Dmytro Struk
1c0ae4b659
* Added shell tool * Fixed CI error * Add ShellTool support for OpenAI and Anthropic providers - Add shell_tool_call, shell_tool_result, and shell_command_output content types - Add ShellTool class and shell_tool decorator to core - Add get_hosted_shell_tool() to OpenAI Responses client - Handle shell_call and shell_call_output parsing in OpenAI (sync and streaming) - Map ShellTool to Anthropic bash tool API format - Parse bash_code_execution_tool_result as shell_tool_result in Anthropic - Add unit tests for all new functionality - Add sample scripts for hosted and local shell execution Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Addressed comments * Reverted ruff change * Fixed tests * Addressed comments --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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:
- 01_hello_agent.py — Create and run your first agent
- 02_add_tools.py — Add function tools with
@tool - 03_multi_turn.py — Multi-turn conversations with
AgentThread - 04_memory.py — Agent memory with
ContextProvider - 05_first_workflow.py — Build a workflow with executors and edges
- 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):
- Copy
.env.exampleto.envin thepython/directory:cp .env.example .env - Edit
.envand 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 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