Building AI Agents with TypeScript: A Practical Guide
Learn how to build production-ready AI agents using TypeScript and LangGraph.js. From simple chatbots to complex multi-agent systems.
Why TypeScript for AI Agents?
TypeScript has become the go-to language for full-stack web development, and it's increasingly the best choice for building AI agents too. With LangGraph.js, you get the same powerful agent orchestration framework used in Python — but with type safety, familiar tooling, and seamless integration into your existing stack.
This post gives a high-level overview. For a hands-on deep dive, check out our LangGraph.js Mastery course.
The Agent Architecture
Every LangGraph.js agent follows a graph-based architecture:
- State — A typed object that flows through the graph
- Nodes — Functions that transform state (call LLMs, run tools, make decisions)
- Edges — Connections between nodes, including conditional routing
import { StateGraph, Annotation } from "@langchain/langgraph";
const AgentState = Annotation.Root({
messages: Annotation<BaseMessage[]>({
reducer: (prev, next) => [...prev, ...next],
}),
});
const graph = new StateGraph(AgentState)
.addNode("agent", callModel)
.addNode("tools", callTools)
.addEdge("__start__", "agent")
.addConditionalEdges("agent", shouldContinue)
.addEdge("tools", "agent")
.compile();Key Patterns
Tool Calling
Modern LLMs can decide when and how to call tools. In LangGraph.js, you bind tools to the model and the framework handles execution:
const tools = [searchTool, calculatorTool];
const model = new ChatAnthropic({ model: "claude-sonnet-4-5-20250929" })
.bindTools(tools);Multi-Turn Conversations
With a checkpointer, your agent maintains conversation state across requests — essential for production chatbots:
const graph = workflow.compile({
checkpointer: new PostgresSaver(pool),
});
// Each invocation continues the conversation
await graph.invoke(
{ messages: [new HumanMessage("Hello!")] },
{ configurable: { thread_id: "user-123" } }
);What's Next?
Building production AI agents requires more than just the basics. You need persistence, error handling, streaming, and testing strategies. Our LangGraph.js Mastery course covers all of this across 36 hands-on lessons.