Multi-Agent Systems: Orchestration, Supervisor Patterns, and When Not To

from langgraph.graph import StateGraph, END
from typing import TypedDict, Literal

class AgentState(TypedDict):
    messages: list
    next: Literal["researcher", "writer", "FINISH"]

def supervisor(state: AgentState):
    # An LLM decides which specialist to call next, or FINISH.
    # In real code this calls Claude with a structured-output prompt.
    last = state["messages"][-1].content
    if "research" in last.lower():
        return {"next": "researcher"}
    if "draft" in last.lower():
        return {"next": "writer"}
    return {"next": "FINISH"}

def researcher(state: AgentState):
    # Specialist with its own system prompt, tools, and Claude call.
    return {"messages": state["messages"] + [...]}

def writer(state: AgentState):
    return {"messages": state["messages"] + [...]}

graph = StateGraph(AgentState)
graph.add_node("supervisor", supervisor)
graph.add_node("researcher", researcher)
graph.add_node("writer", writer)
graph.set_entry_point("supervisor")
graph.add_conditional_edges(
    "supervisor", lambda s: s["next"],
    {"researcher": "researcher", "writer": "writer", "FINISH": END},
)
graph.add_edge("researcher", "supervisor")
graph.add_edge("writer", "supervisor")
app = graph.compile()