refactor: update agentscope[full] to 1.0.11 (#92)

alias/README.md

@@ -288,7 +288,7 @@ The backend server will:
 - Create the initial superuser account (if not exists)
 - Start on `http://localhost:8000` (or the port specified in `.env`)
 
-Verify the server is running by visiting `http://localhost:8000/api/v1/monitor/health`.
+Verify the server is running by visiting `http://localhost:8000/api/v1/health`.
 
 #### Start the Frontend
 
@@ -361,7 +361,7 @@ Once both servers are running:
 - **Frontend UI**: Open `http://localhost:5173` in your browser
 - **Backend API**: Available at `http://localhost:8000`
 - **API Documentation**: Available at `http://localhost:8000/docs` (Swagger UI) or `http://localhost:8000/api/v1/openapi.json` (OpenAPI JSON)
-- **Health Check**: `http://localhost:8000/api/v1/monitor/health`
+- **Health Check**: `http://localhost:8000/api/v1/health`
 
 #### Default Login Credentials
 

alias/README_ZH.md

@@ -289,7 +289,7 @@ python -m uvicorn alias.server.main:app --host 0.0.0.0 --port 8000 --reload
 - 创建初始超级用户账户（如果不存在）
 - 在 `http://localhost:8000` 启动（或 `.env` 中指定的端口）
 
-通过访问 `http://localhost:8000/api/v1/monitor/health` 来验证服务器是否正在运行。
+通过访问 `http://localhost:8000/api/v1/health` 来验证服务器是否正在运行。
 
 #### 启动前端
 
@@ -362,7 +362,7 @@ bash script/start_memory_service.sh
 - **前端 UI**：在浏览器中打开 `http://localhost:5173`
 - **后端 API**：可在 `http://localhost:8000` 访问
 - **API 文档**：可在 `http://localhost:8000/docs` (Swagger UI) 或 `http://localhost:8000/api/v1/openapi.json` (OpenAPI JSON) 访问
-- **健康检查**：`http://localhost:8000/api/v1/monitor/health`
+- **健康检查**：`http://localhost:8000/api/v1/health`
 
 #### 默认登录凭据
 

alias/pyproject.toml

@@ -31,7 +31,7 @@ dependencies = [
     "elasticsearch>=9.0.0",
     "jinja2>=3.1.6",
     "bcrypt==4.0.1",
-    "agentscope[full]==1.0.7",
+    "agentscope[full]==1.0.11",
     "tenacity>=8.5.0",
     "apscheduler>=3.11.0",
     "chardet>=5.2.0",
@@ -42,7 +42,7 @@ dependencies = [
     "alembic>=1.16.1",
     "openpyxl>=3.1.5",
     "sentry-sdk[fastapi]===2.30.0",
-    "agentscope-runtime==0.2.0",
+    "agentscope-runtime>=1.0.0",
     "aiosqlite>=0.21.0",
     "asyncpg>=0.30.0",
     "itsdangerous>=2.2.0"

alias/src/alias/agent/agents/_alias_agent_base.py

@@ -11,7 +11,7 @@ from agentscope.agent import ReActAgent
 from agentscope.model import ChatModelBase
 from agentscope.formatter import FormatterBase
 from agentscope.memory import MemoryBase, LongTermMemoryBase
-from agentscope.message import Msg, TextBlock, ToolUseBlock, ToolResultBlock
+from agentscope.message import Msg, ToolUseBlock, ToolResultBlock
 
 from alias.agent.tools import AliasToolkit
 from alias.agent.utils.constants import DEFAULT_PLANNER_NAME
@@ -23,24 +23,6 @@ from alias.agent.utils.constants import DEFAULT_BROWSER_WORKER_NAME
 from alias.agent.utils.constants import MODEL_MAX_RETRIES
 
 
-def alias_agent_post_reply_hook(
-    self: "AliasAgentBase",
-    kwargs: dict[str, Any],  # pylint: disable=unused-argument
-    output: Any,
-):
-    """
-    This is a monkey patch to ensure that when the agent is interrupted in
-    a tool call, the control returns to user
-    """
-    if (
-        self.tool_call_interrupt_return
-        and isinstance(output, Msg)
-        and output.metadata
-        and output.metadata.get("is_interrupted", False)
-    ):
-        raise asyncio.CancelledError()
-
-
 class AliasAgentBase(ReActAgent):
     def __init__(
         self,
@@ -53,7 +35,6 @@ class AliasAgentBase(ReActAgent):
         state_saving_dir: Optional[str] = None,
         sys_prompt: Optional[str] = None,
         max_iters: int = 10,
-        tool_call_interrupt_return: bool = True,
         long_term_memory: Optional[LongTermMemoryBase] = None,
         long_term_memory_mode: Literal[
             "agent_control",
@@ -77,14 +58,7 @@ class AliasAgentBase(ReActAgent):
         self.message_sending_mapping = {}
         self.state_saving_dir = state_saving_dir
         self.agent_stop_function_names = [self.finish_function_name]
-        self.tool_call_interrupt_return = tool_call_interrupt_return
 
-        # interrupted if the
-        self.register_instance_hook(
-            "post_reply",
-            "alias_agent_post_reply_hook",
-            alias_agent_post_reply_hook,
-        )
         # for message output to backend
         self.register_instance_hook(
             "post_print",
@@ -92,7 +66,16 @@ class AliasAgentBase(ReActAgent):
             alias_post_print_hook,
         )
 
-    async def _reasoning(self):
+        # register finish_function_name
+        if self.finish_function_name not in self.toolkit.tools:
+            self.toolkit.register_tool_function(
+                getattr(self, self.finish_function_name),
+            )
+
+    async def _reasoning(
+        self,
+        tool_choice: Literal["auto", "none", "required"] | None = None,
+    ):
         """Override _reasoning to add retry logic."""
 
         # Call the parent class's _reasoning method directly to
@@ -109,7 +92,8 @@ class AliasAgentBase(ReActAgent):
             if hasattr(original_method, "__wrapped__"):
                 # This is the wrapped version, get the original
                 original_method = original_method.__wrapped__
-            return await original_method(self)
+
+            return await original_method(self, tool_choice=tool_choice)
 
         for i in range(MODEL_MAX_RETRIES - 1):
             try:
@@ -132,21 +116,18 @@ class AliasAgentBase(ReActAgent):
         # final attempt
         await call_parent_reasoning()
 
-    async def _acting(self, tool_call: ToolUseBlock) -> Msg | None:
+    async def _acting(self, tool_call: ToolUseBlock) -> dict | None:
-        """Perform the acting process.
+        """Perform the acting process, and return the structured output if
-
+        it's generated and verified in the finish function call.
-        TODO: (part 2)
-        this is just a monkey patch for AS when not support interruption
-        during tool call; can be remove when AS framework updated
 
         Args:
             tool_call (`ToolUseBlock`):
                 The tool use block to be executed.
 
         Returns:
-            `Union[Msg, None]`:
+            `Union[dict, None]`:
-                Return a message to the user if the `_finish_function` is
+                Return the structured output if it's verified in the finish
-                called, otherwise return `None`.
+                function call.
         """
 
         tool_res_msg = Msg(
@@ -165,7 +146,6 @@ class AliasAgentBase(ReActAgent):
             # Execute the tool call
             tool_res = await self.toolkit.call_tool_function(tool_call)
 
-            response_msg = None
             # Async generator handling
             async for chunk in tool_res:
                 # Turn into a tool result block
@@ -191,28 +171,26 @@ class AliasAgentBase(ReActAgent):
                     tool_call["name"] != self.finish_function_name
                     or (
                         tool_call["name"] == self.finish_function_name
+                        and chunk.metadata
                         and not chunk.metadata.get("success")
                     )
                 ):
                     await self.print(tool_res_msg, chunk.is_last)
 
                 # Return message if generate_response is called successfully
-                if tool_call[
+                if (
-                    "name"
+                    tool_call["name"] in self.agent_stop_function_names
-                ] in self.agent_stop_function_names and chunk.metadata.get(
+                    and chunk.metadata
+                    and chunk.metadata.get(
                         "success",
                         True,
+                    )
                 ):
-                    response_msg = chunk.metadata.get("response_msg")
+                    return chunk.metadata.get("structured_output")
                 elif chunk.is_interrupted:
-                    # TODO: monkey patch happens here
+                    raise asyncio.CancelledError
-                    response_msg = tool_res_msg
-                    if response_msg.metadata is None:
-                        response_msg.metadata = {"is_interrupted": True}
-                    else:
-                        response_msg.metadata["is_interrupted"] = True
 
-            return response_msg
+            return None
         finally:
             # Record the tool result message in the memory
             await self.memory.add(tool_res_msg)
@@ -228,16 +206,16 @@ class AliasAgentBase(ReActAgent):
         """
         response_msg = Msg(
             self.name,
-            content=[
+            "I noticed that you have interrupted me. What can I "
-                TextBlock(
+            "do for you?",
-                    type="text",
+            "assistant",
-                    text="I got interrupted by the user. "
+            metadata={
-                    "Pivot to handle the user's new request.",
+                # Expose this field to indicate the interruption
-                ),
+                "_is_interrupted": True,
-            ],
+            },
-            role="assistant",
-            metadata={},
         )
+
+        await self.print(response_msg, True)
         await self.memory.add(response_msg)
 
         # update and save agent states

alias/src/alias/agent/agents/_browser_agent.py

@@ -10,7 +10,7 @@ import os
 import json
 import inspect
 from functools import wraps
-from typing import Type, Optional, Any
+from typing import Type, Optional, Any, Literal
 import asyncio
 import copy
 from loguru import logger
@@ -104,6 +104,10 @@ with open(
     _BROWSER_AGENT_SUMMARIZE_TASK_PROMPT = f.read()
 
 
+class EmptyModel(BaseModel):
+    pass
+
+
 async def browser_pre_reply_hook(
     self,
     kwargs: dict[str, Any],
@@ -150,6 +154,7 @@ async def browser_post_acting_hook(
                     ] = self._filter_execution_text(return_json["text"])
     if tool_call["name"] != self.finish_function_name or (
         tool_call["name"] == self.finish_function_name
+        and tool_res_msg.metadata
         and not tool_res_msg.metadata.get("success")
     ):
         await self.print(tool_res_msg)
@@ -343,6 +348,7 @@ class BrowserAgent(AliasAgentBase):
             or "gpt-5" in self.model.model_name
         )
 
+    # pylint: disable=R0912,R0915
     async def reply(
         self,
         msg: Msg | list[Msg] | None = None,
@@ -370,20 +376,38 @@ class BrowserAgent(AliasAgentBase):
             else ""
         )
 
+        if structured_model is None:
+            structured_model = EmptyModel
+
+        tool_choice: Literal["auto", "none", "required"] | None = None
+
         self._required_structured_model = structured_model
         # Record structured output model if provided
         if structured_model:
+            # Register generate_response tool only when structured output
+            # is required
+            if self.finish_function_name not in self.toolkit.tools:
+                self.toolkit.register_tool_function(
+                    getattr(self, self.finish_function_name),
+                )
+
             self.toolkit.set_extended_model(
                 self.finish_function_name,
                 structured_model,
             )
+            tool_choice = "required"
+        else:
+            # Remove generate_response tool if no structured output is required
+            self.toolkit.remove_tool_function(self.finish_function_name)
+
         # The reasoning-acting loop
+        structured_output = None
         reply_msg = None
         for iter_n in range(self.max_iters):
             self.iter_n = iter_n + 1
             await self._summarize_mem()
 
-            msg_reasoning = await self._pure_reasoning()
+            msg_reasoning = await self._pure_reasoning(tool_choice)
             tool_calls = msg_reasoning.get_content_blocks("tool_use")
             if tool_calls and tool_calls[0]["name"] == "browser_snapshot":
                 msg_reasoning = await self._reasoning_with_observation()
@@ -397,27 +421,69 @@ class BrowserAgent(AliasAgentBase):
 
             # Parallel tool calls or not
             if self.parallel_tool_calls:
-                acting_responses = await asyncio.gather(*futures)
+                structured_outputs = await asyncio.gather(*futures)
 
             else:
                 # Sequential tool calls
-                acting_responses = [await _ for _ in futures]
+                structured_outputs = [await _ for _ in futures]
 
-            # Find the first non-None replying message from the acting
+            # -------------- Check for exit condition --------------
-            for acting_msg in acting_responses:
+            # If structured output is still not satisfied
-                reply_msg = reply_msg or acting_msg
+            if self._required_structured_model:
+                # Remove None results
+                structured_outputs = [_ for _ in structured_outputs if _]
 
-            if reply_msg:
+                msg_hint = None
+                # If the acting step generates structured outputs
+                if structured_outputs:
+                    # Cache the structured output data
+                    structured_output = structured_outputs[-1]
+
+                    reply_msg = Msg(
+                        self.name,
+                        structured_output.get("subtask_progress_summary", ""),
+                        "assistant",
+                        metadata=structured_output,
+                    )
                     break
-        # When the maximum iterations are reached
-        if not reply_msg:
-            reply_msg = await self._summarizing()
 
+                if not msg_reasoning.has_content_blocks("tool_use"):
+                    # If structured output is required but no tool call is
+                    # made, remind the llm to go on the task
+                    msg_hint = Msg(
+                        "user",
+                        "<system-hint>Structured output is "
+                        f"required, go on to finish your task or call "
+                        f"'{self.finish_function_name}' to generate the "
+                        f"required structured output.</system-hint>",
+                        "user",
+                    )
+                    await self._reasoning_hint_msgs.add(msg_hint)
+                    # Require tool call in the next reasoning step
+                    tool_choice = "required"
+
+                if msg_hint and self.print_hint_msg:
+                    await self.print(msg_hint)
+
+            elif not msg_reasoning.has_content_blocks("tool_use"):
+                # Exit the loop when no structured output is required (or
+                # already satisfied) and only text response is generated
+                msg_reasoning.metadata = structured_output
+                reply_msg = msg_reasoning
+                break
+
+        # When the maximum iterations are reached
+        # and no reply message is generated
+        if reply_msg is None:
+            reply_msg = await self._summarizing()
+            reply_msg.metadata = structured_output
             await self.memory.add(reply_msg)
+
         return reply_msg
 
     async def _pure_reasoning(
         self,
+        tool_choice: Literal["auto", "none", "required"] | None = None,
     ) -> Msg:
         msg = Msg(
             "user",
@@ -433,12 +499,18 @@ class BrowserAgent(AliasAgentBase):
                 Msg("system", self.sys_prompt, "system"),
                 *await self.memory.get_memory(),
                 msg,
+                # The hint messages to guide the agent's behavior, maybe empty
+                *await self._reasoning_hint_msgs.get_memory(),
             ],
         )
 
+        # Clear the hint messages after use
+        await self._reasoning_hint_msgs.clear()
+
         res = await self.model(
             prompt,
             tools=self.no_screenshot_tool_list,
+            tool_choice=tool_choice,
         )
         # handle output from the model
         interrupted_by_user = False
@@ -1260,15 +1332,6 @@ class BrowserAgent(AliasAgentBase):
                     subtask_progress_summary=summary_text,
                     generated_files={},
                 )
-
-                response_msg = Msg(
-                    self.name,
-                    content=[
-                        TextBlock(type="text", text=summary_text),
-                    ],
-                    role="assistant",
-                    metadata=structure_response.model_dump(),
-                )
                 return ToolResponse(
                     content=[
                         TextBlock(
@@ -1278,7 +1341,7 @@ class BrowserAgent(AliasAgentBase):
                     ],
                     metadata={
                         "success": True,
-                        "response_msg": response_msg,
+                        "structured_output": structure_response.model_dump(),
                     },
                     is_last=True,
                 )
@@ -1290,7 +1353,7 @@ class BrowserAgent(AliasAgentBase):
                             text=f"Here is a summary of current status:\n{summary_text}\nPlease continue.\n Following steps \n {finish_status}",
                         ),
                     ],
-                    metadata={"success": False, "response_msg": None},
+                    metadata={"success": False, "structured_output": None},
                     is_last=True,
                 )
         except Exception as e:
@@ -1310,8 +1373,8 @@ class BrowserAgent(AliasAgentBase):
         sys_prompt = (
             "You are an expert in task validation. "
             "Your job is to determine if the agent has completed its task"
-            " based on the provided summary. If finished, strictly reply "
+            " based on the provided summary. If the summary is `NO_ANSWER`, this task is not over. If finished, strictly reply "
-            '"BROWSER_AGENT_TASK_FINISHED", otherwise return the remaining '
+            '"BROWSER_AGENT_TASK_FINISHED" and your reason, otherwise return the remaining '
             "tasks or next steps."
         )
         # Extract user question from memory

alias/src/alias/agent/agents/_data_science_agent.py

@@ -4,10 +4,9 @@ import asyncio
 import json
 import os
 from functools import partial
-from typing import List, Dict, Optional, Any, Type, cast
+from typing import List, Dict, Optional, Any, Type, cast, Literal
 import uuid
 
-import shortuuid
 from agentscope.formatter import FormatterBase
 from agentscope.memory import MemoryBase
 from agentscope.message import Msg, TextBlock, ToolUseBlock, ToolResultBlock
@@ -15,7 +14,7 @@ from agentscope.model import ChatModelBase
 from agentscope.tool import ToolResponse
 from agentscope.tracing import trace_reply
 from loguru import logger
-from pydantic import BaseModel, ValidationError
+from pydantic import BaseModel, ValidationError, Field
 from tenacity import retry, stop_after_attempt, wait_fixed
 
 from alias.agent.agents import AliasAgentBase
@@ -37,6 +36,13 @@ from .ds_agent_utils import (
 from .ds_agent_utils.ds_config import PROMPT_DS_BASE_PATH
 
 
+class DefaultStructuredResponse(BaseModel):
+    response: str = Field(
+        description="Just a placeholder. "
+        "Enter any character to trigger report generation",
+    )
+
+
 class DataScienceAgent(AliasAgentBase):
     def __init__(
         self,
@@ -194,27 +200,140 @@ class DataScienceAgent(AliasAgentBase):
             "pre_reply",
             "files_filter_pre_reply_hook",
         )
-        return await super().reply(msg, structured_model)
+
+        if structured_model is None:
+            structured_model = DefaultStructuredResponse
+
+        # Record the input message(s) in the memory
+        await self.memory.add(msg)
+
+        # -------------- Retrieval process --------------
+        # Retrieve relevant records from the long-term memory if activated
+        await self._retrieve_from_long_term_memory(msg)
+        # Retrieve relevant documents from the knowledge base(s) if any
+        await self._retrieve_from_knowledge(msg)
+
+        # Control if LLM generates tool calls in each reasoning step
+        tool_choice: Literal["auto", "none", "required"] | None = None
+
+        # -------------- Structured output management --------------
+        self._required_structured_model = structured_model
+
+        # Register generate_response tool only when structured output
+        # is required
+        if self.finish_function_name not in self.toolkit.tools:
+            self.toolkit.register_tool_function(
+                getattr(self, self.finish_function_name),
+            )
+
+        # Set the structured output model
+        self.toolkit.set_extended_model(
+            self.finish_function_name,
+            structured_model,
+        )
+        tool_choice = "required"
+
+        # -------------- The reasoning-acting loop --------------
+        # Cache the structured output generated in the finish function call
+        structured_output = None
+        reply_msg = None
+        for _ in range(self.max_iters):
+            # -------------- The reasoning process --------------
+            msg_reasoning = await self._reasoning(tool_choice)
+
+            # -------------- The acting process --------------
+            futures = [
+                self._acting(tool_call)
+                for tool_call in msg_reasoning.get_content_blocks(
+                    "tool_use",
+                )
+            ]
+            # Parallel tool calls or not
+            if self.parallel_tool_calls:
+                structured_outputs = await asyncio.gather(*futures)
+            else:
+                # Sequential tool calls
+                structured_outputs = [await _ for _ in futures]
+
+            # -------------- Check for exit condition --------------
+            # Remove None results
+            structured_outputs = [_ for _ in structured_outputs if _]
+
+            msg_hint = None
+            # If the acting step generates structured outputs
+            if structured_outputs:
+                # Cache the structured output data
+                structured_output = structured_outputs[-1]
+
+                reply_msg = Msg(
+                    self.name,
+                    structured_output.get("response"),
+                    "assistant",
+                    metadata=structured_output,
+                )
+                break
+
+            if not msg_reasoning.has_content_blocks("tool_use"):
+                # If structured output is required but no tool call is
+                # made, remind the llm to go on the task
+                msg_hint = Msg(
+                    "user",
+                    "<system-hint>Structured output is "
+                    f"required, go on to finish your task or call "
+                    f"'{self.finish_function_name}' to generate the "
+                    f"required structured output.</system-hint>",
+                    "user",
+                )
+                await self._reasoning_hint_msgs.add(msg_hint)
+
+            if msg_hint and self.print_hint_msg:
+                await self.print(msg_hint)
+
+        # When the maximum iterations are reached
+        # and no reply message is generated
+        if reply_msg is None:
+            reply_msg = await self._summarizing()
+            reply_msg.metadata = structured_output
+            await self.memory.add(reply_msg)
+
+        # Post-process the memory, long-term memory
+        if self._static_control:
+            await self.long_term_memory.record(
+                [
+                    *([*msg] if isinstance(msg, list) else [msg]),
+                    *await self.memory.get_memory(),
+                    reply_msg,
+                ],
+            )
+
+        return reply_msg
 
     @retry(stop=stop_after_attempt(10), wait=wait_fixed(5), reraise=True)
     async def _reasoning(
         self,
+        tool_choice: str = "required",
     ) -> Msg:
         """Perform the reasoning process."""
         prompt = await self.formatter.format(
             msgs=[
                 Msg("system", self.sys_prompt, "system"),
                 *await self.memory.get_memory(),
+                # The hint messages to guide the agent's behavior, maybe empty
+                *await self._reasoning_hint_msgs.get_memory(),
             ],
         )
 
+        # Clear the hint messages after use
+        await self._reasoning_hint_msgs.clear()
+
         try:
             res = await self.model(
                 prompt,
                 tools=self.toolkit.get_json_schemas(),
+                tool_choice=tool_choice,
             )
         except Exception as e:
-            print(str(e))
+            logger.debug("Error while calling model in _reasoning: {}", e)
 
         # handle output from the model
         interrupted_by_user = False
@@ -238,18 +357,6 @@ class DataScienceAgent(AliasAgentBase):
             raise e from None
 
         finally:
-            if msg and not msg.has_content_blocks("tool_use"):
-                # Turn plain text response into a tool call of the finish
-                # function
-                msg.content = [
-                    ToolUseBlock(
-                        id=shortuuid.uuid(),
-                        type="tool_use",
-                        name=self.think_function_name,
-                        input={"response": msg.get_text_content()},
-                    ),
-                ]
-
             # None will be ignored by the memory
             await self.memory.add(msg)
 
@@ -279,17 +386,10 @@ class DataScienceAgent(AliasAgentBase):
     # pylint: disable=invalid-overridden-method, unused-argument
     async def generate_response(
         self,
-        response: str,
         **kwargs: Any,
     ) -> ToolResponse:
-        """Call this function when you have either completed the task
+        """
-        or cannot continue due to insurmountable reasons.
+        Generate required structured output by this function and return it
-        Provide in the `response` argument any information you believe
-        the user needs to be informed of.
-
-        Args:
-            response (`str`):
-                Your response to the user.
         """
         memory = await self.memory.get_memory()
         memory_log = "\n\n".join(
@@ -350,20 +450,15 @@ class DataScienceAgent(AliasAgentBase):
                 f"{self.detailed_report_path}."
             )
 
-        response_msg = Msg(
+        kwargs["response"] = response
-            self.name,
+        structured_output = {}
-            response,
-            "assistant",
-        )
-
-        await self.print(response_msg, True)
 
         # Prepare structured output
         if self._required_structured_model:
             try:
                 # Use the metadata field of the message to store the
                 # structured output
-                response_msg.metadata = (
+                structured_output = (
                     self._required_structured_model.model_validate(
                         kwargs,
                     ).model_dump()
@@ -379,10 +474,18 @@ class DataScienceAgent(AliasAgentBase):
                     ],
                     metadata={
                         "success": False,
-                        "response_msg": None,
+                        "structured_output": {},
                     },
                 )
 
+        await self.print(
+            Msg(
+                name=self.name,
+                content=response,
+                role="assistant",
+            ),
+            True,
+        )
         return ToolResponse(
             content=[
                 TextBlock(
@@ -392,7 +495,7 @@ class DataScienceAgent(AliasAgentBase):
             ],
             metadata={
                 "success": True,
-                "response_msg": response_msg,
+                "structured_output": structured_output,
             },
             is_last=True,
         )

alias/src/alias/agent/agents/_deep_research_agent_v2.py

@@ -107,6 +107,8 @@ class DeepResearchAgent(AliasAgentBase):
                     x,
                     x.get("task_type", "general"),
                 )
+                if x
+                else None
             ),
         )
         self.node_level_report = node_level_report

alias/src/alias/agent/agents/_meta_planner.py

@@ -245,12 +245,19 @@ class MetaPlanner(AliasAgentBase):
                 long_term_memory.tool_memory_retrieve,
             )
 
+        # register finish_function_name
+        if not self.toolkit.tools.get(self.finish_function_name):
+            self.toolkit.register_tool_function(
+                self.finish_function_name,
+            )
+
+        response_func = self.toolkit.tools.get(self.finish_function_name)
+
         # adjust ReActAgent parameters
         if enable_clarification:
             self._required_structured_model = (
                 MetaPlannerResponseWithClarification
             )
-            response_func = self.toolkit.tools.get(self.finish_function_name)
             response_func.json_schema["function"][
                 "description"
             ] = response_func.json_schema["function"].get(
@@ -265,7 +272,6 @@ class MetaPlanner(AliasAgentBase):
             self._required_structured_model = (
                 MetaPlannerResponseNoClarification
             )
-            response_func = self.toolkit.tools.get(self.finish_function_name)
             response_func.json_schema["function"][
                 "description"
             ] = response_func.json_schema["function"].get(

alias/src/alias/agent/agents/_react_worker.py

@@ -1,15 +1,11 @@
 # -*- coding: utf-8 -*-
 # pylint: disable=C2801, W0611, W0212
-from typing import Optional, Any
+from typing import Optional, Any, Callable
+from functools import partial
 
 from agentscope.model import ChatModelBase
 from agentscope.formatter import FormatterBase
 from agentscope.memory import MemoryBase
-from agentscope.tool import ToolResponse
-from agentscope.message import (
-    Msg,
-    TextBlock,
-)
 from dotenv import load_dotenv
 
 from alias.agent.agents import AliasAgentBase
@@ -50,21 +46,22 @@ class ReActWorker(AliasAgentBase):
             state_saving_dir=state_saving_dir,
         )
 
+        self._required_structured_model = WorkerResponse
+        self.reply: Callable = partial(
+            self.reply,
+            structured_model=self._required_structured_model,
+        )
+
         self.max_iters: int = max(self.max_iters, WORKER_MAX_ITER)
 
     def generate_response(
         self,
-        response: str = "",
+        **kwargs,
-        task_done: bool = True,
+    ):  # pylint: disable=useless-parent-delegation
-        subtask_progress_summary: str = "",
-        generated_files: dict[str, str] = None,
-    ) -> ToolResponse:
         """
         Generate a response summarizing the execution progress of the
         given subtask.
         Args:
-            response (str):
-                The response text (compatible with AgentScope finish function).
             task_done (bool):
                 REQUIRED! Whether the subtask was done or not.
             subtask_progress_summary (str):
@@ -76,37 +73,4 @@ class ReActWorker(AliasAgentBase):
                 paths of generated files (e.g. '/FULL/PATH/OF/FILE_1.md') and
                 the values are short descriptions about the generated files.
         """
-        if generated_files is None:
+        return super().generate_response(**kwargs)
-            generated_files = {}
-
-        # If only response is provided,
-        # use it as subtask_progress_summary
-        if not subtask_progress_summary and response:
-            subtask_progress_summary = response
-
-        structure_response = WorkerResponse(
-            task_done=task_done,
-            subtask_progress_summary=subtask_progress_summary,
-            generated_files=generated_files,
-        )
-        response_msg = Msg(
-            self.name,
-            content=[
-                TextBlock(type="text", text=subtask_progress_summary),
-            ],
-            role="assistant",
-            metadata=structure_response.model_dump(),
-        )
-        return ToolResponse(
-            content=[
-                TextBlock(
-                    type="text",
-                    text="Successfully generated response.",
-                ),
-            ],
-            metadata={
-                "success": True,
-                "response_msg": response_msg,
-            },
-            is_last=True,
-        )

alias/src/alias/agent/agents/common_agent_utils/_common_agent_hooks.py

@@ -106,7 +106,7 @@ async def save_post_reasoning_state(
 async def save_post_action_state(
     self: AliasAgentBase,
     action_input: dict[str, Any],  # pylint: disable=W0613
-    tool_output: Optional[Msg],  # pylint: disable=W0613
+    tool_output: Optional[dict],  # pylint: disable=W0613
 ) -> None:
     """Hook func for save state after action step"""
     await _update_and_save_state_with_session(self)
@@ -115,23 +115,23 @@ async def save_post_action_state(
 async def generate_response_post_action_hook(
     self: AliasAgentBase,
     action_input: dict[str, Any],  # pylint: disable=W0613
-    tool_output: Optional[Msg],  # pylint: disable=W0613
+    tool_output: Optional[dict],  # pylint: disable=W0613
 ) -> None:
     """Hook func for printing clarification"""
     if not (hasattr(self, "session_service") and self.session_service):
         return
 
-    if isinstance(tool_output, Msg):
+    if isinstance(tool_output, dict):
-        if tool_output.metadata and tool_output.metadata.get(
+        if tool_output.get(
             "require_clarification",
             False,
         ):
             clarification_dict = {
-                "clarification_question": tool_output.metadata.get(
+                "clarification_question": tool_output.get(
                     "clarification_question",
                     "",
                 ),
-                "clarification_options": tool_output.metadata.get(
+                "clarification_options": tool_output.get(
                     "clarification_options",
                     "",
                 ),
@@ -144,7 +144,7 @@ async def generate_response_post_action_hook(
                     indent=4,
                 ),
                 role="assistant",
-                metadata=tool_output.metadata,
+                metadata=tool_output,
             )
             await self.print(msg, last=True)
 

alias/src/alias/agent/agents/common_agent_utils/_common_models.py

@@ -76,14 +76,15 @@ class WorkerResponse(BaseModel):
     """
 
     subtask_progress_summary: str = Field(
-        ...,
+        default="",
         description=WORKER_PROGRESS_SUMMARY,
     )
     generated_files: dict = Field(
-        ...,
+        default=dict,
         description=WORKER_FILE_COLLECTION_INSTRUCTION,
     )
     task_done: bool = Field(
-        ...,
+        default=True,
-        description="Whether task is done or it require addition effort",
+        description="Whether the task is done or "
+        "it requires additional effort",
     )

alias/src/alias/agent/agents/ds_agent_utils/__init__.py

@@ -9,7 +9,9 @@ from .utils import (
 )
 from .ds_toolkit import add_ds_specific_tool
 from .prompt_selector import LLMPromptSelector
-from .agent_hook import files_filter_pre_reply_hook
+from .agent_hook import (
+    files_filter_pre_reply_hook,
+)
 
 __all__ = [
     "ReportGenerator",

alias/src/alias/agent/agents/meta_planner_utils/_meta_planner_hooks.py

@@ -128,7 +128,7 @@ async def _planner_save_plan_with_session(
 async def planner_save_post_action_state(
     self: MetaPlanner,
     action_input: dict[str, Any],  # pylint: disable=W0613
-    tool_output: Optional[Msg],  # pylint: disable=W0613
+    tool_output: Optional[dict],  # pylint: disable=W0613
 ) -> None:
     """Hook func for save state after action step"""
     await _update_and_save_state_with_session(self)

alias/src/alias/server/api/v1/__init__.py

@@ -10,6 +10,7 @@ from alias.server.api.v1.file import router as file_router
 from alias.server.api.v1.inner import router as inner_router
 from alias.server.api.v1.share import router as share_router
 from alias.server.api.v1.user import router as user_router
+from alias.server.api.v1.monitor import router as monitor_router
 
 router = APIRouter()
 router.include_router(user_router)
@@ -19,3 +20,4 @@ router.include_router(chat_router)
 router.include_router(file_router)
 router.include_router(inner_router)
 router.include_router(share_router)
+router.include_router(monitor_router)

alias/src/alias/server/services/conversation_service.py

@@ -80,6 +80,7 @@ class ConversationService(BaseService[Conversation]):
             base_url=settings.SANDBOX_URL,
             bearer_token=settings.SANDBOX_BEARER_TOKEN,
         )
+        sandbox.__enter__()
 
         conversation_data = Conversation(
             user_id=user_id,