Introduction
KumoRFM is a pre-trained relational foundation model that generates high-quality predictions directly from your data — no training required. It learns from your existing relational data at query time using in-context learning, enabling fast, production-ready predictions with minimal setup.
Prediction tasks are defined using Predictive Query Language (PQL), a lightweight SQL-like interface. You can also use the Kumo Coding Agent to translate natural language into PQL and iterate on workflows directly in your IDE.
Try the Pre-Trained Quick Start Notebook on Google Colab — run end-to-end with your API key.
The KumoRFM SDK workflow follows these steps:
Install the SDK, authenticate, and connect to your data sources.
Configure your notebook or editor environment for agent-assisted work.
Load tables, define data types, and build a relational graph.
Write PQL queries and generate instant predictions.
Evaluate prediction quality and understand what drives results.
Benchmark against RelBench and explore end-to-end examples.