vercel/develop-ai-functions-example

AI Functions Examples

The examples/ai-functions/ directory contains scripts for validating, testing, and iterating on AI SDK functions across providers.

Example Categories

Examples are organized by AI SDK function in examples/ai-functions/src/:

| 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:

1. Adding a new provider: Create basic examples for each supported API (generateText, streamText, generateObject, etc.)

2. Implementing a new feature: Demonstrate the feature with at least one provider example

3. Reproducing a bug: Create an example that shows the issue for debugging

4. Adding provider-specific options: Show how to use providerOptions for provider-specific settings

5. 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

1. Keep examples focused: Each example should demonstrate one feature or use case

2. Use descriptive prompts: Make it clear what the example is testing

3. Handle errors gracefully: The run() wrapper handles this automatically

4. Use realistic model IDs: Use actual model IDs that work with the provider

5. Add comments for complex logic: Explain non-obvious code patterns

6. Reuse tools when appropriate: Use weatherTool or create new reusable tools in tools/