The market opportunity
TypeScript AI Engineering Is a Career Skill
Weekly npm downloads for Mastra — reached in under a year since launch, making it the fastest-growing TypeScript-native AI framework in the ecosystem.
GitHub stars at 1.0 launch in February 2026, with production use confirmed at Replit, WorkOS, and teams across the YC network.
Projected AI agent market size by 2030, up from $7.6B in 2025. Teams are hiring TypeScript developers who can ship agents — not just call chat APIs.
Faster agent build time with Mastra compared to assembling equivalent infrastructure manually, per VentureBeat — because the hard decisions are already made.
Learning outcomes
What You'll Be Able to Do
Build a production agent with new Agent() in minutes — provider-flexible via createMastraModel(), tool-powered via createTool() with Zod schemas, and instantly testable in the Mastra playground at localhost:4111 without writing a single line of frontend code
Design durable workflows with new Workflow() and new Step() that branch, run in parallel, loop, and suspend for human approval — then resume exactly where they left off, with live SSE progress streaming via .watch()
Give your agents real memory: thread-based conversation history, semantic recall via embeddings, and a working-memory scratchpad — backed by LibSQL, PostgreSQL, or Upstash in production
Build a full RAG pipeline from scratch: chunk documents with MDocument, embed with OpenAI or Cohere, store in pgvector, retrieve with a typed tool, and return cited responses — all wired to an agent with memory integration
Wire your entire project to the MCP ecosystem: connect to filesystem, GitHub, and Slack as an MCPClient, then expose your own agents as an MCPServer usable directly from Claude Desktop and other MCP clients
Hands-on from day one
What You'll Build
A Production-Grade TypeScript AI Backend
You don't build a toy. You build a real TypeScript backend project, incrementally, lesson by lesson. From lesson one, mastra dev gives you a professional playground at localhost:4111 — chat interface for every agent, step graph visualization for every workflow, memory thread viewer, tool call cards. No custom frontend to maintain. By Module 8, your project deploys to Vercel via VercelDeployer, exposes a typed client API via @mastra/client-js, and has a Vitest eval suite running in CI.
- A multi-tool agent with streaming via agent.stream(), Zod-validated structured output, dynamic per-request instructions, and sub-agent delegation via the orchestrator-as-tool pattern
- A content pipeline workflow with parallel steps via .parallel(), conditional branching via .branch(), human approval gates via .suspend() and .resume(), and live progress streaming via .watch()
- A personal assistant agent with thread-based conversation history via threadId and resourceId, semantic recall powered by embeddings, and a structured working-memory scratchpad
- A documentation Q&A RAG system: MDocument chunking, embed() and embedMany() calls, pgvector storage via PgVector, a typed retrieval tool, and source-cited responses wired to an agent with memory
- MCP integrations: MCPClient connecting to stdio servers for filesystem, GitHub, and Slack — plus MCPServer exposing your entire project to Claude Desktop as a registered MCP server
- A Vitest eval suite with FaithfulnessMetric, AnswerRelevancyMetric, and HallucinationMetric, plus a custom BaseMetric — all wired to CI with pass/fail thresholds
- Production deployment via mastra build and VercelDeployer, with OpenTelemetry tracing, Langfuse observability, and a @mastra/client-js code reference showing how any frontend connects to the deployed backend
Before you start
Prerequisites
- —TypeScript or JavaScript experience — comfortable with types, generics, and async/await — no hand-holding on the language fundamentals
- —Node.js and backend familiarity — you've built a Node.js server or API and you know what process.env is — no React or Next.js required for this course
- —No prior AI or ML experience required — this is a software engineering course, not machine learning research — if you can write a typed async function, you have the prerequisites
- —No prior Mastra experience required — the course starts from pnpm create mastra@latest and builds from first principles — you don't need to have read the docs
37 lessons across 8 modules
Course Curriculum
Bootstrap your Mastra project, meet the playground, and build your first agents and workflows. Understand the new Agent() model, createMastraModel() for provider-flexible LLM selection, createTool() with Zod input/output schemas, and new Workflow() with new Step() composition.
Go beyond the basics. Stream responses token-by-token with agent.stream(), extract structured data with Zod output schemas, write dynamic system prompts that adapt per request, compose agents as tools for orchestrator patterns, and add retry logic and model fallbacks for resilience.
Build durable, production-grade workflows. Wire data between steps, fan out with .parallel(), conditionally route with .branch(), loop with .while() and .until(), pause for human approval with .suspend() and .resume(), stream live progress with .watch() SSE, and graduate to the modern vNext createWorkflow() and createStep() API.
Give your agents meaningful context. Implement thread-based conversation history via threadId and resourceId, add semantic recall by storing and querying embeddings, maintain a structured agent scratchpad with working memory, and configure production backends — LibSQL, PostgreSQL, and Upstash.
Build a complete retrieval-augmented generation pipeline. Chunk and transform documents with MDocument, generate embeddings with embed() and embedMany() using OpenAI or Cohere, store and query vectors via LibSQLVector and PgVector, wire a typed RAG retrieval tool, and integrate retrieval into an agent that returns source-cited responses.
Connect to the broader AI tool ecosystem. Use MCPClient to connect to stdio MCP servers for filesystem access and GitHub automation, work with the built-in @mastra/google and @mastra/slack integrations, and use MCPServer to expose your entire Mastra project as a registered MCP server accessible from Claude Desktop and compatible clients.
Measure what your agents actually do. Run built-in evals — FaithfulnessMetric, AnswerRelevancyMetric, HallucinationMetric — on real agent outputs, write a custom BaseMetric for domain-specific quality checks, and integrate the full eval suite into Vitest with per-metric pass/fail thresholds that run in CI.
Ship to production with confidence. Build an AgentNetwork for multi-agent routing, wire OpenTelemetry tracing with Langfuse dashboards, add voice capabilities via agent.speak() and agent.listen(), and complete the capstone: mastra build plus VercelDeployer — your backend deployed to production, with a @mastra/client-js code reference showing how any frontend can call it.
Made for TypeScript engineers
Is This Course For You?
This is for you if…
- You're a TypeScript developer who wants to build real AI agents without managing a Python environment or mentally translating Python idioms into JavaScript
- You've called the OpenAI or Anthropic API directly and hit the limits — no memory, no persistence, no structured tool calling, no way to test your agent without a frontend
- You're a Python developer working with CrewAI, AutoGen, or LangChain who wants to build agents in the language your product is already written in
- You've been asked to ship AI features at work and want an opinionated, batteries-included framework — not a collection of primitives you have to assemble yourself
- You want a framework where pnpm dev gives you a professional agent testing UI immediately, not a blank terminal waiting for curl commands
- You want to finish with a deployable project — not completed exercises that live in a scratch repo
This is NOT for you if…
- You're still learning TypeScript basics — this course won't teach you types, generics, or async/await from scratch
- You need custom graph topology with explicit node-and-edge control — that level of architectural control is what the LangGraph.js course is for
- You want a passive video course — every lesson has a coding exercise, and the Mastra playground gives you immediate feedback on what you built
- You're looking for ML theory, model training, or fine-tuning — this is a software engineering course; the models are APIs you call, not systems you build
Got questions?
Frequently Asked Questions
Do I need prior AI or machine learning experience?
No. This is a software engineering course, not a machine learning course. If you're comfortable with TypeScript and async/await, you have the prerequisites. No statistics, no linear algebra, no Python environment to manage.
Do I need to know Python?
Not at all. Every line of code in this course is TypeScript. Mastra is TypeScript-native — built from the ground up for the JS/TS ecosystem, not ported from Python. That matters more than it sounds: no type-casting workarounds, no gaps where the JS version lags the Python version, no mental translation required.
Which version of Mastra does this course use?
The course is built on Mastra 1.0, which reached general availability in February 2026. Each lesson includes version notes so you know exactly what you're running against. Mastra 1.0 introduced an explicit stability commitment for the core Agent, Workflow, and Memory APIs — the patterns you learn here are built to last.
How long does the course take?
Approximately 30–42 hours at a comfortable pace — 37 lessons across 8 modules. Most developers complete one module per week while working full time. There's no time limit; access is lifetime.
What's the project structure?
A clean TypeScript backend project bootstrapped with pnpm create mastra@latest, built lesson by lesson. No custom frontend to maintain — mastra dev gives you a professional playground at localhost:4111 from lesson one: a chat interface for every agent, a step graph viewer for every workflow, a memory thread inspector, and tool call cards. Module 8 ends with mastra build and VercelDeployer, deploying your backend to production. The final lesson includes a @mastra/client-js code reference showing how any frontend (React, Next.js, plain fetch) can call your deployed server. The final project is deployable, demonstrable, and portfolio-ready.
Do I need an API key from an AI provider?
Yes. The course is designed to work with OpenAI (GPT-4o) or Anthropic (Claude) — your choice per lesson. createMastraModel() makes switching providers a one-line change, and the course covers both. Typical API costs during the course are $10–$25 depending on how much you experiment. The eval module (Module 7) includes lessons on using smaller, cheaper models for evaluation to reduce cost.
Is there a money-back guarantee?
Yes. 30 days, no questions asked.
What's the difference between the Free and Professional tiers?
Module 1 (5 lessons covering Mastra foundations — agents, tools, your first workflow, and the playground) is free — start today, no credit card required. Professional unlocks all 8 modules, the full test suite with solutions, conversational AI quizzes, the Ask the Course assistant, and lifetime access including all future updates.
Is the content kept up to date?
Yes. Mastra 1.0 has an explicit stability commitment for its core APIs, so major rewrites are unlikely. When the framework ships meaningful changes — new workflow primitives, updated deployer APIs, new memory backends — the relevant lessons are revised. You get all updates at no additional cost, and each lesson notes which Mastra version it was last verified against.
How does this relate to the LangGraph.js course?
Different paradigms, zero content overlap. Mastra is batteries-included — new Agent(), new Workflow(), Memory, RAG, evals, MCP, and VercelDeployer are all first-class framework primitives. You move fast because the infrastructure decisions are made. LangGraph.js is maximum control — you define every node, every edge, every state channel explicitly. Nothing happens unless you wire it. Many developers take both: Mastra when the framework does what you need and you want to move fast, LangGraph.js when you need custom graph topology that no framework prescribes. There is no content overlap between the two courses — they teach different mental models for different problem shapes.
How does this relate to the Vercel AI SDK course?
Complementary layers of the same stack. Mastra uses Vercel AI SDK providers internally under createMastraModel(), so these courses reinforce each other rather than overlap. The Vercel AI SDK course covers the frontend and streaming layer: useChat, streamUI, generateObject, provider switching, and React integration. This course covers the backend agent and workflow server: multi-layer memory, durable workflows with suspend/resume, RAG pipelines, MCP client and server, eval frameworks, and deployment. Together they cover the full TypeScript AI stack from browser to production backend.
I'm coming from Python frameworks like CrewAI or LangChain. Is this the right course?
Yes, and you'll feel it immediately. Mastra is the TypeScript-native answer to what CrewAI and LangChain do in Python. You won't find type-casting workarounds, gaps where the JS version lags the Python version, or Python idioms leaking through the API surface. The course is designed for developers making the move: it assumes you understand agents conceptually but explains every Mastra-specific primitive from first principles. The playground at localhost:4111 also replaces the print-statement debugging loop that Python agent development often requires.
What if I get stuck?
Every lesson includes a complete, working solution file. The Professional tier includes the Ask the Course AI assistant — trained on the full course content, code examples, and Mastra documentation. It can answer questions about specific lessons, debug your implementation against the exercise files, and explain why a particular Mastra API behaves the way it does.
Can my team take this course?
Yes — the team license includes 5 seats and is designed for engineering teams adopting Mastra together. Contact us if you need more than 5 seats for a volume arrangement.