3 articles in this tag.
CrewAI wins when your multi-agent workflow maps naturally onto human team roles. AutoGen wins when agents need to deliberate, debate, and build on each other's work conversationally. Both are mature, well-supported, and genuinely different in how they model coordination.
Multi-agent systems assign specialized roles to separate AI agents that coordinate to complete tasks no single agent could handle reliably. The key architectures — supervisor, pipeline, and peer-to-peer — each trade control for flexibility in different ways.
AI agent communication design determines system cost, reliability, and debuggability as much as any individual agent's capability. The two fundamental approaches — message passing and shared state — each have distinct trade-offs, and most production systems use a hybrid.