Distributed AI Agent Orchestration System with OpenAI Swarm Integration

Overview

This article describes a distributed system architecture for managing AI agents across multiple servers, integrating OpenAI’s swarm architecture with RabbitMQ for efficient task distribution and resource management.

Modified OpenAI Swarm Architecture

Core Modifications

graph TD
    A[Original Swarm Agent] --> B[Modified Swarm Agent]
    B --> C[RabbitMQ Integration]
    C --> D[Handoff Functions]
    D --> E[Callback Handlers]

Key Changes

1. Handoff Function Integration

# Conceptual Structure
class ModifiedSwarmAgent:
    async def handoff(self, task):
        # Instead of direct execution
        await self.publish_to_queue(task)
        return await self.await_callback()

2. Queue Integration Points

• Task Reception
• Inter-agent Communication
• Result Publication
• Resource Status Updates

System Architecture

1. Resource Monitor and Management Service

Front end app

• Chat supported deployment of new agent

• state of network dashboard

• prompt repo

Server Agent Component

• Lightweight metrics collector
• Real-time resource monitoring
• Docker container statistics

Metrics Aggregator

• Centralized metrics collection
• Resource availability mapping
• Threshold monitoring

2. API Key Management

Key Store

• Encrypted storage
• Usage quotas
• Cost tracking

Usage Monitor

• Real-time tracking
• Predictive analytics
• Auto-rotation

3. Agent Orchestrator

Deployment Manager

# Example Docker Compose Template
version: '3.8'
services:
  agent:
    image: modified-swarm-agent
    deploy:
      resources:
        limits:
          memory: ${MEM_LIMIT}
          cpus: ${CPU_LIMIT}
    environment:
      - QUEUE_CONNECTION=${RABBIT_URL}
      - API_KEY=${SELECTED_KEY}
      - CALLBACK_ROUTE=${CALLBACK_ENDPOINT}

Queue Consumer Template

# Conceptual Consumer Structure
class AgentConsumer:
    async def callback(self, message):
        agent = SwarmAgent(system_prompt=self.config.prompt)
        result = await agent.execute(message.body)
        await self.publish_result(result)

Message Queue Architecture

Queue Structure

1. Task Queues
   • Priority-based
   • Agent-specific
   • Load-balanced
2. Response Queues
   • Result collection
   • Error handling
   • Status updates

Consumer Implementation

# Example Consumer Setup
async def setup_consumer(queue_name, callback):
    connection = await connect_rabbitmq()
    channel = await connection.channel()
    await channel.basic_consume(
        queue=queue_name,
        callback=callback
    )

Deployment Flow

1. Initial Request

• Task received via API
• Resource check initiated
• API key validation

2. Agent Deployment

graph LR
    A[Request] --> B[Resource Check]
    B --> C[Generate Docker Compose]
    C --> D[Deploy Container]
    D --> E[Start Consumer]
    E --> F[Ready for Tasks]

3. Task Processing

1. Task received in queue
2. Consumer callback triggered
3. Swarm agent execution
4. Result publication

Resource Management

Server Selection Logic

# Conceptual Resource Selection
class ResourceManager:
    async def select_server(self, requirements):
        available_servers = await self.get_available_servers()
        return self.optimize_selection(
            available_servers,
            requirements
        )

Scaling Logic

• Resource-based scaling
• Load-based scaling
• Cost-optimization scaling

Security Implementation

Authentication

• Service-to-service auth
• API authentication
• Queue authentication

Authorization

• Role-based access
• Resource permissions
• Usage quotas

Monitoring Framework

Metrics Collection

1. Resource Usage
2. Performance Metrics
3. Cost Tracking
4. Error Rates

Alerting System

• Resource thresholds
• Cost thresholds
• Error thresholds

Implementation Phases

Phase 1: Core Setup

• Basic resource monitoring
• Simple API key management
• Initial queue integration
• Front-end dashboard with prompts, resources and running services with chat to run AI generations
• Sandbox to test new deployment

Phase 2: Advanced Features

• Auto-scaling
• Advanced monitoring
• Cost optimization

Phase 3: Optimization

• Performance tuning
• Security hardening
• Error handling improvements

Conclusion

This architecture provides a robust foundation for distributed AI agent management, combining the power of OpenAI’s swarm architecture with efficient message queue-based task distribution. The system ensures optimal resource utilization while maintaining scalability and security.

Next Steps

1. Implement core monitoring services
2. Develop queue integration
3. Build deployment system
4. Add advanced features