Ask which agent framework is best and you will get a ranking, and the ranking will be wrong within a quarter. Star counts move monthly, names change (OpenAI’s Swarm became the Agents SDK; Microsoft folded AutoGen and Semantic Kernel into one Agent Framework), and every vendor’s landing page is a marketing surface. The durable question is not which tool wins. It is which abstraction matches the shape of your problem, and which maturity signals are worth trusting. Here is that comparison, starting with the most useful answer of all.
Start by asking whether you need one at all
The most experienced advice on this comes from the company whose models you are probably calling. Anthropic’s guide to building effective agents reports that the most successful implementations it saw were not using complex frameworks; they were built from simple, composable patterns, often a few lines against the model API directly. Its warning is specific: frameworks add layers of abstraction that obscure the underlying prompts and responses and make debugging harder. So the first move is to resist the framework reflex. If your task is a fixed sequence (retrieve, then summarize, then route), you want a workflow in plain code, not an autonomous agent, and you may not need a framework at all. Reach for one when the orchestration genuinely gets hard: branching, retries, shared state across many steps, parallel tool calls, human approvals in the middle.
Match the abstraction to the problem shape
When you do need a framework, the honest way to choose is by its core abstraction, because that is what you will live with. Four shapes dominate. Graph: LangGraph models an agent as a low-level graph of nodes, edges, and shared state, the right fit when you need explicit control, cycles, persistence, and the ability to inspect or pause a run. It is the most controllable and the least magical, which is exactly the trade you are making. Handoff: the OpenAI Agents SDK is built on a small set of primitives (agents, handoffs between them, guardrails, sessions, and built-in tracing) and is the natural pick if you are already on that stack and want orchestration without much ceremony; Microsoft’s Agent Framework occupies the same handoff-and-workflow space for the .NET and Azure world. Role: CrewAI lets you describe a crew of role-playing agents that collaborate, plus event-driven Flows for tighter control, and it shines for getting a multi-agent shape standing quickly. Event-driven: LlamaIndex Workflows expresses an application as async steps that emit and react to events, a good fit when your process is a pipeline of stages rather than a conversation. None of these is best in the abstract. They are best for a shape.
Pick the abstraction you will still understand at 2 a.m. when it breaks, not the one with the most stars this month.
The maturity signals that matter, and the ones that don’t
Two signals get over-weighted, and one gets ignored. The logo wall is the first trap: a framework listing famous companies tells you it has a sales team, not that it will fit your problem, and those testimonials almost never carry a production metric. The certified-developer count is the second: CrewAI’s headline of more than a hundred thousand certified developers measures course completions, not deployments. The signal that actually matters is boring version maturity. Microsoft’s Agent Framework reached a production-grade 1.0 in April 2026, with stable APIs and a long-term-support commitment. The OpenAI Agents SDK, despite being marketed as production-ready, is still on a 0.x version; its own release notes say the leading zero means the SDK is evolving rapidly and may break across minor versions. Neither fact is a verdict, but both are things you want to know before you pin a roadmap to them.
How to choose
Compress it to a few questions. Can you do it in plain code against the model API? Then do that, and revisit only when the orchestration starts to hurt. Do you need explicit, auditable control over state and steps? Look at a graph framework. Are you committed to a vendor’s stack and want managed handoffs and tracing? Use that vendor’s SDK, and check whether it has actually reached a stable version. Do you want a multi-agent or event-pipeline shape fast? The role and event-driven tools earn their place. And whatever you choose, keep the model swappable and keep your own evaluation outside the framework, so the day a better abstraction arrives, and it will, you can move without rewriting what you actually know about your task.