Supercharge Your Cursor Experience with GenAI Agent

Cursor is an AI-powered coding assistant that integrates with big-name LLMs like OpenAI and Anthropic. It helps developers generate code, debug faster, and streamline development.

But here’s the thing—out of the box, it’s just a really smart autocomplete. It doesn’t know you. It won’t instinctively follow your coding style, use your favorite libraries, or structure projects the way you like. And if you’re working with a team that has strict coding standards? Expect to be manually tweaking AI-generated code all the time.

What if your Cursor IDE could actually learn from your coding preferences and deliver your code exactly the way you want them? That’s where DigitalOcean’s GenAI Agent comes in.

I’ve got a stash of “Hello World” snippets for my go-to APIs. It’s way quicker than hunting through docs or wrestling with generic AI suggestions that overcomplicate things. But I wanted a step further: a way to have my assistant return exactly the snippet I need, every time.

DigitalOcean’s GenAI Agent lets you train an AI on your very own, curated knowledge base. Instead of a generic output, you get code that fits your workflow to a T.

It even learns the specific SDKs, environment setups, and libraries I prefer. Say I need a Stripe integration snippet—I don’t want a one-size-fits-all API call. I need it to use the Stripe Python SDK, authenticate with my .env variables using python-dotenv, and follow the structure I prefer – right from the IDE.

Here’s the quick rundown:

1. Create a Spaces Bucket: Upload all your favorite snippets and API setups to a DigitalOcean Spaces bucket.
2. Create a Knowledge Base: Connect that bucket as your data source. DigitalOcean takes care of converting your snippets into vector embeddings so your data is always ready for AI.
3. Deploy an AI Agent: Link your Knowledge Base to an AI Agent. You can choose from models like Llama 3, Anthropic, or DeepSeek.
4. Configure Cursor: Finally, override Cursor’s OpenAI API endpoint with your new DigitalOcean GenAI Agent endpoint and secret key.

Step 1: Create a Knowledge Base in DigitalOcean

1. Head over to DigitalOcean and create a Knowledge Base.
2. Connect it to a Spaces bucket—this is where all your docs, snippets, and references live.
3. Upload your code snippets/projects, and DigitalOcean will handle the vector embeddings automatically, making your data searchable and AI-friendly.

🤖 Step 2: Create an AI Agent

1. Once your Knowledge Base is set, create an AI Agent.
2. Choose your desired model (For example, Llama 3)
3. Link it to your Knowledge Base to ensure the AI agent has access to the right information

1. Create Agent: Wait for the agent to get deployed. Once deployed, make it public, and copy the endpoint URL—you’ll need this in the next step when configuring Cursor.

1. Create an Endpoint Access Key. We will need this to set up Cursor.

Step 3: Configure Cursor to Use the AI Agent

1. Open Cursor and go to Settings > Models.
2. Create a new model and select the option to override the OpenAI API settings.
3. Paste in the Agent endpoint from DigitalOcean.
   • Important: Append /api/v1 to the endpoint URL, like this: Example: https://agent-123457-abcd.ondigitalocean.app/api/v1

Now, when you request a snippet, Cursor retrieves code directly from your curated knowledge base instead of relying on generic online responses.

A custom-trained AI means you get code that fits exactly how you work. It’s not generic—it’s your code, built with your tools, and structured your way, right in your IDE.

And it doesn’t stop at code. An AI trained on your own knowledge base lets you build internal chatbots, support bots, or even an assistant that truly understands your product. It’s not about cookie-cutter responses; it’s about an AI that fits your unique workflow.

Want to learn more? These guides show you how to integrate and build with GenAI Agents: –