# apache/ossie

Apache Ossie, industry wide specification effort to standardize how we exchange semantic metadata across analytics, AI and BI platforms, providing a vendor neutral, single source of truth for semantic data

Repository: https://github.com/apache/ossie
Canonical: https://ross.abutalabs.com/products/ossie
Homepage: https://ossie.apache.org/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: metadata, semantic
Last push: 2026-08-26T19:29:29+00:00

## Health v2 (maintenance only)
Score: 61/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 20
- inputs: {"age_days": 288, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1971, forks 249 (observed 2026-08-28T04:06:00.741604+00:00)

## What it is
Apache Ossie (incubating, formerly Open Semantic Interchange) is a vendor-neutral JSON/YAML specification for exchanging semantic metadata—metrics, dimensions, joins, and AI context—across analytics, AI, and BI platforms. The repository hosts the core spec, machine-readable schemas, reference converters (dbt, GoodData, Polaris, Salesforce), example models, and validation tooling.

## Use cases
- standardize metric definitions across BI tools
- share semantic models between dbt and other platforms
- give LLM agents consistent business metric context
- avoid redefining KPIs in every dashboard
- convert semantic models between vendor formats
- validate semantic model files against a schema

## When to choose
- you need one source of truth for business metrics across multiple analytics/AI tools
- you want to avoid vendor lock-in for semantic layer definitions
- you're building AI agents that need reliable, consistent business logic context

## When to avoid
- you need a full semantic layer engine that executes queries—Ossie is a specification, not a runtime
- your stack is single-vendor with no interchange needs
- you need a mature, finalized standard—the project is incubating and still evolving

## Facets
- artifact type: library
- maturity: active
- function: serialization, data-science, developer-tools
- domain: analytics, large-language-models, data-science, apis
- platform: python, cross-platform
- tags: semantic-layer, semantic-metadata, bi, metrics, specification, interoperability, yaml, json-schema, vendor-neutral, data-engineering

## Member repositories
- apache/ossie (main) score 61

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:00.741604+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:05:04.543060+00:00, confidence not recorded.
  - readme: https://github.com/apache/ossie (fetched 2026-08-28T04:06:00.741604+00:00, sha 0d169b438579)
  - homepage: https://ossie.apache.org/ (fetched 2026-08-29T10:44:30.167096+00:00, sha dfb998837959)
  - site_page: https://ossie.apache.org/index.html (fetched 2026-08-29T10:44:30.169885+00:00, sha dfb998837959)
- Data as of 2026-08-30T08:39:29.467469+00:00.