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.Documentation Index
Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt
Use this file to discover all available pages before exploring further.
Try the Pre-Trained Quick Start Notebook on Google Colab — run end-to-end with your API key.
Setup
Install the SDK, authenticate, and connect to your data sources.
Environment Setup
Configure your notebook or editor environment for agent-assisted work.
Data & Graph
Load tables, define data types, and build a relational graph.
Predictions & Queries
Write PQL queries and generate instant predictions.
Evaluation & Explainability
Evaluate prediction quality and understand what drives results.
Examples
Benchmark against RelBench and explore end-to-end examples.