AI Automation
LangGraph Cyclic & Stateful Agent Architecture: StateGraph, MemorySaver & Human-in-the-Loop
Build production-grade autonomous AI agents with LangChain's LangGraph, stateful persistence, cyclic control flow, and human approval gates.
4 min
Why Linear (DAG) Chains Fall Short
Traditional LLM pipelines follow linear step-by-step paths (A -> B -> C). When an agent encounters an error or failing test, it must loop back to previous nodes and retry.
LangGraph provides stateful cyclic graphs with conditional edges, making it the industry standard for controlled autonomous agents.
agent_graph.py
from typing import TypedDict, Annotated, Sequence
from langchain_core.messages import BaseMessage, HumanMessage
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver
class AgentState(TypedDict):
messages: Sequence[BaseMessage]
next_step: str
def coder_node(state: AgentState):
print("Writing code...")
return {"next_step": "test"}
def test_node(state: AgentState):
print("Running tests...")
# Returns 'end' if passed, or loops back to 'coder'
return {"next_step": "end"}
def should_continue(state: AgentState):
return END if state["next_step"] == "end" else "coder"
workflow = StateGraph(AgentState)
workflow.add_node("coder", coder_node)
workflow.add_node("test", test_node)
workflow.set_entry_point("coder")
workflow.add_edge("coder", "test")
workflow.add_conditional_edges("test", should_continue)
app = workflow.compile(checkpointer=MemorySaver())