agent-framework/python/samples/getting_started/agents/azure_ai

Azure AI Agent Examples

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

Examples

File	Description
azure_ai_basic.py	The simplest way to create an agent using ChatAgent with AzureAIAgentClient. It automatically handles all configuration using environment variables.
azure_ai_with_bing_grounding.py	Shows how to use Bing Grounding search with Azure AI agents to find real-time information from the web. Demonstrates web search capabilities with proper source citations and comprehensive error handling.
azure_ai_with_code_interpreter.py	Shows how to use the HostedCodeInterpreterTool with Azure AI agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks.
azure_ai_with_existing_agent.py	Shows how to work with a pre-existing agent by providing the agent ID to the Azure AI chat client. This example also demonstrates proper cleanup of manually created agents.
azure_ai_with_existing_thread.py	Shows how to work with a pre-existing thread by providing the thread ID to the Azure AI chat client. This example also demonstrates proper cleanup of manually created threads.
azure_ai_with_explicit_settings.py	Shows how to create an agent with explicitly configured AzureAIAgentClient settings, including project endpoint, model deployment, credentials, and agent name.
azure_ai_with_azure_ai_search.py	Demonstrates how to use Azure AI Search with Azure AI agents to search through indexed data. Shows how to configure search parameters, query types, and integrate with existing search indexes.
azure_ai_with_file_search.py	Demonstrates how to use the HostedFileSearchTool with Azure AI agents to search through uploaded documents. Shows file upload, vector store creation, and querying document content. Includes both streaming and non-streaming examples.
azure_ai_with_function_tools.py	Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries).
azure_ai_with_hosted_mcp.py	Shows how to integrate Azure AI agents with hosted Model Context Protocol (MCP) servers for enhanced functionality and tool integration. Demonstrates remote MCP server connections and tool discovery.
azure_ai_with_local_mcp.py	Shows how to integrate Azure AI agents with local Model Context Protocol (MCP) servers for enhanced functionality and tool integration. Demonstrates both agent-level and run-level tool configuration.
azure_ai_with_multiple_tools.py	Demonstrates how to use multiple tools together with Azure AI agents, including web search, MCP servers, and function tools. Shows coordinated multi-tool interactions and approval workflows.
azure_ai_with_openapi_tools.py	Demonstrates how to use OpenAPI tools with Azure AI agents to integrate external REST APIs. Shows OpenAPI specification loading, anonymous authentication, thread context management, and coordinated multi-API conversations using weather and countries APIs.
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:

   cp ../../.env.example ../../.env
   

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

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

3. For samples using Bing Grounding search (like azure_ai_with_bing_grounding.py and azure_ai_with_multiple_tools.py), you'll also need:

   BING_CONNECTION_ID="your-bing-connection-id"
   

   To get your Bing connection details:

   • Go to Azure AI Foundry portal
   • Navigate to your project's "Connected resources" section
   • Add a new connection for "Grounding with Bing Search"
   • Copy the ID

Option 2: Using environment variables directly

Set the environment variables in your shell:

export AZURE_AI_PROJECT_ENDPOINT="your-project-endpoint"
export AZURE_AI_MODEL_DEPLOYMENT_NAME="your-model-deployment-name"
export BING_CONNECTION_ID="your-bing-connection-id"

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)

Optional Variables

• BING_CONNECTION_ID: Your Bing connection ID (required for azure_ai_with_bing_grounding.py and azure_ai_with_multiple_tools.py)