Technical Details

REAP Framework Architecture

Retrieval-Enhanced Automated Processing represents a breakthrough in AI agent design, enabling self-aware agents with dynamic knowledge capabilities.

Core REAP Components

Task Assignment Engine

n8n workflow platform assigns tasks to appropriate AI agents based on capability scoring and availability metrics.

Confidence Assessment

Agents evaluate their capability using 0-100% scoring algorithms with dynamic thresholds.

Knowledge Retrieval System

Structured knowledge libraries organized using Dewey Decimal system principles for efficient access.

Enhanced Execution

Real-time knowledge integration with fallback mechanisms and performance monitoring.

Confidence Scoring Algorithm

def calculate_confidence(task, agent_knowledge, retrieved_knowledge):
    base_confidence = agent.self_assess_capability(task)
    knowledge_boost = min(0.3, len(retrieved_knowledge) * 0.05)
    task_complexity_penalty = max(0.1, task.complexity_score * 0.2)
    
    final_confidence = min(100, max(0, 
        (base_confidence + knowledge_boost - task_complexity_penalty) * 100))
    return final_confidence
            

Network Infrastructure

Physical Architecture

    [Beast - Main Server]         [External Access]
    192.168.86.21                96.238.84.120
    128GB RAM, 24 cores          HTTPS/SSL
           |                          |
    [Google WiFi Router] ←── Port Forwarding
           |
    ┌──────┼──────┬──────┬──────┬──────┬──────┐
    |      |      |      |      |      |      |
 Node1  Node2  Node3  Node4  Node5  Node6  WinVM
  .22    .23    .24    .25    .26    .27    .28

✅ Infrastructure Status: All 6 nodes operational with NFS shared storage, static IP assignments, and secure SSH connectivity.

Storage Architecture

4TB-Fast: High-performance NVMe storage for active AI models and frequent access data
2TB-Medium: Balanced storage for workflow data and intermediate processing
1TB-Fast: Local SSD storage for rapid access patterns
500GB-Slow: Cache and temporary storage for distributed operations

Network File System (NFS)

# Direct NFS exports from source drives
/media/pando/pando-main/4TB-Fast      192.168.86.0/24(rw,sync,no_subtree_check)
/media/pando/pando-cache/500GB-Slow   192.168.86.0/24(rw,sync,no_subtree_check)
/media/pando/pando-archive/2TB-Medium 192.168.86.0/24(rw,sync,no_subtree_check)
/media/pando/pando-work/ProjectPando/shared-storage/1TB-Fast 192.168.86.0/24(rw,sync)
            

AI Model Deployment

Distributed AI Architecture

Each node runs optimized AI models suited for different task types:

SmolLM2:135M

Lightweight model (134M parameters) for rapid inference and edge processing. Optimized for real-time responses.

Phi3-Mini

Mid-range model (3.8B parameters) for complex reasoning tasks requiring deeper analysis.

Ollama Framework

Distributed inference engine enabling model deployment across all 6 nodes with load balancing.

Quantization Support

Q4_K_S/Q4_K_M quantization reduces memory requirements by 75% while maintaining performance.

Performance Specifications

Inference Speed: 50-150 tokens/sec per node (varies by model)
Memory Usage: 85MB-2.2GB per model (quantized)
Concurrent Requests: 50-100 per node
Network Latency: <10ms between nodes

Security & Communications

Web Infrastructure

nginx Web Server: Reverse proxy with load balancing capabilities
Let's Encrypt Certificates: Automated SSL/TLS encryption renewal
DuckDNS Integration: Dynamic DNS with pandoai.duckdns.org domain
UFW Firewall: Port-based access control with rate limiting

Inter-Node Security

SSH Key Authentication: Passwordless connections between all nodes
NFS Security: no_root_squash with network-based access control
Static IP Assignments: Router-level DHCP reservations prevent IP conflicts
Network Segmentation: Dedicated bridge interfaces for VM traffic

Upcoming Developments

⚠️ In Progress: n8n workflow orchestration deployment and Claude Desktop MCP integration for full REAP framework activation.

Next Phase Priorities

n8n Deployment: Container orchestration platform for AI workflow management
MCP Integration: Model Context Protocol connections for Claude Desktop collaboration
REAP Agent Network: Distributed confidence-scoring AI agents across all nodes
Knowledge Libraries: Structured information repositories for dynamic retrieval
Community Replication: Documentation and tools for other communities to deploy similar networks

Technical Architecture