microsoft/spec-to-agents

microsoft/spec-to-agents

Description: A multi-agent event planning workflow built with Microsoft Agent Framework - combining Semantic Kernel's enterprise orchestration with AutoGen's multi-agent patterns.

Language: Python

License: MIT

Stars: 111

Forks: 55

Open issues: 14

Created: 2025-08-20T16:05:40Z

Pushed: 2026-06-12T06:53:42Z

Default branch: main

Fork: no

Archived: no

README:

Event Planning Multi-Agent System


A multi-agent event planning workflow built with [Microsoft Agent Framework](https://github.com/microsoft/agent-framework) - combining Semantic Kernel's enterprise orchestration with AutoGen's multi-agent patterns.

> [!NOTE] > Ignite 2025 Lab: LAB513 - Build A2A and MCP Systems Using SWE Agents and Agent Framework

🎯 What This Demonstrates

This sample shows you how to build a production-ready multi-agent system with:

• Multi-Agent Orchestration: 5 specialized agents coordinating event planning
• Human-in-the-Loop: Interactive approval and feedback during workflow execution
• Tool Integration: Web search, weather APIs, calendar management, and code interpreter
• Azure Deployment: One-click deployment with Azure Developer CLI (azd)

🏗️ Architecture

Multi-Agent Workflow Design

The system uses a coordinator-centric star topology where the Event Coordinator routes tasks to specialized agents and synthesizes their outputs into a comprehensive event plan:


Agent Tools & Capabilities

Each specialist agent has access to domain-specific tools for their area of expertise:


Tool Integration:

• Venue Specialist: Web Search (Bing Grounding)
• Budget Analyst: Code Interpreter (Python REPL)
• Catering Coordinator: Web Search (Bing Grounding)
• Logistics Manager: Weather API (Open-Meteo) + Calendar Tools (iCalendar)
• All Agents: MCP Sequential Thinking (Model Context Protocol for complex reasoning)

🚀 Quick Start

Prerequisites

• Python 3.11+
• uv - Python package manager
• Azure CLI (az)
• Azure Developer CLI (azd)
• Azure subscription

Deploy to Azure

# Clone and navigate to the repository
git clone https://github.com/microsoft/spec-to-agents.git
cd spec-to-agents

# Login to Azure
az login
azd auth login

# Deploy everything (provisions resources, generates .env, installs dependencies)
azd up

What happens:

• ✅ Provisions Microsoft Foundry + OpenAI models
• ✅ Generates .env with connection details
• ✅ Installs Python dependencies via uv sync

Run Locally

Interactive Console (Recommended):

uv run console

DevUI (Visual Interface):

uv run app

Then navigate to the URL shown (by default http://localhost:8080)

Example Input

Try this event planning request:

Plan a corporate holiday party for 50 people on December 6th, 2025 in Seattle
with a budget of $5,000. Include venue options, catering for dietary restrictions,
and check the weather forecast.

The agents will collaborate to: 1. Search for suitable venues 2. Calculate budget breakdown 3. Recommend catering options 4. Check weather and create calendar event 5. Synthesize a comprehensive plan

🛠️ Project Structure

spec-to-agents/
├── src/spec_to_agents/
│ ├── main.py # DevUI entry point (uv run app)
│ ├── console.py # Interactive CLI entry point (uv run console)
│ ├── agents/ # Agent definitions (budget_analyst, venue_specialist, etc.)
│ ├── prompts/ # System prompts for each agent
│ ├── tools/ # Tool implementations (web search, weather, calendar, mcp)
│ ├── workflow/ # Workflow orchestration logic
│ └── utils/ # Shared utilities and clients
├── tests/ # Unit and integration tests
├── infra/ # Azure infrastructure (Bicep templates)
└── scripts/ # Post-provisioning hooks

🔑 Key Features

Service-Managed Threads

All agents use store=True for automatic conversation history management via Azure AI Service - no manual message tracking required.

Human-in-the-Loop

Framework-native ctx.request_info() enables pausing workflows for user input with automatic state preservation.

Structured Output Routing

Agents return Pydantic models with explicit routing decisions (next_agent field), enabling dynamic workflow orchestration.

🧪 Development

Run Tests:

uv run pytest

Detailed Setup: See [DEV_SETUP.md](./DEV_SETUP.md) for debugging instructions and manual configuration.

📦 Azure Resources

Provisioned automatically by azd up:

• Microsoft Foundry: AIServices resource and Project for service managed agents
• Azure OpenAI: gpt-5-mini (primary) and gpt-4.1-mini (web search)
• Bing Search: Grounding API for web searches
• Container Registry & App: For deployment (optional)
• Application Insights: Telemetry and monitoring

🤝 Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA).

📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.