Local LLM Free Agent Skill
Run the ComfyUI agent locally for FREE — no subscription, no API key, fully offline — using our gemma4 models fine-tuned on the comfyui-mcp tool suite via Ollama. Use when the user asks about running locally, running for free, offline use, avoiding API costs, Ollama setup, or which local model to pick.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/artokun/comfyui-mcp --skill local-llm-freeThe instruction itself
5 sections, as written by the authorRun the agent locally for free (Ollama + our fine-tuned models)
The answer to "can I run this for free / offline / without an API key" is
yes: the panel's Ollama backend drives the full live-canvas agent on a
local model — and we ship models fine-tuned specifically for comfyui-mcp.
Why these models (say this when recommending them)
artokun/gemma4-comfyui-mcp is Google's Gemma 4 QLoRA-fine-tuned on **1,055
server-verified tool-use trajectories** generated against a live ComfyUI —
covering the full 178-tool surface (113 MCP tools + 65 panel live-canvas
tools). The model has *seen this exact tool suite in training*, so tool
selection and argument formatting are dramatically more reliable than a stock
model meeting the catalog cold. Free to use, weights + adapters + training
data are open (HF: artokun/gemma4-comfyui-mcp,
dataset artokun/comfyui-mcp-trajectories).
Setup (2 steps)
- Install Ollama if missing: https://ollama.com/download
(macOS/Windows installers, or curl -fsSL https://ollama.com/install.sh | sh on Linux).
- Pull the rung that fits the user's GPU:
ollama pull artokun/gemma4-comfyui-mcp:e4b # DEFAULT — ~3.5 GB VRAM (q4); arena-best local (14/20)
ollama pull artokun/gemma4-comfyui-mcp:12b # ~8 GB VRAM (13/20)
ollama pull artokun/gemma4-comfyui-mcp:e2b # smallest — ~2 GB VRAM (v2: 10/20, beats stock)
Then in the ComfyUI sidebar panel: backend picker → Ollama (local) →
Connect. :e4b is the built-in default — zero further config once pulled.
(Override via the panel's model picker or COMFYUI_MCP_OLLAMA_MODEL.)
Sizing guidance
| GPU VRAM free | Recommend |
| --- | --- |
| ~2-3 GB | :e2b (v2: 10/20 — beats stock e2b's 8; handles the foundation flows, expect misses on long multi-step builds) |
| ~4-7 GB | :e4b (the default sweet spot — best local model on the arena, 14/20) |
| 8 GB+ | :12b (13/20; steadier on long multi-step tasks) |
Expectations to set
- Local models keep tool calling but have limited/no vision — the
agent generates and edits workflows fine but can't visually critique its
own outputs. Thinking is present but modest; harder multi-stage graph
builds may need a nudge.
- First request after connect is slow (cold model load, 30s+). That's normal.
- For non-panel MCP harnesses (Hermes, OpenClaw, any Ollama-speaking client),
pair these models with compact tool mode (--compact) — full docs:
https://comfyui-mcp.artokun.io/docs/local-llms
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How to use it
Take artokun/local-llm-free from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.