# Swarm Architecture Design Document

## Overview

The goal of the Swarm Architecture is to provide a flexible and scalable system to build swarm intelligence models that can solve complex problems. This document details the proposed design to create a plug-and-play system, which makes it easy to create custom swarms, and provides pre-configured swarms with multi-modal agents.

## Design Principles

- **Modularity**: The system will be built in a modular fashion, allowing various components to be easily swapped or upgraded.
- **Interoperability**: Different swarm classes and components should be able to work together seamlessly.
- **Scalability**: The design should support the growth of the system by adding more components or swarms.
- **Ease of Use**: Users should be able to easily create their own swarms or use pre-configured ones with minimal configuration.

## Design Components

### AbstractSwarm

The AbstractSwarm is an abstract base class which defines the basic structure of a swarm and the methods that need to be implemented. Any new swarm should inherit from this class and implement the required methods.

### Swarm Classes

Various Swarm classes can be implemented inheriting from the AbstractSwarm class. Each swarm class should implement the required methods for initializing the components, worker nodes, and boss node, and running the swarm.

Pre-configured swarm classes with multi-modal agents can be provided for ease of use. These classes come with a default configuration of tools and agents, which can be used out of the box.

### Tools and Agents

Tools and agents are the components that provide the actual functionality to the swarms. They can be language models, AI assistants, vector stores, or any other components that can help in problem solving.

To make the system plug-and-play, a standard interface should be defined for these components. Any new tool or agent should implement this interface, so that it can be easily plugged into the system.

## Usage

Users can either use pre-configured swarms or create their own custom swarms.

To use a pre-configured swarm, they can simply instantiate the corresponding swarm class and call the run method with the required objective.

To create a custom swarm, they need to:

1. Define a new swarm class inheriting from AbstractSwarm.
2. Implement the required methods for the new swarm class.
3. Instantiate the swarm class and call the run method.

### Example

```python
# Using pre-configured swarm
swarm = PreConfiguredSwarm(openai_api_key)
swarm.run_swarms(objective)

# Creating custom swarm
class CustomSwarm(AbstractSwarm):
    # Implement required methods

swarm = CustomSwarm(openai_api_key)
swarm.run_swarms(objective)
```

## Conclusion

This Swarm Architecture design provides a scalable and flexible system for building swarm intelligence models. The plug-and-play design allows users to easily use pre-configured swarms or create their own custom swarms.


# Swarming Architectures
Sure, below are five different swarm architectures with their base requirements and an abstract class that processes these components:

1. **Hierarchical Swarm**: This architecture is characterized by a boss/worker relationship. The boss node takes high-level decisions and delegates tasks to the worker nodes. The worker nodes perform tasks and report back to the boss node. 
    - Requirements: Boss node (can be a large language model), worker nodes (can be smaller language models), and a task queue for task management.

2. **Homogeneous Swarm**: In this architecture, all nodes in the swarm are identical and contribute equally to problem-solving. Each node has the same capabilities.
    - Requirements: Homogeneous nodes (can be language models of the same size), communication protocol for nodes to share information.

3. **Heterogeneous Swarm**: This architecture contains different types of nodes, each with its specific capabilities. This diversity can lead to more robust problem-solving.
    - Requirements: Different types of nodes (can be different types and sizes of language models), a communication protocol, and a mechanism to delegate tasks based on node capabilities.

4. **Competitive Swarm**: In this architecture, nodes compete with each other to find the best solution. The system may use a selection process to choose the best solutions.
    - Requirements: Nodes (can be language models), a scoring mechanism to evaluate node performance, a selection mechanism.

5. **Cooperative Swarm**: In this architecture, nodes work together and share information to find solutions. The focus is on cooperation rather than competition.
    - Requirements: Nodes (can be language models), a communication protocol, a consensus mechanism to agree on solutions.


6. **Grid-based Swarm**: This architecture positions agents on a grid, where they can only interact with their neighbors. This is useful for simulations, especially in fields like ecology or epidemiology.
    - Requirements: Agents (can be language models), a grid structure, and a neighborhood definition (i.e., how to identify neighboring agents).

7. **Particle Swarm Optimization (PSO) Swarm**: In this architecture, each agent represents a potential solution to an optimization problem. Agents move in the solution space based on their own and their neighbors' past performance. PSO is especially useful for continuous numerical optimization problems.
    - Requirements: Agents (each representing a solution), a definition of the solution space, an evaluation function to rate the solutions, a mechanism to adjust agent positions based on performance.

8. **Ant Colony Optimization (ACO) Swarm**: Inspired by ant behavior, this architecture has agents leave a pheromone trail that other agents follow, reinforcing the best paths. It's useful for problems like the traveling salesperson problem.
    - Requirements: Agents (can be language models), a representation of the problem space, a pheromone updating mechanism.

9. **Genetic Algorithm (GA) Swarm**: In this architecture, agents represent potential solutions to a problem. They can 'breed' to create new solutions and can undergo 'mutations'. GA swarms are good for search and optimization problems.
    - Requirements: Agents (each representing a potential solution), a fitness function to evaluate solutions, a crossover mechanism to breed solutions, and a mutation mechanism.

10. **Stigmergy-based Swarm**: In this architecture, agents communicate indirectly by modifying the environment, and other agents react to such modifications. It's a decentralized method of coordinating tasks.
    - Requirements: Agents (can be language models), an environment that agents can modify, a mechanism for agents to perceive environment changes.

These architectures all have unique features and requirements, but they share the need for agents (often implemented as language models) and a mechanism for agents to communicate or interact, whether it's directly through messages, indirectly through the environment, or implicitly through a shared solution space. Some also require specific data structures, like a grid or problem space, and specific algorithms, like for evaluating solutions or updating agent positions.





Here is an abstract class that provides the basic structure to process these components:

```python
from abc import ABC, abstractmethod

class AbstractSwarm(ABC):

    def __init__(self, agents, vectorstore, tools):
        self.agents = agents
        self.vectorstore = vectorstore
        self.tools = tools

    @abstractmethod
    def initialize(self):
        pass

    @abstractmethod
    def communicate(self):
        pass

    @abstractmethod
    def process(self):
        pass

    @abstractmethod
    def solve(self):
        pass
```

This abstract class requires four methods to be implemented:

- `initialize`: This method is used to set up the initial state of the swarm, including setting up nodes and tools.
- `communicate`: This method is responsible for facilitating communication between nodes.
- `process`: This method handles the processing logic, which can be different based on the swarm architecture.
- `solve`: This method is called to start the problem-solving process.

This abstract class can be inherited by specific swarm architecture classes to implement their specific behavior.

# 3 Ingredients

* The Individual Agent Configuration with a vectorstore and tools

* The Orchestrator, => task assignment, task completion handling, communication layer 