feat(standalone): 新增单机模式，KILOSTAR_MODE=standalone 时去掉 Ray 依赖

通过 StandaloneProxy 适配层让 .remote() 调用在单机模式下透明降级为
asyncio 协程调用，7 个 Actor 和 workflow task 均可在纯 asyncio 环境运行，
启动快、资源占用低。分布式模式行为完全不变。

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

main.py

@@ -31,8 +31,8 @@ except Exception as e:
     sys.exit(1)
 
 import asyncio
-import ray
-from ray import serve
+
+KILOSTAR_MODE = os.environ.get("KILOSTAR_MODE", "distributed")
 
 from kilostar.worker_cluster import WorkerCluster
 from kilostar.utils.banner import print_banner
@@ -42,10 +42,53 @@ from kilostar.core.global_workflow_manager import GlobalWorkflowManager
 from kilostar.core.individual.regulatory_node import RegulatoryNode
 from kilostar.core.individual.consciousness_node import ConsciousnessNode
 from kilostar.core.individual.control_node import ControlNode
-from kilostar.api import KiloStarGateway
+
+if KILOSTAR_MODE != "standalone":
+    import ray
+    from ray import serve
+    from kilostar.api import KiloStarGateway
 
 
-async def start_system():
+async def start_standalone():
+    """单机模式：纯 asyncio，不依赖 Ray。"""
+    import uvicorn
+    from kilostar.utils.ray_hook import register_standalone
+    from kilostar.api import app
+
+    postgres_database = PostgresDatabase()
+    await postgres_database.init_db()
+    register_standalone("postgres_database", postgres_database)
+
+    global_state_machine = GlobalStateMachine(postgres_database)
+    await global_state_machine.init_state_machine()
+    register_standalone("global_state_machine", global_state_machine)
+
+    global_workflow_manager = GlobalWorkflowManager()
+    await global_workflow_manager.init_manager()
+    register_standalone("global_workflow_manager", global_workflow_manager)
+
+    regulatory_node = RegulatoryNode()
+    register_standalone("regulatory_node", regulatory_node)
+
+    consciousness_node = ConsciousnessNode()
+    register_standalone("consciousness_node", consciousness_node)
+
+    control_node = ControlNode()
+    register_standalone("control_node", control_node)
+
+    worker_cluster = WorkerCluster()
+    await worker_cluster.start()
+    register_standalone("worker_cluster", worker_cluster)
+
+    print(f"✅ KiloStar 单机模式启动完成，监听 0.0.0.0:8000")
+
+    config = uvicorn.Config(app, host="0.0.0.0", port=8000, log_level="info")
+    server = uvicorn.Server(config)
+    await server.serve()
+
+
+async def start_distributed():
+    """分布式模式：使用 Ray Actor + Ray Serve。"""
     env_vars = {
         "POSTGRES_USER": os.getenv("POSTGRES_USER", "postgres"),
         "POSTGRES_PASSWORD": os.getenv("POSTGRES_PASSWORD", ""),
@@ -63,8 +106,9 @@ async def start_system():
         runtime_env={"env_vars": env_vars},
     )
 
-    # 2. 启动数据库组件
-    postgres_database = PostgresDatabase.options(name="postgres_database").remote()
+    postgres_database = PostgresDatabase.options(
+        name="postgres_database"
+    ).remote()
     await postgres_database.init_db.remote()
 
     global_state_machine = GlobalStateMachine.options(
@@ -73,55 +117,52 @@ async def start_system():
 
     print("正在等待 GlobalStateMachine 初始化并加载注册表...")
     try:
-        # 强制执行初始化方法并阻塞等待结果。
-        # 如果 __init__ 或 init_state_machine 中有任何报错，会立刻在这里抛出！
         await global_state_machine.init_state_machine.remote()
         print("GlobalStateMachine 初始化成功！")
     except Exception as e:
-        print(f"\n[致命错误] GlobalStateMachine 启动失败！真实报错如下：\n{e}\n")
+        print(f"\n[致命错误] GlobalStateMachine 启动失败！\n{e}\n")
         return
 
     global_workflow_manager = GlobalWorkflowManager.options(
         name="global_workflow_manager", namespace="kilostar", lifetime="detached"
     ).remote()
 
-    # 4. 启动核心节点
-    regulatory_node = RegulatoryNode.options(name="regulatory_node").remote()
-    consciousness_node = ConsciousnessNode.options(name="consciousness_node").remote()
-    control_node = ControlNode.options(name="control_node").remote()
+    RegulatoryNode.options(name="regulatory_node").remote()
+    ConsciousnessNode.options(name="consciousness_node").remote()
+    ControlNode.options(name="control_node").remote()
 
     try:
         WorkerCluster.options(
-            name="worker_cluster",
-            lifetime="detached",  # 保证它在后台一直运行
+            name="worker_cluster", lifetime="detached"
         ).remote()
         print("✅ WorkerCluster 已成功启动并注册！")
     except ValueError:
         print("WorkerCluster 已经存在。")
 
-    # 工作流以一次性 ray task 形式由 ConsciousnessNode 直接 fire，不再需要常驻 engine actor。
-
     print("正在等待 GlobalWorkflowManager 初始化与恢复工作流...")
     try:
         await global_workflow_manager.init_manager.remote()
         print("GlobalWorkflowManager 初始化成功！")
     except Exception as e:
-        print(f"\n[致命错误] GlobalWorkflowManager 启动失败！真实报错如下：\n{e}\n")
+        print(f"\n[致命错误] GlobalWorkflowManager 启动失败！\n{e}\n")
         return
 
-    # 6. 启动 FastAPI 网关 (使用 Ray Serve)
     serve.start(http_options={"host": "0.0.0.0", "port": 8000})
     serve.run(KiloStarGateway.bind())
 
-    # 挂起主线程以保持系统运行
     while True:
         await asyncio.sleep(3600)
 
 
 def main():
     print_banner()
+    mode = KILOSTAR_MODE
+    print(f"启动模式: {mode}")
     try:
-        asyncio.run(start_system())
+        if mode == "standalone":
+            asyncio.run(start_standalone())
+        else:
+            asyncio.run(start_distributed())
     except KeyboardInterrupt:
         print("系统已退出。")