What Is Mastra AI? TypeScript Agent Framework Guide 2026

Mastra AI has emerged in 2026 as the leading TypeScript-native framework for building production AI agents. It gives developers everything needed for serious agent work — agents, durable workflows, RAG, memory, MCP, and built-in evaluation — without the complexity of LangChain or the Python-first delays of LangGraph.

Last verified: June 28, 2026

The short definition

Mastra AI is an open-source (MIT), TypeScript-first framework for building AI agents and applications that includes agents, workflows, RAG, memory, MCP support, observability, and evaluation — all in one package.

If you’re a TypeScript developer who wants to build production AI agents without stitching together LangChain + LangSmith + something for evals + something for memory, Mastra is designed for you.

Why Mastra matters in 2026

The AI agent framework space in 2026 has settled into clear tiers. Mastra occupies the sweet spot for TypeScript teams:

• LangChain/LangGraph: Most feature-rich but Python-first, complex, and the TypeScript port lags
• Vercel AI SDK: Great for streaming web UIs but not designed for complex agent workflows
• OpenAI Agents SDK: Fastest for prototyping but heavy vendor lock-in
• Mastra: TypeScript-native, production-ready, all-in-one

As of June 2026, Mastra has ~12,000 GitHub stars and is used in production by companies building customer support agents, coding agents, content pipelines, and research tools.

Core capabilities

Agents

Mastra agents combine an LLM with tools, memory, and instructions to solve open-ended tasks autonomously.

import { Agent } from 'mastra';
import { openai } from '@mastra/openai';

const researchAgent = new Agent({
  name: 'research-agent',
  instructions: 'You are a research assistant. Gather information and synthesize findings.',
  model: openai('gpt-5.5-pro'),
  tools: [webSearchTool, documentReaderTool],
  memory: { type: 'conversation' },
});

Key agent features:

• Multi-model support (swap providers per agent)
• Tool calling with MCP integration
• Conversation and entity memory
• Agent-to-agent handoffs
• Guardrails and safety checks

Workflows

Mastra’s workflow engine provides step-based durable execution, similar to Temporal but designed for AI workflows.

• Durable execution: Steps survive restarts and failures
• Retry semantics: Built-in retry with exponential backoff
• Parallel execution: Run multiple agent branches concurrently
• State persistence: Every step’s state is saved for recovery and auditing
• Human-in-the-loop: Pause for human approval at any step

import { Workflow } from 'mastra';

const contentWorkflow = new Workflow({
  name: 'content-pipeline',
  steps: [
    { name: 'research', execute: researchAgent.run },
    { name: 'draft', execute: draftingAgent.run, dependsOn: ['research'] },
    { name: 'review', execute: humanReview, dependsOn: ['draft'] },
    { name: 'publish', execute: publishAgent.run, dependsOn: ['review'] },
  ],
});

RAG (Retrieval-Augmented Generation)

Built-in RAG pipeline with no external dependencies:

• Ingestion: Process documents from PDF, HTML, Markdown
• Chunking: Configurable chunking strategies
• Embedding: Multiple embedding provider support
• Vector storage: Local (SQLite + vector extension) or remote (Pinecone, Weaviate, etc.)
• Retrieval: Hybrid search (semantic + keyword)

Memory

Mastra provides memory primitives that work out of the box:

• Conversation memory: Full chat history with token-aware summarization
• Entity memory: Remember facts about users, projects, and concepts
• Working memory: Short-term context for the current task
• Persistent memory: Long-term storage across sessions (uses SQLite by default)

MCP Integration

Mastra has native support for the Model Context Protocol (MCP), the open standard from Anthropic/Linux Foundation for connecting AI agents to tools and data sources.

This means any MCP server you build or use with Claude works with Mastra agents too:

• Web search and browsing
• Database queries
• API integrations
• File system access
• Custom internal tools

Evaluations

Mastra includes a built-in evaluation framework, eliminating the need for a separate eval tool:

• 10+ built-in metrics (hallucination, faithfulness, relevance, etc.)
• LLM-as-judge evaluation
• Test dataset management
• CI/CD integration
• Visual dashboard in Mastra Studio

Mastra Studio

The local development environment (npx mastra dev) provides:

• Chat interface for testing agents
• Trace viewer for step-by-step debugging
• Workflow runner with state inspection
• Memory inspector
• Eval dashboard
• Log viewer

This is one of Mastra’s strongest advantages: you get a full-featured local UI without needing a separate observability platform.

Architecture

┌─────────────────────────────┐
│       Mastra Application     │
├─────────────────────────────┤
│  Agents  │  Workflows  │ RAG │
├──────────┴─────────────┴────┤
│    Memory  │  Tools  │ MCP  │
├──────────┬──────────────────┤
│  Model Layer (40+ providers) │
├──────────┴──────────────────┤
│   Mastra Studio (Dev UI)    │
└─────────────────────────────┘

Getting started

npm create mastra@latest
# or
npx create-mastra@latest

cd my-mastra-app
npm run dev  # Starts Mastra Studio

Basic agent:

import { Agent } from 'mastra';
import { anthropic } from '@mastra/anthropic';

const agent = new Agent({
  name: 'helper',
  instructions: 'You are a helpful assistant.',
  model: anthropic('claude-sonnet-4-6'),
});

await agent.send('What is the capital of France?');

Comparison at a glance

Feature	Mastra	LangGraph (TS)	Vercel AI SDK	OpenAI Agents SDK
Language	TypeScript	TypeScript (ported)	TypeScript	Python > TS
Agents	✅ Native	✅ Via graph	⚠️ Basic	✅ Native
Workflows	✅ Durable	✅ via StateGraph	❌	❌
RAG	✅ Built-in	✅ Via LangChain	❌	❌
Memory	✅ Built-in	⚠️ Add-on	❌	❌
Evals	✅ Built-in	❌ (LangSmith)	❌	❌
MCP	✅ Native	✅ Via LangChain	❌	❌
Studio/UI	✅ Local	❌	❌	❌
Multi-model	✅ 40+	✅ 40+	✅ 30+	❌ OpenAI
Learning curve	Medium	High	Low	Low
License	MIT	MIT	MIT	MIT

The bottom line

Mastra is the most pragmatic choice for TypeScript teams in 2026 who want to build production AI agents without managing multiple frameworks. It’s not the most feature-rich option (LangGraph still wins on breadth), but it’s the most complete TypeScript-native option.

The ideal Mastra user is a TypeScript developer building a production AI agent system who wants:

• Everything in one framework (no cobbling together)
• Multi-model flexibility (no vendor lock-in)
• Local development tooling (Mastra Studio)
• Production durability (workflow engine, state persistence)
• Open-source control (self-hostable, MIT license)

If that sounds like you, Mastra is worth serious consideration for your next AI agent project.

---

Last verified: June 28, 2026. Sources: Mastra.ai official docs, GitHub repo (github.com/mastra-ai/mastra), CallSphere Mastra review, Speakeasy agent framework comparison, Particula.tech Mastra analysis, Generative.inc Mastra guide.