from langchain_core.runnables import RunnableConfig from typing_extensions import Annotated, TypedDict from langgraph.checkpoint.memory import InMemorySaver from langgraph.graph import StateGraph from langgraph.runtime import Runtime def reducer(a: list, b: int | None) -> list: if b is not None: return a + [b] return a class State(TypedDict): x: Annotated[list, reducer] class Context(TypedDict): r: float graph = StateGraph(state_schema=State, context_schema=Context) def node(state: State, runtime: Runtime[Context]) -> dict: r = runtime.context.get("r", 1.0) x = state["x"][-1] next_value = x * r * (1 - x) return {"x": next_value} graph.add_node("A", node) graph.set_entry_point("A") graph.set_finish_point("A") compiled = graph.compile() step1 = compiled.invoke({"x": 0.5}, context={"r": 3.0}) # {'x': [0.5, 0.75]}

Overrides Graph<N, S, U, StateGraphNodeSpec<S, U>, ToStateDefinition<C>>.constructor · Defined in libs/langgraph-core/dist/graph/state.d.ts:163

Edges: Functions that determine which Node to execute next based on the current state. They can be conditional branches or fixed transitions. By composing Nodes and Edges, you can create complex, looping workflows that evolve the state over time. The real power, though, comes from how LangGraph manages that state.

October 26, 2024 - State is one of the core components of LangGraph. A node takes state as input, updates it and pass it to the next node in the graph. So the next node takes the response from the previous node as input, updates it and pass it onto the next one.

LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) ...

January 17, 2024 - At it's core, LangGraph exposes a pretty narrow interface on top of LangChain. StateGraph is a class that represents the graph. You initialize this class by passing in a state definition.

import { type BaseMessage, AIMessage, HumanMessage, } from "@langchain/core/messages"; import { StateGraph, Annotation } from "@langchain/langgraph"; // Define a state with a single key named "messages" that will // combine a returned BaseMessage or arrays of BaseMessages const StateAnnotation = Annotation.Root({ sentiment: Annotation<string>, messages: Annotation<BaseMessage[]>({ reducer: (left: BaseMessage[], right: BaseMessage | BaseMessage[]) => { if (Array.isArray(right)) { return left.concat(right); } return left.concat([right]); }, default: () => [], }), }); const graphBuilder = new StateGraph(StateAnnotation); // A node in the graph that returns an object with a "messages" key // will update the state by combining the existing value with the returned one.

March 24, 2025 - LangGraph is a powerful framework that allows developers to define workflows as directed graphs. One of its core components is StateGraph, which enables efficient state management and execution flow control.

August 17, 2024 - This simple graph demonstrates the fundamental structure of a LangGraph workflow: We create a StateGraph using our BasicState.

September 5, 2024 - Once we understand why LangGraph exists, we’ll explore the technology practically through something called a “State Graph”. We’ll use this state graph to build an agent which is capable of performing a complex task which requires the agent to deal with natural conversation, hard rules, and application logic.

3 days ago - Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.

Graphs are the core abstraction of LangGraph. Each StateGraph implementation is used to create graph workflows.

November 24, 2024 - For example, a node can integrate with a large language model, process information, call an external API, or any other task. A LangGraph node takes the state of the graph as a parameter and returns an updated state after it is executed.

November 15, 2024 - LangGraph is a versatile Python library designed for stateful, cyclic, and multi-actor Large Language Model (LLM) applications. This tutorial will give you an overview of LangGraph fundamentals through hands-on examples, and the tools needed ...

September 9, 2025 - StateGraph returns a builder for the graph. Note the difference between the LangGraph StateGraph and the state type GraphState!

November 19, 2024 - LangGraph helps us manage such workflows efficiently. States are like checkpoints in your task execution: from typing import TypedDict, List class ShoppingState(TypedDict): # Current state current_step: str # Cart items cart_items: List[str] # Total amount total_amount: float # User input user_input: str class ShoppingGraph(StateGraph): def __init__(self): super().__init__() # Define states self.add_node("browse", self.browse_products) self.add_node("add_to_cart", self.add_to_cart) self.add_node("checkout", self.checkout) self.add_node("payment", self.payment)