Production AI Agents with JavaScript: LangChain & LangGraph

Production-grade AI agents with LangChain.js, LangGraph.js, RAG, Next.js, LangSmith & real JS/TS projects

Preview this Course

GET COUPON CODE

What you'll learn

• Design, build and ship production-grade AI agents using LangChain.js, LangGraph.js and modern TypeScript/JavaScript.
• Implement real projects: web search agent, docs chat (RAG), code-driven tools and agentic workflows with clean, testable APIs.
• Master JSON-first patterns, Zod schemas, tool calling and structured outputs to make agents reliable, debuggable and observable.
• Deploy and monitor agents using LangSmith & LangGraph Cloud, integrate with Next.js UIs, and prepare for real-world production use.

Description

Most LangChain and LangGraph courses are Python-first. This one is built from the ground up for JavaScript & TypeScript engineers who want real, shippable agentic systems—not disconnected demos.

You’ll build a sequence of end-to-end projects that mirror how modern teams ship AI features: clean TypeScript code, clear APIs, JSON contracts, LangGraph orchestration, RAG, proper vector stores, and real Next.js frontends wired to real agents.

By the end, you’ll know exactly how to go from idea → design → implementation → observability → deployment in the JS ecosystem.

Here’s what we’ll cover in Phase 1:

Intro & Mindset

How this course works, what it is / isn’t, and how to follow.

Choosing models (OpenAI / Gemini / Groq / local) smartly for cost, speed & reliability.

How all projects connect into a reusable “agent platform” you can extend.

Foundations: LangChain, Agents & Flow

Modern AI app architecture: UI → orchestration → models → tools → storage.

Simple, honest definition of AI agents and real-world use cases.

Chains vs agents: when a chain is enough, when an agent is worth it.

Where LangChain.js fits, where LangGraph.js fits, and how they work together.

JSON-first mindset teaser: why strings lie and schemas save you.

Orientation & “Hello Agent” Project

TS/Node project setup, tsconfig, env patterns, scripts.

Multi-provider setup: OpenAI, Gemini, Groq via a single provider factory.

First “Hello Agent” function that runs like a clean backend primitive, not a toy script.

LLM Fundamentals: JSON-First Approach

Tokens, context windows, cost-aware thinking.

Sampling knobs: temperature, top_p, max_tokens in practical terms.

Chat vs tools; why structured outputs beat ad-hoc prompts.

Zod schemas as contracts for every response.

Validate → repair → fallback strategies to keep agents stable.

JSON-First Mini Project

Implement a strict Q&A pipeline in TypeScript with:

Centralized env management.

Reusable LLM wrapper.

CLI entrypoint that returns guaranteed JSON, ready for any frontend.

LangChain.js Fundamentals

Why use LangChain.js instead of only raw SDKs.

Prompt templates, models, output parsers in JS.

Runnables & LCEL (RunnableSequence, RunnableMap) as your mental model.

Tool-calling with schemas, low-temperature deterministic behavior.

Tool-Calling 101: Search v1 (LCEL)

Design a search agent that chooses:

Direct answer vs web search route.

Implement:

Typed schemas for search results, open-url, and summaries.

Tavily (or similar) integration via LangChain tools.

LCEL pipeline that routes, fetches, summarizes, and returns strict JSON.

Expose as /search HTTP endpoint and connect to a simple Next.js UI.

RAG Fundamentals

Clear, no-buzzword explanation of RAG.

Ingestion vs query phases; chunking & embeddings.

Vector store concepts: similarity search, metadata, top-k.

Where “light RAG” is enough vs when you need heavy infra.

Light RAG: Docs Helper Project

Build a small RAG system in JS:

Character-based chunker.

In-memory vector store with pluggable embeddings (OpenAI/Gemini).

/kb/ingest, /kb/ask, /kb/reset APIs.

Cited answers with confidence scores.

Next.js UI tab: paste docs → ask questions → view grounded answers + sources.

LangGraph Fundamentals

Why LCEL alone isn’t enough for complex agents.

State, nodes, edges: an intuitive JS mental model.

Linear flows (validate → plan → act → finalize).

Branching, retries, max-iterations & error boundaries.

Checkpointing, replay, and human-in-the-loop approvals.

LangGraph Orchestration Project

Implement a real LangGraph.js graph:

Typed state in types.ts.

Nodes: validate, plan, approve, execute, finalize.

HTTP route to run the graph; Next.js UI to inspect outcomes.

Show how LangChain tools plug into LangGraph nodes cleanly.

Deploying & Observing Agents (LangSmith + LangGraph Cloud)

Why tracing & observability are mandatory in production.

Connect your JS agents to LangSmith for logs, spans, errors, prompts.

Deploy a LangGraph graph to LangGraph Cloud.

Test via API + HITL (approve/deny) flows.

Agentic RAG with Vector DB (Mongo/Supabase style)

Turn RAG + tools into a production-ish agent:

Chunk → embed → upsert into a real vector store.

Ask → retrieve → summarize with citations & confidence.

Add tools like calculator, date planner, summarize.

Use createAgent / tools with strict policies (cite-if-used, no hallucinated sources).

Wire it into a Next.js UI and show how this can power support bots, internal copilots, or SaaS features.

Throughout the course you’ll see one consistent JavaScript architecture, heavy inline explanations, and production-minded patterns you can lift directly into your own products or client work.

Who this course is for:

• JavaScript/TypeScript developers who want to build real AI agents instead of toy chatbots.
• Full-stack, backend and Next.js engineers ready to add LangChain.js, LangGraph.js, RAG and tool-calling skills to their production toolkit.
• Developers coming from Python-only AI content who want a JS-native, framework-driven path to shipping agentic apps.
• Tech leads, indie hackers and SaaS builders who care about correctness, JSON-first APIs, observability, and deployable architectures—not just demos.

Share :