cybertarr-A/CyberVerseAI

# CyberVerse AI CyberVerse AI is an Autonomous Multi-Agent Security Intelligence Platform designed to automate code scanning, vulnerability analysis, and threat intelligence mapping. Powered by a robust FastAPI/Celery backend and a modern Next.js/Three.js frontend, it delivers an "Elite Engineer" dashboard for visualizing zero-day risks and structural code flaws. ## Features - **Multi-Agent Architecture**: Dedicated AI agents for Asset Intelligence, Security Analysis, Validation, and Reporting. - **Asynchronous Task Pipeline**: Built on Celery and Redis to handle deep codebase clones and intensive LLM analysis asynchronously. - **Elite Holographic Dashboard**: A Next.js frontend with Three.js rendering for real-time scan visualization. - **Security-First Architecture**: Integrated rate limiting, secure pseudo-random generators, subprocess isolation, and CWE/OWASP vulnerability mapping. - **Cloud-Ready**: Fully containerized and prepared for AWS and Vercel deployments. ## Local Development Setup To run CyberVerse AI locally on a Linux/macOS machine: ### Prerequisites - Python 3.10+ - Node.js 18+ - Redis (must be installed and running, or available via Docker) ### 1. Environment Configuration Duplicate the `.env.example` files (or create new `.env` files) in both the `backend/` and `frontend/` directories. **Backend (`backend/.env`):** APP_ENV=development # The backend will automatically generate a secure local SQLite DB and secret key if left blank in development. NVIDIA_API_KEY=your_nvidia_nim_api_key_here **Frontend (`frontend/.env.local`):** NEXT_PUBLIC_BACKEND_REST_URL=http://localhost:8090/api/v1 NEXT_PUBLIC_BACKEND_WS_URL=ws://localhost:8090/ws/scan ### 2. Install Dependencies **Backend:** cd backend python -m venv .venv source .venv/bin/activate pip install -r requirements.txt **Frontend:** ### 3. Launch the Stack (Locally) To run the full stack locally: 1. **Start Redis**: Ensure Redis is running locally on port `6379`. 2. **Start Backend API Server**: ```bash cd backend source .venv/bin/activate # Run migrations alembic upgrade head # Start Uvicorn python -m uvicorn app.main:app --host 0.0.0.0 --port 8090 3. **Start Celery Worker**: In a separate terminal window: cd backend source .venv/bin/activate python -m celery -A app.core.celery:celery_app worker --loglevel=info --concurrency=4 --prefetch-multiplier=1 4. **Start Frontend Dev Server**: In a separate terminal window: cd frontend npm run dev - **Frontend Dashboard**: `http://localhost:3000` - **Backend API Docs**: `http://localhost:8090/docs` ## Production Deployment (Railway & Vercel) CyberVerse AI is configured for a robust, production-grade cloud deployment leveraging **Vercel** for the frontend and **Railway** for the backend architecture. ### Target Architecture Frontend (Vercel) ↓ (HTTP / WebSocket) FastAPI Backend (Railway API Service) ├── Railway PostgreSQL ├── Railway Redis └── Celery Worker (Railway Worker Service) ### 1. Railway Services Provisioning 1. **Create a New Project** in Railway. 2. **Add Databases**: - Add a **PostgreSQL** service database. - Add a **Redis** service database. 3. **Add the FastAPI API Backend Service**: - Link the service to your GitHub repository. - Set the **Root Directory** to `backend`. - Railway will automatically detect the `backend/Dockerfile` and `backend/railway.json` configurations. - Add the required environment variables (see below). 4. **Add the Celery Worker Service**: - In the same Railway project, add another service linking to the same GitHub repository. - Set the **Root Directory** to `backend`. - Set the **Start Command** to: celery -A app.core.celery:celery_app worker --loglevel=info --concurrency=4 --prefetch-multiplier=1 - Add the required environment variables (see below). ### 2. Environment Variables Configuration Configure the following environment variables in the Railway dashboard: #### API Service (`backend`): - `APP_ENV`: `production` - `DATABASE_URL`: `${{Postgres.DATABASE_URL}}` (Injected automatically by linking services) - `REDIS_URL`: `${{Redis.REDIS_URL}}` (Injected automatically by linking services) - `SECRET_KEY`: A cryptographically secure random string (Generate via `openssl rand -hex 32`) - `NVIDIA_API_KEY`: Your Nvidia NIM API key (Mandatory in production) - `PREFERRED_LLM_PROVIDER`: `nvidia` - `CORS_ORIGINS`: `https://cyber-verse-ai.vercel.app` (Your custom Vercel domain) #### Celery Worker Service: - `APP_ENV`: `production` - `DATABASE_URL`: `${{Postgres.DATABASE_URL}}` - `REDIS_URL`: `${{Redis.REDIS_URL}}` - `SECRET_KEY`: A cryptographically secure random string (Matching the API service's key) - `NVIDIA_API_KEY`: Your Nvidia NIM API key - `PREFERRED_LLM_PROVIDER`: `nvidia` ### 3. Frontend Deployment (Vercel) 1. Import your GitHub repository to Vercel. 2. Link the `frontend` directory. 3. Configure the following build-time Environment Variables: - `NEXT_PUBLIC_BACKEND_REST_URL`: The production API URL generated by Railway (e.g. `https://cyberverse-api.up.railway.app/api/v1`) - `NEXT_PUBLIC_BACKEND_WS_URL`: The production WebSocket URL generated by Railway (e.g. `wss://cyberverse-api.up.railway.app/ws/scan`) 4. Deploy the frontend application. ## Architecture Overview - **Backend Framework**: FastAPI (Python) - **Task Queue**: Celery + Redis - **Database**: SQLite (Local) / PostgreSQL (Production) via SQLAlchemy/Alembic - **Frontend**: Next.js (React), Zustand, Three.js (React Three Fiber) - **AI Integration**: Nvidia NIM API (`moonshotai/kimi-k2.6`), utilizing dynamic retry loops for rate-limit resilience. <<<<<<< HEAD =======