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How to Build AI Apps with Multi-Agent Systems Using OpenAI Swarm Library ?

Science & Technology

Introduction

Today, we are diving into a fascinating topic centered around a tool called Swarm, which represents a multi-agentic framework or orchestration framework. I will demonstrate how to utilize this innovative system to create a multi-agent chatbot within the healthcare domain.

The Multi-Agentic Chatbot

Let's start with the basics of our multi-agentic chatbot. The first interaction begins with the user greeting the bot, typically initiated with a "Hi." The bot responds through an agent labeled the Triage Agent:

Triage Agent: "Hello! How can I assist you today with your healthcare needs?"

When our user states they have a high fever, a new agent arises from the interaction. This is the Medical Advice Agent, which offers guidance such as staying hydrated, taking rest, and reducing fever.

If the user wishes to visit a doctor, our chatbot transitions again. This time, the Appointment Scheduling Agent comes into play:

**Appointment Scheduling Agent:** "Sure! I can help you with an appointment with the healthcare provider. Please let me know your preferred time."

For example, if the user chooses tomorrow at 6 PM, the agent confirms:

Appointment Scheduling Agent: "Your appointment has been successfully booked for tomorrow at 6 PM."

Once the interaction concludes, a friendly return from the Triage Agent reassures the user.

This engaging process exemplifies how a multi-agent framework operates efficiently, dynamically generating new agents based upon user inputs. This multi-agentic framework was easily developed using just a few lines of Python code, thanks to the new OpenAI Swarm library.

What is Swarm?

Swarm is fundamentally a group of independent agents working collaboratively. In a typical scenario, one agent may handle a specific query while delegating tasks to another agent specialized in a different domain. For instance, when a user asks Agent Bob (the English-speaking agent) for a joke, if Agent Bob lacks the ability to provide humor, it can delegate to Agent Emy, who specializes in humorous content:

Agent Emy: "Why don't scientists trust atoms? Because they make up everything!"

This delegation process among agents—referred to as handoffs—is a crucial concept in the functionality of Swarm.

Although orchestration frameworks have existed before, such as LangChain, Crew AI, and others, the simplicity and elegance of Swarm stood out during my initial experiments with it. The library allows for a compact and organized approach to developing multi-agentic systems without writing extensive amounts of code.

The Cookbook and Functionality

According to Swarm's cookbook, managing language models often requires an effective prompt, coupled with the necessary tools. Functions like function calling within the OpenAI SDK become essential for creating a seamless interaction routine among agents.

Swarm, being experimental and educational, is not intended for production yet, but it certainly proves adequate for building competitive applications when compared to writing vastly longer code. I will guide you through building a simple AI chatbot that demonstrates the multi-agentic capabilities of Swarm using DataButton, an environment where we can easily develop web apps.

Step-by-Step Implementation

To move forward, we will develop a straightforward front end and back end for our chatbot.

  1. Creating the Front End: Selecting a minimal theme as our starting point, a well-done UI emerges, equipped with light and dark modes.
  2. Building the Back End: Using DataButton, we can prompt the system to create a backend named "Multi-Agent". This backend manages the interactions between agents.
  3. Agent Development: Here, we define our agents, such as the English-speaking agent and the Spanish-speaking agent. These can efficiently hand off tasks to one another.

Though this example seems basic, we can enhance it further by incorporating various agent capabilities, such as medical advice and appointment scheduling.

Creating an Example with Health Chatbot

We can create additional agents like the Triage Agent, Medical Advice Agent, and Appointment Scheduling Agent within our AI Health Chatbot. The Triage Agent acts as the primary point of contact, funneling inquiries to specialized agents based on the user’s needs:

  • Triage Agent: Handles initial queries.
  • Medical Advice Agent: Offers medical advice.
  • Appointment Scheduling Agent: Manages appointment bookings.

For the appointment scheduling, a dedicated function verifies availability, thus ensuring that users can book appointments efficiently.

Incorporating Functionality

To streamline the process for each agent within the Swarm framework, we can create specific functions that agents can call upon. The crucial part here is managing handoffs among agents, which should be carefully designed into our system.

In conclusion, with Swarm, creating a robust multi-agent workflow is a streamlined experience requiring minimal code. The tools provided can bolster application development and functionality making it a valuable asset for building AI applications.

Keywords

Swarm, multi-agent systems, OpenAI, Triage Agent, Medical Advice Agent, Appointment Scheduling Agent, AI chatbot, orchestration framework, Python, DataButton, healthcare applications.

FAQ

Q1: What is the Swarm library and what does it do? A1: The Swarm library is a multi-agentic framework from OpenAI allowing developers to create AI applications with independent agents that can collaborate and handle user queries seamlessly.

Q2: Can I use Swarm for production applications? A2: While Swarm is powerful and user-friendly, the OpenAI team has stated that it is experimental and not intended for production usage as of now.

Q3: How can I create agents with Swarm? A3: You can create agents by defining their roles, instructions, and functions within the framework and then implementing their interactions through a concise coding approach.

Q4: What types of agents can I create? A4: You can create various agents based on the needs of your application, including but not limited to customer support, medical advice, and appointment scheduling agents.

Q5: Is programming knowledge required to use Swarm? A5: A basic understanding of Python programming is recommended, but Swarm streamlines many processes, allowing for easier implementation of complex multi-agent frameworks.