# Design Philosophy Document for Zeta

## Usable

### Objective

Our goal is to ensure that Zeta is intuitive and easy to use for all users, regardless of their level of technical expertise. This includes the developers who implement Zeta in their applications, as well as end users who interact with the implemented systems.

### Tactics

- Clear and Comprehensive Documentation: We will provide well-written and easily accessible documentation that guides users through using and understanding Zeta.
- User-Friendly APIs: We'll design clean and self-explanatory APIs that help developers to understand their purpose quickly.
- Prompt and Effective Support: We will ensure that support is readily available to assist users when they encounter problems or need help with Zeta.

## Reliable

### Objective

Zeta should be dependable and trustworthy. Users should be able to count on Zeta to perform consistently and without error or failure.

### Tactics

- Robust Error Handling: We will focus on error prevention, detection, and recovery to minimize failures in Zeta.
- Comprehensive Testing: We will apply various testing methodologies such as unit testing, integration testing, and stress testing to validate the reliability of our software.
- Continuous Integration/Continuous Delivery (CI/CD): We will use CI/CD pipelines to ensure that all changes are tested and validated before they're merged into the main branch.

## Fast

### Objective

Zeta should offer high performance and rapid response times. The system should be able to handle requests and tasks swiftly.

### Tactics

- Efficient Algorithms: We will focus on optimizing our algorithms and data structures to ensure they run as quickly as possible.
- Caching: Where appropriate, we will use caching techniques to speed up response times.
- Profiling and Performance Monitoring: We will regularly analyze the performance of Zeta to identify bottlenecks and opportunities for improvement.

## Scalable

### Objective

Zeta should be able to grow in capacity and complexity without compromising performance or reliability. It should be able to handle increased workloads gracefully.

### Tactics

- Modular Architecture: We will design Zeta using a modular architecture that allows for easy scaling and modification.
- Load Balancing: We will distribute tasks evenly across available resources to prevent overload and maximize throughput.
- Horizontal and Vertical Scaling: We will design Zeta to be capable of both horizontal (adding more machines) and vertical (adding more power to an existing machine) scaling.

### Philosophy

Zeta is designed with a philosophy of simplicity and reliability. We believe that software should be a tool that empowers users, not a hurdle that they need to overcome. Therefore, our focus is on usability, reliability, speed, and scalability. We want our users to find Zeta intuitive and dependable, fast and adaptable to their needs. This philosophy guides all of our design and development decisions.

# 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 zeta, and provides pre-configured zeta 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 zeta.
- **Ease of Use**: Users should be able to easily create their own zeta 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 zeta. 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 zeta or create their own custom zeta.

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_zeta(objective)

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

swarm = CustomSwarm(openai_api_key)
swarm.run_zeta(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 zeta or create their own custom zeta.


# 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 zeta 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.
