5.3 KiB
MatrixSwarm
The MatrixSwarm class provides a framework for managing and operating on matrices of AI agents, enabling matrix-like operations similar to linear algebra. This allows for complex agent interactions and parallel processing capabilities.
Overview
MatrixSwarm treats AI agents as elements in a matrix, allowing for operations like addition, multiplication, and transposition. This approach enables sophisticated agent orchestration and parallel processing patterns.
Installation
pip3 install -U swarms
Basic Usage
from swarms import Agent
from swarms.matrix import MatrixSwarm
# Create a 2x2 matrix of agents
agents = [
[Agent(agent_name="Agent-0-0"), Agent(agent_name="Agent-0-1")],
[Agent(agent_name="Agent-1-0"), Agent(agent_name="Agent-1-1")]
]
# Initialize the matrix
matrix = MatrixSwarm(agents)
Class Constructor
def __init__(self, agents: List[List[Agent]])
Parameters
agents(List[List[Agent]]): A 2D list of Agent instances representing the matrix.
Raises
ValueError: If the input is not a valid 2D list of Agent instances.
Methods
transpose()
Transposes the matrix of agents by swapping rows and columns.
def transpose(self) -> MatrixSwarm
Returns
MatrixSwarm: A new MatrixSwarm instance with transposed dimensions.
add(other)
Performs element-wise addition of two agent matrices.
def add(self, other: MatrixSwarm) -> MatrixSwarm
Parameters
other(MatrixSwarm): Another MatrixSwarm instance to add.
Returns
MatrixSwarm: A new MatrixSwarm resulting from the addition.
Raises
ValueError: If matrix dimensions are incompatible.
scalar_multiply(scalar)
Scales the matrix by duplicating agents along rows.
def scalar_multiply(self, scalar: int) -> MatrixSwarm
Parameters
scalar(int): The multiplication factor.
Returns
MatrixSwarm: A new MatrixSwarm with scaled dimensions.
multiply(other, inputs)
Performs matrix multiplication (dot product) between two agent matrices.
def multiply(self, other: MatrixSwarm, inputs: List[str]) -> List[List[AgentOutput]]
Parameters
other(MatrixSwarm): The second MatrixSwarm for multiplication.inputs(List[str]): Input queries for the agents.
Returns
List[List[AgentOutput]]: Matrix of operation results.
Raises
ValueError: If matrix dimensions are incompatible for multiplication.
subtract(other)
Performs element-wise subtraction of two agent matrices.
def subtract(self, other: MatrixSwarm) -> MatrixSwarm
Parameters
other(MatrixSwarm): Another MatrixSwarm to subtract.
Returns
MatrixSwarm: A new MatrixSwarm resulting from the subtraction.
identity(size)
Creates an identity matrix of agents.
def identity(self, size: int) -> MatrixSwarm
Parameters
size(int): Size of the identity matrix (NxN).
Returns
MatrixSwarm: An identity MatrixSwarm.
determinant()
Computes the determinant of a square agent matrix.
def determinant(self) -> Any
Returns
Any: The determinant result.
Raises
ValueError: If the matrix is not square.
save_to_file(path)
Saves the matrix structure and metadata to a JSON file.
def save_to_file(self, path: str) -> None
Parameters
path(str): File path for saving the matrix data.
Extended Example
Here's a comprehensive example demonstrating various MatrixSwarm operations:
from swarms import Agent
from swarms.matrix import MatrixSwarm
# Create agents with specific configurations
agents = [
[
Agent(
agent_name=f"Agent-{i}-{j}",
system_prompt="Your system prompt here",
model_name="gpt-4",
max_loops=1,
verbose=True
) for j in range(2)
] for i in range(2)
]
# Initialize matrix
matrix = MatrixSwarm(agents)
# Example operations
transposed = matrix.transpose()
scaled = matrix.scalar_multiply(2)
# Run operations with inputs
inputs = ["Query 1", "Query 2"]
results = matrix.multiply(transposed, inputs)
# Save results
matrix.save_to_file("matrix_results.json")
Output Schema
The AgentOutput class defines the structure for operation results:
class AgentOutput(BaseModel):
agent_name: str
input_query: str
output_result: Any
metadata: dict
Best Practices
-
Initialization
- Ensure all agents in the matrix are properly configured before initialization
- Validate matrix dimensions for your use case
-
Operation Performance
- Consider computational costs for large matrices
- Use appropriate batch sizes for inputs
-
Error Handling
- Implement proper error handling for agent operations
- Validate inputs before matrix operations
-
Resource Management
- Monitor agent resource usage in large matrices
- Implement proper cleanup procedures
Limitations
- Matrix operations are constrained by the underlying agent capabilities
- Performance may vary based on agent configuration and complexity
- Resource usage scales with matrix dimensions