DREAMv1 Architecture

A hierarchical multi-agent system (HMAS) designed for practical, modular and extensible AGI development.

Architecture Diagram

The diagram visualization has been temporarily removed. The DREAMv1 architecture consists of hierarchical agent organization with a Master Control Program at the top, coordinating Executive and Worker agents, with Memory Systems and External Tool integration.

System Overview

DREAMv1 (Distributed Reasoning and Execution Architecture for Models) is a hierarchical multi-agent system that organizes AI agents in a structured, layered architecture. This design philosophy enables both top-down control and bottom-up emergent behaviors, allowing the system to handle complex tasks through coordinated agent activities.

The architecture addresses several fundamental challenges in AGI development:

Core Components

Master Control Program (MCP)

The MCP serves as the central coordinator of the entire system, responsible for:

  • Orchestrating the initialization and termination of system processes
  • Managing resource allocation and scheduling
  • Supervising agent activities and intervening when necessary
  • Maintaining system stability and ensuring proper execution flow
  • Distributing tasks and monitoring their completion

Agent Hierarchy

The agent hierarchy is organized into three principal layers:

Strategic Layer

The strategic layer contains high-level planning and decision-making agents:

  • Executive Agent: Sets overall goals, priorities, and directs the planning process
  • Planning Agent: Develops strategies and allocates resources to achieve system goals

Tactical Layer

The tactical layer translates strategies into actionable tasks:

  • Task Manager: Coordinates task execution and monitors progress
  • Knowledge Agent: Synthesizes information and manages contextual understanding

Operational Layer

The operational layer executes specific tasks and interacts with the environment:

  • Domain Experts: Specialized agents with deep knowledge in particular domains
  • Worker Agents: Performs specific functions including perception, reasoning, tool use, and output generation

Memory Systems

DREAMv1 implements a multi-faceted memory architecture inspired by cognitive science:

  • Working Memory: Temporary storage for active processing and current context
  • Episodic Memory: Records of events, interactions, and experiences
  • Semantic Memory: Structured knowledge about facts, concepts, and relationships
  • Procedural Memory: Storage of methods, skills, and procedural knowledge

These memory systems use a combination of vector databases, structured storage, and retrievable indexes to enable efficient storage and retrieval.

Communication Protocol

The standardized communication protocol enables efficient exchange of information between agents:

  • Task Assignment: Distribution of tasks with clear specifications
  • Status Updates: Regular progress reports and completion notifications
  • Information Requests: Queries for specific data or knowledge
  • Resource Allocation: Requests and grants for computational resources
  • System Notifications: Alerts about system state changes or issues

Monitoring & Reflection

The system includes components for self-monitoring and improvement:

  • Performance Monitor: Tracks system performance metrics and identifies bottlenecks
  • Reflection Engine: Analyzes past operations to improve future behavior
  • Safety Protocols: Implements guardrails and risk assessment mechanisms

Data Flow

Information flows through DREAMv1 in a structured but flexible manner:

  1. Input Processing

    External inputs are received and processed by perception agents in the operational layer.

  2. Context Integration

    Information is integrated with existing knowledge in the tactical layer.

  3. Goal Alignment

    The strategic layer evaluates how the new information relates to current goals.

  4. Task Distribution

    Tasks are distributed to appropriate specialized agents via the task manager.

  5. Parallel Processing

    Multiple agents work simultaneously on different aspects of the problem.

  6. Result Aggregation

    Results are collected and synthesized into a coherent output.

  7. Response Generation

    Final output is formatted and delivered via output agents.

Throughout this process, memory systems are continuously updated, and the monitoring components ensure proper execution.

Technical Implementation

System Initialization


# Sample initialization code
from dreamv1.core import MasterControlProgram
from dreamv1.agents import AgentRegistry
from dreamv1.memory import MemoryManager

# Initialize the Master Control Program
mcp = MasterControlProgram(config_path="config/system.yaml")

# Register core agents
agent_registry = AgentRegistry()
agent_registry.register_strategic_agents()
agent_registry.register_tactical_agents()
agent_registry.register_operational_agents()

# Initialize memory systems
memory_manager = MemoryManager()
memory_manager.initialize_all()

# Start the system
mcp.initialize(agent_registry, memory_manager)
mcp.start()
                

Core Technologies

DREAMv1 is built using a combination of the following technologies:

Extensibility

DREAMv1 is designed to be highly extensible, allowing for customization and expansion across multiple dimensions:

Custom Agents

Developers can create new specialized agents by implementing the base Agent interface:


# Example of creating a custom specialized agent
from dreamv1.agents.base import Agent
from dreamv1.protocol import Message, MessageType

class CustomAnalysisAgent(Agent):
    def __init__(self, agent_id, config):
        super().__init__(agent_id, "operational", config)
        self.domain = "data_analysis"
        
    async def process_message(self, message: Message):
        if message.message_type == MessageType.TASK_REQUEST:
            # Process the task
            result = self._analyze_data(message.content)
            
            # Send response
            response = Message(
                sender=self.agent_id,
                receiver=message.sender,
                message_type=MessageType.TASK_RESPONSE,
                content=result
            )
            await self.send_message(response)
    
    def _analyze_data(self, data):
        # Custom analysis logic here
        return {"analysis_result": "..."}
                

Plugin System

The architecture includes a plugin system for adding new capabilities:

Next Steps

DREAMv1 is under active development with several planned enhancements:

In Progress

Enhanced Agent Learning

Implementing more sophisticated learning mechanisms for agents to improve over time

Planned

Distributed Deployment

Full support for running the system across multiple machines for increased scale

Planned

Advanced Security Features

Enhanced security measures to ensure system integrity and data protection