IMPORTANT! All samples and other resources made available in this GitHub repository ("samples") are designed to assist in accelerating development of agents, solutions, and agent workflows for various scenarios. Review all provided resources and carefully test output behavior in the context of your use case. AI responses may be inaccurate and AI actions should be monitored with human oversight. Learn more in the transparency documents for Agent Service and Agent Framework.

Agents, solutions, or other output you create may be subject to legal and regulatory requirements, may require licenses, or may not be suitable for all industries, scenarios, or use cases. By using any sample, you are acknowledging that any output created using those samples are solely your responsibility, and that you will comply with all applicable laws, regulations, and relevant safety standards, terms of service, and codes of conduct.

Third-party samples contained in this folder are subject to their own designated terms, and they have not been tested or verified by Microsoft or its affiliates.

Microsoft has no responsibility to you or others with respect to any of these samples or any resulting output.

This sample demonstrates a math solver agent with local Python tools:

• Basic arithmetic — Evaluate expressions like 2 + 3 * 4
• Quadratic equations — Solve ax² + bx + c = 0 (real & complex roots)
• Statistics — Mean, median, mode, std dev, min, max
• Factorials — Compute n! for non-negative integers
• GCD & LCM — Greatest common divisor and least common multiple
• Base conversion — Convert between binary, octal, decimal, and hex

The agent is hosted using the Azure AI AgentServer SDK and can be deployed to Microsoft Foundry using the Azure Developer CLI.

In main.py, the agent exposes six local Python tool functions:

The agent is hosted using the Azure AI AgentServer SDK, which provisions a REST API endpoint compatible with the OpenAI Responses protocol.

The hosted agent can be deployed to Microsoft Foundry using the Azure Developer CLI ai agent extension.

Before running this sample, ensure you have:

1. Azure AI Foundry Project

   • Project created in Azure AI Foundry
   • Chat model deployed (e.g., gpt-4o or gpt-4.1)
   • Note your project endpoint URL and model deployment name

2. Azure CLI

   • Installed and authenticated
   • Run az login and verify with az account show

3. Python 3.10 or higher

   • Verify your version: python --version
   • If you have Python 3.9 or older, install a newer version:
     • Windows: winget install Python.Python.3.12
     • macOS: brew install [email protected]
     • Linux: Use your package manager

Set the following environment variables (matching agent.yaml):

• PROJECT_ENDPOINT - Your Azure AI Foundry project endpoint URL (required)
• MODEL_DEPLOYMENT_NAME - The deployment name for your chat model (defaults to gpt-4.1-mini)

This sample loads environment variables from a local .env file if present.

Create a .env file in this directory with the following content:

PROJECT_ENDPOINT=https://<your-resource>.services.ai.azure.com/api/projects/<your-project>
MODEL_DEPLOYMENT_NAME=gpt-4.1-mini

Or set them via PowerShell:

# Replace with your actual values
$env:PROJECT_ENDPOINT="https://<your-resource>.services.ai.azure.com/api/projects/<your-project>"
$env:MODEL_DEPLOYMENT_NAME="gpt-4.1-mini"

It's recommended to use a virtual environment to isolate project dependencies:

macOS/Linux:

python -m venv .venv
source .venv/bin/activate

Windows (PowerShell):

python -m venv .venv
.\.venv\Scripts\Activate.ps1

Install the required Python dependencies using pip:

pip install -r requirements.txt

The required packages are:

• azure-ai-agentserver-agentframework - Agent Framework and AgentServer SDK
• python-dotenv - Load environment variables from .env file
• azure-identity - Azure authentication
• azure-monitor-opentelemetry-exporter - Azure Monitor telemetry export
• opentelemetry-sdk / opentelemetry-api - OpenTelemetry for tracing

To run the agent, execute the following command in your terminal:

python main.py

This will start the hosted agent locally on http://localhost:8088/.

PowerShell (Windows):

$body = @{
   input = "I need a hotel in Seattle from 2025-03-15 to 2025-03-18, budget under $200 per night"
    stream = $false
} | ConvertTo-Json

Invoke-RestMethod -Uri http://localhost:8088/responses -Method Post -Body $body -ContentType "application/json"

Bash/curl (Linux/macOS):

curl -sS -H "Content-Type: application/json" -X POST http://localhost:8088/responses \
   -d '{"input": "Find me hotels in Seattle for March 20-23, 2025 under $200 per night","stream":false}'

The agent will use the get_available_hotels tool to search for available hotels matching your criteria.

To deploy your agent to Microsoft Foundry, follow the comprehensive deployment guide at https://learn.microsoft.com/en-us/azure/ai-foundry/agents/concepts/hosted-agents?view=foundry&tabs=cli

We recommend using azd cloud build, which always builds images with the correct architecture.

If you choose to build locally, and your machine is not linux/amd64 (for example, an Apple Silicon Mac), the image will not be compatible with our service, causing runtime failures.

Fix for local builds

Use this command to build the image locally:

docker build --platform=linux/amd64 -t image .

This forces the image to be built for the required amd64 architecture.