fix(agent): Fix agent loss message bug (#1283)

dbgpt/agent/actions/plugin_action.py

@@ -83,10 +83,10 @@ class PluginAction(Action[PluginInput]):
             if not resource_plugin_client:
                 raise ValueError("No implementation of the use of plug-in resources！")
             response_success = True
-            status = Status.TODO.value
+            status = Status.RUNNING.value
+            tool_result = ""
             err_msg = None
             try:
-                status = Status.RUNNING.value
                 tool_result = await resource_plugin_client.a_execute_command(
                     param.tool_name, param.args, plugin_generator
                 )

dbgpt/agent/agents/agent_new.py

@@ -4,6 +4,12 @@ import dataclasses
 from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Optional, Tuple, Union
 
+from dbgpt.agent.resource.resource_loader import ResourceLoader
+from dbgpt.core import LLMClient
+from dbgpt.util.annotations import PublicAPI
+
+from ..memory.gpts_memory import GptsMemory
+
 
 class Agent(ABC):
     async def a_send(
@@ -72,6 +78,8 @@ class Agent(ABC):
     async def a_act(
         self,
         message: Optional[str],
+        sender: Optional[Agent] = None,
+        reviewer: Optional[Agent] = None,
         **kwargs,
     ) -> Union[str, Dict, None]:
         """
@@ -101,3 +109,42 @@ class Agent(ABC):
         Returns:
 
         """
+
+
+@dataclasses.dataclass
+class AgentContext:
+    conv_id: str
+    gpts_app_name: str = None
+    language: str = None
+    max_chat_round: Optional[int] = 100
+    max_retry_round: Optional[int] = 10
+    max_new_tokens: Optional[int] = 1024
+    temperature: Optional[float] = 0.5
+    allow_format_str_template: Optional[bool] = False
+
+    def to_dict(self) -> Dict[str, Any]:
+        return dataclasses.asdict(self)
+
+
+@dataclasses.dataclass
+@PublicAPI(stability="beta")
+class AgentGenerateContext:
+    """A class to represent the input of a Agent."""
+
+    message: Optional[Dict]
+    sender: Agent
+    reviewer: Agent
+    silent: Optional[bool] = False
+
+    rely_messages: List[Dict] = dataclasses.field(default_factory=list)
+    final: Optional[bool] = True
+
+    memory: Optional[GptsMemory] = None
+    agent_context: Optional[AgentContext] = None
+    resource_loader: Optional[ResourceLoader] = None
+    llm_client: Optional[LLMClient] = None
+
+    round_index: int = None
+
+    def to_dict(self) -> Dict:
+        return dataclasses.asdict(self)

dbgpt/agent/agents/base_agent_new.py

@@ -8,8 +8,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 from pydantic import BaseModel, Field
 
 from dbgpt.agent.actions.action import Action, ActionOutput
-from dbgpt.agent.agents.agent import AgentContext
-from dbgpt.agent.agents.agent_new import Agent
+from dbgpt.agent.agents.agent_new import Agent, AgentContext
 from dbgpt.agent.agents.llm.llm import LLMConfig, LLMStrategyType
 from dbgpt.agent.agents.llm.llm_client import AIWrapper
 from dbgpt.agent.agents.role import Role
@@ -31,7 +30,7 @@ class ConversableAgent(Role, Agent):
     llm_config: Optional[LLMConfig] = None
     memory: GptsMemory = Field(default_factory=GptsMemory)
     resource_loader: Optional[ResourceLoader] = None
-    max_retry_count: int = 10
+    max_retry_count: int = 3
     consecutive_auto_reply_counter: int = 0
     llm_client: Optional[AIWrapper] = None
     oai_system_message: List[Dict] = Field(default_factory=list)
@@ -178,54 +177,75 @@ class ConversableAgent(Role, Agent):
         logger.info(
             f"generate agent reply!sender={sender}, rely_messages_len={rely_messages}"
         )
-
-        reply_message = self._init_reply_message(recive_message=recive_message)
-        await self._system_message_assembly(
-            recive_message["content"], reply_message.get("context", None)
-        )
-
-        fail_reason = None
-        current_retry_counter = 0
-        is_sucess = True
-        while current_retry_counter < self.max_retry_count:
-            if current_retry_counter > 0:
-                retry_message = self._init_reply_message(recive_message=recive_message)
-                retry_message["content"] = fail_reason
-                # The current message is a self-optimized message that needs to be recorded.
-                # It is temporarily set to be initiated by the originating end to facilitate the organization of historical memory context.
-                await sender.a_send(retry_message, self, reviewer, request_reply=False)
-
-            # 1.Think about how to do things
-            llm_reply, model_name = await self.a_thinking(
-                self._load_thinking_messages(recive_message, sender, rely_messages)
+        try:
+            reply_message = self._init_reply_message(recive_message=recive_message)
+            await self._system_message_assembly(
+                recive_message["content"], reply_message.get("context", None)
             )
-            reply_message["model_name"] = model_name
-            reply_message["content"] = llm_reply
 
-            # 2.Review whether what is being done is legal
-            approve, comments = await self.a_review(llm_reply, self)
-            reply_message["review_info"] = {"approve": approve, "comments": comments}
+            fail_reason = None
+            current_retry_counter = 0
+            is_sucess = True
+            while current_retry_counter < self.max_retry_count:
+                if current_retry_counter > 0:
+                    retry_message = self._init_reply_message(
+                        recive_message=recive_message
+                    )
+                    retry_message["content"] = fail_reason
+                    retry_message["current_goal"] = recive_message.get(
+                        "current_goal", None
+                    )
+                    # The current message is a self-optimized message that needs to be recorded.
+                    # It is temporarily set to be initiated by the originating end to facilitate the organization of historical memory context.
+                    await sender.a_send(
+                        retry_message, self, reviewer, request_reply=False
+                    )
 
-            # 3.Act based on the results of your thinking
-            act_extent_param = self.prepare_act_param()
-            act_out: ActionOutput = await self.a_act(
-                message=llm_reply,
-                **act_extent_param,
-            )
-            reply_message["action_report"] = act_out.dict()
+                # 1.Think about how to do things
+                llm_reply, model_name = await self.a_thinking(
+                    self._load_thinking_messages(recive_message, sender, rely_messages)
+                )
+                reply_message["model_name"] = model_name
+                reply_message["content"] = llm_reply
 
-            # 4.Reply information verification
-            check_paas, reason = await self.a_verify(reply_message, sender, reviewer)
-            is_sucess = check_paas
-            # 5.Optimize wrong answers myself
-            if not check_paas:
-                current_retry_counter += 1
-                # Send error messages and issue new problem-solving instructions
-                await self.a_send(reply_message, sender, reviewer, request_reply=False)
-                fail_reason = reason
-            else:
-                break
-        return is_sucess, reply_message
+                # 2.Review whether what is being done is legal
+                approve, comments = await self.a_review(llm_reply, self)
+                reply_message["review_info"] = {
+                    "approve": approve,
+                    "comments": comments,
+                }
+
+                # 3.Act based on the results of your thinking
+                act_extent_param = self.prepare_act_param()
+                act_out: ActionOutput = await self.a_act(
+                    message=llm_reply,
+                    sender=sender,
+                    reviewer=reviewer,
+                    **act_extent_param,
+                )
+                reply_message["action_report"] = act_out.dict()
+
+                # 4.Reply information verification
+                check_paas, reason = await self.a_verify(
+                    reply_message, sender, reviewer
+                )
+                is_sucess = check_paas
+                # 5.Optimize wrong answers myself
+                if not check_paas:
+                    current_retry_counter += 1
+                    # Send error messages and issue new problem-solving instructions
+                    if current_retry_counter < self.max_retry_count:
+                        await self.a_send(
+                            reply_message, sender, reviewer, request_reply=False
+                        )
+                    fail_reason = reason
+                else:
+                    break
+            return is_sucess, reply_message
+
+        except Exception as e:
+            logger.exception("Generate reply exception!")
+            return False, {"content": str(e)}
 
     async def a_thinking(
         self, messages: Optional[List[Dict]], prompt: Optional[str] = None
@@ -265,7 +285,13 @@ class ConversableAgent(Role, Agent):
     ) -> Tuple[bool, Any]:
         return True, None
 
-    async def a_act(self, message: Optional[str], **kwargs) -> Optional[ActionOutput]:
+    async def a_act(
+        self,
+        message: Optional[str],
+        sender: Optional[ConversableAgent] = None,
+        reviewer: Optional[ConversableAgent] = None,
+        **kwargs,
+    ) -> Optional[ActionOutput]:
         last_out = None
         for action in self.actions:
             # Select the resources required by acton
@@ -335,6 +361,7 @@ class ConversableAgent(Role, Agent):
     #######################################################################
 
     def _init_actions(self, actions: List[Action] = None):
+        self.actions = []
         for idx, action in enumerate(actions):
             if not isinstance(action, Action):
                 self.actions.append(action())
@@ -426,7 +453,9 @@ class ConversableAgent(Role, Agent):
             for item in self.resources:
                 resource_client = self.resource_loader.get_resesource_api(item.type)
                 resource_prompt_list.append(
-                    await resource_client.get_resource_prompt(item, qustion)
+                    await resource_client.get_resource_prompt(
+                        self.agent_context.conv_id, item, qustion
+                    )
                 )
             if context is None:
                 context = {}
@@ -525,7 +554,11 @@ class ConversableAgent(Role, Agent):
             content = item.content
             if item.action_report:
                 action_out = ActionOutput.from_dict(json.loads(item.action_report))
-                if action_out is not None and action_out.content is not None:
+                if (
+                    action_out is not None
+                    and action_out.is_exe_success
+                    and action_out.content is not None
+                ):
                     content = action_out.content
             oai_messages.append(
                 {

dbgpt/agent/memory/gpts_memory.py

@@ -1,15 +1,18 @@
 from __future__ import annotations
 
 import json
-from collections import defaultdict
+from collections import OrderedDict, defaultdict
 from typing import Dict, List, Optional
 
+from dbgpt.agent.actions.action import ActionOutput
 from dbgpt.util.json_utils import EnhancedJSONEncoder
 from dbgpt.vis.client import VisAgentMessages, VisAgentPlans, vis_client
 
 from .base import GptsMessage, GptsMessageMemory, GptsPlansMemory
 from .default_gpts_memory import DefaultGptsMessageMemory, DefaultGptsPlansMemory
 
+NONE_GOAL_PREFIX: str = "none_goal_count_"
+
 
 class GptsMemory:
     def __init__(
@@ -32,6 +35,41 @@ class GptsMemory:
     def message_memory(self):
         return self._message_memory
 
+    async def _message_group_vis_build(self, message_group):
+        if not message_group:
+            return ""
+        num: int = 0
+        last_goal = next(reversed(message_group))
+        last_goal_messages = message_group[last_goal]
+
+        last_goal_message = last_goal_messages[-1]
+        vis_items = []
+
+        plan_temps = []
+        for key, value in message_group.items():
+            num = num + 1
+            if key.startswith(NONE_GOAL_PREFIX):
+                vis_items.append(await self._messages_to_plan_vis(plan_temps))
+                plan_temps = []
+                num = 0
+                vis_items.append(await self._messages_to_agents_vis(value))
+            else:
+                num += 1
+                plan_temps.append(
+                    {
+                        "name": key,
+                        "num": num,
+                        "status": "complete",
+                        "agent": value[0].receiver if value else "",
+                        "markdown": await self._messages_to_agents_vis(value),
+                    }
+                )
+
+        if len(plan_temps) > 0:
+            vis_items.append(await self._messages_to_plan_vis(plan_temps))
+        vis_items.append(await self._messages_to_agents_vis([last_goal_message]))
+        return "\n".join(vis_items)
+
     async def _plan_vis_build(self, plan_group: dict[str, list]):
         num: int = 0
         plan_items = []
@@ -48,6 +86,37 @@ class GptsMemory:
             )
         return await self._messages_to_plan_vis(plan_items)
 
+    async def one_chat_competions_v2(self, conv_id: str):
+        messages = self.message_memory.get_by_conv_id(conv_id=conv_id)
+        temp_group = OrderedDict()
+        none_goal_count = 1
+        count: int = 0
+        for message in messages:
+            count = count + 1
+            if count == 1:
+                continue
+            current_gogal = message.current_goal
+
+            last_goal = next(reversed(temp_group)) if temp_group else None
+            if last_goal:
+                last_goal_messages = temp_group[last_goal]
+                if current_gogal:
+                    if current_gogal == last_goal:
+                        last_goal_messages.append(message)
+                    else:
+                        temp_group[current_gogal] = [message]
+                else:
+                    temp_group[f"{NONE_GOAL_PREFIX}{none_goal_count}"] = [message]
+                    none_goal_count += 1
+            else:
+                if current_gogal:
+                    temp_group[current_gogal] = [message]
+                else:
+                    temp_group[f"{NONE_GOAL_PREFIX}{none_goal_count}"] = [message]
+                    none_goal_count += 1
+
+        return await self._message_group_vis_build(temp_group)
+
     async def one_chat_competions(self, conv_id: str):
         messages = self.message_memory.get_by_conv_id(conv_id=conv_id)
         temp_group = defaultdict(list)
@@ -76,12 +145,14 @@ class GptsMemory:
             vis_items.append(await self._plan_vis_build(temp_group))
             temp_group.clear()
         if len(temp_messages) > 0:
-            vis_items.append(await self._messages_to_agents_vis(temp_messages))
+            vis_items.append(await self._messages_to_agents_vis(temp_messages, True))
             temp_messages.clear()
 
         return "\n".join(vis_items)
 
-    async def _messages_to_agents_vis(self, messages: List[GptsMessage]):
+    async def _messages_to_agents_vis(
+        self, messages: List[GptsMessage], is_last_message: bool = False
+    ):
         if messages is None or len(messages) <= 0:
             return ""
         messages_view = []
@@ -89,10 +160,11 @@ class GptsMemory:
             action_report_str = message.action_report
             view_info = message.content
             if action_report_str and len(action_report_str) > 0:
-                action_report = json.loads(action_report_str)
-                if action_report:
-                    view = action_report.get("view", None)
-                    view_info = view if view else action_report.get("content", "")
+                action_out = ActionOutput.from_dict(json.loads(action_report_str))
+                if action_out is not None:
+                    if action_out.is_exe_success or is_last_message:
+                        view = action_out.view
+                        view_info = view if view else action_out.content
 
             messages_view.append(
                 {
@@ -102,9 +174,8 @@ class GptsMemory:
                     "markdown": view_info,
                 }
             )
-        return await vis_client.get(VisAgentMessages.vis_tag()).display(
-            content=messages_view
-        )
+        vis_compent = vis_client.get(VisAgentMessages.vis_tag())
+        return await vis_compent.display(content=messages_view)
 
     async def _messages_to_plan_vis(self, messages: List[Dict]):
         if messages is None or len(messages) <= 0:

dbgpt/agent/resource/resource_api.py

@@ -14,7 +14,10 @@ class ResourceType(Enum):
     Knowledge = "knowledge"
     Internet = "internet"
     Plugin = "plugin"
-    File = "file"
+    TextFile = "text_file"
+    ExcelFile = "excel_file"
+    ImageFile = "image_file"
+    AwelFlow = "awel_flow"
 
 
 class AgentResource(BaseModel):
@@ -80,7 +83,7 @@ class ResourceClient(ABC):
         return ""
 
     async def get_resource_prompt(
-        self, resource: AgentResource, question: Optional[str] = None
+        self, conv_uid, resource: AgentResource, question: Optional[str] = None
     ) -> str:
         return resource.resource_prompt_template().format(
             data_type=self.get_data_type(resource),

dbgpt/serve/agent/agents/controller.py

@@ -10,7 +10,7 @@ from fastapi import APIRouter, Body
 from fastapi.responses import StreamingResponse
 
 from dbgpt._private.config import Config
-from dbgpt.agent.agents.agent import Agent, AgentContext
+from dbgpt.agent.agents.agent_new import Agent, AgentContext
 from dbgpt.agent.agents.agents_manage import agent_manage
 from dbgpt.agent.agents.base_agent_new import ConversableAgent
 from dbgpt.agent.agents.llm.llm import LLMConfig, LLMStrategyType
@@ -121,7 +121,7 @@ class MultiAgents(BaseComponent, ABC):
                 )
             )
 
-        asyncio.create_task(
+        task = asyncio.create_task(
             multi_agents.agent_team_chat_new(
                 user_query, agent_conv_id, gpt_app, is_retry_chat
             )
@@ -129,17 +129,19 @@ class MultiAgents(BaseComponent, ABC):
 
         async for chunk in multi_agents.chat_messages(agent_conv_id):
             if chunk:
-                logger.info(chunk)
                 try:
                     chunk = json.dumps(
                         {"vis": chunk}, default=serialize, ensure_ascii=False
                     )
-                    yield f"data: {chunk}\n\n"
+                    if chunk is None or len(chunk) <= 0:
+                        continue
+                    resp = f"data:{chunk}\n\n"
+                    yield task, resp
                 except Exception as e:
                     logger.exception(f"get messages {gpts_name} Exception!" + str(e))
                     yield f"data: {str(e)}\n\n"
 
-        yield f'data:{json.dumps({"vis": "[DONE]"}, default=serialize, ensure_ascii=False)} \n\n'
+        yield task, f'data:{json.dumps({"vis": "[DONE]"}, default=serialize, ensure_ascii=False)} \n\n'
 
     async def app_agent_chat(
         self,
@@ -164,19 +166,30 @@ class MultiAgents(BaseComponent, ABC):
         current_message.save_to_storage()
         current_message.start_new_round()
         current_message.add_user_message(user_query)
+        agent_conv_id = conv_uid + "_" + str(current_message.chat_order)
+        agent_task = None
         try:
             agent_conv_id = conv_uid + "_" + str(current_message.chat_order)
-            async for chunk in multi_agents.agent_chat(
+            async for task, chunk in multi_agents.agent_chat(
                 agent_conv_id, gpts_name, user_query, user_code, sys_code
             ):
+                agent_task = task
                 yield chunk
 
-            final_message = await self.stable_message(agent_conv_id)
+        except asyncio.CancelledError:
+            # Client disconnects
+            print("Client disconnected")
+            if agent_task:
+                logger.info(f"Chat to App {gpts_name}:{agent_conv_id} Cancel!")
+                agent_task.cancel()
         except Exception as e:
             logger.exception(f"Chat to App {gpts_name} Failed!" + str(e))
+            raise
         finally:
-            logger.info(f"save agent chat info！{conv_uid},{agent_conv_id}")
-            current_message.add_view_message(final_message)
+            logger.info(f"save agent chat info！{conv_uid}")
+            final_message = await self.stable_message(agent_conv_id)
+            if final_message:
+                current_message.add_view_message(final_message)
             current_message.end_current_round()
             current_message.save_to_storage()
 
@@ -288,7 +301,7 @@ class MultiAgents(BaseComponent, ABC):
                     ]
                     else False
                 )
-            message = await self.memory.one_chat_competions(conv_id)
+            message = await self.memory.one_chat_competions_v2(conv_id)
             yield message
 
             if is_complete:
@@ -308,11 +321,12 @@ class MultiAgents(BaseComponent, ABC):
                 else False
             )
             if is_complete:
-                return await self.memory.one_chat_competions(conv_id)
+                return await self.memory.one_chat_competions_v2(conv_id)
             else:
-                raise ValueError(
-                    "The conversation has not been completed yet, so we cannot directly obtain information."
-                )
+                pass
+                # raise ValueError(
+                #     "The conversation has not been completed yet, so we cannot directly obtain information."
+                # )
         else:
             raise ValueError("No conversation record found!")
 

dbgpt/serve/agent/resource_loader/plugin_hub_load_client.py

@@ -27,14 +27,15 @@ class PluginHubLoadClient(ResourcePluginClient):
         self, value: str, plugin_generator: Optional[PluginPromptGenerator] = None
     ) -> PluginPromptGenerator:
         logger.info(f"PluginHubLoadClient load plugin:{value}")
-        plugins_prompt_generator = PluginPromptGenerator()
-        plugins_prompt_generator.command_registry = CFG.command_registry
+        if plugin_generator is None:
+            plugin_generator = PluginPromptGenerator()
+            plugin_generator.command_registry = CFG.command_registry
 
         agent_module = CFG.SYSTEM_APP.get_component(
             ComponentType.PLUGIN_HUB, ModulePlugin
         )
-        plugins_prompt_generator = agent_module.load_select_plugin(
-            plugins_prompt_generator, json.dumps(value)
+        plugin_generator = agent_module.load_select_plugin(
+            plugin_generator, json.dumps(value)
         )
 
-        return plugins_prompt_generator
+        return plugin_generator

dbgpt/serve/agent/team/layout/agent_operator.py

@@ -1,7 +1,7 @@
 from abc import ABC
 from typing import Dict, List, Optional
 
-from dbgpt.agent.agents.agent import Agent, AgentGenerateContext
+from dbgpt.agent.agents.agent_new import Agent, AgentGenerateContext
 from dbgpt.agent.agents.agents_manage import agent_manage
 from dbgpt.agent.agents.base_agent_new import ConversableAgent
 from dbgpt.agent.agents.llm.llm import LLMConfig
@@ -191,6 +191,7 @@ class AwelAgentOperator(
             agent_context=input_value.agent_context,
             resource_loader=input_value.resource_loader,
             llm_client=input_value.llm_client,
+            round_index=agent.consecutive_auto_reply_counter,
         )
 
     async def get_agent(
@@ -208,11 +209,19 @@ class AwelAgentOperator(
                 llm_config = LLMConfig(llm_client=input_value.llm_client)
             else:
                 llm_config = LLMConfig(llm_client=self.llm_client)
+        else:
+            if not llm_config.llm_client:
+                if input_value.llm_client:
+                    llm_config.llm_client = input_value.llm_client
+                else:
+                    llm_config.llm_client = self.llm_client
+
         kwargs = {}
         if self.awel_agent.role_name:
             kwargs["name"] = self.awel_agent.role_name
         if self.awel_agent.fixed_subgoal:
             kwargs["fixed_subgoal"] = self.awel_agent.fixed_subgoal
+
         agent = (
             await agent_cls(**kwargs)
             .bind(input_value.memory)
@@ -222,6 +231,7 @@ class AwelAgentOperator(
             .bind(input_value.resource_loader)
             .build()
         )
+
         return agent
 
 

dbgpt/serve/agent/team/layout/team_awel_layout_new.py

@@ -1,12 +1,12 @@
 import json
 import logging
-from typing import Any, List, Optional
+from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Field, validator
 
 from dbgpt._private.config import Config
 from dbgpt.agent.actions.action import ActionOutput, T
-from dbgpt.agent.agents.agent import Agent, AgentContext, AgentGenerateContext
+from dbgpt.agent.agents.agent_new import Agent, AgentContext, AgentGenerateContext
 from dbgpt.agent.agents.base_agent_new import ConversableAgent
 from dbgpt.agent.agents.base_team import ManagerAgent
 from dbgpt.core.awel import DAG
@@ -35,6 +35,9 @@ class AwelLayoutChatNewManager(ManagerAgent):
         assert value is not None and value != "", "dag must not be empty"
         return value
 
+    async def _a_process_received_message(self, message: Optional[Dict], sender: Agent):
+        pass
+
     async def a_act(
         self,
         message: Optional[str],
@@ -60,7 +63,7 @@ class AwelLayoutChatNewManager(ManagerAgent):
                     "content": message,
                     "current_goal": message,
                 },
-                sender=self,
+                sender=sender,
                 reviewer=reviewer,
                 memory=self.memory,
                 agent_context=self.agent_context,
@@ -73,8 +76,11 @@ class AwelLayoutChatNewManager(ManagerAgent):
             last_message = final_generate_context.rely_messages[-1]
 
             last_agent = await last_node.get_agent(final_generate_context)
+            last_agent.consecutive_auto_reply_counter = (
+                final_generate_context.round_index
+            )
             await last_agent.a_send(
-                last_message, self, start_message_context.reviewer, False
+                last_message, sender, start_message_context.reviewer, False
             )
 
             return ActionOutput(

dbgpt/serve/agent/team/plan/team_auto_plan.py

@@ -35,7 +35,9 @@ class AutoPlanChatManager(ManagerAgent):
 
         if now_plan.rely and len(now_plan.rely) > 0:
             rely_tasks_list = now_plan.rely.split(",")
-            rely_tasks = self.memory.plans_memory.get_by_conv_id_and_num(conv_id, [])
+            rely_tasks = self.memory.plans_memory.get_by_conv_id_and_num(
+                conv_id, rely_tasks_list
+            )
             if rely_tasks:
                 rely_prompt = "Read the result data of the dependent steps in the above historical message to complete the current goal:"
                 for rely_task in rely_tasks: