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Martin-Bruel
Martin-Bruel

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NestJS + LangChain: Build an AI Agent in 5 Minutes

#nestjs #ai #langchain #typescript

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
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@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 {}
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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;
  }
}
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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?"}'
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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;
  }
}
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Then tools: [MathModule] in register() hands it to the agent. The next article in this series covers tools in detail.

• Full sample: samples/chat
• Package: nestjs-langchain on npm
• Source, issues and stars: GitHub

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