核心模块

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

class AgentState(TypedDict):
    query: str
    result: str

def plan_node(state: AgentState):
    # 简化版规划逻辑
    return {"result": f"Planning: {state['query']}"}

def tool_node(state: AgentState):
    # 模拟工具调用
    return {"result": state['result'] + " → Tool executed"}

workflow = StateGraph(AgentState)
workflow.add_node("plan", plan_node)
workflow.add_node("tool", tool_node)
workflow.add_edge("plan", "tool")
workflow.add_edge("tool", END)
workflow.set_entry_point("plan")

app = workflow.compile()
result = app.invoke({"query": "查询用户订单状态"})
print(result)

使用 LangGraph 实现 Planning→Tool→State 基础链路

{
  "query": "查询用户订单状态",
  "result": "Planning: 查询用户订单状态 → Tool executed",
  "metadata": {
    "nodes_executed": [
      "plan",
      "tool"
    ],
    "execution_time_ms": 1250,
    "state_snapshot": {
      "query": "查询用户订单状态",
      "result": "Planning: 查询用户订单状态 → Tool executed"
    }
  }
}