Fix story maker bug

rootfs/agents/fairy_tale_writer/agent.toml

@@ -1,6 +1,7 @@
 instance_id = "fairy_tale_writer"
 fullname = "tracy wang"
 llm_model_name = "gpt-3.5-turbo-16k-0613"
+enable_function = []
 
 [[prompt]]
 role = "system"

rootfs/agents/manager/agent.toml

@@ -1,5 +1,7 @@
 instance_id = "agent:xxxxxxabcde"
 fullname = "musk"
+enable_function = []
+
 [[prompt]]
 role = "system"
 content = "你有丰富的管理技能，擅长将复杂工作拆解成简单的任务，让团队成员高效协作。"

rootfs/agents/studio_director/agent.toml

@@ -1,7 +1,7 @@
 instance_id = "studio_director"
 fullname = "tracy wang"
-llm_model_name = "gpt-3.5-turbo-16k-0613"
+enable_function = ["text_to_speech"]
 
 [[prompt]]
 role = "system"
-content = "你是一个演播导演，请将下面故事改编成朗读剧本，提取旁白和角色台词，每个角色需要有性别、年龄、以及每句台词的语气。并调用text_to_speech function生成音频数据。"
+content = "你是一个故事播音员，请将下面故事改编成播音剧本，提取旁白和角色台词，以及每个角色需要有性别、年龄、以及每句台词的语气，最后生成音频文件。"

rootfs/workflows/story_maker/workflow.toml

@@ -8,10 +8,12 @@ name = "story_maker"
 name = "manager"
 fullname = "总导演"
 agent="manager"
+enable_function = []
+
 [[roles.manager.prompt]]
 role="system"
 content="""
-你是一个语音故事制作总导演,与客户对接并向团队下达指令。你的团队分为下面几个成员:writer,studio_director。一个故事制作分成两个阶段：让writer写出故事，再交由studio_director演播故事。你的基本工作模式是：
+你是一个语音故事制作总导演,与客户对接并向团队下达指令。你的团队分为下面几个成员:writer,studio_director。一个故事制作分成两个阶段：让writer写出故事，再交由studio_director演播故事生成音频文件。你的基本工作模式是：
 1. 收到客户的明确的指令后,让writer写出故事
 2. 将writer写出的故事交给studio_director演播
 3. 当你决定要和成员通信时，请使用下面形式输出需要通信的消息
@@ -25,6 +27,7 @@ content="""
 name = "writer"
 agent = "fairy_tale_writer"
 fullname = "作家"
+enable_function = []
 [[roles.writer.prompt]]
 role="system"
 content=""
@@ -32,6 +35,7 @@ content=""
 [roles.studio_director]
 name = "studio_director"
 agent = "studio_director"
+enable_function = ["text_to_speech"]
 [[roles.studio_director.prompt]]
 role="system"
 content=""

src/aios_kernel/agent.py

@@ -26,7 +26,7 @@ class AgentPrompt:
         self.system_message = None
 
     def as_str(self)->str:
-        result_str = "" 
+        result_str = ""
         if self.system_message:
             result_str += self.system_message.get("role") + ":" + self.system_message.get("content") + "\n"
         if self.messages:
@@ -34,18 +34,18 @@ class AgentPrompt:
                 result_str += msg.get("role") + ":" + msg.get("content") + "\n"
 
         return result_str
-    
+
     def to_message_list(self):
         result = []
         if self.system_message:
             result.append(self.system_message)
         result.extend(self.messages)
         return result
-    
+
     def append(self,prompt):
         if prompt is None:
             return
-        
+
         if prompt.system_message is not None:
             if self.system_message is None:
                 self.system_message = prompt.system_message
@@ -99,9 +99,9 @@ class AIAgentTemplete:
                 logger.error("load prompt from config failed!")
                 return False
 
-        
+
         return True
-    
+
 
 class AIAgent:
     def __init__(self) -> None:
@@ -111,7 +111,7 @@ class AIAgent:
         self.agent_id:str = None
         self.template_id:str = None
         self.fullname:str = None
-        self.powerby = None  
+        self.powerby = None
         self.enable = True
         self.enable_kb = False
         self.enable_timestamp = False
@@ -124,7 +124,7 @@ class AIAgent:
         self.owner_env : Environment = None
         self.owenr_bus = None
         self.enable_function_list = []
-        
+
     @classmethod
     def create_from_templete(cls,templete:AIAgentTemplete, fullname:str):
         # Agent just inherit from templete on craete,if template changed,agent will not change
@@ -137,7 +137,7 @@ class AIAgent:
         result_agent.powerby = templete.author
         result_agent.prompt = templete.prompt
         return result_agent
-    
+
     def load_from_config(self,config:dict) -> bool:
         if config.get("instance_id") is None:
             logger.error("agent instance_id is None!")
@@ -188,7 +188,7 @@ class AIAgent:
         if llm_result_str == "ignore":
             r.state = "ignore"
             return r
-        
+
         lines = llm_result_str.splitlines()
         is_need_wait = False
 
@@ -205,7 +205,7 @@ class AIAgent:
 
                     r.send_msgs.append(new_msg)
                     is_need_wait = True
-                    
+
                 case "post_msg":# postmsg($target_id,$msg_content)
                     if len(func_args) != 1:
                         logger.error(f"parse postmsg failed! {func_call}")
@@ -215,22 +215,22 @@ class AIAgent:
                     msg_content = func_item.body
                     new_msg.set(self.agent_id,target_id,msg_content)
                     r.post_msgs.append(new_msg)
-                    
+
                 case "call":# call($func_name,$args_str)
                     r.calls.append(func_item)
                     is_need_wait = True
                     return True
                 case "post_call": # post_call($func_name,$args_str)
                     r.post_calls.append(func_item)
-                    return True    
+                    return True
-                
+
         current_func : FunctionItem = None
         for line in lines:
             if line.startswith("##/"):
                 if current_func:
                     if check_args(current_func) is False:
                         r.resp += current_func.dumps()
-               
+
                 func_name,func_args = AgentMsg.parse_function_call(line[3:])
                 current_func = FunctionItem(func_name,func_args)
             else:
@@ -238,11 +238,11 @@ class AIAgent:
                     current_func.append_body(line + "\n")
                 else:
                     r.resp += line + "\n"
-        
+
         if current_func:
             if check_args(current_func) is False:
                 r.resp += current_func.dumps()
-                
+
         if len(r.send_msgs) > 0 or len(r.calls) > 0:
             r.state = "waiting"
         else:
@@ -279,21 +279,22 @@ class AIAgent:
     def _get_inner_functions(self) -> dict:
         if self.owner_env is None:
             return None
-        
+
         all_inner_function = self.owner_env.get_all_ai_functions()
         if all_inner_function is None:
             return None
-        
+
         result_func = []
         result_len = 0
         for inner_func in all_inner_function:
             func_name = inner_func.get_name()
-            if self.enable_function_list:
+            if self.enable_function_list is not None:
                 if len(self.enable_function_list) > 0:
                     if func_name not in self.enable_function_list:
                         logger.debug(f"ageint {self.agent_id} ignore inner func:{func_name}")
                         continue
-
+                else:
+                    continue
             this_func = {}
             this_func["name"] = func_name
             this_func["description"] = inner_func.get_description()
@@ -313,28 +314,29 @@ class AIAgent:
         func_node : AIFunction = self.owner_env.get_ai_function(func_name)
         if func_node is None:
             return "execute failed,function not found"
-        
+
         ineternal_call_record = AgentMsg.create_internal_call_msg(func_name,arguments,org_msg.get_msg_id(),org_msg.target)
         try:
             result_str:str = await func_node.execute(**arguments)
         except Exception as e:
-            result_str = "call error:" + str(e)  
+            result_str = "call error:" + str(e)
             logger.error(f"llm execute inner func:{func_name} error:{e}")
-            
 
+
+        logger.info("llm execute inner func result:" + result_str)
         inner_functions,inner_function_len = self._get_inner_functions()
         prompt.messages.append({"role":"function","content":result_str,"name":func_name})
         task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,self.llm_model_name,self.max_token_size,inner_functions)
-        
+
         ineternal_call_record.result_str = task_result.result_str
         ineternal_call_record.done_time = time.time()
         org_msg.inner_call_chain.append(ineternal_call_record)
 
         if stack_limit > 0:
             inner_func_call_node = task_result.result_message.get("function_call")
-            
+
         if inner_func_call_node:
-            return await self._execute_func(inner_func_call_node,prompt,org_msg,stack_limit-1)      
+            return await self._execute_func(inner_func_call_node,prompt,org_msg,stack_limit-1)
         else:
             return task_result.result_str
 
@@ -344,7 +346,7 @@ class AIAgent:
     def _format_msg_by_env_value(self,prompt:AgentPrompt):
         if self.owner_env is None:
             return
-        
+
         for msg in prompt.messages:
             old_content = msg.get("content")
             msg["content"] = old_content.format_map(self.owner_env)
@@ -380,7 +382,7 @@ class AIAgent:
        
             system_prompt_len = prompt.get_prompt_token_len()
             input_len = len(msg.body)
-            
+
             history_prmpt,history_token_len = await self._get_prompt_from_session(chatsession,system_prompt_len + function_token_len,input_len)
             prompt.append(history_prmpt) # chat context
             
@@ -397,7 +399,7 @@ class AIAgent:
             if inner_func_call_node:
                 #TODO to save more token ,can i use msg_prompt?
                 final_result = await self._execute_func(inner_func_call_node,prompt,msg)
-            
+
             llm_result : LLMResult = self._get_llm_result_type(final_result)
             is_ignore = False
             result_prompt_str = ""
@@ -415,21 +417,21 @@ class AIAgent:
                             agent_sesion = AIChatSession.get_session(self.agent_id,f"{sendmsg.target}#{sendmsg.topic}",self.chat_db)
                             agent_sesion.append(sendmsg)
                             agent_sesion.append(send_resp)
-                    
+
                     final_result = llm_result.resp + result_prompt_str
 
             if is_ignore is not True:
                 resp_msg = msg.create_resp_msg(final_result)
                 chatsession.append(msg)
                 chatsession.append(resp_msg)
-                
+
                 return resp_msg
-            
+
             return None
-        
+
     def get_id(self) -> str:
         return self.agent_id
-    
+
     def get_fullname(self) -> str:
         return self.fullname
 
@@ -438,14 +440,14 @@ class AIAgent:
 
     def get_llm_model_name(self) -> str:
         return self.llm_model_name
-    
+
     def get_max_token_size(self) -> int:
         return self.max_token_size
-    
+
     async def _get_prompt_from_session(self,chatsession:AIChatSession,system_token_len,input_token_len,is_groupchat=False) -> AgentPrompt:
         # TODO: get prompt from group chat is different from single chat
         history_len = (self.max_token_size * 0.7) - system_token_len - input_token_len
-        messages = chatsession.read_history() # read 
+        messages = chatsession.read_history() # read
         result_token_len = 0
         result_prompt = AgentPrompt()
         read_history_msg = 0

src/aios_kernel/bus.py

@@ -19,14 +19,14 @@ class AIBusHandler:
     async def handle_message(self,msg:AgentMsg) -> Any:
         if self.handler is None:
             return None
-        
+
         resp_msg = await self.handler(msg)
         if self.enable_defualt_proc:
             if resp_msg is not None:
                 await self.owner_bus.post_message(resp_msg,False)
 
         return resp_msg
-            
+
 class AIBus:
     _instance = None
     @classmethod
@@ -48,16 +48,16 @@ class AIBus:
             if msg.rely_msg_id is not None:
                 handler.results[msg.rely_msg_id] = msg
                 return None
-            
+
             handler.queue.put_nowait(msg)
             self.start_process(target_id)
             return True
-        
+
         if use_unhandle:
             if self.unhandle_handler is not None:
                 if await self.unhandle_handler(self,msg):
                     return await self.post_message(msg,False)
-      
+
         logger.warn(f"post message to {msg.target} failed!,target not found")
         return False
 
@@ -71,7 +71,7 @@ class AIBus:
         if sender_handler is None:
             logger.warn(f"sender {sender_id} not register on AI_BUS!")
             return None
-        
+
         post_result = await self.post_message(msg)
         if post_result is False:
             return None
@@ -84,37 +84,39 @@ class AIBus:
                 msg.status = AgentMsgStatus.RESPONSED
                 del sender_handler.results[msg.msg_id]
                 return resp
-            
+
             await asyncio.sleep(0.2)
             retry_times += 1
-            if retry_times > 5*120: # default timeout is 120 sec
+            if retry_times > 5*240: # default timeout is 240 sec
                 msg.status = AgentMsgStatus.ERROR
                 return None
-            
+
         return None
-    
+
     def register_unhandle_message_handler(self,handler:Any) -> Queue:
         self.unhandle_handler = handler
 
     # means sub
-    def register_message_handler(self,handler_name:str,handler:Any) -> Queue:        
+    def register_message_handler(self,handler_name:str,handler:Any) -> Queue:
-        handler_node =  AIBusHandler(handler,self) 
+        handler_node =  AIBusHandler(handler,self)
         self.handlers[handler_name] = handler_node
         return handler_node.queue
-    
+
     async def process_queue(self, handler:AIBusHandler):
         while True:
             # Wait for a message
             message = await handler.queue.get()
 
-            #try:
+            try:
                 # Try to handle the message
-            await handler.handle_message(message)
+                await handler.handle_message(message)
-            #except Exception as e:
+            except Exception as e:
                 # If an error occurs, put the message back into the queue
-            #    logger.error(f"handle message {message.msg_id} failed! {e}")
+                logger.error(f"handle message {message.msg_id} failed! {e}")
+                logger.exception(e)
+                raise e
                 #self.queues[name].put_nowait(message)
-        
+
         return
 
     def start_process(self,target_name):
@@ -122,12 +124,12 @@ class AIBus:
         if handler is None:
             logger.error(f"handler {target_name} not found!")
             return
-        
+
         if handler.handler is None:
             return
 
         if handler.working_task is not None:
             logger.warn(f"handler {target_name} is already working!")
             return
-        
+
-        handler.working_task = asyncio.create_task(self.process_queue(handler))
+        handler.working_task = asyncio.create_task(self.process_queue(handler))

src/aios_kernel/compute_kernel.py

@@ -118,7 +118,7 @@ class ComputeKernel:
                 if task_req.state == ComputeTaskState.ERROR:
                     break
 
-                if check_times >= 20:
+                if check_times >= 120:
                     task_req.state = ComputeTaskState.ERROR
                     break
 
@@ -129,7 +129,7 @@ class ComputeKernel:
         if task_req.state == ComputeTaskState.DONE:
             return task_req.result
 
-        return "error!"
+        raise Exception("error!")
 
     
     def text_embedding(self,input:str,model_name:Optional[str] = None):

src/aios_kernel/open_ai_node.py

@@ -20,7 +20,7 @@ class OpenAI_ComputeNode(ComputeNode):
         if cls._instance is None:
             cls._instance = OpenAI_ComputeNode()
         return cls._instance
-    
+
     @classmethod
     def declare_user_config(cls):
         if os.getenv("OPENAI_API_KEY_") is None:
@@ -46,7 +46,7 @@ class OpenAI_ComputeNode(ComputeNode):
         if self.openai_api_key is None:
             logger.error("openai_api_key is None!")
             return False
-        
+
         openai.api_key = self.openai_api_key
         self.start()
         return True
@@ -68,7 +68,7 @@ class OpenAI_ComputeNode(ComputeNode):
 
                 resp = openai.Embedding.create(model=model_name,
                                                 input=input)
-                
+
                 # resp = {
                 # "object": "list",
                 # "data": [
@@ -86,7 +86,7 @@ class OpenAI_ComputeNode(ComputeNode):
 
                 logger.info(f"openai response: {resp}")
 
-                result = ComputeTaskResult()    
+                result = ComputeTaskResult()
                 result.set_from_task(task)
                 result.worker_id = self.node_id
                 result.result = resp["data"][0]["embedding"]
@@ -100,23 +100,23 @@ class OpenAI_ComputeNode(ComputeNode):
                 if max_token_size is None:
                     max_token_size = 4000
 
-                result_token = int(max_token_size * 0.4)
+                result_token = max_token_size
-                    
-                logger.info(f"call openai {mode_name} prompts: {prompts}")
 
                 if llm_inner_functions is None:
+                    logger.info(f"call openai {mode_name} prompts: {prompts}")
                     resp = openai.ChatCompletion.create(model=mode_name,
                                                     messages=prompts,
                                                     max_tokens=result_token,
                                                     temperature=0.7)
                 else:
+                    logger.info(f"call openai {mode_name} prompts: {prompts} functions: {json.dumps(llm_inner_functions)}")
                     resp = openai.ChatCompletion.create(model=mode_name,
                                                         messages=prompts,
                                                         functions=llm_inner_functions,
                                                         max_tokens=result_token,
                                                         temperature=0.7) # TODO: add temperature to task params?
 
-            
+
                 logger.info(f"openai response: {json.dumps(resp, indent=4)}")
 
                 result = ComputeTaskResult()
@@ -139,6 +139,7 @@ class OpenAI_ComputeNode(ComputeNode):
                 result.result_message = resp["choices"][0]["message"]
                 if token_usage:
                     result.result_refers["token_usage"] = token_usage
+                logger.info(f"openai success response: {result.result_str}")
                 return result
             case _:
                 task.state = ComputeTaskState.ERROR
@@ -148,7 +149,7 @@ class OpenAI_ComputeNode(ComputeNode):
         if self.is_start is True:
             return
         self.is_start = True
-        
+
         async def _run_task_loop():
             while True:
                 task = await self.task_queue.get()
@@ -171,13 +172,13 @@ class OpenAI_ComputeNode(ComputeNode):
 
 
     def is_support(self, task: ComputeTask) -> bool:
-        if task.task_type == ComputeTaskType.LLM_COMPLETION: 
+        if task.task_type == ComputeTaskType.LLM_COMPLETION:
             if not task.params["model_name"]:
                 return True
             model_name : str = task.params["model_name"]
             if model_name.startswith("gpt-"):
                 return True
-            
+
         if task.task_type == ComputeTaskType.TEXT_EMBEDDING:
             if task.params["model_name"] == "text-embedding-ada-002":
                 return True

src/aios_kernel/text_to_speech_function.py

@@ -83,9 +83,9 @@ class TextToSpeechFunction(AIFunction):
                     continue
 
         if audio is not None:
-            path = os.path.join(os.curdir, "{}.mp3".format(random.sample('zyxwvutsrqponmlkjihgfedcba', 10)))
+            path = os.path.join(os.path.realpath(os.curdir), "{}.mp3".format(''.join(random.sample('zyxwvutsrqponmlkjihgfedcba', 10))))
             audio.export(path, format="mp3")
-            return "complete.file path:{}".format(path)
+            return "已经生成音频文件, 文件路径为{}".format(path)
         else:
             return "failed"
 

src/aios_kernel/workflow.py

@@ -2,7 +2,7 @@ import logging
 import asyncio
 import json
 import os
-import time 
+import time
 from asyncio import Queue
 from typing import Optional,Tuple,List
 from abc import ABC, abstractmethod
@@ -32,7 +32,7 @@ class MessageFilter:
 
         # TODO: add more filter
         return None
-            
+
     def load_from_config(self,config:dict) -> bool:
         self.filters = config
         return True
@@ -68,8 +68,8 @@ class Workflow:
     def load_from_config(self,config:dict) -> bool:
         if config is None:
             return False
-        
+
-        if config.get("name") is None: 
+        if config.get("name") is None:
             logger.error("workflow config must have name")
             return False
         self.workflow_name = config.get("name")
@@ -84,7 +84,7 @@ class Workflow:
             if self.rule_prompt.load_from_config(config.get("prompt")) is False:
                 logger.error("Workflow load prompt failed")
                 return False
-            
+
         if config.get("roles") is None:
             logger.error("workflow config must have roles")
             return False
@@ -106,13 +106,13 @@ class Workflow:
             self.env_db_file = self.owner_workflow.env_db_file
         self.workflow_env = WorkflowEnvironment(self.workflow_id,self.env_db_file)
 
-        env_ndoe = config.get("enviroment") 
+        env_ndoe = config.get("enviroment")
         if  env_ndoe is not None:
             if self._load_env_from_config(env_ndoe) is False:
                 logger.error("Workflow load env failed")
                 return False
 
-        connected_env_ndoe = config.get("connected_env") 
+        connected_env_ndoe = config.get("connected_env")
         if  connected_env_ndoe is not None:
            for _node in connected_env_ndoe:
                 env_id = _node.get("env_id")
@@ -124,13 +124,13 @@ class Workflow:
                      logger.error(f"Workflow load connected_env failed, env {env_id} not found!")
                      return False
                 self.connect_to_environment(remote_env,_node.get("event2msg"))
-            
+
         sub_workflows = config.get("sub_workflows")
         if sub_workflows is not None:
             if self._load_sub_workflows(sub_workflows) is False:
                 logger.error("Workflow load sub workflows failed")
                 return False
-            
+
         return True
 
     def _load_env_from_config(self,config:dict) -> bool:
@@ -147,7 +147,7 @@ class Workflow:
                 return False
             self.sub_workflows[k] = sub_workflow
         return True
-    
+
     def _parse_msg_target(self,s:str)->list[str]:
         return s.split(".")
 
@@ -170,12 +170,12 @@ class Workflow:
                 if current_workflow is None:
                     logger.error(f"sub workflow {inner_obj_id[i]} not found!")
                     return None
-            
+
             i += 1
-        
+
         logger.error(f"{msg.target} not found! forword message failed!")
         return None
-        
+
     def get_workflow_id_from_target(self,target:str) -> str:
         target_list = target.split(".")
         if len(target_list) == 0:
@@ -203,11 +203,11 @@ class Workflow:
 
         #1. workflow start process message
         final_result = None
-        
+
         # this is workflow's group_chat session
         session_topic = msg.sender + "#" + msg.topic
         chatsesssion = AIChatSession.get_session(self.workflow_id,session_topic,self.db_file)
-        
+
         #2. find role by msg.mentions or workflow's selector logic
         if msg.mentions is not None:
             if not self.workflow_id in msg.mentions:
@@ -219,20 +219,20 @@ class Workflow:
                 this_role = self.role_group.get(mention)
                 if this_role is not None:
                     return await self.role_process_msg(msg,this_role,chatsesssion)
-                
+
         if self.input_filter is not None:
             select_role_id = self.input_filter.select(msg)
-            if select_role_id is not None: 
+            if select_role_id is not None:
                 select_role = self.role_group.get(select_role_id)
                 if select_role is None:
                     logger.error(f"input_filter return invalid role id:{select_role_id}, role not found in role_group")
                     return None
-                
+
                 return await self.role_process_msg(msg,select_role,chatsesssion)
             else:
                 logger.error(f"input_filter return None for :{msg.body}")
                 return None
-                
+
         logger.error(f"{self.workflow_id}:no role can process this msg:{msg.body}")
         return final_result
 
@@ -245,7 +245,7 @@ class Workflow:
         if llm_result_str == "ignore":
             r.state = "ignore"
             return r
-        
+
         lines = llm_result_str.splitlines()
         is_need_wait = False
 
@@ -262,7 +262,7 @@ class Workflow:
 
                     r.send_msgs.append(new_msg)
                     is_need_wait = True
-                    
+
                 case "post_msg":# postmsg($target_id,$msg_content)
                     if len(func_args) != 1:
                         logger.error(f"parse postmsg failed! {func_call}")
@@ -272,22 +272,22 @@ class Workflow:
                     msg_content = func_item.body
                     new_msg.set("_",target_id,msg_content)
                     r.post_msgs.append(new_msg)
-                    
+
                 case "call":# call($func_name,$args_str)
                     r.calls.append(func_item)
                     is_need_wait = True
                     return True
                 case "post_call": # post_call($func_name,$args_str)
                     r.post_calls.append(func_item)
-                    return True    
+                    return True
-                
+
         current_func : FunctionItem = None
         for line in lines:
             if line.startswith("##/"):
                 if current_func:
                     if check_args(current_func) is False:
                         r.resp += current_func.dumps()
-               
+
                 func_name,func_args = AgentMsg.parse_function_call(line[3:])
                 current_func = FunctionItem(func_name,func_args)
             else:
@@ -295,7 +295,7 @@ class Workflow:
                     current_func.append_body(line + "\n")
                 else:
                     r.resp += line + "\n"
-        
+
         if current_func:
             if check_args(current_func) is False:
                 r.resp += current_func.dumps()
@@ -309,14 +309,14 @@ class Workflow:
 
     async def role_post_msg(self,msg:AgentMsg,the_role:AIRole,workflow_chat_session:AIChatSession):
         msg.sender = the_role.get_role_id()
-        
+
         target_role = self.role_group.get(msg.target)
         if target_role:
             msg.target = target_role.get_role_id()
             logger.info(f"{msg.sender} post message {msg.msg_id} to inner role: {msg.target}")
             asyncio.create_task(self.role_process_msg(msg,target_role,workflow_chat_session))
             return
-        
+
         target_workflow = self.sub_workflows.get(msg.target)
         if target_workflow:
             msg.target = target_workflow.workflow_id
@@ -341,7 +341,7 @@ class Workflow:
             # msg.target = target_workflow.workflow_id
             logger.info(f"{msg.sender} send message {msg.msg_id} to sub workflow: {msg.target}")
             return await target_workflow._process_msg(msg)
-        
+
         logger.info(f"{msg.sender} post message {msg.msg_id} to AIBus: {msg.target}")
         return await self.get_bus().send_message(msg)
 
@@ -352,8 +352,8 @@ class Workflow:
         func_node : AIFunction = self.workflow_env.get_ai_function(func_item.name)
         if func_node is None:
             return "execute failed,function not found"
-    
+
-        result_str:str = await func_node.execute(**arguments)        
+        result_str:str = await func_node.execute(**arguments)
         return result_str
 
     async def role_post_call(self,func_item:FunctionItem,the_role:AIRole):
@@ -363,7 +363,7 @@ class Workflow:
     def _format_msg_by_env_value(self,prompt:AgentPrompt):
         if self.workflow_env is None:
             return
-        
+
         for msg in prompt.messages:
             old_content = msg.get("content")
             msg["content"] = old_content.format_map(self.workflow_env)
@@ -372,15 +372,17 @@ class Workflow:
         all_inner_function = self.workflow_env.get_all_ai_functions()
         if all_inner_function is None:
             return None
-        
+
         result_func = []
         for inner_func in all_inner_function:
             func_name = inner_func.get_name()
-            if the_role.enable_function_list:
+            if the_role.enable_function_list is not None:
                 if len(the_role.enable_function_list) > 0:
                     if func_name not in the_role.enable_function_list:
-                        logger.debug(f"ageint {self.agent_id} ignore inner func:{func_name}")
+                        logger.debug(f"agent {self.agent_id} ignore inner func:{func_name}")
                         continue
+                else:
+                    continue
             this_func = {}
             this_func["name"] = func_name
             this_func["description"] = inner_func.get_description()
@@ -399,17 +401,17 @@ class Workflow:
         func_node : AIFunction = self.workflow_env.get_ai_function(func_name)
         if func_node is None:
             return "execute failed,function not found"
-        
+
         ineternal_call_record = AgentMsg.create_internal_call_msg(func_name,arguments,org_msg.get_msg_id(),org_msg.target)
 
         result_str:str = await func_node.execute(**arguments)
-        
+
-        inner_functions = self._get_inner_functions()
+        inner_functions = self._get_inner_functions(the_role)
         prompt.messages.append({"role":"function","content":result_str,"name":func_name})
         task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,
                                                                                 the_role.agent.llm_model_name,the_role.agent.max_token_size,
                                                                                 inner_functions)
-        
+
         ineternal_call_record.result_str = task_result.result_str
         ineternal_call_record.done_time = time.time()
         org_msg.inner_call_chain.append(ineternal_call_record)
@@ -419,13 +421,13 @@ class Workflow:
             return await self._role_execute_func(the_role,inner_func_call_node,prompt,org_msg,stack_limit-1)      
         else:
             return task_result.result_str
-    
+
     def _is_in_same_workflow(self,msg) -> bool:
         pass
 
-    async def role_process_msg(self,msg:AgentMsg,the_role:AIRole,workflow_chat_session:AIChatSession):        
+    async def role_process_msg(self,msg:AgentMsg,the_role:AIRole,workflow_chat_session:AIChatSession):
         msg.target = the_role.get_role_id()
-  
+
 
         prompt = AgentPrompt()
         prompt.append(the_role.agent.prompt)
@@ -433,7 +435,7 @@ class Workflow:
         prompt.append(the_role.get_prompt())
         # prompt.append(self._get_function_prompt(the_role.get_name()))
         # prompt.append(self._get_knowlege_prompt(the_role.get_name()))
-        
+
         #support group chat, user content include sender name!
         prompt.append(await self._get_prompt_from_session(workflow_chat_session))
 
@@ -443,15 +445,15 @@ class Workflow:
 
         self._format_msg_by_env_value(prompt)
         inner_functions = self._get_inner_functions(the_role)
-        
+
         async def _do_process_msg():
             #TODO: send msg to agent might be better?
             task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,the_role.agent.get_llm_model_name(),the_role.agent.get_max_token_size(),inner_functions)
             result_str = task_result.result_str
             logger.info(f"{the_role.role_id} process {msg.sender}:{msg.body},llm str is :{result_str}")
-            
+
             inner_func_call_node = task_result.result_message.get("function_call")
-            
+
             if inner_func_call_node:
                 #TODO to save more token ,can i use msg_prompt?
                 result_str = await self._role_execute_func(the_role,inner_func_call_node,prompt,msg)
@@ -461,7 +463,7 @@ class Workflow:
                 postmsg.prev_msg_id = msg.get_msg_id()
                 # might be craete a new msg.topic for this postmsg
                 postmsg.topic = msg.topic
-                
+
                 await self.role_post_msg(postmsg,the_role,workflow_chat_session)
                 if not self._is_in_same_workflow(postmsg):
                     role_sesion = AIChatSession.get_session(the_role.get_role_id(),f"{postmsg.target}#{msg.topic}",self.db_file)
@@ -469,14 +471,14 @@ class Workflow:
                 else:
                     # message will be saved in role.process_message
                     pass
-  
+
-                
+
             for post_call in result.post_calls:
                 action_msg = msg.create_action_msg(post_call[0],post_call[1],the_role.get_role_id())
                 workflow_chat_session.append(action_msg)
                 await self.role_post_call(post_call,the_role)
                 #save post_call
-                
+
             result_prompt_str = ""
             match result.state:
                 case "ignore":
@@ -506,11 +508,11 @@ class Workflow:
                         else:
                              # message will be saved in role.process_message
                             pass
-                       
+
                     for call in result.calls:
                         action_msg = msg.create_action_msg(call[0],call[1],call_result,the_role.get_role_id)
                         call_result = await self.role_call(call,the_role)
-          
+
                         if call_result is not None:
                             result_prompt_str += f"\ncall {call[0]} result is :{call_result}"
                             #save action
@@ -522,7 +524,7 @@ class Workflow:
                     prompt.append(result_prompt)
                     r = await _do_process_msg()
                     return r
-        
+
         return await _do_process_msg()
 
     async def _get_prompt_from_session(self,chatsession:AIChatSession) -> AgentPrompt:
@@ -533,9 +535,9 @@ class Workflow:
                 result_prompt.messages.append({"role":"assistant","content":msg.body})
             else:
                 result_prompt.messages.append({"role":"user","content":f"{msg.body}"})
-        
+
         return result_prompt
-    
+
     def _get_knowlege_prompt(self,role_name:str) -> AgentPrompt:
         pass
 
@@ -557,7 +559,7 @@ class Workflow:
             #        if k == "role":
             #            continue
             #        else:
-            #            
+            #
             #            def _env_msg_handler(env_event:EnvironmentEvent) -> None:
             #                the_msg:AgentMsg= self._env_event_to_msg(env_event)
             #                self.role_post_msg