LangGraph: Durable Stateful Workflows

~32 min · langgraph, stategraph

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Graphs make state explicit

LangGraph focuses on low-level orchestration for long-running, stateful agents. Instead of hiding everything in a chat loop, you define nodes, edges, state, and persistence.

This is valuable when your agent needs branching, human-in-the-loop, replay, durable execution, or a state machine that operators can reason about.

Graph does not mean complicated by default

A graph can be two nodes. The win is explicit structure: model node, tool node, approval node, summarize node. Conditional edges show why the run moved where it moved.

If you are building a complex workflow, explicit edges are a gift to future debugging. Future you will take any gift available. Trust me.

Code

Tiny StateGraph shape·python

from langgraph.graph import StateGraph, MessagesState, START, END

def call_model(state: MessagesState):
    return {"messages": [model.invoke(state["messages"])]}

graph = StateGraph(MessagesState)
graph.add_node("model", call_model)
graph.add_edge(START, "model")
graph.add_edge("model", END)
app = graph.compile()
app.invoke({"messages": [{"role": "user", "content": "hi"}]})

External links

Exercise

Draw a four-node graph for a research agent: model, search, read, summarize. Add one conditional edge.

Hint

A conditional edge might route to read only if search found sources.

Progress

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