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templates/text-agents/agent-structured-output.ts

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+/**
+ * Structured Output with Zod Schemas
+ *
+ * Demonstrates:
+ * - Defining output schemas with Zod
+ * - Type-safe structured responses
+ * - Extracting specific data formats
+ * - Using reasoning in structured outputs
+ */
+
+import { z } from 'zod';
+import { Agent, run } from '@openai/agents';
+
+// ========================================
+// Example 1: Contact Information Extraction
+// ========================================
+
+const contactInfoSchema = z.object({
+  name: z.string().describe('Full name of the person'),
+  email: z.string().email().describe('Email address'),
+  phone: z.string().optional().describe('Phone number if mentioned'),
+  company: z.string().optional().describe('Company name if mentioned'),
+  reasoning: z.string().describe('Brief explanation of how you extracted this information'),
+});
+
+const contactExtractorAgent = new Agent({
+  name: 'Contact Extractor',
+  instructions: 'Extract contact information from text. Be thorough but only extract information that is explicitly mentioned.',
+  outputType: contactInfoSchema,
+});
+
+// ========================================
+// Example 2: Sentiment Analysis
+// ========================================
+
+const sentimentSchema = z.object({
+  sentiment: z.enum(['positive', 'negative', 'neutral', 'mixed']),
+  confidence: z.number().min(0).max(1).describe('Confidence score from 0 to 1'),
+  keyPhrases: z.array(z.string()).describe('Key phrases that indicate the sentiment'),
+  reasoning: z.string().describe('Explanation of the sentiment analysis'),
+});
+
+const sentimentAgent = new Agent({
+  name: 'Sentiment Analyzer',
+  instructions: 'Analyze the sentiment of the given text. Consider tone, word choice, and context.',
+  outputType: sentimentSchema,
+});
+
+// ========================================
+// Example 3: Task Breakdown
+// ========================================
+
+const taskSchema = z.object({
+  title: z.string(),
+  priority: z.enum(['low', 'medium', 'high', 'urgent']),
+  estimatedHours: z.number(),
+  dependencies: z.array(z.string()),
+});
+
+const taskBreakdownSchema = z.object({
+  projectName: z.string(),
+  tasks: z.array(taskSchema),
+  totalEstimatedHours: z.number(),
+  reasoning: z.string().describe('Explanation of how you broke down the project'),
+});
+
+const taskPlannerAgent = new Agent({
+  name: 'Task Planner',
+  instructions: 'Break down project descriptions into concrete tasks with priorities and time estimates.',
+  outputType: taskBreakdownSchema,
+});
+
+// ========================================
+// Usage Examples
+// ========================================
+
+async function exampleContactExtraction() {
+  const text = `
+    Hi, I'm Sarah Johnson from TechCorp.
+    You can reach me at sarah.j@techcorp.com or call me at (555) 123-4567.
+  `;
+
+  const result = await run(contactExtractorAgent, text);
+
+  console.log('\n📇 Contact Extraction:');
+  console.log('Name:', result.finalOutput.name);
+  console.log('Email:', result.finalOutput.email);
+  console.log('Phone:', result.finalOutput.phone);
+  console.log('Company:', result.finalOutput.company);
+  console.log('Reasoning:', result.finalOutput.reasoning);
+}
+
+async function exampleSentimentAnalysis() {
+  const review = `
+    I absolutely love this product! The design is beautiful and it works flawlessly.
+    However, the customer service could be better. Overall, very satisfied with my purchase.
+  `;
+
+  const result = await run(sentimentAgent, review);
+
+  console.log('\n😊 Sentiment Analysis:');
+  console.log('Sentiment:', result.finalOutput.sentiment);
+  console.log('Confidence:', result.finalOutput.confidence);
+  console.log('Key Phrases:', result.finalOutput.keyPhrases);
+  console.log('Reasoning:', result.finalOutput.reasoning);
+}
+
+async function exampleTaskPlanning() {
+  const project = `
+    Build a user authentication system with email/password login,
+    social OAuth, password reset, and two-factor authentication.
+    Should integrate with our existing PostgreSQL database.
+  `;
+
+  const result = await run(taskPlannerAgent, project);
+
+  console.log('\n📋 Task Breakdown:');
+  console.log('Project:', result.finalOutput.projectName);
+  console.log('Total Hours:', result.finalOutput.totalEstimatedHours);
+  console.log('\nTasks:');
+  result.finalOutput.tasks.forEach((task, i) => {
+    console.log(`\n${i + 1}. ${task.title}`);
+    console.log(`   Priority: ${task.priority}`);
+    console.log(`   Hours: ${task.estimatedHours}`);
+    console.log(`   Dependencies: ${task.dependencies.join(', ') || 'None'}`);
+  });
+  console.log('\nReasoning:', result.finalOutput.reasoning);
+}
+
+async function main() {
+  try {
+    await exampleContactExtraction();
+    await exampleSentimentAnalysis();
+    await exampleTaskPlanning();
+  } catch (error) {
+    console.error('❌ Error:', error);
+    process.exit(1);
+  }
+}
+
+// Uncomment to run
+// main();
+
+export {
+  contactExtractorAgent,
+  sentimentAgent,
+  taskPlannerAgent,
+  contactInfoSchema,
+  sentimentSchema,
+  taskBreakdownSchema,
+};