Here's a step-by-step tutorial for setting up and deploying an AI Agent with watsonx.ai and LangGraph, including installing necessary tools, deploying the app, and running it locally.

This example consists of the following pieces:

• watsonx.ai or ollama (models)
• LangGraph (agent)
• Next.js (user inteface)
• sqlite3 (in-memory database)

You can use any of the supported chat models.

This guide will walk you through installing the dependencies, initializing and deploying a project, and running the application locally. We’ll use sqlite3 to run a local in-memory database.

Clone this repository and open the right directory:

git clone https://github.com/IBM/wxflows.git
cd examples/text-to-sql-agent

To run the application you need to install the necessary dependencies:

npm i

This command installs all required packages, including LangGraph and any dependencies specified in the project.

Copy the sample environment file to create your .env file:

cp .env.sample .env

Edit the .env file and add your credentials, such as API keys and other required environment variables. Ensure the credentials are correct to allow the tools to authenticate and interact with external services.

Finally, start the application by running:

npm run dev

This command initiates your application, allowing you to asking questions like:

• How many order are there?
• Return all orders
• Which customers placed the most orders

Have a look at the datavase schema in /src/app/database.ts to learn more about the data structure.

You’ve now successfully set up, deployed, and run an agentic application. This setup provides a flexible environment to leverage external tools for data retrieval, allowing you to further build and expand your app with wxflows. See the instructions in tools to add more tools or create your own tools from Databases, NoSQL, REST or GraphQL APIs.