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Documentation Index

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The Mastra adapter lets you use AgentMark prompts with Mastra’s agentic workflow framework.

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Installation

npm install @agentmark-ai/mastra-v0-adapter @mastra/core @ai-sdk/openai

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Setup

Create your AgentMark client with a MastraModelRegistry. Use .registerProviders() to register AI SDK provider packages; Mastra uses the same @ai-sdk/* providers under the hood, so model IDs written as "openai/gpt-4o" auto-resolve:
agentmark.client.ts
import { createAgentMarkClient, MastraModelRegistry } from "@agentmark-ai/mastra-v0-adapter";
import { openai } from "@ai-sdk/openai";
import { anthropic } from "@ai-sdk/anthropic";

const modelRegistry = new MastraModelRegistry();
modelRegistry.registerProviders({ openai, anthropic });

// Or register models explicitly:
// modelRegistry.registerModels(["gpt-4o"], (name) => openai(name));

export const client = createAgentMarkClient({
  loader,
  modelRegistry,
});

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Running prompts

Mastra prompts go through four steps: formatAgent() returns an AgentConfig, you construct a new Agent(agentConfig), formatMessages() (async) returns a [messages, options] tuple, and finally agent.generate(messages, options) runs the prompt: 
import { client } from "./agentmark.client";
import { Agent } from "@mastra/core/agent";

const prompt = await client.loadTextPrompt("greeting.prompt.mdx");
const agentConfig = await prompt.formatAgent({
  props: { name: "Alice" },
});

const agent = new Agent(agentConfig);
const [messages, options] = await agentConfig.formatMessages();
const result = await agent.generate(messages, options);

console.log(result.text);
formatMessages() is async — always await it. The returned tuple is [messages, options], both required by agent.generate() / agent.stream().

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Object generation

For structured output, use object prompts. The schema lives in your .prompt.mdx frontmatter (object_config.schema), not as a second argument to loadObjectPrompt(): 
import { client } from "./agentmark.client";
import { Agent } from "@mastra/core/agent";

const prompt = await client.loadObjectPrompt("sentiment.prompt.mdx");
const agentConfig = await prompt.formatAgent({
  props: { text: "This product is amazing!" },
});

const agent = new Agent(agentConfig);
const [messages, options] = await agentConfig.formatMessages();
const result = await agent.generate(messages, options);

console.log(result.object);
// { sentiment: 'positive', confidence: 0.95 }

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Streaming

Stream responses using agent.stream(): 
import { Agent } from "@mastra/core/agent";

const prompt = await client.loadTextPrompt("story.prompt.mdx");
const agentConfig = await prompt.formatAgent({
  props: { topic: "space exploration" },
});

const agent = new Agent(agentConfig);
const [messages, options] = await agentConfig.formatMessages();
const stream = await agent.stream(messages, options);

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

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Tools

Mastra tools use the ai v4 tool() helper with parameters: (not inputSchema: — that’s AI SDK v5). This matches Mastra’s internals:
agentmark.client.ts
import { createAgentMarkClient, MastraModelRegistry } from "@agentmark-ai/mastra-v0-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({
  loader,
  modelRegistry,
  tools: {
    weather: weatherTool,
  },
});
Then reference tools in your prompts:
weather.prompt.mdx
---
name: weather
text_config:
  model_name: openai/gpt-4o
  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

Configure MCP servers for extended capabilities:
agentmark.client.ts
import { createAgentMarkClient, MastraModelRegistry } from "@agentmark-ai/mastra-v0-adapter";

export const client = createAgentMarkClient({
  loader,
  modelRegistry,
  mcpServers: {
    filesystem: {
      command: "npx",
      args: ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/files"],
    },
  },
});

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Limitations

  • No image generation — use the AI SDK adapter for experimental_generateImage.
  • No speech generation — use the AI SDK adapter for experimental_generateSpeech.

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Next steps

Prompts

Learn about prompt syntax

Testing

Test your prompts with datasets

Observability

Monitor your agents in production

Other integrations

Explore other AI frameworks

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