OmniMind - Human-like Thinking AI System

OmniMind is an advanced AI system that mimics human cognitive processes through continuous thinking, learning, and reasoning capabilities. It provides a framework for building intelligent applications with human-like problem-solving abilities.

🌟 Features

• Continuous Thinking Engine: Background cognitive processing that generates thoughts, connections, and insights
• Adaptive Learning System: Learns from interactions and improves over time
• Multi-Model Support: Works with Ollama (local), OpenAI, and other LLM providers
• Modular Architecture: Clean, extensible design for easy customization
• Comprehensive Testing: 200+ tests covering unit, integration, security, and performance
• Production Ready: Pre-commit hooks, CI/CD pipeline, and quality gates

🚀 Quick Start

Prerequisites

• Python 3.9 or higher
• Ollama (for local models)
• Git

Installation

1. Clone the repository:

git clone https://github.com/prakashgbid/omnimind.git
cd omnimind

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment:

cp .env.example .env
# Edit .env with your configuration

4. Run setup script:

./setup_local.sh

Usage

Run OmniMind in interactive mode:

python omnimind.py

Process a specific task:

python omnimind.py "Create a web scraper in Python"

With options:

python omnimind.py --model llama3.2:3b --verbose "Explain quantum computing"

📁 Project Structure

omnimind/
├── src/                  # Source code
│   ├── core/            # Core OSA modules
│   │   ├── osa.py       # Main OSA implementation
│   │   ├── logger.py    # Logging utilities
│   │   └── modules/     # Core modules
│   │       ├── thinking.py   # Thinking engine
│   │       ├── learning.py   # Learning system
│   │       └── architecture_reviewer.py
│   ├── providers/       # LLM providers
│   ├── agents/          # Agent system
│   └── utils/           # Utilities
├── tests/               # Test suite
│   ├── unit/           # Unit tests
│   ├── integration/    # Integration tests
│   ├── security/       # Security tests
│   ├── performance/    # Performance tests
│   └── regression/     # Regression tests
├── tools/              # Development tools
├── docs/               # Documentation
├── web/                # Web interface
└── omnimind.py         # Main entry point

🧪 Testing

Run all tests:

pytest tests/

Run specific test categories:

pytest tests/unit/          # Unit tests
pytest tests/security/      # Security tests
pytest tests/performance/   # Performance tests

Run with coverage:

pytest --cov=src tests/

🔧 Development

Setup Development Environment

# Install development dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

# Run quality checks
python tools/quality_checks.py

Code Quality

The project uses:

• Black for code formatting
• Flake8 for linting
• MyPy for type checking
• Pytest for testing
• Pre-commit hooks for quality gates

🤝 Contributing

Please see docs/CONTRIBUTING.md for contribution guidelines.

📄 License

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

🔗 Links

• GitHub Repository
• Documentation
• Wiki

💡 Core Concepts

OmniMind implements several key cognitive concepts:

1. Continuous Thinking: Background processing that generates thoughts and connections
2. Pattern Recognition: Identifies and learns from patterns in data and interactions
3. Contextual Memory: Maintains context across conversations and tasks
4. Adaptive Learning: Improves performance based on feedback and experience
5. Multi-Model Reasoning: Combines insights from multiple AI models

⚡ Performance

• Supports concurrent task processing
• Memory-efficient with automatic cleanup
• Optimized for both local and cloud deployments
• Benchmarked for speed and resource usage

🛡️ Security

• Input validation and sanitization
• Protection against injection attacks
• Secure handling of API keys and credentials
• Regular security audits via automated testing

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Built with passion for advancing AI capabilities 🚀