CrewAI

CrewAI organizes multi-agent systems around a workplace metaphor: you define agents with a role, goal, and backstory, give them tasks with expected outputs, and assemble them into a crew that works through the tasks sequentially or under a manager agent. It’s a standalone Python framework — notably independent of LangChain — designed to make collaborative agent teams feel declarative.

CrewAI’s spot in the landscape is approachability for multi-agent work. Where LangGraph asks you to design a state machine and AutoGen asks you to choreograph conversations, CrewAI asks you to describe a team — which makes it the fastest route from idea to a working researcher-plus-writer or analyst-plus-reviewer pipeline, and a favorite for automating business workflows. The trade-off is that the crew abstraction is opinionated: fine-grained control over each step is harder than in a graph framework. CrewAI’s answer is Flows, its event-driven layer for precise, conditional orchestration, letting you mix free-form crews into deterministic pipelines when production demands it.

You’ll pip install crewai and scaffold a project with crewai create crew myproject, which generates agents.yaml and tasks.yaml alongside Python glue. Define two or three agents, attach tools (built-in web search, file, and scraping tools, or your own via a @tool decorator), list tasks with expected_output, then call crew.kickoff() and watch the agents delegate and hand off results. Start sequential; reach for hierarchical mode and Flows once the simple version works.