OpenAI's Swarm vs. AutoGen: A Quick Comparison

tl;dr: AutoGen wins, but see the summary table.

Before all else, an admission that this is a little silly - OpenAI’s freshly released Swarm is a fresh educational example of a multi-agent system, whereas Microsoft’s AutoGen is a seasoned year-old framework. Nonetheless, I haven’t seen anyone do this yet, and it’s interesting to see how you’d do the same things in either.

For a review of AutoGen, see my previous post. Note that both that review and this article pertains to AutoGen 0.2, even though a major release (0.4) is happening at the time of writing.

Swarm

At the heart of the Swarm are two notions: routines and handoffs (I’ll explain below, though this OpenAI handbook explains each well). Autogen implements both, albeit slightly differently. But before we get into that, let’s talk about the agents themselves.

Agents

In both AutoGen and Swarm, agents are the basic unit of operation. In the previous post, I described AutoGen agents as ‘a fancy word for “LLM prompts with tool access and execution environment”’, and that’s not really different in Swarm¹.

Likewise, tools are the same in both frameworks - they’re Python functions that you register to an agent².

Routines

This is the same as an agent prompt in AutoGen - each Swarm agent has a natural-language routine (imagine “task list”) that it follows.

Handoffs

This is a little different - in Swarm, the agent keeps going until it explicitly makes a tool call that “hands off” to another agent (i.e. returns an Agent object from the tool call). In AutoGen, a multi-agent conversation is managed by GroupChatManager, which selects the next agent (which can be the same agent!) to speak based on the conversation history and the agent’s description.

AutoGen allows you to define “StateFlow” - a set of allowable/disallowed transitions between agents. I find this to be a little more concise than Swarm’s approach, but it does have its own abstraction.

Other differences

Terminating the conversation

AutoGen has a notion of TERMINATE message that an agent issues once done with the task at hand. (Weaker models tend to not get there, getting stuck in “gratitude loops”, which is my favorite fact about AutoGen.)

As far as I could tell from looking at the code, Swarm doesn’t have this notion - rather, I think there’s an assumption that the user and the agent will take turns until the conversation is done. (Currently looking through the examples to confirm.)

Of note, both frameworks implement a notion of max_turns (Swarm) / max_rounds and max_consecutive_auto_reply (AutoGen) to cap execution loops.

Evaluations

Unlike AutoGen, Swarm is at least eval-aware - it ships with an airline example, which tests whether a tool is called when the conversation implies. The test framework, such as it is, is hand-rolled and not terribly flexible.

AutoGen is technically amenable to using e.g. pytest/DeepEval for evaluations, but it’s not built-in and it’s hard to test multi-agent workflows end-to-end. (You can use DeepEval the way I have in the “cover letter generator” hobby project, but it only implements per-agent tests without any tool calling.)

Observability

Swarm wins here: it’s just an OpenAI call, so all observability wrappers (like Langfuse) should “just work”. AutoGen has several options, all a little unsatisfying - talk to the SQLite database or use the AgentOps third-party cloud solution, which trades comfort off for privacy.

Provenance, maintenance, and community

Swarm is OpenAI’s child with no promises of updates or maintenance attached. AutoGen is maintained by a group at Microsoft, has been actively developed for the past year, and has a thriving Discord community.

Summary table

Feature	Swarm	AutoGen
Agents	LLM prompts with tool access	Same
Tools	Python functions registered to agents	Same
Execution Environment	Current environment; no executing agent concept	Containerized environment; agents can be either caller, executor, or both
Handoffs	Agent continues until explicit handoff via tool call	Managed by GroupChatManager; supports StateFlow for agent transitions
Terminating the Conversation	No explicit termination; uses max_turns	TERMINATE message; also uses max_rounds and max_consecutive_auto_reply
Evaluations	Eval-aware, with a hand-rolled test framework, not very flexible	No built-in evaluations; flexible with pytest/DeepEval but hard to test multi-agent
Observability	Integrated with OpenAI, works with wrappers like Langfuse	SQLite or third-party solutions like AgentOps
Provenance, Maintenance, Community	OpenAI project with no guarantees of updates or maintenance	Actively developed by Microsoft with a thriving community

Conclusion

Swarm is a great educational tool to build a multi-agent system from scratch, allowing you to understand each bit. AutoGen, however, has taken the lessons learned from that endeavor and implemented them in a way that’s more flexible and more powerful. Consequently, this is not really a fair comparison.

I’ll be watching the development of Swarm with interest, though. (If there is any.)

Footnotes

1. In Swarm, the execution environment is whatever env you’re running in; in AutoGen, there’s a notion of automatic containerization. I assume it’s going to take E2B.dev about a day to implement an out-of-the-box solution for this, though. ↩

2. Due to the execution environment sameness, there’s no notion of “executing agent” in Swarm like there is in AutoGen - the agent calls the tool, and that’s that. In AutoGen, you can register the tool to the agent and make it both the caller and the executor to achieve a similar effect. ↩