Build a Content Creation Multi-Agent in TypeScript
A guide to collaborative agents, tools, and orchestration with LangChain
- Starting July 13, 2026, new lesson every week
- Expert strategies for beginners & advanced developers
- Practical knowledge you can apply immediately
🇬🇧 English
From Single Prompts to Collaborative AI Systems
Modern AI applications rarely rely on a single model call. Instead, they combine multiple specialized AI agents that collaborate to solve complex tasks. In this tutorial, you’ll build a complete multi-agent content creation system from scratch using LangChain and TypeScript. Step by step, you’ll create agents that research, write, and refine content while learning the architectural principles behind effective agent orchestration.
Build Production-Ready Agent Workflows
This hands-on tutorial goes beyond simple examples and explains why each architectural decision matters. You’ll learn how to structure collaborative AI systems, assign responsibilities to specialized agents, integrate external tools, and coordinate workflows that scale to real-world applications. By the end, you’ll have the practical knowledge to design your own production-ready agentic applications instead of simply copying existing examples.
Lesson-Overview
Foundations: Environment, Models, and Your First Agent
Set up a TypeScript project from scratch, configure API keys securely with .env, and create standardized chat models using LangChain. Build and run your first AI agent with createAgent, understand the role of system prompts, and learn how to invoke agents and process their responses.
Tools & Prompt Engineering: The Research Agent
Discover how LangChain tools extend your agents by integrating Tavily Web Search and building custom tools with Zod schemas. Apply proven prompt engineering techniques to create a research agent that gathers information from the web and generates structured research in Markdown.
Multi-Agent Patterns: Supervisor & Sub-Agents
Learn how to split complex tasks across multiple specialized AI agents using the Supervisor Pattern. Build dedicated research, technical writer, and fact-checking agents, then orchestrate a complete multi-agent workflow that coordinates tasks and exchanges information through shared Markdown files.
Images, Skills & the Full Studio
Take your application to the next level by integrating AI image generation, reusable agent skills, and advanced agent orchestration. Build a complete AI-powered content creation studio where research, writing, fact-checking, image generation, and social media agents collaborate to produce a polished content package from a single command.
Expert Knowledge for...
TypeScript Developers who want to build multi-agent AI systems
Full-stack Developers exploring LangChain in real applications
AI Engineers who want to move beyond single-prompt workflows
Software Architects interested in agent orchestration patterns
Complete this Tutorial and Learn how to...
- build collaborative multi-agent systems with LangChain and TypeScript.
- design specialized AI agents with clearly defined roles and responsibilities.
- orchestrate multiple agents into efficient content creation workflows.
- integrate tools that extend the capabilities of AI agents.
- apply production-ready architecture patterns for scalable agentic AI applications.
Expert-Led Knowledge for Real-World Applications
Nir Kaufman - Tikal
Nir Kaufman is a software architect, consultant, and international speaker specializing in modern web development, TypeScript, Angular, and AI-powered applications. He helps development teams build scalable frontend architectures and modern full-stack systems, with a strong focus on developer experience, performance, and emerging AI workflows. Nir is known for his practical, hands-on teaching style and regularly shares his expertise through workshops, conference talks, and technical training sessions.
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