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Mastra

Mastra is a TypeScript framework for building AI-powered applications. It provides a unified Mastra() constructor that wires together agents, workflows, tools, memory, RAG, MCP, voice, evals, and observability. Projects scaffold via npm create mastra@latest and run with mastra dev (dev server + Studio UI at localhost:4111). Built on Hono, deployable to Node.js 22+, Bun, Deno, Cloudflare, Vercel, Netlify, AWS, and Azure.

When to use this skill

Trigger this skill when the user:

• Creates or configures a Mastra agent with tools, memory, or structured output
• Defines workflows with steps, branching, loops, or parallel execution
• Creates custom tools with createTool and Zod schemas
• Sets up memory (message history, working memory, semantic recall)
• Builds RAG pipelines (chunking, embeddings, vector stores)
• Configures MCP clients to connect to external tool servers
• Exposes Mastra agents/tools as an MCP server
• Runs Mastra CLI commands (mastra dev, mastra build, mastra init)
• Deploys a Mastra application to any cloud provider

Do NOT trigger this skill for:

• General TypeScript/Node.js questions unrelated to Mastra
• Other AI frameworks (LangChain, CrewAI, AutoGen) unless comparing to Mastra

Setup & authentication

Environment variables

# Required - at least one LLM provider
OPENAI_API_KEY=sk-...
# Or: ANTHROPIC_API_KEY, GOOGLE_GENERATIVE_AI_API_KEY, OPENROUTER_API_KEY

# Optional
POSTGRES_CONNECTION_STRING=postgresql://...   # for pgvector RAG/memory
PINECONE_API_KEY=...                          # for Pinecone vector store

Installation

# New project
npm create mastra@latest

# Existing project
npx mastra init --components agents,tools,workflows --llm openai

Basic initialization

import { Mastra } from '@mastra/core'
import { Agent } from '@mastra/core/agent'
import { createTool } from '@mastra/core/tool'
import { z } from 'zod'

const myAgent = new Agent({
  id: 'my-agent',
  instructions: 'You are a helpful assistant.',
  model: 'openai/gpt-4.1',
  tools: {},
})

export const mastra = new Mastra({
  agents: { myAgent },
})

Always access agents via mastra.getAgent('myAgent') - not direct imports. Direct imports bypass logger, telemetry, and registered resources.

Core concepts

Mastra instance - the central registry. Pass agents, workflows, tools, memory, MCP servers, and config to the new Mastra({}) constructor. Everything registered here gets wired together (logging, telemetry, resource access).

Agents - LLM-powered entities created with new Agent({}). They take instructions, a model string (e.g. 'openai/gpt-4.1'), and optional tools. Call agent.generate() for complete responses or agent.stream() for streaming. Both accept maxSteps (default 5) to cap tool-use loops.

Workflows - typed multi-step pipelines built with createWorkflow() and createStep(). Steps have Zod inputSchema/outputSchema. Chain with .then(), branch with .branch(), loop with .dountil()/.dowhile(), parallelize with .parallel(), iterate with .foreach(). Always call .commit() at the end.

Tools - typed functions via createTool({ id, description, inputSchema, outputSchema, execute }). The description field guides the LLM's tool selection.

Memory - four types: message history (recent messages), working memory (persistent user profile), observational memory (background summarization), and semantic recall (RAG over past conversations). Configure via new Memory({}).

MCP - MCPClient connects to external tool servers; MCPServer exposes Mastra tools/agents as an MCP endpoint. Use listTools() for static single-user setups, listToolsets() for dynamic multi-user scenarios.

Common tasks

Create an agent with tools

import { Agent } from '@mastra/core/agent'
import { createTool } from '@mastra/core/tool'
import { z } from 'zod'

const weatherTool = createTool({
  id: 'get-weather',
  description: 'Fetches current weather for a city',
  inputSchema: z.object({ city: z.string() }),
  outputSchema: z.object({ temp: z.number(), condition: z.string() }),
  execute: async ({ city }) => {
    const res = await fetch(`https://wttr.in/${city}?format=j1`)
    const data = await res.json()
    return { temp: Number(data.current_condition[0].temp_F), condition: data.current_condition[0].weatherDesc[0].value }
  },
})

const agent = new Agent({
  id: 'weather-agent',
  instructions: 'Help users check weather. Use the get-weather tool.',
  model: 'openai/gpt-4.1',
  tools: { [weatherTool.id]: weatherTool },
})

Stream agent responses

const stream = await agent.stream('What is the weather in Tokyo?')
for await (const chunk of stream.textStream) {
  process.stdout.write(chunk)
}

Define a workflow with steps

import { createWorkflow, createStep } from '@mastra/core/workflow'
import { z } from 'zod'

const summarize = createStep({
  id: 'summarize',
  inputSchema: z.object({ text: z.string() }),
  outputSchema: z.object({ summary: z.string() }),
  execute: async ({ inputData, mastra }) => {
    const agent = mastra.getAgent('summarizer')
    const res = await agent.generate(`Summarize: ${inputData.text}`)
    return { summary: res.text }
  },
})

const workflow = createWorkflow({
  id: 'summarize-workflow',
  inputSchema: z.object({ text: z.string() }),
  outputSchema: z.object({ summary: z.string() }),
}).then(summarize).commit()  // .commit() is required!

const run = workflow.createRun()
const result = await run.start({ inputData: { text: 'Long article...' } })
if (result.status === 'success') console.log(result.result)

Always check result.status before accessing result.result or result.error. Possible statuses: success, failed, suspended, tripwire, paused.

Configure agent memory

import { Memory } from '@mastra/memory'
import { LibSQLStore, LibSQLVector } from '@mastra/libsql'

const memory = new Memory({
  storage: new LibSQLStore({ id: 'mem', url: 'file:./local.db' }),
  vector: new LibSQLVector({ id: 'vec', url: 'file:./local.db' }),
  options: {
    lastMessages: 20,
    semanticRecall: { topK: 3, messageRange: 2 },
    workingMemory: { enabled: true, template: '# User\n- Name:\n- Preferences:' },
  },
})

const agent = new Agent({ id: 'mem-agent', model: 'openai/gpt-4.1', memory })

// Use with thread context
await agent.generate('Remember my name is Alice', {
  memory: { thread: { id: 'thread-1' }, resource: 'user-123' },
})

Connect to MCP servers

import { MCPClient } from '@mastra/mcp'

const mcp = new MCPClient({
  id: 'my-mcp',
  servers: {
    github: { command: 'npx', args: ['-y', '@modelcontextprotocol/server-github'] },
    custom: { url: new URL('https://my-mcp-server.com/sse') },
  },
})

const agent = new Agent({
  id: 'mcp-agent',
  model: 'openai/gpt-4.1',
  tools: await mcp.listTools(),  // static - fixed at init
})

// For multi-user (dynamic credentials per request):
const res = await agent.generate(prompt, {
  toolsets: await mcp.listToolsets(),
})
await mcp.disconnect()

Run CLI commands

mastra dev              # Dev server + Studio at localhost:4111
mastra build            # Bundle to .mastra/output/
mastra build --studio   # Include Studio UI in build
mastra start            # Serve production build
mastra lint             # Validate project structure
mastra migrate          # Run DB migrations

Error handling

Gotchas

1. Forgetting .commit() causes a silent no-op workflow - A workflow chain that is missing .commit() at the end will not throw an error when defined, but calling workflow.createRun() will either fail or produce unexpected behavior. Always end every workflow chain with .commit() as the final call.

2. Accessing agents directly (not via mastra.getAgent()) bypasses telemetry and logging - Importing and calling an agent instance directly skips the Mastra registry's wiring, meaning no trace data, no logger output, and no resource access via the registered Mastra instance. Always resolve agents through mastra.getAgent('id') in step execute functions.

3. mcp.listTools() caches tools at initialization time - If the MCP server's available tools change after MCPClient initializes, the agent will not see the new tools until the process restarts. For dynamic multi-user scenarios where credentials or available tools differ per request, use mcp.listToolsets() per request instead of the static listTools() pattern.

4. Memory resource scope isolation can cause cross-user data leakage if resource IDs are not unique - If two users share the same resource ID (e.g., a static string like "default"), their working memory and semantic recall overlap. Always derive the resource ID from a unique identifier (user ID, session token) before passing it to agent.generate().

5. Workflow step schema mismatches produce cryptic runtime errors - When a step's outputSchema does not match the next step's inputSchema, Mastra throws a Zod parse error at runtime, not at workflow definition time. Use .map() between steps to transform data shapes, and verify schema compatibility during development by running the workflow with a test payload before deploying.

References

For detailed content on specific Mastra sub-domains, read the relevant file from the references/ folder:

• references/workflows-advanced.md - branching, loops, parallel, foreach, suspend/resume, state management
• references/memory-and-rag.md - full memory config, working memory schemas, RAG pipeline, vector stores, semantic recall
• references/mcp-and-voice.md - MCP client/server patterns, voice providers, CompositeVoice, realtime audio
• references/deployment-and-server.md - server config, middleware, auth, CLI reference, deployment targets, evals/observability

Only load a references file if the current task requires it - they are long and will consume context.

Companion check

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Skip entirely if recommended_skills is empty or all companions are already installed.