Build AI Agents in Minutes,
Not Months

// Multiple import statements
import { OpenAI } from 'openai';
import Anthropic from '@anthropic-ai/sdk';
import { PineconeClient } from '@pinecone-database/pinecone';
import { S3Client } from '@aws-sdk/client-s3';

// Managing connections
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
const pinecone = new PineconeClient();
await pinecone.init({
  environment: process.env.PINECONE_ENV,
  apiKey: process.env.PINECONE_API_KEY
});
                
// Different libraries for each service
const openaiResponse = await openai.chat.completions.create({
  model: "gpt-4",
  messages: [{ role: "user", content: prompt }]
});

// Separate auth and api keys, costs for each provider
const anthropicResponse = await anthropic.messages.create({
  model: "claude-3",
  max_tokens: 1000,
  messages: [{ role: "user", content: prompt }]
});

// Different response formats for each provider
const imageResponse = await openai.images.generate({
  prompt: "A beautiful sunset over a calm ocean",
  n: 1,
  size: "1024x1024"
});

// Manual error handling and retries
try {
  const embeddings = await embedding.embed(text);
  await vectorDB.upsert(embeddings);
} catch (error) {
  // Complex error handling
}

// Scheduling and monitoring
queue.addJob("my-content-workflow", {
  input: userInput,
  userId: "123"
});

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