AutoPattern

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,
    )

@classmethod
def create_default(
    cls,
    initial_agent: "ConversableAgent",
    agents: list["ConversableAgent"],
    user_agent: Optional["ConversableAgent"] = None,
    group_manager_args: Optional[dict[str, Any]] = None,
    context_variables: Optional[ContextVariables] = None,
    exclude_transit_message: bool = True,
    summary_method: Optional[Union[str, Callable[..., Any]]] = "last_msg",
) -> "DefaultPattern":
    """Create a default pattern with minimal configuration.

    This replaces the need for a separate BasePattern class by providing
    a factory method that creates a simple DefaultPattern instance.

    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.
        exclude_transit_message: Whether to exclude transit messages from the conversation.
        summary_method: Method for summarizing the conversation.

    Returns:
        A DefaultPattern instance with basic configuration.
    """
    return DefaultPattern(
        initial_agent=initial_agent,
        agents=agents,
        user_agent=user_agent,
        group_manager_args=group_manager_args,
        context_variables=context_variables,
        exclude_transit_message=exclude_transit_message,
        summary_method=summary_method,
    )