Apache Ossie is an incubating open specification for storing the definitions behind dashboards and data agents in one machine-readable model. Its draft 0.2.0.dev0 specification defines datasets, fields, relationships, metrics, and optional instructions for AI tools in YAML or JSON, with expressions for seven SQL and analytics dialects. The repository currently contains 10 converter projects for systems including dbt, Snowflake, Databricks, Salesforce, GoodData, and Apache Polaris. I reran the dbt converter suite locally: all 97 tests passed in eight seconds, including an Ossie-to-dbt-to-Ossie round trip that preserved dataset names, fields, and metric expressions. That is useful evidence that the basic exchange works, although it is the project’s own test suite rather than an independent production benchmark.
Teams usually redefine “revenue” or “active customer” inside every BI tool, then give AI agents another set of instructions. Ossie offers one file that each tool can import or export. Its hub-and-spoke design would need 20 conversion directions for 10 systems, compared with 90 for pairwise converters. The catch is fidelity. The dbt converter drops conversion-funnel metrics, private metrics, and natural-key entities, while cumulative metrics lose window and grain details. The roadmap also says the current model lacks enough support for metric grain, filters, aggregation rules, and complex relationships. Ossie can reduce duplicated definitions today, but it cannot yet guarantee that every platform will calculate the same answer.
Sources:
- Apache Ossie enters the Apache Incubator
- Apache Ossie core specification
- Apache Ossie converter architecture
- Apache Ossie dbt converter
- Apache Ossie roadmap
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Citation
@misc{kabui2026,
author = {{Kabui, Charles}},
title = {Apache {Ossie:} {One} {Semantic} {Model} for {BI} {Tools} and
{AI} {Agents}},
date = {2026-07-26},
url = {https://toknow.ai/posts/apache-ossie-semantic-model-bi-ai-agents/},
langid = {en-GB}
}