comfyui LLM party

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C‌‌​‎​‎‏​‍‎​‎​‎‏​‌‎​‎‍​‍‏​‍‌​‌‏omfyui_llm_party aims to develop a complete set of nodes for LLM workflow construction based on comfyui as the front end. It allows users to quickly and conveniently build their own LLM workflows and easily integrate them into their existing image workflows.

Effect display

https://github.com/user-attachments/assets/945493c0-92b3-4244-ba8f-0c4b2ad4eba6

Project Overview

ComfyUI LLM Party, from the most basic LLM multi-tool call, role setting to quickly build your own exclusive AI assistant, to the industry-specific word vector RAG and GraphRAG to localize the management of the industry knowledge base; from a single agent pipeline, to the construction of complex agent-agent radial interaction mode and ring interaction mode; from the access to their own social APP (QQ, Feishu, Discord) required by individual users, to the one-stop LLM + TTS + ComfyUI workflow required by streaming media workers; from the simple start of the first LLM application required by ordinary students, to the various parameter debugging interfaces commonly used by scientific researchers, model adaptation. All of this, you can find the answer in ComfyUI LLM Party.

Quick Start

0. If you have never used ComfyUI and encounter some dependency issues while installing the LLM party in ComfyUI, please click here to download the Windows portable package that includes the LLM party. Please note that this portable package contains only the party and manager plugins, and is exclusively compatible with the Windows operating system.(If you need to install LLM party into an existing comfyui, this step can be skipped.)
1. Drag the following workflows into your comfyui, then use comfyui-Manager to install the missing nodes.

• Use API to call LLM: start_with_LLM_api
• Using aisuite to call LLM: start_with_aisuite
• Manage local LLM with ollama: start_with_Ollama
• Use local LLM in distributed format: start_with_LLM_local
• Use local LLM in GGUF format: start_with_LLM_GGUF
• Use local VLM in distributed format: start_with_VLM_local (Currently, support is extended for Llama-3.2-Vision/Qwen/Qwen2.5-VL/deepseek-ai/Janus-Pro.)
• Use local VLM in GGUF format: start_with_VLM_GGUF
• Utilize API calls to LLM for generating SD prompts and images: start_with_VLM_API_for_SD
• Employ ollama to call minicpm for generating SD prompts and images: start_with_ollama_minicpm_for_SD
• Utilize the local qwen-vl to generate SD prompts and images: start_with_qwen_vl_local_for_SD

2. If you are using API, fill in your