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AP

apache/ossie

AI Agent

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

1.4k Stars164 Forks58 Open Issues1.4k WatchersPythonApache-2.0
Data Tool
Source and compliance noteLast synced: Jul 21, 2026

Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.

Original GitHub sourceMethodologyEditorial Policy
Editorial Evaluation

apache/ossie is tracked as a Python project in the Data Tool area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.

Momentum check: the repository has 1.4k total stars, with +0 today, +827 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.

Maintenance check: current activity is Active; the latest push was 1 days ago, and the open issue queue is 58, about 4.07% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.

Adoption check: 164 forks and 1.4k watchers suggest how often the project is reused or followed. License signal: Apache-2.0. Always verify license compatibility before commercial or internal use.

Practical fit: this project is most relevant when you need Python teams evaluating ecosystem-native tooling. Be cautious when you need a legal review, security audit, or production SLA.

Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 827. Follow the original GitHub repository for final install, security, and release information.

Best For
  • Python teams evaluating ecosystem-native tooling
  • use cases where recent maintenance matters
Avoid When
  • you need a legal review, security audit, or production SLA
Adoption Signals

Momentum

1.4k Stars

Reuse

164 Forks

Attention

1.4k Watchers

Maintenance

active

License

Apache-2.0

Open issues

58

Overview

Apache Ossie is an open-source project that standardizes semantic model exchange across data analytics, AI, and BI tools, providing a vendor-agnostic specification to ensure consistent data definitions and interoperability.

Key Features

- Provides a single JSON/YAML specification for semantic models that any tool can read and write. - Includes reference converters for popular formats like dbt, GoodData, Polaris, and Salesforce. - Offers validation tooling and example models to ensure compliance and ease adoption.

Tool Positioning

Data Tool

Databases, data pipelines, ETL, analytics, and vector search

Quick Start
Clone the repository and use the provided converters and validation tools; no single install command yet.
View on GitHub Project Homepage
Project Activity

83

Health Score

Active

Commit Activity

Nov 18, 2025

Created

Jul 20, 2026

Last push

Source Trail

GitHub repository metadata

metadata

GitHub README

readme_summary

Star History

+0

Today's growth

+0

7-day growth

+0

30-day growth

Jul 21, 2026Jul 21, 2026
Community Health
164

Forks

58

Open

1.4k

Watchers

Owner
AP

apache

GitHub profile
Topics & Language
Pythonmetadatasemantic
Ecosystem & Usage
GitHub Repository Project Website
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License
Apache-2.0
CreatedNov 18, 2025
Last pushJul 20, 2026
Last syncedJul 21, 2026
Community Standards

✓

License

✓

Forked

✓ Active

Maintained

AI AnalysisAnalyzed by Git-Stars

Problem Solved

It solves semantic fragmentation where the same KPI is defined differently across tools, requiring manual reconciliation. It also ensures AI agents produce reliable outputs grounded in consistent business logic by providing a common semantic model.

Capabilities

Developers can build interoperable data pipelines where semantic definitions are shared across dbt, GoodData, Polaris, Salesforce, and other tools. Real-world use cases include unifying business metrics across an organization, enabling AI agents to reason with consistent definitions, and automating semantic model validation. The ceiling is a fully standardized semantic layer across the entire data ecosystem.

Bottom Line

This is for data engineers and architects seeking to eliminate semantic inconsistency across their toolchain. Avoid if you have no multi-tool interoperability needs. The key trade-off is adopting a new standard versus existing proprietary solutions.

Full AI Analysis