CrewAI vs AutoGen

Two different mental models

CrewAI thinks "team of specialists doing a job." You define a Crew with Agents (each with a role, goal, and backstory), Tasks assigned to specific agents, and a Process (sequential or hierarchical). The mental model is a project plan: each step has an owner and a deliverable, and the work moves forward in a known order.

AutoGen thinks "agents in a conversation." You define Agents and put them into a group chat; each turn, an agent reads the conversation and responds, and termination is explicit (a message pattern or a maximum number of turns). The mental model is a meeting where the next speaker is chosen by a manager -- closer to Slack than to Jira.

If you find yourself writing "the researcher gathers facts, then the writer drafts, then the reviewer edits," that is CrewAI shaped. Existing AutoGen systems may already encode a conversational critic loop; preserve it only while it remains useful, and use Microsoft Agent Framework for new Microsoft-centered work.

Use cases: when each one wins

CrewAI fits content production pipelines (researcher, writer, editor, fact-checker in sequence), market and competitor research crews, compliance and audit workflows where each agent owns a check, cross-provider experiments mixing GPT-4 for one role and Claude for another, and fast prototypes of "team" ideas where the goal is to validate whether a multi-agent shape works at all.

Existing AutoGen systems can fit code generation with critique, planner/executor loops, multi-agent debate, research synthesis, and human-in-the-loop conversations. For greenfield Microsoft work, evaluate Microsoft Agent Framework instead.

Learning curve

CrewAI presents a direct role-based model: define agents with roles and goals, attach tasks, and run the crew. That can be easier to explain for a clearly multi-role workflow, but onboarding speed depends on tools, state, evaluation, and deployment requirements.

AutoGen requires choosing a conversation pattern (group chat, round-robin, nested chat) and a termination condition. Existing systems may still benefit from free-form turns such as critique and revision after a tool failure. For greenfield work, do not treat that pattern fit as a reason to adopt a maintenance-mode runtime: use CrewAI for an active role-and-task path or Microsoft Agent Framework for new Microsoft-centered work.

Pricing comparison

Both frameworks are MIT-licensed and free; the real bill is model inference and, optionally, hosted observability. Both self-host on any Python host and call any provider pay-per-token. CrewAI offers CrewAI Plus (tiered subscription) for hosted runtime and observability; AutoGen has no official hosted runtime -- you self-host and roll your own observability (logs, OpenTelemetry).

The pattern: both frameworks are MIT-licensed. Runtime cost depends on agents, turns, context, tools, models, and hosted services. AutoGen conversations and CrewAI role flows produce different traces; measure the representative task instead of assuming a fixed multiplier.

Final verdict

These frameworks use different orchestration models, but maintenance status changes the greenfield decision. CrewAI remains an active candidate for role-and-task pipelines. Existing AutoGen conversation loops can remain in service while compatibility and migration value are measured.

For a new Microsoft-centered multi-agent project, begin with Microsoft Agent Framework. For an existing AutoGen deployment, inventory conversation semantics, executors, state, and evaluation before moving; do not rewrite solely because a successor exists.