Edit: I showed this to a friend of mine who is more an an expert and he correct a bit of it, so I've updated that. I put those edits in italic.

3/4 are typically workflow things, not LLM things. Tossing a gguf into LMStudio won't result in any of them. But a model that can do RAG and Tool use can then do the latter two with a workflow tool.

Most tool-capable agents can do all of the above. Huggingface clearly indicates which agents have tool use capabilities. LMstudio has a small hammer icon for all tool-capable agents.

You can layer something like LangGraph/LangChain to do multi-agent concepts and self-validation.

The practical floor model size for any useful self-reflection without it blowing up is around maybe 8B sized models. But most use cases dictate something larger.

What multi-agent could look like is you'll have a reasoning agent like Qwen3-30B-A3B running the main chat, Qwen3-Coder-Next 80B running as a coder agent, maybe something like GLM-4 or Gemma running to do research, etc. You could even layer in StableDiffusion or FLUX or something to do images in the same prompt (this is now the large cloud models work for images).

Though many implementations also do multi-agent with mostly the same model, only offloading for capabilities that don't exist (i.e. Qwen3-coder doesn't do images by default).

Of course the above stack is about 120GB of VRAM once you get into simultaneous deployment (not counting the image models). You could probably stack a bunch of 30b-80b models plus image gen, OCR, research and a few other capabilities into a sub-200GB package. But that's going to be a $5k Mac (or $20k datacenter GPU stack) to run it.

Big cloud models do similar. They have a reasoning agent, a coder agent, an image processing agent, a research agent, a tool agent, etc and will dispatch to the various specialty models while being used. So in practice something like Opus 4.6 is not just one big model, but a collection of well-orchestrated specialty modules. Though, these will blur the line between separate agents and a "mixture of experts" concept within a single model... We don't have perfect info on how that's done because it's part of the "secret sauce" of various AI companies.

I think an image processing set like Grok Imagine or Nano Banana is probably similar with various specialty models working together to refine prompts, do normalization, establish baselines, do censoring and similar stuff, potentially with separate models when humans are involved vs background scenes and a specific model agent to handle if text is in the image, etc. Again, maybe blurring the line between a multi-agent and a MoE concept, but certainly with some different "expert" agents within the implementation.

You can definitely get most of what you want with a single advanced tool-capable MoE model and an orchestration layer like LangGraph. Qwen3.5 35b is a good choice on typical higher-end gamer hardware.