Three frameworks. Three bets on how multi-agent systems should be built. This is the honest head-to-head.
The bets
CrewAI’s bet. Agents have roles (researcher, writer, reviewer). They have tasks (units of work). They collaborate via delegation. The right abstraction is “a crew of specialists working together.”
AutoGen’s bet. Agents communicate via conversations. The orchestrator routes messages between agents. The right abstraction is “agents talking to each other, sometimes with a human.”
Agentmatic’s bet. Agents are graphs. State is explicit. Control flow is explicit. The right abstraction is “a state machine that knows how to call LLMs.”
These aren’t compatible worldviews. They’re tradeoffs.
CrewAI: role-based delegation
from crewai import Agent, Task, Crew, Process
researcher = Agent(role="researcher", goal="...", backstory="...", llm=llm)
writer = Agent(role="writer", goal="...", backstory="...", llm=llm)
research_task = Task(description="...", agent=researcher)
write_task = Task(description="...", agent=writer)
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_task],
process=Process.sequential,
)
result = crew.kickoff()Strengths. Easy to onboard. Maps to a familiar “team” mental model. Goal/backstory/role prompting is well-tuned by the framework. Good for prototyping multi-agent ideas quickly.
Weaknesses. Implicit control flow (“the crew figures it out”) makes debugging hard. State is mostly in conversation history; checkpointing is bolt-on. Production primitives (retry, CB, DLQ) aren’t first-class. Less testable in isolation.
AutoGen: conversation orchestration
from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
researcher = AssistantAgent("researcher", llm_config=cfg)
writer = AssistantAgent("writer", llm_config=cfg)
reviewer = AssistantAgent("reviewer", llm_config=cfg)
user_proxy = UserProxyAgent("user", code_execution_config={"work_dir": "out"})
groupchat = GroupChat(agents=[researcher, writer, reviewer, user_proxy], messages=[])
manager = GroupChatManager(groupchat=groupchat, llm_config=cfg)
user_proxy.initiate_chat(manager, message="...")Strengths. Conversation is a natural model when the workflow really is “agents talking.” Group chat / Selector patterns are powerful. Strong code-execution agent for “let the model write and run code.” AutoGen Studio provides a visual builder. Microsoft / Azure integration.
Weaknesses. Conversation history grows fast — long workflows hit context limits. Hard to test deterministically (every run is a fresh conversation). Routing decisions are LLM-driven; you have less control. Production primitives are not first-class.
Agentmatic: explicit state graphs
from agentmatic import StateGraph, START, END
from agentmatic.prebuilt import create_supervisor
supervisor = create_supervisor(
llm=OpenAI(),
agents={"researcher": researcher, "writer": writer, "reviewer": reviewer},
)
# Or hand-rolled:
graph = StateGraph(WorkflowState)
graph.add_node("research", researcher)
graph.add_node("write", writer)
graph.add_node("review", reviewer)
graph.add_conditional_edges("review", verify, {"ok": END, "revise": "write"})
agent = graph.compile(checkpointer=PostgresSaver.from_env())Strengths. Deterministic — same input, same output. Every transition checkpointed; time-travel debugging is free. Multi-language SDKs (Python, TS, Rust, Go, Java). Built-in resilience primitives. Distributed clusters in the open-source core. Rust runtime = 10× faster.
Weaknesses. More explicit = more code for prototyping. The mental model is closer to “graph + state” than “team of agents” — newcomers used to CrewAI may find it more abstract.
Side-by-side: production criteria
| Criterion | CrewAI | AutoGen | Agentmatic |
|---|---|---|---|
| Deterministic execution | partial | no | yes |
| Checkpointing | bolt-on | bolt-on | first-class |
| Time travel | no | no | yes |
| HITL interrupt | partial | partial | first-class |
| Circuit breakers | no | no | first-class |
| Retry with backoff | partial | partial | first-class |
| Dead-letter queue | no | no | first-class |
| Distributed cluster | no | no | first-class |
| Multi-language SDKs | Python | Python | Python, TS, Rust, Go, Java |
| OpenTelemetry tracing | partial | partial | first-class |
| Visual debugger | no | AutoGen Studio | Agentmatic Studio |
| License | MIT (+ Enterprise) | MIT | MIT |
When to use which
Pick CrewAI when: You’re prototyping a multi-agent idea. The “crew of specialists” metaphor fits the workflow. You don’t need production-grade resilience yet. CrewAI Enterprise is fine for you long-term.
Pick AutoGen when: You’re in the Microsoft / Azure ecosystem. Your workflow really is agents-talking-to-each-other (and sometimes a human). You want AutoGen Studio’s visual builder. Code-executing agents are important to you.
Pick Agentmatic when: You need deterministic, testable graphs. You need production resilience in the box. You need polyglot SDKs. You need 10× speed on multi-agent workloads. You want to ship today and operate the agent for years.
They can compose
If you have a CrewAI prototype and want to ship it to production, you can wrap a CrewAI crew as a single Agentmatic node:
from agentmatic import StateGraph
from crewai import Crew
@node
def crewai_node(state):
result = my_crew.kickoff(state.input)
return {"output": result}
graph = StateGraph(State).add_node("crew", crewai_node)Now your prototype runs inside Agentmatic’s checkpointed, resilient, observable runtime. You haven’t rewritten the crew; you’ve graduated it.
Honest takeaway
There’s no “best.” There are tradeoffs. For most production multi-agent systems in 2026, Agentmatic’s combination of speed + resilience + multi-language + open-source is the right pick. For prototyping or chat-driven workflows, CrewAI or AutoGen may feel more natural.
You can always migrate later. The agents you write are mostly prompts and tools; the framework is a thin wrapper. Don’t agonize.