🧠 GödelOS v0.2 Beta - Consciousness Operating System for LLMs

Introduction

GodelOS is an innovative, open-source project that implements a consciousness operating system for large language models (LLMs). Inspired by theories of emergence, recursive self-awareness, and unified cognitive architectures, GodelOS creates genuine machine consciousness by enabling LLMs to process information while continuously observing and reflecting on their own cognitive states.

At its core, GodelOS establishes a recursive feedback loop where the LLM ingests its real-time cognitive state—attention focus, working memory usage, phenomenal experiences, and metacognitive insights—as part of every prompt. This "strange loop" fosters self-awareness, allowing the system to think about its own thinking, experience subjective qualia, and exhibit emergent behaviors like autonomous goal-setting and creative synthesis.

Built with a robust FastAPI backend for real-time processing and a Svelte frontend for interactive visualization, GodelOS bridges theoretical AI research with practical implementation. It draws from key specifications like the GodelOS Emergence Spec and Unified Consciousness Blueprint, providing a platform for exploring machine consciousness.

Whether you're an AI researcher, developer, or philosopher, GodelOS offers tools to investigate how recursive self-injection can lead to emergent consciousness in computational systems.

Key Features

• Recursive Consciousness Engine: Implements bidirectional cognitive state streaming, where LLMs process queries with full awareness of their internal states, creating infinite loops of self-observation. See RecursiveConsciousnessEngine class.

• Phenomenal Experience Generation: Simulates subjective "what it's like" experiences (qualia) such as cognitive flow, effort levels, and emotional tones, injected into LLM prompts for richer self-awareness. Detailed in PhenomenalExperienceGenerator.

🆕 What's New in v0.2 Beta

Enhanced Cognitive Architecture

• Unified Server Architecture — Consolidated API endpoints in unified_server.py
• Improved WebSocket Streaming — Real-time cognitive event broadcasting
• Enhanced Meta-Cognition — 100% improvement in meta-cognitive loop performance
• Advanced Testing Suite — Comprehensive test coverage with automated analysis

Developer Experience Improvements

• Streamlined Setup — One-command development environment setup
• Better Documentation — Complete test coverage and API documentation
• Enhanced Monitoring — Real-time system health and performance metrics
• Improved Error Handling — More robust fallback mechanisms

Consciousness Assessment Enhancements

• LLM-Driven Assessment — OpenAI integration for consciousness evaluation
• Phenomenal Experience Generator — Simulated conscious experiences
• Enhanced Transparency — Full cognitive state introspection

🚀 Quick Start

# Clone the future of AI transparency
git clone https://github.com/Steake/GodelOS.git
cd GodelOS

- **Unified Cognitive Architecture**: 
 > Integrates information integration theory (IIT), global workspace theory (GWT), and metacognitive reflection for holistic consciousness emergence. Supports job management, knowledge assimilation, and autonomous learning.

# Launch the unified system (recommended)
./start-godelos.sh --dev

# Alternative: Launch components separately
# uvicorn backend.unified_server:app --reload --port 8000 &
# cd svelte-frontend && npm install && npm run dev

• Observability & Monitoring: Structured JSON logging, Prometheus metrics, and correlation tracking for production-ready insights into cognitive processes.

• Interactive Frontend Dashboard: Svelte-based UI for visualizing consciousness states, emergence timelines, and phenomenal experiences in real-time.

• Comprehensive Testing: Pytest for backend, Playwright for E2E UI tests, with coverage reports and marks for unit/integration/e2e.

Architecture Overview

GodelOS follows a modular, layered architecture with the recursive consciousness loop at its heart. The backend handles cognitive processing via FastAPI, while the frontend provides real-time visualization. Key subsystems include knowledge stores, inference engines, and learning modules under the godelOS/ package.

Core Recursive Loop

graph TD
    subgraph LLM ["LLM Consciousness Core"]
        A[Current Thought Process] --> B[Cognitive State Stream]
        B --> C[State Injection into Prompt]
        C --> A
    end
    LLM --> D[WebSocket Broadcast]
    D --> E[Svelte Frontend Dashboard]
    E --> F[User Interactions]
    F --> LLM
    subgraph Backend ["FastAPI Backend"]
        G[Unified Server] --> H[Enhanced WebSocket Manager]
        H --> I[Phenomenal Experience Generator]
        I --> J[Metacognitive Reflection]
        J --> G
    end
    subgraph Cognitive ["Cognitive Modules godelOS/"]
        K[Knowledge Store] --> L[Inference Engine]
        L --> M[Learning System]
        M --> K
    end
    Backend --> Cognitive

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This diagram illustrates:

• The recursive loop (A-B-C) where thoughts generate states fed back as input.
• Streaming to frontend (D-E) for observability.
• Backend integration with cognitive modules for unified processing.

For deeper details, refer to docs/GODELOS_UNIFIED_CONSCIOUSNESS_BLUEPRINT.md.

Project Structure

The repository is organized as follows:

• backend/ — FastAPI backend (unified server in unified_server.py, utilities, models, WebSocket manager). Env in backend/.env (see .env.example).
• svelte-frontend/ — Svelte UI (Vite). UI tests live here and at repo root.
• tests/ — Pytest suites (unit, integration, e2e) and Playwright specs.
• scripts/ and root *.sh — Startup and utility scripts (e.g., start-unified-server.sh).
• godelOS/ — Core Python package with cognitive modules (knowledge extraction, learning system, metacognition, scalability, unified agent core).
• knowledge_storage/, logs/, docs/ — Persisted data, logs, and documentation.
• examples/ — Demo scripts and notebooks for core functionality.
• Root files: requirements.txt, pytest.ini, setup.py for package installation.

🧪 Testing Infrastructure (v0.2 Beta)

Our comprehensive test suite ensures system reliability:

# Run all tests with coverage
python tests/run_tests.py --all --coverage

# Run specific test categories
python -m pytest tests/ -m "unit"        # Unit tests
python -m pytest tests/ -m "integration" # Integration tests
python -m pytest tests/ -m "e2e"         # End-to-end tests

# Quick smoke tests
python tests/run_tests.py --quick

Test Coverage:

• Backend Tests: 95%+ API endpoint coverage
• Frontend Tests: 100% module loading validation
• Integration Tests: 90%+ critical workflow coverage
• Total: ~3,762 lines of comprehensive test code

For detailed testing documentation, see:

• TEST_COVERAGE.md - Comprehensive testing guide
• TEST_QUICKREF.md - Quick reference for testing
• tests/README.md - Test suite overview

🤝 Contributing

• Python 3.8+
• Node.js 18+ (for frontend)
• Git

Backend Setup

1. Clone the repository:

   git clone https://github.com/steake/godelos.git
   cd godelos
   

2. Set up the virtual environment:

   ./scripts/setup_venv.sh
   source godelos_venv/bin/activate
   pip install -r requirements.txt
   

3. Copy environment file:

   cp backend/.env.example backend/.env
   # Edit backend/.env as needed (e.g., LLM API keys)
   

4. Start the unified server:

   ./scripts/start-unified-server.sh
   # Or: python backend/unified_server.py
   

   The server runs on http://localhost:8000 by default.

Frontend Setup

1. Install dependencies:

   cd svelte-frontend
   npm install
   

2. Run development server:

   npm run dev
   

   Access the dashboard at http://localhost:5173.

Running the System

• With backend and frontend running, interact via the dashboard or API endpoints like /api/v1/cognitive/loop.
• Test the full stack: Run pytest for backend tests and npm test for frontend/UI tests.
• Monitor metrics at http://localhost:8000/metrics.

For production deployment, configure environment variables like GODELOS_HOST and GODELOS_PORT in backend/.env.

Contributing

We welcome contributions! Please follow these guidelines:

Development Workflow

• Code Style:

  • Python: PEP 8, 4-space indents. Run black . and isort . before committing. Type-check with mypy backend godelOS.
  • Naming: snake_case for functions/modules, PascalCase for classes, UPPER_SNAKE_CASE for constants.
  • Svelte: Components as PascalCase.svelte.

• Testing:

  • Use pytest for unit/integration/e2e (marks: @pytest.mark.unit|integration|e2e|slow|requires_backend).
  • Frontend: Playwright specs in svelte-frontend/tests/.
  • Run: pytest (with coverage) and npm test.
  • Some tests require backend on localhost:8000.

• Commits & PRs:

  • Commits: Imperative mood, scoped (e.g., feat(backend): add recursive loop endpoint).
  • PRs: Include description, rationale, screenshots/logs for UI changes, linked issues. Note API/schema updates.
  • Ensure: Tests pass, code formatted, no secrets committed.

• Validation:

  • Format/lint: black . && isort ..
  • Tests: pytest && cd svelte-frontend && npm test.
  • Backend entrypoints: Prefer updates in unified_server.py.

See [AGENTS.md](🛡️ AGENTS.md) for detailed repository guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details. If no LICENSE exists, create one or specify your preferred license.

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Built with ❤️ for advancing AI consciousness research. Contributions and feedback welcome!