# Azure AI Agent Examples

This folder contains examples demonstrating different ways to create and use agents with the Azure AI client from the `agent_framework.azure` package.

## Examples

| File | Description |
|------|-------------|
| [`azure_ai_basic.py`](azure_ai_basic.py) | The simplest way to create an agent using `AzureAIClient`. Demonstrates both streaming and non-streaming responses with function tools. Shows automatic agent creation and basic weather functionality. |
| [`azure_ai_use_latest_version.py`](azure_ai_use_latest_version.py) | Demonstrates how to reuse the latest version of an existing agent instead of creating a new agent version on each instantiation using the `use_latest_version=True` parameter. |
| [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use the `HostedCodeInterpreterTool` with Azure AI agents to write and execute Python code for mathematical problem solving and data analysis. |
| [`azure_ai_with_existing_agent.py`](azure_ai_with_existing_agent.py) | Shows how to work with a pre-existing agent by providing the agent name and version to the Azure AI client. Demonstrates agent reuse patterns for production scenarios. |
| [`azure_ai_with_existing_conversation.py`](azure_ai_with_existing_conversation.py) | Demonstrates how to use an existing conversation created on the service side with Azure AI agents. Shows two approaches: specifying conversation ID at the client level and using AgentThread with an existing conversation ID. |
| [`azure_ai_with_explicit_settings.py`](azure_ai_with_explicit_settings.py) | Shows how to create an agent with explicitly configured `AzureAIClient` settings, including project endpoint, model deployment, and credentials rather than relying on environment variable defaults. |
| [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Shows how to use the `HostedFileSearchTool` with Azure AI agents to upload files, create vector stores, and enable agents to search through uploaded documents to answer user questions. |
| [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate hosted Model Context Protocol (MCP) tools with Azure AI Agent. |
| [`azure_ai_with_response_format.py`](azure_ai_with_response_format.py) | Shows how to use structured outputs (response format) with Azure AI agents using Pydantic models to enforce specific response schemas. |
| [`azure_ai_with_thread.py`](azure_ai_with_thread.py) | Demonstrates thread management with Azure AI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |

## Environment Variables

Before running the examples, you need to set up your environment variables. You can do this in one of two ways:

### Option 1: Using a .env file (Recommended)

1. Copy the `.env.example` file from the `python` directory to create a `.env` file:

   ```bash
   cp ../../../../.env.example ../../../../.env
   ```

2. Edit the `.env` file and add your values:

   ```env
   AZURE_AI_PROJECT_ENDPOINT="your-project-endpoint"
   AZURE_AI_MODEL_DEPLOYMENT_NAME="your-model-deployment-name"
   ```

### Option 2: Using environment variables directly

Set the environment variables in your shell:

```bash
export AZURE_AI_PROJECT_ENDPOINT="your-project-endpoint"
export AZURE_AI_MODEL_DEPLOYMENT_NAME="your-model-deployment-name"
```

### Required Variables

- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI project endpoint (required for all examples)
- `AZURE_AI_MODEL_DEPLOYMENT_NAME`: The name of your model deployment (required for all examples)

## Authentication

All examples use `AzureCliCredential` for authentication by default. Before running the examples:

1. Install the Azure CLI
2. Run `az login` to authenticate with your Azure account
3. Ensure you have appropriate permissions to the Azure AI project

Alternatively, you can replace `AzureCliCredential` with other authentication options like `DefaultAzureCredential` or environment-based credentials.

## Running the Examples

Each example can be run independently. Navigate to this directory and run any example:

```bash
python azure_ai_basic.py
python azure_ai_with_code_interpreter.py
# ... etc
```

The examples demonstrate various patterns for working with Azure AI agents, from basic usage to advanced scenarios like thread management and structured outputs.