swarms/utils/README.md

A high-level pseudocode for creating the classes and functions for your desired system:

1. Swarms
   • The main class. It initializes the swarm with a specified number of worker nodes and sets up self-scaling if required.
   • Methods include add_worker, remove_worker, execute, and scale.
2. WorkerNode
   • Class for each worker node in the swarm. It has a task_queue and a completed_tasks queue.
   • Methods include receive_task, complete_task, and communicate.
3. HierarchicalSwarms
   • Inherits from Swarms and overrides the execute method to execute tasks in a hierarchical manner.
4. CollaborativeSwarms
   • Inherits from Swarms and overrides the execute method to execute tasks in a collaborative manner.
5. CompetitiveSwarms
   • Inherits from Swarms and overrides the execute method to execute tasks in a competitive manner.
6. MultiAgentDebate
   • Inherits from Swarms and overrides the execute method to execute tasks in a debating manner.

To implement this in Python, you would start by setting up the base Swarm class and WorkerNode class. Here's a simplified Python example:

class WorkerNode:
    def __init__(self, llm: BaseLLM):
        self.llm = llm
        self.task_queue = deque()
        self.completed_tasks = deque()

    def receive_task(self, task):
        self.task_queue.append(task)

    def complete_task(self):
        task = self.task_queue.popleft()
        result = self.llm.execute(task)
        self.completed_tasks.append(result)
        return result

    def communicate(self, other_node):
        # Placeholder for communication method
        pass


class Swarms:
    def __init__(self, num_nodes: int, llm: BaseLLM, self_scaling: bool):
        self.nodes = [WorkerNode(llm) for _ in range(num_nodes)]
        self.self_scaling = self_scaling

    def add_worker(self, llm: BaseLLM):
        self.nodes.append(WorkerNode(llm))

    def remove_worker(self, index: int):
        self.nodes.pop(index)

    def execute(self, task):
        # Placeholder for main execution logic
        pass

    def scale(self):
        # Placeholder for self-scaling logic
        pass

Then, you would build out the specialized classes for each type of swarm:

class HierarchicalSwarms(Swarms):
    def execute(self, task):
        # Implement hierarchical task execution
        pass


class CollaborativeSwarms(Swarms):
    def execute(self, task):
        # Implement collaborative task execution
        pass


class CompetitiveSwarms(Swarms):
    def execute(self, task):
        # Implement competitive task execution
        pass


class MultiAgentDebate(Swarms):
    def execute(self, task):
        # Implement debate-style task execution
        pass

WorkerNode class

Here's the pseudocode algorithm for a WorkerNode class that includes a vector embedding database for communication:

1. WorkerNode
   • Initialize a worker node with an LLM and a connection to the vector embedding database.
   • The worker node maintains a task_queue and completed_tasks queue. It also keeps track of the status of tasks (e.g., "pending", "completed").
   • The receive_task method accepts a task and adds it to the task_queue.
   • The complete_task method takes the oldest task from the task_queue, executes it, and then stores the result in the completed_tasks queue. It also updates the task status in the vector embedding database to "completed".
   • The communicate method uses the vector embedding database to share information with other nodes. It inserts the task result into the vector database and also queries for tasks marked as "completed".

In Python, this could look something like:

from langchain.vectorstores import FAISS
from langchain.docstore import InMemoryDocstore
from langchain.embeddings import OpenAIEmbeddings
import faiss
from swarms.workers.auto_agent import AutoGPT
from collections import deque
from typing import Dict, Any

class WorkerNode:
    def __init__(self, llm: AutoGPT, vectorstore: FAISS):
        self.llm = llm
        self.vectorstore = vectorstore
        self.task_queue = deque()
        self.completed_tasks = deque()
        self.task_status: Dict[Any, str] = {}

    def receive_task(self, task):
        self.task_queue.append(task)
        self.task_status[task] = 'pending'

    def complete_task(self):
        task = self.task_queue.popleft()
        result = self.llm.run(task)
        self.completed_tasks.append(result)
        self.task_status[task] = 'completed'
        # Insert task result into the vectorstore
        self.vectorstore.insert(task, result)
        return result

    def communicate(self):
        # Share task results and status through vectorstore
        completed_tasks = [(task, self.task_status[task]) for task in self.task_queue if self.task_status[task] == 'completed']
        for task, status in completed_tasks:
            self.vectorstore.insert(task, status)

This example assumes that tasks are hashable and can be used as dictionary keys. The vectorstore.insert method is used to share task results and status with other nodes, and you can use methods like vectorstore.query or vectorstore.regex_search to retrieve this information. Please remember this is a simplified implementation and might need changes according to your exact requirements.