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Structured Output

The Structured Output node runs after the conversation ends. It uses an LLM to extract structured data from the full conversation transcript — returning a JSON object with the fields you define. You can then pipe the result to a Webhook.

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Adding a Structured Output node

  1. Drag the Structured Output node from the sidebar (Post-Processing category) onto the canvas
  2. Connect the End node’s output to the Structured Output node’s end handle
  3. Connect an LLM node to its llm_in handle
  4. Define the schema fields you want extracted
  5. Optionally, connect the Structured Output node’s output to a Webhook node

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Parameters

ParameterTypeDescription
commandtextInstruction for extraction (e.g., “Extract the customer’s name, issue type, and whether the issue was resolved”)
schemaFieldsschema_fieldsThe fields to extract — each with a name, type, and optional description

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Schema field types

TypeDescription
stringFree-form text
intInteger number
floatDecimal number
booleanTrue/false value
listArray of strings
enumOne of a fixed set of values

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Output

The extracted data is available as a JSON object matching your schema. When chained to a Webhook, it is sent as the structuredOutput field in the POST body: 
{
  "structuredOutput": {
    "customerName": "Jane Smith",
    "issueType": "billing",
    "sentiment": "negative",
    "resolved": false
  }
}

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Typical pattern

[End] ──▶ [Structured Output] ──▶ [Webhook]

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Use cases

  • Extract leads (name, email, interest level) from a sales call
  • Classify support tickets and collect issue details at the end of the call
  • Score call quality or customer sentiment
  • Build a CRM record automatically from each conversation