# Project export: Ping AI

This document was generated by HackStack to give an AI agent context about a hackathon project. Sections are labeled with their provenance; content marked as truncated was cut to keep this document small.

## Project metadata

- Hackathon: Cal Hacks 12.0
- Tagline: Ping AI: The Cursor for DevOps Engineers. An AI-native platform that turns Linux administration into a conversational experience, automating infrastructure and workflows with natural language.
- Devpost: https://devpost.com/software/otium-iq59wz
- GitHub: https://github.com/CadeNahama/ping-calhacks
- Video: https://www.youtube.com/embed/7JcNe2-exDI?enablejsapi=1&hl=en_US&rel=0&start=&version=3&wmode=transparent
- Team: 1 GitHub contributor(s) — CadeNahama (3 commits)

## Devpost submission (written by the team)

### Inspiration

Modern DevOps engineers are drowning in tool sprawl, juggling Ansible, Terraform, Jenkins, PagerDuty, Datadog, and 15+ other specialized tools. The current landscape forces a painful trade-off: AI tools are fast but unsafe for production, while traditional tools are safe but require manual playbooks and deep expertise. Whether deploying updates or diagnosing a 3 AM production outage, engineers spend more time coordinating between tools than solving problems. We built Ping AI to bridge this gap, combining the speed of AI with the safety and compliance of traditional DevOps tools.

### What it does

Ping AI is an intelligent DevOps agent that transforms natural language into production-ready server operations. Here's how it works: The Workflow: Connect - User provides SSH credentials to their server through our secure web interface Connect - User provides SSH credentials to their server through our secure web interface Describe - User types what they want in plain English: "Set up a complete LEMP stack with Nginx, MySQL 8.0, and PHP 8.1" Describe - User types what they want in plain English: "Set up a complete LEMP stack with Nginx, MySQL 8.0, and PHP 8.1" Analyze - Ping automatically connects to the server and profiles the entire environment: Operating system type and version (Ubuntu 20.04, CentOS 7, etc.) Available resources (RAM, CPU, disk space) Installed tools and package managers (apt, yum, docker, etc.) Running services and port bindings User permissions and capabilities Analyze - Ping automatically connects to the server and profiles the entire environment: Operating system type and version (Ubuntu 20.04, CentOS 7, etc.) Available resources (RAM, CPU, disk space) Installed tools and package managers (apt, yum, docker, etc.) Running services and port bindings User permissions and capabilities Generate - Using Claude Sonnet 4.5, Ping creates a sequence of production-ready commands tailored specifically to that server. For a 1GB RAM Ubuntu server, MySQL gets different memory settings than a 16GB CentOS server. If nginx is already running, Ping configures around it. Generate - Using Claude Sonnet 4.5, Ping creates a sequence of production-ready commands tailored specifically to that server. For a 1GB RAM Ubuntu server, MySQL gets different memory settings than a 16GB CentOS server. If nginx is already running, Ping configures around it. Review - User sees each command before it runs, along with: What the command does in plain English Risk level (low/medium/high) Expected outcome Potential issues Review - User sees each command before it runs, along with: What the command does in plain English Risk level (low/medium/high) Expected outcome Potential issues Execute - After approval, Ping runs commands with real-time output streaming. Users see exactly what's happening as it happens. Execute - After approval, Ping runs commands with real-time output streaming. Users see exactly what's happening as it happens. Verify - Ping confirms success, logs everything for compliance, and provides troubleshooting if issues arise. Verify - Ping confirms success, logs everything for compliance, and provides troubleshooting if issues arise. This entire workflow reduces multi-hour DevOps tasks to minutes. A complete LEMP stack that normally takes 2-3 hours of manual configuration completes in 90 seconds. Docker installation drops from 30-45 minutes to 45 seconds. The key is context awareness: every command is generated with complete knowledge of the specific server infrastructure, not generic scripts from Stack Overflow.

### How we built it

The backend uses FastAPI with Anthropic Claude Sonnet 4.5, featuring 180+ lines of carefully engineered system prompts optimized for DevOps operations. We built a sophisticated system detection pipeline that profiles the OS, resources, tool inventory, running services, and user capabilities before every command generation. This context feeds into Claude's 200K token window for truly intelligent adaptation. For safety, we engineered a multi-stage system with pre-validation checks, AI-powered risk assessment, approval workflows, idempotency guarantees, error handling, and audit logging. We implemented robust JSON parsing with multiple fallback strategies to handle edge cases in AI output, achieving 99.9%+ reliability. Security uses Fernet symmetric encryption for SSH credentials with auto-generated keys and session-based authentication. The real-time execution engine streams SSH output non-blocking with exit code tracking and detailed error reporting. The frontend is built with Next.js 15, TypeScript, and Tailwind CSS, providing real-time execution feedback and a clean interface engineers actually want to use.

### Challenges we ran into

Claude occasionally returned malformed JSON or wrapped responses in markdown code blocks. We solved this with a multi-stage parsing pipeline featuring three fallback strategies: direct JSON parsing, markdown code block extraction, and regex-based extraction with error correction. Maintaining persistent SSH connections across multiple commands while handling network interruptions and concurrent sessions required building a robust connection pooling system with automatic reconnection, health checks, and graceful degradation. Each user session gets isolated connections with automatic cleanup. Different Linux distributions use different package managers, init systems, and tool availability. We implemented comprehensive system detection that profiles the OS before command generation, enabling Claude to generate native commands for each distribution. Ensuring commands could be safely re-run after partial failures required engineering prompts that generate idempotent commands with existence checks, proper flags, and rollback procedures.

### Accomplishments we're proud of

Ping AI eliminates hours of monotonous DevOps work, transforming multi-hour tasks into 90-second operations. Take a real example: "Set up a complete LEMP stack with Nginx, MySQL 8.0, PHP 8.1 with FPM, configure Nginx to serve PHP applications, create a test phpinfo page, and ensure all services start on boot." This task typically requires 1-2 hours of manual configuration, documentation lookup, and troubleshooting. Ping completed it in 90 seconds with 18 production-ready commands tailored to the specific server environment. What makes this possible is our context-aware architecture. Every single command Ping generates is built with deep knowledge of the target server: its operating system and version, available RAM and CPU resources, installed tools and package managers, running services and port bindings, and existing configurations. This isn't generic script execution. When Ping sets up MySQL on a 957MB RAM system, it automatically adjusts memory configurations. When it detects Ubuntu versus CentOS, it uses apt versus yum. When it finds nginx already running, it avoids port conflicts. We achieved 99.9%+ JSON parsing reliability through our multi-stage fallback system, and sub-second response times for command generation. The system works across six major Linux distributions with zero-downtime deployment support. Our safety layer makes AI-driven DevOps genuinely trustworthy with pre-execution validation, risk assessment, approval workflows, and encrypted credential storage. Ping AI is production-ready from day one, successfully deploying complete infrastructure stacks, Docker environments, database automation, and performance monitoring on real servers.

### What we learned

AI prompt engineering requires meticulous iteration. Our final 180-line system prompt resulted from hundreds of refinements. Explicit formatting instructions are critical, context prioritization matters, and temperature tuning (we use 0.1) ensures consistent outputs. Generic commands from Stack Overflow rarely work out of the box. Truly useful automation requires deep context awareness: OS-specific nuances, resource constraints, tool availability, and service dependencies. We learned that security must be built in from the start with encryption, audit logging, secure failure modes, and input validation. Real-time feedback is critical for trust. Users need to see live command output, execution time tracking, and detailed error messages. Production systems need multiple fallback strategies for robustness: our multi-stage JSON parsing, connection pooling with health checks, and graceful degradation keep the system usable even when components fail.

### What's next

We're building multi-server orchestration to execute commands across fleets simultaneously with intelligent coordination for rolling deployments and centralized logging. Advanced rollback capabilities will include automatic snapshot creation before risky operations and one-click rollback to known-good states. Infrastructure-as-Code integration will parse existing Terraform and Ansible configurations, generate IaC from natural language, and detect drift. We're adding Windows Server support with PowerShell command generation for hybrid infrastructure management. Enhanced security features include role-based access control for team collaboration, integration with Vault and AWS Secrets Manager, and security policy enforcement. We're also building Kubernetes support for natural language deployments and intelligent pod scaling. The ultimate goal is creating the next abstraction layer for infrastructure management. Engineers will describe what they want in plain English and trust AI to execute it safely in production, fundamentally rethinking how humans interact with infrastructure.

## README (from the GitHub repository)

# Otium - AI-Powered System Administration

Full-stack application for AI-powered Linux system administration with SSH support. Built for hackathons with **session-based in-memory storage** - no database required!

## 🏗️ Project Structure

This is a monorepo containing both frontend and backend:

```
CALHACKS-OTIUM/
├── backend/          # Python FastAPI backend (in-memory storage)
│   └── llm-os-agent/ # Main application code
└── frontend/         # Next.js frontend
```

## 🚀 Quick Start (Automated)

### Prerequisites
- Python 3.8+
- Node.js 18+
- OpenAI API key
- **No Docker needed!** (in-memory storage)

### One-Command Setup

```bash
# 1. Setup backend
cd backend
chmod +x setup_local.sh
./setup_local.sh

# 2. Add your OpenAI API key to backend/.env
# Edit the file and replace: OPENAI_API_KEY=your_openai_api_key_here

# 3. Start backend
cd llm-os-agent
source ../venv/bin/activate
uvicorn api_server_memory:app --reload --host 0.0.0.0 --port 8000

# 4. In a new terminal, setup frontend
cd frontend
npm install
npm run dev
```

## 🔧 Manual Setup

### 1. Backend Setup

```bash
cd backend

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Copy environment template
cp env.example .env

# Generate encryption key
python3 -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"

# Edit .env and add:
# - Your OpenAI API key
# - The generated encryption key

# Start backend server
cd llm-os-agent
uvicorn api_server_memory:app --reload --host 0.0.0.0 --port 8000
```

Backend runs on: `http://localhost:8000`
API docs: `http://localhost:8000/docs`

### 2. Frontend Setup

```bash
cd frontend

# Install dependencies
npm install

# Start development server
npm run dev
```

Frontend runs on: `http://localhost:3000`

## 🔑 Environment Variables

### Backend (`backend/.env`)
```bash
OPENAI_API_KEY=your_openai_api_key_here
OTIUM_ENCRYPTION_KEY=your_generated_encryption_key
PORT=8000
HOST=0.0.0.0
```

### Frontend
No environment variables needed for local development. The frontend automatically connects to `http://localhost:8000`.

## 📚 Tech Stack

### Backend
- **FastAPI** - Web framework
- **Uvicorn** - ASGI server
- **In-Memory Storage** - Session-based (no database!)
- **OpenAI** - AI command generation
- **Paramiko** - SSH connections
- **Cryptography** - Encrypted credentials

### Frontend
- **Next.js 15** - React framework
- **TypeScript** - Type safety
- **Tailwind CSS** - Styling
- **Radix UI** - Component library
- **Local Auth** - Simple demo authentication

## 🎯 Features

- ✅ AI-powered command generation (OpenAI)
- ✅ SSH-based system administration
- ✅ Step-by-step command approval
- ✅ Real-time command execution
- ✅ **In-memory session storage** (no database setup!)
- ✅ Encrypted credential storage
- ✅ Audit logging
- ✅ Auto-cleanup on inactivity
- ✅ Local-first development (no external services)

## 🧪 Testing

### Test Backend
```bash
cd backend/llm-os-agent
python3 -m pytest tests/ -v
```

### Test Frontend
```bash
cd frontend
npm run lint
```

## 🛠️ Troubleshooting

### Backend Issues
```bash
# Check if backend is running
curl http://localhost:8000/api/health

# View backend logs (in terminal where uvicorn is running)

# Restart backend to clear all session data
# Just stop (Ctrl+C) and restart uvicorn
```

### Frontend Issues
```bash
# Clear Next.js cache
cd frontend
rm -rf .next
npm run dev
```

### Session Data Issues
```bash
# All data is in-memory - just restart the backend to reset everything
# No database to clean up or reset!
```

## 📝 Development Notes

- **Storage**: In-memory (session-based) - all data lost on restart
- **Authentication**: Auto-login with `demo_user` for hackathon demos
- **Backend**: FastAPI on port 8000
- **Frontend**: Next.js on port 3000
- **No Database**: No Docker, PostgreSQL, or database setup needed!
- **No External Services**: Everything runs locally

## 🎓 For Hackathon Judges

This is a **super simple** local setup requiring:
1. Python 3.8+ (for backend)
2. Node.js 18+ (for frontend)
3. OpenAI API key (for AI features)
4. SSH access to a test server (for demo)

**No Docker, Database, Railway, Vercel, or WorkOS needed!**

Perfect for quick demos - just restart the backend to reset everything!

## 📝 License

MIT


## Detected evidence (automated analysis)

Indexed codebase: 74 recognized source files, 503 KB.
- CSS (language) — detected in the code
- FastAPI (technology) — detected in the code
- Next.js (technology) — detected in the code
- OpenAI (technology) — detected in the code
- Python (language) — detected in the code
- React (technology) — detected in the code
- Tailwind CSS (technology) — detected in the code
- TypeScript (language) — detected in the code
- JavaScript (language) — claimed on Devpost, not found in the code
- PostgreSQL (technology) — claimed on Devpost, not found in the code
- SQL (language) — claimed on Devpost, not found in the code

## Codebase structure (from repository index)

### Files (85 of 85)

```
.gitignore
backend/.gitignore
backend/DATABASE_CHANGES_FOR_STATE_AWARE_EXECUTION.md
backend/DATABASE_MANAGEMENT.md
backend/database_viewer.py
backend/env.example
backend/llm-os-agent/.env.example
backend/llm-os-agent/agent.py
backend/llm-os-agent/api_server_enhanced.py
backend/llm-os-agent/api_server_memory.py
backend/llm-os-agent/api_server.py
backend/llm-os-agent/approval_service.py
backend/llm-os-agent/auth_service.py
backend/llm-os-agent/command_executor.py
backend/llm-os-agent/command_generator.py
backend/llm-os-agent/config.py
backend/llm-os-agent/database_service.py
backend/llm-os-agent/database.py
backend/llm-os-agent/memory_storage.py
backend/llm-os-agent/secrets_manager.py
backend/llm-os-agent/security.py
backend/llm-os-agent/ssh_manager.py
backend/llm-os-agent/ssh_system_detector.py
backend/llm-os-agent/state_evaluator.py
backend/llm-os-agent/tests/__init__.py
backend/llm-os-agent/tests/test_database.py
backend/llm-os-agent/tests/test_security.py
backend/llm-os-agent/tests/test_state_evaluator.py
backend/migrate_state_aware_tables.py
backend/PROJECT_STRUCTURE.md
backend/README.md
backend/requirements.txt
backend/setup_database.py
backend/setup_local.sh
backend/test_new_tables.py
frontend/.gitignore
frontend/app/api/commands/[id]/approve/route.ts
frontend/app/api/commands/[id]/reject/route.ts
frontend/app/api/commands/[id]/route.ts
frontend/app/api/commands/route.ts
frontend/app/api/commands/submit/route.ts
frontend/app/api/get-name/route.ts
frontend/app/api/ssh/connect/route.ts
frontend/app/api/ssh/disconnect/route.ts
frontend/app/api/ssh/status/route.ts
frontend/app/components/CodeBlock.tsx
frontend/app/components/CommandPreviewCard.tsx
frontend/app/components/Footer.tsx
frontend/app/components/LoginButton.tsx
frontend/app/components/SSHCard.tsx
frontend/app/components/TaskSubmissionCard.tsx
frontend/app/components/TaskSubmissionCardEnhanced.tsx
frontend/app/components/Toast.tsx
frontend/app/config/api.ts
frontend/app/contexts/ConnectionContext.tsx
frontend/app/contexts/UserContext.tsx
frontend/app/dashboard/page.tsx
frontend/app/globals.css
frontend/app/hooks/useToast.ts
frontend/app/layout.tsx
frontend/app/page.tsx
frontend/app/test-enhanced/page.tsx
frontend/CLEAN_START.sh
frontend/components.json
frontend/components/LoginButton.tsx
frontend/components/ui/badge.tsx
frontend/components/ui/button.tsx
frontend/components/ui/card.tsx
frontend/components/ui/drawer.tsx
frontend/components/ui/input.tsx
frontend/components/ui/label.tsx
frontend/components/ui/select.tsx
frontend/components/ui/textarea.tsx
frontend/eslint.config.mjs
frontend/lib/utils.ts
frontend/middleware.ts
frontend/next.config.ts
frontend/package.json
frontend/postcss.config.mjs
frontend/setup_local.sh
frontend/tsconfig.json
HACKATHON_PROJECT_DESCRIPTION.md
IN_MEMORY_CONVERSION.md
README.md
START_HERE.md
```

### Dependencies

- backend/requirements.txt: bcrypt@>=4.0.0, cachetools@>=5.0.0, cryptography@>=41.0.0, dataclasses-json@>=0.6.0, fastapi@>=0.104.0, openai@>=1.3.0, paramiko@>=3.3.0, pydantic@>=2.0.0, PyJWT@>=2.8.0, python-dotenv@>=1.0.0, PyYAML@>=6.0, slowapi@>=0.1.9, structlog@>=23.0.0, uvicorn[standard]@>=0.24.0
- frontend/package.json: @eslint/eslintrc@^3, @radix-ui/react-dialog@^1.1.15, @radix-ui/react-label@^2.1.7, @radix-ui/react-select@^2.2.6, @radix-ui/react-slot@^1.2.3, @tailwindcss/postcss@^4, @types/node@^20, @types/react@^19, @types/react-dom@^19, class-variance-authority@^0.7.1, clsx@^2.1.1, eslint@^9, eslint-config-next@15.4.6, lucide-react@^0.540.0, next@15.4.6, react@19.1.0, react-dom@19.1.0, tailwind-merge@^3.3.1, tailwindcss@^4, tw-animate-css@^1.3.7, typescript@^5, vaul@^1.1.2

### Recent commits (newest first)

- Add concise hackathon project description with clear workflow
- Add frontend clean start script
- Convert to in-memory session-based architecture
- Initial commit - CalHacks hackathon project

## Key source files (fetched from GitHub, selected and truncated for size)

### START_HERE.md

```markdown
# 🚀 Quick Start Guide

## What You Need

1. **OpenAI API Key** - Get from https://platform.openai.com/api-keys
2. **Python 3.8+** - Check with `python3 --version`
3. **Node.js 18+** - Check with `node --version`
4. **(Optional) SSH Server** - For actual command execution demo

## 🏃 Run the Platform (5 Steps)

### Step 1: Setup Backend
```bash
cd backend
./setup_local.sh
```

### Step 2: Add Your OpenAI API Key
```bash
# Edit backend/.env
nano backend/.env
# or
code backend/.env

# Replace this line:
OPENAI_API_KEY=your_openai_api_key_here
# With your actual key:
OPENAI_API_KEY=sk-proj-xxxxxxxxxxxxx
```

### Step 3: Start Backend
```bash
cd backend/llm-os-agent
source ../venv/bin/activate
uvicorn api_server_memory:app --reload --host 0.0.0.0 --port 8000
```

You should see:
```
INFO:     Uvicorn running on http://0.0.0.0:8000
[MEMORY] Starting in-memory backend
[MEMORY] All data will be lost on restart
```

### Step 4: Start Frontend (New Terminal)
```bash
cd frontend
npm install
npm run dev
```

You should see:
```
▲ Next.js 15.4.6
- Local:        http://localhost:3000
```

### Step 5: Open Browser
```
http://localhost:3000
```

## ✅ What Should Work

### Without SSH Server
- ✅ Frontend loads
- ✅ Auto-login as `demo_user`
- ✅ UI is functional
- ❌ Can't connect to SSH (need server)
- ❌ Can't execute commands (need SSH)

### With SSH Server
- ✅ Everything above
- ✅ Connect to SSH server
- ✅ Submit tasks
- ✅ AI generates commands
- ✅ Approve/reject steps
- ✅ Execute commands
- ✅ View command history

## 🧪 Test Without SSH Server

You can test the API directly:

```bash
# Test health endpoint
curl http://localhost:8000/api/health

# Should return:
{
  "status": "healthy",
  "storage_type": "in-memory (session-based)",
  "stats": {
    "users": 0,
    "connections": 0,
    "commands": 0
  }
}
```

## 🔧 Troubleshooting

### Backend won't start
```bash
# Check if port 8000 is in use
lsof -i :8000

# Kill process if needed
kill -9 <PID>
```

### Frontend won't start
```bash
# Check if port 3000 is in use
lsof -i :3000

# Clear cache and retry
cd frontend
rm -rf .next node_modules
npm install
npm run dev
```

### Missing OpenAI Key
```
Error: OPENAI_API_KEY not set
```
Solution: Add your key to `backend/.env`

### Module not found errors
```bash
# Reinstall backend dependencies
cd backend
source venv/bin/activate
pip install -r requirements.txt

# Reinstall frontend dependencies
cd frontend
rm -rf node_modules
npm install
```

## 🎯 Quick Demo Flow

1. **Start both servers** (backend + frontend)
2. **Open http://localhost:3000**
3. **Connect to SSH server** (if you have one)
4. **Submit a task**: "list all files in the home directory"
5. **Review AI-generated commands**
6. **Approve each step**
7. **Watch execution results**
8. **View command history**

## 📝 Notes

- **All data is in-memory** - Restart backend to reset everything
- **Auto-login** - No need to sign up/login
- **Local only** - No external services except OpenAI
- **Perfect for demos** 
[truncated — 568 more characters]
```

### IN_MEMORY_CONVERSION.md

```markdown
# In-Memory Conversion Summary

## Overview
Successfully converted Otium from database-backed storage to **session-based in-memory storage**. All data now persists only while the backend is running and resets on restart.

## What Changed

### Removed
- ❌ PostgreSQL database
- ❌ Docker Compose (`docker-compose.yml`)
- ❌ Database dependencies (`psycopg2-binary`, `sqlalchemy`, `alembic`)
- ❌ Database initialization scripts
- ❌ Database migration files

### Added
- ✅ `backend/llm-os-agent/memory_storage.py` - In-memory storage service
- ✅ `backend/llm-os-agent/api_server_memory.py` - Simplified API server
- ✅ Session-based storage for:
  - Users
  - SSH Connections
  - Commands
  - Command Approvals
  - Audit Logs

### Modified
- 📝 `backend/requirements.txt` - Removed database dependencies
- 📝 `backend/setup_local.sh` - Removed Docker/database setup
- 📝 `backend/env.example` - Removed DATABASE_URL
- 📝 `README.md` - Updated for in-memory setup

## Architecture

### Before (Database)
```
Frontend → Backend → PostgreSQL
                ↓
         (persistent storage)
```

### After (In-Memory)
```
Frontend → Backend → In-Memory Storage
                ↓
         (session-based, resets on restart)
```

## Storage Structure

### InMemoryStorage Class
```python
class InMemoryStorage:
    users: Dict[str, Dict]              # User accounts
    connections: Dict[str, Dict]        # SSH connections
    commands: Dict[str, Dict]           # Command history
    command_approvals: Dict[str, List]  # Approval records
    audit_logs: List[Dict]              # Audit trail
```

### Data Lifecycle
1. **Startup**: Empty storage
2. **Runtime**: Data accumulates in memory
3. **Restart**: All data lost
4. **Inactivity**: Auto-cleanup after 60 minutes

## API Changes

### Endpoints (Unchanged)
All API endpoints remain the same:
- `POST /api/connect` - SSH connection
- `POST /api/commands` - Submit task
- `POST /api/commands/{id}/approve-step` - Approve step
- `GET /api/commands/{id}/approval-status` - Get approval status
- `GET /api/ssh/status` - Connection status
- `POST /api/disconnect` - Disconnect
- `GET /api/commands` - List commands
- `GET /api/health` - Health check

### Health Check Response
Now includes storage statistics:
```json
{
  "status": "healthy",
  "version": "3.0.0",
  "storage_type": "in-memory (session-based)",
  "stats": {
    "users": 5,
    "connections": 3,
    "active_connections": 2,
    "commands": 15,
    "audit_logs": 47
  }
}
```

## Running the Backend

### Old Command (Database)
```bash
uvicorn api_server_enhanced:app --reload
```

### New Command (In-Memory)
```bash
uvicorn api_server_memory:app --reload
```

## Benefits

### For Development
- ✅ **No Docker required** - One less dependency
- ✅ **No database setup** - Instant start
- ✅ **Fast reset** - Just restart the backend
- ✅ **Simpler debugging** - All data in memory
- ✅ **Faster tests** - No database I/O

### For Hackathons
- ✅ **Quick demos** - Setup in < 2 minutes
- ✅ **Easy r
[truncated — 3323 more characters]
```

### backend/requirements.txt

```
# Otium AI Agent - In-Memory Version
# Session-based storage - no database required

# Core AI and OpenAI Integration
openai>=1.3.0

# FastAPI and Web Framework
fastapi>=0.104.0
uvicorn[standard]>=0.24.0
pydantic>=2.0.0

# SSH and Remote Execution
paramiko>=3.3.0

# Configuration and Environment
python-dotenv>=1.0.0
PyYAML>=6.0

# Security
cryptography>=41.0.0
bcrypt>=4.0.0
PyJWT>=2.8.0

# Logging
structlog>=23.0.0

# Performance and Caching
cachetools>=5.0.0

# Data Handling
dataclasses-json>=0.6.0

# Rate Limiting
slowapi>=0.1.9
```

### frontend/package.json

```
{
  "name": "otium-web-test",
  "version": "0.1.0",
  "private": true,
  "scripts": {
    "dev": "next dev --turbopack",
    "build": "next build",
    "start": "next start",
    "lint": "next lint"
  },
  "dependencies": {
    "@radix-ui/react-dialog": "^1.1.15",
    "@radix-ui/react-label": "^2.1.7",
    "@radix-ui/react-select": "^2.2.6",
    "@radix-ui/react-slot": "^1.2.3",
    "class-variance-authority": "^0.7.1",
    "clsx": "^2.1.1",
    "lucide-react": "^0.540.0",
    "next": "15.4.6",
    "react": "19.1.0",
    "react-dom": "19.1.0",
    "tailwind-merge": "^3.3.1",
    "vaul": "^1.1.2"
  },
  "devDependencies": {
    "@eslint/eslintrc": "^3",
    "@tailwindcss/postcss": "^4",
    "@types/node": "^20",
    "@types/react": "^19",
    "@types/react-dom": "^19",
    "eslint": "^9",
    "eslint-config-next": "15.4.6",
    "tailwindcss": "^4",
    "tw-animate-css": "^1.3.7",
    "typescript": "^5"
  }
}

```

### frontend/app/page.tsx

```typescript
import { redirect } from 'next/navigation';

export default async function Home() {
  // Auto-redirect to dashboard (local demo - no auth needed)
  redirect('/dashboard');
}

```

### frontend/app/layout.tsx

```typescript
import type { Metadata } from "next";
import "./globals.css";
import { UserProvider } from "./contexts/UserContext";
import { ConnectionProvider } from "./contexts/ConnectionContext";

export const metadata: Metadata = {
  title: "Otium - Review, approve, and execute",
  description: "Review, approve, and execute AI-generated commands securely with Otium's AI-powered server management platform.",
};

export default function RootLayout({
  children,
}: Readonly<{
  children: React.ReactNode;
}>) {
  return (
    <html lang="en" className="dark">
      <body className="antialiased">
          <UserProvider>
            <ConnectionProvider>
              {children}
            </ConnectionProvider>
          </UserProvider>
      </body>
    </html>
  );
}

```

### frontend/app/test-enhanced/page.tsx

```typescript
"use client";

import { TaskSubmissionCardEnhanced } from '../components/TaskSubmissionCardEnhanced';
import { useUser } from '../contexts/UserContext';

export default function TestEnhancedPage() {
  const { userId, isAuthenticated, isLoading } = useUser();

  if (isLoading) {
    return (
      <div className="min-h-screen bg-background flex items-center justify-center">
        <div className="w-6 h-6 border-2 border-primary border-t-transparent rounded-full animate-spin" />
      </div>
    );
  }

  if (!isAuthenticated) {
    return (
      <div className="min-h-screen bg-background flex items-center justify-center">
        <div className="text-center">
          <h1 className="text-2xl font-bold mb-4">Authentication Required</h1>
          <a href="/login" className="text-primary hover:underline">Sign In</a>
        </div>
      </div>
    );
  }

  return (
    <div className="min-h-screen bg-background p-8">
      <div className="max-w-4xl mx-auto">
        <div className="mb-8 text-center">
          <h1 className="text-3xl font-bold text-foreground mb-4">
            🎯 Enhanced Step-by-Step Approval Test
          </h1>
          <div className="grid grid-cols-1 md:grid-cols-3 gap-4 mb-6">
            <div className="p-4 bg-green-50 border border-green-200 rounded-lg">
              <div className="text-green-600 font-semibold">✅ Database Persistence</div>
              <div className="text-sm text-green-600">All data stored in PostgreSQL</div>
            </div>
            <div className="p-4 bg-blue-50 border border-blue-200 rounded-lg">
              <div className="text-blue-600 font-semibold">🔐 Encrypted Credentials</div>
              <div className="text-sm text-blue-600">SSH passwords secured</div>
            </div>
            <div className="p-4 bg-purple-50 border border-purple-200 rounded-lg">
              <div className="text-purple-600 font-semibold">🎯 Step-by-Step Approval</div>
              <div className="text-sm text-purple-600">Like Cursor workflow</div>
            </div>
          </div>
          <p className="text-muted-foreground">
            Test the new step-by-step approval system. Each command step requires individual approval.
          </p>
        </div>

        <div className="grid grid-cols-1 gap-8">
          <TaskSubmissionCardEnhanced
            userId={userId}
            onCommandStatusChange={() => {
              console.log('Command status changed - refreshing...');
            }}
          />
        </div>

        <div className="mt-8 p-6 bg-muted/20 rounded-lg border border-border/20">
          <h3 className="font-semibold mb-4">🧪 Test Instructions:</h3>
          <ol className="list-decimal list-inside space-y-2 text-sm text-muted-foreground">
            <li>Connect to an SSH server using the connection form</li>
            <li>Submit a task like &quot;Check system status&quot; or &quot;List files and check disk space&quot;</li>
            <li>You should see each command step displayed individually</li>
            <li>Approve or reject each step separately</li>
            <li>Safe commands (ls, pwd, df) should be auto-approved</li>
            <li>Risky commands require manual approval</li>
            <li>Only when all steps are approved can you execute</li>
          </ol>
        </div>

        <div className="mt-6 flex items-center justify-center gap-4">
          <a
            href="/dashboard"
            className="px-4 py-2 bg-primary text-primary-foreground rounded-lg hover:bg-primary/90 transition-colors"
          >
            ← Back to Dashboard
          </a>
          <a
            href="https://otium-backend-production.up.railway.app/api/health"
            target="_blank"
            rel="noopener noreferrer"
            className="px-4 py-2 border border-border text-foreground rounded-lg hover:bg-muted/20 transition-colors"
          >
            Check API Health →
          </a>
        </div>
      </div>
    </div>
  );
}

```

### frontend/app/dashboard/page.tsx

```typescript
"use client";

import { useState } from 'react';
import { SSHCard } from '../components/SSHCard';
import { CommandPreviewCard } from '../components/CommandPreviewCard';
import { TaskSubmissionCardEnhanced } from '../components/TaskSubmissionCardEnhanced';
import { Footer } from '../components/Footer';
import { useUser } from '../contexts/UserContext';
import { Zap, Shield, Bot } from 'lucide-react';
import { ToastContainer } from '../components/Toast';
import { useToast } from '../hooks/useToast';

export default function Dashboard() {
  const { userId, isAuthenticated, isLoading } = useUser();
  const { toasts, removeToast } = useToast();
  const [commandRefreshTrigger, setCommandRefreshTrigger] = useState(0);

  if (isLoading) {
    return (
      <div className="min-h-screen bg-background flex flex-col">
        <div className="flex-1 flex items-center justify-center">
          <div className="text-center">
            <div className="w-12 h-12 border-4 border-primary border-t-transparent rounded-full animate-spin mx-auto mb-6" />
            <p className="text-body-regular text-muted-foreground">Loading authentication...</p>
          </div>
        </div>
        <Footer />
      </div>
    );
  }

  if (!isAuthenticated) {
    return (
      <div className="min-h-screen bg-background flex flex-col">
        <div className="flex-1">
          <main className="px-8 py-16">
            <div className="max-w-4xl mx-auto">
              <div className="text-center py-24">
                <h1 className="mb-8 leading-tight">
                  Welcome to Otium
                </h1>
                <p className="text-body-large text-muted-foreground mb-12 max-w-2xl mx-auto leading-relaxed">
                  Review, approve, and execute AI-generated commands securely.
                </p>
                <div className="flex items-center justify-center gap-12 mb-12">
                  <div className="flex items-center gap-3 text-muted-foreground">
                    <Shield className="w-5 h-5" />
                    <span className="text-body-regular">Secure SSH</span>
                  </div>
                  <div className="flex items-center gap-3 text-muted-foreground">
                    <Bot className="w-5 h-5" />
                    <span className="text-body-regular">AI Commands</span>
                  </div>
                  <div className="flex items-center gap-3 text-muted-foreground">
                    <Zap className="w-5 h-5" />
                    <span className="text-body-regular">Fast Execution</span>
                  </div>
                </div>
                <a 
                  href="/login" 
                  className="inline-flex items-center gap-2 px-8 py-3 bg-primary hover:bg-primary/90 text-primary-foreground font-medium rounded-xl shadow-sm transition-colors"
                >
                  Sign In
                </a>
              </div>
            </div>
          </main>
        </div>
        <Footer />
      </div>
    );
  }

  return (
    <div className="min-h-screen bg-background flex flex-col">
      <div className="flex-1">
        <main className="px-8 py-16">
          <div className="max-w-7xl mx-auto">
            <div className="mb-16 text-center">
              <h1 className="text-4xl font-bold text-foreground leading-tight">
                Welcome to Otium.
              </h1>
            </div>

            <div className="grid grid-cols-1 xl:grid-cols-3 gap-10">
              <SSHCard userId={userId} />
              <TaskSubmissionCardEnhanced 
                userId={userId} 
                onCommandStatusChange={() => {
                  // Refresh command list when status changes
                  setCommandRefreshTrigger(prev => prev + 1);
                }}
              />
              <CommandPreviewCard userId={userId} refreshTrigger={commandRefreshTrigger} />
            </div>
          </div>
        </main>

        <ToastContainer toasts={toasts} onRemove={removeToast} />
      </div>
      <Footer />
    </div>
  );
}
```

### frontend/app/api/get-name/route.ts

```typescript
import { NextRequest, NextResponse } from "next/server";

export const GET = async (request: NextRequest) => {
  // Local demo - return demo user name
  return NextResponse.json({ name: "Demo User" });
};

```

### frontend/app/api/commands/route.ts

```typescript
import { NextRequest, NextResponse } from 'next/server';
import { API_CONFIG } from '@/app/config/api';

export async function GET(request: NextRequest) {
  try {
    const { searchParams } = new URL(request.url);
    const status = searchParams.get('status');
    const connectionId = searchParams.get('connection_id');
    const limit = searchParams.get('limit') || '50';

    // Get user ID from request headers
    const userId = request.headers.get('X-User-ID');
    if (!userId) {
      return NextResponse.json(
        { error: 'User ID header is required' },
        { status: 400 }
      );
    }

    // Validate limit
    const limitNum = parseInt(limit);
    if (isNaN(limitNum) || limitNum < 1 || limitNum > 100) {
      return NextResponse.json(
        { error: 'Invalid limit. Must be between 1 and 100' },
        { status: 400 }
      );
    }

    // Build query parameters
    const queryParams = new URLSearchParams();
    if (status) queryParams.append('status', status);
    if (connectionId) queryParams.append('connection_id', connectionId);
    queryParams.append('limit', limit);

    // Call Otium backend with user ID header
    const response = await fetch(`${API_CONFIG.OTIUM_BACKEND_URL}/api/commands?${queryParams}`, {
      method: 'GET',
      headers: {
        'Content-Type': 'application/json',
        'user-id': userId, // Forward user ID to Otium backend using correct header name
      },
    });

    if (!response.ok) {
      const errorData = await response.json().catch(() => ({}));
      return NextResponse.json(
        { 
          error: errorData.error || 'Failed to get commands',
          details: errorData.details || 'Command retrieval failed'
        },
        { status: response.status }
      );
    }

    const data = await response.json();
    return NextResponse.json(data);

  } catch (error) {
    console.error('Command listing error:', error);
    return NextResponse.json(
      { error: 'Internal server error', details: 'Failed to retrieve commands' },
      { status: 500 }
    );
  }
}

```

[63 more indexed source files omitted to keep this export small. The full file list is in the Codebase structure section above.]