Flowise + Luma Cloud

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.

Get an API key and a model ID →

Set up Flowise

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.

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}

Question: {question}
Answer clearly and briefly.

Check the connection

1. Save the flow and wait for a completed reply in its chat preview.
2. Confirm the Luma credential and exact Model Name are selected on the node used by this flow.
3. After the reply, open Dashboard → Usage. Check API Wallet for a wallet key, or subscription usage for a Builder subscription key.

For errors or a request that stops, see troubleshooting. A visible model list alone does not confirm that a chat or editing task can complete.

What to expect

• 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 Flowise