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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.

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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

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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.

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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.

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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

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Error handling

Error	Cause	Resolution
Schema mismatch between steps	Step outputSchema doesn't match next step's inputSchema	Use .map() between steps to transform data
Workflow not committed	Forgot .commit() after chaining steps	Add .commit() as the final call on the workflow chain
maxSteps exceeded	Agent loops through tools beyond limit (default 5)	Increase maxSteps or improve tool descriptions to reduce loops
Memory scope mismatch	Using resource-scoped memory but not passing resource in generate	Always pass memory: { thread, resource } when using resource-scoped memory
MCP resource leak	Dynamic listToolsets() without disconnect()	Always call mcp.disconnect() after multi-user requests

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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.

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Related skills

When this skill is activated, check if the following companion skills are installed. For any that are missing, mention them to the user and offer to install before proceeding with the task. Example: "I notice you don't have [skill] installed yet - it pairs well with this skill. Want me to install it?"

• ai-agent-design - Designing AI agent architectures, implementing tool use, building multi-agent systems, or creating agent memory.
• llm-app-development - Building production LLM applications, implementing guardrails, evaluating model outputs,...
• a2a-protocol - Working with the A2A (Agent-to-Agent) protocol - agent interoperability, multi-agent...
• prompt-engineering - Crafting LLM prompts, implementing chain-of-thought reasoning, designing few-shot...

Install a companion: npx skills add AbsolutelySkilled/AbsolutelySkilled --skill <name>