OpenAI Compatible LLM

ComfyUI is genuinely great at making images and famously dumb at words. OpenAICompatibleLLM is the bridge: a single node that lets any OpenAI-compatible chat server - Ollama, LM Studio, vLLM, LocalAI, or OpenAI itself - answer inside your graph. Hook the reply into your prompt, and you've turned the workflow into something that can rephrase a prompt until the model likes it, describe a reference image into a caption, or just explain a broken output in plain text. It's a pattern people have been bolting together by hand for a while - building the same wrapper for personal use is practically a rite of passage on r/comfyui - and this is the packaged version.

The name is a small lie in the best way: this node calls no API of its own and needs no key. The whole thing is a ~200-line requests wrapper. You point it at an endpoint, it posts a standard OpenAI Chat Completions payload, and hands the reply back as a string. Nothing to download, no model files, no GPU to babysit. If you can run the LLM elsewhere, this node can reach it.

How it works. generate() does one POST to whatever endpoint you give it, with model, messages, max_tokens, temperature, and a seed in the body. If you feed an image in, it takes the first frame of the batch tensor, base64-encodes it as PNG, and drops it into the message as an image_url part with a detail level. That's the whole vision story - "a chat completion with a picture attached." It also means the model you point at has to actually be a vision model (Qwen-VL, LLaVA, GPT-4o, that family); a text-only model will just complain or hallucinate.

The inputs that actually matter:

• endpoint - the full path, including /v1/chat/completions. Default is http://localhost:3010/v1/chat/completions, which is nobody's real default: Ollama listens on 11434, vLLM on 8000. You will change this, and the first thing to check when nothing works is that you did.
• model - the model name exactly as your server knows it.
• prompt - multiline; this is your actual message to the LLM.
• max_tokens (1–32000, default 2000) and temperature (0–2) - the knobs you'd expect.
• seed + seed_control - default random means a fresh seed every run, so an LLM won't give you the same answer twice. Pick fixed when you actually want reproducibility; increment/decrement walk from the last seed.

Optional extras: api_key (adds a Bearer header - blank for local servers), image_detail (auto/low/high, mostly a token-cost trade), and enable_thinking, which tacks chat_template_kwargs: {"enable_thinking": true} onto the request for reasoning models like QwQ or DeepSeek-R1 served via vLLM. Skip it unless your server/model support it.

Output: a single STRING named response. Feed it into Show Text to read it, or into any prompt/string input - using this node as an automatic prompt writer for your KSampler is the classic move. One nice touch: errors don't crash the graph, they get returned as text in that same output, so a failed call is readable rather than a red wall.

Installing. Grab it via ComfyUI Manager (search "OpenAI Compatible"), or:

cd ComfyUI/custom_nodes
git clone https://github.com/yeeyou/ComfyUI-OpenAI-Compatible-API

then restart. Dependencies are requests, pillow, torch, numpy - every one of which ComfyUI already ships, so there's no heavy install and no model download. Heads-up: the README's own clone command says git clone https://github.com/your-repo/... - a literal placeholder, the classic copy-paste trap. Use the URL above.

Where people get burned. Connection failures are almost always endpoint/port/path, not the node. Vision fails when the model isn't vision-capable. And remember this node ships your prompt and your image to whatever endpoint you type - the ecosystem has a real horror story in the ComfyUI_LLMVISION malware incident, so point it at a local Ollama/vLLM/LM Studio instance you control, not some random remote service. It's a small, new, MIT-licensed community node, so expect a light feature set - but for a two-minute text-in-text-out bridge, it does exactly one job and does it fine.