Swarm by OpenAI: Guide to Multi-Agent AI Systems

Some problems are too big for one person to solve alone.

You need a team, with each member bringing a different skill. In the world of artificial intelligence (AI), the same idea applies.

Sometimes, one AI agent isn’t enough to handle a task.

That’s where multi-agent systems come in—teams of AI agents working together.

Swarm by OpenAI is a tool that helps you build these teams.

Each agent can have its own job, like answering questions or doing calculations, and they can pass tasks to each other to get things done.

Swarm is an experimental project, so it’s not finished or perfect, but it’s a great way to learn how AI agents can team up to solve problems.

What Makes Swarm Special

Swarm has some key features that make it useful for building multi-agent systems.

Here’s what stands out:

• No Memory Between Tasks: Many AI tools keep track of past actions or conversations. Swarm doesn’t. It starts fresh every time you use it. This might seem like a downside, but it keeps things simple. You don’t have to worry about the AI mixing up old details with new ones. It’s like giving it a clean whiteboard for each job.
• Agents with Specific Jobs: With Swarm, you can create agents and give them clear roles. One agent might be good at math, while another searches the internet. You decide what they do by giving them instructions and tools to use.
• Passing Tasks Around: Agents in Swarm can hand tasks to each other. If one agent can’t handle something, it passes it to another that can. Think of it like a worker passing a customer to a specialist in a store.
• Shared Notes: To avoid confusion, agents share information using something called context variables. It’s like a shared notebook where they write down important details so everyone stays on the same page.

These features make Swarm a handy tool for experimenting with AI teams.

Why Multi-Agent Systems Are Useful

Having multiple AI agents work together brings some big advantages:

• Specialization: Each agent can focus on what it’s best at, like a team of experts working together.
• Scalability: If a task gets bigger, you can add more agents to help out.
• Flexibility: You can change or update one agent without breaking the whole system.
• Resilience: If one agent stops working, others can step in and keep things going.

Swarm lets you play around with these ideas and see how they work in real situations.

Examples of Swarm at Work

OpenAI includes examples to show what Swarm can do.

One example is an airline customer service system. It starts with a main agent that talks to customers and finds out what they want.

If someone needs to book a flight, the main agent hands them off to a sales agent. If they want a refund, they go to a refund agent.

Each agent has its own job, and they team up to help the customer from start to finish. Another example is a weather agent.

You can ask it something like, “What’s the weather in Chicago?” and it uses its tools to check and tell you the answer.

These examples show how Swarm can handle different tasks by splitting them up among agents.

How to Start Using Swarm

Want to try Swarm yourself? Here’s a simple way to get going.

You’ll need Python, a popular programming language, version 3.10 or higher. If you don’t have it, you can download it online—it’s free and not too hard to learn.

Once Python is ready, open a terminal (a command window on your computer) and type “pip install swarm” to get the Swarm library.

Next, you need an OpenAI API key, which is like a passcode to use OpenAI’s AI features. Sign up on OpenAI’s website to get one. Then, tell your computer the key. On Windows, type “set OPENAI_API_KEY=your_key_here” in the terminal. On Mac or Linux, type “export OPENAI_API_KEY=your_key_here”.

Now, let’s make a basic agent. Open a text editor, create a file called “hello_agent.py”, and copy this code:

from swarm import Swarm, Agent

client = Swarm()

def say_hello():
    return "Hello, how can I help you?"

hello_agent = Agent(
    name="Hello Agent",
    instructions="You are a friendly agent that says hello.",
    functions=[say_hello]
)

response = client.run(agent=hello_agent, messages=[{"role": "user", "content": "Hi"}])
print(response.messages[-1]["content"])

Save the file and run it with Python. The agent will say, “Hello, how can I help you?” back to you. It’s a small start, but you can add more agents or tasks from here.

Swarm vs. Other Tools

Swarm isn’t the only tool out there.

OpenAI also has the Assistants API, which is good for single agents that remember past chats—like a virtual helper you talk to over time. Swarm, though, is built for multiple agents working as a team, not just one.

Other tools like Langroid do multi-agent stuff too, but Swarm is easier to use and simpler to customize, especially if you’re new to this. It’s less about fancy features and more about learning the basics of AI teamwork.

Where Swarm Stands Now

Swarm is still an experimental tool, so it’s not ready for serious, real-world projects where everything has to work perfectly.

OpenAI has come out with something called the Agents SDK, which is a stronger, more reliable version of Swarm.

The Agents SDK is meant for people building AI systems to use in actual businesses or apps. If you’re just messing around or learning, Swarm is fine. But if you need something solid for a real job, the Agents SDK is the way to go.

Wrapping Up

Swarm by OpenAI is a great way to dive into multi-agent AI systems.

It’s simple to use and lets you experiment with teams of AI agents, each doing its own job.

You can test out different roles, watch how agents work together, and figure out the challenges of making them cooperate.

It’s not built for big, real-world projects yet—that’s where the Agents SDK comes in—but it’s perfect for learning or trying out ideas. If you’re curious about how AI can work as a team, Swarm is a fun and easy place to start.

Give it a shot and see what you can build!