NSAF MCP Server

# Installation Guide for NSAF Prototype This guide provides instructions for installing and setting up the Neuro-Symbolic Autonomy Framework (NSAF) prototype. ## Prerequisites - Python 3.8 or higher - pip (Python package installer) - Virtual environment (recommended) ## Installation Steps ### 1. Clone the Repository ```bash git clone https://github.com/yourusername/nsaf_prototype.git cd nsaf_prototype ``` ### 2. Create a Virtual Environment (Recommended) ```bash # Create a virtual environment python -m venv venv # Activate the virtual environment # On Windows: venv\Scripts\activate # On macOS/Linux: source venv/bin/activate ``` ### 3. Install the Package #### Option 1: Install in Development Mode This option is recommended for development as it allows you to modify the code without reinstalling. ```bash pip install -e . ``` #### Option 2: Install from Requirements File ```bash pip install -r requirements.txt ``` ### 4. Verify Installation Run the tests to verify that the installation was successful: ```bash pytest tests/ ``` ## Running the Examples To run the example script: ```bash python main.py ``` This will demonstrate the Self-Constructing Meta-Agents (SCMA) component of the NSAF framework. ## Troubleshooting ### TensorFlow Installation Issues If you encounter issues with TensorFlow installation: 1. Make sure you have the latest pip version: ```bash pip install --upgrade pip ``` 2. For GPU support, ensure you have the appropriate CUDA and cuDNN versions installed. 3. Consider installing TensorFlow separately: ```bash pip install tensorflow ``` ### Other Issues If you encounter any other issues, please check the following: 1. Make sure all dependencies are installed: ```bash pip install -r requirements.txt ``` 2. Ensure your Python version is compatible (Python 3.8 or higher). 3. Check for any error messages and search for solutions in the TensorFlow or Python documentation. ## Next Steps After installation, you can: 1. Explore the example code in `main.py` 2. Read the documentation in the code comments 3. Modify the configuration parameters to experiment with different settings 4. Implement your own fitness functions and datasets