Enterprise-grade AI-powered software development platform with real-time visibility into the planning→coding→testing loop. Built with modern full-stack technologies and production-ready architecture.
Gizmo AI is a sophisticated multi-agent orchestration platform that demonstrates advanced software engineering capabilities. The system integrates AI agents (Planner, Coder, Tester) with a secure execution environment, real-time monitoring, and enterprise-grade reliability features.
Key Highlights:
- Multi-Agent AI System: Intelligent orchestration of AI agents for software development
- Production-Ready Architecture: Microservices, containerization, and scalable design
- Real-Time Monitoring: WebSocket-powered live updates and progress tracking
- Security-First Approach: Sandbox isolation, resource limits, and input validation
- Enterprise Reliability: Auto-retries, memory learning, and failure quarantine
Shashank B
Lead Developer & Architect
Last Updated: Aug 2025
# Clone and setup
git clone https://github.com/ShashankBejjanki1241/GIZMO.git
cd GIZMO
# Install dependencies
npm install
pip install -r requirements.txt
# Start development stack
docker-compose up -d
npm run devExperience Gizmo AI in action: [Demo Link Coming Soon]
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Frontend │ │ Backend │ │ Database │
│ (Next.js) │◄──►│ (FastAPI) │◄──►│ (PostgreSQL) │
│ │ │ │ │ │
│ - React │ │ - Orchestrator │ │ - Real-time │
│ - WebSocket │ │ - WebSocket │ │ │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│
▼
┌─────────────────┐
│ Cache │
│ (Redis) │
│ │
│ - Pub/Sub │
└─────────────────┘
- Framework: Next.js 14 with TypeScript
- UI Library: React 18 with Tailwind CSS
- State Management: React Hooks + Context
- Real-time: WebSocket integration
- API Framework: FastAPI (Python 3.11)
- Orchestration: Custom AI agent orchestrator
- Authentication: JWT-based (planned)
- Validation: Pydantic models
- Database: PostgreSQL 15
- Caching: Redis 7
- Containerization: Docker & Docker Compose
- AI Integration: OpenAI GPT-4o-mini
- Package Manager: npm (Node.js), pip (Python)
- Testing: Jest, pytest
- Linting: ESLint, Prettier, Black
- Version Control: Git
- Frontend: React, TypeScript, Next.js, Tailwind CSS
- Backend: Python, FastAPI, RESTful APIs, WebSocket
- Database: PostgreSQL, Redis, SQL, Database Design
- DevOps: Docker, Docker Compose, CI/CD, Deployment
- LLM Integration: OpenAI API, GPT-4o-mini
- AI Agents: Multi-agent orchestration, prompt engineering
- Natural Language Processing: JSON parsing, structured output
- AI Workflows: Planning, coding, testing automation
- Microservices: Service-oriented architecture
- Real-time Systems: WebSocket, event-driven design
- Security: Sandbox isolation, input validation, resource limits
- Scalability: Containerization, load balancing, caching
- Testing: Unit, integration, and end-to-end testing
- Code Quality: Linting, formatting, type safety
- Documentation: Technical writing, API docs, project structure
- Version Control: Git workflow, branching strategy
- Agile Development: Iterative development, phase-based approach
- Requirements Analysis: PRD creation, scope management
- Technical Planning: Architecture design, technology selection
- Quality Assurance: Testing strategies, reliability metrics
- Response Time: < 5 seconds to first event
- Success Rate: Target ≥ 80% on curated tasks
- Scalability: Containerized microservices architecture
- Reliability: Auto-retry with intelligent failure handling
- Network Isolation: Zero outbound access from sandbox
- Resource Limits: CPU, memory, and execution time caps
- Input Validation: Strict JSON parsing and diff validation
- Critical File Protection: Prevents deletion of core system files
- Multi-Agent Workflow: Seamless Planner → Coder → Tester coordination
- Memory Learning: Pattern recognition from successful executions
- Graceful Degradation: Fallback mechanisms for LLM failures
- Deterministic Replay: Task replay without additional API calls
- Planner Agent: Generates structured development plans
- Coder Agent: Implements code changes with unified diffs
- Tester Agent: Runs tests and provides detailed reports
- Intelligent Loop: Self-improving workflow with memory
- Secure Sandbox: Network isolation and resource limits
- Command Allowlist: Restricted execution environment
- Auto-retries: Graceful error handling and recovery
- Memory Layer: Learning from successful patterns
- Live Updates: WebSocket-powered real-time communication
- Status Tracking: Visual progress indicators and timelines
- Artifact Management: Downloadable diffs, logs, and reports
- Performance Metrics: Success rates and execution times
- Template System: Pre-built React, Express, and Flask templates
- Showcase Tasks: Curated examples for demonstration
- Replay System: Deterministic task replay without LLM calls
- Professional UI: Modern, responsive interface
The project follows a clean, modular architecture. For detailed information, see:
- Project Index - Complete file mapping and organization
- Project Structure - Directory layout and architecture
GIZMO/
├── app/ # Next.js frontend application
│ ├── pages/ # React components and routing
│ ├── types.ts # TypeScript interfaces
│ └── package.json # Frontend dependencies
├── api/ # FastAPI backend service
│ ├── main.py # API entry point
│ └── .env # Environment configuration
├── orchestrator/ # AI orchestration engine
│ ├── engine.py # Main orchestrator logic
│ ├── sandbox.py # Secure execution environment
│ └── protocol.py # Data models and contracts
├── templates/ # Testing templates
│ ├── react/ # React + Jest template
│ ├── express/ # Express + Supertest template
│ └── flask/ # Flask + pytest template
├── postgres/ # Database initialization
├── docker-compose.yml # Development environment
├── requirements.txt # Python dependencies
└── README.md # This file
- Node.js 18+ and npm
- Python 3.11+
- Docker and Docker Compose
- Git
# Clone repository
git clone https://github.com/ShashankBejjanki1241/GIZMO.git
cd GIZMO
# Install dependencies
npm install
pip install -r requirements.txt
# Start development stack
docker-compose up -d
npm run dev# Frontend tests
npm test
# Backend tests
python -m pytest
# Integration tests
make test-integrationmake help # Show all available commands
make status # Check service health
make logs # View service logs
make clean # Clean up containers and volumes- Frontend: Vercel deployment ready
- Backend: Fly.io configuration included
- Database: Supabase integration planned
- Monitoring: Health checks and metrics
# Required for production
OPENAI_API_KEY=your_openai_api_key
AGENT_MODEL=gpt-4o-mini
DB_HOST=your_db_host
REDIS_HOST=your_redis_host- Response Time: < 5s to first event
- Success Rate: Target ≥ 80% on curated tasks
- Scalability: Containerized microservices
- Reliability: Auto-retry with failure quarantine
- Network Isolation: Sandboxed execution environment
- Command Allowlist: Restricted command execution
- Resource Limits: CPU, memory, and time caps
- Input Validation: Strict JSON and diff validation
MIT License - see LICENSE file for details.
We welcome contributions! Please follow these steps:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
- Follow the existing code style
- Add tests for new functionality
- Update documentation as needed
- Ensure all tests pass before submitting
- AI Agent Orchestration: Novel approach to AI-assisted development
- Secure Execution Environment: Production-ready sandbox with enterprise security
- Real-time Workflow Visibility: Unprecedented transparency in AI development
- Memory-Augmented AI: Learning from successful patterns for improved reliability
- Developer Productivity: Automated planning, coding, and testing workflows
- Quality Assurance: Consistent code quality through AI-driven validation
- Cost Efficiency: Reduced development time and iteration cycles
- Knowledge Transfer: Captured development patterns and best practices
- AI-Powered Development: Addresses the growing demand for AI-assisted coding
- DevOps Integration: Seamless integration with modern development workflows
- Scalable Architecture: Enterprise-ready design for team and organizational use
- Security Compliance: Built-in security measures for enterprise environments
- Developer: Shashank B
- Repository: https://github.com/ShashankBejjanki1241/GIZMO
- Issues: GitHub Issues
🚀 Ready to revolutionize AI-assisted development with Gizmo AI!
Built with ❤️ by Shashank B