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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 the cloud provider’s Gradient Agent comes in.
Why This Matters
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.
the cloud provider’s Gradient 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.
How It Works
Here’s the quick rundown:
- Create a Spaces Bucket: Upload all your favorite snippets and API setups to a object storage bucket.
- Create a Knowledge Base: Connect that bucket as your data source. the cloud provider takes care of converting your snippets into vector embeddings so your data is always ready for AI.
- Deploy an AI Agent: Link your Knowledge Base to an AI Agent. You can choose from models like Llama 3, Anthropic, or DeepSeek.
- Configure Cursor: Finally, override Cursor’s OpenAI API endpoint with your new the cloud provider Gradient Agent endpoint and secret key.
How to Set It Up
🚀 Step 1: Create a Knowledge Base in the cloud provider
- Head over to the cloud provider and create a Knowledge Base.
- Connect it to a Spaces bucket—this is where all your docs, snippets, and references live.
- Upload your code snippets/projects, and the cloud provider will handle the vector embeddings automatically, making your data searchable and AI-friendly.
🤖 Step 2: Create an AI Agent
- Once your Knowledge Base is set, create an AI Agent.
- Choose your desired model *(For example, Llama 3)*
- Link it to your Knowledge Base to ensure the AI agent has access to the right information
- 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.
- Create an Endpoint Access Key. We will need this to set up Cursor.
🛠 Step 3: Configure Cursor to Use the AI Agent
- Open Cursor and go to Settings \> Models.
- Create a new model and select the option to override the OpenAI API settings.
- Paste in the Agent endpoint from the cloud provider.
- Important: Append
/api/v1to the endpoint URL, like this: Example:https://developer.mozilla.org/en-US/docs/Web/HTTP
Now, when you request a snippet, Cursor retrieves code directly from your curated knowledge base instead of relying on generic online responses.
Your Knowledge, Your Way
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 Gradient Agents: –
