# Python Samples

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

## Structure

| Folder | Description |
|--------|-------------|
| [`01-get-started/`](./01-get-started/) | Progressive tutorial: hello agent → hosting |
| [`02-agents/`](./02-agents/) | Deep-dive by concept: tools, middleware, providers, orchestrations |
| [`03-workflows/`](./03-workflows/) | Workflow patterns: sequential, concurrent, state, declarative |
| [`04-hosting/`](./04-hosting/) | Deployment: Azure Functions, Durable Tasks, A2A |
| [`05-end-to-end/`](./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](./01-get-started/01_hello_agent.py)** — Create and run your first agent
2. **[02_add_tools.py](./01-get-started/02_add_tools.py)** — Add function tools with `@tool`
3. **[03_multi_turn.py](./01-get-started/03_multi_turn.py)** — Multi-turn conversations with `AgentSession`
4. **[04_memory.py](./01-get-started/04_memory.py)** — Agent memory with `ContextProvider`
5. **[05_first_workflow.py](./01-get-started/05_first_workflow.py)** — Build a workflow with executors and edges
6. **[06_host_your_agent.py](./01-get-started/06_host_your_agent.py)** — Host your agent via Azure Functions

## Prerequisites

```bash
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:
   ```bash
   cp .env.example .env
   ```
2. Edit `.env` and set your values (API keys, endpoints, etc.)

**Option 2: Export environment variables directly**:
```bash
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:

```python
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 getting-started samples, you'll need at minimum:
```bash
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](https://learn.microsoft.com/agent-framework/)
- [AGENTS.md](./AGENTS.md) — Structure documentation for maintainers
- [SAMPLE_GUIDELINES.md](./SAMPLE_GUIDELINES.md) — Coding conventions for samples
