LangGraph concept in this app is the shared state object:
python
class PlannerState(TypedDict):
messages: Annotated[List[AnyMessage], add_messages]
user_request: str
session_id: str
origin: str
destination: str
departure: str
return_date: str
travellers: int
flight_summary: Optional[str]
hotel_summary: Optional[str]
activities_summary: Optional[str]
final_itinerary: Optional[str]
current_agent: strThis state moves through the graph from node to node.
Each node:
- reads values from state
- does some work
- writes new values back to state
- sets current_agent to control what happens next
This is a key LangGraph mental model: stateful workflow orchestration.
Knowledge Check #
How would you explain the syntax used for the messages field?
python
messages: Annotated[List[AnyMessage], add_messages]Click here to see the answer
messages: Annotated[List[AnyMessage], add_messages] does two things.
List[AnyMessage]defines the type of the field: it’s a list of LangChain message objects (system, human, or AI messages).Annotated[..., add_messages]adds LangGraph behavior that tells the graph how updates to this field should be handled.
Specifically, add_messages means that when a node writes new messages, LangGraph will append them to the existing list instead of overwriting it.
So the conversation history grows as each node adds messages.