OpenMetadata: An Open-Source Platform Providing Data Context for AI

OpenMetadata is an open-source data context layer whose core goal is unifying metadata scattered across the data ecosystem into a knowledge graph. It uses 130+ connectors to ingest technical metadata (databases, tables, columns, topics, dashboards, pipelines, ML models, etc.) and integrates quality and trust signals (test cases, freshness checks, incident alerts), lineage and impact analysis (table-, column-, dashboard-, pipeline-, metric-, and ML-model-level lineage), semantics (glossaries, metrics, classifications, data domains, policies, data products), governance (owners, admins, policies, data contracts), and memory (conversations, AI threads, decisions, hypotheses, runbooks, remediation notes). The platform offers an MCP server, semantic search, APIs, SDKs, events, and webhooks so AI assistants and agents can query and act on metadata via natural language. For data engineers and platform teams, OpenMetadata provides out-of-the-box metadata ingestion, lineage tracking, and quality monitoring without building a metadata warehouse. For developers building data agents, the MCP server and AI SDK let LLMs directly understand data's business meaning, ownership, quality status, and change impact—not just raw table schemas. Memory entities tie conversational decisions and hypotheses to specific data assets, so later AI sessions or teammates can reuse that context and avoid redundant discovery. Python teams can install the openmetadata-ingestion package to read and write metadata, lineage, glossaries, and quality info, or install the data-ai-sdk package for governed MCP and LangChain access for LLMs or agents. TypeScript and Java developers have corresponding SDKs. SDK versions must match the server version; connections use a JWT token and point to the server's API endpoint.