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// Production Control

Structured Output

Demonstrates outputSchema on AgentConfig. The agent's response is automatically parsed as JSON and validated against a Zod schema. On validation failure, the framework retries once with error feedback.

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OMA APIsOpenMultiAgent
Production Control73 lines

From a clone of the repo — this exact file:

terminal
npx tsx packages/core/examples/patterns/structured-output.ts
Prerequisites
  • ANTHROPIC_API_KEY env var must be set.

OMA is provider-agnostic — this example is written for the key above, but you can run it on OpenAI, Gemini, Groq and others. All providers →

Open the complete, synchronized source · 73 lines

The complete example, synchronized from the pinned Framework commit.

patterns/structured-output.ts
/**
* Structured Output
*
* Demonstrates `outputSchema` on AgentConfig. The agent's response is
* automatically parsed as JSON and validated against a Zod schema.
* On validation failure, the framework retries once with error feedback.
*
* The validated result is available via `result.structured`.
*
* Run:
* npx tsx packages/core/examples/patterns/structured-output.ts
*
* Prerequisites:
* ANTHROPIC_API_KEY env var must be set.
*/
 
import { z } from 'zod'
import { OpenMultiAgent } from '../../src/index.js'
import type { AgentConfig } from '../../src/types.js'
 
// ---------------------------------------------------------------------------
// Define a Zod schema for the expected output
// ---------------------------------------------------------------------------
 
const ReviewAnalysis = z.object({
summary: z.string().describe('One-sentence summary of the review'),
sentiment: z.enum(['positive', 'negative', 'neutral']),
confidence: z.number().min(0).max(1).describe('How confident the analysis is'),
keyTopics: z.array(z.string()).describe('Main topics mentioned in the review'),
})
 
type ReviewAnalysis = z.infer<typeof ReviewAnalysis>
 
// ---------------------------------------------------------------------------
// Agent with outputSchema
// ---------------------------------------------------------------------------
 
const analyst: AgentConfig = {
name: 'analyst',
model: 'claude-sonnet-4-6',
systemPrompt: 'You are a product review analyst. Analyze the given review and extract structured insights.',
outputSchema: ReviewAnalysis,
}
 
// ---------------------------------------------------------------------------
// Run
// ---------------------------------------------------------------------------
 
const orchestrator = new OpenMultiAgent({ defaultModel: 'claude-sonnet-4-6' })
 
const reviews = [
'This keyboard is amazing! The mechanical switches feel incredible and the RGB lighting is stunning. Build quality is top-notch. Only downside is the price.',
'Terrible experience. The product arrived broken, customer support was unhelpful, and the return process took 3 weeks.',
'It works fine. Nothing special, nothing bad. Does what it says on the box.',
]
 
console.log('Analyzing product reviews with structured output...\n')
 
for (const review of reviews) {
const result = await orchestrator.runAgent(analyst, `Analyze this review: "${review}"`)
 
if (result.structured) {
const data = result.structured as ReviewAnalysis
console.log(`Sentiment: ${data.sentiment} (confidence: ${data.confidence})`)
console.log(`Summary: ${data.summary}`)
console.log(`Topics: ${data.keyTopics.join(', ')}`)
} else {
console.log(`Validation failed. Raw output: ${result.output.slice(0, 100)}`)
}
 
console.log(`Tokens: ${result.tokenUsage.input_tokens} in / ${result.tokenUsage.output_tokens} out`)
console.log('---')
}
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// Enterprise

Taking this to production?

open-multi-agent is MIT-licensed and free to run yourself. When you need it delivered, integrated, or supported on a deadline, 元定义科技 (YuanASI) offers commercial delivery and support.

Enterprise support