Agents

import { openai } from '@ai-sdk/openai';
import { generateText, generateObject } from 'ai';
import { z } from 'zod';

async function generateMarketingCopy(input: string) {
  const model = openai('gpt-4o');

  // First step: Generate marketing copy
  const { text: copy } = await generateText({
    model,
    prompt: `Write persuasive marketing copy for: ${input}. Focus on benefits and emotional appeal.`,
  });

  // Perform quality check on copy
  const { object: qualityMetrics } = await generateObject({
    model,
    schema: z.object({
      hasCallToAction: z.boolean(),
      emotionalAppeal: z.number().min(1).max(10),
      clarity: z.number().min(1).max(10),
    }),
    prompt: `Evaluate this marketing copy for:
    1. Presence of call to action (true/false)
    2. Emotional appeal (1-10)
    3. Clarity (1-10)

    Copy to evaluate: ${copy}`,
  });

  // If quality check fails, regenerate with more specific instructions
  if (
    !qualityMetrics.hasCallToAction ||
    qualityMetrics.emotionalAppeal < 7 ||
    qualityMetrics.clarity < 7
  ) {
    const { text: improvedCopy } = await generateText({
      model,
      prompt: `Rewrite this marketing copy with:
      ${!qualityMetrics.hasCallToAction ? '- A clear call to action' : ''}
      ${qualityMetrics.emotionalAppeal < 7 ? '- Stronger emotional appeal' : ''}
      ${qualityMetrics.clarity < 7 ? '- Improved clarity and directness' : ''}

      Original copy: ${copy}`,
    });
    return { copy: improvedCopy, qualityMetrics };
  }

  return { copy, qualityMetrics };
}

import { openai } from '@ai-sdk/openai';
import { generateObject, generateText } from 'ai';
import { z } from 'zod';

async function handleCustomerQuery(query: string) {
  const model = openai('gpt-4o');

  // First step: Classify the query type
  const { object: classification } = await generateObject({
    model,
    schema: z.object({
      reasoning: z.string(),
      type: z.enum(['general', 'refund', 'technical']),
      complexity: z.enum(['simple', 'complex']),
    }),
    prompt: `Classify this customer query:
    ${query}

    Determine:
    1. Query type (general, refund, or technical)
    2. Complexity (simple or complex)
    3. Brief reasoning for classification`,
  });

  // Route based on classification
  // Set model and system prompt based on query type and complexity
  const { text: response } = await generateText({
    model:
      classification.complexity === 'simple'
        ? openai('gpt-4o-mini')
        : openai('o3-mini'),
    system: {
      general:
        'You are an expert customer service agent handling general inquiries.',
      refund:
        'You are a customer service agent specializing in refund requests. Follow company policy and collect necessary information.',
      technical:
        'You are a technical support specialist with deep product knowledge. Focus on clear step-by-step troubleshooting.',
    }[classification.type],
    prompt: query,
  });

  return { response, classification };
}

import { openai } from '@ai-sdk/openai';
import { generateText, generateObject } from 'ai';
import { z } from 'zod';

// Example: Parallel code review with multiple specialized reviewers
async function parallelCodeReview(code: string) {
  const model = openai('gpt-4o');

  // Run parallel reviews
  const [securityReview, performanceReview, maintainabilityReview] =
    await Promise.all([
      generateObject({
        model,
        system:
          'You are an expert in code security. Focus on identifying security vulnerabilities, injection risks, and authentication issues.',
        schema: z.object({
          vulnerabilities: z.array(z.string()),
          riskLevel: z.enum(['low', 'medium', 'high']),
          suggestions: z.array(z.string()),
        }),
        prompt: `Review this code:
      ${code}`,
      }),

      generateObject({
        model,
        system:
          'You are an expert in code performance. Focus on identifying performance bottlenecks, memory leaks, and optimization opportunities.',
        schema: z.object({
          issues: z.array(z.string()),
          impact: z.enum(['low', 'medium', 'high']),
          optimizations: z.array(z.string()),
        }),
        prompt: `Review this code:
      ${code}`,
      }),

      generateObject({
        model,
        system:
          'You are an expert in code quality. Focus on code structure, readability, and adherence to best practices.',
        schema: z.object({
          concerns: z.array(z.string()),
          qualityScore: z.number().min(1).max(10),
          recommendations: z.array(z.string()),
        }),
        prompt: `Review this code:
      ${code}`,
      }),
    ]);

  const reviews = [
    { ...securityReview.object, type: 'security' },
    { ...performanceReview.object, type: 'performance' },
    { ...maintainabilityReview.object, type: 'maintainability' },
  ];

  // Aggregate results using another model instance
  const { text: summary } = await generateText({
    model,
    system: 'You are a technical lead summarizing multiple code reviews.',
    prompt: `Synthesize these code review results into a concise summary with key actions:
    ${JSON.stringify(reviews, null, 2)}`,
  });

  return { reviews, summary };
}

import { openai } from '@ai-sdk/openai';
import { generateObject } from 'ai';
import { z } from 'zod';

async function implementFeature(featureRequest: string) {
  // Orchestrator: Plan the implementation
  const { object: implementationPlan } = await generateObject({
    model: openai('o3-mini'),
    schema: z.object({
      files: z.array(
        z.object({
          purpose: z.string(),
          filePath: z.string(),
          changeType: z.enum(['create', 'modify', 'delete']),
        }),
      ),
      estimatedComplexity: z.enum(['low', 'medium', 'high']),
    }),
    system:
      'You are a senior software architect planning feature implementations.',
    prompt: `Analyze this feature request and create an implementation plan:
    ${featureRequest}`,
  });

  // Workers: Execute the planned changes
  const fileChanges = await Promise.all(
    implementationPlan.files.map(async file => {
      // Each worker is specialized for the type of change
      const workerSystemPrompt = {
        create:
          'You are an expert at implementing new files following best practices and project patterns.',
        modify:
          'You are an expert at modifying existing code while maintaining consistency and avoiding regressions.',
        delete:
          'You are an expert at safely removing code while ensuring no breaking changes.',
      }[file.changeType];

      const { object: change } = await generateObject({
        model: openai('gpt-4o'),
        schema: z.object({
          explanation: z.string(),
          code: z.string(),
        }),
        system: workerSystemPrompt,
        prompt: `Implement the changes for ${file.filePath} to support:
        ${file.purpose}

        Consider the overall feature context:
        ${featureRequest}`,
      });

      return {
        file,
        implementation: change,
      };
    }),
  );

  return {
    plan: implementationPlan,
    changes: fileChanges,
  };
}

import { openai } from '@ai-sdk/openai';
import { generateText, generateObject } from 'ai';
import { z } from 'zod';

async function translateWithFeedback(text: string, targetLanguage: string) {
  let currentTranslation = '';
  let iterations = 0;
  const MAX_ITERATIONS = 3;

  // Initial translation
  const { text: translation } = await generateText({
    model: openai('gpt-4o-mini'), // use small model for first attempt
    system: 'You are an expert literary translator.',
    prompt: `Translate this text to ${targetLanguage}, preserving tone and cultural nuances:
    ${text}`,
  });

  currentTranslation = translation;

  // Evaluation-optimization loop
  while (iterations < MAX_ITERATIONS) {
    // Evaluate current translation
    const { object: evaluation } = await generateObject({
      model: openai('gpt-4o'), // use a larger model to evaluate
      schema: z.object({
        qualityScore: z.number().min(1).max(10),
        preservesTone: z.boolean(),
        preservesNuance: z.boolean(),
        culturallyAccurate: z.boolean(),
        specificIssues: z.array(z.string()),
        improvementSuggestions: z.array(z.string()),
      }),
      system: 'You are an expert in evaluating literary translations.',
      prompt: `Evaluate this translation:

      Original: ${text}
      Translation: ${currentTranslation}

      Consider:
      1. Overall quality
      2. Preservation of tone
      3. Preservation of nuance
      4. Cultural accuracy`,
    });

    // Check if quality meets threshold
    if (
      evaluation.qualityScore >= 8 &&
      evaluation.preservesTone &&
      evaluation.preservesNuance &&
      evaluation.culturallyAccurate
    ) {
      break;
    }

    // Generate improved translation based on feedback
    const { text: improvedTranslation } = await generateText({
      model: openai('gpt-4o'), // use a larger model
      system: 'You are an expert literary translator.',
      prompt: `Improve this translation based on the following feedback:
      ${evaluation.specificIssues.join('\n')}
      ${evaluation.improvementSuggestions.join('\n')}

      Original: ${text}
      Current Translation: ${currentTranslation}`,
    });

    currentTranslation = improvedTranslation;
    iterations++;
  }

  return {
    finalTranslation: currentTranslation,
    iterationsRequired: iterations,
  };
}

import { openai } from '@ai-sdk/openai';
import { generateText, tool } from 'ai';
import * as mathjs from 'mathjs';
import { z } from 'zod';

const { text: answer } = await generateText({
  model: openai('gpt-4o-2024-08-06', { structuredOutputs: true }),
  tools: {
    calculate: tool({
      description:
        'A tool for evaluating mathematical expressions. ' +
        'Example expressions: ' +
        "'1.2 * (2 + 4.5)', '12.7 cm to inch', 'sin(45 deg) ^ 2'.",
      parameters: z.object({ expression: z.string() }),
      execute: async ({ expression }) => mathjs.evaluate(expression),
    }),
  },
  maxSteps: 10,
  system:
    'You are solving math problems. ' +
    'Reason step by step. ' +
    'Use the calculator when necessary. ' +
    'When you give the final answer, ' +
    'provide an explanation for how you arrived at it.',
  prompt:
    'A taxi driver earns $9461 per 1-hour of work. ' +
    'If he works 12 hours a day and in 1 hour ' +
    'he uses 12 liters of petrol with a price  of $134 for 1 liter. ' +
    'How much money does he earn in one day?',
});

console.log(`ANSWER: ${answer}`);

import { openai } from '@ai-sdk/openai';
import { generateText, tool } from 'ai';
import 'dotenv/config';
import { z } from 'zod';

const { toolCalls } = await generateText({
  model: openai('gpt-4o-2024-08-06', { structuredOutputs: true }),
  tools: {
    calculate: tool({
      description:
        'A tool for evaluating mathematical expressions. Example expressions: ' +
        "'1.2 * (2 + 4.5)', '12.7 cm to inch', 'sin(45 deg) ^ 2'.",
      parameters: z.object({ expression: z.string() }),
      execute: async ({ expression }) => mathjs.evaluate(expression),
    }),
    // answer tool: the LLM will provide a structured answer
    answer: tool({
      description: 'A tool for providing the final answer.',
      parameters: z.object({
        steps: z.array(
          z.object({
            calculation: z.string(),
            reasoning: z.string(),
          }),
        ),
        answer: z.string(),
      }),
      // no execute function - invoking it will terminate the agent
    }),
  },
  toolChoice: 'required',
  maxSteps: 10,
  system:
    'You are solving math problems. ' +
    'Reason step by step. ' +
    'Use the calculator when necessary. ' +
    'The calculator can only do simple additions, subtractions, multiplications, and divisions. ' +
    'When you give the final answer, provide an explanation for how you got it.',
  prompt:
    'A taxi driver earns $9461 per 1-hour work. ' +
    'If he works 12 hours a day and in 1 hour he uses 14-liters petrol with price $134 for 1-liter. ' +
    'How much money does he earn in one day?',
});

console.log(`FINAL TOOL CALLS: ${JSON.stringify(toolCalls, null, 2)}`);

import { generateText } from 'ai';

const { steps } = await generateText({
  model: openai('gpt-4o'),
  maxSteps: 10,
  // ...
});

// extract all tool calls from the steps:
const allToolCalls = steps.flatMap(step => step.toolCalls);

import { generateText } from 'ai';

const result = await generateText({
  model: yourModel,
  maxSteps: 10,
  onStepFinish({ text, toolCalls, toolResults, finishReason, usage }) {
    // your own logic, e.g. for saving the chat history or recording usage
  },
  // ...
});