DeerFlow 2.0 Multi-Agent Workflow Deep Dive — StateGraph, Plan-Execute, Human-in-the-Loop

from langgraph.graph import StateGraph, START, END

# Define state
class AgentState(TypedDict):
    messages: list[BaseMessage]
    plan: list[dict]
    current_step: int
    results: dict

# Build graph
workflow = StateGraph(AgentState)
workflow.add_node("supervisor", supervisor_node)
workflow.add_node("researcher", researcher_node)
workflow.add_node("coder", coder_node)
workflow.add_node("reporter", reporter_node)

# Define edges
workflow.add_edge(START, "supervisor")
workflow.add_conditional_edges("supervisor", route_to_agent)
workflow.add_edge("reporter", END)