Structured reasoning for AI agents

TypeDB gives AI agents the ability to understand, plan, and act safely through a semantic data layer, allowing them to truly understand and intuit from the data itself.

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Why TypeDB is built for agentic systems

Building intelligent agents requires a grounded understanding of data. Agents must recall facts, infer relationships, plan actions, and verify outcomes.

TypeDB is purpose-built for this kind of reasoning. It models entities, relationships, and functions natively, allowing agents to construct, query, and evolve a knowledge base that mirrors their environment.

TypeDB enables agents to understand the data they interact with, which gives them a level of capability unparalelled by any other database provider. We give them a world model that your agents can based their capabilities upon.

Structured memory

Store and retrieve information through a schema that enforces meaning and prevents drift.

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Functions that scale

TypeDB functions can abstract away layers of logic, hence deriving new insights automatically, detecting hidden relationships that would take manual correlation to find.

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Explainability

Constraints and roles ensure agents act within safe, interpretable boundaries.

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Smart Databases

In the same way AI-powered code generation tools can improve developer productivity, so can an AI-powered database.

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Constraining TypeDB data

TypeDB allows tight constraints to be defined on the types in the schema. For example, these constraints can restrict the cardinality of an interface, the domain of values of an attribute type.

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Queries as Functions

Functions provide powerful abstractions of query logic, which can be nested, recursed, or negated, and they natively embed into TypeQL's declarative patterns.

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Structured, semantic memory

TypeDB stores information as entities, relationships, and roles, all governed by a formal schema. Why that matters for agents:

  • Agents need to remember and reason over state, not just text.
  • Vector databases retrieve similarity; TypeDB retrieves meaning (“Which tool belongs to which agent?”, “What’s dependent on this task?”).
  • Semantic structure lets agents persist long-term memory without losing coherence.

Impact: Agents gain persistent, interpretable memory that survives context resets and scales beyond a prompt window.

TypeDB Studio
A visualization of the Social Network TypeDB schema in TypeDB Studio

The schema defines what’s true and what’s allowed. TypeDB enforces roles, constraints, and inheritance at the database level.

Why it matters for agents:

  • Prevents invalid or contradictory world states (e.g., circular dependencies, impossible goals).
  • Keeps multi-agent environments coherent even as many actors update shared knowledge.
  • Enables deterministic rollback and debugging of reasoning steps.

Impact: Agents reason safely and predictably, even in dynamic, concurrent systems.

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