LLM processing

The AI Agent will use LLMs to generate a text response based on the provided inputs and context.

Action — Sends a prompt to an LLM mid-chat and stores the response in a variable for downstream actions.

What it does

Generates or transforms text at any point in the flow — summarising what a person said, classifying their request, or drafting a tailored reply — and makes the result available to the next action.

When to use it

To process free-form input intelligently mid-chat, such as extracting structured fields from a long description or deciding which path to take based on sentiment.

Settings

Setting	Description
Input	The content the LLM acts on. Use plain text or a chat variable in curly brackets, e.g. {description}.
Model	The LLM to use: DeepSeek V2, GPT-4o, Qwen 2.5, or Llama 3.2. Defaults to DeepSeek V2.
Temperature	Controls response randomness. Range 0.0–2.0, default 1. Lower values produce more deterministic output.
System prompt	The standing instruction that shapes how the LLM responds, separate from the input. See example system prompt for guidance.
Use knowledge info	When toggled on, the LLM can also draw on the AI agent's Knowledge training sources.
Resulting variable	The chat variable that stores the LLM's response. Must start with a letter and contain only letters, numbers, or underscores.

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Outcome branches

The action branches into Success, Error, and Timeout. Nest subsequent actions under each as needed.

Example

LLM processing: "complaint details"
• Input:              {description}
• Model:              GPT-4o
• System prompt:      extraction instructions
• Resulting variable: complaint_details
└─ Success
   └─ Create ticket → Description: {complaint_details}

Related

BranchSet variable