LLM
Call an AI model with a prompt built from what the flow has already produced.
The LLM node is where the flow uses an AI model. It processes natural language: it classifies, summarizes, extracts information from free text and generates content.
It is the most expensive node on the canvas, because every run consumes tokens.
Choose the model
The side panel lists the models enabled in the workspace, under Settings › Models. A workspace with no models enabled doesn't let you configure this node.
Different LLM nodes in the same flow can use different models. It is common to use a cheap model to classify and a better one to write the final text.
Write the prompt
The prompt is built with variables from earlier nodes, in the {{ node.field }} format. The / key opens the menu of variables available at that point in the flow.
Classify the request below into one of the categories:
refund, exchange, question.
Answer only with the category, without explanation.
Request: {{ entrada.mensagem_cliente }}If the next node consumes this one's output, be explicit about the format. A prompt that doesn't constrain the answer returns free text, and the Condition or Code node that follows can't handle it.
Combine with knowledge
An LLM node on its own answers with what the model knows. To answer from the company's documents, put a Vector database node before it and inject the retrieved passages into the prompt.
That pair, search followed by prompt, is what implements RAG inside a Workflow. The equivalent in an Agent is attaching Collections to it.
Cost
This node's consumption shows up in the workspace's Observability, attributed to the chosen model.
Flows with an LLM node inside an Iteration multiply the cost by the size of the list. A list of 200 items means 200 calls.