LangGraph Cyclic & Stateful Agent Architecture: StateGraph, MemorySaver & Human-in-the-Loop

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())