From Flows to Graphs

Linear workflows (like n8n or Make) are fantastic for automation. Input -> Process -> Output. But AI Agents don't work in straight lines. They need Loops, Retries, and State Memory.

Enter LangGraph.

What is LangGraph?

Built on top of LangChain, LangGraph allows you to define your agent's logic as a Graph (Nodes and Edges) rather than a Chain (DAG). The key difference? Cycles. You can tell an agent: "Try to fetch the data. If it fails, summarize the error, then try a different query strategy."

Why n8n Struggles Here (For Now)

n8n is a DAG (Directed Acyclic Graph). It flows in one direction. Designing a loop where an agent "thinks" and "refines" its answer 5 times before outputting require messy loop nodes. LangGraph handles this state natively.

Code Example: The "Thinking Loop"

from langgraph.graph import StateGraph, END

# Define the Agent State
class AgentState(TypedDict):
    messages: list
    next_step: str

# Define Nodes
def reason(state):
    # LLM decides what to do next
    return {"next_step": "search"}

def act(state):
    # Execute tool
    return {"messages": ["Search Result Found"]}

# Build Graph
workflow = StateGraph(AgentState)
workflow.add_node("reason", reason)
workflow.add_node("act", act)

# The Magic: Cyclic Edge
workflow.add_edge("act", "reason") # Loops back!

Verdict

If you are building simple automations (Email -> Slack), stick to n8n. If you are building autonomous employees that check their own work? Learn LangGraph.