OpenMetadata
The Open Context Layer for Data and AI , OpenMetadata is the open platform for building trusted data context and business semantics for humans, AI assistants, and agents.
OpenMetadata is an open-source platform that maps all your company's data sources, databases, dashboards, pipelines, and ML models, into a single searchable catalog, adding context about quality, ownership, and data lineage so teams and AI tools can find and trust data faster.
OpenMetadata is an open-source platform for keeping track of an organization's data. In a large company, information lives in many separate places: databases, data warehouses, dashboards, reports, pipelines, and machine-learning models. OpenMetadata does not store that data itself. Instead it collects metadata, which is data about the data, such as what tables exist, what each column means, who owns it, where it came from, and how fresh and trustworthy it is. It pulls this together into a single connected map the README calls a metadata knowledge graph.
The README frames much of this around making the information usable by both people and AI assistants. Its argument is that connecting an AI tool straight to a raw database only gives it the bare structure, not the meaning or the context: whether a dataset can be trusted, who is responsible for it, or what other systems rely on it. OpenMetadata aims to supply that missing context so users and AI can find, understand, and safely use data.
The platform groups its work into a few areas. Context covers the technical facts about each data asset plus quality test results, freshness checks, and lineage, which is the record of where data flows from and to. It tracks lineage even down to the level of individual columns, so you can see what might break if one column changes. Semantics adds business meaning on top, letting teams define shared vocabularies (glossaries), business concepts like Customer or Revenue, metrics, and classification tags such as PII or Confidential for sensitive information.
The README says OpenMetadata connects to more than 120 data services and offers search, APIs, and software development kits so other programs can read and write this metadata. It also mentions an MCP server, a standard way to plug AI assistants into the catalog. Common uses include data discovery, data quality monitoring (called observability), and data governance, which is the practice of controlling who can use what data and how. The document ends with quickstart, documentation, community, contributing, and license sections. The project's code is written mainly in TypeScript.
Where it fits
- Search and discover data assets across 120+ connected services without tracking down data owners manually.
- Audit column-level data lineage to see exactly where values come from and what downstream systems would break if something changed.
- Monitor data quality and freshness with automated checks on your most critical datasets.
- Plug AI assistants into your data catalog via the built-in MCP server to give them business context alongside raw schema.