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:
1parentWorkflow
2 .step(nestedWorkflowA)
3 .step(nestedWorkflowB)
4 .after([nestedWorkflowA, nestedWorkflowB])
5 .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:
1// Create nested workflows for different paths
2const workflowA = new Workflow({ name: "workflow-a" })
3 .step(stepA1)
4 .then(stepA2)
5 .commit();
6
7const workflowB = new Workflow({ name: "workflow-b" })
8 .step(stepB1)
9 .then(stepB2)
10 .commit();
11
12// Use the new if-else syntax with nested workflows
13parentWorkflow
14 .step(initialStep)
15 .if(
16 async ({ context }) => {
17 // Your condition here
18 return someCondition;
19 },
20 workflowA, // if branch
21 workflowB, // else branch
22 )
23 .then(finalStep)
24 .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:
1
2 const workflowA = new Workflow({
3 name: 'workflow-a',
4 result: {
5 schema: z.object({
6 activities: z.string(),
7 }),
8 mapping: {
9 activities: {
10 step: planActivities,
11 path: 'activities',
12 },
13 },
14 },
15})
16 .step(fetchWeather)
17 .then(planActivities)
18 .commit();
19
20const weatherWorkflow = new Workflow({
21 name: 'weather-workflow',
22 triggerSchema: z.object({
23 city: z.string().describe('The city to get the weather for'),
24 }),
25 result: {
26 schema: z.object({
27 activities: z.string(),
28 }),
29 mapping: {
30 activities: {
31 step: workflowA,
32 path: 'result.activities',
33 },
34 },
35 },
36})
37 .step(workflowA, {
38 variables: {
39 city: {
40 step: 'trigger',
41 path: 'city',
42 },
43 },
44 })
45 .step(doSomethingElseStep)
46 .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 useagent.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:
1import { Agent } from "@mastra/core/agent";
2import { OpenAIRealtimeVoice } from "@mastra/voice-openai-realtime";
3
4const agent = new Agent({
5 name: 'Agent',
6 instructions: `You are a helpful assistant with real-time voice capabilities.`,
7 model: openai('gpt-4o'),
8 voice: new OpenAIRealtimeVoice(),
9});
10
11// Connect to the voice service
12await agent.voice.connect();
13
14// Listen for agent audio responses
15agent.voice.on('speaker', (stream) => {
16 // The 'speaker' can be any audio output implementation that accepts streams for instance, node-speaker
17 stream.pipe(speaker)
18});
19
20// Initiate the conversation by emitting a 'speaking' event
21await agent.voice.speak('How can I help you today?');
22
23// Send continuous audio from microphone
24const micStream = mic.getAudioStream();
25await 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…