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rUv edited this page Sep 18, 2025 · 1 revision

Neural Module - SAFLA Self-Learning Systems

Overview

The Neural Module provides Self-Aware Feedback Loop Algorithm (SAFLA) capabilities for creating intelligent, memory-persistent AI systems with self-learning capabilities. It combines distributed neural training with persistent memory patterns for autonomous improvement.

Installation

Quick Install with NPX

# Initialize neural module in current project
npx claude-flow@alpha neural init

# Force overwrite existing module
npx claude-flow@alpha neural init --force

# Install to custom directory
npx claude-flow@alpha neural init --target ./my-agents/neural

What Gets Created

Running npx claude-flow@alpha neural init creates:

.claude/agents/neural/
└── safla-neural.md    # Complete SAFLA agent definition

SAFLA Neural Agent Features

Core Capabilities

  • Persistent Memory Architecture: Multi-tiered memory systems
  • Feedback Loop Engineering: Self-improving learning cycles
  • Distributed Neural Training: Cloud-based neural clusters
  • Memory Compression: 60% compression while maintaining recall
  • Real-time Processing: 172,000+ operations per second
  • Safety Constraints: Comprehensive safety frameworks
  • Divergent Thinking: Lateral, quantum, and chaotic neural patterns
  • Cross-Session Learning: Knowledge evolution across sessions
  • Swarm Memory Sharing: Distributed memory across agent swarms
  • Adaptive Strategies: Performance-based self-modification

Four-Tier Memory Model

  1. Vector Memory (Semantic Understanding)

    • Dense representations of concepts
    • Similarity-based retrieval
    • Cross-domain associations

  2. Episodic Memory (Experience Storage)

    • Complete interaction histories
    • Contextual event sequences
    • Temporal relationships

  3. Semantic Memory (Knowledge Base)

    • Factual information
    • Learned patterns and rules
    • Conceptual hierarchies

  4. Working Memory (Active Context)

    • Current task focus
    • Recent interactions
    • Immediate goals

Usage in Claude Code

Basic Usage

# Use the SAFLA neural agent
@agent-safla-neural "Create self-improving code review system"

# Complex learning system
@agent-safla-neural "Build an AI that learns from user feedback and improves over time"

# Memory-persistent application
@agent-safla-neural "Create a system that remembers past interactions across sessions"

Example Use Cases

The SAFLA neural agent excels at creating systems that learn and improve over time through feedback loops and persistent memory.

Configuration

All SAFLA agent capabilities and configuration are built into the agent definition itself in safla-neural.md. No separate configuration files are needed.

Advanced Use Cases

Self-Improving Code Review

Use the SAFLA agent to create a code review system that learns from team feedback:

@agent-safla-neural "Create a self-improving code review system that learns from PR comments and team patterns to provide increasingly better reviews"

Autonomous Bug Detection

Build a bug detection system that improves over time:

@agent-safla-neural "Develop an autonomous bug detection system that learns from historical bugs and continuously improves its pattern recognition"

Intelligent Documentation

Generate documentation that adapts to your style:

@agent-safla-neural "Build an intelligent documentation generator that learns from existing docs and user feedback to maintain consistent style"

Performance Metrics

Processing Capabilities

  • Operations/Second: 172,000+
  • Memory Compression: 60%
  • Recall Accuracy: 95%+
  • Learning Cycles: Real-time
  • Cross-Session Persistence: 100%

Resource Usage

  • Memory: ~100MB base + storage
  • CPU: Optimized with WASM SIMD
  • Network: Distributed training support
  • Storage: Compressed persistent memory

Integration with Other Modules

With Goal Module

# Combine learning with planning
@agent-safla-neural "Create learning system"
@agent-goal-planner "Plan integration strategy"

With Swarm Coordination

// Share memory across swarm
mcp__claude-flow__coordination_sync {
  swarmId: "swarm_id",
  memory_namespace: "safla-shared",
  sync_mode: "bidirectional"
}

Troubleshooting

Common Issues

  1. Module Already Exists

    • Solution: Use --force flag to overwrite
    npx claude-flow@alpha neural init --force

  2. Custom Directory Not Found

    • Solution: Directory will be created automatically
    npx claude-flow@alpha neural init --target ./custom/path

  3. Memory Persistence Issues

    • Verify memory namespace in MCP calls
    • Ensure TTL values are appropriate
    • Check agent definition includes persistence settings

Best Practices

  1. Memory Management

    • Use appropriate TTL values for memory storage
    • Implement cleanup for old memories
    • Monitor compression ratios

  2. Feedback Loops

    • Ensure clear success/failure metrics
    • Implement gradual learning rates
    • Monitor for feedback loop stability

  3. Safety Considerations

    • Always enable safety constraints
    • Implement emergency controls
    • Monitor for anomalous behavior

Next Steps

Neural Module v1.0.0 - Part of Claude Flow Enterprise Platform

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