Exploring OpenAI’s Swarm: An experimental framework for multi-agent systems

OpenAI has recently and somehow surprisingly released Swarm, a lightweight and experimental framework designed to support the development of multi-agent systems (in their GitHub they specifically mention that it is experimental and educational). Unlike traditional approaches that rely heavily on the underlying LLM API, Swarm offers a stateless abstraction for managing interactions and handoffs between multiple agents. It is an interesting release but also has sparked a mix of excitement and skepticism within the developer community, mainly due to its experimental state while released and the growing landscape of similar frameworks that already exist.

What is Swarm?

Swarm aims to simplify the orchestration of multiple agents by providing a minimalist and transparent interface. Here is how it works from a high level perspective:

1. Each agent in Swarm comes with its own set of instructions, a designated role (e.g., “Sales Agent”), and a collection of available functions. These functions are transformed into JSON structures, allowing for seamless integration and execution.
2. Swarm enables dynamic handoffs between agents based on the conversation flow or specific criteria within agent functions. This is achieved by returning the next agent to be called within a function, facilitating smooth transitions and specialised handling of tasks.
3. To maintain state and share information across agents, Swarm uses something called context variables. These variables provide initial context and are updated throughout the conversation, ensuring consistency and coherence.
4. The client.run() method initiates and oversees the multi-agent conversation. It requires the initial agent, user messages and context variables, returning a response that includes updated messages, context variables and the last active agent.

Key features and insights

The Swarm framework manages a loop of agent interactions, function calls and potential handoffs, enabling complex conversation flows and task delegation. It operates using a stateless architecture, meaning it does not retain information between calls. This design offers transparency and allows developers fine-grained control over agent behaviours without the overhead of maintaining persistent states.

The framework supports direct Python function calls within agents, providing flexibility and enabling developers to integrate existing codebases. Effective state management across interactions is achieved through context variables, ensuring that relevant information is accessible when needed during agent operations. Swarm also supports streaming responses, enhancing real-time interactions and contributing to a smoother user experience.

Swarm is presented as an experimental framework, with OpenAI emphasising that it is intended for exploring ergonomic interfaces in multi-agent systems. It is primarily aimed at educational purposes and is not yet recommended for production environments. This is a bit of surprise knowing how OpenAI usually releases their products.

The release of Swarm has also provoked a range of responses from the developer community. Some are skeptical of its practical utility, pointing to the existence of similar frameworks and questioning the effectiveness of cyclic agent interactions given the current capabilities of language models. Comparisons have been drawn to other multi-agent frameworks, such as Langroid, developed by researchers at CMU and UW-Madison, which is recognised for its mature orchestration mechanisms and broader compatibility with other LLMs.

There is also a serious allegation that OpenAI took the idea and code for their implementation from Swarms (see also here for ongoing discussions on this: https://x.com/KyeGomezB/status/1844959936972791907)

This release is also seen within the context of a larger industry trend, where major tech companies are moving up the stack to create differentiation in the enterprise agents space, which is being viewed as a significant commercial opportunity.

Sample code

Below is a simplified example illustrating how to define agents and manage interactions in Swarm:

from swarm import Swarm, Agent

# Define agent functions
def get_weather(location, time="now"):
    # Function to get weather information
    return f"The weather in {location} at {time} is sunny."
def send_email(recipient, subject, body):
    # Function to send an email
    return "Email sent successfully."
# Define agents
weather_agent = Agent(
    name="Weather Agent",
    instructions="Provide weather updates.",
    functions=[get_weather, send_email],
)
# Initialise Swarm client and run conversation
client = Swarm()
response = client.run(
    agent=weather_agent,
    messages=[{"role": "user", "content": "What's the weather in New York?"}],
)
print(response.messages[-1]["content"])

Downsides and challenges

Although interesting Swarm comes with several challenges and downsides…

One key challenge lies in its stateless architecture. Although statelessness allows for fine-grained control and transparency, it can complicate scenarios where persistent information across interactions is important. In complex workflows or long-running tasks, the lack of an intrinsic mechanism to maintain state across calls may result in inefficiencies, as developers may need to manually recreate context or devise external state management solutions.

Another potential downside is the experimental nature of Swarm, as OpenAI explicitly mentions that it is primarily intended for educational purposes. This experimental status means that the framework lacks the robustness and optimisations typically found in production-ready systems. Without official support or detailed documentation, developers might encounter unexpected limitations, bugs, or performance bottlenecks when scaling their applications. Furthermore, since Swarm is still in its early stages, critical features that are necessary for real-world applications might be missing or underdeveloped, reducing its practicality in enterprise environments.

The reliance on manual handoffs between agents also introduces complexity. Although dynamic agent transitions offer flexibility, developers need to carefully design and test agent interactions to avoid potential deadlocks or miscommunications. Poorly managed handoffs could disrupt the conversation flow, leading to suboptimal performance and user dissatisfaction.

Swarm’s sustainability as a framework is another concern. The stateless design, while simplifying certain aspects of agent interactions, poses challenges for long-term sustainability in agent-based systems. Each agent interaction begins without memory of previous states, requiring the system to manage external context carefully. This approach can lead to inefficiencies in resource usage, as agents must constantly retrieve or rebuild context, increasing computational overhead. Additionally, as the complexity and number of agents grow, maintaining an efficient and scalable system becomes more difficult without a built-in mechanism for sustainable state management. This can strain both computational resources and development efforts, making it harder to build and maintain systems that are both scalable and resource-efficient over time.

In addition, Swarm’s compatibility with existing systems may present challenges. While it supports direct Python function calls, integrating Swarm with large, existing codebases or other multi-agent frameworks could require significant effort. The lack of a mature ecosystem around Swarm, compared to other more established frameworks, may deter developers who require seamless integration with diverse tools and environments.

Finally, Swarm’s limited novelty in the growing landscape of multi-agent frameworks raises concerns about its relevance. Competing frameworks like Langroid offer more mature orchestration mechanisms and greater compatibility with various language models. Developers may find it difficult to justify adopting Swarm over other frameworks that already provide the functionality and stability needed for advanced multi-agent systems.

Considerations and future outlook

While Swarm offers an interesting and more integrated approach to building multi-agent systems, its experimental status and lack of official support limit its immediate applicability for production environments. Developers are encouraged to explore Swarm for educational purposes and to contribute feedback that could shape future developments.

The release of Swarm also reflects a broader industry movement toward more sophisticated agent-based systems, particularly in the enterprise domain. As companies like OpenAI continue to innovate, we can expect more robust and production-ready tools that address the current limitations and capitalise on the growing demand for advanced AI orchestration frameworks.

I think that OpenAI’s Swarm is an interesting framework to explore. While it may not yet be suitable for production use and is also not very novel since many of these frameworks already exist, Swarm can provide insights into the possibilities of stateless agent orchestration and serves as possible foundation for future advancements in the field.

References

• OpenAI Swarm GitHub repository
• Langroid: Multi-Agent framework
• Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N.V., Wiest, O. and Zhang, X., 2024. Large language model based multi-agents: A survey of progress and challenges. arXiv preprint arXiv:2402.01680.