Why Structured JSON Output Matters for AI Automation

The Parsing Problem

When building AI agents, one of the biggest challenges is parsing LLM responses. Standard APIs return:

• Markdown text
• Unstructured paragraphs
• Inconsistent formatting
• Mixed content types

This leads to:

• Fragile regex parsers
• Breaking changes when LLM updates
• Hours spent on error handling
• Unreliable data extraction

The JSON Advantage

Structured JSON output changes everything:

Deterministic Structure

With a defined schema, you know exactly what to expect:

interface MarketData {
  current_price: number
  weekly_change: string
  sentiment: "bullish" | "bearish" | "neutral"
  key_events: string[]
}

No more guessing if the LLM will use bullet points, numbered lists, or tables.

Type Safety

Use TypeScript interfaces that match your schema:

const result: MarketData = await deepResearch({
  schema: {
    current_price: "number",
    weekly_change: "string",
    sentiment: "bullish | bearish | neutral",
    key_events: "string[]"
  }
})
// TypeScript knows exactly what fields exist
// result.key_events is string[], not any

Error Handling

With structured output, you can validate responses:

const response = await deepResearch({...})

// Validate required fields
if (!response.current_price) {
  throw new Error('Missing required field: current_price')
}

// Type checking
const sentiment: MarketData['sentiment'] = response.sentiment
// TypeScript will error if invalid value

Real-World Example

Here's a comparison of parsing Markdown vs JSON:

Markdown (Painful)

const text = await standardLLM(query)
// "The current price is $850, with a weekly change of +5.2%..."

const price = text.match(/\$([\d,]+)/)?.[1]
const change = text.match(/\+([\d.]+)%/)?.[1]
// Fragile, breaks easily

JSON (Reliable)

const data = await deepResearch({
  schema: { current_price: "number", weekly_change: "string" }
})
// data.current_price = 850
// data.weekly_change = "+5.2%"
// Reliable, typed, no parsing needed

Deep Research API Schemas

Deep Research API supports custom schemas for any use case:

await deepResearch({
  query: "Bitcoin price analysis",
  mode: "schema",
  schema: {
    current_price: "number",
    weekly_change: "string",
    sentiment: "bullish | bearish | neutral",
    key_events: "string[]"
  }
})

You can define:

• Primitive types: string, number, boolean
• Enums: "option1" | "option2" | "option3"
• Arrays: string[], number[]
• Nested objects: complex structures

Get Started

Stop parsing Markdown. Get structured JSON.

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Read Schema Documentation