Mastra

Concept

Mastra rethinks AI “glue code” as a coherent developer kit. Instead of piecing together prompts, function calls and vector-DB hacks, you compose agents objects with memory, tools, and deterministic workflows. Whether you’re shipping an AI recruiter, a RAG-powered chatbot or a multi-step eval pipeline, Mastra supplies the primitives so you focus on product, not plumbing.

Core Features

• Agents & Workflows – chain LLM calls, invoke tools and persist long-term memory.
• RAG & Knowledge Bases – hydrate agents with domain data in minutes.
• Deterministic Evals – score outputs automatically to keep drift in check.
• Local Dev Sandbox – chat with agents, tweak prompts, hot-reload.
• Batteries-Included Type Safety – authored in TS, shipped to Node, edge or serverless.

Developer Experience & Performance

Mastra’s docs load as a static Astro site (LCP ≈ 0.9s), code snippets copy with one click and every example is runnable via npx. A CLI scaffolds new agents, while typed generics surface model capabilities at compile time—no “any” gymnastics.

Takeaway

Mastra shows that AI infra can feel like a modern front-end framework: opinionated defaults, tight type-checking and an ethos of “minimal magic, maximal velocity.” If you can write a React component, you can stand up an agent that remembers, plans and executes without reinventing LangChain-sized boilerplate.