Mastra

Mastra is a TypeScript framework for building AI agents and workflows with tool integration, memory persistence, and multi-step orchestration. It covers agent configuration, tool registration, workflow composition, memory management, and deployment patterns that enable developers to create structured AI-powered applications.

What Is This?

Overview

Mastra provides structured approaches to building AI agent systems in TypeScript. It handles defining agents with specific roles, instructions, and model configurations, registering tools that agents can invoke during conversation or task execution, composing multi-step workflows with conditional branching and parallel execution, persisting conversation memory and agent state across sessions, connecting to external APIs and services through typed integrations, and deploying agent workflows as API endpoints or background workers.

Who Should Use This

This skill serves TypeScript developers building AI-powered applications, teams creating multi-agent systems with tool integration, engineers automating complex workflows with LLM reasoning, and product teams prototyping AI features with structured orchestration.

Why Use It?

Problems It Solves

Building AI agents from scratch requires implementing tool calling, memory management, and conversation handling manually. Without a framework, orchestrating multi-step workflows with LLM decisions produces fragile, hard-to-test code. Managing state across agent interactions demands custom persistence logic. Connecting agents to external APIs without typed interfaces leads to runtime errors.

Core Highlights

Agent abstraction encapsulates model selection, instructions, and tool access in reusable definitions. Workflow engine supports sequential, parallel, and conditional step execution with typed inputs. Tool system provides a typed interface for registering functions that agents can discover and invoke. Memory persistence stores conversation history and extracted knowledge across sessions.

How to Use It?

Basic Usage

import { Agent, Tool } from "@mastra/core";

const searchTool = new Tool({
  name: "search",
  description: "Search a knowledge base by query",
  parameters: {
    type: "object",
    properties: {
      query: { type: "string", description: "Search query" }
    },
    required: ["query"]
  },
  execute: async ({ query }) => {
    const results = await knowledgeBase.search(query);
    return results.map(r => r.content).join("\n");
  }
});

const agent = new Agent({
  name: "support-agent",
  model: "gpt-4o",
  instructions: "You are a support agent. Use the search "
    + "tool to find answers from the knowledge base.",
  tools: [searchTool]
});

const response = await agent.generate(
  "How do I reset my password?"
);
console.log(response.text);

Real-World Examples

import { Workflow, Step } from "@mastra/core";

const classifyStep = new Step({
  name: "classify",
  execute: async ({ input, agent }) => {
    const result = await agent.generate(
      `Classify this ticket: ${input.text}. `
      + `Return: bug, feature, or question.`
    );
    return { category: result.text.trim() };
  }
});

const routeStep = new Step({
  name: "route",
  execute: async ({ prev }) => {
    const routes = {
      bug: "engineering",
      feature: "product",
      question: "support"
    };
    const team = routes[prev.category] || "support";
    return { team };
  }
});

const respondStep = new Step({
  name: "respond",
  execute: async ({ input, prev, agent }) => {
    const reply = await agent.generate(
      `Draft a response for this ${prev.category} `
      + `ticket: ${input.text}. Routed to ${prev.team}.`
    );
    return {
      reply: reply.text,
      team: prev.team,
      category: prev.category
    };
  }
});

const ticketWorkflow = new Workflow({
  name: "ticket-handler",
  steps: [classifyStep, routeStep, respondStep]
});

const result = await ticketWorkflow.run({
  input: { text: "Login button returns a 500 error" },
  agent
});
console.log(result.reply, result.team);

Advanced Tips

Define tools with detailed descriptions so the model selects them accurately during generation. Use workflow steps with explicit return types to enforce data contracts between stages. Store agent memory in a vector database for semantic retrieval of past conversation context.

When to Use It?

Use Cases

Use Mastra when building customer support agents with tool access and memory, when creating multi-step automation workflows driven by LLM classification, when prototyping AI features in TypeScript with structured orchestration, or when deploying agent systems that need persistent conversation state.

Related Topics

LangChain and LlamaIndex frameworks, tool-calling LLM patterns, workflow orchestration engines, vector memory storage, and AI agent deployment strategies complement Mastra development.

Important Notes

Requirements

Node.js runtime with TypeScript support. API keys for the target LLM provider. Package installation of Mastra core and any integration modules.

Usage Recommendations

Do: define focused agents with clear instructions and limited tool sets for predictable behavior. Use workflows for multi-step processes where each stage has defined inputs and outputs. Test agent tool selection with representative queries before deploying to production.

Don't: give a single agent access to many unrelated tools, which increases selection errors. Skip typed parameters on tools, as this reduces the model ability to call them correctly. Store unbounded conversation history without summarization, which exhausts context limits.

Limitations

Agent behavior depends on the underlying LLM capabilities and may vary between model versions. Workflow complexity grows with the number of conditional branches and parallel steps. Real-time agent interactions require WebSocket or streaming infrastructure beyond the core framework.