The market opportunity
The Best Time to Add AI Engineering to Your Stack
TypeScript overtook Python as the most-used language on GitHub in August 2025 — the AI tooling is finally catching up.
Job postings explicitly listing LangGraph experience on Glassdoor alone — and growing fast.
Average AI engineer base salary in 2025, with an 18.7% pay premium over general software engineers.
Year-over-year growth in TypeScript contributors on GitHub. Companies building AI products are doing it in TypeScript.
Learning outcomes
What You'll Be Able to Do
Designing LangGraph.js graphs from scratch using TypeScript-native patterns: typed state channels, reducer functions, and strongly-typed tool schemas
Persistent agents with checkpointed state that survive restarts, resume mid-conversation, and maintain cross-session memory
A working retrieval-augmented generation pipeline: document chunking, pgvector embeddings, semantic search, and context injection — all in TypeScript
Supervisor-worker multi-agent architectures that route tasks between specialized agents, with shared state and handoff protocols
Full LangSmith observability: trace every node execution, inspect state at each step, catch errors before they reach production
Hands-on from day one
What You'll Build
AI Assistant Portfolio App
You don't just read about LangGraph.js — you build with it. The course is backed by a real pnpm monorepo you clone locally: a Next.js application where each lesson extends a working AI agent with new capabilities. Agents grow from simple chat to multi-agent systems with RAG, streaming, and human-in-the-loop workflows. By the final module, you have a deployable application you can actually demo.
- Multi-turn conversational agent with persistent memory across sessions
- RAG pipeline with pgvector semantic search over a real document corpus
- Supervisor-worker multi-agent system with dynamic task routing
- Streaming responses from LangGraph nodes to a Next.js UI in real time
- Human-in-the-loop approval steps with generative UI components
- LangSmith observability: traces, state inspection, and error tracking
- Production guardrails: input validation, rate limiting, and graceful error recovery
Before you start
Prerequisites
- —TypeScript or JavaScript experience — comfortable with types, generics, and async/await — no hand-holding on the basics
- —Basic familiarity with React and Next.js — you can read and write component code
- —Some experience with REST APIs and JSON — you know what a fetch call looks like
- —Curiosity about AI systems — a genuine interest in building LLM-powered applications — no ML background needed
36 lessons across 8 modules
Course Curriculum
Get up and running with LangGraph.js. Understand the graph model, TypeScript-specific state patterns, and multi-turn conversation flows.
Add durable memory to your agents. Checkpointing strategies, conversation history management, and cross-session state with embeddings.
Give your agents access to external knowledge. Build retrieval-augmented generation pipelines with vector search, document chunking, and context injection.
Master the core patterns of autonomous agents. Tool calling, structured output, decision-making loops, and error recovery.
Design complex workflows with branching, parallel execution, human-in-the-loop approval, and conditional routing.
Orchestrate multiple specialized agents. Supervisor patterns, agent handoff, shared state, and collaborative problem-solving.
Stream rich, interactive UI components directly from your agents. Real-time rendering of dynamic content powered by AI decision-making.
Deploy with confidence. Observability, error handling, rate limiting, testing strategies, and monitoring for production AI systems.
Made for TypeScript engineers
Is This Course For You?
This is for you if…
- You're a TypeScript developer who wants to add AI to your toolkit without switching to Python
- You've built a basic LLM integration but hit a wall with persistence, multi-step workflows, or production deployment
- You've been asked to ship AI features at work and want an architecture-first path — not another playground demo
- You think in components, types, and APIs — and want agent systems explained the same way
- You want to finish with a working, deployable application — not just completed exercises
This is NOT for you if…
- You're still learning TypeScript basics — this course won't teach you the language from scratch
- You're looking for Python content — every line of code here is TypeScript
- You want a passive video course — you'll write code in every lesson, and the tests tell you when you're done
- You're looking for ML theory or model training — this is a software engineering course, not machine learning research
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 linear algebra, no statistics, no Python.
Do I need to know Python?
Not at all. Every line of code in this course is TypeScript. That's the entire point — LangGraph.js in the language you actually use, not a port you have to mentally translate.
Which version of LangGraph.js does this course use?
The course is built on LangGraph.js 1.0, which reached general availability in October 2025 with an explicit no-breaking-changes commitment until 2.0. Each lesson includes version notes so you know exactly what you're running against.
How long does the course take?
Approximately 40 hours at a comfortable pace — 36 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 sandbox repo?
A real pnpm monorepo you clone locally: a Next.js application with a LangGraph.js agent backend, wired together end to end. Each lesson ships with skeleton files (typed interfaces, test stubs) and a full solution. You write the implementation; the tests tell you when you're done.
Do I need an API key from an AI provider?
Yes. Lessons work with OpenAI (GPT-4o) or Anthropic (Claude) — your choice. Each lesson includes notes on switching providers. Typical API costs during the course are $5–$20 depending on how much you experiment.
Is there a money-back guarantee?
Yes. 30 days, no questions asked.
What's the difference between the Free and Professional tiers?
Module 1 (9 lessons covering LangGraph.js fundamentals) 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. LangGraph.js 1.0 has an explicit stability commitment, so major rewrites are unlikely before 2.0. When the library updates in meaningful ways, lessons are revised. You get all updates at no additional cost.
What if I get stuck?
Every lesson includes a complete, working solution file. Professional tier includes the Ask the Course AI assistant — trained on the full course content, code examples, and LangGraph.js documentation. It can answer questions about specific lessons, debug your implementation, and explain concepts in context.
Can my team take this course?
Yes — the team license includes 5 seats and is designed for engineering teams adopting LangGraph.js together. Contact us if you need more than 5 seats for a volume arrangement.
I already know LangChain. Is this still useful?
LangGraph.js is the next layer on top of LangChain for stateful, graph-based agent workflows. If you're comfortable with basic chains and tool calling, this course picks up exactly where LangChain ends — persistent state, multi-agent orchestration, streaming, and production deployment.