AI SDK Core

---

AI SDK 6 (Stable - January 2026)

BREAKING: Output API Replaces generateObject/streamObject

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// ❌ 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',
});
`**After (v6 - USE THIS):**`
// ✅ 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 }
`### Output Types`
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()
`### Streaming with Output API`
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;

v6 New Features

• Full control over execution flow, tool loops, and state management
• Replaces manual tool calling orchestration

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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),
  }),
}

• Use dynamic approval for operations where risk depends on parameters (e.g., payment amount)
• Always require approval for destructive operations (delete, modify, purchase)
• Don't require approval for safe read operations
• Add system instruction: "When a tool execution is not approved, do not retry it"
• Implement timeout for approval requests to prevent stuck states
• Store user preferences for repeat actions

• Next.js Human-in-the-Loop Guide
• Cloudflare Agents Human-in-the-Loop
• Permit.io Best Practices

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import { rerank } from 'ai';

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

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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',
});

• Tool definitions become part of your agent's prompt
• Can change unexpectedly without warning
• Compromised MCP server can inject malicious prompts
• New tools can escalate user privileges (e.g., adding delete to read-only server)

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// ❌ 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',
});

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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
`**6\. Telemetry (OpenTelemetry)**`
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,
  },
});

---

Latest AI Models (2025-2026)

OpenAI

• 400k context window, 128k output tokens
• Enhanced reasoning capabilities
• Available in API platform

• Improved speed and efficiency over GPT-5
• "Warmer" and more intelligent responses

• 45% less hallucination than GPT-4o
• State-of-the-art in math, coding, visual perception

• o3, o3-pro, o3-mini - Advanced reasoning
• o4-mini - Fast reasoning

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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');

Anthropic

• Opus 4 (May 22): Best for complex reasoning, $15/$75 per million tokens
• Sonnet 4 (May 22): Balanced performance, $3/$15 per million tokens
• Opus 4.1 (Aug 5): Enhanced agentic tasks, real-world coding
• Sonnet 4.5 (Sept 29): Most capable for coding, agents, computer use
• Haiku 4.5 (Oct 15): Small, fast, low-latency model

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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');

Google

• Pro (March 2025): Most intelligent, #1 on LMArena at launch
• Pro Deep Think (May 2025): Enhanced reasoning mode
• Flash (May 2025): Fast, cost-effective
• Flash-Lite (Sept 2025): Updated efficiency

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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');

---

Core Functions

Text Generation

Structured Output (v6 Output API)

Multi-Modal Capabilities

Speech Synthesis (Text-to-Speech)

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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;

• OpenAI: tts-1, tts-1-hd, gpt-4o-mini-tts
• ElevenLabs: eleven_multilingual_v2, eleven_turbo_v2
• LMNT, Hume

Transcription (Speech-to-Text)

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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

• OpenAI: whisper-1
• ElevenLabs, Deepgram, AssemblyAI, Groq, Rev.ai

Image Generation

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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;

• OpenAI: dall-e-2, dall-e-3
• Google: imagen-3.0
• Fal AI, Black Forest Labs (Flux), Luma AI, Replicate

Embeddings

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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

• OpenAI: text-embedding-3-small, text-embedding-3-large
• Google: text-embedding-004
• Cohere, Voyage AI, Mistral, Amazon Bedrock

Multi-Modal Prompts (Files, Images, PDFs)

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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 },
    ],
  }],
});

---

v5 Stream Response Methods

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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();

---

workers-ai-provider Version Compatibility

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# ✅ 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"

---

Cloudflare Workers Startup Fix

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// ❌ 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 });
  // ...
});

• Minimize top-level Zod schemas
• Move complex schemas into route handlers
• Monitor startup time with Wrangler

---

v5 Tool Calling Changes

• parameters → inputSchema (Zod schema)
• Tool properties: args → input, result → output
• ToolExecutionError removed (now tool-error content parts)
• maxSteps parameter removed → Use stopWhen(stepCountIs(n))

• Dynamic tools (add tools at runtime based on context)
• Agent class (multi-step execution with tools)

---

Critical v4→v5 Migration

Breaking Changes Overview

1. Parameter Renames
   • maxTokens → maxOutputTokens
   • providerMetadata → providerOptions
2. Tool Definitions
   • parameters → inputSchema
   • Tool properties: args → input, result → output
3. Message Types
   • CoreMessage → ModelMessage
   • Message → UIMessage
   • convertToCoreMessages → convertToModelMessages
4. Tool Error Handling
   • ToolExecutionError class removed
   • Now tool-error content parts
   • Enables automated retry
5. Multi-Step Execution
   • maxSteps → stopWhen
   • Use stepCountIs() or hasToolCall()
6. Message Structure
   • Simple content string → parts array
   • Parts: text, file, reasoning, tool-call, tool-result
7. Streaming Architecture
   • Single chunk → start/delta/end lifecycle
   • Unique IDs for concurrent streams
8. Tool Streaming
   • Enabled by default
   • toolCallStreaming option removed
9. Package Reorganization
   • ai/rsc → @ai-sdk/rsc
   • ai/react → @ai-sdk/react
   • LangChainAdapter → @ai-sdk/langchain

Migration Examples

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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,
});
`**After (v5):**`
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),
});

Migration Checklist

• Update all maxTokens to maxOutputTokens
• Update providerMetadata to providerOptions
• Convert tool parameters to inputSchema
• Update tool execute functions: args → input
• Replace maxSteps with stopWhen(stepCountIs(n))
• Update message types: CoreMessage → ModelMessage
• Remove ToolExecutionError handling
• Update package imports (ai/rsc → @ai-sdk/rsc)
• Test streaming behavior (architecture changed)
• Update TypeScript types

Automated Migration

---

Top 15 Errors & Solutions

1. AI_APICallError

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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
    }
  }
}

• Validate API keys at startup
• Implement retry logic with exponential backoff
• Monitor rate limits
• Handle network errors gracefully

---

2. AI_NoObjectGeneratedError

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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)
  }
}

• Start with simple schemas, add complexity incrementally
• Include examples in prompt: "Generate a person like: { name: 'Alice', age: 30 }"
• Use GPT-4 for complex structured output
• Test schemas with sample data first

---

3. Worker Startup Limit (270ms+)

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// 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
  }
}

• Move AI SDK imports inside route handlers
• Minimize top-level Zod schemas
• Monitor Worker startup time (must be <400ms)
• Use Wrangler's startup time reporting

---

4. streamText Fails Silently

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// 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);
}
`**Alternative (Manual try-catch):**`
// 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);
}

• Use onError callback for proper error capture (recommended)
• Implement server-side error monitoring
• Test stream error handling explicitly
• Always log on server side in production

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5. AI_LoadAPIKeyError

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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-)
  }
}

• Validate API keys at application startup
• Use environment variable validation (e.g., zod)
• Provide clear error messages in development
• Document required environment variables

---

6. AI_InvalidArgumentError

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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
  }
}

• Use TypeScript for type checking
• Validate inputs before calling AI SDK functions
• Read function signatures carefully
• Check official docs for parameter constraints

---

7. AI_NoContentGeneratedError

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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.' };
  }
}

• Sanitize user inputs
• Avoid prompts that may trigger safety filters
• Have fallback messaging
• Log occurrences for analysis

---

8. AI_TypeValidationError

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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
  }
}

• Start with lenient schemas, tighten gradually
• Use .optional() for fields that may not always be present
• Add validation hints in field descriptions
• Test with various prompts

---

9. AI_RetryError

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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
  }
}

• Investigate root cause of failures
• Adjust retry configuration if needed
• Implement circuit breaker pattern for provider outages
• Have fallback providers

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10. Rate Limiting Errors

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// 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');
}

• Monitor rate limit headers
• Queue requests to stay under limits
• Upgrade provider tier if needed
• Implement request throttling

---

11. TypeScript Performance with Zod

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// 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(),
  })
);

• Avoid top-level complex schemas
• Use z.lazy() for recursive types
• Split large schemas into smaller ones
• Use type assertions where appropriate

---

12. Invalid JSON Response (Provider-Specific)

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// 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',
  });
}

• Use mode: 'json' when available
• Prefer GPT-4 for structured output
• Implement retry logic
• Validate responses

---

13. Gemini Implicit Caching Fails with Tools

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// 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,
});

---

14. Anthropic Tool Error Results Cause JSON Parse Crash

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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;
}

---

15. Tool-Result in Assistant Message (Anthropic)

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// 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',
});

---

---

Known Issues & Limitations

useChat Stale Closures with Memoized Options

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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);
`**Workaround 1 - Don't Memoize Callbacks**:`
const { messages, append } = useChat({
  onFinish: (message) => {
    console.log('Count:', count); // Now sees current count
  },
});
`**Workaround 2 - Use useRef**:`
const countRef = useRef(count);
useEffect(() => { countRef.current = count; }, [count]);

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

---

Stream Resumption Fails on Tab Switch

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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();
    }
  },
});
`**Workaround 2 - Detect Visibility Change**:`
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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When to Use This Skill

Use ai-sdk-core when:

• Building backend AI features (server-side text generation)
• Implementing server-side text generation (Node.js, Workers, Next.js)
• Creating structured AI outputs (JSON, forms, data extraction)
• Building AI agents with tools (multi-step workflows)
• Integrating multiple AI providers (OpenAI, Anthropic, Google, Cloudflare)
• Migrating from AI SDK v4 to v5
• Encountering AI SDK errors (AI_APICallError, AI_NoObjectGeneratedError, etc.)
• Using AI in Cloudflare Workers (with workers-ai-provider)
• Using AI in Next.js Server Components/Actions
• Need consistent API across different LLM providers

Don't use this skill when:

• Building React chat UIs (use ai-sdk-ui skill instead)
• Need frontend hooks like useChat (use ai-sdk-ui skill instead)
• Need advanced topics like embeddings or image generation (check official docs)
• Building native Cloudflare Workers AI apps without multi-provider (use cloudflare-workers-ai skill instead)
• Need Generative UI / RSC (see https://ai-sdk.dev/docs/ai-sdk-rsc)

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Versions

• Stable: ai@6.0.26 (Jan 2026)
• ⚠️ Skip v6.0.40 - Breaking streaming change (reverted in v6.0.41)
• Legacy v5: ai@5.0.117 (ai-v5 tag)
• Zod 3.x/4.x both supported

• OpenAI: GPT-5.2, GPT-5.1, GPT-5, o3, o3-mini, o4-mini
• Anthropic: Claude Sonnet 4.5, Opus 4.1, Haiku 4.5
• Google: Gemini 2.5 Pro/Flash/Lite

Copy
npm view ai version
npm view ai dist-tags

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Official Docs

• AI SDK v6: https://ai-sdk.dev/docs
• AI SDK Core: https://ai-sdk.dev/docs/ai-sdk-core/overview
• Output API: https://ai-sdk.dev/docs/ai-sdk-core/generating-structured-data
• v4→v5 Migration: https://ai-sdk.dev/docs/migration-guides/migration-guide-5-0
• All Errors (31): https://ai-sdk.dev/docs/reference/ai-sdk-errors
• Providers (69+): https://ai-sdk.dev/providers/overview

• Speech: https://ai-sdk.dev/docs/ai-sdk-core/speech
• Transcription: https://ai-sdk.dev/docs/ai-sdk-core/transcription
• Image Generation: https://ai-sdk.dev/docs/ai-sdk-core/image-generation
• Embeddings: https://ai-sdk.dev/docs/ai-sdk-core/embeddings

• Repository: https://github.com/vercel/ai
• Issues: https://github.com/vercel/ai/issues

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