AI nodes
The AI node is where the reasoning happens. Flux ships two — GPT Model (OpenAI) and Gemini Model (Google) — and they behave the same way on the canvas.
What an AI node does
Given its configuration and whatever is wired into it, an AI node will:
- Read its input — usually the user's message or a previous node's output.
- Read its attachments — conversation memory, and a knowledge base if one is attached.
- Decide whether any of its tools should be called, and call them with parameters it fills in itself.
- Produce a final answer as its output.
A GPT Model node on the canvas with an input wire, two tool nodes on its tool connector and a memory attachment below it
Screenshot to be addedConfiguring one
| Setting | What it controls |
|---|---|
| Credential | Whose API key is used — Flux's default, or one of yours from the credential store |
| Model | Which model in that family to call |
| System prompt | Who the agent is and how it should behave. The single most important field |
| Input | What it is being asked, usually a {{POUT ...}} expression |
Writing the system prompt
The prompt is where an agent becomes reliable or unreliable. What tends to work:
- Say what it is. "You are a support agent for Acme, a company that sells industrial sensors."
- Say what it must not do. Refunds, pricing commitments, medical or legal advice — name the boundaries explicitly.
- Say what to do when it does not know. Otherwise a model will guess. "If the knowledge base does not answer it, call the transfer tool."
- Say how to sound. Length, formality, whether to use the customer's name.
Tools
Tools attach to the node's tool connector, not its input. The model reads
each tool's description and decides whether this request needs it. Describe parameters
with the DESC keyword so it knows what to fill in.
See Tool nodes.
Attachments
Memory
GPT Memory or Gemini Memory carries the conversation through the session, so the agent can follow "and what about tomorrow?".
Knowledge Base
Your documents, so the agent answers from your material rather than from what the model happens to know.
Use the matching memory for the model — GPT Memory with GPT, Gemini Memory with Gemini.
See Attachment nodes.
GPT or Gemini?
Both are first-class in Flux, take tools and memory the same way, and are wired identically. The practical differences are cost, speed and which provider you already have keys and an agreement with.
Because they are interchangeable on the canvas, the honest approach is to build with one, keep the prompt, and try the other on the same workflow. Compare the answers and the token cost in usage analytics.
What AI nodes cost
AI nodes are billed in tokens — input and output. A node with several tools and a long memory sends more context on every call, so cost per session grows with conversation length rather than with node count.
Using Flux's built-in keys bills through your project. Using your own credential bills you directly with the provider.