A Comprehensive Guide to Advanced Multi-Agent Workflows with Microsoft AutoGen
Introduction
This guide explores how Microsoft’s AutoGen framework enables developers to create sophisticated multi-agent workflows with ease. By utilizing AutoGen’s features, you can integrate various specialized assistants, such as Researchers, FactCheckers, Critics, Summarizers, and Editors, into a unified tool called “DeepDive.” This approach simplifies the complexities of managing interactions between agents, allowing you to concentrate on defining their roles and expertise.
Installation
To begin, you need to install the required packages. Use the following command:
pip install -q autogen-agentchat[gemini] autogen-ext[openai] nest_asyncio
This command installs the necessary components to run asynchronous, multi-agent workflows in your environment.
Environment Setup
Next, set up your environment by importing the required libraries:
import os, nest_asyncio
from getpass import getpass
nest_asyncio.apply()
os.environ["GEMINI_API_KEY"] = getpass("Enter your Gemini API key: ")This code ensures that your environment is prepared for running the AutoGen framework securely.
Initialize the Model Client
Now, set up an OpenAI-compatible chat client:
from autogen_ext.models.openai import OpenAIChatCompletionClient
model_client = OpenAIChatCompletionClient(
model="gemini-1.5-flash-8b",
api_key=os.environ["GEMINI_API_KEY"],
api_type="google",
)This initializes the model client with the specified parameters, allowing it to interact with the Gemini model.
Define Specialized Agents
Next, create five specialized assistant agents:
from autogen_agentchat.agents import AssistantAgent
researcher = AssistantAgent(name="Researcher", system_message="Gather and summarize factual info.", model_client=model_client)
factchecker = AssistantAgent(name="FactChecker", system_message="Verify facts and cite sources.", model_client=model_client)
critic = AssistantAgent(name="Critic", system_message="Critique clarity and logic.", model_client=model_client)
summarizer = AssistantAgent(name="Summarizer",system_message="Condense into a brief executive summary.", model_client=model_client)
editor = AssistantAgent(name="Editor", system_message="Polish language and signal APPROVED when done.", model_client=model_client)Each agent is assigned a specific role, enabling them to perform tasks effectively within the AutoGen workflow.
Create the Round-Robin Team
Now, set up the round-robin team:
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination
max_msgs = MaxMessageTermination(max_messages=20)
text_term = TextMentionTermination(text="APPROVED", sources=["Editor"])
termination = max_msgs | text_term
team = RoundRobinGroupChat(
participants=[researcher, factchecker, critic, summarizer, editor],
termination_condition=termination
)This code establishes a team that will operate in a round-robin fashion, stopping based on defined conditions.
Wrap the Team in a Tool
Next, encapsulate the round-robin team in a callable tool:
from autogen_agentchat.tools import TeamTool
deepdive_tool = TeamTool(team=team, name="DeepDive", description="Collaborative multi-agent deep dive")This creates a unified tool that can be easily accessed for collaborative tasks.
Create the Host Agent
Now, create a “Host” assistant agent:
host = AssistantAgent(
name="Host",
model_client=model_client,
tools=[deepdive_tool],
system_message="You have access to a DeepDive tool for in-depth research."
)This agent will manage the workflow and utilize the DeepDive tool for research tasks.
Run the DeepDive Workflow
Finally, define a function to execute the DeepDive workflow:
import asyncio
async def run_deepdive(topic: str):
result = await host.run(task=f"Deep dive on: {topic}")
print("DeepDive result:\n", result)
await model_client.close()
topic = "Impacts of Model Context Protocol on Agentic AI"
loop = asyncio.get_event_loop()
loop.run_until_complete(run_deepdive(topic))This function runs the DeepDive tool on a specified topic and outputs the results.
Conclusion
Utilizing Microsoft AutoGen allows for the creation of advanced, modular workflows that enhance collaboration among AI agents. By abstracting complex processes, AutoGen enables rapid development and iteration on agent roles, paving the way for more sophisticated AI applications. This framework not only streamlines workflows but also sets the stage for future advancements in AI technology.
Next Steps
Explore how AI can transform your business processes. Identify areas for automation, set key performance indicators (KPIs) to measure impact, and start with small projects to gather data. For guidance on implementing AI in your business, feel free to reach out to us.
