# Flowise + Luma Cloud Use a custom OpenAI-compatible chat model in a Flowise Chatflow. API connection: Check client requirements. Instructions checked: 2026-09-28. Client capabilities and versions may vary; follow the verification steps below. This is an API setup guide. SuperGPT desktop installation and sign-in are separate; use a customer API key for the client described here. ## Before you start - Create a named key in Dashboard → API. Use API Wallet credit, or an eligible Builder subscription key. - Copy an exact model ID from the catalog returned for this key. Replace MODEL_ID_FROM_CATALOG wherever it appears below. - Use a separate named key for this app. Paste it only into the app's credential field; keep it out of chat prompts, screenshots, shared configuration and workflow exports. - Access to a trusted Flowise installation with a custom OpenAI chat-model node. Use a model compatible with the parameters that your node sends. ## Setup 1. **Create a test Chatflow** Open Chatflows and create a new flow. Add Chains → LLM Chain, Prompts → Prompt Template and Chat Models → OpenAI Custom Model. Some versions call the last node ChatOpenAI Custom. Use the custom node when the standard ChatOpenAI dropdown cannot select your exact model ID. 2. **Create a Luma credential** In the model node's Connect Credential field, choose Create New. Give the credential a recognizable name such as Luma Cloud and put your Luma key in OpenAI API Key. Select this saved credential on the node; do not put the key in the prompt or Base Options. 3. **Configure the model** Enter the catalog ID in Model Name. Open Additional Parameters and set Base Path to https://api.lumaos.cloud/v1. Leave Base Options blank unless you need a documented custom header. Set output limits only within the selected model's allowance. 4. **Check parameter compatibility** Inspect Temperature and other parameters the node supplies by default. Use values accepted by the selected model. Some node versions always send Temperature; if your model rejects it and the node cannot omit it, use another compatible client instead of repeatedly retrying the same flow. 5. **Connect the prompt and model** Set the Prompt Template to Question: {question}. Connect the custom model output to the LLM Chain's Language Model input, and the template output to its Prompt input. Save the Chatflow before testing. 6. **Run one text request** Open the flow's chat preview and send Reply with hello. Start without tools, memory, file uploads or retrieval. After a completed answer, add those features individually and check the extra model calls they create. ## Connection fields - Node: OpenAI Custom Model / ChatOpenAI Custom - Credential: A separate Luma Cloud OpenAI API credential - Model Name: MODEL_ID_FROM_CATALOG - Additional Parameters → Base Path: https://api.lumaos.cloud/v1 - Base Options: Leave blank - Temperature: A value accepted by the chosen model; see compatibility step - Prompt Template: Question: {question} ## Prompt Template ```text Question: {question} Answer clearly and briefly. ``` Enter this into the Prompt Template node. The chat question fills {question}. This is prompt content, not a Flowise import file or a place for credentials. ## Verify - Save the flow and wait for a completed reply in its chat preview. - Confirm the Luma credential and exact Model Name are selected on the node used by this flow. - After the reply, open Dashboard → Usage. Check API Wallet for a wallet key, or subscription usage for a Builder subscription key. ## Limits - This guide covers Chat Completions in Chatflows. It does not configure OpenAI Assistants, hosted tools, image generation or embeddings. - An agent node may send tool schemas and several follow-up requests. Tool calling needs separate verification with the chosen model; a successful LLM Chain test is not an agent test. - Anyone who can run a shared flow may consume its credential's balance or quota. Configure access to your Flowise app before sharing its chat or prediction endpoint. - The custom model node's exact parameters vary by release. A model that rejects an unconfigurable default parameter needs a different compatible node/client; do not patch application files to bypass the error. ## Troubleshooting ### 401 or an invalid-key message Confirm that the saved credential contains a Luma Cloud API key, without surrounding quotes, spaces or a Bearer prefix. Check that this key is still active in Dashboard → API. A dashboard password or another provider's key will not work. ### The model is missing from the dropdown Use OpenAI Custom Model / ChatOpenAI Custom and enter the exact catalog ID in Model Name. A predefined model dropdown is not a complete list of models your Luma key can use. ### 404 or requests go to the default OpenAI address Check Additional Parameters → Base Path on the model node connected to this flow. It must be https://api.lumaos.cloud/v1, without /chat/completions. Save the flow and verify that an older model node is not still connected. ### Missing prompt variable or no output Use {question} in the Prompt Template and connect that template to the LLM Chain. Check both graph connections and save before opening chat preview. Start without an output parser or a retrieval chain. ### Temperature or another parameter is rejected Read the named field in the error. Remove optional overrides when the node permits it. If the node always sends a parameter your model rejects, stop here and use a compatible client or the API quickstart; changing the key will not fix the request format. ### 429, a balance warning or an interrupted run Read the error and check the usage associated with this key. A wallet key uses API Wallet funds; a Builder subscription key uses its eligible subscription quota. Pause retries, wait for the indicated reset or retry time, and reduce parallel requests. After a timeout, check the existing result before running a costly job again. ## Official client documentation - [Flowise: custom OpenAI base URL and model](https://docs.flowiseai.com/integrations/langchain/chat-models/azure-chatopenai) - [Flowise: custom model node fields](https://github.com/FlowiseAI/Flowise/blob/main/packages/components/nodes/chatmodels/ChatOpenAICustom/ChatOpenAICustom.ts) - [Flowise: connecting a model and prompt to LLM Chain](https://docs.flowiseai.com/integrations/langchain/chat-models/chathuggingface)