Mastra Changelog 2025-03-27

This week brings significant improvements across Mastra's ecosystem, with major updates to workflow capabilities, Mastra Voice, and more.

Nested Workflows & Concurrency

The core package received substantial enhancements to its workflow system.

You can now give a workflow directly as a step, which makes executing parallel branches easier:

parentWorkflow
  .step(nestedWorkflowA)
  .step(nestedWorkflowB)
  .after([nestedWorkflowA, nestedWorkflowB])
  .step(finalStep);

Conditional branching is also easier to follow with the new structure that accepts workflows. You can execute a whole nested workflow, and then continue execution as normal on the next .then() ’d step:

// Create nested workflows for different paths
const workflowA = new Workflow({ name: "workflow-a" })
  .step(stepA1)
  .then(stepA2)
  .commit();

const workflowB = new Workflow({ name: "workflow-b" })
  .step(stepB1)
  .then(stepB2)
  .commit();

// Use the new if-else syntax with nested workflows
parentWorkflow
  .step(initialStep)
  .if(
    async ({ context }) => {
      // Your condition here
      return someCondition;
    },
    workflowA, // if branch
    workflowB, // else branch
  )
  .then(finalStep)
  .commit();

And to pass arguments to nested workflows, you can use variable mappings.

We also added new workflow result schemas to make it easier to propagate results back from a nested workflow execution back to the parent workflow:

 const workflowA = new Workflow({
	 name: 'workflow-a',
   result: {
   schema: z.object({
	 activities: z.string(),
   }),
   mapping: {
   activities: {
   step: planActivities,
   path: 'activities',
	    },
	  },
	},
})
  .step(fetchWeather)
  .then(planActivities)
  .commit();

const weatherWorkflow = new Workflow({
  name: 'weather-workflow',
  triggerSchema: z.object({
    city: z.string().describe('The city to get the weather for'),
  }),
  result: {
    schema: z.object({
      activities: z.string(),
    }),
    mapping: {
      activities: {
        step: workflowA,
        path: 'result.activities',
      },
    },
  },
})
  .step(workflowA, {
    variables: {
      city: {
        step: 'trigger',
        path: 'city',
      },
    },
  })
  .step(doSomethingElseStep)
  .commit();

Check out our docs to explore all things nested workflows.

Voice

We shipped some big changes and new features to Mastra Voice. They include:

• Support for speech to speech providers, with OpenAI Realtime API being our first.
• Support for WebSocket connections to establish a persistent connection to voice providers (like OpenAI) instead of separate HTTP requests. This enables bidirectional audio streaming without the request-wait-response pattern of traditional HTTP.
• A new event-driven architecture for voice interactions. Instead of const text = await agent.voice.listen(audio), you now use agent.voice.on('writing', ({ text }) => { ... }), creating a more responsive experience without managing any WebSocket complexity.

Here's an example of setting up a real-time voice agent that can participate in continuous conversations:

import { Agent } from "@mastra/core/agent";
import { OpenAIRealtimeVoice } from "@mastra/voice-openai-realtime";

const agent = new Agent({
  name: 'Agent',
  instructions: `You are a helpful assistant with real-time voice capabilities.`,
  model: openai('gpt-4o'),
  voice: new OpenAIRealtimeVoice(),
});

// Connect to the voice service
await agent.voice.connect();

// Listen for agent audio responses
agent.voice.on('speaker', (stream) => {
  // The 'speaker' can be any audio output implementation that accepts streams for instance, node-speaker
  stream.pipe(speaker)
});

// Initiate the conversation by emitting a 'speaking' event
await agent.voice.speak('How can I help you today?');

// Send continuous audio from microphone
const micStream = mic.getAudioStream();
await agent.voice.send(micStream);

Additional details, including code examples of the new event-driven architecture, can be found in our blog post.

New LLM Provider Options

Both Cerebras and Groq were added as LLM provider options in create-mastra expanding the available choices for AI backends.

Performance optimizations

Serverless deployments saw improvements like:

• A marked reduction in bundle sizes. We went from 90mb to 8mb when using memory and our Postgres store
• Improved deployments to Vercel, Netlify and Cloudflare edge functions
• Improved cold start times

Find details on our most recent optimizations here.

Memory improvements

Memory operations also saw notable optimization, like:

• The addition of a new Mem0 integration @mastra/mem0
• Improved semantic recall for long message inputs
• Complete compatibility for using memory with AI SDK's useChat() hooks

Stay tuned for more updates next week…