use tool_done in state in chatty

lang_agent/graphs/tool_nodes.py

@@ -98,7 +98,6 @@ class ChattyToolNode(ToolNodeBase):
         self.config = config
         self.tool_manager = tool_manager
         self.mem = memory
-        self.tool_done = False
 
         self.chat_key = "[CHATTY_OUT]"
         self.tool_key = "[TOOL_OUT]"
@@ -138,9 +137,7 @@ class ChattyToolNode(ToolNodeBase):
         return [["chatty_llm"], ["reit_llm"]]
 
     def invoke(self, state:State):
-        self.tool_done = False
-
-        inp = {"inp": state["inp"]}
+        inp = {"inp": state["inp"], "tool_done": False}
         out = self.workflow.invoke(inp)
         chat_msgs = out.get("chatty_messages")["messages"]
         tool_msgs = out.get("tool_messages")["messages"]
@@ -150,9 +147,7 @@ class ChattyToolNode(ToolNodeBase):
 
     async def ainvoke(self, state:State):
         """Async version of invoke using LangGraph's native async support."""
-        self.tool_done = False
-
-        inp = {"inp": state["inp"]}
+        inp = {"inp": state["inp"], "tool_done": False}
         out = await self.workflow.ainvoke(inp)
         chat_msgs = out.get("chatty_messages")["messages"]
         tool_msgs = out.get("tool_messages")["messages"]
@@ -170,14 +165,13 @@ class ChattyToolNode(ToolNodeBase):
 
         out = self.tool_agent.invoke(state)
 
-        self.tool_done = True
-        return {"tool_messages": out["messages"]}
+        return {"tool_messages": out["messages"], "tool_done": True}
 
     
     def _chat_node_call(self, state:ChattyToolState):
         outs:List[BaseMessage] = []
 
-        while not self.tool_done:
+        while not state.get("tool_done", False):
             inp = {"messages":[
                         SystemMessage(
                             f"回复的最开始应该是{self.chat_key}\n"+self.chatty_sys_prompt