← Back to Blog
AI / MLFebruary 20, 20266 min read

LangGraph (2024): Orchestrating Complex Agents

LangGraph is a framework to orchestrate complex agents built with LangChain, supporting cycles, state, and multi-agent workflows.

Beyond linear chains

LangGraph is a framework to orchestrate complex agents built with LangChain. Introduced by the LangChain team in early 2024, it addresses what linear chains cannot do: cyclic workflows, persistent state, human-in-the-loop interactions, and multi-agent orchestration.

In an AI booking platform, trip planning is not one LLM call. It requires destination discovery, hotel search, activity planning, budget validation, and itinerary assembly. Each step can fail, retry, or branch. LangGraph models that as a graph, not a chain.

Booking agent graph

booking_agent_graph.pyPython
from langgraph.graph import StateGraph, END

def discover_destinations(state):
    state["candidates"] = hybrid_search(state["query"])
    return state

def recommend_hotels(state):
    state["hotels"] = hotel_agent.run(state["candidates"], state["budget"])
    return state

def plan_activities(state):
    state["activities"] = activity_agent.run(state["candidates"])
    return state

def validate_budget(state):
    state["plan"] = budget_agent.filter(state["hotels"], state["activities"], state["budget"])
    return state

def generate_itinerary(state):
    state["itinerary"] = itinerary_agent.assemble(state["plan"])
    return state

graph = StateGraph(dict)
graph.add_node("discover", discover_destinations)
graph.add_node("hotels", recommend_hotels)
graph.add_node("activities", plan_activities)
graph.add_node("budget", validate_budget)
graph.add_node("itinerary", generate_itinerary)

graph.set_entry_point("discover")
graph.add_edge("discover", "hotels")
graph.add_edge("hotels", "activities")
graph.add_edge("activities", "budget")
graph.add_edge("budget", "itinerary")
graph.add_edge("itinerary", END)

booking_agent = graph.compile()

Where LangGraph fits in the stack

RAG retrieves documents. LangChain builds the tools and chains. LangGraph orchestrates when those tools run, in what order, and with what state. For any multi-step AI product, LangGraph is the orchestration layer on top of LangChain.