Harnessing Multi-Agent Orchestration with Autogen: A Comprehensive Guide

In today’s rapidly evolving tech landscape, the need for more advanced and collaborative artificial intelligence (AI) solutions is ever-increasing. Microsoft’s Autogen, which infuses the capabilities of AutoGPT along with Crew AI, introduces an exciting feature: multi-agent orchestration. This feature allows for the creation of multiple AI agents that can communicate and collaborate to solve complex problems efficiently. This article delves into how to practically implement multi-agent orchestration using Autogen, based on a detailed tutorial demonstration.

Introduction to Multi-Agent Orchestration

Multi-agent orchestration represents a significant leap in AI technology, where multiple AI agents (each with specialized capabilities or roles) can interact to perform tasks or solve problems that are too complex for a single AI. This not only enhances the efficiency but also the scope of what AI can achieve in real-world applications.

Setting Up Your Environment

Before diving into multi-agent orchestration, it’s crucial to set up an environment capable of supporting such intricate operations. For instance, using Hugging Face’s Endo as an LLM for integration with Autogen offers a streamlined approach. Setting up involves:

• Ensuring access to an API, like opening an AAP.
• Deploying your Hugging Face endpoint, and configuring relevant settings such as the base URL and timeout – crucial for handling operations that require more extended processing times, especially in Windows environments.

Creating AI Agents with Autogen

The core of multi-agent orchestration in Autogen involves creating and configuring multiple AI agents, each designed for specific tasks. For example:

1. User Proxy Agent: Acts as a substitute for human interaction, controlling the flow and nature of the AI-mediated conversation.
2. Coder Agent: Specializes in understanding and handling code-related queries and tasks.
3. Product Manager Agent: Focuses on generating creative software product ideas and solutions.

These agents can then interact within a controlled environment – an orchestrated group chat setting – to tackle a predefined problem.

Launching and Managing Group Discussions

Once the agents are set up, the next step involves launching a group discussion where these agents can collaborate. Here’s how you can initialize and manage a group chat:

• Define the group chat with the list of participating agents.
• Set parameters like the maximum number of conversation rounds to ensure discussions are concise and goal-oriented.
• Monitor and manage the conversation flow using the Group Chat Manager, which also incorporates your previously configured LLM settings.

This setup allows agents to discuss and iterate on a problem collaboratively – in this tutorial, the problem was adding OTP-based authentication to an app.

Observing AI Interaction and Execution

Once the group chat is live, the AI agents begin to communicate based on the input prompts and their specialized roles. For instance, the coder might suggest technical solutions or code snippets, whereas the product manager could propose strategic ideas or enhancements. The user proxy not only facilitates this discussion but can also execute code snippets in real-time if required, thanks to Autogen’s execution capabilities.

Evaluating Outcomes and Enhancing AI Discussions

After the AI agents have completed their discussion, evaluating the outcome is crucial to understand the effectiveness of the multi-agent orchestration. This could involve reviewing the proposed solutions and the efficiency of the inter-agent communication. Additionally, adjustments can be made for future sessions to optimize the agents’ performance based on previous outcomes.

Conclusion

Multi-agent orchestration using Autogen presents a revolutionary step toward more dynamic and capable AI systems. By allowing multiple AI agents to communicate and collaborate on complex problems, solutions can be reached faster and with a higher degree of innovation. The potential applications of this technology are vast, from software development to complex systems engineering and beyond.

Experimenting with different configurations and problem statements can help users leverage the full potential of Autogen and multi-agent setups. Each session provides valuable insights, driving towards more sophisticated and capable AI-driven solutions. So, whether you’re an AI enthusiast, a developer, or a product manager, exploring Autogen’s capabilities could be a game-changer for your projects and workflows.

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


This video demonstrates how to enable group discussion between multiple AI agents ilusing Autogen by Microsoft

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[h3]Transcript[/h3]
so hi everyone my new book lanch in your pocket beginner’s guide to building gen applications using llm is out now on Amazon the book is already a best seller as you can see it is trending on hash 3 on Amazon best sellers so go grab your copies and find the link in the description below uh so hi everyone today uh we will be demonstrating how you can use multi-agent orchestration using autogen so autogen is a very very popular package coming from Microsoft which has the qualities of Auto GPT as well as crew AI hence and it is introducing the concept of multi-agent orchestration so what is that in this case we would be creating multiple dummy AI agents that would be talking to each other and then coming up with a solution for a complex problem so let’s get started I’ve already discussed what is multi-agent orchestration in my previous video you can check that out so in this case I would be using the hugging phase Endo for integrating with autogen as an llm hence to set that up first of all if you have an open a AP then it’s fine but if you don’t have it then you can go with hugging phas endpoint so I’ve already discussed this in length in my previous video you can check this out so here you can see that using light llm I have deployed my hugging face endpoint at this particular location Local Host Port is 4,000 now I’m also setting up the base URL here the same as the mention for the light LM hosted server and I’m setting up a timeout also this timeout is basically if you’re using Ama apps AMA llm because AMA inferencing is quite slow in Windows and eventually you might need this parameter if you’re using AMA uh then I’m assigning the LM config this is similar to what we have done in my previous video in autogen so I’m skipping a few things here you can check back on why certain things are done now here comes the main part of this particular tutorial where we will be creating three agents one is a user proxy what is a user proxy agent it is a alternate for the human intervention in the conversation and in this case uh as you can see the system message is also a human admin it would be one of our agents I have discussed user proxy agent in my previous video on autogen in detail you can check that out the rest of the two agents that I’m creating is one is coder who understand codes and technicalities of any development and a product manager creative in having software product ideas right now I’d be throwing them with a feature development option and eventually they would be coming up with the fin solution uh so after you have created these three agents as you can see that in the first case the user proxy I’m using user proxy agent else I’m using assistant agent so autogen does have the qualities of code execution also and this power has been given to the user proxy agent only so if the assistant any assistant agent comes up with any code snippet the user proxy agent has the powers to execute that also and that is the user proxy agents are used in this particular use case that I would be displaying I won’t be requiring IR ing to execute any codes but you can change the prompt you can use it for your problem statement and if any output from the codes are also required while discussing I think that would be visible to you so here you can see that I’ve have created three agents now now I’m creating a auto. group chat where I’m providing the list of agents that I would be using the messages and maximum round equals to 12 so the conversation should last for at Max 12 conversation and then using the group chat object I am creating a group chat manager here I’m providing the LM config also quite easy to understand I think it’s quite clear uh now let’s get started so I’m providing them discuss product design technicalities for adding OTP based authentication in an app right using the user proxy so as we have discussed the codes now let’s execute this code and see the results this might take some time and you would be able to see how the agents are uh conversating having a conversation amongst themselves to come up to a solution tion also as the LM would get hit you will be seeing here the API hits that would be coming up this might take some time so do wait for a while now here you can see that the discussion has started the coder is chatting to the chat manager telling out what uh its response then the product manager is chatting Out Auto rep is getting used for user proxy so basically user proxy intends to have user input but you can uh override that with auto reply also this is a feature provided in autogen and you can see that they are having quite a discussion here and then they have come to a conclusion also now these are the cleaned up part of the conversation that I have formatted so you can see that uh the assistant started off with a problem statement then the coder came out with certain input from his end here you can see that the product manager is talking about certain things for additional security and stuff you can you need to check out how the different the two major agents that we have created the product manager and the coder are discussing the project uh problem amongst themselves again a statement by product manager then the coder is speaking here and then coming to a final conclusion given by the product manager so this was a very easy use case also the model that I was using gamma 2 billion is not the state-of-the-art model and hence the results are uh decent not the best but eventually what you can do for the coder part you can have different llm config also which is more specific to codes like code Lama for example in that case it will be able to generate better codes you can execute them and eventually you can even see uh you can see the full potential of autogen with multiple agent so this is how Auto can be used for creating multiple AI agents which can con have a discussion among themselves and then come up with a solution for a given problem statement thank you so much do try out with different problem statements