Directory	Purpose
`generate-text/`	Non-streaming text generation with `generateText()`
`stream-text/`	Streaming text generation with `streamText()`
`generate-object/`	Structured output generation with `generateObject()`
`stream-object/`	Streaming structured output with `streamObject()`
`agent/`	`ToolLoopAgent` examples for agentic workflows
`embed/`	Single embedding generation with `embed()`
`embed-many/`	Batch embedding generation with `embedMany()`
`generate-image/`	Image generation with `generateImage()`
`generate-speech/`	Text-to-speech with `generateSpeech()`
`transcribe/`	Audio transcription with `transcribe()`
`rerank/`	Document reranking with `rerank()`
`middleware/`	Custom middleware implementations
`registry/`	Provider registry setup and usage
`telemetry/`	OpenTelemetry integration
`complex/`	Multi-component examples (agents, routers)
`lib/`	Shared utilities (not examples)
`tools/`	Reusable tool definitions

File Naming Convention

Examples follow the pattern: {provider}-{feature}.ts

Pattern	Example	Description
`{provider}.ts`	`openai.ts`	Basic provider usage
`{provider}-{feature}.ts`	`openai-tool-call.ts`	Specific feature
`{provider}-{sub-provider}.ts`	`amazon-bedrock-anthropic.ts`	Provider with sub-provider
`{provider}-{sub-provider}-{feature}.ts`	`google-vertex-anthropic-cache-control.ts`	Sub-provider with feature

Example Structure

All examples use the run() wrapper from lib/run.ts which:

Loads environment variables from .env
Provides error handling with detailed API error logging

Basic Template

import { providerName } from '@ai-sdk/provider-name';
import { generateText } from 'ai';
import { run } from '../lib/run';

run(async () => {
  const result = await generateText({
    model: providerName('model-id'),
    prompt: 'Your prompt here.',
  });

  console.log(result.text);
  console.log('Token usage:', result.usage);
  console.log('Finish reason:', result.finishReason);
});

Streaming Template

import { providerName } from '@ai-sdk/provider-name';
import { streamText } from 'ai';
import { printFullStream } from '../lib/print-full-stream';
import { run } from '../lib/run';

run(async () => {
  const result = streamText({
    model: providerName('model-id'),
    prompt: 'Your prompt here.',
  });

  await printFullStream({ result });
});

Tool Calling Template

import { providerName } from '@ai-sdk/provider-name';
import { generateText, tool } from 'ai';
import { z } from 'zod';
import { run } from '../lib/run';

run(async () => {
  const result = await generateText({
    model: providerName('model-id'),
    tools: {
      myTool: tool({
        description: 'Tool description',
        inputSchema: z.object({
          param: z.string().describe('Parameter description'),
        }),
        execute: async ({ param }) => {
          return { result: `Processed: ${param}` };
        },
      }),
    },
    prompt: 'Use the tool to...',
  });

  console.log(JSON.stringify(result, null, 2));
});

Structured Output Template

import { providerName } from '@ai-sdk/provider-name';
import { generateObject } from 'ai';
import { z } from 'zod';
import { run } from '../lib/run';

run(async () => {
  const result = await generateObject({
    model: providerName('model-id'),
    schema: z.object({
      name: z.string(),
      items: z.array(z.string()),
    }),
    prompt: 'Generate a...',
  });

  console.log(JSON.stringify(result.object, null, 2));
  console.log('Token usage:', result.usage);
});

Running Examples

From the examples/ai-functions directory:

pnpm tsx src/generate-text/openai.ts
pnpm tsx src/stream-text/openai-tool-call.ts
pnpm tsx src/agent/openai-generate.ts

When to Write Examples

Write examples when:

Adding a new provider: Create basic examples for each supported API (generateText, streamText, generateObject, etc.)
Implementing a new feature: Demonstrate the feature with at least one provider example
Reproducing a bug: Create an example that shows the issue for debugging
Adding provider-specific options: Show how to use providerOptions for provider-specific settings
Creating test fixtures: Use examples to generate API response fixtures (see capture-api-response-test-fixture skill)

Utility Helpers

The lib/ directory contains shared utilities:

File	Purpose
`run.ts`	Error-handling wrapper with `.env` loading
`print.ts`	Clean object printing (removes undefined values)
`print-full-stream.ts`	Colored streaming output for tool calls, reasoning, text
`save-raw-chunks.ts`	Save streaming chunks for test fixtures
`present-image.ts`	Display images in terminal
`save-audio.ts`	Save audio files to disk

Using print utilities

import { print } from '../lib/print';

// Pretty print objects without undefined values
print('Result:', result);
print('Usage:', result.usage, { depth: 2 });

Using printFullStream

import { printFullStream } from '../lib/print-full-stream';

const result = streamText({ ... });
await printFullStream({ result }); // Colored output for text, tool calls, reasoning

Reusable Tools

The tools/ directory contains reusable tool definitions:

import { weatherTool } from '../tools/weather-tool';

const result = await generateText({
  model: openai('gpt-4o'),
  tools: { weather: weatherTool },
  prompt: 'What is the weather in San Francisco?',
});

Best Practices

Keep examples focused: Each example should demonstrate one feature or use case
Use descriptive prompts: Make it clear what the example is testing
Handle errors gracefully: The run() wrapper handles this automatically
Use realistic model IDs: Use actual model IDs that work with the provider
Add comments for complex logic: Explain non-obvious code patterns
Reuse tools when appropriate: Use weatherTool or create new reusable tools in tools/

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AI Functions Examples

Example Categories