Mastra: a TypeScript framework for AI agents and workflows

What Mastra actually does

Mastra bundles the building blocks of an AI product into one framework. An AI agent receives a goal, decides which tools to use through tool calling, and loops until it has a final answer. When you need tighter control, the workflow engine lets you spell out each step yourself, with sequential steps, branches and parallel runs. The framework also handles conversation history, retrieval over your own data (known as RAG) and a so-called observational memory.

Two features matter a lot once you ship. First, suspend and resume: an agent or workflow can pause to wait for a person’s approval and pick up exactly where it stopped, the human-in-the-loop pattern. Second, built-in evals and observability that help you measure answer quality over time. Mastra can also expose your agents and tools as an MCP server, and coordinate several agents through subagents.

Who it is for

Mastra targets TypeScript developers. In practice, it suits a team that already runs a Next.js or Node.js app and wants to add an assistant, a support agent or an internal automation without changing languages. Expect an intermediate learning curve: you should be comfortable with npm, environment variables and an API key.

Getting started

The official docs recommend the project scaffolder, npm create mastra@latest, which asks for a project name and a model provider. Once the dev server is running, a local interface called Mastra Studio lets you try agents, workflows and tools in the browser. Templates and an online course are available if agents are new to you.

Strengths, limits and alternatives

Strengths. Pure TypeScript, agents and workflows in one place, pause-and-resume, built-in evals, Apache 2.0 for the core.

Limits. It is a young project: version 1.0 of the core package shipped in January 2026 and the APIs keep moving. The dual license means you should check that you aren’t running an “ee” feature in production without a license. Pull requests are limited to collaborators, so the community mostly contributes through bug reports.

Alternatives. If you want a lightweight toolkit rather than a full framework, the Vercel AI SDK is leaner. On the Python side, LangGraph gives similar control over workflows and Agno takes a platform approach. To compare them all, see our AI agent frameworks overview.

Official site: mastra.ai.