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langgraph-math-agent

README.md

Using watsonx.ai to build a Math agent

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

This example consists of the following pieces:

  • LangGraph SDK (agent)
  • watsonx.ai (models)
  • wxflows SDK (tools)
  • Next.js (user interface)

You can use any of the supported chat models.

This guide will walk you through installing the wxflows CLI, initializing and deploying a project, and running the application locally. We’ll use math tool as examples for tool calling with wxflows.

Before you start

Clone this repository and open the right directory:

git clone https://github.com/IBM/wxflows.git
cd examples/langgraph-math-agent

Step 1: Set up wxflows

Before you can start building AI applications using watsonx.ai Flows Engine:

  1. Sign up for a free account
  2. Download & install the Node.js CLI
  3. Authenticate your account

Step 2: Deploy a Flows Engine project

Move into the wxflows directory:

cd wxflows

There's already a wxflows project for you set up this repository with the following values:

  • Defines an endpoint api/langgraph-math-agent for the project.
  • Imports math tool with a description for answering mathematical question using WolframAlpha and specifying fields wolframAlpha.

You can deploy this tool configuration to a Flows Engine endpoint by running:

wxflows deploy

This command deploys the endpoint and tools defined, these will be used by the wxflows SDK in your application.

Step 3: Install Dependencies in the Application

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

cd ../
npm i

This command installs all required packages, including the @wxflows/sdk package and any dependencies specified in the project.

Step 4: Set Up Environment Variables

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.

Step 5: Run the Application

Finally, start the application by running:

npm run dev

This command initiates your application, allowing you to call and test the math tools through wxflows.

Summary

You’ve now successfully set up, deployed, and run a wxflows project with math tools. 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.