Every Agent Frameworks comparison and buyer's guide for building AI agents — 10 pieces and counting. Each is a head-to-head or a “best X for Y” roundup with a sources-backed verdict.
Microsoft just deprecated its two most-starred agent frameworks to ship a third. If you're choosing today, the decision is already made for you — here's why, and where it still loses.
Since the 1.0 release, LangChain's agent helper runs on LangGraph's engine — so the real question isn't which to pick, but which layer of the same stack to write against.
All three converged on the same runtime shape, so the old 'which can build an agent' question is dead. What's left is a bet on which layer each treats as first-class — and one differentiator nobody can copy.
All three build Python agents, but they disagree on one thing — who owns the loop. That contract, not the benchmark, is what you live with for years.
The frameworks that get the most attention disagree on something basic — what an agent's action even is. One writes code, one wires a graph, one casts a team.
The second wave of agent frameworks is leaner, typed, and vendor-backed — and underneath the branding, they're quietly converging on the same idea.
The three names a JavaScript team keeps hitting when it tries to build an agent aren't competing for the same job. Two of them stack on top of the third.
One hands you Anthropic's production agent loop already wired up; the other hands you a blank graph and a state machine. The choice is less "which framework" than "how much of the loop do you want to own."
They started on opposite ends — one indexed your documents, one chained your calls. In 2026 they've converged. The real choice is which abstraction you want to debug at 3am.
All three claim to build multi-agent systems. The real question isn't features — it's who owns the control flow, and the answer changes which one is the right call.