OpenMetadata
The Open Context Layer for AI
The largest and fastest-growing open-source project for AI context, data cataloging, and metadata management.
OpenMetadata is the open platform for trusted data context, organizational memory, and business semantics for every data user, AI assistant, and agent.
OpenMetadata connects technical metadata, data quality signals, lineage, column-level lineage, ownership, usage, policies, conversations, memories, glossaries, classifications, metrics, domains, data contracts, and data products into a unified metadata knowledge graph. With 130+ connectors, open metadata standards, semantic search, APIs, SDKs, and an MCP server, OpenMetadata gives every user and AI system the governed context it needs to discover, understand, trust, remember, and use data.
AI does not need another raw database connector. AI needs context + memory.
OpenMetadata provides the context AI needs to know:
- what data exists
- what it means
- who owns it
- how it is used
- where it came from
- where it flows
- whether it is fresh, tested, certified, and trusted
- which business concepts, classifications, glossary terms, policies, contracts, and data products apply
- what downstream dashboards, pipelines, metrics, ML models, and applications depend on it
- what conversations, decisions, assumptions, and memory nuggets have already been captured about it
Why OpenMetadata for AI?
AI systems need more than data access. They need governed context, business meaning, trust signals, lineage, usage, ownership, standards, and organizational memory.
A direct connection to a warehouse, lake, dashboard, or pipeline exposes raw structures. It does not tell an AI assistant what the data means, whether it is certified, who owns it, which policies apply, what contract governs it, what breaks if it changes, or what the organization has already learned about it.
OpenMetadata is the open context layer that gives every data user and AI agent the full picture of enterprise data.
OpenMetadata brings together five capabilities:
- Context — technical, operational, trust, usage, and lineage metadata from across the data ecosystem.
- Semantics — business meaning through glossaries, metrics, classifications, domains, policies, ontologies, and data products.
- Knowledge Graph — relationships connecting assets, columns, people, teams, quality, lineage, policies, memories, contracts, and business concepts.
- Memory — conversations, AI threads, decisions, assumptions, runbooks, remediation notes, and reusable memory nuggets that preserve tribal knowledge.
- Activation — MCP, Semantic Search, APIs, SDKs, events, and workflows that make context usable by AI assistants, agents, applications, and humans.
With OpenMetadata, users and AI agents can answer:
- What does this metric mean and how is it calculated?
- Which datasets power this dashboard?
- Who owns this data product?
- Which data contract applies?
- Is this dataset fresh, tested, certified, and trusted?
- Which downstream dashboards, pipelines, or ML models are affected by this column change?
- Which columns contain sensitive customer information?
- Which glossary terms, policies, standards, and business concepts apply?
- What decisions, assumptions, incidents, or conversations have already been captured about this asset?
The Context OpenMetadata Connects
OpenMetadata collects and connects the context AI needs to reason safely over enterprise data.
| Context type | What OpenMetadata captures | Why it matters for AI |
|---|
| Technical metadata | Databases, schemas, tables, columns, topics, dashboards, charts, pipelines, APIs, search indexes, ML models, storage assets, data types, constraints, descriptions, joins, sample queries, service metadata, owners, teams, usage, domains, and data products | Helps AI discover what exists and understand how assets are structured |
| Quality and trust | Test cases, test suites, freshness checks, volume checks, null, uniqueness, distribution, custom tests, profiling results, observability signals, incidents, alerts, and quality history | Helps AI avoid treating every dataset as equally trustworthy |
| Lineage and impact | Upstream and downstream lineage, table lineage, column-level lineage, dashboard lineage, pipeline lineage, metric lineage, ML model lineage, API and topic dependencies, and OpenLineage events | Helps AI explain where data came from, where it flows, and what changes may break |
| Semantics | Glossaries, business terms, synonyms, related terms, metrics, KPIs, classifications, tags, domains, data products, policies, personas, lifecycle states, and ontologies | Helps AI map technical names to business meaning |
| Governance | Owners, stewards, teams, policies, roles, classifications, access context, certification, review workflows, lifecycle states, and data contracts | Helps AI act with policy-aware context |
| Memory and tribal knowledge | Conversations, AI threads, decisions, assumptions, runbooks, remediation notes, incident learnings, and reusable memory nuggets attached to assets, users, teams, data products, and agent workflows | Helps humans and agents inherit what the organization already learned instead of rediscovering it in every conversation |
| Standards and interoperability | DCAT, DPROD, PROV-O, OpenLineage, ODCS, RDF/OWL, JSON-LD, SHACL, JSON Schema, APIs, events, and metadata schemas | Helps context move across tools, agents, catalogs, contracts, and knowledge graphs |
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