Jackspace Claudeskillz AI Sdk Core

AI SDK Core

Production-ready backend AI with Vercel AI SDK v5.

Quick Start (5 Minutes)

Installation

# Core package
npm install ai

# Provider packages (install what you need)
npm install @ai-sdk/openai       # OpenAI (GPT-5, GPT-4, GPT-3.5)
npm install @ai-sdk/anthropic    # Anthropic (Claude Sonnet 4.5, Opus 4, Haiku 4)
npm install @ai-sdk/google       # Google (Gemini 2.5 Pro/Flash/Lite)
npm install workers-ai-provider  # Cloudflare Workers AI

# Schema validation
npm install zod

Environment Variables

# .env
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_GENERATIVE_AI_API_KEY=...

First Example: Generate Text

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

const result = await generateText({
  model: openai('gpt-4-turbo'),
  prompt: 'What is TypeScript?',
});

console.log(result.text);

First Example: Streaming Chat

import { streamText } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';

const stream = streamText({
  model: anthropic('claude-sonnet-4-5-20250929'),
  messages: [
    { role: 'user', content: 'Tell me a story' },
  ],
});

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

First Example: Structured Output

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

const result = await generateObject({
  model: openai('gpt-4'),
  schema: z.object({
    name: z.string(),
    age: z.number(),
    skills: z.array(z.string()),
  }),
  prompt: 'Generate a person profile for a software engineer',
});

console.log(result.object);
// { name: "Alice", age: 28, skills: ["TypeScript", "React"] }

Core Functions

generateText()

Generate text completion with optional tools and multi-step execution.

Signature:

async function generateText(options: {
  model: LanguageModel;
  prompt?: string;
  messages?: Array<ModelMessage>;
  system?: string;
  tools?: Record<string, Tool>;
  maxOutputTokens?: number;
  temperature?: number;
  stopWhen?: StopCondition;
  // ... other options
}): Promise<GenerateTextResult>

Basic Usage:

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

const result = await generateText({
  model: openai('gpt-4-turbo'),
  prompt: 'Explain quantum computing',
  maxOutputTokens: 500,
  temperature: 0.7,
});

console.log(result.text);
console.log(`Tokens: ${result.usage.totalTokens}`);

With Messages (Chat Format):

const result = await generateText({
  model: openai('gpt-4-turbo'),
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What is the weather?' },
    { role: 'assistant', content: 'I need your location.' },
    { role: 'user', content: 'San Francisco' },
  ],
});

With Tools:

import { tool } from 'ai';
import { z } from 'zod';

const result = await generateText({
  model: openai('gpt-4'),
  tools: {
    weather: tool({
      description: 'Get the weather for a location',
      inputSchema: z.object({
        location: z.string(),
      }),
      execute: async ({ location }) => {
        // API call here
        return { temperature: 72, condition: 'sunny' };
      },
    }),
  },
  prompt: 'What is the weather in Tokyo?',
});

When to Use:

• Need final response (not streaming)
• Want to wait for tool executions to complete
• Simpler code when streaming not needed
• Building batch/scheduled tasks

Error Handling:

import { AI_APICallError, AI_NoContentGeneratedError } from 'ai';

try {
  const result = await generateText({
    model: openai('gpt-4-turbo'),
    prompt: 'Hello',
  });
  console.log(result.text);
} catch (error) {
  if (error instanceof AI_APICallError) {
    console.error('API call failed:', error.message);
    // Check rate limits, API key, network
  } else if (error instanceof AI_NoContentGeneratedError) {
    console.error('No content generated');
    // Prompt may have been filtered
  } else {
    console.error('Unknown error:', error);
  }
}

streamText()

Stream text completion with real-time chunks.

Signature:

function streamText(options: {
  model: LanguageModel;
  prompt?: string;
  messages?: Array<ModelMessage>;
  system?: string;
  tools?: Record<string, Tool>;
  maxOutputTokens?: number;
  temperature?: number;
  stopWhen?: StopCondition;
  // ... other options
}): StreamTextResult

Basic Streaming:

import { streamText } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';

const stream = streamText({
  model: anthropic('claude-sonnet-4-5-20250929'),
  prompt: 'Write a poem about AI',
});

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

// Or get final result
const finalResult = await stream.result;
console.log(finalResult.text);

Streaming with Tools:

const stream = streamText({
  model: openai('gpt-4'),
  tools: {
    // ... tools definition
  },
  prompt: 'What is the weather?',
});

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

Handling the Stream:

const stream = streamText({
  model: openai('gpt-4-turbo'),
  prompt: 'Explain AI',
});

// Option 1: Text stream
for await (const text of stream.textStream) {
  console.log(text);
}

// Option 2: Full stream (includes metadata)
for await (const part of stream.fullStream) {
  if (part.type === 'text-delta') {
    console.log(part.textDelta);
  } else if (part.type === 'tool-call') {
    console.log('Tool called:', part.toolName);
  }
}

// Option 3: Wait for final result
const result = await stream.result;
console.log(result.text, result.usage);

When to Use:

• Real-time user-facing responses
• Long-form content generation
• Want to show progress
• Better perceived performance

Production Pattern:

// Next.js API Route
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

export async function POST(request: Request) {
  const { messages } = await request.json();

  const stream = streamText({
    model: openai('gpt-4-turbo'),
    messages,
  });

  // Return stream to client
  return stream.toDataStreamResponse();
}

Error Handling:

// Recommended: Use 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 handling
  },
});

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

// Alternative: Manual try-catch
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);
}

generateObject()

Generate structured output validated by Zod schema.

Signature:

async function generateObject<T>(options: {
  model: LanguageModel;
  schema: z.Schema<T>;
  prompt?: string;
  messages?: Array<ModelMessage>;
  system?: string;
  mode?: 'auto' | 'json' | 'tool';
  // ... other options
}): Promise<GenerateObjectResult<T>>

Basic Usage:

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

const result = await generateObject({
  model: openai('gpt-4'),
  schema: z.object({
    recipe: z.object({
      name: z.string(),
      ingredients: z.array(z.object({
        name: z.string(),
        amount: z.string(),
      })),
      instructions: z.array(z.string()),
    }),
  }),
  prompt: 'Generate a recipe for chocolate chip cookies',
});

console.log(result.object.recipe);

Nested Schemas:

const PersonSchema = z.object({
  name: z.string(),
  age: z.number(),
  address: z.object({
    street: z.string(),
    city: z.string(),
    country: z.string(),
  }),
  hobbies: z.array(z.string()),
});

const result = await generateObject({
  model: openai('gpt-4'),
  schema: PersonSchema,
  prompt: 'Generate a person profile',
});

Arrays and Unions:

// Array of objects
const result = await generateObject({
  model: openai('gpt-4'),
  schema: z.object({
    people: z.array(z.object({
      name: z.string(),
      role: z.enum(['engineer', 'designer', 'manager']),
    })),
  }),
  prompt: 'Generate a team of 5 people',
});

// Union types
const result = await generateObject({
  model: openai('gpt-4'),
  schema: z.discriminatedUnion('type', [
    z.object({ type: z.literal('text'), content: z.string() }),
    z.object({ type: z.literal('image'), url: z.string() }),
  ]),
  prompt: 'Generate content',
});

When to Use:

• Need structured data (JSON, forms, etc.)
• Validation is critical
• Extracting data from unstructured input
• Building AI workflows that consume JSON

Error Handling:

import { AI_NoObjectGeneratedError, AI_TypeValidationError } from 'ai';

try {
  const result = await generateObject({
    model: openai('gpt-4'),
    schema: z.object({ name: z.string() }),
    prompt: 'Generate a person',
  });
} catch (error) {
  if (error instanceof AI_NoObjectGeneratedError) {
    console.error('Model did not generate valid object');
    // Try simplifying schema or adding examples
  } else if (error instanceof AI_TypeValidationError) {
    console.error('Zod validation failed:', error.message);
    // Schema doesn't match output
  }
}

streamObject()

Stream structured output with partial updates.

Signature:

function streamObject<T>(options: {
  model: LanguageModel;
  schema: z.Schema<T>;
  prompt?: string;
  messages?: Array<ModelMessage>;
  mode?: 'auto' | 'json' | 'tool';
  // ... other options
}): StreamObjectResult<T>

Basic Usage:

import { streamObject } from 'ai';
import { google } from '@ai-sdk/google';
import { z } from 'zod';

const stream = streamObject({
  model: google('gemini-2.5-pro'),
  schema: z.object({
    characters: z.array(z.object({
      name: z.string(),
      class: z.string(),
      stats: z.object({
        hp: z.number(),
        mana: z.number(),
      }),
    })),
  }),
  prompt: 'Generate 3 RPG characters',
});

// Stream partial updates
for await (const partialObject of stream.partialObjectStream) {
  console.log(partialObject);
  // { characters: [{ name: "Aria" }] }
  // { characters: [{ name: "Aria", class: "Mage" }] }
  // { characters: [{ name: "Aria", class: "Mage", stats: { hp: 100 } }] }
  // ...
}

UI Integration Pattern:

// Server endpoint
export async function POST(request: Request) {
  const { prompt } = await request.json();

  const stream = streamObject({
    model: openai('gpt-4'),
    schema: z.object({
      summary: z.string(),
      keyPoints: z.array(z.string()),
    }),
    prompt,
  });

  return stream.toTextStreamResponse();
}

// Client (with useObject hook from ai-sdk-ui)
const { object, isLoading } = useObject({
  api: '/api/analyze',
  schema: /* same schema */,
});

// Render partial object as it streams
{object?.summary && <p>{object.summary}</p>}
{object?.keyPoints?.map(point => <li key={point}>{point}</li>)}

When to Use:

• Real-time structured data (forms, dashboards)
• Show progressive completion
• Large structured outputs
• Better UX for slow generations

Provider Setup & Configuration

OpenAI

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

// API key from environment (recommended)
// OPENAI_API_KEY=sk-...
const model = openai('gpt-4-turbo');

// Or explicit API key
const model = openai('gpt-4', {
  apiKey: process.env.OPENAI_API_KEY,
});

// Available models
const gpt5 = openai('gpt-5');           // Latest (released August 2025)
const gpt4 = openai('gpt-4-turbo');
const gpt35 = openai('gpt-3.5-turbo');

const result = await generateText({
  model: gpt4,
  prompt: 'Hello',
});

Common Errors:

• AI_LoadAPIKeyError: Check OPENAI_API_KEY environment variable
• 429 Rate Limit: Implement exponential backoff, upgrade tier
• 401 Unauthorized: Invalid API key format

Rate Limiting: OpenAI enforces RPM (requests per minute) and TPM (tokens per minute) limits. Implement retry logic:

const result = await generateText({
  model: openai('gpt-4'),
  prompt: 'Hello',
  maxRetries: 3,  // Built-in retry
});

Anthropic

import { anthropic } from '@ai-sdk/anthropic';

// ANTHROPIC_API_KEY=sk-ant-...
const claude = anthropic('claude-sonnet-4-5-20250929');

// Available models (Claude 4.x family, released 2025)
const sonnet45 = anthropic('claude-sonnet-4-5-20250929');  // Latest, recommended
const sonnet4 = anthropic('claude-sonnet-4-20250522');     // Released May 2025
const opus4 = anthropic('claude-opus-4-20250522');         // Highest quality

// Legacy models (Claude 3.x, deprecated)
// const sonnet35 = anthropic('claude-3-5-sonnet-20241022');  // Use Claude 4.x instead
// const opus3 = anthropic('claude-3-opus-20240229');
// const haiku3 = anthropic('claude-3-haiku-20240307');

const result = await generateText({
  model: sonnet45,
  prompt: 'Explain quantum entanglement',
});

Common Errors:

• AI_LoadAPIKeyError: Check ANTHROPIC_API_KEY environment variable
• overloaded_error: Retry with exponential backoff
• rate_limit_error: Wait and retry

Best Practices:

• Claude excels at long-context tasks (200K+ tokens)
• Claude 4.x recommended: Anthropic deprecated Claude 3.x in 2025
• Use Sonnet 4.5 for balance of speed/quality (latest model)
• Use Sonnet 4 for production stability (if avoiding latest)
• Use Opus 4 for highest quality reasoning and complex tasks

Google

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

// GOOGLE_GENERATIVE_AI_API_KEY=...
const gemini = google('gemini-2.5-pro');

// Available models (all GA since June-July 2025)
const pro = google('gemini-2.5-pro');
const flash = google('gemini-2.5-flash');
const lite = google('gemini-2.5-flash-lite');

const result = await generateText({
  model: pro,
  prompt: 'Analyze this data',
});

Common Errors:

• AI_LoadAPIKeyError: Check GOOGLE_GENERATIVE_AI_API_KEY
• SAFETY: Content filtered by safety settings
• QUOTA_EXCEEDED: Rate limit hit

Best Practices:

• Gemini Pro: Best for reasoning and analysis
• Gemini Flash: Fast, cost-effective for most tasks
• Free tier has generous limits
• Good for multimodal tasks (combine with image inputs)

Cloudflare Workers AI

import { Hono } from 'hono';
import { generateText } from 'ai';
import { createWorkersAI } from 'workers-ai-provider';

interface Env {
  AI: Ai;
}

const app = new Hono<{ Bindings: Env }>();

app.post('/chat', async (c) => {
  // Create provider inside handler (avoid startup overhead)
  const workersai = createWorkersAI({ binding: c.env.AI });

  const result = await generateText({
    model: workersai('@cf/meta/llama-3.1-8b-instruct'),
    prompt: 'What is Cloudflare?',
  });

  return c.json({ response: result.text });
});

export default app;

wrangler.jsonc:

{
  "name": "ai-sdk-worker",
  "compatibility_date": "2025-10-21",
  "ai": {
    "binding": "AI"
  }
}

Important Notes:

Startup Optimization: AI SDK v5 + Zod can cause >270ms startup time in Workers. Solutions:

1. Move imports inside handler:

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

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

2. Minimize top-level Zod schemas:

// Move complex schemas into route handlers

When to Use workers-ai-provider:

• Multi-provider scenarios (OpenAI + Workers AI)
• Using AI SDK UI hooks with Workers AI
• Need consistent API across providers

When to Use Native Binding: For Cloudflare-only deployments without multi-provider support, use the cloudflare-workers-ai skill instead for maximum performance.

Tool Calling & Agents

Basic Tool Definition

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

const result = await generateText({
  model: openai('gpt-4'),
  tools: {
    weather: tool({
      description: 'Get the weather for a location',
      inputSchema: z.object({
        location: z.string().describe('The city and country, e.g. "Paris, France"'),
        unit: z.enum(['celsius', 'fahrenheit']).optional(),
      }),
      execute: async ({ location, unit = 'celsius' }) => {
        // Simulate API call
        const data = await fetch(`https://api.weather.com/${location}`);
        return { temperature: 72, condition: 'sunny', unit };
      },
    }),
    convertTemperature: tool({
      description: 'Convert temperature between units',
      inputSchema: z.object({
        value: z.number(),
        from: z.enum(['celsius', 'fahrenheit']),
        to: z.enum(['celsius', 'fahrenheit']),
      }),
      execute: async ({ value, from, to }) => {
        if (from === to) return { value };
        if (from === 'celsius' && to === 'fahrenheit') {
          return { value: (value * 9/5) + 32 };
        }
        return { value: (value - 32) * 5/9 };
      },
    }),
  },
  prompt: 'What is the weather in Tokyo in Fahrenheit?',
});

console.log(result.text);
// Model will call weather tool, potentially convertTemperature, then answer

v5 Tool Changes:

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

Agent Class

The Agent class simplifies multi-step execution with tools.

import { Agent, tool } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';
import { z } from 'zod';

const weatherAgent = new Agent({
  model: anthropic('claude-sonnet-4-5-20250929'),
  system: 'You are a weather assistant. Always convert temperatures to the user's preferred unit.',
  tools: {
    getWeather: tool({
      description: 'Get current weather for a location',
      inputSchema: z.object({
        location: z.string(),
      }),
      execute: async ({ location }) => {
        return { temp: 72, condition: 'sunny', unit: 'fahrenheit' };
      },
    }),
    convertTemp: tool({
      description: 'Convert temperature between units',
      inputSchema: z.object({
        fahrenheit: z.number(),
      }),
      execute: async ({ fahrenheit }) => {
        return { celsius: (fahrenheit - 32) * 5/9 };
      },
    }),
  },
});

const result = await weatherAgent.run({
  messages: [
    { role: 'user', content: 'What is the weather in SF in Celsius?' },
  ],
});

console.log(result.text);
// Agent will call getWeather, then convertTemp, then respond

When to Use Agent vs Raw generateText:

• Use Agent when: Multiple tools, complex workflows, multi-step reasoning
• Use generateText when: Simple single-step, one or two tools, full control needed

Multi-Step Execution

Control when multi-step execution stops with stopWhen conditions.

import { generateText, stopWhen, stepCountIs, hasToolCall } from 'ai';
import { openai } from '@ai-sdk/openai';

// Stop after specific number of steps
const result = await generateText({
  model: openai('gpt-4'),
  tools: { /* ... */ },
  prompt: 'Research TypeScript and create a summary',
  stopWhen: stepCountIs(5),  // Max 5 steps (tool calls + responses)
});

// Stop when specific tool is called
const result = await generateText({
  model: openai('gpt-4'),
  tools: {
    research: tool({ /* ... */ }),
    finalize: tool({ /* ... */ }),
  },
  prompt: 'Research and finalize a report',
  stopWhen: hasToolCall('finalize'),  // Stop when finalize is called
});

// Combine conditions
const result = await generateText({
  model: openai('gpt-4'),
  tools: { /* ... */ },
  prompt: 'Complex task',
  stopWhen: (step) => step.stepCount >= 10 || step.hasToolCall('finish'),
});

v5 Change: maxSteps parameter removed. Use stopWhen(stepCountIs(n)) instead.

Dynamic Tools (v5 New Feature)

Add tools at runtime based on context:

const result = await generateText({
  model: openai('gpt-4'),
  tools: (context) => {
    // Context includes messages, step count, etc.
    const baseTool = {
      search: tool({ /* ... */ }),
    };

    // Add tools based on context
    if (context.messages.some(m => m.content.includes('weather'))) {
      baseTool.weather = tool({ /* ... */ });
    }

    return baseTools;
  },
  prompt: 'Help me with my task',
});

Critical v4→v5 Migration

AI SDK v5 introduced extensive breaking changes. If migrating from v4, follow this guide.

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

Before (v4):

import { generateText } from 'ai';

const result = await generateText({
  model: openai.chat('gpt-4'),
  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'),
  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

AI SDK provides a migration tool:

npx ai migrate

This will update most breaking changes automatically. Review changes carefully.

Official Migration Guide: https://ai-sdk.dev/docs/migration-guides/migration-guide-5-0

Top 12 Errors & Solutions

1. AI_APICallError

Cause: API request failed (network, auth, rate limit).

Solution:

import { AI_APICallError } from 'ai';

try {
  const result = await generateText({
    model: openai('gpt-4'),
    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
    }
  }
}

Prevention:

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

2. AI_NoObjectGeneratedError

Cause: Model didn't generate valid object matching schema.

Solution:

import { AI_NoObjectGeneratedError } from 'ai';

try {
  const result = await generateObject({
    model: openai('gpt-4'),
    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-4 better than gpt-3.5 for complex objects)
  }
}

Prevention:

• 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+)

Cause: AI SDK v5 + Zod initialization overhead in Cloudflare Workers exceeds startup limits.

Solution:

// 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
  }
}

Prevention:

• 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

GitHub Issue: Search for "Workers startup limit" in Vercel AI SDK issues

4. streamText Fails Silently

Cause: Stream errors can be swallowed by createDataStreamResponse.

Status: ✅ RESOLVED - Fixed in ai@4.1.22 (February 2025)

Solution (Recommended):

// Use the onError callback (added in v4.1.22)
const stream = streamText({
  model: openai('gpt-4'),
  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'),
    prompt: 'Hello',
  });

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

Prevention:

• 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

GitHub Issue: #4726 (RESOLVED)

5. AI_LoadAPIKeyError

Cause: Missing or invalid API key.

Solution:

import { AI_LoadAPIKeyError } from 'ai';

try {
  const result = await generateText({
    model: openai('gpt-4'),
    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-)
  }
}

Prevention:

• 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

Cause: Invalid parameters passed to function.

Solution:

import { AI_InvalidArgumentError } from 'ai';

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

Prevention:

• 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

Cause: Model generated no content (safety filters, etc.).

Solution:

import { AI_NoContentGeneratedError } from 'ai';

try {
  const result = await generateText({
    model: openai('gpt-4'),
    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.' };
  }
}

Prevention:

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

8. AI_TypeValidationError

Cause: Zod schema validation failed on generated output.

Solution:

import { AI_TypeValidationError } from 'ai';

try {
  const result = await generateObject({
    model: openai('gpt-4'),
    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
  }
}

Prevention:

• 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

Cause: All retry attempts failed.

Solution:

import { AI_RetryError } from 'ai';

try {
  const result = await generateText({
    model: openai('gpt-4'),
    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
  }
}

Prevention:

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

10. Rate Limiting Errors

Cause: Exceeded provider rate limits (RPM/TPM).

Solution:

// 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'),
        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');
}

Prevention:

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

11. TypeScript Performance with Zod

Cause: Complex Zod schemas slow down TypeScript type checking.

Solution:

// 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'), 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(),
  })
);

Prevention:

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

Official Docs: https://ai-sdk.dev/docs/troubleshooting/common-issues/slow-type-checking

12. Invalid JSON Response (Provider-Specific)

Cause: Some models occasionally return invalid JSON.

Solution:

// Use built-in retry and mode selection
const result = await generateObject({
  model: openai('gpt-4'),
  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'),
    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',
  });
}

Prevention:

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

GitHub Issue: #4302 (Imagen 3.0 Invalid JSON)

For More Errors: See complete error reference at https://ai-sdk.dev/docs/reference/ai-sdk-errors

Production Best Practices

Performance

1. Always use streaming for long-form content:

// User-facing: Use streamText
const stream = streamText({ model: openai('gpt-4'), prompt: 'Long essay' });
return stream.toDataStreamResponse();

// Background tasks: Use generateText
const result = await generateText({ model: openai('gpt-4'), prompt: 'Analyze data' });

2. Set appropriate maxOutputTokens:

const result = await generateText({
  model: openai('gpt-4'),
  prompt: 'Short answer',
  maxOutputTokens: 100,  // Limit tokens to save cost
});

3. Cache provider instances:

// Good: Reuse provider instances
const gpt4 = openai('gpt-4-turbo');
const result1 = await generateText({ model: gpt4, prompt: 'Hello' });
const result2 = await generateText({ model: gpt4, prompt: 'World' });

4. Optimize Zod schemas:

// Avoid complex nested schemas at top level in Workers
// Move into route handlers to prevent startup overhead

Error Handling

1. Wrap all AI calls in try-catch:

try {
  const result = await generateText({ /* ... */ });
} catch (error) {
  // Handle specific errors
  if (error instanceof AI_APICallError) { /* ... */ }
  else if (error instanceof AI_NoContentGeneratedError) { /* ... */ }
  else { /* ... */ }
}

2. Implement retry logic:

const result = await generateText({
  model: openai('gpt-4'),
  prompt: 'Hello',
  maxRetries: 3,
});

3. Log errors properly:

console.error('AI SDK Error:', {
  type: error.constructor.name,
  message: error.message,
  statusCode: error.statusCode,
  timestamp: new Date().toISOString(),
});

Cost Optimization

1. Choose appropriate models:

// Simple tasks: Use cheaper models
const simple = await generateText({ model: openai('gpt-3.5-turbo'), prompt: 'Hello' });

// Complex reasoning: Use GPT-4
const complex = await generateText({ model: openai('gpt-4'), prompt: 'Analyze...' });

2. Set maxOutputTokens appropriately:

const result = await generateText({
  model: openai('gpt-4'),
  prompt: 'Summarize in 2 sentences',
  maxOutputTokens: 100,  // Prevent over-generation
});

3. Cache results when possible:

const cache = new Map();

async function getCachedResponse(prompt: string) {
  if (cache.has(prompt)) return cache.get(prompt);

  const result = await generateText({ model: openai('gpt-4'), prompt });
  cache.set(prompt, result.text);
  return result.text;
}

Cloudflare Workers Specific

1. Move imports inside handlers:

// Avoid startup overhead
export default {
  async fetch(request, env) {
    const { generateText } = await import('ai');
    const { openai } = await import('@ai-sdk/openai');
    // Use here
  }
}

2. Monitor startup time:

# Wrangler reports startup time
wrangler deploy
# Check output for startup duration (must be <400ms)

3. Handle streaming properly:

// Return ReadableStream for streaming responses
const stream = streamText({ model: openai('gpt-4'), prompt: 'Hello' });
return new Response(stream.toTextStream(), {
  headers: { 'Content-Type': 'text/plain; charset=utf-8' },
});

Next.js / Vercel Specific

1. Use Server Actions for mutations:

'use server';

export async function generateContent(input: string) {
  const result = await generateText({
    model: openai('gpt-4'),
    prompt: input,
  });
  return result.text;
}

2. Use Server Components for initial loads:

// app/page.tsx
export default async function Page() {
  const result = await generateText({
    model: openai('gpt-4'),
    prompt: 'Welcome message',
  });

  return <div>{result.text}</div>;
}

3. Implement loading states:

'use client';

import { useState } from 'react';
import { generateContent } from './actions';

export default function Form() {
  const [loading, setLoading] = useState(false);

  async function handleSubmit(formData: FormData) {
    setLoading(true);
    const result = await generateContent(formData.get('input'));
    setLoading(false);
  }

  return (
    <form action={handleSubmit}>
      <input name="input" />
      <button disabled={loading}>
        {loading ? 'Generating...' : 'Submit'}
      </button>
    </form>
  );
}

4. For deployment: See Vercel's official deployment documentation: https://vercel.com/docs/functions

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 doc

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