OpenAI Swarm Agents Outperform Crew AI and LangGraph — The Future of Multi-Agent AI

An Introduction to OpenAI Swarm — The Next Evolution in Multi-Agent AI Systems.

Credit: Md Monsur ali

Introduction to OpenAI Swarm

Multi-agent orchestration is a trending topic in the field of large language models (LLMs). Instead of relying on a single, all-encompassing LLM, multi-agent systems employ a team of specialized agents, each designed to excel at a particular task. This approach allows for more complex and nuanced problem-solving, as agents can collaborate, share information, and leverage their individual strengths. OpenAI Swarm is an experimental framework designed to make multi-agent orchestration more accessible and user-friendly. Swarm is built on a practical, lightweight approach, prioritizing ease of use and clear, intuitive interactions between agents.

OpenAI’s Swarm framework is designed to create a user-friendly and flexible environment for coordinating multiple agents. While it’s primarily intended for educational and experimental use, OpenAI advises against using Swarm in production settings, but it is a framework worth exploring. Its core purpose is to demonstrate the concepts of “handoffs” and “routines,” patterns that help agents collaborate efficiently. Swarm isn’t a standalone library but a tool to explore these patterns. Let’s dive into what routines and handoffs are and how they play a role in orchestrating agent behavior.

What is OpenAI Swarm?

OpenAI has bundled these ideas into a sample library called Swarm, designed as a proof of concept. While Swarm is not meant for production use, it serves as a great starting point for experimentation, offering ideas and code you can build upon to create your own systems.

Swarm focuses on making agent coordination and task execution lightweight, easy to control, and simple to test. It does this by relying on two core concepts: Agents and handoffs. An Agent represents a set of instructions and tools, and at any point, it can hand off a conversation to another Agent.

These core abstractions are powerful enough to model complex interactions between tools and networks of agents. This makes building scalable, real-world systems possible without facing a steep learning curve.

Why Use OpenAI Swarm?

Open AI Swarm explores lightweight, scalable, and inherently customizable patterns. It’s ideal for scenarios involving many independent tasks and instructions, which are hard to capture in a single prompt.

The Assistants API might be a better fit for developers looking for fully hosted solutions with built-in memory management. However, Swarm is a fantastic educational resource for those who want to dive into the mechanics of multi-agent orchestration. Running mostly on the client, Swarm is similar to the Chat Completions API and doesn’t store state between calls, making it an effective tool for learning and experimenting.

Key Features of OpenAI Swarm

• Multi-Agent Coordination: Swarm allows multiple AI agents to work together and handle different tasks, enabling efficient teamwork.
• Customizable Roles: Each agent receives a specific role (e.g., “Sales Agent,” “Support Agent”), which makes it easy to define their tasks and responsibilities.
• Dynamic Handoffs: Agents smoothly pass tasks to each other based on the conversation flow or specific conditions, ensuring that the right agent completes the work.
• Context Sharing: Swarm uses context variables to help agents share important information with each other, ensuring consistency throughout a conversation or task.
• Scalable Design: The framework builds to scale, allowing developers to manage complex systems with many agents efficiently.
• Open-Source: Swarm is open-source and available on GitHub, making it accessible for developers to explore, experiment with, and contribute to its development.
• Easy Integration: Swarm offers simplicity for users and seamless integration with other systems, making it easier for developers to build multi-agent applications.

OpenAI Swarm vs Other AI agents

Coordination Between Agents

Agents in CrewAI have structured roles and responsibilities. Each agent receives a specific “Task” object that outlines the type of work it can perform, providing clarity and specificity in agent functions.

Swarm allows for more flexible agent behavior without imposing strict task limits. Agents can act independently and do not require a centralized manager as required in CrewAI, promoting a decentralized approach to task execution.

Autogen provides even more flexibility by emphasizing on dynamic collaboration among agents that can adjust their roles based on real-time task demands. Agents can work in pairs or groups, making the collaboration more fluid and adaptable compared to the more structured approach of OpenAI Swarm.

Memory Management

Swarm maintains persistent context by storing information through context_variables across agent interactions. Autogen provides a similar memory object to track relevant data for agent communication. CrewAI differentiates itself with an advanced memory object that manages both short- and long-term memory, automatically generating embeddings for key terms and memories.

Tools Utilized

Swarm defines functions with docstrings, which are useful for general purposes but can be challenging when detailed parameter descriptions are needed. Autogen uses function annotations to simplify customization of agent capabilities by specifying parameters. CrewAI allows agents to utilize tools from its own toolkit or Langchain, offering good compatibility.

AutoGen is particularly strong in code generation and managing intricate multi-agent programming workflows. On the other hand, OpenAI Swarm and CrewAI are designed to be user-friendly, making them perfect for beginners in multi-agent AI, as they don’t require complicated setups.

Use Cases of OpenAI Swarm

Some of the potential use cases of a open AI Swarm could be the following:

• Virtual Customer Support: Specialized agents can efficiently tackle distinct types of customer queries, such as resolving billing issues or addressing technical glitches, while routing complex concerns to the appropriate expert.
• Smart Personal Assistance: Autonomous agents can collaborate to handle everyday responsibilities, including managing schedules, sending reminders, and drafting email responses, offering comprehensive support for daily activities.
• Real-time Data Workflows: In dynamic data environments, agents can seamlessly handle various stages such as gathering, analyzing, and generating insights from data streams, ensuring a streamlined pipeline.
• Enhanced Retail Interactions: Swarm agents can work together in e-commerce settings to assist with customer inquiries, recommend suitable products, and process returns, creating a unified and efficient customer journey.

How to use OpenAI Swarm Framework

Setting the Stage

First, we import the essentials:

This creates the Swarm client, which orchestrates the interactions between our agents. Think of it as the mastermind behind the scenes, ensuring the agents do their thing.

The Agents Take the Stage

Next, we define a simple yet crucial function:

This function is the handoff mechanic. It allows Agent A to politely pass the conversation to Agent B when the time is right.

Now, let’s meet the agents:

Agent A is your friendly helper — always ready to assist but also smart enough to know when it’s time to bring in a colleague. Agent B is a bit more poetic and mysterious, only communicating in the elegant form of haikus.

Now, we bring it all together:

This starts a conversation with Agent A, but the user requests a chat with Agent B. Thanks to the function transfer_to_agent_b, Agent A recognizes that it’s time to step aside and lets Agent B take over. Agent B, true to form, will respond in haikus, adding a creative twist to the interaction!

Output

Building a Complex Customer Service Multi-Agent System

We will approach this with understanding how routine and handoffs work in the swarm.

Importing our dependencies

Routines

A “routine” isn’t rigidly defined but instead captures the idea of a sequence of actions. Think of it as a set of natural language instructions (provided via a system prompt) and the tools needed to carry them out.

Let’s break it down with an example:

Imagine building a customer service agent that helps users solve their problems. The agent follows these steps:

1. Gather information: First, the agent asks the user about the issue they’re facing.
2. Ask for more details (if needed): If the agent needs more information to understand the problem, it asks follow-up questions.
3. Provide a solution: Based on the information, the agent suggests a solution to fix the issue.
4. Offer a refund (if needed): If the user is still not satisfied, the agent offers a refund.
5. Process the refund: If the refund is accepted, the agent will find the relevant ID and complete the refund process.

This step-by-step process helps the agent efficiently resolve user issues while ensuring the user is satisfied.

The real power of routines lies in their simplicity and adaptability. Notice how the tasks are conditional, much like branches in a state machine. But routines go a step further. With “soft” adherence, the LLM doesn’t get stuck in a loop; it skillfully guides the conversation, making these routines highly effective for small and medium tasks.

Executing Routines

Start with a basic loop: gather user input, append the message to the conversation history, call the model, and then append the model’s response back to the history.

Since we haven’t integrated function calls yet, we need to add that next. Functions should be formatted as function schemas according to the model’s specifications. To make this easier, we can create a helper function that converts Python functions into the correct schema format.

Now, when the model triggers a tool, we need to run the appropriate function and return the result. This can be done by mapping tool names to python functions in a

In practice, we should allow the model to react differently depending on the result of the tool call. This process can repeat as long as there are more tool calls, since the model’s response may prompt another one. Here’s how the loop looks when we bring everything together:

Once the basic routine is up and running, we can consider adding more steps and tools. By loading the necessary tools and processes, we can expand routines to handle different kinds of user requests. However, as we try to stretch routines across too many tasks, they may begin to falter.

That’s where the concept of multiple routines comes in handy. We can switch to the appropriate routine with the right tools to handle different user requests. At first, dynamically changing tools and instructions might feel complex. But if we think of routines as individual “agents,” the concept of handoffs makes this easier — one agent can simply pass the conversation to another, keeping the workflow seamless.

Handoffs in the OpenAI Swarm Framework

Similar to being transferred to another representative during a phone call, a “handoff” in the Swarm framework happens when one agent (or routine) passes an ongoing conversation to another. But unlike real-life handoffs, these agents are fully aware of your previous interactions, ensuring a smooth transition!

To implement handoffs in code, we first need to define a class for an Agent. This will allow agents to manage conversations and transfer them when necessary.

Next, we’ll modify the existing routine code to support agents. Instead of passing a system_message and tools directly into the run_full_turn function, we’ll have it accept an Agent object instead.

With this setup, running multiple agents becomes straightforward:

In this example, handoffs are performed manually, but ideally, we want agents to pass tasks between each other automatically. A simple way to achieve this is through function calling. Each agent can invoke a specific handoff function, like transfer_to_xxx, to smoothly hand over the conversation to the next agent in line.

This method allows agents to handle conversations seamlessly, without manual intervention!

Handoff Functions

Now that our agent can communicate its intention to transfer a task, we need to implement the actual handoff. While there are several ways to do this, one particularly elegant approach is available.

So far, we’ve been returning strings from our agent functions, such as execute_refund or place_order. But what if we return an Agent object when it’s time to transfer instead of just returning a string? For example:

Now, let’s update the run_full_turn function to accommodate this kind of handoff:

Let’s take a look at an example where multiple agents are involved, allowing them to transfer tasks between one another:

Finally, we can run this in a loop to see everything in action. Since this won’t work directly in a Python notebook, try it in a separate Python file:

Using this method, agents can seamlessly hand off tasks to each other, enabling fluid transitions without extra complexity!

Conclusion

The Open ai Swarm framework provides an innovative approach to coordinating multiple agents in a dynamic and user-friendly manner. By focusing on the principles of routines and handoffs, Swarm facilitates seamless interactions between agents, allowing them to work collaboratively and adaptively to fulfil user requests.

This framework simplifies the management of agent behaviors and enhances the overall user experience by ensuring smooth transitions and continuity in conversations. With its lightweight and customizable architecture, Swarm serves as an excellent starting point for developers looking to explore multi-agent orchestration in their applications.