The framework design of the aios kernel has been basically completed, as well as the key logic code centered on workflow.

doc/mvp/pkg_install_procedure.drawio

@@ -1,4 +1,4 @@
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+<mxfile host="Electron" modified="2023-08-17T05:39:49.397Z" agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/21.6.8 Chrome/114.0.5735.289 Electron/25.5.0 Safari/537.36" etag="4Rsz4qjJ1LkgbdKfJbeI" version="21.6.8" type="device" pages="2">
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       <root>
@@ -241,7 +241,7 @@
     </mxGraphModel>
   </diagram>
   <diagram id="TOYXUel0h6b4Yd0VOcvu" name="Page-2">
-    <mxGraphModel dx="848" dy="810" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
+    <mxGraphModel dx="848" dy="806" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
       <root>
         <mxCell id="0" />
         <mxCell id="1" parent="0" />

doc/mvp/workflow.drawio

@@ -1,6 +1,6 @@
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+<mxfile host="Electron" modified="2023-08-21T08:17:59.615Z" agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/21.6.8 Chrome/114.0.5735.289 Electron/25.5.0 Safari/537.36" etag="B6guG2NTWSku4GvC0ygf" version="21.6.8" type="device" pages="3">
   <diagram id="C5RBs43oDa-KdzZeNtuy" name="Page-1">
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+    <mxGraphModel dx="981" dy="563" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="827" pageHeight="1169" math="0" shadow="0">
       <root>
         <mxCell id="WIyWlLk6GJQsqaUBKTNV-0" />
         <mxCell id="WIyWlLk6GJQsqaUBKTNV-1" parent="WIyWlLk6GJQsqaUBKTNV-0" />
@@ -211,7 +211,7 @@
           <mxGeometry x="510" y="180" width="165" height="30" as="geometry" />
         </mxCell>
         <mxCell id="q_eHDVuD3DBh6_VEKgap-5" value="如果一个工作流有3个role参与，每个人都需要调用函数并分析结果，那么要进行至少6次推理" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" vertex="1" parent="1">
-          <mxGeometry x="500" y="640" width="174" height="40" as="geometry" />
+          <mxGeometry x="483" y="640" width="174" height="40" as="geometry" />
         </mxCell>
         <mxCell id="q_eHDVuD3DBh6_VEKgap-6" value="这里是基于规则的合并，给另一个Role合并是多输入Agent流程" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" vertex="1" parent="1">
           <mxGeometry x="635" y="570" width="160" height="30" as="geometry" />
@@ -251,7 +251,7 @@
         <mxCell id="8StUH8aFnwlRlHm0zShF-6" value="Workflow Rules助记词" style="shape=document;whiteSpace=wrap;html=1;boundedLbl=1;size=0.25;" vertex="1" parent="1">
           <mxGeometry x="40" y="235.5" width="120" height="52.5" as="geometry" />
         </mxCell>
-        <mxCell id="8StUH8aFnwlRlHm0zShF-7" value="访问Knowlegegou构造助记词" style="shape=document;whiteSpace=wrap;html=1;boundedLbl=1;size=0.25;" vertex="1" parent="1">
+        <mxCell id="8StUH8aFnwlRlHm0zShF-7" value="访问Knowlege构造助记词" style="shape=document;whiteSpace=wrap;html=1;boundedLbl=1;size=0.25;" vertex="1" parent="1">
           <mxGeometry x="40" y="378" width="120" height="50" as="geometry" />
         </mxCell>
         <mxCell id="Tz-ThZOieDqqDBRXvXnh-1" value="Invoke the function and wait for the necessary response." style="shape=hexagon;perimeter=hexagonPerimeter2;whiteSpace=wrap;html=1;fixedSize=1;fontSize=11;" vertex="1" parent="1">

src/aios_kernel/README

@@ -1 +1,3 @@
-TODO
+TODO:
+Embading Pipline 
+Knowlege Base

src/aios_kernel/__init__.py

@@ -0,0 +1,7 @@
+from .environment import environment,environment_event
+from .agent import agent_msg,ai_agent,ai_agent_templete
+from .compute_kernel import compute_kernel,compute_task
+from .compute_node import compute_node,local_compute_node
+from .open_ai_node import open_ai_compute_node 
+from .role import ai_role,ai_role_group
+from .workflow import ai_workflow

src/aios_kernel/agent.py

@@ -1,58 +1,88 @@
 from typing import Optional
+
 import logging
-import llm_kernel,llm_work_task
 
 logger = logging.getLogger(__name__)
 
-
-
 class agent_msg:
     def __init__(self) -> None:
+        self.sender = None
+        self.target = None
+        self.body = None
+
+    def get_msg_id(self) -> str:
+        pass
+
+    def get_sender(self) -> str:
+        return self.sender
+
+    def get_target(self) -> str:
+        return self.target
+
+    # return workflow_name, role_name, session_id
+    def parser_target(self,target:str) -> None:
         pass
 
 class agent_prompt:
     def __init__(self) -> None:
         pass
 
-    def as_str()->str:
+    def as_str(self)->str:
         pass
 
-class agent_chat_session:
+    def append(self,prompt) -> None:
+        pass
+
+# chat session store the chat history between owner and agent
+# chat session might be large, so can read / write at stream mode.
+class ai_chat_session:
     def __init__(self) -> None:
-        self.llm_model_name = None
-        self.llm_instance = None
-        self.max_token_size = 0
-        self.chat_msg_list = None
-        self.enable_function = True
-
         pass
 
-    def chat(self,message:str) -> None:
+    def get_owner_id(self) -> str:
         pass
-    # Key functions, let the AI Agent try to run.
-    def completion(self)->llm_work_task:
-        if self.llm_instance is None:
-            self.llm_instance = llm_kernel.craete(self.llm_model_name)
-            if self.llm_instance is None:
-                logger.fatal(f"cann't get llm_kerenel : {self.llm_model_name}")
-                return 
 
-        llm_work_task = self.llm_instance.completion(self._get_prompt(),self.max_token_size)
-        return llm_work_task
+    def append_post(self,msg:agent_msg) -> None:
+        """append msg to session, msg is post from session (owner => msg.target)"""
+        pass
 
-    def _get_prompt(str) -> str:
+    def append_recv(self,msg:agent_msg) -> None:
+        """append msg to session, msg is recv from msg'sender (msg.sender => owner)"""
+        pass    
+
+    def attach_event_handler(self,handler) -> None:
+        """chat session changed event handler"""
+        pass
+
+    #TODO : add iterator interface for read chat history 
+
+class ai_agent_templete:
+    def __init__(self) -> None:
         pass
     
 class ai_agent:
     def __init__(self) -> None:
+        self.chat_sessions = None    
+        self.llm_model_name = None
+        self.max_token_size = 0
+        self.instance_id = None
+        self.template_id = None
+
+    def get_id(self) -> str:
+        return self.instance_id
+
+    def get_template_id(self) -> str:
+        return self.template_id
+
+    def get_chat_session_for_msg(self,msg:agent_msg) -> ai_chat_session:
         pass
 
-    def get_chat_session(self,chat_user_name:str,session_id:Optional[str]) -> agent_chat_session:
+    def get_chat_session(self,sender:str,session_id:str) -> ai_chat_session:
         pass
 
+    def get_llm_model_name(self) -> str:
+        return self.llm_model_name
     
+    def get_max_token_size(self) -> int:
+        return self.max_token_size
 
-    #chat_session = agent.get_default_chat_session("master");
-    #chat_session.chat("给我讲一个英文笑话!");
-    #chat_session.completion();
-    #print(chat_session.last_msg());

src/aios_kernel/agent_group.py

@@ -1,20 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Any
-from .agent import agent,agent_msg
-
-class ai_role:
-    def __init__(self) -> None:
-        pass
-
-class agent_group:
-    def __init__(self) -> None:
-        self.roles = None
-        pass
-
-    def add_role(self,role_name:str,agent_id:str) -> None:
-        pass
-
-    def send_msg(self,role_name:str,msg:agent_msg) -> None:
-        pass
-
-    

src/aios_kernel/ai_function.py

@@ -0,0 +1,32 @@
+class ai_function:
+    def __init__(self) -> None:
+        self.intro : str = None
+    
+    def load_from_config(self,config:dict) -> bool:
+        pass
+
+    def is_local(self) -> bool:
+        pass
+
+    def is_in_zone(self) -> bool:
+        pass
+    
+    def is_readyonly(self) -> bool:
+        pass
+
+    def get_intro(self) -> str:
+        return self.intro
+    
+    async def execute(self):
+        pass
+
+# call chain is a combination of ai_function,group of ai_function.
+class call_chain:
+    def __init__(self) -> None:
+        pass
+
+    def load_from_config(self,config:dict) -> bool:
+        pass
+
+    async def execute(self):
+        pass

src/aios_kernel/aios_shell.py

@@ -1,6 +1,14 @@
+# aiso shell like bash of linux
 from .workflow import ai_workflow
-from agent import agent_msg
+
+
 class aios_shell:
-    def send_msg(self,msg:agent_msg,target:ai_workflow) -> None:
+    def __init__(self,username:str) -> None:
+        pass
+
+    async def send_msg(self,msg:str,target_workflow:str) -> str:
+        pass
+
+    async def install_workflow(self,workflow_id:ai_workflow) -> None:
         pass
     

src/aios_kernel/compute_kernel.py

@@ -1,5 +1,12 @@
-from compute_node import compute_node
 from abc import ABC, abstractmethod
+from typing import Optional
+import logging
+import asyncio
+
+from .agent import agent_prompt
+from .compute_node import compute_node
+
+logger = logging.getLogger(__name__)
 
 # How to dispatch different computing tasks (some tasks may contain a large amount of state for correct execution)
 # to suitable computing nodes, achieving a balance of speed, cost, and power consumption, 
@@ -11,15 +18,42 @@ class compute_task(ABC):
 
 
 class compute_kernel:
+    _instance = None
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super(compute_kernel, cls).__new__(cls)
+        return cls._instance
+
     def __init__(self) -> None:
+        self.task_queue = []
+        self.is_start = False
         pass
 
     def run(self,task:compute_task) -> None:
         # check there is compute node can support this task
+        if self.is_task_support(task) is False:
+            logger.error(f"task {task.display()} is not support by any compute node")
+            return
         # add task to working_queue
-        pass
+        self.task_queue.append(task)
+        
 
     def start(self):
+        if self.is_start is True:
+            logger.warn("compute_kernel is already start")
+            return
+        
+        self.is_start = True
+        async def _run_task_loop():
+            while True:
+                task = self.task_queue.pop(0)
+                c_node:compute_node= await self._schedule(task)
+                c_node.push_task(task)
+        
+        asyncio.create_task(_run_task_loop())
+        
+
+    async def _schedule(self,task) -> compute_node:
         pass
 
     def add_compute_node(self,node:compute_node):
@@ -27,3 +61,17 @@ class compute_kernel:
 
     def disable_compute_node(self,):
         pass
+
+    def is_task_support(self,task:compute_task) -> bool:
+        pass
+
+
+    # friendly interface for use:
+    def llm_completion(self,prompt:agent_prompt,mode_name:Optional[str] = None,max_token:int = 0) -> compute_task:
+        # craete a llm_work_task ,push on queue's end
+        # then task_schedule would run this task.(might schedule some work_task to another host)
+        pass
+
+    async def do_llm_completion(self,prompt:agent_prompt,mode_name:Optional[str] = None,max_token:int = 0) -> str:
+        pass
+    

src/aios_kernel/compute_node.py

@@ -1,13 +1,48 @@
 from abc import ABC, abstractmethod
-
+from .compute_kernel import compute_task
 
 class compute_node(ABC):
+    @abstractmethod
+    async def push_task(self,task:compute_task,proiority:int = 0):
+        pass
+    
+    async def remove_task(self,task_id:str):
+        pass
+
+    @abstractmethod
+    def get_task_state(self,task_id:str):
+        pass
+
     @abstractmethod
     def display(self) -> str:
         pass
 
+    @abstractmethod
+    def get_capacity(self):
+        pass
+
+    @abstractmethod
+    def is_support(self,task_type:str) -> bool:
+        pass
+
+    @abstractmethod
+    def is_local(self) -> bool:
+        pass
+
+    @abstractmethod
+    def is_trusted(self) -> bool:
+        return True
+    
+    def get_fee_type(self) -> str:
+        return "free"
+    
+    
+
 class local_compute_node(compute_node):
     def display(self) -> str:
         return super().display()
     
+    def is_local(self) -> bool:
+        return True
+    
 

src/aios_kernel/environment.py

@@ -1,12 +1,23 @@
+# basic environment class
+# we have some built-in environment: Calender(include timer),Home(connect to IoT device in your home), ,KnwoledgeBase,FileSystem,
+
+from abc import ABC, abstractmethod
+from typing import Callable
+
+from .agent import agent_msg
+
 class environment_event(ABC):
     @abstractmethod
     def display(self) -> str:
         pass    
 
-
 class environment:
     def __init__(self) -> None:
         pass
 
-    def event_to_msg(self,) -> environment_event:
+    def get_id(self) -> str:
         pass
+
+    def attach_event_handler(self,event_id:str,handler:Callable) -> None:
+        pass
+

src/aios_kernel/llm_kernel.py

@@ -1,14 +0,0 @@
-
-class llm_work_task:
-    def __init__(self) -> None:
-        pass
-
-
-class llm_kernel:
-    def __init__(self) -> None:
-        pass
-    def completion(self,prompt:str,max_token:int) -> llm_work_task:
-        # craete a llm_work_task ,push on queue's end
-        # then task_schedule would run this task.(might schedule some work_task to another host)
-        pass
-    

src/aios_kernel/open_ai_node.py

@@ -1,6 +1,8 @@
-from compute_node import compute_node
+from .compute_node import compute_node
 
 class open_ai_compute_node(compute_node):
     def display(self) -> str:
         return super().display()
     
+    
+    

src/aios_kernel/role.py

@@ -0,0 +1,25 @@
+from .agent import ai_agent
+
+class ai_role:
+    def __init__(self) -> None:
+        self.agent_instance_id : str = None
+        self.role_name : str = None
+        self.agent : ai_agent = None
+        self.introduce : str = None
+
+    def load_from_config(self,config:dict) -> bool:
+        pass
+    
+    def get_intro(self) -> str:
+        return self.introduce
+
+    def get_name(self) -> str:
+        return self.role_name
+    
+class ai_role_group:
+    def __init__(self) -> None:
+        self.roles : dict[str,str]= None
+        pass
+
+
+    

src/aios_kernel/workflow.py

@@ -1,38 +1,206 @@
-import environment
-import agent_prompt,agent_msg
+
+import logging
+import asyncio
+from typing import Optional,Tuple
+
+from .environment import environment,environment_event
+from .agent import agent_prompt,agent_msg,ai_chat_session
+from .role import ai_role
+from .ai_function import call_chain
+from .compute_kernel import compute_kernel
+
+logger = logging.getLogger(__name__)
+
+class ai_message_filter:
+    def __init__(self) -> None:
+        pass
+    def select(self,msg:agent_msg) -> ai_role:
+        pass
 
 class ai_workflow:
     def __init__(self) -> None:
         self.rule_prompt : agent_prompt = None
         self.workflow_config = None
-        self.context = None
+        self.role_group = None
+        self.input_filter : ai_message_filter= None
+        self.msg_queue = []
+        self.connected_environment = {}
         
     def load_from_disk(self,config_path:str,context_dir_path) -> int:
         pass
 
-    def send_msg(self,msg:agent_msg,target_group:str = None) -> None:
-        if target_group is None:
-            target_group = self.get_default_group()   
+    #workflow is asynchronous. 
+    # When processing one message, it can process another message at the same time. 
+    # chatsession is synchronous, it has to wait for the previous message to finish processing before it can process the next message. 
+    # Therefore, post a message needs to specify the session_id explicitly, if not specified it will be automatically created by workflow.
+    def post_msg(self,msg:agent_msg) -> None:
+        self.msg_queue.append(msg)
+        return
+    
+    async def send_msg(self,msg:agent_msg) -> str:
         pass
     
-    def run(self):
+    async def run(self):
+        # TODO add tracking design of msg processing
+        while True:
+            the_msg = await self._pop_msg()
+            chatsession:ai_chat_session = self._get_chat_session_for_msg(the_msg)
+            if chatsession is None:
+                logger.error(f"get_chat_session_for_msg return None for :{the_msg}")
+                continue
 
+            chatsession.append_recv(the_msg)
+        
+            async def _process_msg(msg:agent_msg,the_role) -> None:
+                # prompt generat progress is most important part of workflow(app) develope
+                prompt = agent_prompt()
+                prompt.append(the_role.get_prompt())
+                prompt.append(self.get_workflow_rule_prompt())
+                prompt.append(self._get_function_prompt(the_role.get_name()))
+                prompt.append(self._get_knowlege_prompt(the_role.get_name()))
+                prompt.append(await self._get_prompt_from_session(chatsession,the_role.get_name())) # chat context
+
+                result = await compute_kernel().do_llm_completion(prompt,the_role.agent.get_llm_model_name(),the_role.agent.get_max_token_size())
+                final_result = result
+                result_type : str = self._get_llm_result_type(result)
+                is_ignore = False
+                match result_type:
+                    case "function":
+                        callchain:call_chain = self._parse_function_call_chain(result)
+                        resp = await callchain.exec()
+                        if callchain.have_result():
+                            # generator proc resp prompt with WAITING state
+                            proc_resp_prompt:agent_prompt = self._get_resp_prompt(resp,msg,the_role,prompt,chatsession)
+                            final_result = await compute_kernel().do_llm_completion(proc_resp_prompt,the_role.agent.get_llm_model_name(),the_role.agent.get_max_token_size())
+                            return final_result
+    
+                    
+                    case "send_message":
+                        # send message to other / sub workflow
+                        next_msg:agent_msg = self._parse_to_msg(result)
+                        if next_msg is not None:
+                            # TODO: Next Target can be another role in workflow
+                            next_workflow:ai_workflow = self.get_workflow(next_msg.get_target())
+                            inner_chat_session = the_role.agent.get_chat_session(next_msg.get_target(),next_msg.get_session_id())
+
+                            inner_chat_session.append_post(next_msg)
+                            resp = await next_workflow.send_msg(next_msg)
+                            inner_chat_session.append_recv(resp)
+                            # generator proc resp prompt with WAITING state
+                            proc_resp_prompt:agent_prompt = self._get_resp_prompt(resp,msg,the_role,prompt,chatsession)
+                            final_result = await compute_kernel().do_llm_completion(proc_resp_prompt,the_role.agent.get_llm_model_name(),the_role.agent.get_max_token_size())
+                           
+                            return final_result
+                        
+                    case "post_message":
+                        # post message to other / sub workflow
+                        next_msg:agent_msg = self._parse_to_msg(result)
+                        if next_msg is not None:
+                            next_workflow:ai_workflow = self.get_workflow(next_msg.get_target())
+                            inner_chat_session = the_role.agent.get_chat_session(next_msg.get_target(),next_msg.get_session_id())
+                            inner_chat_session.append_post(next_msg)
+                            next_workflow.post_msg(next_msg)
+
+                    case "ignore":
+                        is_ignore = True
+                        
+                if is_ignore is not True:
+                    # TODO : how to get inner chat session?
+                    inner_chat_session = the_role.agent.get_chat_session_for_msg(msg)
+                    if inner_chat_session is not None:
+                        inner_chat_session.append_input(msg)
+                        inner_chat_session.append_result(final_result)
+
+                return result
+    
+            async def _workflow_process_msg(msg:agent_msg) -> None:
+                final_result = None
+                if self.input_filter is not None:
+                    select_role = self.input_filter.select(msg)
+                    if select_role is not None: 
+                        result = await _process_msg(msg,select_role)
+                        if result is None:
+                            logger.error(f"_process_msg return None for :{msg}")
+                            return
+                        if chatsession is not None:
+                            chatsession.append_post(result)
+                        final_result = result
+                        
+                else:
+                    results = {}
+                    for this_role in self.role_group.roles:
+                        a_result = asyncio.create_task(_process_msg(msg,this_role))
+                        results[this_role.get_name()] = a_result
+
+                    # merge result from all roles 
+                    # TODO: one input msg can have multiple result msg, at this while ,we only support one result msg
+                    final_result:agent_msg = self._merge_msg_result(results)
+                    if chatsession is not None:
+                        chatsession.append_post(final_result)
+
+                if final_result is not None:
+                    # TODO post message to source
+                    pass 
+              
+            asyncio.create_task(_workflow_process_msg(the_msg))
+
+    async def _pop_msg(self) -> agent_msg:
         pass
 
-    def _pop_msg(self) -> Tuple[agent_msg,str]:
+    def _get_chat_session_for_msg(self,msg:agent_msg) -> ai_chat_session:
         pass
 
-    def get_default_group(self) -> agent_group:
+    async def _get_prompt_from_session(self,chatsession:ai_chat_session,role_name:str) -> agent_prompt:
         pass
     
-    def get_group(self,group_name:str) -> agent_group:
+    def _get_msg_queue(self,session_id:str):
+        pass
+
+    def _merge_msg_result(self,results:dict) -> agent_msg:
+        pass
+
+    def _get_function_prompt(self,role_name:str) -> agent_prompt:
+        pass
+    
+    def _get_knowlege_prompt(self,role_name:str) -> agent_prompt:
+        pass
+
+    def _get_resp_prompt(self,resp:str,msg:agent_msg,role:ai_role,prompt:agent_prompt,chatsession:ai_chat_session) -> agent_prompt:
         pass
 
     def get_workflow_rule_prompt(self) -> agent_prompt:
         return self.rule_prompt
     
-    def get_inner_environment(self) -> environment:
+    def _get_llm_result_type(self,llm_resp_str:str) -> str:
+        pass
+
+    def _parse_function_call_chain(self,llm_resp_str) -> call_chain:
+        pass
+
+    def _parse_to_msg(self,llm_resp_str) -> agent_msg:
+        pass
+    
+    def get_workflow(self,workflow_name:str) -> ai_workflow:
+        """get workflow from known workflow list or sub workflow list"""
+        pass
+
+
+    def _env_event_to_msg(self,env_event:environment_event) -> agent_msg:
+        pass
+
+    def get_inner_environment(self,env_id:str) -> environment:
         pass
 
     def connect_to_environment(self,env:environment) -> None:
-        pass
+        the_env = self.connected_environment.get(env.get_id())
+        if the_env is None:
+            self.connected_environment[env.get_id()] = env
+            def _env_msg_handler(env_event:environment_event) -> None:
+                the_msg:agent_msg= self._env_event_to_msg(env_event)
+                self.post_msg(the_msg)
+            
+            # register all event handler
+            the_env.attach_event_handler(None,_env_msg_handler)
+        else:
+            logger.warn(f"environment {env.get_id()} already connected!")
+

src/component/agent_manager/templete.py

@@ -1,3 +0,0 @@
-class ai_agent_templete:
-    def __init__(self) -> None:
-        pass

test/agent_test.py

@@ -23,8 +23,9 @@ def test_agent():
         agent = am.create(agent_templete,"Tracy","Wang","Tracy Wang is my english teacher")
     
     print("Agent Tracy Wang load success!");
+    print(agent.get_introduce());
+
       
-    #print(agent.get_introduce());
 
     #chat_session = agent.get_default_chat_session("master");
     #chat_session.chat("给我讲一个英文笑话!");

test/async_test.py

@@ -0,0 +1,66 @@
+import asyncio
+import aiohttp
+import aiofiles
+STATE_DONE = 0
+STATE_DOWNLOADING = 1
+class install_task:
+    def __init__(self) -> None:
+        self.download_task = None
+        self.state = STATE_DOWNLOADING
+        self.recv_bytes = 0
+        self.total_bytes = 0 
+
+class install_task_mgr:
+    def __init__(self) -> None:
+        self.all_tasks = {}
+        
+    def create_install_task(self,url:str,target:str):
+        owner = self 
+        this_task = self.all_tasks.get(url)
+        if this_task is not None:
+            return this_task
+    
+        this_task = install_task()
+        self.all_tasks[url] = this_task
+        async def down_and_write():
+            async with aiofiles.open(target, 'wb') as file:
+                async with aiohttp.ClientSession() as session:
+                    async with session.get(url) as response:
+                        while True:
+                            chunk = await response.content.read(1024*1024*16) 
+                            this_task.recv_bytes += len(chunk)
+                            if not chunk:
+                                break
+                            await file.write(chunk)
+                        print(f"download task {url} done!")    
+                        this_task.state = STATE_DONE           
+                        del owner.all_tasks[url]  
+                        
+        this_task.download_task = asyncio.create_task(down_and_write())
+        return this_task
+            
+
+
+async def test_wait_download(mgr):
+    this_task = mgr.create_install_task("https://www.cyfs.com/download/beta/cyberchat/android/latest","test.pkg")
+    await this_task.download_task
+
+def test_timer_download(mgr):
+    this_task = mgr.create_install_task("https://www.cyfs.com/download/beta/cyberchat/android/latest","test.pkg")
+    # start timer
+    async def check_timer():
+        while this_task.state == STATE_DOWNLOADING:
+            await r = asyncio.sleep(1)
+            print(f"download bytes:{this_task.recv_bytes}")
+        print("download complete!")
+    
+    asyncio.create_task(check_timer())
+
+async def test_main():
+    mgr = install_task_mgr()
+    test_timer_download(mgr)
+    await test_wait_download(mgr)
+    await asyncio.sleep(1)
+
+if __name__ == "__main__":
+    asyncio.run(test_main())