What’s the point of LangGraph now that frontier AI providers are getting better at agent building?
It feels like nowadays, almost everything you might want to build with LangGraph is already being implemented — and arguably better — directly by the frontier AI providers.
OpenAI, Anthropic, Google, Microsoft, etc. are increasingly providing models with better tool use, reasoning, memory/context handling, agent loops, and orchestration capabilities out of the box.
So what is the real advantage of building your own agent architecture with LangGraph?
Is it mainly about control and customization — e.g. deterministic workflows, state management, human-in-the-loop, custom routing, retries, parallel execution, observability, and being model/provider agnostic?
Or are there use cases where LangGraph actually produces materially better agents than simply using the agent frameworks provided by the frontier model companies?
I’m particularly interested in hearing from people who have deployed LangGraph agents in production. What made you choose LangGraph instead of the native agent tooling from OpenAI/Anthropic/etc., and would you still make the same choice today?