npm install ai @ai-sdk/openai @ai-sdk/anthropic @ai-sdk/google zod

// ❌ DEPRECATED - will be removed
import { generateObject } from 'ai';

const result = await generateObject({
  model: openai('gpt-5'),
  schema: z.object({ name: z.string(), age: z.number() }),
  prompt: 'Generate a person',
});

// ✅ NEW OUTPUT API
import { generateText, Output } from 'ai';

const result = await generateText({
  model: openai('gpt-5'),
  output: Output.object({ schema: z.object({ name: z.string(), age: z.number() }) }),
  prompt: 'Generate a person',
});

// Access the typed object
console.log(result.object); // { name: "Alice", age: 30 }

import { generateText, Output } from 'ai';

// Object with Zod schema
output: Output.object({ schema: myZodSchema })

// Array of typed objects
output: Output.array({ schema: personSchema })

// Enum/choice from options
output: Output.choice({ choices: ['positive', 'negative', 'neutral'] })

// Plain text (explicit)
output: Output.text()

// Unstructured JSON (no schema validation)
output: Output.json()

import { streamText, Output } from 'ai';

const result = streamText({
  model: openai('gpt-5'),
  output: Output.object({ schema: personSchema }),
  prompt: 'Generate a person',
});

// Stream partial objects
for await (const partialObject of result.objectStream) {
  console.log(partialObject); // { name: "Ali..." } -> { name: "Alice", age: ... }
}

// Get final object
const finalObject = await result.object;

tools: {
  payment: tool({
    // Dynamic approval based on input
    needsApproval: async ({ amount }) => amount > 1000,
    inputSchema: z.object({ amount: z.number() }),
    execute: async ({ amount }) => { /* process payment */ },
  }),

  readFile: tool({
    needsApproval: false, // Safe operations don't need approval
    inputSchema: z.object({ path: z.string() }),
    execute: async ({ path }) => fs.readFile(path),
  }),

  deleteFile: tool({
    needsApproval: true, // Destructive operations always need approval
    inputSchema: z.object({ path: z.string() }),
    execute: async ({ path }) => fs.unlink(path),
  }),
}

import { rerank } from 'ai';

const result = await rerank({
  model: cohere.reranker('rerank-v3.5'),
  query: 'user question',
  documents: searchResults,
  topK: 5,
});

import { experimental_createMCPClient } from 'ai';

const mcpClient = await experimental_createMCPClient({
  transport: { type: 'stdio', command: 'npx', args: ['-y', '@modelcontextprotocol/server-filesystem'] },
});

const tools = await mcpClient.tools();

const result = await generateText({
  model: openai('gpt-5'),
  tools,
  prompt: 'List files in the current directory',
});

// ❌ RISKY: Dynamic tools change without your control
const mcpClient = await experimental_createMCPClient({ /* ... */ });
const tools = await mcpClient.tools(); // Can change anytime!

// ✅ SAFE: Generate static, versioned tool definitions
// Step 1: Install mcp-to-ai-sdk
npm install -g mcp-to-ai-sdk

// Step 2: Generate static tools (one-time, version controlled)
npx mcp-to-ai-sdk generate stdio 'npx -y @modelcontextprotocol/server-filesystem'

// Step 3: Import static tools
import { tools } from './generated-mcp-tools';

const result = await generateText({
  model: openai('gpt-5'),
  tools, // Static, reviewed, versioned
  prompt: 'Use tools',
});

import { wrapLanguageModel, extractReasoningMiddleware } from 'ai';

const wrappedModel = wrapLanguageModel({
  model: anthropic('claude-sonnet-4-5-20250929'),
  middleware: extractReasoningMiddleware({ tagName: 'think' }),
});

// Reasoning extracted automatically from <think>...</think> tags

const result = await generateText({
  model: openai('gpt-5'),
  prompt: 'Hello',
  experimental_telemetry: {
    isEnabled: true,
    functionId: 'my-chat-function',
    metadata: { userId: '123' },
    recordInputs: true,
    recordOutputs: true,
  },
});

import { openai } from '@ai-sdk/openai';
const gpt52 = openai('gpt-5.2');
const gpt51 = openai('gpt-5.1');
const gpt5 = openai('gpt-5');
const o3 = openai('o3');
const o3mini = openai('o3-mini');

import { anthropic } from '@ai-sdk/anthropic';
const sonnet45 = anthropic('claude-sonnet-4-5-20250929');  // Latest
const opus41 = anthropic('claude-opus-4-1-20250805');
const haiku45 = anthropic('claude-haiku-4-5-20251015');

import { google } from '@ai-sdk/google';
const pro = google('gemini-2.5-pro');
const flash = google('gemini-2.5-flash');
const lite = google('gemini-2.5-flash-lite');

import { experimental_generateSpeech as generateSpeech } from 'ai';
import { openai } from '@ai-sdk/openai';

const result = await generateSpeech({
  model: openai.speech('tts-1-hd'),
  voice: 'alloy',
  text: 'Hello, how can I help you today?',
});

// result.audio is an ArrayBuffer containing the audio
const audioBuffer = result.audio;

import { experimental_transcribe as transcribe } from 'ai';
import { openai } from '@ai-sdk/openai';

const result = await transcribe({
  model: openai.transcription('whisper-1'),
  audio: audioFile, // File, Blob, ArrayBuffer, or URL
});

console.log(result.text); // Transcribed text
console.log(result.segments); // Timestamped segments

import { generateImage } from 'ai';
import { openai } from '@ai-sdk/openai';

const result = await generateImage({
  model: openai.image('dall-e-3'),
  prompt: 'A futuristic city at sunset',
  size: '1024x1024',
  n: 1,
});

// result.images is an array of generated images
const imageUrl = result.images[0].url;
const imageBase64 = result.images[0].base64;

import { embed, embedMany, cosineSimilarity } from 'ai';
import { openai } from '@ai-sdk/openai';

// Single embedding
const result = await embed({
  model: openai.embedding('text-embedding-3-small'),
  value: 'Hello world',
});
console.log(result.embedding); // number[]

// Multiple embeddings (parallel processing)
const results = await embedMany({
  model: openai.embedding('text-embedding-3-small'),
  values: ['Hello', 'World', 'AI'],
  maxParallelCalls: 5, // Parallel processing
});

// Compare similarity
const similarity = cosineSimilarity(
  results.embeddings[0],
  results.embeddings[1]
);
console.log(`Similarity: ${similarity}`); // 0.0 to 1.0

import { generateText } from 'ai';
import { google } from '@ai-sdk/google';

const result = await generateText({
  model: google('gemini-2.5-pro'),
  messages: [{
    role: 'user',
    content: [
      { type: 'text', text: 'Summarize this document' },
      { type: 'file', data: pdfBuffer, mimeType: 'application/pdf' },
    ],
  }],
});

// Or with images
const result = await generateText({
  model: openai('gpt-5'),
  messages: [{
    role: 'user',
    content: [
      { type: 'text', text: 'What is in this image?' },
      { type: 'image', image: imageBuffer },
    ],
  }],
});

const result = streamText({
  model: workersai('@cf/qwen/qwen3-30b-a3b-fp8'),
  messages,
  system: 'You are helpful.',
});

// ✅ For chat UIs - returns SSE with JSON events
return result.toUIMessageStreamResponse({
  headers: { 'Access-Control-Allow-Origin': '*' },
});

// ❌ For simple text - returns plain text chunks only
return result.toTextStreamResponse();

# ✅ Correct - AI SDK v5 with workers-ai-provider v2
npm install ai@^5.0.0 workers-ai-provider@^2.0.0 zod@^3.25.0

# ❌ Wrong - AI SDK v4 causes error
npm install ai@^4.0.0 workers-ai-provider@^2.0.0
# Error: "AI SDK 4 only supports models that implement specification version v1"

// ❌ BAD: Top-level imports cause startup overhead
import { createWorkersAI } from 'workers-ai-provider';
const workersai = createWorkersAI({ binding: env.AI });

// ✅ GOOD: Lazy initialization inside handler
app.post('/chat', async (c) => {
  const { createWorkersAI } = await import('workers-ai-provider');
  const workersai = createWorkersAI({ binding: c.env.AI });
  // ...
});

import { generateText } from 'ai';

const result = await generateText({
  model: openai.chat('gpt-4-turbo'),
  maxTokens: 500,
  providerMetadata: { openai: { user: 'user-123' } },
  tools: {
    weather: {
      description: 'Get weather',
      parameters: z.object({ location: z.string() }),
      execute: async (args) => { /* args.location */ },
    },
  },
  maxSteps: 5,
});

import { generateText, tool, stopWhen, stepCountIs } from 'ai';

const result = await generateText({
  model: openai('gpt-4-turbo'),
  maxOutputTokens: 500,
  providerOptions: { openai: { user: 'user-123' } },
  tools: {
    weather: tool({
      description: 'Get weather',
      inputSchema: z.object({ location: z.string() }),
      execute: async ({ location }) => { /* input.location */ },
    }),
  },
  stopWhen: stepCountIs(5),
});

npx ai migrate

import { AI_APICallError } from 'ai';

try {
  const result = await generateText({
    model: openai('gpt-4-turbo'),
    prompt: 'Hello',
  });
} catch (error) {
  if (error instanceof AI_APICallError) {
    console.error('API call failed:', error.message);
    console.error('Status code:', error.statusCode);
    console.error('Response:', error.responseBody);

    // Check common causes
    if (error.statusCode === 401) {
      // Invalid API key
    } else if (error.statusCode === 429) {
      // Rate limit - implement backoff
    } else if (error.statusCode >= 500) {
      // Provider issue - retry
    }
  }
}

import { AI_NoObjectGeneratedError } from 'ai';

try {
  const result = await generateObject({
    model: openai('gpt-4-turbo'),
    schema: z.object({ /* complex schema */ }),
    prompt: 'Generate data',
  });
} catch (error) {
  if (error instanceof AI_NoObjectGeneratedError) {
    console.error('No valid object generated');

    // Solutions:
    // 1. Simplify schema
    // 2. Add more context to prompt
    // 3. Provide examples in prompt
    // 4. Try different model (gpt-5 or claude-sonnet-4-5 for complex objects)
  }
}

// BAD: Top-level imports cause startup overhead
import { createWorkersAI } from 'workers-ai-provider';
import { complexSchema } from './schemas';

const workersai = createWorkersAI({ binding: env.AI });

// GOOD: Lazy initialization inside handler
export default {
  async fetch(request, env) {
    const { createWorkersAI } = await import('workers-ai-provider');
    const workersai = createWorkersAI({ binding: env.AI });

    // Use workersai here
  }
}

// Use the onError callback (added in v4.1.22)
const stream = streamText({
  model: openai('gpt-4-turbo'),
  prompt: 'Hello',
  onError({ error }) {
    console.error('Stream error:', error);
    // Custom error logging and handling
  },
});

// Stream safely
for await (const chunk of stream.textStream) {
  process.stdout.write(chunk);
}

// Fallback if not using onError callback
try {
  const stream = streamText({
    model: openai('gpt-4-turbo'),
    prompt: 'Hello',
  });

  for await (const chunk of stream.textStream) {
    process.stdout.write(chunk);
  }
} catch (error) {
  console.error('Stream error:', error);
}

import { AI_LoadAPIKeyError } from 'ai';

try {
  const result = await generateText({
    model: openai('gpt-4-turbo'),
    prompt: 'Hello',
  });
} catch (error) {
  if (error instanceof AI_LoadAPIKeyError) {
    console.error('API key error:', error.message);

    // Check:
    // 1. .env file exists and loaded
    // 2. Correct env variable name (OPENAI_API_KEY)
    // 3. Key format is valid (starts with sk-)
  }
}

import { AI_InvalidArgumentError } from 'ai';

try {
  const result = await generateText({
    model: openai('gpt-4-turbo'),
    maxOutputTokens: -1,  // Invalid!
    prompt: 'Hello',
  });
} catch (error) {
  if (error instanceof AI_InvalidArgumentError) {
    console.error('Invalid argument:', error.message);
    // Check parameter types and values
  }
}

import { AI_NoContentGeneratedError } from 'ai';

try {
  const result = await generateText({
    model: openai('gpt-4-turbo'),
    prompt: 'Some prompt',
  });
} catch (error) {
  if (error instanceof AI_NoContentGeneratedError) {
    console.error('No content generated');

    // Possible causes:
    // 1. Safety filters blocked output
    // 2. Prompt triggered content policy
    // 3. Model configuration issue

    // Handle gracefully:
    return { text: 'Unable to generate response. Please try different input.' };
  }
}

import { AI_TypeValidationError } from 'ai';

try {
  const result = await generateObject({
    model: openai('gpt-4-turbo'),
    schema: z.object({
      age: z.number().min(0).max(120),  // Strict validation
    }),
    prompt: 'Generate person',
  });
} catch (error) {
  if (error instanceof AI_TypeValidationError) {
    console.error('Validation failed:', error.message);

    // Solutions:
    // 1. Relax schema constraints
    // 2. Add more guidance in prompt
    // 3. Use .optional() for unreliable fields
  }
}

import { AI_RetryError } from 'ai';

try {
  const result = await generateText({
    model: openai('gpt-4-turbo'),
    prompt: 'Hello',
    maxRetries: 3,  // Default is 2
  });
} catch (error) {
  if (error instanceof AI_RetryError) {
    console.error('All retries failed');
    console.error('Last error:', error.lastError);

    // Check root cause:
    // - Persistent network issue
    // - Provider outage
    // - Invalid configuration
  }
}

// Implement exponential backoff
async function generateWithBackoff(prompt: string, retries = 3) {
  for (let i = 0; i < retries; i++) {
    try {
      return await generateText({
        model: openai('gpt-4-turbo'),
        prompt,
      });
    } catch (error) {
      if (error instanceof AI_APICallError && error.statusCode === 429) {
        const delay = Math.pow(2, i) * 1000;  // Exponential backoff
        console.log(`Rate limited, waiting ${delay}ms`);
        await new Promise(resolve => setTimeout(resolve, delay));
      } else {
        throw error;
      }
    }
  }
  throw new Error('Rate limit retries exhausted');
}

// Instead of deeply nested schemas at top level:
// const complexSchema = z.object({ /* 100+ fields */ });

// Define inside functions or use type assertions:
function generateData() {
  const schema = z.object({ /* complex schema */ });
  return generateObject({ model: openai('gpt-4-turbo'), schema, prompt: '...' });
}

// Or use z.lazy() for recursive schemas:
type Category = { name: string; subcategories?: Category[] };
const CategorySchema: z.ZodType<Category> = z.lazy(() =>
  z.object({
    name: z.string(),
    subcategories: z.array(CategorySchema).optional(),
  })
);

// Use built-in retry and mode selection
const result = await generateObject({
  model: openai('gpt-4-turbo'),
  schema: mySchema,
  prompt: 'Generate data',
  mode: 'json',  // Force JSON mode (supported by GPT-4)
  maxRetries: 3,  // Retry on invalid JSON
});

// Or catch and retry manually:
try {
  const result = await generateObject({
    model: openai('gpt-4-turbo'),
    schema: mySchema,
    prompt: 'Generate data',
  });
} catch (error) {
  // Retry with different model
  const result = await generateObject({
    model: openai('gpt-4-turbo'),
    schema: mySchema,
    prompt: 'Generate data',
  });
}

// Conditionally add tools only when needed
const needsTools = await analyzePrompt(userInput);

const result = await generateText({
  model: google('gemini-3-flash'),
  tools: needsTools ? { weather: weatherTool } : undefined,
  prompt: userInput,
});

try {
  const result = await generateText({
    model: anthropic('claude-sonnet-4-5-20250929'),
    tools: { web_fetch: { type: 'anthropic_defined', name: 'web_fetch' } },
    prompt: userPrompt,
  });
} catch (error) {
  if (error.message.includes('is not valid JSON')) {
    // Tool returned error result, handle gracefully
    console.error('Tool execution failed - likely blocked URL or permission issue');
    // Retry without tool or use custom tool
  }
  throw error;
}

// Workaround: Filter messages before sending
const filteredMessages = messages.map(msg => {
  if (msg.role === 'assistant') {
    return {
      ...msg,
      content: msg.content.filter(part => part.type !== 'tool-result'),
    };
  }
  return msg;
});

const result = await generateText({
  model: anthropic('claude-sonnet-4-5-20250929'),
  tools: { database: databaseTool },
  messages: filteredMessages,
  prompt: 'Get user data',
});

const [count, setCount] = useState(0);

const chatOptions = useMemo(() => ({
  onFinish: (message) => {
    console.log('Count:', count); // ALWAYS 0, never updates!
  },
}), []); // Empty deps = stale closure

const { messages, append } = useChat(chatOptions);

const { messages, append } = useChat({
  onFinish: (message) => {
    console.log('Count:', count); // Now sees current count
  },
});

const countRef = useRef(count);
useEffect(() => { countRef.current = count; }, [count]);

const chatOptions = useMemo(() => ({
  onFinish: (message) => {
    console.log('Count:', countRef.current); // Always current
  },
}), []);

const { messages, append, reload } = useChat({
  api: '/api/chat',
  onError: (error) => {
    if (error.message.includes('stream') || error.message.includes('aborted')) {
      // Attempt to reload last message
      reload();
    }
  },
});

useEffect(() => {
  const handleVisibilityChange = () => {
    if (document.visibilityState === 'visible') {
      // Check if stream was interrupted
      const lastMessage = messages[messages.length - 1];
      if (lastMessage?.role === 'assistant' && !lastMessage.content) {
        reload();
      }
    }
  };

  document.addEventListener('visibilitychange', handleVisibilityChange);
  return () => document.removeEventListener('visibilitychange', handleVisibilityChange);
}, [messages, reload]);

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