refactor RpcAgent

docs/sphinx_doc/source/agentscope.agents.rst

@@ -48,11 +48,3 @@ dict_dialog_agent module
    :members:
    :undoc-members:
    :show-inheritance:
-
-rpc_dialog_agent module
--------------------------------
-
-.. automodule:: agentscope.agents.dict_dialog_agent
-   :members:
-   :undoc-members:
-   :show-inheritance:

examples/distributed/distributed_debate.py

@@ -6,7 +6,7 @@
 
 import agentscope
 from agentscope.msghub import msghub
-from agentscope.agents.rpc_dialog_agent import RpcDialogAgent
+from agentscope.agents.dialog_agent import DialogAgent
 from agentscope.agents.rpc_agent import RpcAgentServerLauncher
 from agentscope.message import Msg
 from agentscope.utils.logging_utils import logger
@@ -72,7 +72,7 @@ def setup_server(parsed_args: argparse.Namespace) -> None:
             host=host,
             port=port,
             local_mode=False,
-            agent_class=RpcDialogAgent,
+            agent_class=DialogAgent,
             **config,
         )
         server_launcher.launch()
@@ -84,20 +84,23 @@ def run_main_process(parsed_args: argparse.Namespace) -> None:
     agentscope.init(
         model_configs="configs/model_configs.json",
     )
-    pro_agent = RpcDialogAgent(
+    pro_agent = DialogAgent(
         name="Pro",
+    ).to_distributed(
         host=parsed_args.pro_host,
         port=parsed_args.pro_port,
         launch_server=False,
     )
-    con_agent = RpcDialogAgent(
+    con_agent = DialogAgent(
         name="Con",
+    ).to_distributed(
         host=parsed_args.con_host,
         port=parsed_args.con_port,
         launch_server=False,
     )
-    judge_agent = RpcDialogAgent(
+    judge_agent = DialogAgent(
         name="Judge",
+    ).to_distributed(
         host=parsed_args.judge_host,
         port=parsed_args.judge_port,
         launch_server=False,

examples/distributed/distributed_dialog.py

@@ -7,7 +7,7 @@
 
 import agentscope
 from agentscope.agents.user_agent import UserAgent
-from agentscope.agents.rpc_dialog_agent import RpcDialogAgent
+from agentscope.agents.dialog_agent import DialogAgent
 from agentscope.agents.rpc_agent import RpcAgentServerLauncher
 
 
@@ -38,14 +38,16 @@ def setup_assistant_server(assistant_host: str, assistant_port: int) -> None:
         model_configs="configs/model_configs.json",
     )
     assistant_server_launcher = RpcAgentServerLauncher(
-        name="Assitant",
-        agent_class=RpcDialogAgent,
-        host=assistant_host,
-        port=assistant_port,
-        sys_prompt="You are a helpful assistant.",
-        model="gpt-3.5-turbo",
-        use_memory=True,
-        local_mode=False,
+        agent_class=DialogAgent,
+        agent_kwargs={
+            "name": "Assitant",
+            "host": assistant_host,
+            "port": assistant_port,
+            "sys_prompt": "You are a helpful assistant.",
+            "model": "gpt-3.5-turbo",
+            "use_memory": True,
+            "local_mode": False,
+        },
     )
     assistant_server_launcher.launch()
     assistant_server_launcher.wait_until_terminate()
@@ -56,8 +58,9 @@ def run_main_process(assistant_host: str, assistant_port: int) -> None:
     agentscope.init(
         model_configs="configs/model_configs.json",
     )
-    assistant_agent = RpcDialogAgent(
+    assistant_agent = DialogAgent(
         name="Assistant",
+    ).to_distributed(
         host=assistant_host,
         port=assistant_port,
         launch_server=False,

notebook/distributed_debate.ipynb

@@ -84,28 +84,28 @@
    "outputs": [],
    "source": [
     "import agentscope\n",
-    "from agentscope.agents.rpc_dialog_agent import RpcDialogAgent\n",
+    "from agentscope.agents.dialog_agent import DialogAgent\n",
     "\n",
     "agentscope.init(model_configs=model_configs)\n",
     "\n",
-    "pro_agent = RpcDialogAgent(\n",
+    "pro_agent = DialogAgent(\n",
     "    name=\"Pro\",\n",
     "    model=\"gpt-3.5-turbo\",\n",
     "    use_memory=True,\n",
     "    sys_prompt=\"Assume the role of a debater who is arguing in favor of the proposition that AGI (Artificial General Intelligence) can be achieved using the GPT model framework. Construct a coherent and persuasive argument, including scientific, technological, and theoretical evidence, to support the statement that GPT models are a viable path to AGI. Highlight the advancements in language understanding, adaptability, and scalability of GPT models as key factors in progressing towards AGI.\",\n",
-    ")\n",
-    "con_agent = RpcDialogAgent(\n",
+    ").to_distributed()\n",
+    "con_agent = DialogAgent(\n",
     "    name=\"Con\",\n",
     "    model=\"gpt-3.5-turbo\",\n",
     "    use_memory=True,\n",
     "    sys_prompt=\"Assume the role of a debater who is arguing against the proposition that AGI can be achieved using the GPT model framework. Construct a coherent and persuasive argument, including scientific, technological, and theoretical evidence, to support the statement that GPT models, while impressive, are insufficient for reaching AGI. Discuss the limitations of GPT models such as lack of understanding, consciousness, ethical reasoning, and general problem-solving abilities that are essential for true AGI.\",\n",
-    ")\n",
-    "judge_agent = RpcDialogAgent(\n",
+    ").to_distributed()\n",
+    "judge_agent = DialogAgent(\n",
     "    name=\"Judge\",\n",
     "    model=\"gpt-3.5-turbo\",\n",
     "    use_memory=True,\n",
     "    sys_prompt=\"Assume the role of an impartial judge in a debate where the affirmative side argues that AGI can be achieved using the GPT model framework, and the negative side contests this. Listen to both sides' arguments and provide an analytical judgment on which side presented a more compelling and reasonable case. Consider the strength of the evidence, the persuasiveness of the reasoning, and the overall coherence of the arguments presented by each side.\"\n",
-    ")"
+    ").to_distributed()"
    ]
   },
   {

notebook/distributed_dialog.ipynb

@@ -80,18 +80,18 @@
    "source": [
     "import agentscope\n",
     "from agentscope.agents.user_agent import UserAgent\n",
-    "from agentscope.agents.rpc_dialog_agent import RpcDialogAgent\n",
+    "from agentscope.agents.dialog_agent import DialogAgent\n",
     "\n",
     "agentscope.init(\n",
     "    model_configs=model_configs\n",
     ")\n",
     "\n",
-    "assistant_agent = RpcDialogAgent(\n",
+    "assistant_agent = DialogAgent(\n",
     "    name=\"Assistant\",\n",
     "    sys_prompt=\"You are a helpful assistant.\",\n",
     "    model=\"gpt-3.5-turbo\",\n",
     "    use_memory=True,\n",
-    ")\n",
+    ").to_distributed()\n",
     "user_agent = UserAgent(\n",
     "    name=\"User\",\n",
     ")"
@@ -140,7 +140,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.1.0"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

src/agentscope/agents/__init__.py

@@ -2,7 +2,6 @@
 """ Import all agent related modules in the package. """
 from .agent import AgentBase
 from .operator import Operator
-from .rpc_agent import RpcAgentBase
 from .dialog_agent import DialogAgent
 from .dict_dialog_agent import DictDialogAgent
 from .user_agent import UserAgent
@@ -11,7 +10,6 @@
 __all__ = [
     "AgentBase",
     "Operator",
-    "RpcAgentBase",
     "DialogAgent",
     "DictDialogAgent",
     "UserAgent",

src/agentscope/agents/agent.py

@@ -2,20 +2,29 @@
 """ Base class for Agent """
 
 from __future__ import annotations
+from abc import ABCMeta
 from typing import Optional
 from typing import Sequence
 from typing import Union
 from typing import Any
 from typing import Callable
-
 from loguru import logger
 
-from .operator import Operator
-from ..models import load_model_by_name
-from ..memory import TemporaryMemory
+from agentscope.agents.operator import Operator
+from agentscope.models import load_model_by_name
+from agentscope.memory import TemporaryMemory
+
+
+class _RecordInitSettingMeta(ABCMeta):
+    """A wrapper to record the init args into `init_settings` field."""
+
+    def __call__(cls, *args: tuple, **kwargs: dict) -> Any:
+        instance = super().__call__(*args, **kwargs)
+        instance.init_settings = {"args": args, "kwargs": kwargs}
+        return instance
 
 
-class AgentBase(Operator):
+class AgentBase(Operator, metaclass=_RecordInitSettingMeta):
     """Base class for all agents.
 
     All agents should inherit from this class and implement the `reply`
@@ -172,3 +181,31 @@ def _broadcast_to_audience(self, x: dict) -> None:
         """Broadcast the input to all audiences."""
         for agent in self._audience:
             agent.observe(x)
+
+    def to_distributed(
+        self,
+        host: str = "localhost",
+        port: int = None,
+        max_pool_size: int = 100,
+        max_timeout_seconds: int = 1800,
+        launch_server: bool = True,
+        local_mode: bool = True,
+        lazy_launch: bool = True,
+    ) -> AgentBase:
+        """Convert current agent instance into a distributed version"""
+        from .rpc_agent import RpcAgent
+
+        if issubclass(self.__class__, RpcAgent):
+            return self
+        return RpcAgent(
+            agent_class=self.__class__,
+            agent_configs=self.init_settings,
+            name=self.name,
+            host=host,
+            port=port,
+            max_pool_size=max_pool_size,
+            max_timeout_seconds=max_timeout_seconds,
+            launch_server=launch_server,
+            local_mode=local_mode,
+            lazy_launch=lazy_launch,
+        )