Technical
AIMatrix Architecture

AIMatrix Architecture

AIMatrix Master Agent Architecture

The revolutionary approach to AI orchestration that automatically manages multiple AI systems, deploys local models, and creates a distributed compute network.

The Master Agent Concept

What is a Master Agent?

The Master Agent is the orchestrator of orchestrators - a sophisticated AI system that:

  • Detects your environment and capabilities
  • Automatically installs required components
  • Manages multiple AI agents and models
  • Routes tasks to optimal executors
  • Maintains context across all systems
┌──────────────────────────────────────────────┐
│            Master Agent                      │
│         (AIMatrix Runtime)                   │
├──────────────────────────────────────────────┤
│  Environment Detection | Auto-Configuration  │
│  Model Management | Agent Orchestration      │
└────────────┬─────────────────────────────────┘
             │
    ┌────────┴────────┬─────────┬──────────┐
    ▼                 ▼         ▼          ▼
┌─────────┐    ┌──────────┐ ┌────────┐ ┌────────┐
│ Claude  │    │  Gemini  │ │ Local  │ │Custom │
│  CLI    │    │   API    │ │  LLMs  │ │Agents │
└─────────┘    └──────────┘ └────────┘ └────────┘

Why Master Agent Architecture?

1. Intelligent Resource Management

The Master Agent understands what’s available and what’s needed:

  • Detects GPU availability for local models
  • Checks API credentials for cloud services
  • Monitors system resources (RAM, CPU, disk)
  • Dynamically allocates tasks based on capabilities

2. Automatic Environment Setup

No manual configuration needed:

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# Master Agent automatically detects and installs:
- Ollama (if NVIDIA/AMD GPU detected)
- LM Studio (if Apple Silicon detected)
- llama.cpp (CPU inference fallback)
- Python environments for AutoGen
- Node.js for JavaScript agents
- Docker for containerized agents

3. Multi-Model Orchestration

Use the best model for each task:

  • Vision tasks → GPT-4V or Gemini Vision
  • Code generation → Claude or Codex
  • Local privacy → Llama 3, Mistral, Phi-4
  • Fast responses → Gemma 2 (2B parameters)
  • Complex reasoning → GPT-4 or Claude

Local LLM Integration

Supported Local Model Frameworks

Ollama 

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# Master Agent auto-installs and configures Ollama
aimatrix detect
> GPU detected: NVIDIA RTX 4090
> Installing Ollama...
> Pulling recommended models:
  - llama3:8b (general purpose)
  - codellama:13b (code generation)
  - gemma2:2b (fast responses)

LM Studio 

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# For Apple Silicon Macs
aimatrix detect
> Apple M2 Max detected
> Installing LM Studio...
> Optimizing for Metal Performance Shaders
> Loading models:
  - Phi-4 (14B) for complex tasks
  - Gemma 2 (9B) for general use

Direct Model Loading 

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# Master Agent configuration
master_agent:
  local_models:
    - provider: ollama
      models:
        - llama3:70b
        - mixtral:8x7b
    - provider: llamacpp
      models:
        - path: /models/phi-4-Q4_K_M.gguf
    - provider: transformers
      models:
        - microsoft/phi-4
        - google/gemma-2-27b

Benefits of Local LLM Orchestration

🔒 Privacy & Security

  • Sensitive data never leaves your infrastructure
  • Complete control over model behavior
  • Audit trails for all processing
  • Compliance with data regulations

💰 Cost Optimization

  • Zero API costs for local processing
  • Predictable infrastructure costs
  • Mix local and cloud for optimal pricing
  • Use expensive models only when needed

⚡ Performance & Latency

  • No network latency for local models
  • Parallel processing across multiple models
  • Edge deployment capabilities
  • Offline operation support

🎯 Task-Specific Optimization 

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# Master Agent routing rules
routing:
  - task: code_review
    model: local/codellama:34b
    reason: "Specialized for code, no API costs"
  
  - task: customer_email
    model: api/claude-3
    reason: "Best writing quality"
  
  - task: data_extraction
    model: local/gemma2:2b
    reason: "Fast, cheap, good enough"
  
  - task: complex_reasoning
    model: api/gpt-4
    reason: "Highest capability needed"

Distributed Compute Network

P2P Job Distribution

The Master Agent enables a revolutionary distributed compute model:

How It Works

  1. Job Creation at Studio
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{
  "job_id": "proj_123_training",
  "type": "model_fine_tuning",
  "requirements": {
    "gpu": "RTX 3080+",
    "vram": "10GB",
    "duration": "2 hours"
  },
  "reward": "500 credits"
}
  1. Local CLI Pulls Jobs
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# Your CLI checks for compatible jobs
aimatrix compute available
> Found 3 compatible jobs:
  1. Fine-tuning job (500 credits, 2 hrs)
  2. Batch processing (200 credits, 30 min)
  3. Model serving (10 credits/hour)

aimatrix compute accept proj_123_training
> Job accepted, downloading workspace...
  1. Master Agent Executes
  • Validates job requirements
  • Sets up isolated environment
  • Monitors resource usage
  • Reports progress to Studio
  • Handles failures gracefully

Job Types & Restrictions

✅ Allowed Job Types

  • Model Training: Fine-tuning on provided datasets
  • Batch Processing: Document analysis, data extraction
  • Model Serving: Host models for inference
  • Data Processing: ETL, transformations
  • Agent Testing: Run test suites

❌ Restricted Operations

  • Direct access to local file system
  • Network requests outside whitelist
  • System-level operations
  • Access to local credentials
  • Modification of Master Agent

Security & Isolation 

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# Job execution sandbox
security:
  isolation: docker
  network: restricted
  filesystem: temporary
  resources:
    cpu_limit: 80%
    memory_limit: specified
    gpu_access: dedicated
  monitoring:
    - resource_usage
    - network_traffic
    - file_operations

Studio API Integration

Webhook & Communication Channels

Studio.AIMatrix.com provides centralized services that local agents can’t handle:

Telegram Bot Integration 

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# Studio handles Telegram webhooks
@studio.webhook('/telegram/{workspace_id}')
async def telegram_handler(message):
    # Route to appropriate workspace
    workspace = get_workspace(workspace_id)
    
    # Master Agent processes locally
    response = await workspace.master_agent.process(
        channel="telegram",
        message=message
    )
    
    # Studio sends response
    await telegram.send(response)

WhatsApp Business API 

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// Studio provides WhatsApp gateway
studio.whatsapp.on('message', async (msg) => {
  // Forward to workspace's Master Agent
  const result = await masterAgent.process({
    channel: 'whatsapp',
    from: msg.from,
    content: msg.body,
    media: msg.media
  });
  
  // Studio handles delivery
  await studio.whatsapp.send(result);
});

Email Services 

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# Studio email configuration
email:
  inbound:
    - workspace1@aimatrix.com
    - support@company.aimatrix.com
  outbound:
    smtp: studio-managed
    dkim: configured
    tracking: enabled

Phone Calls (Twilio Integration) 

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# Studio manages phone infrastructure
@studio.voice.incoming
async def handle_call(call):
    # Convert speech to text
    text = await studio.transcribe(call.audio)
    
    # Process with Master Agent
    response = await master_agent.process_voice(text)
    
    # Convert back to speech
    audio = await studio.synthesize(response)
    
    # Play to caller
    await call.play(audio)

Benefits of Hybrid Architecture

🌐 Centralized Services

Studio provides what can’t be done locally:

  • Public webhooks and APIs
  • Email sending/receiving
  • SMS and phone calls
  • OAuth integrations
  • Certificate management

🏠 Local Processing

Master Agent handles sensitive operations:

  • Data processing
  • Model inference
  • Business logic
  • Private integrations
  • Custom workflows

🔄 Best of Both Worlds 

Internet Services          Local Processing
    (Studio)                (Master Agent)
       │                          │
   Webhooks ──────────────► Message Queue
   APIs ──────────────────► Local Agents
   Email ─────────────────► Processing
   Phone ─────────────────► Business Logic
       │                          │
       └──────── Results ─────────┘

Implementation Examples

Example 1: Multi-Channel Customer Service 

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class CustomerServiceMaster(MasterAgent):
    def __init__(self):
        # Auto-detect and setup models
        self.setup_models()
        
        # Connect to Studio for channels
        self.studio = StudioConnection()
        
    async def handle_inquiry(self, channel, message):
        # Determine best model for response
        if self.is_sensitive(message):
            # Use local model for privacy
            model = self.local_models['llama3']
        else:
            # Use cloud for better quality
            model = self.cloud_models['claude']
            
        # Process with appropriate agent
        response = await model.process(message)
        
        # Route response through Studio
        await self.studio.send(channel, response)

Example 2: Document Processing Pipeline 

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# Master Agent configuration
pipeline:
  - stage: receive
    source: studio.email
    
  - stage: extract
    model: local/gemma2:2b
    task: extract_data
    
  - stage: classify
    model: local/llama3:8b
    task: categorize
    
  - stage: process
    model: api/gpt-4
    task: complex_analysis
    condition: "category == 'complex'"
    
  - stage: store
    destination: local_database
    
  - stage: notify
    service: studio.email
    template: completion_notice

Example 3: Distributed Training Network 

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# Organization creates training job
studio create-job \
  --type=fine-tuning \
  --model=llama3:8b \
  --dataset=customer-service \
  --participants=10 \
  --reward=5000

# 10 different CLI users pull chunks
aimatrix compute join-training proj_456
> Downloading dataset chunk 3/10...
> Training on local GPU...
> Uploading gradients...

# Studio aggregates results
> Training completed across 10 nodes
> Model accuracy improved by 15%
> Credits distributed to participants

Performance Optimization

Model Selection Strategy 

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class ModelSelector:
    def select_model(self, task):
        # Check task requirements
        complexity = self.assess_complexity(task)
        privacy = self.assess_privacy(task)
        latency = self.get_latency_requirement(task)
        
        # Smart selection logic
        if privacy == "high":
            return self.local_models.best_fit(complexity)
        elif latency < 100:  # ms
            return self.local_models.fastest()
        elif complexity == "high":
            return self.cloud_models.most_capable()
        else:
            return self.local_models.most_efficient()

Resource Management 

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# Master Agent resource allocation
resources:
  local_models:
    max_loaded: 3
    swap_strategy: lru
    preload:
      - gemma2:2b  # Always loaded (fast)
      - llama3:8b  # General purpose
    
  cloud_apis:
    rate_limits:
      openai: 100/min
      anthropic: 50/min
    fallback_chain:
      - primary: gpt-4
      - secondary: claude-3
      - tertiary: local/llama3:70b

Getting Started

Quick Setup 

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# Install AIMatrix CLI
npm install -g @aimatrix/cli

# Initialize Master Agent
aimatrix init --master

# Auto-detection begins
> Detecting environment...
> GPU: NVIDIA RTX 4090 (24GB)
> RAM: 64GB
> CPU: AMD Ryzen 9 5950X
> 
> Installing optimal configuration...
> ✓ Ollama installed
> ✓ Pulling llama3:70b
> ✓ Pulling codellama:34b
> ✓ Setting up Claude CLI
> ✓ Configuring API connections
>
> Master Agent ready!

# Start the Master Agent
aimatrix serve
> Master Agent running on http://localhost:8080
> Studio connection established
> P2P compute network: available
> Local models: 3 loaded, 5 available
> Cloud APIs: configured and ready

Next Steps

  1. Configure Your Models
  2. Connect to Studio
  3. Join P2P Network
  4. Build Your First Agent

AIMatrix Master Agent - The AI that manages AI