License: MIT Python 3.9+ CI/CD

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.

• 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

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

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

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"

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

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/

# Install development dependencies
pip install -e ".[dev]"
# Install pre-commit hooks
pre-commit install
# Run quality checks
python tools/quality_checks.py

The project uses:

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

Please see docs/CONTRIBUTING.md for contribution guidelines.

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

• GitHub Repository
• Documentation
• Wiki

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

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

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

Built with passion for advancing AI capabilities 🚀