You shouldn’t have to go away Cursor to construct, deploy, or monitor a production-grade agent. You possibly can wire collectively LangChain, a vector DB, a monitoring software, and a deployment pipeline your self, however you’ll spend extra time on that plumbing than on the agent itself. DataRobot is the shortcut. It now lives the place you construct, integrating straight into your IDE throughout the coding agent, software layer, and mannequin gateway.
Image what this unlocks. A platform engineer at a fintech firm wires up the International MCP, factors their present LangGraph agent at it, and ships a ruled deployment with monitoring and tracing all earlier than lunch, with out touching their agent code.
DataRobot is the pathway to that workflow. 4 items, one per layer of the stack:
Abilities: drop DataRobot experience into any coding agent
datarobot-agent-skills ships Agent Context Protocol folders for the issues builders ask DataRobot to do: mannequin coaching, predictions, deployment, function engineering, monitoring, explainability, knowledge prep. One set up reaches Claude Code, Cursor, Codex, Gemini CLI, Amp, VS Code Copilot, Goose, Letta, Kilo Code, and OpenCode:
npx ai-agent-skills set up datarobot-oss/datarobot-agent-skillsAfter that, “create a buyer churn undertaking and begin AutoML” works in your IDE with out memorizing the SDK sample. DataRobot can be within the Cursor market for one-click set up.
Use DataRobot from any MCP shopper
The International MCP is auto-deployed to each DataRobot occasion. 5 traces in .cursor/mcp.json and also you’re performed:
{
"mcpServers": {
"datarobot-mcp": {
"url": "https://{DATAROBOT_URL}/api/v2/genai/globalmcp/mcp",
"headers": { "Authorization": "Bearer " }
}
}
}
Want customized instruments or inner providers uncovered over MCP? The af-component-datarobot-mcp template is a FastMCP scaffold with @dr_mcp_tool decorators and Pulumi-managed deployment as a Customized Mannequin App. Native on port 8080, manufacturing on DataRobot serverless.
Brokers cease containing software code. They ask the server what’s obtainable and name it once they want it.
That’s the architectural payoff: add or change instruments with out redeploying the agent. See the LangGraph integration sample for the mcp_tools property that auto-converts MCP instruments into LangChain instruments.
Go from spec to ruled deployment — templates, Agent Help, and the LLM Gateway
datarobot-agent-templates supplies scaffolds for CrewAI, LangGraph, and LlamaIndex. Every one ships with Pulumi infrastructure, a dev server, OpenTelemetry tracing, and the required plumbing that turns an area agent right into a ruled DataRobot deployment.
Agent Help (dr help) is the design-before-you-code path. It walks by way of agent specification, generates agent_spec.md, simulates tool-calling so you may validate mannequin and gear selection with out burning actual LLM calls, then scaffolds towards the templates.
Beneath all of it: the LLM Gateway, an OpenAI-compatible endpoint at {DATAROBOT_URL}/api/v2/genai/llmgw. Brokers written towards the OpenAI Python SDK work as-is. Switching suppliers is a model-string change. Metering, governance, and credentialing occur on the gateway. All 4 interfaces share auth by way of a documented credential decision order, with separate Private, Software, and Agent API key varieties whenever you want scoped service-to-service calls.
The way it’s composed
In Cursor: set up Abilities, clone a LangGraph template, level your OpenAI shopper on the LLM Gateway, expose instruments through International MCP, run dr activity run deploy. Consequence: a ruled DataRobot deployment with monitoring and tracing.
Each functionality obtainable within the DataRobot UI can be obtainable to your IDE and CI pipeline, so you may select the floor that matches the duty.
