Confident AI provides evaluation, observability, red-teaming, and governance tools for testing and monitoring AI systems.
OpenMetadata
OpenMetadata provides data cataloging, discovery, quality, observability, governance, and lineage tools.
About OpenMetadata
OpenMetadata is a context layer that combines technical metadata, business semantics, and records of decisions and changes into a knowledge graph for people and AI agents. It covers data cataloging, discovery, quality, observability, governance, lineage, and collaboration.
It uses a schema-first model with JSON Schema and RDF/JSON-LD specifications, provides an MCP server and AI SDK for agent access, and connects to 130+ data services. It can be deployed in its own environment, explored in a live sandbox with demo data, or used as a cloud service.
Key features
- Catalogs tables, columns, dashboards, pipelines, ML models and owners
- Tracks lineage showing origin and connections between data assets
- Monitors data quality and observability across connected sources
- Manages glossary terms, metric definitions and W3C-standard relationships
- Provides MCP server and AI SDK for agent access
- Supports connectors for 130+ database, dashboard and pipeline services
Best for: Technical and non-technical data teams that need shared data context for staff and AI agents.
Good for
- Ingestion framework supports connectors for 130+ data services
- Schema-first model with JSON Schema and RDF/JSON-LD specifications
- Architecture uses four system components for deployment and upgrades
OpenMetadata at a glance
- Website
- open-metadata.org
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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OpenMetadata FAQ
How can OpenMetadata be tried or deployed?
The site lists self-hosted deployment in your own environment, a live sandbox with demo data, and a cloud service with your own data.
What types of systems can it connect to?
Its ingestion framework supports 130+ data services across categories including database, dashboard, pipeline, messaging, ML model, storage, API, and search.
How do AI agents access its context?
Through a native MCP server for direct integration, an AI SDK for programmatic calls, and a Context Center for articles, documents, and memories.
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