swarms/swarms/structs/base_swarm.py

import os
import asyncio
import json
import uuid
from swarms.utils.file_processing import create_file_in_folder
from abc import ABC
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import (
    Any,
    Callable,
    Dict,
    List,
    Optional,
    Sequence,
)

import yaml

from swarms_memory import BaseVectorDatabase
from swarms.structs.agent import Agent
from swarms.structs.conversation import Conversation
from swarms.structs.omni_agent_types import AgentType
from pydantic import BaseModel
from swarms.utils.pandas_utils import (
    dict_to_dataframe,
    display_agents_info,
    pydantic_model_to_dataframe,
)
from swarms.utils.loguru_logger import initialize_logger

logger = initialize_logger(log_folder="base_swarm")


class BaseSwarm(ABC):
    """
    Base Swarm Class for all multi-agent systems

    Attributes:
        agents (List[Agent]): A list of agents
        max_loops (int): The maximum number of loops to run


    Methods:
        communicate: Communicate with the swarm through the orchestrator, protocols, and the universal communication layer
        run: Run the swarm
        step: Step the swarm
        add_agent: Add a agent to the swarm
        remove_agent: Remove a agent from the swarm
        broadcast: Broadcast a message to all agents
        reset: Reset the swarm
        plan: agents must individually plan using a workflow or pipeline
        direct_message: Send a direct message to a agent
        autoscaler: Autoscaler that acts like kubernetes for autonomous agents
        get_agent_by_id: Locate a agent by id
        get_agent_by_name: Locate a agent by name
        assign_task: Assign a task to a agent
        get_all_tasks: Get all tasks
        get_finished_tasks: Get all finished tasks
        get_pending_tasks: Get all penPding tasks
        pause_agent: Pause a agent
        resume_agent: Resume a agent
        stop_agent: Stop a agent
        restart_agent: Restart agent
        scale_up: Scale up the number of agents
        scale_down: Scale down the number of agents
        scale_to: Scale to a specific number of agents
        get_all_agents: Get all agents
        get_swarm_size: Get the size of the swarm
        get_swarm_status: Get the status of the swarm
        save_swarm_state: Save the swarm state
        loop: Loop through the swarm
        run_async: Run the swarm asynchronously
        run_batch_async: Run the swarm asynchronously
        run_batch: Run the swarm asynchronously
        batched_run: Run the swarm asynchronously
        abatch_run: Asynchronous batch run with language model
        arun: Asynchronous run

    """

    def __init__(
        self,
        name: Optional[str] = None,
        description: Optional[str] = None,
        agents: Optional[List[Agent]] = None,
        models: Optional[List[Any]] = None,
        max_loops: Optional[int] = 200,
        callbacks: Optional[Sequence[callable]] = None,
        autosave: Optional[bool] = False,
        logging: Optional[bool] = False,
        return_metadata: Optional[bool] = False,
        metadata_filename: Optional[
            str
        ] = "multiagent_structure_metadata.json",
        stopping_function: Optional[Callable] = None,
        stopping_condition: Optional[str] = "stop",
        stopping_condition_args: Optional[Dict] = None,
        agentops_on: Optional[bool] = False,
        speaker_selection_func: Optional[Callable] = None,
        rules: Optional[str] = None,
        collective_memory_system: Optional[
            BaseVectorDatabase
        ] = False,
        agent_ops_on: bool = False,
        output_schema: Optional[BaseModel] = None,
        *args,
        **kwargs,
    ):
        """Initialize the swarm with agents"""
        self.name = name
        self.description = description
        self.agents = agents
        self.models = models
        self.max_loops = max_loops
        self.callbacks = callbacks
        self.autosave = autosave
        self.logging = logging
        self.return_metadata = return_metadata
        self.metadata_filename = metadata_filename
        self.stopping_function = stopping_function
        self.stopping_condition = stopping_condition
        self.stopping_condition_args = stopping_condition_args
        self.agentops_on = agentops_on
        self.speaker_selection_func = speaker_selection_func
        self.rules = rules
        self.collective_memory_system = collective_memory_system
        self.agent_ops_on = agent_ops_on
        self.output_schema = output_schema

        logger.info("Reliability checks activated.")
        # Ensure that agents is exists
        if self.agents is None:
            logger.info("Agents must be provided.")
            raise ValueError("Agents must be provided.")

        # Ensure that agents is a list
        if not isinstance(self.agents, list):
            logger.error("Agents must be a list.")
            raise TypeError("Agents must be a list.")

        # Ensure that agents is not empty
        if len(self.agents) == 0:
            logger.error("Agents list must not be empty.")
            raise ValueError("Agents list must not be empty.")

        # Initialize conversation
        self.conversation = Conversation(
            time_enabled=True, rules=self.rules, *args, **kwargs
        )

        # Handle callbacks
        if callbacks is not None:
            for callback in self.callbacks:
                if not callable(callback):
                    raise TypeError("Callback must be callable.")

        # Handle autosave
        if autosave:
            self.save_to_json(metadata_filename)

        # Handle stopping function
        if stopping_function is not None:
            if not callable(stopping_function):
                raise TypeError("Stopping function must be callable.")
            if stopping_condition_args is None:
                stopping_condition_args = {}
            self.stopping_condition_args = stopping_condition_args
            self.stopping_condition = stopping_condition
            self.stopping_function = stopping_function

        # Handle stopping condition
        if stopping_condition is not None:
            if stopping_condition_args is None:
                stopping_condition_args = {}
            self.stopping_condition_args = stopping_condition_args
            self.stopping_condition = stopping_condition

        # If agentops is enabled, try to import agentops
        if agentops_on is True:
            for agent in self.agents:
                agent.agent_ops_on = True

        # Handle speaker selection function
        if speaker_selection_func is not None:
            if not callable(speaker_selection_func):
                raise TypeError(
                    "Speaker selection function must be callable."
                )
            self.speaker_selection_func = speaker_selection_func

        # Add the check for all the agents to see if agent ops is on!
        if agent_ops_on is True:
            for agent in self.agents:
                agent.agent_ops_on = True

        # Agents dictionary with agent name as key and agent object as value
        self.agents_dict = {
            agent.agent_name: agent for agent in self.agents
        }

    def communicate(self):
        """Communicate with the swarm through the orchestrator, protocols, and the universal communication layer"""
        ...

    def run(self):
        """Run the swarm"""
        ...

    def __call__(
        self,
        task,
        *args,
        **kwargs,
    ):
        """Call self as a function

        Args:
            task (_type_): _description_

        Returns:
            _type_: _description_
        """
        try:
            return self.run(task, *args, **kwargs)
        except Exception as error:
            logger.error(f"Error running {self.__class__.__name__}")
            raise error

    def step(self):
        """Step the swarm"""

    def add_agent(self, agent: AgentType):
        """Add a agent to the swarm"""
        self.agents.append(agent)

    def add_agents(self, agents: List[AgentType]):
        """Add a list of agents to the swarm"""
        self.agents.extend(agents)

    def add_agent_by_id(self, agent_id: str):
        """Add a agent to the swarm by id"""
        agent = self.get_agent_by_id(agent_id)
        self.add_agent(agent)

    def remove_agent(self, agent: AgentType):
        """Remove a agent from the swarm"""
        self.agents.remove(agent)

    def get_agent_by_name(self, name: str):
        """Get a agent by name"""
        for agent in self.agents:
            if agent.name == name:
                return agent

    def reset_all_agents(self):
        """Resets the state of all agents."""
        for agent in self.agents:
            agent.reset()

    def broadcast(
        self, message: str, sender: Optional[AgentType] = None
    ):
        """Broadcast a message to all agents"""

    def reset(self):
        """Reset the swarm"""

    def plan(self, task: str):
        """agents must individually plan using a workflow or pipeline"""

    def self_find_agent_by_name(self, name: str):
        """
        Find an agent by its name.

        Args:
            name (str): The name of the agent to find.

        Returns:
            Agent: The Agent object if found, None otherwise.
        """
        for agent in self.agents:
            if agent.agent_name == name:
                return agent
        return None

    def self_find_agent_by_id(self, id: uuid.UUID):
        """
        Find an agent by its id.

        Args:
            id (str): The id of the agent to find.

        Returns:
            Agent: The Agent object if found, None otherwise.
        """
        for agent in self.agents:
            if agent.id == id:
                return agent
        return None

    def agent_exists(self, name: str):
        """
        Check if an agent exists in the swarm.

        Args:
            name (str): The name of the agent to check.

        Returns:
            bool: True if the agent exists, False otherwise.
        """
        return self.self_find_agent_by_name(name) is not None

    def direct_message(
        self,
        message: str,
        sender: AgentType,
        recipient: AgentType,
    ):
        """Send a direct message to a agent"""

    def autoscaler(self, num_agents: int, agent: List[AgentType]):
        """Autoscaler that acts like kubernetes for autonomous agents"""

    def get_agent_by_id(self, id: str) -> AgentType:
        """Locate a agent by id"""

    def assign_task(self, agent: AgentType, task: Any) -> Dict:
        """Assign a task to a agent"""

    def get_all_tasks(self, agent: AgentType, task: Any):
        """Get all tasks"""

    def get_finished_tasks(self) -> List[Dict]:
        """Get all finished tasks"""

    def get_pending_tasks(self) -> List[Dict]:
        """Get all pending tasks"""

    def pause_agent(self, agent: AgentType, agent_id: str):
        """Pause a agent"""

    def resume_agent(self, agent: AgentType, agent_id: str):
        """Resume a agent"""

    def stop_agent(self, agent: AgentType, agent_id: str):
        """Stop a agent"""

    def restart_agent(self, agent: AgentType):
        """Restart agent"""

    def scale_up(self, num_agent: int):
        """Scale up the number of agents"""

    def scale_down(self, num_agent: int):
        """Scale down the number of agents"""

    def scale_to(self, num_agent: int):
        """Scale to a specific number of agents"""

    def get_all_agents(self) -> List[AgentType]:
        """Get all agents"""

    def get_swarm_size(self) -> int:
        """Get the size of the swarm"""

    # #@abstractmethod
    def get_swarm_status(self) -> Dict:
        """Get the status of the swarm"""

    # #@abstractmethod
    def save_swarm_state(self):
        """Save the swarm state"""

    def batched_run(self, tasks: List[Any], *args, **kwargs):
        """_summary_

        Args:
            tasks (List[Any]): _description_
        """
        # Implement batched run
        return [self.run(task, *args, **kwargs) for task in tasks]

    async def abatch_run(self, tasks: List[str], *args, **kwargs):
        """Asynchronous batch run with language model

        Args:
            tasks (List[str]): _description_

        Returns:
            _type_: _description_
        """
        return await asyncio.gather(
            *(self.arun(task, *args, **kwargs) for task in tasks)
        )

    async def arun(self, task: Optional[str] = None, *args, **kwargs):
        """Asynchronous run

        Args:
            task (Optional[str], optional): _description_. Defaults to None.
        """
        loop = asyncio.get_event_loop()
        result = await loop.run_in_executor(
            None, self.run, task, *args, **kwargs
        )
        return result

    def loop(
        self,
        task: Optional[str] = None,
        *args,
        **kwargs,
    ):
        """Loop through the swarm

        Args:
            task (Optional[str], optional): _description_. Defaults to None.
        """
        # Loop through the self.max_loops
        for i in range(self.max_loops):
            self.run(task, *args, **kwargs)

    async def aloop(
        self,
        task: Optional[str] = None,
        *args,
        **kwargs,
    ):
        """Asynchronous loop through the swarm

        Args:
            task (Optional[str], optional): _description_. Defaults to None.
        """
        # Async Loop through the self.max_loops
        loop = asyncio.get_event_loop()
        result = await loop.run_in_executor(
            None, self.loop, task, *args, **kwargs
        )
        return result

    def run_async(self, task: Optional[str] = None, *args, **kwargs):
        """Run the swarm asynchronously

        Args:
            task (Optional[str], optional): _description_. Defaults to None.
        """
        loop = asyncio.get_event_loop()
        result = loop.run_until_complete(
            self.arun(task, *args, **kwargs)
        )
        return result

    def run_batch_async(self, tasks: List[str], *args, **kwargs):
        """Run the swarm asynchronously

        Args:
            task (Optional[str], optional): _description_. Defaults to None.
        """
        loop = asyncio.get_event_loop()
        result = loop.run_until_complete(
            self.abatch_run(tasks, *args, **kwargs)
        )
        return result

    def run_batch(self, tasks: List[str], *args, **kwargs):
        """Run the swarm asynchronously

        Args:
            task (Optional[str], optional): _description_. Defaults to None.
        """
        return self.batched_run(tasks, *args, **kwargs)

    def select_agent_by_name(self, agent_name: str):
        """
        Select an agent through their name
        """
        # Find agent with id
        for agent in self.agents:
            if agent.name == agent_name:
                return agent

    def task_assignment_by_id(
        self, task: str, agent_id: str, *args, **kwargs
    ):
        """
        Assign a task to an agent
        """
        # Assign task to agent by their agent id
        agent = self.select_agent(agent_id)
        return agent.run(task, *args, **kwargs)

    def task_assignment_by_name(
        self, task: str, agent_name: str, *args, **kwargs
    ):
        """
        Assign a task to an agent
        """
        # Assign task to agent by their agent id
        agent = self.select_agent_by_name(agent_name)
        return agent.run(task, *args, **kwargs)

    def concurrent_run(self, task: str) -> List[str]:
        """Synchronously run the task on all llms and collect responses"""
        with ThreadPoolExecutor() as executor:
            future_to_llm = {
                executor.submit(agent, task): agent
                for agent in self.agents
            }
            responses = []
            for future in as_completed(future_to_llm):
                try:
                    responses.append(future.result())
                except Exception as error:
                    print(
                        f"{future_to_llm[future]} generated an"
                        f" exception: {error}"
                    )
        self.last_responses = responses
        self.task_history.append(task)
        return responses

    def add_llm(self, agent: Callable):
        """Add an llm to the god mode"""
        self.agents.append(agent)

    def remove_llm(self, agent: Callable):
        """Remove an llm from the god mode"""
        self.agents.remove(agent)

    def run_all(self, task: str = None, *args, **kwargs):
        """Run all agents

        Args:
            task (str, optional): _description_. Defaults to None.

        Returns:
            _type_: _description_
        """
        responses = []
        for agent in self.agents:
            responses.append(agent(task, *args, **kwargs))
        return responses

    def run_on_all_agents(self, task: str = None, *args, **kwargs):
        """Run on all agents

        Args:
            task (str, optional): _description_. Defaults to None.

        Returns:
            _type_: _description_
        """
        with ThreadPoolExecutor() as executor:
            responses = executor.map(
                lambda agent: agent(task, *args, **kwargs),
                self.agents,
            )
        return list(responses)

    def add_swarm_entry(self, swarm):
        """
        Add the information of a joined Swarm to the registry.

        Args:
            swarm (SwarmManagerBase): Instance of SwarmManagerBase representing the joined Swarm.

        Returns:
            None
        """

    def add_agent_entry(self, agent: Agent):
        """
        Add the information of an Agent to the registry.

        Args:
            agent (Agent): Instance of Agent representing the Agent.

        Returns:
            None
        """

    def retrieve_swarm_information(self, swarm_id: str):
        """
        Retrieve the information of a specific Swarm from the registry.

        Args:
            swarm_id (str): Unique identifier of the Swarm.

        Returns:
            SwarmManagerBase: Instance of SwarmManagerBase representing the retrieved Swarm, or None if not found.
        """

    def retrieve_joined_agents(self, agent_id: str) -> List[Agent]:
        """
        Retrieve the information the Agents which have joined the registry.

        Returns:
            Agent: Instance of Agent representing the retrieved Agent, or None if not found.
        """

    def join_swarm(
        self, from_entity: Agent | Agent, to_entity: Agent
    ):
        """
        Add a relationship between a Swarm and an Agent or other Swarm to the registry.

        Args:
            from (Agent | SwarmManagerBase): Instance of Agent or SwarmManagerBase representing the source of the relationship.
        """

    def metadata(self):
        """
        Get the metadata of the multi-agent structure.

        Returns:
            dict: The metadata of the multi-agent structure.
        """
        return {
            "agents": self.agents,
            "callbacks": self.callbacks,
            "autosave": self.autosave,
            "logging": self.logging,
            "conversation": self.conversation,
        }

    def save_to_json(self, filename: str):
        """
        Save the current state of the multi-agent structure to a JSON file.

        Args:
            filename (str): The name of the file to save the multi-agent structure to.

        Returns:
            None
        """
        try:
            with open(filename, "w") as f:
                json.dump(self.__dict__, f)
        except Exception as e:
            logger.error(e)

    def load_from_json(self, filename: str):
        """
        Load the state of the multi-agent structure from a JSON file.

        Args:
            filename (str): The name of the file to load the multi-agent structure from.

        Returns:
            None
        """
        try:
            with open(filename) as f:
                self.__dict__ = json.load(f)
        except Exception as e:
            logger.error(e)

    def save_to_yaml(self, filename: str):
        """
        Save the current state of the multi-agent structure to a YAML file.

        Args:
            filename (str): The name of the file to save the multi-agent structure to.

        Returns:
            None
        """
        try:
            with open(filename, "w") as f:
                yaml.dump(self.__dict__, f)
        except Exception as e:
            logger.error(e)

    def load_from_yaml(self, filename: str):
        """
        Load the state of the multi-agent structure from a YAML file.

        Args:
            filename (str): The name of the file to load the multi-agent structure from.

        Returns:
            None
        """
        try:
            with open(filename) as f:
                self.__dict__ = yaml.load(f)
        except Exception as e:
            logger.error(e)

    def __repr__(self):
        return f"{self.__class__.__name__}({self.__dict__})"

    def __str__(self):
        return f"{self.__class__.__name__}({self.__dict__})"

    def __len__(self):
        return len(self.agents)

    def __getitem__(self, index):
        return self.agents[index]

    def __setitem__(self, index, value):
        self.agents[index] = value

    def __delitem__(self, index):
        del self.agents[index]

    def __iter__(self):
        return iter(self.agents)

    def __reversed__(self):
        return reversed(self.agents)

    def __contains__(self, value):
        return value in self.agents

    def agent_error_handling_check(self):
        try:
            if self.agents is None:
                message = "You have not passed in any agents, you need to input agents to run a swarm"
                logger.info(message)
                raise ValueError(message)
        except Exception as error:
            logger.info(error)
            raise error

    def swarm_initialization(self, *args, **kwargs):
        """
        Initializes the hierarchical swarm.

        Args:
            *args: Additional positional arguments.
            **kwargs: Additional keyword arguments.

        Returns:
            None

        """
        logger.info(
            f"Initializing the hierarchical swarm: {self.name}"
        )
        logger.info(f"Purpose of this swarm: {self.description}")

        # Now log number of agnets and their names
        logger.info(f"Number of agents: {len(self.agents)}")
        logger.info(
            f"Agent names: {[agent.name for agent in self.agents]}"
        )

        # Now see if agents is not empty
        if len(self.agents) == 0:
            logger.info(
                "No agents found. Please add agents to the swarm."
            )
            return None

        # Now see if director is not empty
        if self.director is None:
            logger.info(
                "No director found. Please add a director to the swarm."
            )
            return None

        logger.info(
            f"Initialization complete for the hierarchical swarm: {self.name}"
        )

    def export_output_schema(self):
        """
        Export the output schema of the swarm.

        Returns:
            dict: The output schema of the swarm.

        """
        return self.output_schema.model_dump_json(indent=4)

    def export_output_schema_dict(self):
        return self.output_schema.model_dump()

    def export_and_autosave(self):
        content = self.export_output_schema()

        create_file_in_folder(
            os.getenv("WORKSPACE_DIR"),
            self.metadata_filename,
            content=content,
        )

        return logger.info(
            f"Metadata saved to {self.metadata_filename}"
        )

    def list_agents(self):
        """
        List all agents in the swarm.

        Returns:
            None
        """
        display_agents_info(self.agents)

    def agents_to_dataframe(self):
        """
        Convert agents to a pandas DataFrame.
        """
        data = [agent.agent_output.dict() for agent in self.agents]
        return dict_to_dataframe(data)

    def model_to_dataframe(self):
        """
        Convert the Pydantic model to a pandas DataFrame.
        """
        return pydantic_model_to_dataframe(self.output_schema)