Single agents struggle with complex, multi-domain tasks. Multi-agent systems assign specialized roles — researcher, writer, critic, executor — each with distinct tools and prompts. They collaborate, delegate, and produce higher-quality outputs than monolithic agents.
LangGraph models agent workflows as directed graphs. Each node is a function (LLM call, tool use, human review). Edges define transitions based on state. This enables cycles (retry loops), conditional branching, and persistent state across steps.
CrewAI organizes agents into "crews" with defined roles, goals, and backstories. A manager agent delegates tasks, agents execute in sequence or parallel, and results are synthesized. Ideal for content pipelines, research reports, and code reviews.