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https://github.com/microsoft/agent-framework.git
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Evan Mattson
35a8565495
* Add AG-UI integration * Fix tests. PR feedback * Cleanup * PR Feedback * Improve README and getting started experience * Fix links
101 lines
2.7 KiB
Python
101 lines
2.7 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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"""Example agent demonstrating agentic generative UI with custom events during execution."""
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import asyncio
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from agent_framework import ChatAgent, ai_function
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework_ag_ui import AgentFrameworkAgent
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@ai_function
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async def research_topic(topic: str) -> str:
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"""Research a topic and generate a comprehensive report.
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Args:
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topic: The topic to research
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Returns:
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Research report
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"""
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# Simulate multi-step research process
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steps = [
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("Searching databases", 1.0),
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("Analyzing sources", 1.5),
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("Synthesizing information", 1.0),
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("Generating report", 0.5),
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]
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results: list[str] = []
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for step_name, duration in steps:
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await asyncio.sleep(duration)
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results.append(f"- {step_name}: completed")
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return f"Research report on '{topic}':\n" + "\n".join(results)
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@ai_function
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async def create_presentation(title: str, num_slides: int) -> str:
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"""Create a presentation with multiple slides.
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Args:
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title: Presentation title
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num_slides: Number of slides to create
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Returns:
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Presentation summary
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"""
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# Simulate slide generation
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slides: list[str] = []
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for i in range(num_slides):
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await asyncio.sleep(0.5)
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slides.append(f"Slide {i + 1}: Content for {title}")
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return f"Created presentation '{title}' with {num_slides} slides:\n" + "\n".join(slides)
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@ai_function
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async def analyze_data(dataset: str) -> str:
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"""Analyze a dataset and produce insights.
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Args:
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dataset: The dataset name to analyze
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Returns:
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Analysis results
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"""
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# Simulate data analysis phases
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phases = [
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("Loading data", 0.8),
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("Cleaning data", 1.0),
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("Running statistical analysis", 1.2),
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("Generating visualizations", 0.7),
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]
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insights: list[str] = []
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for phase_name, duration in phases:
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await asyncio.sleep(duration)
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insights.append(f"- {phase_name}: done")
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return f"Analysis of '{dataset}':\n" + "\n".join(insights)
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agent = ChatAgent(
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name="research_assistant",
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instructions=(
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"You are a research and analysis assistant. "
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"You can research topics, create presentations, and analyze data. "
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"Use the available tools to help users with their research needs."
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),
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chat_client=AzureOpenAIChatClient(),
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tools=[research_topic, create_presentation, analyze_data],
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)
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research_assistant_agent = AgentFrameworkAgent(
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agent=agent,
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name="ResearchAssistant",
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description="Research assistant that emits progress events during task execution",
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)
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