No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by comfy-deploy · LLM Plugin · ★ 107
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is comfyui-llm-toolkit safe to install? View the security audit →
ComfyUI LLM Toolkit A custom node collection for integrating various LLM (Large Language Model) providers with ComfyUI. NEW GENERAL IMAGE AND VIDEO CFGs Features True Context-to-Context node connections one-input / one-output design Generators run even if nothing is connected, and they are the only node in the workflow Streaming Output directly on the node UI Runs openai latest image model GPT-image-1 and we include various templates Text generation using various LLM providers (OpenAI and local models, etc.) Provider selection and configuration with dynamic model fetching API key management Seamless integration with ComfyUI workflows Installation @@ -33,6 +43,7 @@ The following Python packages are required: pyyaml python-dotenv requests openai Installation Clone this repository into your ComfyUI customnodes directory: bash cd
| Stars | 107 |
| Forks | 19 |
| Language | Python |
| Category | LLM Plugin |
| Quality Score | 64.0292094748617/100 |
| Open Issues | 5 |
| Last Updated | 2025-10-01 |
| Created | 2025-04-17 |
| Platforms | python |
| Est. Tokens | ~792k |
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comfyui-llm-toolkit is an open-source llm plugin by comfy-deploy with 107 GitHub stars.
comfyui-llm-toolkit is primarily written in Python.
You can find installation instructions and usage details in the comfyui-llm-toolkit GitHub repository at github.com/comfy-deploy/comfyui-llm-toolkit. The project has 107 stars and 19 forks, indicating an active community.
The top alternatives to comfyui-llm-toolkit on Agent Skills Hub include codinit-dev, bpmn-assistant, lm-proxy. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: