TypeDB

TypeDB is a strongly-typed knowledge graph database (database / knowledge graph) designed to model complex domains and execute semantic queries over interconnected data.

• Strongly-typed knowledge graph database with an entity–relationship–attribute–rule model (database / knowledge graph).
• TypeQL query language for pattern-matching, schema definition, and data manipulation (query language).
• Logical inference via rules for deriving implicit facts from stored data (rule-based reasoning).
• Support for complex and nested relations across entities, attributes, and roles (data modeling).
• Client drivers and tooling for integration into application and analytics stacks (developer tooling / integration).

More About TypeDB

TypeDB is a knowledge graph database (database / knowledge graph) developed by Vaticle to address data modeling and querying requirements in complex domains, where entities, relationships, and constraints must be expressed with high semantic precision. It uses a strongly-typed data model and a logical query engine to represent and query networks of interconnected data rather than isolated tables or documents. The project targets use cases where data has multiple overlapping relationships, role-based interactions, and domain rules that need to be enforced and computed at the database layer.

At the core of TypeDB is its schema-first, type-theoretic model (data modeling). Data is organized into entity types, relation types, and attribute types, with roles defining how entities participate in relations. This approach allows designers to encode domain semantics directly into the schema, including inheritance, constraints, and role participation. The schema enforces consistency for stored data and provides a foundation for querying based on conceptual structure rather than low-level storage layout.

TypeDB uses TypeQL (query language) for defining schemas and querying data. TypeQL supports pattern-matching queries that express complex graph patterns, joins across many entity and relation types, and conditions on attributes and roles. The same language is used to define rules, insert and delete data, and update the schema. Rule definitions enable logical inference (rule-based reasoning), so that when data satisfies rule conditions, TypeDB can derive additional facts, which are then queryable as if they were explicitly stored.

The database engine provides transactional access (database operations), allowing concurrent clients to read and write data while preserving consistency guarantees documented by the project. TypeDB exposes client libraries (developer tooling / integration) for multiple programming languages, which enterprises can use to embed the database into services, data pipelines, and analytic workloads. The system can be deployed in server mode, allowing remote access from clients and integration into containerized or cloud infrastructure, as described in official materials.

In enterprise and institutional environments, TypeDB is positioned for domains such as life sciences, financial services, cybersecurity, and industrial systems where data is highly relational and rule-based reasoning is required. Its category placement fits under graph databases, knowledge graphs, and rule-based reasoning engines (database / knowledge graph / reasoning). Organizations use it to centralize complex domain models, support investigative and analytic queries, and encode domain logic directly in the database schema and rules. The combination of a typed schema, semantic relations, and a rule engine enables use cases in which downstream applications rely on the database to maintain a coherent, semantically rich representation of domain knowledge.