deployment

Deployment

To deploy your LangGraph agent, create and configure a LangGraph app. This setup supports both local development and production deployments.

Features:

• 🖥️ Local server for development
• 🧩 Studio Web UI for visual debugging
• ☁️ Cloud and 🔧 self-hosted deployment options
• 📊 LangSmith integration for tracing and observability

!!! info "Requirements"

- ✅ You **must** have a [LangSmith account](https://www.langchain.com/langsmith). You can sign up for **free** and get started with the free tier.

Create a LangGraph app

npm install -g create-langgraph
create-langgraph path/to/your/app

Follow the prompts and select New LangGraph Project. This will create an empty LangGraph project. You can modify it by replacing the code in src/agent/graph.ts with your agent code. For example:

import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { initChatModel } from "langchain/chat_models/universal";
import { tool } from "@langchain/core/tools";
import { z } from "zod";

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

const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
// make sure to export the graph that will be used in the LangGraph API server
// highlight-next-line
export const graph = createReactAgent({
  llm,
  tools: [getWeather],
  prompt: "You are a helpful assistant"
})

Install dependencies

In the root of your new LangGraph app, install the dependencies:

yarn
# install these to use initChatModel with Anthropic
yarn add langchain
yarn add @langchain/anthropic

Create an .env file

You will find a .env.example in the root of your new LangGraph app. Create a .env file in the root of your new LangGraph app and copy the contents of the .env.example file into it, filling in the necessary API keys: