Learn How to Build a Multi-Agent AI System Using AutoGen

The digital world teems with advancements, and among the most intriguing is the development of AI systems, particularly multi-agent AI systems. Today, we’ll dive deep into the construction of robust, multi-agent AI frameworks utilizing Microsoft’s open-source programming framework, AutoGen. With AI agents increasingly being employed across various industries for enhanced interactivity and problem-solving capabilities, understanding the creation of these systems not only paves the way for technical innovation but also significantly enhances enterprise capabilities in handling complex tasks.

Introduction to Multi-Agent AI Systems

Multi-agent AI systems consist of multiple artificial intelligence agents that interact and cooperate to perform tasks that are usually beyond the scope of a single AI agent. Unlike standard chatbots, these AI agents possess advanced capabilities, including logic and reasoning, allowing them to execute complex, collaborative tasks with precision and efficiency.

What Is AutoGen?

AutoGen is an open-source framework designed by Microsoft, tailored specifically for the creation and management of sophisticated AI agents. Hosted on Microsoft’s GitHub, it offers developers robust tools to build and facilitate cooperation among AI agents in multi-agent systems. The framework is designed to enrich large language model (LLM) inference and optimization, making it a top pick for developers aiming to leverage advanced AI capabilities in their applications.

Setting Up Your Multi-Agent System with AutoGen

Step 1: Installation and Initial Setup

To kick off, navigate to the AutoGen framework on Microsoft’s GitHub page and download the necessary installation files. Follow the setup instructions to get started. You’ll need to create different agent roles within the framework, each tailored to perform specific functions as per your system’s requirements.

Step 2: Defining AI Agent Roles

A fascinating use case example is creating AI agents representing fans of notable figures like Sam Altman and Elon Musk, who would debate their respective viewpoints. Another crucial role is the judge agent, responsible for overseeing the debate, summarizing discussions, and announcing a winner based on logical assessments.

Example: Creating Agents for a Debate Scenario

• Elon Musk Fan Agent: Programmed to advocate Elon Musk’s leadership skills and contributions.
• Sam Altman Fan Agent: Created to argue in favor of Sam Altman’s capabilities as a leader.
• Judge Agent: Acts as the mediator and decision-maker in the debate.

Step 3: Programming Interaction and Flow

Using Python or another suitable programming language supported by AutoGen, define how your agents will interact. Setup involves importing the necessary AutoGen classes and defining the parameters for interaction. For each agent, you’ll assign scripts or guidelines they will follow during their interaction.

from autogen import ConversationFlow, Agent

elon_agent = Agent(name='ElonMuskFan', role='Supporter of Elon Musk')
sam_agent = Agent(name='SamAltmanFan', role='Supporter of Sam Altman')
judge_agent = Agent(name='Judge', role='Debate Facilitator')

debate_flow = ConversationFlow(participants=[elon_agent, sam_agent, judge_agent])
debate_flow.setup_interaction()  # Define the interaction specifics here

Step 4: Running the Agents and Analyzing the Output

After setting up your agents and defining their interactions, initiate the conversation. The system will use the defined roles and interaction rules to conduct the debate. Once completed, analyze the output, where the judge agent determines the winner based on the arguments presented by the AI agents.

Customizing Your Multi-Agent System

The versatility of AutoGen allows for extensive customization of your multi-agent system. Whether it’s changing the debate topic, introducing more agents into the scenario, or adjusting the decision-making criteria of the judge agent, AutoGen provides a flexible platform suitable for various applications beyond the initial setup.

Conclusion

Building a multi-agent AI system is a formidable task that promises revolutionary changes in how tasks are managed and executed in digital environments. With tools like AutoGen, developers are not only equipped to create sophisticated systems but also poised to innovate on how collaborative tasks can be enhanced through AI technology. Whether for educational purposes, business applications, or research, diving into the construction of multi-agent AI systems opens up a world of possibilities for proactive problem solving and decision-making in any field.

Happy coding, and may your AI systems be as intelligent and collaborative as you envisage!

[h3]Watch this video for the full details:[/h3]


Explore how to build a cutting-edge multi-agent AI system using AutoGen, an open-source programming framework designed for creating AI agents and enabling seamless cooperation among them to tackle complex tasks.

This tutorial walks you through the entire process, from setting up your environment to integrating multiple agents, ensuring they work together efficiently. Ideal for developers and AI enthusiasts, this video will help you unlock the full potential of multi-agent systems with AutoGen, enhancing your ability to solve intricate problems through collaborative AI.

Here is the complete notebook code: https://github.com/pavanbelagatti/Multi-AI-Agent-Debate-Tutorial

I am using SingleStore notebooks (just like Jupyter Notebooks but with added features) to run the code and understand how function calling works.

With SingleStore, you can also build LLM powered applications and AI agents.

Sign up to SingleStore & get started for free: https://bit.ly/3AzExJm

[h3]Transcript[/h3]
hey guys so we all know that the whole world is going crazy about these AI agents while chatbots can just offer help these AI agents go in extra mile to make sure the job is done perfectly chat Bots like chat GPT and jini can generate code and do all that but EI agents are capable of logic and reasoning to get the work done how cool is that so that’s what makes them so special so today I’m going to talk about building a multi-agent workflow using autogen uh so autogen is an open- Source programming framework from Microsoft for building EI agents and facilitating cooperation among multiple agents to solve different tasks so autogen uh basically enables uh multi-agent conversation workflow to the finest so let’s see how we can build uh multi- a agent workflow using autogen hey guys so you can basically go to autogen uh website uh which is from Microsoft which is listed under microsoft. github.io autogen so uh like I said it’s an open source programming framework for agentic eii so it’s a framework where uh you can create different EI agents uh combine them together and uh basically build a complete workflow to uh to uh solve different uh complex uh tasks or problems right so it’s a multi agent conversation work framework and where developers can easily build diverse applications to uh basically uh enhance their uh llm inference and optimization so yeah let’s see let’s see uh how we can use autogen and create yeah agents and uh in our uh in our tutorial hey guys so here we are building a multii agent workflow where we have basically three AI agents created Creed one is uh a fan of Sam Altman and the other one is a fan of Alon musk so these two agents are going to debate and the judge agent the third agent is the judge agent it’s going to summarize everything and basically announce a winner so we are doing this using autogen the autogen uh framework so uh these two EI agents uh a fan of Sam Alman and the other one is f fan of uh Elon Musk they are both basically going to debate uh about the leadership skills that these two personalities have so let’s see it in action in the next part of this video hey guys so here is our um tutorial uh here is my complete notebook code uh I’m going to share this in the uh video description so that you can go and try uh building multi agents and see how they can collaborate collaboratively work together uh to solve any complex problem so uh so what we we are doing here is uh we are basically creating uh three agents uh one is a fan of Sam Alman and the other one is a a fan of Elon Musk and they both are going to debate on their you know leadership skills uh and uh there is a judge uh agent which is going to um summarize everything and basically uh announce and announce a winner so uh we are doing this using autogen so first things first we are uh basically installing uh autogen and um uh we are uh importing the um conversible agent from autogen and we are mentioning our uh open IPI key uh because we are using the model GPT 4 mini and uh GPT 3.5 turbo uh and then we are basically uh constructing our agents so as you can see this is the Elon Musk agent uh this is the Sam Aron uh agent and the judge agent uh and their respective roles right so uh as you can see the role of Alon musk um agent is like you are a person who admires Alon musk and believes he’s the best leader and BL blah blah blah so similarly um we are creating a role for the uh Sam Alman agent uh you are a person who admires Sam Alman and believes he’s the best leader uh in the world uh blah blah blah and uh here is a judge agent uh and uh judge jent also has a role uh you are you are acting as the ultimate facilitator uh your job is to guide the debate between the two and declare a winner right so that’s what we are doing we are basically defining uh the role and the agents and then we are importing um um import group chat from autogen that’s what we are doing and then formally the uh agents uh involved in the group chat so these are the agents involved in the group chat Alon musk agent Sam Alman agent and the judge agent so uh after that we are basically um you know uh here is a group chat management system uh that’s what we are doing the model the API key and uh uh yeah and then uh finally we going to initiate the debate all right the chat result um uh message this debate will be used as a sample in a university class uh winner must be decided the debate will continue until the uh facilitator reaches a conclusion on whether Alon musk or samman is the better leader right so uh yeah so once you uh run this uh you can see different agents coming and uh taking their role uh their um and and talking about the you know uh who is better for example there is a judge agent uh so this is uh this uh agent uh coming and uh this role everything is assigned randomly who is coming uh next so the judge agent is coming he’s like this debate will be used as a sample you know so he’s just saying he’s just introducing like what the debate is all about and how we’re going to decide the winner and next uh speaker is Elon Musk fan the Alon musk agent so he’s going to say right um why Alon musk is the better leader he has the whole um script here uh he’s talking about and next speaker is uh uh Sam Alman fan uh and uh he’s going to say like uh he’s part about why Sam Alman is the better leader and then uh next speaker is the judge agent so both the parties presented their um we on who is a better leader from Sam Alman and the from Elon Musk so judge agent uh comes and then says like hey thank you both for your passionate arguments uh and uh basically summarizes uh both of what the both the agents uh said uh and then uh basically at the end it will announce the winner right that’s enough Sam Alman wins this debate and it also says why I declare that Sam Alman is the more convincing leader in this context uh owing to his um emphasis on collaboration and ethical consideration alongside Innovation so that’s how you can basically build a multi-on conversational uh EI agent workflow using autogen so I’m going to share this you can basically change the whole scenario and uh use your own Concepts here to uh come up with different ideas uh so yeah hopefully I think um uh uh you like this tutorial and uh this um video on Multi agents uh let’s meet in the next video soon um thanks