Mastra

You define an agent in a TypeScript file as a typed object with an id, instructions, a model and a set of tools, and you register it on a Mastra instance alongside any workflows. Workflows are composed from typed steps with retries and branches, so a multi-step process is something your team can read and review rather than a prompt chain. Memory is configured separately, with options for recent message windows, semantic recall and observational memory, so agents keep durable context across threads. The harness coordinates multi-mode agents that share state, threads and storage. You run the whole thing on one Mastra server locally on a port you choose, and deploy the same application to Mastra Cloud or to your own infrastructure without rewriting it. Every run produces traces, metrics and eval scores you can inspect afterwards.

• •Typed Agents - Define instructions, model, tools and runtime behaviour in one place, with TypeScript types across the whole definition rather than dictionaries checked at runtime
• •Workflows - Compose typed steps, retries and branches into production workflows your team can inspect and ship, instead of an opaque sequence of model calls
• •Agent Harness - Coordinate multi-mode agents with shared state, threads and storage, using the same harness that runs Mastra Code
• •Memory - Durable context through observational memory, semantic recall and thread-aware storage, configured declaratively rather than assembled by hand
• •Built-in Observability - Traces, metrics, logs, datasets and evals in the framework itself, so you can inspect every model call, tool call and handoff from one searchable timeline and score runs against repeatable checks before changes reach production
• •One Server, Any Deployment - Register agents and workflows on a single Mastra server, run it locally, and deploy the same app to Mastra Cloud or self-host it

TypeScript and JavaScript teams who want to build agents without adding a Python service to their stack, and who would rather have types and a compiler than discover a malformed tool call in production. It suits people building agents into an existing React, Next.js or Node application, and teams who want evals and tracing from day one rather than bolted on later. It is a reasonable choice if you want the framework to stay open source and self-hosted, and to treat the paid platform as optional. It is a poor fit if your team is Python-first, since LangGraph and CrewAI are more mature there and Mastra gains you nothing. It is also a poor fit if you need predictable flat billing, because the hosted platform meters more than twenty dimensions and the $250 tier is a floor rather than a price.