chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a1b2c3d4",
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"metadata": {},
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"source": [
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"# Lesson 02 - Exploring Microsoft Agent Framework\n",
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"\n",
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"The **Microsoft Agent Framework (MAF)** is a unified framework for building AI agents. It provides a clean, composable architecture with four core building blocks:\n",
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"\n",
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"- **Client** – connects to an AI model endpoint and handles communication\n",
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"- **Agent** – wraps a client with instructions and tool definitions\n",
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"- **Tools** – extend agent capabilities with custom functions the model can call\n",
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"- **Session** – maintains conversation history for multi-turn interactions\n",
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"\n",
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"In this lesson, we'll build a **travel booking agent** that checks destination availability using these concepts."
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]
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},
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{
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"cell_type": "markdown",
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"id": "b2c3d4e5",
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"metadata": {},
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"source": [
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"## Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c3d4e5f6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Install the Microsoft Agent Framework package\n",
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"! pip install agent-framework azure-ai-projects -U -q\n",
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"! pip install python-dotenv -q"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d4e5f6a7",
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"metadata": {},
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"outputs": [],
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"source": [
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"import logging\n",
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"logging.getLogger(\"agent_framework.foundry\").setLevel(logging.ERROR)\n",
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"\n",
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"import os\n",
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"import asyncio\n",
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"import dotenv\n",
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"from typing import Annotated\n",
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"\n",
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"from agent_framework import tool\n",
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"from agent_framework.foundry import FoundryChatClient\n",
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"from azure.identity import AzureCliCredential\n",
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"\n",
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"dotenv.load_dotenv(dotenv.find_dotenv())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e5f6a7b8",
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"metadata": {},
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"source": [
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"## Understanding the Agent Framework Architecture\n",
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"\n",
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"The Microsoft Agent Framework follows a layered architecture:\n",
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"\n",
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"```\n",
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"Client → Agent → Tools\n",
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" → Session\n",
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"```\n",
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"\n",
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"1. **Client** – A `FoundryChatClient` connects to an Azure OpenAI deployment. It handles authentication, request formatting, and response parsing.\n",
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"2. **Agent** – Created from the client via `provider.create_agent()`, the agent combines model access with instructions (system prompt) and tools.\n",
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"3. **Tools** – Python functions decorated with `@tool` that the agent can invoke to perform actions or retrieve data.\n",
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"4. **Session** – An `AgentSession` object (created via `agent.create_session()`) that stores conversation history, enabling multi-turn dialogue where the agent remembers prior context.\n",
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"\n",
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"Let's build each layer step by step."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f6a7b8c9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create the client – this is the connection to the AI model\n",
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"endpoint = os.getenv(\"AZURE_AI_PROJECT_ENDPOINT\")\n",
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"model = os.getenv(\"AZURE_AI_MODEL_DEPLOYMENT_NAME\")\n",
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"\n",
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"if not endpoint or not model:\n",
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" raise ValueError(\n",
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" \"Missing required environment variables. \"\n",
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" \"Please set AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME as environment variables (e.g., in your .env file or shell environment).\"\n",
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" )\n",
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"\n",
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"provider = FoundryChatClient(\n",
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" project_endpoint=endpoint,\n",
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" model=model,\n",
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" credential=AzureCliCredential()\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a7b8c9d0",
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"metadata": {},
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"source": [
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"## Adding Tools with the @tool Decorator\n",
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"\n",
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"Tools let agents take actions beyond generating text. The `@tool` decorator converts a regular Python function into something the agent can call.\n",
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"\n",
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"Key points:\n",
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"- Use `Annotated[type, \"description\"]` so the model understands each parameter.\n",
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"- The docstring becomes the tool description the model sees.\n",
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"- `approval_mode=\"never_require\"` means the tool runs automatically without user confirmation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b8c9d0e1",
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"metadata": {},
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"outputs": [],
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"source": [
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"@tool(approval_mode=\"never_require\")\n",
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"def check_destination_availability(\n",
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" destination: Annotated[str, \"The destination to check availability for\"]\n",
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") -> str:\n",
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" \"\"\"Check if a vacation destination is currently available for booking.\"\"\"\n",
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" available = {\n",
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" \"Barcelona\": True,\n",
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" \"Tokyo\": True,\n",
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" \"Cape Town\": False,\n",
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" \"Vancouver\": True,\n",
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" \"Dubai\": False,\n",
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" }\n",
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" is_available = available.get(destination, False)\n",
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" return f\"{destination} is {'available' if is_available else 'not available'} for booking.\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "c9d0e1f2",
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"metadata": {},
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"source": [
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"## Creating an Agent with Tools\n",
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"\n",
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"Now we combine the client, instructions, and tools into an agent. The `instructions` act as the system prompt — they define the agent's persona and behaviour."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d0e1f2a3",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = provider.as_agent(\n",
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" name=\"TravelAvailabilityAgent\",\n",
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" instructions=(\n",
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" \"You are a travel booking agent. Help users check destination availability \"\n",
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" \"and make recommendations. Always check availability before recommending a destination.\"\n",
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" ),\n",
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" tools=[check_destination_availability],\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e1f2a3b4",
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"metadata": {},
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"source": [
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"## Multi-Turn Conversations with Sessions\n",
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"\n",
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"An `AgentSession` (created via `agent.create_session()`) keeps track of all messages in a conversation. By passing the same session to each `agent.run()` call, the agent has access to the full conversation history and can refer back to earlier messages.\n",
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"\n",
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"We pass `tools=[check_destination_availability]` so the agent can call our availability checker during each turn."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f2a3b4c5",
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"metadata": {},
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"outputs": [],
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"source": [
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"session = agent.create_session()\n",
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"\n",
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"# Turn 1: Ask about available destinations\n",
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"response = await agent.run(\n",
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" \"Which destinations do you have available?\",\n",
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" session=session,\n",
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")\n",
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"print(f\"Agent: {response}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a3b4c5d6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Turn 2: Follow-up question — the agent remembers the conversation\n",
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"response = await agent.run(\n",
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" \"I'd like to go somewhere warm. What's available?\",\n",
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" session=session,\n",
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")\n",
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"print(f\"Agent: {response}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b4c5d6e7",
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"metadata": {},
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"source": [
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"## Summary\n",
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"\n",
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"In this lesson you explored the four pillars of the Microsoft Agent Framework:\n",
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"\n",
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"| Concept | What You Learned |\n",
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"|---------|------------------|\n",
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"| **Client** | `FoundryChatClient` connects to Azure OpenAI with credential-based auth |\n",
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"| **Agent** | `provider.create_agent()` bundles a model connection with instructions and a name |\n",
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"| **Tools** | The `@tool` decorator exposes Python functions for the agent to call |\n",
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"| **Session** | `agent.create_session()` maintains conversation history across multiple turns |\n",
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"\n",
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"These building blocks compose together to create agents that can hold natural conversations, call external functions, and maintain context — the foundation for more advanced agentic patterns in later lessons."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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