EvoOntology vs slayer
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. — versus — Embeddable semantic layer for AI agents: define metrics once, compose them with expressions and time shifts, row-level security and read-only SQL, over MCP, REST, CLI, Python or a Postgres facade.
Both give data agents a semantic map of the warehouse over MCP; SLayer's definitions are curated by you or your agent and compiled to SQL, EvoOntology builds its ontology from the workload and evolves it behind evaluation gates.
| EvoOntology | slayer | |
|---|---|---|
| Stars | 493 | 227 |
| Forks | 41 | 31 |
| Language | Python | Python |
| License | MIT | MIT |
| Last activity | today | today |
| Topics | data, knowledge-graphs | data |
| Curated connections | 4 | 5 |
EvoOntology — the 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.
slayer — the curator's take
Reach for SLayer when agents keep writing plausible-but-wrong SQL against your warehouse: define columns and metrics once, and agents compose them (ratios, time shifts, alternate aggregations, multi-stage queries) through a search, inspect, query flow instead of free-hand joins, with read-only connections and row-level security handled for them. It imports dbt and Cube definitions, embeds as a Python library, and speaks MCP, REST, Flight SQL and the Postgres wire protocol, so BI tools hit the same definitions. It is the core of Motley's product and still young (a few hundred stars, six contributors). Skip it if you want dashboards out of the box (wrenai) or a layer that builds itself from your existing BI assets (ktx).