NestJS + LangChain: Build an AI Agent in 5 Minutes

dev.to TypeScript2026-10-13 22:490 views

To add an AI agent to a NestJS app, register it as a module with nestjs-langchain, inject it like any provider, and call run(). This tutorial goes from nest new to a working POST /ask endpoint in about 30 lines of TypeScript.
Tested with NestJS 12, LangChain 1.x, nestjs-langchain 2.0 and Node 22.12.

How do I add LangChain to a NestJS project?

Create the app, then install the library and a model provider:

npx @nestjs/cli new my-app --package-manager npm cd my-app npm install nestjs-langchain @langchain/openai

@langchain/openai is the provider: @langchain/anthropic, @langchain/ollama, @langchain/mistralai or any other LangChain chat integration works the same way.

The provider reads its key from the environment:
export OPENAI_API_KEY=sk-...

How do I register the agent?

Import LangChainModule in app.module.ts:

import { Module } from '@nestjs/common'; import { LangChainModule } from 'nestjs-langchain'; import { AppController } from './app.controller.js'; import { AppService } from './app.service.js'; import { AskController } from './ask.controller.js'; @Module({ imports: [ LangChainModule.register({ // `provider:model`; the provider reads OPENAI_API_KEY. model: { model: 'openai:gpt-5-mini' }, systemPrompt: 'You are a helpful assistant. Answer in one paragraph.', }), ], controllers: [AppController, AskController], providers: [AppService], }) export class AppModule {}

ts
model names the provider and the model: switching to Claude is anthropic:claude-sonnet-5-5, with @langchain/anthropic installed. The agent is built when the app starts, so a wrong configuration shows up there, not on the first request.

How do I call the agent from a controller?

Inject Agent like any provider, in a new ask.controller.ts:

import { Body, Controller, Post } from '@nestjs/common'; import { Agent } from 'nestjs-langchain'; @Controller('ask') export class AskController { constructor(private readonly agent: Agent) {} @Post() async ask(@Body('question') question: string): Promise<string> { const { output } = await this.agent.run(question); return output; } }

Besides output, the answer, run() returns the tools the model called, the tokens it used and how long the run took.
Start the app and ask it something:

npm run start:dev curl -X POST http://localhost:3000/ask \ -H 'Content-Type: application/json' \ -d '{"question": "What is dependency injection?"}'

The response is the model's answer, as plain text.

Next: give the agent tools

An agent becomes useful when it can act on your app. With @tool(), a service method becomes a tool the model can call:

@Injectable() export class MathService { @Tool({ description: 'Adds two numbers.' }) add( @ToolParam({ name: 'a' }) a: number, @ToolParam({ name: 'b' }) b: number, ): number { return a + b; } }