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Python: AutoGen migration samples (#1738)
* add autogen migration samples * fix typo * remove comment * fix typo * fix lab pyright * fix for HuggingFace change
This commit is contained in:
Eric Zhu
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# Copyright (c) Microsoft. All rights reserved.
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"""Basic AutoGen AssistantAgent vs Agent Framework ChatAgent.
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Both samples expect OpenAI-compatible environment variables (OPENAI_API_KEY or
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Azure OpenAI configuration). Update the prompts or client wiring to match your
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model of choice before running.
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"""
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import asyncio
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async def run_autogen() -> None:
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"""Call AutoGen's AssistantAgent for a simple question."""
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from autogen_agentchat.agents import AssistantAgent
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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# AutoGen agent with OpenAI model client
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client = OpenAIChatCompletionClient(model="gpt-4.1-mini")
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agent = AssistantAgent(
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name="assistant",
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model_client=client,
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system_message="You are a helpful assistant. Answer in one sentence.",
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)
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# Run the agent (AutoGen maintains conversation state internally)
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result = await agent.run(task="What is the capital of France?")
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print("[AutoGen]", result.messages[-1].to_text())
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async def run_agent_framework() -> None:
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"""Call Agent Framework's ChatAgent created from OpenAIChatClient."""
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from agent_framework.openai import OpenAIChatClient
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# AF constructs a lightweight ChatAgent backed by OpenAIChatClient
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client = OpenAIChatClient(model_id="gpt-4.1-mini")
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agent = client.create_agent(
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name="assistant",
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instructions="You are a helpful assistant. Answer in one sentence.",
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)
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# Run the agent (AF agents are stateless by default)
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result = await agent.run("What is the capital of France?")
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print("[Agent Framework]", result.text)
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async def main() -> None:
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print("=" * 60)
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print("Basic Assistant Agent Comparison")
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print("=" * 60)
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await run_autogen()
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print()
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await run_agent_framework()
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if __name__ == "__main__":
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asyncio.run(main())
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# Copyright (c) Microsoft. All rights reserved.
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"""AutoGen AssistantAgent vs Agent Framework ChatAgent with function tools.
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Demonstrates how to create and attach tools to agents in both frameworks.
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"""
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import asyncio
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async def run_autogen() -> None:
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"""AutoGen agent with a FunctionTool."""
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from autogen_agentchat.agents import AssistantAgent
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from autogen_core.tools import FunctionTool
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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# Define a simple tool function
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def get_weather(location: str) -> str:
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"""Get the weather for a location.
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Args:
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location: The city name or location.
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Returns:
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A weather description.
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"""
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return f"The weather in {location} is sunny and 72°F."
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# Wrap function in FunctionTool
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weather_tool = FunctionTool(
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func=get_weather,
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description="Get weather information for a location",
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)
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# Create agent with tool
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client = OpenAIChatCompletionClient(model="gpt-4.1-mini")
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agent = AssistantAgent(
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name="assistant",
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model_client=client,
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tools=[weather_tool],
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system_message="You are a helpful assistant. Use available tools to answer questions.",
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)
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# Run with tool usage
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result = await agent.run(task="What's the weather in Seattle?")
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print("[AutoGen]", result.messages[-1].to_text())
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async def run_agent_framework() -> None:
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"""Agent Framework agent with @ai_function decorator."""
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from agent_framework import ai_function
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from agent_framework.openai import OpenAIChatClient
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# Define tool with @ai_function decorator (automatic schema inference)
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@ai_function
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def get_weather(location: str) -> str:
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"""Get the weather for a location.
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Args:
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location: The city name or location.
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Returns:
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A weather description.
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"""
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return f"The weather in {location} is sunny and 72°F."
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# Create agent with tool
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client = OpenAIChatClient(model_id="gpt-4.1-mini")
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agent = client.create_agent(
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name="assistant",
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instructions="You are a helpful assistant. Use available tools to answer questions.",
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tools=[get_weather],
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)
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# Run with tool usage
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result = await agent.run("What's the weather in Seattle?")
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print("[Agent Framework]", result.text)
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async def main() -> None:
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print("=" * 60)
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print("Assistant Agent with Tools Comparison")
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print("=" * 60)
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await run_autogen()
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print()
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await run_agent_framework()
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if __name__ == "__main__":
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asyncio.run(main())
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# Copyright (c) Microsoft. All rights reserved.
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"""AutoGen vs Agent Framework: Thread management and streaming responses.
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Demonstrates conversation state management and streaming in both frameworks.
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"""
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import asyncio
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async def run_autogen() -> None:
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"""AutoGen agent with conversation history and streaming."""
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from autogen_agentchat.agents import AssistantAgent
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from autogen_agentchat.ui import Console
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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client = OpenAIChatCompletionClient(model="gpt-4.1-mini")
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agent = AssistantAgent(
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name="assistant",
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model_client=client,
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system_message="You are a helpful math tutor.",
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model_client_stream=True,
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)
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print("[AutoGen] Conversation with history:")
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# First turn - AutoGen maintains state internally with Console for streaming
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result = await agent.run(task="What is 15 + 27?")
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print(f" Q1: {result.messages[-1].to_text()}")
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# Second turn - agent remembers context
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result = await agent.run(task="What about that number times 2?")
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print(f" Q2: {result.messages[-1].to_text()}")
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print("\n[AutoGen] Streaming response:")
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# Stream response with Console for token streaming
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await Console(agent.run_stream(task="Count from 1 to 5"))
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async def run_agent_framework() -> None:
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"""Agent Framework agent with explicit thread and streaming."""
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from agent_framework.openai import OpenAIChatClient
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client = OpenAIChatClient(model_id="gpt-4.1-mini")
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agent = client.create_agent(
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name="assistant",
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instructions="You are a helpful math tutor.",
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)
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print("[Agent Framework] Conversation with thread:")
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# Create a thread to maintain state
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thread = agent.get_new_thread()
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# First turn - pass thread to maintain history
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result1 = await agent.run("What is 15 + 27?", thread=thread)
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print(f" Q1: {result1.text}")
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# Second turn - agent remembers context via thread
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result2 = await agent.run("What about that number times 2?", thread=thread)
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print(f" Q2: {result2.text}")
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print("\n[Agent Framework] Streaming response:")
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# Stream response
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print(" ", end="")
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async for chunk in agent.run_stream("Count from 1 to 5"):
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print()
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async def main() -> None:
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print("=" * 60)
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print("Thread Management and Streaming Comparison")
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print("=" * 60)
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await run_autogen()
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print()
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await run_agent_framework()
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if __name__ == "__main__":
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asyncio.run(main())
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# Copyright (c) Microsoft. All rights reserved.
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"""AutoGen vs Agent Framework: Agent-as-a-Tool pattern.
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Demonstrates hierarchical agent architectures where one agent delegates
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work to specialized sub-agents wrapped as tools.
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"""
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import asyncio
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async def run_autogen() -> None:
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"""AutoGen's AgentTool for hierarchical agents with streaming."""
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from autogen_agentchat.agents import AssistantAgent
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from autogen_agentchat.tools import AgentTool
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from autogen_agentchat.ui import Console
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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# Create a specialized writer agent
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writer_client = OpenAIChatCompletionClient(model="gpt-4.1-mini")
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writer = AssistantAgent(
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name="writer",
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model_client=writer_client,
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system_message="You are a creative writer. Write short, engaging content.",
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model_client_stream=True,
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)
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# Wrap writer agent as a tool (description is taken from agent.description)
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writer_tool = AgentTool(agent=writer)
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# Create coordinator agent with writer as a tool
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# IMPORTANT: Disable parallel_tool_calls when using AgentTool
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coordinator_client = OpenAIChatCompletionClient(
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model="gpt-4.1-mini",
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parallel_tool_calls=False,
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)
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coordinator = AssistantAgent(
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name="coordinator",
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model_client=coordinator_client,
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tools=[writer_tool],
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system_message="You coordinate with specialized agents. Delegate writing tasks to the writer agent.",
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model_client_stream=True,
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)
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# Run coordinator with streaming - it will delegate to writer
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print("[AutoGen]")
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await Console(coordinator.run_stream(task="Create a tagline for a coffee shop"))
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async def run_agent_framework() -> None:
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"""Agent Framework's as_tool() for hierarchical agents with streaming."""
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from agent_framework import FunctionCallContent, FunctionResultContent
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from agent_framework.openai import OpenAIChatClient
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client = OpenAIChatClient(model_id="gpt-4.1-mini")
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# Create specialized writer agent
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writer = client.create_agent(
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name="writer",
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instructions="You are a creative writer. Write short, engaging content.",
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)
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# Convert writer to a tool using as_tool()
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writer_tool = writer.as_tool(
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name="creative_writer",
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description="Generate creative content",
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arg_name="request",
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arg_description="What to write",
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)
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# Create coordinator agent with writer tool
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coordinator = client.create_agent(
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name="coordinator",
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instructions="You coordinate with specialized agents. Delegate writing tasks to the writer agent.",
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tools=[writer_tool],
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)
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# Run coordinator with streaming - it will delegate to writer
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print("[Agent Framework]")
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# Track accumulated function calls (they stream in incrementally)
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accumulated_calls: dict[str, FunctionCallContent] = {}
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async for chunk in coordinator.run_stream("Create a tagline for a coffee shop"):
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# Stream text tokens
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if chunk.text:
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print(chunk.text, end="", flush=True)
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# Process streaming function calls and results
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if chunk.contents:
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for content in chunk.contents:
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if isinstance(content, FunctionCallContent):
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# Accumulate function call content as it streams in
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call_id = content.call_id
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if call_id in accumulated_calls:
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# Add to existing call (arguments stream in gradually)
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accumulated_calls[call_id] = accumulated_calls[call_id] + content
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else:
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# First chunk of this function call
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accumulated_calls[call_id] = content
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print("\n[Function Call - streaming]", flush=True)
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print(f" Call ID: {call_id}", flush=True)
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print(f" Name: {content.name}", flush=True)
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# Show accumulated arguments so far
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current_args = accumulated_calls[call_id].arguments
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print(f" Arguments: {current_args}", flush=True)
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elif isinstance(content, FunctionResultContent):
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# Tool result - shows writer's response
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result_text = content.result if isinstance(content.result, str) else str(content.result)
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if result_text.strip():
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print("\n[Function Result]", flush=True)
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print(f" Call ID: {content.call_id}", flush=True)
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print(f" Result: {result_text[:150]}{'...' if len(result_text) > 150 else ''}", flush=True)
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print()
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async def main() -> None:
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print("=" * 60)
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print("Agent-as-Tool Pattern Comparison")
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print("=" * 60)
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print("Note: AutoGen requires parallel_tool_calls=False for AgentTool")
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print(" Agent Framework handles this automatically\n")
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await run_autogen()
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print()
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await run_agent_framework()
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if __name__ == "__main__":
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asyncio.run(main())
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