ComfyUI-ThinkingLLM

ThinkingLLM is a multimodal local-LLM node pack for ComfyUI. It provides workflows for Qwen, Gemma 4, GGUF, Whisper ASR, prompt enhancement, image/video understanding, audio analysis, local image and text decision routing, and secure OpenAI-compatible providers.

Watch the demo on YouTube

GPL-3.0 fork of Deaquay/ComfyUI-Qwen3.5-Uncensored, itself derived from huchukato/ComfyUI-QwenVL-Mod and 1038lab/ComfyUI-QwenVL. See LICENSE.

Highlights

• Local Transformers/Hugging Face and GGUF inference
• Image and video understanding with optional mask-focused analysis
• Gemma 4 audio understanding and Whisper speech-to-text
• Prompt enhancers for image and video generation workflows
• Thinking-mode control, live terminal streaming, and a separate RAW_TRACE output
• Local image and text decision routing to named, lazily executed workflow branches
• Curated model recommendations and preset-specific generation guidance
• Secure OpenAI-compatible API node for approved remote providers
• Existing compatible models are discovered instead of downloaded twice

Quick install

ComfyUI Manager (recommended)

1. Open ComfyUI Manager → Install via Registry.
2. Search for ThinkingLLM.
3. Click Install and restart ComfyUI.

Manual install

cd ComfyUI/custom_nodes
git clone https://github.com/goodguy1963/ComfyUI-ThinkingLLM.git
cd ComfyUI-ThinkingLLM
pip install -r requirements.txt

For GGUF vision or Gemma 4 audio, also follow the llama.cpp backend installation guide.

The Decision Router needs native llama.cpp decision APIs that are not present in every llama-cpp-python build. Check the Decision Router runtime guide before using it; the node checks compatibility but does not replace an already installed native runtime.

Nodes at a glance

Node	Backend	Purpose
ThinkingLLM	Transformers / HF	Image and video understanding
ThinkingLLM (Advanced)	Transformers / HF	Full controls and MASK_PREVIEW
ThinkingLLM Prompt Enhancer	Transformers / HF	Text prompt enhancement
ThinkingLLM (GGUF)	llama.cpp	GGUF image and video understanding
ThinkingLLM (GGUF Advanced)	llama.cpp	GGUF advanced controls and MASK_PREVIEW
ThinkingLLM Decision Router (Image)	llama.cpp	Image decisions routed to named branches
ThinkingLLM Decision Router (Text)	llama.cpp	Text decisions routed to named branches
ThinkingLLM Gemma 4 Audio (GGUF)	llama.cpp	Gemma 4 audio understanding
ThinkingLLM Whisper ASR	faster-whisper	Speech-to-text transcription
ThinkingLLM Prompt Enhancer (GGUF)	llama.cpp	GGUF prompt enhancement
ThinkingLLM System Prompt Preset	Utility	Reusable system prompts as STRING
ThinkingLLM API (OpenAI Compatible)	Remote API	Approved OpenAI-compatible providers

The Transformers/HF nodes are the recommended default when you want the simplest installation. Choose GGUF when you specifically need quantized llama.cpp models or Gemma 4 audio.

Decision Router

The separate Image and Text Decision Router nodes classify their input and send each image or text item to one named route. Configure a system prompt plus route names and descriptions for any task, such as subject, document, or product classification. They offer Clef-Flash 9B, Clef 27B, and OpenJev 27B GGUFs. Model files download on first use into ComfyUI/models/LLM/GGUF; image routing also downloads the matching vision projector. Both run in-process without a separate llama.cpp server. Each node exposes up to 52 configured route sockets and returns per-item decisions and normalized route probabilities on decision_json. Empty route outputs are blocked, so their downstream branches do not execute.

Clef models require llama.cpp's native decision-batch APIs; OpenJev uses llama.cpp's final-position logits API. Image routing additionally requires the MTMD vision APIs; text routing does not. Each node checks the selected capabilities before downloading model weights and reports missing symbols with an installation hint. See Decision Router llama.cpp requirements for the runtime details. OpenJev is CC BY-NC 4.0 and is hidden in commercial builds; Clef models are Apache-2.0.

Start a workflow

1. Add the ThinkingLLM node that matches your backend and task.
2. Select a model or an already installed local model.
3. Connect image, video, audio, mask, or text inputs as needed.
4. Connect RESPONSE or ENHANCED_OUTPUT to a text display or downstream prompt input.
5. Connect RAW_TRACE when you want generation diagnostics.

Useful defaults:

• Start with enable_thinking=false for transcription and short direct answers.
• Enable stream_tokens_to_terminal when diagnosing a slow or unhelpful generation.
• Resize very large images before inference to reduce context and memory pressure.
• Use the dedicated Gemma 4 Audio or Whisper node instead of assuming every multimodal model supports audio.

For explanations of thinking mode, model guidance, raw traces, audio, masks, and object-removal prompting, see Features and workflows.

Model families

ThinkingLLM includes curated entries for:

• Qwen3.8, Qwen3.5, Qwen3-VL, Qwen2.5-VL, and Qwen3
• Gemma 4 vision, text, and audio-capable GGUF variants
• Whisper tiny through large-v3 and distil-large-v3
• Local compatible Transformers checkpoints and GGUF/mmproj pairs

See Models and backends for exact families, model locations, local discovery, platform notes, and backend requirements.

Remote API node

ThinkingLLM API (OpenAI Compatible) supports approved OpenAI-compatible providers while keeping credentials on the ComfyUI server. Provider keys are not stored in workflows.

• API setup, profiles, keys, and security
• API image inputs

Documentation

Topic	Guide
Thinking, streaming, traces, audio, masks, and workflow behavior	Features and workflows
Supported models, discovery, locations, and backend selection	Models and backends
GGUF / llama.cpp installation on Windows and Linux	llama.cpp backend installation
Decision model runtime requirements	Decision Router llama.cpp requirements
Duration-aware LTX and MiniMax H3 prompting	Duration-aware video prompts
Video preset recommendations and rationale	Video preset settings
OpenAI-compatible API node	API setup and security
OpenAI-compatible image inputs	API image inputs
Maintainer and fork notes	Maintainer notes

Production deployment

This repository provides ComfyUI nodes, not a complete production runtime. For exposed or serverless workflows, review dependency locking, validation, secrets, cost controls, monitoring, and rollback before deployment. The ComfyUI on RunPod production-readiness checklist is a useful starting point.

Credits

• Deaquay/ComfyUI-Qwen3.5-Uncensored
• huchukato/ComfyUI-QwenVL-Mod
• 1038lab/ComfyUI-QwenVL
• Qwen Team (Alibaba Cloud)
• JamePeng/llama-cpp-python
• comfyanonymous/ComfyUI

Maintainer: goodguy1963

License

GPL-3.0. See LICENSE.