Running agents on local models is a great idea with one classic failure mode, and I have watched people hit it over and over: they pick a framework that quietly assumes one specific provider. Then their local agent turns out to be an API client with extra steps. The demo works. The architecture lies.
The fix is architectural, not ideological. Pick a framework with a pluggable model layer, one where the model is a swappable component instead of a hardcoded dependency. With that in place you can develop against an API, test against a local model, and deploy on whatever your privacy or budget demands. Without it, you are rewriting when requirements change. And requirements always change. A framework welded to one provider is a future migration wearing a friendly face. I keep saying this because people keep not listening.
Local models also change the money math, and this part is underrated. They are slower per token than the big APIs but cost zero per token and leak zero data. Agents loop, retry, and chatter. An agent that makes a hundred internal calls is cheap locally and terrifying on metered pricing. That cost difference compounds fast. Half the point of local inference is the economics, not the ideology.
There is a security angle too. Local inference shrinks your attack surface, which is real. But it does not cover everything, so pair it with an AI agent security audit SaaS review before launch for the layer local models cannot touch: your prompts, your tools, your permissions. Prompts leak. Tools overreach. Nobody audits that part until something breaks, and by then it is a story.
So the checklist for the best AI agent framework for local LLMs: pluggable model layer, real support for open-weight models, sane handling of smaller context windows, and examples that actually show local setups instead of pretending. Our database flags local-LLM support per framework across the open source AI agent frameworks we track, precisely because this question comes up weekly. Filter for it at localaiagents.fyi. Or hand me the whole problem: I build custom AI agents scoped to your actual workflow, local models included.