import Anthropic from '@anthropic-ai/sdk';
const anthropic = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});
// Advanced code generation with streaming
async function generateComponent(description: string) {
  const stream = await anthropic.messages.create({
    model: "claude-sonnet-4-20250514",
    max_tokens: 4096,
    messages: [{
      role: "user",
      content: Generate a React component: ${description}
    }],
    stream: true,
  });
  for await (const chunk of stream) {
    if (chunk.type === 'content_block_delta') {
      process.stdout.write(chunk.delta.text);
    }
  }
}
// Example: Complex reasoning task
const response = await anthropic.messages.create({
  model: "claude-sonnet-4-20250514",
  max_tokens: 2048,
  messages: [{
    role: "user",
    content: "Analyze this codebase architecture and suggest improvements..."
  }],
});
console.log(response.content[0].text);

import { generateText, streamText } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';
import { openai } from '@ai-sdk/openai';
// Generate text with any provider
const { text } = await generateText({
  model: anthropic('claude-sonnet-4-20250514'),
  prompt: 'Explain quantum computing in simple terms',
});
// Stream responses for better UX
const result = await streamText({
  model: openai('gpt-4-turbo'),
  prompt: 'Write a technical blog post about WebAssembly',
});
for await (const chunk of result.textStream) {
  process.stdout.write(chunk);
}
// Build chat interfaces with built-in state management
import { useChat } from 'ai/react';
export function ChatComponent() {
  const { messages, input, handleInputChange, handleSubmit } = useChat({
    api: '/api/chat',
    initialMessages: [
      { role: 'assistant', content: 'How can I help you today?' }
    ],
  });
  return (
    

      {messages.map((m, i) => (
        
message ${m.role}}>
          {m.content}
        
      ))}
      

        
        Send
      
    
  );
}

import OpenAI from 'openai';
const openrouter = new OpenAI({
  baseURL: 'https://openrouter.ai/api/v1',
  apiKey: process.env.OPENROUTER_API_KEY,
});
// Access any model through one API
async function compareModels(prompt: string) {
  const models = [
    'anthropic/claude-sonnet-4',
    'openai/gpt-4-turbo',
    'google/gemini-pro-1.5',
    'meta-llama/llama-3.1-70b',
  ];
  const responses = await Promise.all(
    models.map(model =>
      openrouter.chat.completions.create({
        model,
        messages: [{ role: 'user', content: prompt }],
      })
    )
  );
  return responses.map((r, i) => ({
    model: models[i],
    response: r.choices[0].message.content,
    cost: r.usage?.total_tokens * 0.000002, // Example pricing
  }));
}
// Smart routing based on requirements
const response = await openrouter.chat.completions.create({
  model: 'auto', // Automatically select best model
  messages: [{ role: 'user', content: 'Complex reasoning task...' }],
  route: 'balanced', // Options: fastest, cheapest, balanced
});

import { ElevenLabsClient } from 'elevenlabs';
const elevenlabs = new ElevenLabsClient({
  apiKey: process.env.ELEVENLABS_API_KEY,
});
// Generate natural-sounding speech
async function generateSpeech(text: string, voiceId: string) {
  const audio = await elevenlabs.textToSpeech.convert({
    voice_id: voiceId,
    text: text,
    model_id: 'eleven_multilingual_v2',
    voice_settings: {
      stability: 0.5,
      similarity_boost: 0.8,
      style: 0.5,
      use_speaker_boost: true,
    },
  });
  // Stream audio directly to browser or save to file
  return audio;
}
// Voice cloning from samples
async function cloneVoice(name: string, audioFiles: File[]) {
  const voice = await elevenlabs.voices.add({
    name: name,
    files: audioFiles,
    description: 'Custom cloned voice',
  });
  return voice.voice_id;
}
// Advanced: Conversational AI with emotions
const conversation = await elevenlabs.conversationalAI.create({
  agent_id: 'agent_123',
  conversation: [
    { role: 'user', content: 'Tell me about your day' },
    { role: 'assistant', content: 'It was wonderful!', emotion: 'happy' },
  ],
});

import Banana from '@banana-dev/banana-dev';
const banana = new Banana(process.env.BANANA_API_KEY);
// Generate images with SDXL Turbo
async function generateImage(prompt: string) {
  const result = await banana.run('sdxl-turbo', {
    prompt: prompt,
    negative_prompt: 'blurry, low quality, distorted',
    num_inference_steps: 4, // Turbo needs fewer steps
    guidance_scale: 1.5,
    width: 1024,
    height: 1024,
  });
  return result.modelOutputs[0].image_base64;
}
// Batch generation for efficiency
async function generateBatch(prompts: string[]) {
  const results = await Promise.all(
    prompts.map(prompt => generateImage(prompt))
  );
  return results;
}
// Advanced: ControlNet for precise composition
const controlledImage = await banana.run('sdxl-controlnet', {
  prompt: 'Professional headshot of a software engineer',
  control_image: 'base64_encoded_pose_guide',
  controlnet_conditioning_scale: 0.8,
});

import { LumaAI } from 'lumaai';
const luma = new LumaAI({ apiKey: process.env.LUMA_API_KEY });
// Generate video from text
async function generateVideo(prompt: string) {
  const generation = await luma.generations.create({
    prompt: prompt,
    aspect_ratio: '16:9',
    loop: false,
  });
  // Poll for completion
  let video = await luma.generations.get(generation.id);
  while (video.state === 'processing') {
    await new Promise(resolve => setTimeout(resolve, 3000));
    video = await luma.generations.get(generation.id);
  }
  return video.assets.video;
}
// Extend existing video
async function extendVideo(videoId: string, direction: 'forward' | 'backward') {
  return await luma.generations.create({
    extend: {
      generation_id: videoId,
      direction: direction,
    },
  });
}
// Image-to-video animation
async function animateImage(imageUrl: string, prompt: string) {
  return await luma.generations.create({
    image_url: imageUrl,
    prompt: Animate this image: ${prompt},
  });
}

import { StateGraph, END } from '@langchain/langgraph';
import { ChatAnthropic } from '@langchain/anthropic';
// Define agent state
interface AgentState {
  messages: Message[];
  currentTask: string;
  completedSteps: string[];
}
// Create research agent
const researchAgent = new StateGraph({
  channels: {
    messages: { value: (prev, next) => [...prev, ...next] },
    currentTask: { value: (prev, next) => next },
    completedSteps: { value: (prev, next) => [...prev, next] },
  },
});
// Define agent nodes
async function researcher(state: AgentState) {
  const model = new ChatAnthropic({ model: 'claude-sonnet-4-20250514' });
  const response = await model.invoke([
    { role: 'system', content: 'You are a research assistant' },
    ...state.messages,
  ]);
  return {
    messages: [response],
    completedSteps: ['research'],
  };
}
async function writer(state: AgentState) {
  const model = new ChatAnthropic({ model: 'claude-sonnet-4-20250514' });
  const response = await model.invoke([
    { role: 'system', content: 'You are a technical writer' },
    { role: 'user', content: 'Write an article based on this research' },
    ...state.messages,
  ]);
  return {
    messages: [response],
    completedSteps: ['writing'],
  };
}
// Build workflow
researchAgent
  .addNode('research', researcher)
  .addNode('write', writer)
  .addEdge('research', 'write')
  .addEdge('write', END)
  .setEntryPoint('research');
const workflow = researchAgent.compile();
// Execute multi-agent task
const result = await workflow.invoke({
  messages: [{ role: 'user', content: 'Research and write about quantum computing' }],
  currentTask: 'article_generation',
  completedSteps: [],
});

// Full-stack content generation pipeline
import { anthropic } from '@ai-sdk/anthropic';
import { ElevenLabsClient } from 'elevenlabs';
import { generateText } from 'ai';
class ContentPipeline {
  async generateBlogPost(topic: string) {
    // Step 1: Research and outline
    const outline = await generateText({
      model: anthropic('claude-sonnet-4-20250514'),
      prompt: Create a detailed outline for a blog post about: ${topic},
    });
    // Step 2: Write article
    const article = await generateText({
      model: anthropic('claude-sonnet-4-20250514'),
      prompt: Write a comprehensive blog post following this outline:
${outline.text},
    });
    // Step 3: Generate audio version
    const elevenlabs = new ElevenLabsClient({ apiKey: process.env.ELEVENLABS_API_KEY });
    const audio = await elevenlabs.textToSpeech.convert({
      voice_id: 'professional_narrator',
      text: article.text,
    });
    // Step 4: Create social media snippets
    const snippets = await generateText({
      model: anthropic('claude-sonnet-4-20250514'),
      prompt: Create 5 engaging social media posts to promote this article:
${article.text},
    });
    return {
      article: article.text,
      audio: audio,
      socialPosts: snippets.text.split('\n\n'),
      wordCount: article.text.split(' ').length,
    };
  }
}

import { anthropic } from '@ai-sdk/anthropic';
import { generateText } from 'ai';
import { ElevenLabsClient } from 'elevenlabs';
class MultimodalAssistant {
  async processRequest(input: {
    text?: string;
    image?: string;
    audio?: string;
  }) {
    let context = '';
    // Process image if provided
    if (input.image) {
      const imageAnalysis = await generateText({
        model: anthropic('claude-sonnet-4-20250514'),
        messages: [{
          role: 'user',
          content: [
            { type: 'image', image: input.image },
            { type: 'text', text: 'Describe this image in detail' },
          ],
        }],
      });
      context += imageAnalysis.text;
    }
    // Process audio if provided
    if (input.audio) {
      // Transcribe with Deepgram or similar
      const transcription = await this.transcribeAudio(input.audio);
      context += transcription;
    }
    // Generate response
    const response = await generateText({
      model: anthropic('claude-sonnet-4-20250514'),
      prompt: Context: ${context}

User question: ${input.text},
    });
    // Convert to speech
    const elevenlabs = new ElevenLabsClient({ apiKey: process.env.ELEVENLABS_API_KEY });
    const audioResponse = await elevenlabs.textToSpeech.convert({
      voice_id: 'assistant_voice',
      text: response.text,
    });
    return {
      text: response.text,
      audio: audioResponse,
    };
  }
}

import { anthropic } from '@ai-sdk/anthropic';
import { generateObject } from 'ai';
import { z } from 'zod';
const reviewSchema = z.object({
  summary: z.string(),
  issues: z.array(z.object({
    severity: z.enum(['critical', 'warning', 'suggestion']),
    line: z.number(),
    description: z.string(),
    suggestedFix: z.string(),
  })),
  positiveAspects: z.array(z.string()),
  overallScore: z.number().min(0).max(10),
});
async function reviewCode(code: string, language: string) {
  const review = await generateObject({
    model: anthropic('claude-sonnet-4-20250514'),
    schema: reviewSchema,
    prompt: Review this ${language} code and provide structured feedback:

${code},
  });
  return review.object;
}
// Usage
const codeReview = await reviewCode(
function processData(data) {
  var result = []
  for (var i = 0; i < data.length; i++) {
    result.push(data[i] * 2)
  }
  return result
}
, 'javascript');
console.log(codeReview);
// {
//   summary: "Function is functional but uses outdated patterns",
//   issues: [
//     {
//       severity: "warning",
//       line: 2,
//       description: "Using 'var' instead of 'const/let'",
//       suggestedFix: "Replace 'var result = []' with 'const result = []'"
//     },
//     {
//       severity: "suggestion",
//       line: 3,
//       description: "Can be replaced with Array.map()",
//       suggestedFix: "return data.map(item => item * 2)"
//     }
//   ],
//   positiveAspects: ["Clear function name", "Simple logic"],
//   overallScore: 6
// }

import { generateText } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';
async function robustGeneration(prompt: string, maxRetries = 3) {
  for (let attempt = 0; attempt < maxRetries; attempt++) {
    try {
      const result = await generateText({
        model: anthropic('claude-sonnet-4-20250514'),
        prompt,
        maxTokens: 2048,
      });
      return result.text;
    } catch (error) {
      if (attempt === maxRetries - 1) throw error;
      // Exponential backoff
      await new Promise(resolve =>
        setTimeout(resolve, Math.pow(2, attempt) * 1000)
      );
    }
  }
}

// Cache expensive AI operations
import { cache } from '@/lib/cache';
async function getCachedAnalysis(text: string) {
  const cacheKey = analysis:${hashString(text)};
  const cached = await cache.get(cacheKey);
  if (cached) return cached;
  const analysis = await generateText({
    model: anthropic('claude-sonnet-4-20250514'),
    prompt: Analyze: ${text},
  });
  await cache.set(cacheKey, analysis.text, { ttl: 3600 });
  return analysis.text;
}

import { Ratelimit } from '@upstash/ratelimit';
import { Redis } from '@upstash/redis';
const ratelimit = new Ratelimit({
  redis: Redis.fromEnv(),
  limiter: Ratelimit.slidingWindow(10, '1 m'), // 10 requests per minute
});
export async function POST(req: Request) {
  const ip = req.headers.get('x-forwarded-for') ?? 'anonymous';
  const { success } = await ratelimit.limit(ip);
  if (!success) {
    return new Response('Rate limit exceeded', { status: 429 });
  }
  // Process AI request
  const result = await generateText({ /* ... */ });
  return Response.json(result);
}

import { streamText } from 'ai';
export async function POST(req: Request) {
  const { prompt } = await req.json();
  const result = await streamText({
    model: anthropic('claude-sonnet-4-20250514'),
    prompt,
  });
  // Stream response to client
  return result.toAIStreamResponse();
}

import { track } from '@/lib/analytics';
async function monitoredGeneration(prompt: string) {
  const startTime = Date.now();
  try {
    const result = await generateText({
      model: anthropic('claude-sonnet-4-20250514'),
      prompt,
    });
    track('ai_generation_success', {
      model: 'claude-sonnet-4',
      latency: Date.now() - startTime,
      tokens: result.usage?.totalTokens,
      cost: calculateCost(result.usage),
    });
    return result.text;
  } catch (error) {
    track('ai_generation_error', {
      model: 'claude-sonnet-4',
      error: error.message,
      latency: Date.now() - startTime,
    });
    throw error;
  }
}

Vercel AI SDK (recommended for most projects)
npm install ai @ai-sdk/anthropic @ai-sdk/openai
Direct provider SDKs
npm install @anthropic-ai/sdk openai elevenlabs
Specialized tools
npm install @banana-dev/banana-dev lumaai @langchain/langgraph

.env.local
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...
ELEVENLABS_API_KEY=...
OPENROUTER_API_KEY=...

// app/api/chat/route.ts
import { streamText } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';
export async function POST(req: Request) {
  const { messages } = await req.json();
  const result = await streamText({
    model: anthropic('claude-sonnet-4-20250514'),
    messages,
    system: 'You are a helpful coding assistant',
  });
  return result.toAIStreamResponse();
}