What is Mastra? The TypeScript AI Agent Framework, Explained
Mastra is an open-source TypeScript framework for building AI agents, workflows, memory, and RAG. Here's what it does, where it came from, and when it's the right choice for your product.
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What is Mastra?
Mastra is an open-source TypeScript framework for building AI-powered applications: agents that use tools, workflows that orchestrate multi-step processes, and the memory, retrieval, and evaluation plumbing that production AI features actually need. If your product already lives in JavaScript or TypeScript — Next.js, React, Node — Mastra lets your AI logic live in the same language, the same repo, and the same type system as the rest of your code.
Where Mastra came from
Mastra was built by the team behind Gatsby, the React site framework, and went through Y Combinator's Winter 2025 batch. It hit version 1.0 in January 2026 and now sees roughly 300,000 npm downloads a week. The timing explains the traction: for years the serious AI tooling was Python-first, while most teams shipping web products live in TypeScript. Mastra closed that gap — you no longer need a second language and a second deployment story just to add an agent to your app.
The core primitives
Mastra is deliberately un-magical. It hands you a small set of composable pieces rather than one black-box "do everything" abstraction:
Agents
An LLM plus instructions plus tools. The agent reasons about a goal, picks tools, and iterates until it produces an answer or hits a stopping condition you define.
Workflows
Graph-based orchestration with .then(), .branch(), and .parallel(). Deterministic control flow wrapped around your nondeterministic model calls.
Memory
Conversation history plus working and semantic memory, so an agent remembers who it's talking to and what happened last session.
RAG
Chunking, embedding, and retrieval primitives for grounding answers in your own documents and data instead of the model's guesses.
Evals & scorers
Automated checks that score agent output, so quality is measured on every change instead of eyeballed before launch.
Observability
Tracing for every step, tool call, and model response — including tool hooks to observe and control each tool call an agent makes.
What building with it looks like
An agent is a few lines of ordinary TypeScript. Model routing is a string — Mastra speaks to thousands of models across the major providers through one interface:
import { Agent } from "@mastra/core/agent";
export const supportAgent = new Agent({
name: "support-agent",
instructions:
"Answer product questions using the docs search tool. Cite the page you used.",
model: "anthropic/claude-sonnet-5",
tools: { searchDocs },
});Because it's just TypeScript, the agent ships inside your existing app — a Next.js route handler, a Node service, or Mastra's own standalone server — and your existing types flow through tool inputs and outputs. There's also a local playground for poking at agents and workflows while you develop.
Durable workflows are the headline feature
The piece that separates Mastra from "a nice wrapper around an LLM API" is its workflow engine. Workflows persist their execution state, which means one can suspend — waiting on a human approval, a webhook, or a slow external system — and resume days later exactly where it left off. That suspend-and-resume capability is the difference between a chatbot demo and real back-office automation: refund approvals, content review queues, multi-day onboarding sequences. Human-in-the-loop isn't an afterthought; it's a first-class control-flow primitive.
When Mastra is the right call
- Your product and team are TypeScript. One language across product and AI code is a real velocity and hiring advantage.
- The AI feature is user-facing. Chat inside your app, an assistant on your dashboard, AI-powered search — places where the agent lives inside the product.
- You need human-in-the-loop. Suspendable, resumable workflows make approval gates natural instead of hacky.
- You want evals and tracing in the same stack instead of bolting on three observability vendors later.
Weighing it against a Python-side framework? We wrote a full comparison: Mastra vs CrewAI — when to choose which, and how they work together.
Thinking about AI agents in your product?
We design and ship production AI features for growing businesses — scoped, built, and measured. Let's talk about what an agent could take off your plate.