langgraph-fundamentals

Directed graph framework for building stateful, multi-step agent workflows with fine-grained control. Graphs must be compile() d before execution. Follow these 5 steps when building a new graph: class State(TypedDict): name: str # Default: overwrites on update messages: Annotated[list, operator.add] # Appends to list total: Annotated[int, operator.add] # Sums integers from typing import Annotated import operator class State(TypedDict): messages: Annotated[list, operator.add] def my_node(state: State) -> dict: return {"field": "updated"} Node functions accept these arguments: class State(TypedDict): input: str output: str def process_input(state: State) -> dict: return {"output": f"Processed: {state['input']}"} def finalize(state: State) -> dict: return {"output": state["output"].upper()} graph = ( StateGraph(State) .add_node("process", process_input) .add_node("finalize", finalize) .add_edge(START, "process") .add_edge("process", "finalize") .add_edge("finalize", END) .compile() ) result = graph.invoke({"input": "hello"}) print(result["output"]) # "PROCESSED: HELLO" class State(TypedDict): query: str route: str result: str def classify(state: State) -> dict: if "weather" in state["query"].lower(): return {"route": "weather"} return {"route": "general"} def route_query(state: State) -> Literal["weather", "general"]: return state["route"] graph = ( StateGraph(State)...