Local AI beyond just running the model

I’ve been experimenting with local AI lately, and one thing I’m finding interesting is what comes after getting a model running. Ollama makes local model access pretty straightforward, but for an actual application, there are usually other pieces to deal with as well, like RAG, document processing, agents, and connecting everything to the application itself. I recently came across LM-Kit while looking into this. It seems to take a broader approach to running AI locally rather than focusing only on the model runtime. For people here who use Ollama in real projects, how do you handle those additional pieces around the model? Do you keep everything separate, or use a more integrated stack? submitted by /u/Sufficient-Clerk7530 [link] [comments]

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Source: r/ollama | 2026-09-24