SAFLA - Self-Aware Feedback Loop Algorithm

A sophisticated AI/ML system implementing autonomous learning and adaptation with comprehensive safety mechanisms, hybrid memory architecture, meta-cognitive capabilities, and full Model Context Protocol (MCP) integration.

🚀 Overview

SAFLA is a production-ready autonomous AI system that combines advanced memory management, meta-cognitive reasoning, distributed orchestration, and safety validation. The system implements a multi-layered architecture for intelligent agents capable of self-awareness, continuous learning, and safe autonomous operation.

🌟 Key Features

🧠 Hybrid Memory Architecture: Multi-layered memory system with vector, episodic, semantic, and working memory
🤖 Meta-Cognitive Engine: Self-awareness, goal management, strategy selection, and adaptive learning
🔗 MCP Integration: Full Model Context Protocol support with 24 tools across 6 domains
🛡️ Safety & Validation: Comprehensive safety constraints, risk assessment, and rollback mechanisms
📊 Delta Evaluation: Formal quantification of system improvements across multiple dimensions
🔧 CLI Management System: Complete command-line interface for system administration and operations
📈 Benchmarking: Comprehensive performance monitoring and optimization tools

🎯 Novel Applications

SAFLA enables breakthrough applications in:

Autonomous Research Agents: Self-directed research with memory consolidation and knowledge building
Adaptive Learning Systems: Continuous improvement with safety-constrained self-modification
Distributed AI Orchestration: Multi-agent coordination via MCP protocol
Safe AI Development: Production-ready AI with built-in safety mechanisms and validation
Cognitive Computing: Meta-cognitive reasoning and self-aware decision making
Enterprise AI Integration: Seamless integration with existing systems via MCP

🏆 Benefits

Production Ready: Comprehensive testing, benchmarking, and safety validation
Scalable Architecture: Distributed design supporting enterprise-scale deployments
Safety First: Built-in constraints, monitoring, and emergency stop mechanisms
Easy Integration: Comprehensive CLI tools, Python SDK, and MCP protocol support
Performance Optimized: Advanced memory management and optimization algorithms
Extensible Design: Modular architecture supporting custom components and integrations

🏗️ Architecture

Core Components

1. Hybrid Memory Architecture

Vector Memory: High-dimensional vector storage with similarity search (cosine, euclidean, dot product, manhattan)
Episodic Memory: Sequential experience storage with temporal indexing and event clustering
Semantic Memory: Knowledge graph implementation with nodes, edges, and relationship mapping
Working Memory: Active context management with attention mechanisms and temporal decay
Memory Consolidation: Automated transfer between memory types with importance weighting

2. Meta-Cognitive Engine

Self-Awareness Module: System state monitoring and introspective capabilities
Goal Manager: Dynamic goal setting, tracking, adaptation, and conflict resolution
Strategy Selector: Context-aware strategy selection and optimization with learning
Performance Monitor: Real-time performance tracking, alerting, and trend analysis
Adaptation Engine: Continuous learning and controlled self-modification

3. CLI Management System

Complete CLI Interface: Comprehensive command-line tools for all system operations
System Management: Start, stop, restart, status monitoring, and health diagnostics
Configuration Management: View, edit, backup, restore configuration with multiple formats
Real-time Monitoring: Live dashboards, metrics, logs, and performance monitoring
Optimization Tools: System analysis, automated optimizations, memory and cache tuning
Benchmarking Suite: Performance testing with quick, standard, and comprehensive modes
Agent Management: Deploy, scale, monitor, and manage agent instances
Interactive Features: TUI dashboard, setup wizard, and comprehensive help system

4. MCP Integration & SDK

24 Comprehensive Tools: Deployment, optimization, admin, testing, benchmarking, and agent interaction
15 Real-time Resources: Configuration, status, metrics, logs, and system information
Full Protocol Support: JSON-RPC 2.0 compliant MCP server with stdio communication
JWT Authentication: Secure token-based authentication with role-based access control
Python SDK: Easy-to-use Python API for programmatic access and integration

5. Safety & Validation Framework

Safety Constraints: Hard and soft limits with configurable violation actions
Validation Pipeline: Multi-stage validation with timeout and error handling
Risk Assessment: Quantitative risk scoring with weighted factor analysis
Rollback Mechanisms: Safe reversion to previous system states via checkpoints
Safety Monitoring: Real-time monitoring with configurable alert thresholds

6. Delta Evaluation System

Performance Delta: Reward improvements per token with historical tracking
Efficiency Delta: Throughput improvements per resource with multi-resource support
Stability Delta: Divergence-based stability measurement with trend analysis
Capability Delta: New capabilities acquisition tracking relative to total capability space
Adaptive Weighting: Context-aware weight adjustment for different operational priorities

📁 Project Structure

SAFLA/
├── safla/                    # Main package
│   ├── core/                 # Core system components
│   │   ├── hybrid_memory.py     # Hybrid memory architecture
│   │   ├── meta_cognitive_engine.py  # Meta-cognitive engine
│   │   ├── mcp_orchestration.py     # MCP orchestration
│   │   ├── safety_validation.py     # Safety validation framework
│   │   └── delta_evaluation.py      # Delta evaluation system
│   ├── mcp/                  # MCP server and handlers
│   ├── utils/                # Utilities and helpers
│   ├── cli_manager.py        # Main CLI structure
│   ├── cli_implementations.py # CLI command implementations
│   ├── cli_interactive.py    # Interactive CLI components
│   └── cli_main.py          # CLI entry point
├── tests/                    # Test suite
│   ├── test_cli_comprehensive.py # CLI test suite
│   └── integration/          # Integration tests
├── docs/                     # Documentation
├── examples/                 # Usage examples
├── CLI_USAGE_GUIDE.md       # Complete CLI documentation
└── benchmarks/              # Performance benchmarks

🚀 Quick Start

Installation

Option 1: Package Installation (Recommended)

# Install from PyPI
pip install safla

# Or install from source
pip install git+https://github.com/ruvnet/SAFLA.git

Option 2: Interactive Installation

For a guided installation experience with rich UI:

# Install the package first
pip install safla

# Run the interactive installer
safla-install

The interactive installer provides:

System requirements validation
Dependency checking
Configuration setup
Progress tracking with rich UI
Installation verification

Option 3: Development Installation

# Clone the repository
git clone https://github.com/ruvnet/SAFLA.git
cd SAFLA

# Install in development mode
pip install -e .

# Or install dependencies manually
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

Requirements

Python 3.8 or higher
Operating System: Windows, macOS, or Linux
Memory: Minimum 512MB RAM
Disk Space: At least 100MB free space

📁 Project Structure

SAFLA follows a well-organized project structure to maintain clarity and ease of development:

SAFLA/
├── safla/                    # Core SAFLA package
│   ├── core/                 # Core system components
│   ├── mcp/                  # MCP server implementation
│   ├── utils/                # Utility functions
│   └── cli/                  # Command-line interface
├── development/              # Development utilities and tools
│   ├── debug_config.json     # Debug configuration
│   ├── demo_script.py        # Demo and example scripts
│   ├── fix_*.py             # Bug fixes and patches
│   └── implementation_*.md   # Implementation summaries
├── testing/                  # Test files and results
│   ├── test_*.py            # Python test files
│   ├── test_*.js            # JavaScript test files
│   ├── benchmark_*.json     # Benchmark results
│   └── validation_*.md      # Validation reports
├── config/                   # Configuration files
│   ├── *.json               # Environment configurations
│   └── sample.env           # Sample environment file
├── integration/              # MCP integration components
│   ├── mcp_integration.*    # MCP integration files
│   └── *.md                 # Integration documentation
├── benchmarks/               # Performance benchmarking
├── data/                     # Data files and datasets
├── docs/                     # Documentation
├── examples/                 # Usage examples
├── memory_bank/              # Memory system data
├── plans/                    # Project planning documents
├── research/                 # Research and analysis
├── scripts/                  # Utility scripts
├── tests/                    # Main test directory
└── requirements.txt          # Python dependencies

Key Directories

safla/: The main Python package containing all core functionality
development/: Development utilities, debug configs, and implementation tools
testing/: Comprehensive test files, benchmark results, and validation reports
config/: Configuration files for different environments and setups
integration/: MCP integration components and related documentation
benchmarks/: Performance benchmarking tools and results
docs/: Comprehensive documentation and guides
examples/: Usage examples and sample implementations

Basic Usage

Python SDK

from safla.core.hybrid_memory import HybridMemoryArchitecture
from safla.core.meta_cognitive_engine import MetaCognitiveEngine
from safla.core.safety_validation import SafetyValidationFramework

# Initialize core components
memory = HybridMemoryArchitecture()
meta_engine = MetaCognitiveEngine()
safety_framework = SafetyValidationFramework()

# Start the system
await memory.start()
await meta_engine.start()
await safety_framework.start()

# Store and retrieve memories
memory_id = await memory.store_vector_memory(
    content="Example content",
    embedding=[0.1, 0.2, 0.3, ...],  # 512-dimensional vector
    metadata={"type": "example", "timestamp": time.time()}
)

# Retrieve similar memories
similar_memories = await memory.search_similar_memories(
    query_embedding=[0.1, 0.2, 0.3, ...],
    top_k=5,
    similarity_threshold=0.8
)

CLI Interface

SAFLA includes a comprehensive command-line interface for complete system management:

# System Management
python safla/cli_main.py system status                    # Show system health
python safla/cli_main.py system start                     # Start all components
python safla/cli_main.py system stop                      # Stop system
python safla/cli_main.py system validate                  # Validate installation

# Configuration Management
python safla/cli_main.py config show                      # Display configuration
python safla/cli_main.py config set SAFLA_DEBUG true     # Set configuration
python safla/cli_main.py config backup                    # Backup configuration
python safla/cli_main.py config edit                      # Edit in preferred editor

# Monitoring & Metrics
python safla/cli_main.py monitor live                     # Live monitoring dashboard
python safla/cli_main.py monitor logs --follow            # Follow system logs
python safla/cli_main.py monitor metrics --detailed       # Show detailed metrics
python safla/cli_main.py monitor performance              # Performance monitoring

# Optimization & Benchmarking
python safla/cli_main.py optimize analyze --auto          # Auto-apply optimizations
python safla/cli_main.py optimize memory                  # Optimize memory usage
python safla/cli_main.py benchmark run --suite comprehensive  # Run benchmarks
python safla/cli_main.py benchmark stress                 # Stress testing

# Agent Management
python safla/cli_main.py agents list                      # List deployed agents
python safla/cli_main.py agents deploy my-agent --replicas 3  # Deploy agent
python safla/cli_main.py agents scale my-agent --replicas 5   # Scale agent
python safla/cli_main.py agents logs my-agent --follow    # View agent logs

# Interactive Features
python safla/cli_main.py dashboard                        # Launch TUI dashboard
python safla/cli_main.py setup                           # Interactive setup wizard
python safla/cli_main.py doctor                          # System health diagnostics
python safla/cli_main.py search memory                   # Search commands/docs

# Utility Commands
python safla/cli_main.py version --format json           # Version information
python safla/cli_main.py help-menu                       # Comprehensive help

CLI Command Groups

system - System management and operations (start, stop, status, validate)
config - Configuration management (show, set, edit, backup, restore)
monitor - Real-time monitoring (live dashboard, logs, metrics, performance)
optimize - System optimization (analyze, apply, memory, cache)
benchmark - Performance testing (run suites, component tests, stress tests)
agents - Agent management (list, deploy, scale, remove, logs)
dashboard - Interactive TUI dashboard with real-time updates
setup - Interactive system setup wizard
doctor - Comprehensive system health check and diagnostics
version - System and component version information
search - Search commands, settings, and documentation

🔧 CLI Management System

SAFLA includes a comprehensive command-line interface that provides complete system administration and operational control. The CLI supports multiple output formats (table, JSON, YAML), interactive features, and automation-friendly commands.

Quick CLI Start

# Get help and available commands
python safla/cli_main.py --help

# Check system status
python safla/cli_main.py system status

# Launch interactive dashboard
python safla/cli_main.py dashboard

# Run setup wizard for first-time configuration
python safla/cli_main.py setup

CLI Features & Capabilities

🖥️ System Management

Complete lifecycle management of SAFLA components:

Status monitoring with health checks and component details
Service control (start, stop, restart) for individual components or full system
Installation validation with comprehensive dependency checking
System diagnostics with the built-in doctor command

⚙️ Configuration Management

Flexible configuration with multiple formats and backup/restore:

View/edit configuration in YAML, JSON, or environment variable format
Hot configuration updates with immediate effect
Configuration backup/restore with timestamped snapshots
Environment-specific configs (development, production, testing)

📊 Real-time Monitoring

Live system monitoring with rich interfaces:

Interactive live dashboard with real-time updates and component status
Log streaming with filtering and component-specific views
Performance metrics with detailed system and component statistics
Performance monitoring with configurable duration and alerting

🚀 Optimization & Performance

Automated and manual system optimization:

Performance analysis with auto-discovery of optimization opportunities
Targeted optimizations (memory, cache, CPU) with impact assessment
Benchmark suites (quick, standard, comprehensive) with detailed reporting
Stress testing with configurable load levels and duration

🤖 Agent Management

Complete agent lifecycle management:

Agent deployment with custom configurations and resource requirements
Scaling operations with horizontal scaling and resource adjustment
Health monitoring with status tracking and log access
Multi-agent orchestration with centralized management

🎛️ Interactive Features

Rich interactive experiences for complex operations:

TUI Dashboard - Full-featured terminal UI with live updates (requires Textual)
Setup Wizard - Guided configuration for first-time setup
Health Diagnostics - Comprehensive system analysis with detailed reporting
Command Search - Built-in help system with command and setting search

CLI Output Formats

The CLI supports multiple output formats for automation and integration:

# Table format (default, human-readable)
python safla/cli_main.py system status

# JSON format (for automation/parsing)
python safla/cli_main.py system status --format json

# YAML format (for configuration files)
python safla/cli_main.py config show --format yaml

Automation & Scripting

The CLI is designed for automation with:

Exit codes for success/failure detection
JSON output for parsing and integration
Non-interactive modes with --quiet flag
Configuration via environment variables
Batch operations for multiple commands

Example automation script:

#!/bin/bash
# Health monitoring script
STATUS=$(python safla/cli_main.py system status --format json | jq -r '.health')
if [ "$STATUS" != "healthy" ]; then
    echo "System unhealthy, restarting..."
    python safla/cli_main.py system restart
fi

Complete CLI Reference

For detailed usage of all commands, options, and examples, see the CLI Usage Guide.

🔗 MCP Integration

SAFLA provides comprehensive Model Context Protocol integration with 24 tools across 6 domains:

Available Tools

Core System (4 tools)

validate_installation - Validate SAFLA installation and configuration
get_system_info - Get comprehensive system information and status
check_gpu_status - Check GPU availability and CUDA status
get_config_summary - Get SAFLA configuration summary

Deployment (3 tools)

deploy_safla_instance - Deploy new SAFLA instances with custom configurations
check_deployment_status - Monitor deployment health and resource usage
scale_deployment - Scale deployment resources (CPU/memory)

Optimization (3 tools)

optimize_memory_usage - Optimize memory usage with configurable levels
optimize_vector_operations - Optimize vector operations with GPU acceleration
analyze_performance_bottlenecks - Analyze system performance and identify bottlenecks

Administration (4 tools)

manage_user_sessions - Manage user sessions (list, create, delete, suspend)
backup_safla_data - Create compressed backups of SAFLA data
restore_safla_data - Restore SAFLA data from backups with integrity verification
monitor_system_health - Monitor system health with configurable alerts

Testing (3 tools)

run_integration_tests - Run comprehensive integration test suites
validate_memory_operations - Validate memory operations and data integrity
test_mcp_connectivity - Test MCP protocol connectivity and compliance

Benchmarking (3 tools)

benchmark_vector_operations - Benchmark vector operations performance
benchmark_memory_performance - Benchmark memory subsystem performance
benchmark_mcp_throughput - Benchmark MCP protocol throughput and latency

Agent Interaction (4 tools)

create_agent_session - Create new agent interaction sessions
interact_with_agent - Send commands to agent sessions
list_agent_sessions - List active/inactive agent sessions
terminate_agent_session - Terminate agent sessions

Available Resources

SAFLA provides 15 real-time resources for system monitoring and information:

safla://config - Current SAFLA configuration settings
safla://status - Current system status and health
safla://deployments - Information about SAFLA deployments
safla://deployment-templates - Available deployment configuration templates
safla://performance-metrics - Real-time performance metrics and statistics
safla://optimization-recommendations - AI-generated optimization recommendations
safla://system-logs - SAFLA system logs and audit trail
safla://user-sessions - Active user sessions and access information
safla://backup-status - Backup and restore operation status
safla://test-results - Latest test execution results and reports
safla://test-coverage - Code coverage and test quality metrics
safla://benchmark-results - Performance benchmark results and trends
safla://performance-baselines - Established performance baselines for comparison
safla://agent-sessions - Active agent interaction sessions
safla://agent-capabilities - Available agent types and their capabilities

MCP Configuration

Add SAFLA to your MCP configuration (.roo/mcp.json):

{
  "mcpServers": {
    "safla": {
      "command": "python",
      "args": ["safla/mcp_stdio_server.py"],
      "alwaysAllow": [
        "validate_installation",
        "get_system_info",
        "check_gpu_status",
        "get_config_summary",
        "deploy_safla_instance",
        "check_deployment_status",
        "scale_deployment",
        "optimize_memory_usage",
        "optimize_vector_operations",
        "analyze_performance_bottlenecks",
        "manage_user_sessions",
        "backup_safla_data",
        "restore_safla_data",
        "monitor_system_health",
        "run_integration_tests",
        "validate_memory_operations",
        "test_mcp_connectivity",
        "benchmark_vector_operations",
        "benchmark_memory_performance",
        "benchmark_mcp_throughput",
        "create_agent_session",
        "interact_with_agent",
        "list_agent_sessions",
        "terminate_agent_session"
      ],
      "timeout": 60
    }
  }
}

JWT Authentication

SAFLA MCP Server supports JWT authentication for secure access control:

Configuration

Set the following environment variables:

# Required for JWT authentication
export JWT_SECRET_KEY="your-secret-key-here"

# Optional (defaults shown)
export JWT_EXPIRATION_TIME=3600  # Access token expiration in seconds

Authentication Flow

Login to get tokens:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "auth/login",
  "params": {
    "username": "developer",
    "password": "dev123"
  }
}

Use token in requests:

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/list",
  "params": {
    "headers": {
      "Authorization": "Bearer <access_token>"
    }
  }
}

Demo Users

Username	Password	Role	Permissions
admin	admin123	admin	Full access
developer	dev123	developer	Read/write access
reader	read123	reader	Read-only access

See JWT Authentication Documentation for complete details.

Using MCP Tools

# Example: Using MCP tools programmatically
from safla.integrations import FastMCPClient

client = FastMCPClient()

# Validate installation
result = await client.call_tool("validate_installation", {})
print(f"Installation valid: {result['status'] == 'valid'}")

# Deploy new instance
deployment = await client.call_tool("deploy_safla_instance", {
    "instance_name": "production-safla",
    "environment": "production",
    "config_overrides": {
        "memory": {"max_memories": 50000},
        "safety": {"memory_limit": 2000000000}
    }
})

# Monitor system health
health = await client.call_tool("monitor_system_health", {
    "check_interval": 30,
    "alert_thresholds": {
        "memory_usage": 0.8,
        "cpu_usage": 0.9
    }
})

📊 Delta Evaluation

The system implements formal quantification of improvements using:

Δ_total = α₁ × Δ_performance + α₂ × Δ_efficiency + α₃ × Δ_stability + α₄ × Δ_capability

Where:

Δ_performance: (current_reward - previous_reward) / tokens_used
Δ_efficiency: (current_throughput - previous_throughput) / resource_used
Δ_stability: 1 - divergence_score (with trend analysis)
Δ_capability: new_capabilities / total_capabilities

from safla.core.delta_evaluation import DeltaEvaluator

evaluator = DeltaEvaluator()

# Evaluate system improvements
result = evaluator.evaluate_delta(
    performance_data={
        'current_reward': 0.92,
        'previous_reward': 0.85,
        'tokens_used': 1000
    },
    efficiency_data={
        'current_throughput': 150,
        'previous_throughput': 120,
        'resource_used': 0.8
    },
    stability_data={
        'divergence_score': 0.15
    },
    capability_data={
        'new_capabilities': 2,
        'total_capabilities': 10
    },
    context="performance_critical"
)

print(f"Total Delta: {result.total_delta}")
print(f"Improvement Detected: {result.is_improvement()}")

🛡️ Safety Features

Safety Constraints

from safla.core.safety_validation import SafetyConstraint, ConstraintType

# Define safety constraints
memory_constraint = SafetyConstraint(
    name="memory_limit",
    constraint_type=ConstraintType.HARD,
    description="Maximum memory usage limit",
    rule="memory_usage <= 1000000000",  # 1GB
    threshold=1000000000,
    violation_action="emergency_stop"
)

# Add to safety framework
safety_framework.constraint_engine.add_constraint(memory_constraint)

Risk Assessment

from safla.core.safety_validation import RiskFactor

# Define risk factors
def calculate_memory_risk(data):
    memory_usage = data.get('memory_usage', 0)
    return min(memory_usage / 1000000000, 1.0)  # Normalize to 0-1

memory_risk = RiskFactor(
    name="memory_risk",
    description="Risk based on memory usage",
    weight=0.3,
    calculator=calculate_memory_risk
)

safety_framework.risk_scorer.add_risk_factor(memory_risk)

🧠 Memory Management

Vector Memory Operations

# Store vector memories with different embedding dimensions
await memory.vector_memory.store_memory(
    content="Technical documentation",
    embedding_512=[...],  # 512-dimensional
    embedding_768=[...],  # 768-dimensional
    metadata={"type": "documentation", "domain": "technical"}
)

# Search with different similarity metrics
results = await memory.vector_memory.search_memories(
    query_embedding=[...],
    similarity_metric="cosine",  # or "euclidean", "dot_product", "manhattan"
    top_k=10,
    threshold=0.8
)

Episodic Memory

# Store episodic experiences
episode_id = await memory.episodic_memory.store_episode(
    content="User interaction session",
    context={"user_id": "123", "session_type": "support"},
    outcome="resolved",
    metadata={"duration": 300, "satisfaction": 0.9}
)

# Retrieve episodes by time range
episodes = await memory.episodic_memory.get_episodes_by_timerange(
    start_time=start_timestamp,
    end_time=end_timestamp
)

Semantic Memory

# Add knowledge to semantic memory
node_id = await memory.semantic_memory.add_node(
    content="Machine Learning",
    node_type="concept",
    properties={"domain": "AI", "complexity": "high"}
)

# Create relationships
await memory.semantic_memory.add_edge(
    source_id=node_id,
    target_id=other_node_id,
    relationship="is_related_to",
    weight=0.8
)

# Query knowledge graph
related_concepts = await memory.semantic_memory.get_related_nodes(
    node_id=node_id,
    relationship_type="is_related_to",
    max_depth=2
)

🔧 Configuration

Environment Variables

# Memory Configuration
SAFLA_VECTOR_DIMENSIONS=512,768,1024,1536
SAFLA_MAX_MEMORIES=10000
SAFLA_SIMILARITY_THRESHOLD=0.8

# Safety Configuration
SAFLA_MEMORY_LIMIT=1000000000
SAFLA_CPU_LIMIT=0.9
SAFLA_SAFETY_MONITORING_INTERVAL=1.0

# MCP Configuration
SAFLA_MCP_TIMEOUT=30
SAFLA_MCP_MAX_RETRIES=3
SAFLA_MCP_HEALTH_CHECK_INTERVAL=60

Configuration Templates

# Initialize different configuration templates
safla init-config --template minimal      # Basic configuration
safla init-config --template development # Development with debug enabled
safla init-config --template production  # Production-optimized settings

🧪 Testing

# Run all tests
python -m pytest tests/

# Run specific test suites
python -m pytest tests/test_hybrid_memory.py
python -m pytest tests/test_meta_cognitive.py
python -m pytest tests/test_safety_validation.py

# Test CLI functionality
python -m pytest tests/test_cli_comprehensive.py

# Run with coverage
python -m pytest --cov=safla tests/

# Test MCP integration
python test_comprehensive_mcp_server.py

# CLI-based system validation
python safla/cli_main.py system validate
python safla/cli_main.py doctor

🛠️ Utilities

SAFLA includes a comprehensive collection of utility scripts for system administration, testing, and development:

System Utilities

# Generate system status reports
python scripts/system_status_report.py

# Verify system installation and health
python scripts/verify_system.py

# Build and packaging utilities
python scripts/build.py

# Installation utilities
python scripts/install.py

Testing and Verification

# Comprehensive capability testing
python scripts/comprehensive_capability_test.py

# Final system verification
python scripts/final_capability_verification.py
python scripts/final_system_test.py

# Quick capability tests
python scripts/quick_capability_test.py

# Security testing
python scripts/minimal_security_test.py

Demo Programs

# JWT MCP client demonstration
python scripts/demo_jwt_mcp_client.py

Optimization Documentation

The optimization process and progress are documented in:

docs/optimization/optimization_plan.md - Comprehensive optimization strategy
docs/optimization/optimization_progress.md - Current progress tracking
docs/optimization/claude-flow-optimization-guide.md - Agent coordination guide

All utilities are designed to be run from the SAFLA root directory and require the SAFLA package to be installed or available in the Python path. See scripts/README.md for detailed information about each utility.

📊 Benchmarking & Performance

SAFLA includes a comprehensive benchmarking framework for measuring and tracking performance across all system components.

Running Benchmarks

# Run benchmark suites via CLI
python safla/cli_main.py benchmark run --suite quick
python safla/cli_main.py benchmark run --suite standard
python safla/cli_main.py benchmark run --suite comprehensive

# Component-specific benchmarks
python safla/cli_main.py benchmark component --component memory --iterations 1000
python safla/cli_main.py benchmark component --component cognition --iterations 500

# Stress testing
python safla/cli_main.py benchmark stress --duration 300 --load-level 0.8

# Export results
python safla/cli_main.py benchmark run --output benchmark_results.json

# Compare with previous results
python safla/cli_main.py benchmark run --compare previous_results.json

Available Benchmarks

The framework includes comprehensive benchmarks:

CLI Performance - Tests command response times and memory usage
Memory Operations - Benchmarks vector, episodic, and semantic memory performance
MCP Protocol - Tests MCP communication throughput and latency
Safety Validation - Benchmarks constraint checking and risk assessment
Delta Evaluation - Tests improvement quantification performance

Performance Targets

Current benchmark performance targets:

Component	Target Time	Current Performance
CLI Help	< 1.0s	~0.4s
CLI Version	< 0.5s	~0.4s
Memory Store	< 10ms	~5ms
Memory Search	< 50ms	~25ms
MCP Tool Call	< 100ms	~75ms
Safety Validation	< 5ms	~2ms

All benchmarks currently meet or exceed their performance targets with 100% success rate.

📚 API Reference

Core Classes

HybridMemoryArchitecture: Main memory management system
MetaCognitiveEngine: Meta-cognitive reasoning and adaptation
MCPOrchestrator: Distributed agent coordination
SafetyValidationFramework: Safety constraints and validation
DeltaEvaluator: System improvement quantification

Key Methods

Memory Operations

store_vector_memory(content, embedding, metadata): Store vector memory
search_similar_memories(query_embedding, top_k, threshold): Search similar memories
consolidate_memories(): Transfer memories between layers

Meta-Cognitive Operations

add_goal(description, priority, target_metrics): Add system goal
select_strategy(context, available_strategies): Select optimal strategy
monitor_performance(metrics): Monitor system performance

Safety Operations

validate_system_modification(data): Validate proposed changes
create_safety_checkpoint(name, description): Create system checkpoint
emergency_stop(reason): Trigger emergency stop

MCP Operations

call_tool(tool_name, arguments): Call MCP tool
read_resource(uri): Read MCP resource
list_tools(): List available tools
list_resources(): List available resources

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🧾 Credits

Created by rUv

SAFLA represents a comprehensive approach to autonomous AI systems with built-in safety, sophisticated memory management, meta-cognitive capabilities, and full MCP integration. The system demonstrates how advanced AI architectures can be implemented with proper safety constraints, validation mechanisms, and seamless protocol integration.

This README reflects the actual implementation of SAFLA as a sophisticated AI/ML system with comprehensive MCP integration, not a conceptual framework.

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
.roo-orginal		.roo-orginal
.roo		.roo
benchmarks		benchmarks
config		config
development		development
docs		docs
examples		examples
integration		integration
memory		memory
memory_bank/safla_research		memory_bank/safla_research
plans		plans
research		research
safla		safla
scripts		scripts
testing		testing
tests		tests
tutorial		tutorial
.env copy		.env copy
.env.example		.env.example
.gitignore		.gitignore
.roomodes		.roomodes
AGENT_CAPABILITIES.md		AGENT_CAPABILITIES.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
CLI_USAGE_GUIDE.md		CLI_USAGE_GUIDE.md
DEPLOYMENT_GUIDE.md		DEPLOYMENT_GUIDE.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
claude-flow		claude-flow
py.typed		py.typed
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
setup.cfg		setup.cfg
setup.py		setup.py

License

inde5media/SAFLA

Folders and files

Latest commit

History

Repository files navigation

SAFLA - Self-Aware Feedback Loop Algorithm

🚀 Overview

🌟 Key Features

🎯 Novel Applications

🏆 Benefits

🏗️ Architecture

Core Components

1. Hybrid Memory Architecture

2. Meta-Cognitive Engine

3. CLI Management System

4. MCP Integration & SDK

5. Safety & Validation Framework

6. Delta Evaluation System

📁 Project Structure

🚀 Quick Start

Installation

Option 1: Package Installation (Recommended)

Option 2: Interactive Installation

Option 3: Development Installation

Requirements

📁 Project Structure

Key Directories

Basic Usage

Python SDK

CLI Interface

CLI Command Groups

🔧 CLI Management System

Quick CLI Start

CLI Features & Capabilities

🖥️ System Management

⚙️ Configuration Management

📊 Real-time Monitoring

🚀 Optimization & Performance

🤖 Agent Management

🎛️ Interactive Features

CLI Output Formats

Automation & Scripting

Complete CLI Reference

🔗 MCP Integration

Available Tools

Core System (4 tools)

Deployment (3 tools)

Optimization (3 tools)

Administration (4 tools)

Testing (3 tools)

Benchmarking (3 tools)

Agent Interaction (4 tools)

Available Resources

MCP Configuration

JWT Authentication

Configuration

Authentication Flow

Demo Users

Using MCP Tools

📊 Delta Evaluation

🛡️ Safety Features

Safety Constraints

Risk Assessment

🧠 Memory Management

Vector Memory Operations

Episodic Memory

Semantic Memory

🔧 Configuration

Environment Variables

Configuration Templates

🧪 Testing

🛠️ Utilities

System Utilities

Testing and Verification

Demo Programs

Optimization Documentation

📊 Benchmarking & Performance

Running Benchmarks

Available Benchmarks

Packages