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GEMINI.md - RAVANA AGI Project

Project Overview

This project, RAVANA AGI, is a Python-based autonomous, evolving agentic system. The goal is to create an AI that can run continuously, build and recall memory, model emotions, and self-improve over time. It is designed as a research platform to explore the concepts of autonomous general intelligence.

The architecture is modular, with a clear separation of concerns:

  • core/: Contains the main orchestration, state management, and other core services.
  • modules/: Houses plug-and-play intelligence modules like reflection, experimentation, and conversational AI.
  • services/: Provides abstractions for memory, knowledge, and other high-level services.
  • database/: Manages data persistence with schemas and a database engine.
  • main.py: The main entry point for the application.

Building and Running

1. Environment Setup:

Create a virtual environment and install the required dependencies.

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Note: The requirements.txt file is currently empty. You may need to install dependencies manually based on the imports in the source code.

2. Running the System:

The main entry point is main.py. You can run it directly to start the autonomous loop:

python main.py

3. Running Specific Modes:

The application supports several command-line arguments to run in different modes:

  • Run a specific physics experiment: 
    python main.py --physics-experiment "<experiment_name>"
  • Run in discovery mode: 
    python main.py --discovery-mode
  • Run experiment tests: 
    python main.py --test-experiments
  • Run a single task: 
    python main.py --single-task "<task_prompt>"

Development Conventions

  • Configuration: The core configuration is managed in core/config.py. It uses environment variables to allow for flexible configuration.
  • State Management: The system's state is managed by the StateManager class in core/state_manager.py. It handles saving, loading, and validating the state.
  • Modularity: The project is highly modular, with different functionalities separated into different modules and services. This makes it easy to extend and maintain.
  • Logging: The project uses the logging module for logging. The log format and level can be configured in core/config.py.
  • Signal Handling: The application has robust signal handling for graceful shutdown, which is implemented in main.py.