Ross ROSS = Recommend OSS · open-source software intelligence for agents

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 observed · 2026-08-28

github.com/apache/ossie · homepage · Python · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

61/100

  • Activity 99
  • Release rhythm 35
  • Longevity 20

Flags: no_releases

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 288
  • days_rel: n/a
  • days_push: 7
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1971 stars · 249 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity active

serialization data-science developer-tools analytics large-language-models data-science apis python cross-platform semantic-layer semantic-metadata bi metrics specification interoperability yaml json-schema vendor-neutral data-engineering

3 sources

Member repositories

RepositoryRoleHealth v2
apache/ossiemain61

For agents

markdown · JSON · MCP: product_card(name="apache/ossie")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem