CoACT initialize (#292)

mm_agents/coact/autogen/agentchat/group/patterns/auto.py

@@ -0,0 +1,159 @@
+# Copyright (c) 2023 - 2025, AG2ai, Inc., AG2ai open-source projects maintainers and core contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import TYPE_CHECKING, Any, Callable, Optional, Tuple, Union
+
+from ..context_variables import ContextVariables
+from ..targets.group_manager_target import GroupManagerSelectionMessage, GroupManagerTarget
+from ..targets.transition_target import TransitionTarget
+from .pattern import Pattern
+
+if TYPE_CHECKING:
+    from ...conversable_agent import ConversableAgent
+    from ...groupchat import GroupChat, GroupChatManager
+    from ..group_tool_executor import GroupToolExecutor
+
+
+class AutoPattern(Pattern):
+    """AutoPattern implements a flexible pattern where agents are selected based on their expertise.
+
+    In this pattern, a group manager automatically selects the next agent to speak based on the context
+    of the conversation and agent descriptions. The after_work is always set to "group_manager" as
+    this is the defining characteristic of this pattern.
+    """
+
+    def __init__(
+        self,
+        initial_agent: "ConversableAgent",
+        agents: list["ConversableAgent"],
+        user_agent: Optional["ConversableAgent"] = None,
+        group_manager_args: Optional[dict[str, Any]] = None,
+        context_variables: Optional[ContextVariables] = None,
+        selection_message: Optional[GroupManagerSelectionMessage] = None,
+        exclude_transit_message: bool = True,
+        summary_method: Optional[Union[str, Callable[..., Any]]] = "last_msg",
+    ):
+        """Initialize the AutoPattern.
+
+        The after_work is always set to group_manager selection, which is the defining
+        characteristic of this pattern. You can customize the selection message used
+        by the group manager when selecting the next agent.
+
+        Args:
+            initial_agent: The first agent to speak in the group chat.
+            agents: List of all agents participating in the chat.
+            user_agent: Optional user proxy agent.
+            group_manager_args: Optional arguments for the GroupChatManager.
+            context_variables: Initial context variables for the chat.
+            selection_message: Custom message to use when the group manager is selecting agents.
+            exclude_transit_message: Whether to exclude transit messages from the conversation.
+            summary_method: Method for summarizing the conversation.
+        """
+        # Create the group_manager after_work with the provided selection message
+        group_manager_after_work = GroupManagerTarget(selection_message=selection_message)
+
+        super().__init__(
+            initial_agent=initial_agent,
+            agents=agents,
+            user_agent=user_agent,
+            group_manager_args=group_manager_args,
+            context_variables=context_variables,
+            group_after_work=group_manager_after_work,
+            exclude_transit_message=exclude_transit_message,
+            summary_method=summary_method,
+        )
+
+        # Store the selection message for potential use
+        self.selection_message = selection_message
+
+    def prepare_group_chat(
+        self,
+        max_rounds: int,
+        messages: Union[list[dict[str, Any]], str],
+    ) -> Tuple[
+        list["ConversableAgent"],
+        list["ConversableAgent"],
+        Optional["ConversableAgent"],
+        ContextVariables,
+        "ConversableAgent",
+        TransitionTarget,
+        "GroupToolExecutor",
+        "GroupChat",
+        "GroupChatManager",
+        list[dict[str, Any]],
+        Any,
+        list[str],
+        list[Any],
+    ]:
+        """Prepare the group chat for organic agent selection.
+
+        Ensures that:
+        1. The group manager has a valid LLM config
+        2. All agents have appropriate descriptions for the group manager to use
+
+        Args:
+            max_rounds: Maximum number of conversation rounds.
+            messages: Initial message(s) to start the conversation.
+
+        Returns:
+            Tuple containing all necessary components for the group chat.
+        """
+        # Validate that group_manager_args has an LLM config which is required for this pattern
+        if not self.group_manager_args.get("llm_config", False):
+            # Check if any agent has an LLM config we can use
+            has_llm_config = any(getattr(agent, "llm_config", False) for agent in self.agents)
+
+            if not has_llm_config:
+                raise ValueError(
+                    "AutoPattern requires the group_manager_args to include an llm_config, "
+                    "or at least one agent to have an llm_config"
+                )
+
+        # Check that all agents have descriptions for effective group manager selection
+        for agent in self.agents:
+            if not hasattr(agent, "description") or not agent.description:
+                agent.description = f"Agent {agent.name}"
+
+        # Use the parent class's implementation to prepare the agents and group chat
+        components = super().prepare_group_chat(
+            max_rounds=max_rounds,
+            messages=messages,
+        )
+
+        # Extract the group_after_work and the rest of the components
+        (
+            agents,
+            wrapped_agents,
+            user_agent,
+            context_variables,
+            initial_agent,
+            _,
+            tool_executor,
+            groupchat,
+            manager,
+            processed_messages,
+            last_agent,
+            group_agent_names,
+            temp_user_list,
+        ) = components
+
+        # Ensure we're using the group_manager after_work
+        group_after_work = self.group_after_work
+
+        # Return all components with our group_after_work
+        return (
+            agents,
+            wrapped_agents,
+            user_agent,
+            context_variables,
+            initial_agent,
+            group_after_work,
+            tool_executor,
+            groupchat,
+            manager,
+            processed_messages,
+            last_agent,
+            group_agent_names,
+            temp_user_list,
+        )