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python/samples/getting_started/agents/azure_openai/README.md

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+# Azure OpenAI Agent Examples
+
+This folder contains examples demonstrating different ways to create and use agents with the different Azure OpenAI chat client from the `agent_framework.azure` package.
+
+## Examples
+
+| File | Description |
+|------|-------------|
+| [`azure_assistants_basic.py`](azure_assistants_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureOpenAIAssistantsClient`. Shows both streaming and non-streaming responses with automatic assistant creation and cleanup. |
+| [`azure_assistants_with_code_interpreter.py`](azure_assistants_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with Azure agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
+| [`azure_assistants_with_existing_assistant.py`](azure_assistants_with_existing_assistant.py) | Shows how to work with a pre-existing assistant by providing the assistant ID to the Azure Assistants client. Demonstrates proper cleanup of manually created assistants. |
+| [`azure_assistants_with_explicit_settings.py`](azure_assistants_with_explicit_settings.py) | Shows how to initialize an agent with a specific assistants client, configuring settings explicitly including endpoint and deployment name. |
+| [`azure_assistants_with_function_tools.py`](azure_assistants_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_assistants_with_thread.py`](azure_assistants_with_thread.py) | Demonstrates thread management with Azure agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
+| [`azure_chat_client_basic.py`](azure_chat_client_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureOpenAIChatClient`. Shows both streaming and non-streaming responses for chat-based interactions with Azure OpenAI models. |
+| [`azure_chat_client_with_explicit_settings.py`](azure_chat_client_with_explicit_settings.py) | Shows how to initialize an agent with a specific chat client, configuring settings explicitly including endpoint and deployment name. |
+| [`azure_chat_client_with_function_tools.py`](azure_chat_client_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_chat_client_with_thread.py`](azure_chat_client_with_thread.py) | Demonstrates thread management with Azure agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
+| [`azure_responses_client_basic.py`](azure_responses_client_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureOpenAIResponsesClient`. Shows both streaming and non-streaming responses for structured response generation with Azure OpenAI models. |
+| [`azure_responses_client_code_interpreter_files.py`](azure_responses_client_code_interpreter_files.py) | Demonstrates using HostedCodeInterpreterTool with file uploads for data analysis. Shows how to create, upload, and analyze CSV files using Python code execution with Azure OpenAI Responses. |
+| [`azure_responses_client_image_analysis.py`](azure_responses_client_image_analysis.py) | Shows how to use Azure OpenAI Responses for image analysis and vision tasks. Demonstrates multi-modal messages combining text and image content using remote URLs. |
+| [`azure_responses_client_with_code_interpreter.py`](azure_responses_client_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with Azure agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
+| [`azure_responses_client_with_explicit_settings.py`](azure_responses_client_with_explicit_settings.py) | Shows how to initialize an agent with a specific responses client, configuring settings explicitly including endpoint and deployment name. |
+| [`azure_responses_client_with_file_search.py`](azure_responses_client_with_file_search.py) | Demonstrates using HostedFileSearchTool with Azure OpenAI Responses Client for direct document-based question answering and information retrieval from vector stores. |
+| [`azure_responses_client_with_function_tools.py`](azure_responses_client_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_responses_client_with_local_mcp.py`](azure_responses_client_with_local_mcp.py) | Shows how to integrate Azure OpenAI Responses Client with local Model Context Protocol (MCP) servers using MCPStreamableHTTPTool for extended functionality. |
+| [`azure_responses_client_with_thread.py`](azure_responses_client_with_thread.py) | Demonstrates thread management with Azure agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
+
+## Environment Variables
+
+Make sure to set the following environment variables before running the examples:
+
+- `AZURE_OPENAI_ENDPOINT`: Your Azure OpenAI endpoint
+- `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME`: The name of your Azure OpenAI chat model deployment
+- `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME`: The name of your Azure OpenAI Responses deployment
+
+Optionally, you can set:
+- `AZURE_OPENAI_API_VERSION`: The API version to use (default is `2024-02-15-preview`)
+- `AZURE_OPENAI_API_KEY`: Your Azure OpenAI API key (if not using `AzureCliCredential`)
+- `AZURE_OPENAI_BASE_URL`: Your Azure OpenAI base URL (if different from the endpoint)
+
+## Authentication
+
+All examples use `AzureCliCredential` for authentication. Run `az login` in your terminal before running the examples, or replace `AzureCliCredential` with your preferred authentication method.
+
+## Required role-based access control (RBAC) roles
+
+To access the Azure OpenAI API, your Azure account or service principal needs one of the following RBAC roles assigned to the Azure OpenAI resource:
+
+- **Cognitive Services OpenAI User**: Provides read access to Azure OpenAI resources and the ability to call the inference APIs. This is the minimum role required for running these examples.
+- **Cognitive Services OpenAI Contributor**: Provides full access to Azure OpenAI resources, including the ability to create, update, and delete deployments and models.
+
+For most scenarios, the **Cognitive Services OpenAI User** role is sufficient. You can assign this role through the Azure portal under the Azure OpenAI resource's "Access control (IAM)" section.
+
+For more detailed information about Azure OpenAI RBAC roles, see: [Role-based access control for Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control)