Structured reasoning for AI agents

TypeDB provides AI agents with structured knowledge they can query, understand, and reason over. Model entities, relationships, and functions in a semantic database built for reliable AI systems.

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match 
  $user isa full-time-employee;
fetch {
  "employee": { $user.* }
};

[{
    "employee": {
        "type": { "root": "entity", "label": "full-time-employee" },
	"attribute": [
            { "value": "Chloé Dupond", "value_type": "string", "type": { "root": "attribute", "label": "full-name" } },
            { "value": "chloe@typedb.com", "value_type": "string", "type": { "root": "attribute", "label": "email" } },
            { "value": 185, "value_type": "long", "type": { "root": "attribute", "label": "employee-id" } },
            { "value": 35, "value_type": "long", "type": { "root": "attribute", "label": "weekly-hours" } }
        ]
    }
}]

Beyond memory

Most AI infrastructure helps agents retrieve information or invoke tools. TypeDB gives agents a shared understanding of the world they operate in, allowing them to reason over structured knowledge rather than isolated pieces of context.

Semantic models

Give agents a shared understanding of your business domain

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Reliable execution

Apply domain logic consistently through reusable functions

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Auditable decisions

Ground every action in validated, explainable knowledge.

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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.

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Safety and consistency by schema

Reliable reasoning starts with reliable knowledge. TypeDB's schema ensures every fact conforms to a shared model, giving agents consistent data to reason over even as systems evolve.

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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define
  attribute weekly-hours value integer;
  entity full-time-employee sub employee;
  entity part-time-employee sub employee, owns weekly-hours;

insert
  $annette isa full-time-employee,
    has full-name "Annette Durand",
    has email "annette@typedb.com",
    has employee-id 184,
    has weekly-hours 35;

# [THW03] Invalid Write: Attribute of type 'weekly-hours' is
# not defined to be owned by type 'full-time-employee'.
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