🏔️ AlpineAI – Multi-Agent Ski Resort Demo

A distributed, multi-agent ski resort system built with Microsoft Agent Framework (MAF), the A2A protocol, and Aspire.

An AI-powered ski resort concierge that coordinates weather intelligence, lift traffic, safety evaluation, and personalized coaching through a network of specialist agents — all orchestrated by a central advisor and displayed on a real-time dashboard.

Architecture

┌─────────────────────────────────────────────────────┐
│                   Frontend (Vite + React)            │
│         Real-time dashboard + AI Chat (A2A)         │
└──────────────┬──────────────────────┬───────────────┘
               │ REST                 │ A2A (streaming)
               ▼                      ▼
┌──────────────────────┐  ┌──────────────────────────┐
│   Data Generator     │  │   Advisor Agent (.NET)   │
│   (Python/FastAPI)   │  │   Orchestrator via A2A   │
└──────────────────────┘  └────┬───┬───┬───┬─────────┘
                               │   │   │   │  A2A
                    ┌──────────┘   │   │   └──────────┐
                    ▼              ▼   ▼              ▼
             ┌───────────┐ ┌──────────┐ ┌───────────┐ ┌──────────┐
             │  Weather   │ │   Lift   │ │  Safety   │ │Ski Coach │
             │  Agent     │ │  Traffic │ │  Agent    │ │  Agent   │
             │ (Python)   │ │  (.NET)  │ │ (Python)  │ │ (Python) │
             └───────────┘ └──────────┘ └───────────┘ └──────────┘

Component	Language	Role
Advisor Agent	.NET	Central orchestrator — routes questions to specialist agents via A2A
Weather Agent	Python	Current conditions, forecasts, storm alerts
Lift Traffic Agent	.NET	Lift status, wait times, congestion analysis
Safety Agent	Python	Risk evaluation, slope safety, closures
Ski Coach Agent	Python	Personalized slope recommendations, day plans
Data Generator	Python	Continuously generates synthetic resort telemetry
Frontend	React/Vite	Real-time dashboard with AI chat panel

Prerequisites

• .NET 10 SDK
• Python 3.11+
• uv (Python package manager)
• Node.js 20+
• .NET Aspire CLI
• An Azure AI Foundry resource with a gpt-4.1 (or similar) deployment
• Azure CLI authenticated (az login)

Install Aspire CLI

Refer to the official Aspire documentation for installation instructions.

Setup

1. Clone the repository

git clone https://github.com/tommasodotNET/ski-resort-demo.git
cd ski-resort-demo

2. Configure Azure settings

Edit src/apphost.settings.Development.json with your Azure details:

{
    "Azure": {
        "TenantId": "<your-tenant-id>",
        "SubscriptionId": "<your-subscription-id>",
        "AllowResourceGroupCreation": true,
        "ResourceGroup": "<your-resource-group>",
        "Location": "<your-azure-region>",
        "CredentialSource": "AzureCli"
    }
}

Note: The Azure AI Foundry resource must have a chat completion model deployed (e.g., gpt-4.1). The deployment name is configured in the Aspire AppHost.

3. Run the application

From the src/ directory:

cd src
aspire run

This single command starts all services:

• 2 .NET agents (advisor + lift traffic)
• 3 Python agents (weather + safety + ski coach)
• Data generator (Python/FastAPI)
• Frontend (Vite dev server)
• Cosmos DB emulator

Open the Aspire dashboard (URL shown in terminal output) to see all services, logs, and distributed traces.

The frontend will be available at the URL assigned by Aspire (shown in the dashboard).

Project Structure

src/
├── apphost.cs                      # Aspire orchestration (all services wired here)
├── apphost.settings.Development.json  # Azure configuration
├── advisor-agent-dotnet/           # .NET orchestrator agent (A2A)
├── lift-traffic-agent-dotnet/      # .NET lift traffic agent (A2A)
├── weather-agent-python/           # Python weather agent (A2A)
├── safety-agent-python/            # Python safety agent (A2A)
├── ski-coach-agent-python/         # Python ski coach agent (A2A)
├── data-generator/                 # Python FastAPI data generator
├── frontend/                       # Vite + React + Tailwind dashboard
├── shared-services/                # .NET shared library (Cosmos, thread store)
└── service-defaults/               # Aspire service defaults

Configuration

Data Generator

The data generation speed and drift magnitudes are configurable via src/data-generator/data_generator/config.json:

{
  "update_interval_seconds": { "min": 5, "max": 10 },
  "weather": { "temperature_drift": 0.1, "wind_speed_drift": 0.5, ... },
  "lifts": { "queue_drift": 3, "status_change_probability": 0.002 },
  ...
}

Frontend

The dashboard polling interval is configurable via src/frontend/public/config.json:

{
  "pollingIntervalMs": 10000
}

Changes are picked up automatically without restarting.

How It Works

1. Data Generator continuously produces synthetic weather, lift, slope, and safety telemetry via a REST API.

2. Specialist agents (weather, lift, safety, coach) each wrap specific tools using MAF and expose them over the A2A protocol. Each agent calls the data generator's API to fetch current conditions.

3. Advisor Agent is the central orchestrator. It registers all specialist agents as tools (via A2A) and selectively invokes only the relevant agents based on the user's question.

4. Frontend displays real-time data panels (weather, lifts, slopes, safety) by polling the data generator, and provides an AI chat panel that streams responses from the advisor agent via the A2A protocol.

Key Technologies

• Microsoft Agent Framework (MAF) — Agent creation, tool registration, and orchestration
• A2A Protocol — Agent-to-agent communication over JSON-RPC + SSE streaming
• Aspire — Distributed app orchestration, service discovery, observability
• Azure AI Foundry — LLM backend (GPT-4.1)
• Vite + React — Frontend dashboard
• Azure Cosmos DB — Conversation thread persistence

Further Reading

See ARCHITECTURE.md for the detailed system architecture document.