LangChain JS agent with a custom tool

Use a LangChain agent when you want a model loop that can choose tools and respond with the result.

This example creates an agent with one custom weather tool.

Install

npm install langchain @langchain/openai zod

Set your API key:

export OPENAI_API_KEY="your_api_key_here"

Code

import { createAgent, tool } from 'langchain'
import { z } from 'zod'

const getWeather = tool(
  async ({ city }) => {
    return `It is always sunny in ${city}.`
  },
  {
    name: 'get_weather',
    description: 'Get the weather for a given city.',
    schema: z.object({
      city: z.string().describe('The city to get weather for.'),
    }),
  }
)

const agent = createAgent({
  model: 'gpt-5.5',
  tools: [getWeather],
})

const result = await agent.invoke({
  messages: [
    {
      role: 'user',
      content: 'What is the weather in San Francisco?',
    },
  ],
})

console.log(result)

What this gives you

The agent receives the user message, decides whether to call get_weather, runs the tool, and returns the model's final response.

Use this pattern when you want:

A small agent with one or two tools
A portable model/tool setup
A path toward middleware, tracing, or LangGraph later

LangChain JS agent with a custom tool

Install

Code

What this gives you

References

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