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:
- Modularity: Components can be developed, upgraded, and replaced independently
- Scaling: The system can scale horizontally by adding more specialized agents
- Specialization: Agents can focus on specific tasks while contributing to collective intelligence
- Coordination: Strict communication protocols enable efficient collaboration
- Adaptability: The system can reconfigure itself based on task requirements
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:
-
Input Processing
External inputs are received and processed by perception agents in the operational layer.
-
Context Integration
Information is integrated with existing knowledge in the tactical layer.
-
Goal Alignment
The strategic layer evaluates how the new information relates to current goals.
-
Task Distribution
Tasks are distributed to appropriate specialized agents via the task manager.
-
Parallel Processing
Multiple agents work simultaneously on different aspects of the problem.
-
Result Aggregation
Results are collected and synthesized into a coherent output.
-
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:
- Python: Primary programming language for system implementation
- PyTorch/TensorFlow: For neural network models used by various agents
- Redis: Fast in-memory data structure store for working memory
- PostgreSQL: Relational database for structured knowledge storage
- Vector Databases: For efficient similarity search in semantic memory
- ZeroMQ/gRPC: For efficient inter-agent communication
- Docker: For containerization and deployment
- Kubernetes: For orchestration in distributed deployments
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:
- Tool Plugins: Expand the system's interaction capabilities with external systems
- Model Plugins: Integrate new AI models and techniques
- Memory Plugins: Add new memory storage and retrieval mechanisms
- UI Plugins: Create custom interfaces for specific applications
Next Steps
DREAMv1 is under active development with several planned enhancements:
Enhanced Agent Learning
Implementing more sophisticated learning mechanisms for agents to improve over time
Distributed Deployment
Full support for running the system across multiple machines for increased scale
Advanced Security Features
Enhanced security measures to ensure system integrity and data protection