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The Vercel AI SDK adapter allows you to use AgentMark prompts with Vercel AI SDK’s AI functions.

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Installation

npm install @agentmark/ai-sdk-v4-adapter ai @ai-sdk/openai

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Setup

Create your AgentMark client with Vercel AI SDK’s models:
agentmark.client.ts
import { createAgentMarkClient } from '@agentmark/sdk';
import { VercelAIModelRegistry } from '@agentmark/ai-sdk-v4-adapter';
import { anthropic } from '@ai-sdk/anthropic';
import { openai } from '@ai-sdk/openai';

export const client = createAgentMarkClient({
  models: new VercelAIModelRegistry({
    'claude-3-5-sonnet-20241022': anthropic('claude-3-5-sonnet-20241022'),
    'gpt-4o': openai('gpt-4o'),
  }),
});

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Running Prompts

Load and run prompts with generateText(): 
import { client } from './agentmark.client';
import { generateText } from 'ai';

const prompt = await client.loadTextPrompt('greeting.prompt.mdx');
const input = await prompt.format({
  props: { name: 'Alice' }
});

const result = await generateText(input);
console.log(result.text);

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Object Generation

For structured output, use generateObject(): 
import { client } from './agentmark.client';
import { generateObject } from 'ai';
import { z } from 'zod';

const prompt = await client.loadObjectPrompt('extract.prompt.mdx', {
  schema: z.object({
    sentiment: z.enum(['positive', 'negative', 'neutral']),
    confidence: z.number(),
  }),
});

const input = await prompt.format({
  props: { text: 'This product is amazing!' }
});

const result = await generateObject(input);
console.log(result.object);
// { sentiment: 'positive', confidence: 0.95 }

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Streaming

Stream responses for real-time output: 
import { streamText } from 'ai';

const prompt = await client.loadTextPrompt('story.prompt.mdx');
const input = await prompt.format({
  props: { topic: 'space exploration' }
});

const result = streamText(input);

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

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Tools

Configure tools in your client:
agentmark.client.ts
import { createAgentMarkClient } from '@agentmark/sdk';
import { VercelAIModelRegistry, VercelAIToolRegistry } from '@agentmark/ai-sdk-v4-adapter';
import { tool } from 'ai';
import { z } from 'zod';

const weatherTool = tool({
  description: 'Get current weather for a location',
  parameters: z.object({
    location: z.string(),
  }),
  execute: async ({ location }) => {
    return `The weather in ${location} is sunny and 72°F`;
  },
});

export const client = createAgentMarkClient({
  models: new VercelAIModelRegistry({ /* ... */ }),
  tools: new VercelAIToolRegistry({
    weather: weatherTool,
  }),
});
Then use tools in your prompts:
weather.prompt.mdx
---
name: weather
text_config:
  model_name: claude-3-5-sonnet-20241022
  tools:
    - weather
---

<System>You are a helpful weather assistant.</System>
<User>What's the weather in {props.location}?</User>

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MCP Servers

AgentMark supports Model Context Protocol servers for extended capabilities:
agentmark.client.ts
import { createAgentMarkClient } from '@agentmark/sdk';
import { VercelAIModelRegistry, McpServerRegistry } from '@agentmark/ai-sdk-v4-adapter';

export const client = createAgentMarkClient({
  models: new VercelAIModelRegistry({ /* ... */ }),
  mcpServers: new McpServerRegistry({
    filesystem: {
      command: 'npx',
      args: ['-y', '@modelcontextprotocol/server-filesystem', '/path/to/files'],
    },
  }),
});

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Observability

Enable telemetry to track performance and debug issues: 
const input = await prompt.format({
  props: { name: 'Alice' },
  telemetry: {
    isEnabled: true,
    functionId: 'greeting-handler',
    metadata: {
      userId: 'user-123',
      sessionId: 'session-abc',
    }
  }
});

const result = await generateText(input);
Learn more in the Observability documentation.

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Next Steps

Prompts

Learn about prompt syntax

Testing

Test your prompts with datasets

Observability

Monitor your prompts in production

Other Integrations

Explore other AI frameworks

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Have Questions?

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