agent-framework/python/samples

Python Samples

This directory contains samples demonstrating the capabilities of Microsoft Agent Framework for Python.

Structure

Folder	Description
01-get-started/	Progressive tutorial: hello agent → hosting
02-agents/	Deep-dive by concept: tools, middleware, providers, orchestrations
03-workflows/	Workflow patterns: sequential, concurrent, state, declarative
04-hosting/	Deployment: Azure Functions, Durable Tasks, A2A
05-end-to-end/	Full applications, evaluation, demos

Getting Started

Start with 01-get-started/ and work through the numbered files:

1. 01_hello_agent.py — Create and run your first agent
2. 02_add_tools.py — Add function tools with @tool
3. 03_multi_turn.py — Multi-turn conversations with AgentSession
4. 04_memory.py — Agent memory with ContextProvider
5. 05_first_workflow.py — Build a workflow with executors and edges
6. 06_host_your_agent.py — Host your agent via Azure Functions

Prerequisites

pip install agent-framework --pre

Environment Variables

Samples call load_dotenv() to automatically load environment variables from a .env file in the python/ directory. This is a convenience for local development and testing.

For local development, set up your environment using any of these methods:

Option 1: Using a .env file (recommended for local development):

1. Copy .env.example to .env in the python/ directory:

   cp .env.example .env
   

2. Edit .env and set your values (API keys, endpoints, etc.)

Option 2: Export environment variables directly:

export AZURE_AI_PROJECT_ENDPOINT="your-foundry-project-endpoint"
export AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME="gpt-4o"

Option 3: Using env_file_path parameter (for per-client configuration):

All client classes (e.g., OpenAIChatClient, AzureOpenAIResponsesClient) support an env_file_path parameter to load environment variables from a specific file:

from agent_framework.openai import OpenAIChatClient

# Load from a custom .env file
client = OpenAIChatClient(env_file_path="path/to/custom.env")

This allows different clients to use different configuration files if needed.

For the generic OpenAI clients (OpenAIChatClient and OpenAIChatCompletionClient), routing precedence is:

1. Explicit Azure inputs such as credential, azure_endpoint, or api_version
2. OPENAI_API_KEY / explicit OpenAI API-key parameters
3. Azure environment fallback such as AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY

If you keep both OpenAI and Azure variables in your shell, the generic clients stay on OpenAI until you pass an explicit Azure input.

For the getting-started samples, you'll need at minimum:

AZURE_AI_PROJECT_ENDPOINT="your-foundry-project-endpoint"
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME="gpt-4o"

Note for production: In production environments, set environment variables through your deployment platform (e.g., Azure App Settings, Kubernetes ConfigMaps/Secrets) rather than using .env files. The load_dotenv() call in samples will have no effect when a .env file is not present, allowing environment variables to be loaded from the system.

For Azure authentication, run az login before running samples.

Note on XML tags

Some sample files include XML-style snippet tags (for example <snippet_name> and </snippet_name>). These are used by our documentation tooling and can be ignored or removed when you use the samples outside this repository.

Additional Resources

• Agent Framework Documentation
• AGENTS.md — Structure documentation for maintainers
• SAMPLE_GUIDELINES.md — Coding conventions for samples