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https://github.com/microsoft/agent-framework.git
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SergeyMenshykh
4dad26fcae
* support code skills * address pr review comments * address package and syntax checks * address pr review comments * address pr review comment * address failed check * rename agentskill and agetnskillprovider * move agent skills related assets to _skills.py * address pr review comments * address review comments
89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from pathlib import Path
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from agent_framework import Agent, SkillsProvider
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from agent_framework.azure import AzureOpenAIResponsesClient
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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"""
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Agent Skills Sample
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This sample demonstrates how to use file-based Agent Skills with a SkillsProvider.
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Agent Skills are modular packages of instructions and resources that extend an agent's
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capabilities. They follow the progressive disclosure pattern:
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1. Advertise — skill names and descriptions are injected into the system prompt
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2. Load — full instructions are loaded on-demand via the load_skill tool
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3. Read resources — supplementary files are read via the read_skill_resource tool
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This sample includes the expense-report skill:
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- Policy-based expense filing with references and assets
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"""
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# Load environment variables from .env file
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load_dotenv()
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async def main() -> None:
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"""Run the Agent Skills demo."""
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# --- Configuration ---
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endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
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deployment = os.environ.get("AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME", "gpt-4o-mini")
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# --- 1. Create the chat client ---
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client = AzureOpenAIResponsesClient(
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project_endpoint=endpoint,
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deployment_name=deployment,
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credential=AzureCliCredential(),
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)
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# --- 2. Create the skills provider ---
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# Discovers skills from the 'skills' directory and makes them available to the agent
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skills_dir = Path(__file__).parent / "skills"
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skills_provider = SkillsProvider(skill_paths=str(skills_dir))
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# --- 3. Create the agent with skills ---
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async with Agent(
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client=client,
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instructions="You are a helpful assistant.",
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context_providers=[skills_provider],
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) as agent:
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# --- Example 1: Expense policy question (loads FAQ resource) ---
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print("Example 1: Checking expense policy FAQ")
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print("---------------------------------------")
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response1 = await agent.run(
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"Are tips reimbursable? I left a 25% tip on a taxi ride and want to know if that's covered."
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)
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print(f"Agent: {response1}\n")
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# --- Example 2: Filing an expense report (uses template asset) ---
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print("Example 2: Filing an expense report")
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print("---------------------------------------")
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session = agent.create_session()
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response2 = await agent.run(
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"I had 3 client dinners and a $1,200 flight last week. "
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"Return a draft expense report and ask about any missing details.",
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session=session,
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)
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print(f"Agent: {response2}\n")
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if __name__ == "__main__":
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asyncio.run(main())
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"""
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Sample output:
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Example 1: Checking expense policy FAQ
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---------------------------------------
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Agent: Tips up to 20% are reimbursable for meals, taxi/ride-share, and hotel housekeeping.
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Since you left a 25% tip, the portion above 20% would require written justification...
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Example 2: Filing an expense report
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---------------------------------------
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Agent: Here's a draft expense report based on what you've told me. I'll need a few more details...
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"""
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