Squadron vs AutoGen

AutoGen (Microsoft) and Squadron both let you compose multiple LLM agents to solve a task, but the two frameworks have very different worldviews. AutoGen treats multi-agent collaboration as a conversation. Squadron treats it as a workflow.

TL;DR

What is AutoGen?

AutoGen  is a Python framework from Microsoft Research for building applications around conversations between multiple LLM agents. Its central abstraction is the GroupChat (and its more recent successor, AutoGen Studio’s actor model in v0.4), where agents like UserProxyAgent, AssistantAgent, and custom subclasses take turns speaking. A manager decides who speaks next, and the conversation continues until a termination condition fires.

AutoGen is research-forward and has produced influential patterns for agent collaboration — debate, critique, executor/reviewer pairs, and so on.

What is Squadron?

Squadron is a declarative framework for multi-agent AI workflows. You don’t write a conversation; you write a task graph. The runtime — a single Go binary — executes the graph, assigning tasks to agents, collecting structured outputs, and routing dynamically when the commander LLM decides which branch to take next.

The core difference: conversation vs workflow

AutoGen’s mental model is: put several agents in a room and let them talk until they finish. You get emergent behavior, which is powerful for research patterns but harder to reason about for production.

Squadron’s mental model is: describe what work happens, in what order, with what data flowing between steps. The commander LLM still makes runtime decisions (which route to take, when a task is complete) but it does so inside an explicit harness.

If you’ve ever needed to debug why an AutoGen GroupChat went off the rails for 40 turns, you know the cost of the conversational model. If you’ve ever tried to encode a strict process flow into a GroupChat, you know the cost of forcing it.

Side-by-side

A two-step research workflow in AutoGen:

from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
 
researcher = AssistantAgent(name="researcher", llm_config={"model": "gpt-4o"})
analyst    = AssistantAgent(name="analyst",    llm_config={"model": "gpt-4o"})
user       = UserProxyAgent(name="user", human_input_mode="NEVER")
 
group = GroupChat(agents=[user, researcher, analyst], messages=[], max_round=20)
manager = GroupChatManager(groupchat=group, llm_config={"model": "gpt-4o"})
 
user.initiate_chat(
    manager,
    message="Find the top 5 papers on post-quantum cryptography and extract key findings.",
)

The same in Squadron:

mission "research" {
  commander { model = models.anthropic.claude_sonnet_4 }
  agents    = [agents.researcher, agents.analyst]
 
  task "gather"  { objective = "Find the top 5 papers on ${inputs.topic}"; agents = [agents.researcher] }
  task "analyze" { depends_on = [tasks.gather]; objective = "Extract key findings"; agents = [agents.analyst] }
}

squadron mission research -c ./config --topic "post-quantum cryptography"

AutoGen lets the chat manager decide order. Squadron makes order explicit. Both are valid — they suit different problems.

When to pick AutoGen

• You’re researching multi-agent collaboration patterns themselves (debate, critique, role-playing).
• You want emergent behavior from a conversation, not a fixed flow.
• You’re invested in OpenAI / Azure OpenAI and the broader Microsoft ML stack.
• You’re building an interactive agent system where the human is in the loop turn-by-turn.

When to pick Squadron

• You’re shipping a production pipeline and want deterministic structure, not emergent dialogue.
• You want typed structured outputs flowing between tasks instead of replaying conversation history.
• You need scheduled or webhook-triggered runs with concurrency limits and budgets.
• You want automatic state persistence and resume — Squadron writes every step to SQLite or Postgres.
• You want workflows as config that non-Python teammates can review.
• You need MCP both directions — consume external MCP servers and expose your missions as MCP tools to Claude Desktop / Cursor.
• You want to mix LLM providers at the task level without writing adapter code.

Extension model: plugins are the primary primitive

Squadron’s primary way to give agents new capabilities is plugins — standalone programs you write in Go or Python that the runtime spawns as subprocesses and talks to over gRPC (hashicorp/go-plugin ).

plugin "domain" {
  source  = "./plugin_domain"   # local Go or Python source
  version = "local"             # auto-built on every config load
}
 
agent "executor" {
  tools = [plugins.domain.all]
}

In AutoGen, tools are Python functions registered with an agent at construction time. They run in the same process as the rest of your code. Squadron’s plugin model differs on several axes:

• Language choice per plugin. Go for systems-level or performance-critical tools (e.g., a high-throughput crawler, a static binary you can ship without a Python runtime). Python for tools that need PyPI dependencies.
• Process isolation via gRPC. A misbehaving plugin returns a clean error to the agent rather than crashing the runtime. Especially valuable when you’re running a long-lived squadron serve process with scheduled missions — one bad tool can’t take down everything else.
• Auto-build with content-hash caching. Edit a plugin’s source, reload, the runtime rebuilds it. Unchanged source skips the rebuild. No manual install loop.
• Cross-task state. The plugin subprocess lives for the lifetime of the Squadron process, not per-task. A browser plugin opens Chromium once; every task in every mission reuses it. AutoGen tools are functions — sharing state means writing your own singleton or module-level cache.
• Typed schemas and distribution. Each plugin declares typed input/output schemas; plugins distribute as GitHub releases and other Squadron configs install them via source = "github.com/owner/repo".

MCP is complementary. Squadron’s MCP support covers tools someone else built (the official MCP registry, third-party servers). Plugins cover tools you build — your domain logic, your performance-critical paths, things you want versioned in your own repo. AutoGen now has some MCP integration but does not have a comparable first-party plugin primitive in two languages.

What AutoGen does better

• Conversational multi-agent patterns where the content of the dialogue is the artifact.
• Tight Microsoft ecosystem integration.
• Research-grade flexibility — easy to invent new agent role patterns.

What Squadron does better

• Predictability. The DAG is explicit; the commander’s runtime choices are constrained to declared routes.
• Operational maturity. Resume, schedules, webhooks, budgets, and a web command center are all first-class.
• Reviewable workflows. One HCL file you diff in a PR.
• MCP-first. Squadron speaks the Model Context Protocol natively in both directions.

See also

• What is Squadron?
• Routing — how Squadron handles dynamic decisions
• Squadron vs LangGraph
• Squadron vs CrewAI