EvoOntology

Renmin University's self-evolving ontology layer for data agents: builds a workload-grounded ontology over tables, files and databases, serves it via MCP, and evolves it from agent trajectories.

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Curator's take

The research answer to stale semantic layers: instead of hand-maintained definitions, EvoOntology builds an ontology from your data and real workload, lets agents fetch only the semantics a step needs through MCP, and proposes targeted updates from execution trajectories, publishing a new version only when paired evaluation beats its parent. Claude Code and Codex plugins make it easy to try. It is two-week-old research code from a university lab: treat the evolution loop as an experiment, keep versions reviewable, and give it read-only credentials. If you want definitions humans own and govern, a curated semantic layer (ktx, neocarta) is the conservative choice.

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README.md1 min read

EvoOntology: A Self-Evolving Ontology Layer for Data Agents

English | 简体中文

Authors: Meiduo Chong, Shaolei Zhang^*, Ju Fan, Xiaoyong Du
Renmin University of China

EvoOntology is the first self-evolving ontology layer for data agents, aiming to bridge the agent-data gap over heterogeneous tables, files, and databases. It exposes a versioned Ontology Layer through MCP tools, grounds that layer in real workload evidence, and continuously adapts it from execution trajectories.

🔌 Universal Agent Plugin as MCP: Seamlessly integrates with Claude Code, Codex, and other AI agents.
🤖 Automatic Ontology Construction: Builds a tailored ontology layer directly from your data.
♻️ Continuous Self-Evolution: Continuously evolves and refines the ontology layer based on interaction history.

🎬 Demo

The Codex and Claude Code plugins build and evolve ontology layers over your data.

https://github.com/user-attachments/assets/e15f4acd-7161-4ae1-ba41-f2f3ea05488b

🧭 EvoOntology Makes Your Data Valuable!

Raw data leaves semantics implicit. Table names, columns, file paths, and isolated observations rarely explain metric definitions, entity relationships, or business constraints. Agents must infer them repeatedly and are prone to semantic errors.
Static semantic layers do not scale with use. Hand-authored layers require sustained expert maintenance, become stale as data and workloads change, and consume increasing context when injected in full.
Agents need semantics that can adapt. EvoOntology provides a workload-grounded Ontology Layer that agents query on demand and that evolves from observed execution behavior under controlled evaluation.

Data Agents with and without EvoOntology

An agent-first, self-evolving ontology layer for Data Agent