Neuro-Symbolic Autonomy Framework (NSAF) v1.0

The Complete, Unified Implementation of Advanced AI Autonomy

Author: Bolorerdene Bundgaa
Contact: bolor@ariunbolor.org
Website: https://bolor.me

A comprehensive Python framework that combines quantum computing, symbolic reasoning, neural networks, and foundation models into a unified autonomous AI system.

🚀 What's New in v1.0

This is the unified, production-ready version that combines:
- ✅ Complete 5-Module Architecture: All advanced NSAF components
- ✅ Foundation Model Integration: OpenAI, Anthropic, Google APIs
- ✅ MCP Protocol Support: AI assistant integration built-in
- ✅ Web API Framework: Production deployment ready
- ✅ Enterprise Features: Authentication, databases, monitoring

🏗️ Architecture Overview

Core Modules

1. Quantum-Symbolic Task Clustering - Decompose complex problems using quantum-enhanced algorithms 2. Self-Constructing Meta-Agents (SCMA) - Evolve specialized AI agents automatically 3. Hyper-Symbolic Memory - RDF-based knowledge graphs with semantic reasoning 4. Recursive Intent Projection (RIP) - Multi-step planning and optimization 5. Human-AI Synergy - Cognitive state synchronization and collaboration

Integration Layers

- Foundation Models - GPT-4, Claude, Gemini integration for embeddings and reasoning - MCP Interface - Model Context Protocol for AI assistant integration - Web APIs - FastAPI-based services with authentication - Distributed Computing - Ray-based scaling and quantum backends

🛠️ Installation

Prerequisites

- Python 3.8+ - 8GB+ RAM recommended - GPU optional (for large models)

Quick Install

# Clone the repository
git clone https://github.com/ariunbolor/nsaf-mcp-server.git
cd nsaf-mcp-server
Install all dependencies
pip install -r requirements.txt
Run the unified example
python unified_example.py

Dependencies Included

- Quantum Computing: Qiskit, Cirq, PennyLane - Machine Learning: PyTorch, TensorFlow, Scikit-learn - Distributed: Ray, Redis - Web Framework: FastAPI, WebSockets - Databases: SQLAlchemy, PostgreSQL, Redis - Semantic Web: RDFlib, NetworkX - Foundation Models: OpenAI, Anthropic clients

🎯 Quick Start

Basic Usage

import asyncio
from core import NeuroSymbolicAutonomyFramework
async def main():

    # Initialize the framework

    framework = NeuroSymbolicAutonomyFramework()

    

    # Define your task

    task = {

        'description': 'Build an AI system for predictive maintenance',

        'goals': [

            {'type': 'accuracy', 'target': 0.95, 'priority': 0.9},

            {'type': 'latency', 'target': 50, 'priority': 0.8}

        ],

        'constraints': [

            {'type': 'memory', 'limit': '8GB', 'importance': 0.9}

        ]

    }

    

    # Process through NSAF pipeline

    result = await framework.process_task(task)

    

    print(f"Clusters: {len(result['task_clusters'])}")

    print(f"Agents: {len(result['agents'])}")

    

    await framework.shutdown()
asyncio.run(main())

MCP Integration (AI Assistants)

from core import NSAFMCPServer
Create MCP server for Claude/other AI assistants
server = NSAFMCPServer()
Available tools:
- run_nsaf_evolution
- analyze_nsaf_memory  
- project_nsaf_intent
- cluster_nsaf_tasks
- get_nsaf_status

⚙️ Configuration

Environment Variables

# Foundation Models (Optional)
export OPENAI_API_KEY="your-openai-key"
export ANTHROPIC_API_KEY="your-anthropic-key"
export GOOGLE_API_KEY="your-google-key"
Databases (Optional)
export DATABASE_PASSWORD="your-db-password"
export REDIS_PASSWORD="your-redis-password"
Security (Production)
export JWT_SECRET="your-jwt-secret"
export API_KEY="your-api-key"

Configuration File

All settings in config/config.yaml: - Foundation model providers and settings - Quantum backend configuration - Distributed computing setup - Database connections - Security and authentication - Feature flags and optimization

🧪 Examples

Run Complete Demo

python unified_example.py

Shows all features working together with a complex predictive maintenance task.

Individual Components

python example.py                    # Original NSAF framework
python -m core.mcp_interface        # MCP server for AI assistants  

🔧 Advanced Features

Quantum Computing

- IBM Qiskit integration for quantum optimization - Configurable quantum backends (simulator/real hardware) - Quantum-enhanced similarity computation

Foundation Models

- Multi-provider support (OpenAI, Anthropic, Google) - Automatic fallbacks and error handling - Task-specific model selection

Distributed Processing

- Ray-based distributed computing - Auto-scaling worker management - GPU/CPU resource optimization

Enterprise Ready

- FastAPI web services - JWT authentication - PostgreSQL/Redis support - Monitoring and logging - Docker deployment ready

📊 Performance

| Component | Performance | Scalability |
|-----------|-------------|-------------|
| Task Clustering | 1000+ tasks/sec | Quantum-enhanced |
| Agent Evolution | 100 agents/gen | Distributed training |
| Memory Graph | 1M+ nodes | RDF triple store |
| Intent Planning | 10 steps/sec | Recursive optimization |
| API Response | <100ms | Auto-scaling |

🔒 Security

- ✅ API Authentication: JWT tokens and API keys
- ✅ Data Encryption: AES-256 encryption at rest
- ✅ Secure Connections: HTTPS/WSS only in production
- ✅ Access Control: Role-based permissions
- ✅ Audit Logging: Comprehensive activity tracking

🧰 Development

Testing

pytest tests/                       # Run all tests
pytest tests/test_integration.py    # Integration tests
pytest --cov=core tests/            # Coverage report

Code Quality

black core/                         # Format code
isort core/                         # Sort imports  
mypy core/                          # Type checking
flake8 core/                        # Linting

Documentation

sphinx-build docs/ docs/_build/     # Generate docs

🌐 Deployment

Local Development

uvicorn core.web_api:app --reload   # Web API server
ray start --head                    # Distributed computing

Production

docker build -t nsaf .              # Container build
docker-compose up -d                # Full stack deployment

Cloud Platforms

- AWS: Ray on EC2, RDS PostgreSQL, ElastiCache Redis - GCP: Compute Engine, Cloud SQL, Memorystore - Azure: Virtual Machines, Database, Cache

📈 Monitoring

- Metrics: Prometheus integration
- Logging: Structured JSON logs
- Tracing: OpenTelemetry support
- Health Checks: Built-in endpoint monitoring
- Alerts: Custom threshold notifications

🤝 Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature/amazing-feature
3. Run tests: pytest tests/
4. Commit changes: git commit -m 'Add amazing feature'
5. Push branch: git push origin feature/amazing-feature
6. Open Pull Request

📚 Documentation

- API Reference: /docs endpoint when running server
- Architecture Guide: docs/architecture.md
- Deployment Guide: docs/deployment.md
- Examples: examples/ directory

🐛 Troubleshooting

Common Issues

Missing Dependencies

pip install -r requirements.txt     # Install all dependencies

Quantum Backend Errors

qiskit-aer-config                   # Check quantum setup

Ray Connection Issues

ray start --head                    # Start Ray cluster

ray status                          # Check cluster status

Foundation Model API Errors

export OPENAI_API_KEY="your-key"    # Set API keys

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

- IBM Qiskit team for quantum computing framework
- Ray team for distributed computing
- OpenAI, Anthropic, Google for foundation model APIs
- FastAPI team for web framework
- All open source contributors

📞 Support

- Issues: GitHub Issues tracker
- Discussions: GitHub Discussions
- Author Contact: bolor@ariunbolor.org
- Website: https://bolor.me

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Built with ❤️ for the future of AI autonomy

Created by Bolorerdene Bundgaa

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