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Some memory is naturally graph-shaped: Alice → works at → Acme, Order 42 → refunded on → 2026-07-15. Storing these as relationship triples lets the Agent traverse — following relationships to answer structural questions.

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Where relationship graphs fit

You can store this info as free-text notes and rely on retrieval to find them. That works. But relationship graphs are better when:
  • The Agent needs to answer multi-hop questions (“who else works at the same company as Alice?”).
  • You have canonical entities with stable IDs (customers, products, tickets, projects).
  • You need to filter or aggregate over relationships (“how many refunds did we issue this quarter?”).

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Structure

A triple is (subject, relationship, object). Both subject and object are entities with IDs. The relationship is a named edge type.
Entities can have attributes (name, created_at, custom fields) beyond just an ID.

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Creating a graph memory space

Choose Index type when creating a memory space, and turn on Graph mode. This enables the entity/relationship model on top of the standard vector index.

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Writing triples

From an Agent:
  • Use the remember_fact tool with subject, relationship, object.
  • Or turn on Auto-extract triples on the space — Nora extracts entities and relationships from freeform Agent notes automatically.
From the CLI:

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Traversing

The Agent gets two tools when attached to a graph space:
  • recall_facts — retrieve facts about an entity by ID or name.
  • traverse — follow edges. Ask “what does Alice work at?” and get the answer as a set of (entity, path) pairs.
Both tools are automatically added when a graph space is attached.

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Bidirectional edges

Relationships can be marked symmetric (same_as, sibling_of) so traversal works both ways. Or explicitly directed (works_at, parent_of) with a defined inverse (employs, child_of). Configure per relationship type in the space schema.

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Entity resolution

Multiple mentions of the same entity should resolve to the same node. Nora auto-resolves on:
  • Exact ID match.
  • Alias match (list aliases per entity).
  • Fuzzy name match above a similarity threshold (adjustable).
Manual merge available: Space → Entities → select duplicates → Merge.

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Contradictions

If a new triple contradicts an existing one (works_at says Acme, new triple says Beta), Nora doesn’t silently overwrite. It flags the contradiction for review or, if you turn on auto-supersede on conflict, the newer triple wins and the older becomes historical.

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Graph vs. causal graph

Relationship graph memory is entity-relationship data the Agent accumulates. A causal graph (see Causal Graph overview) is a domain-authored graph of cause-and-effect variables, used for verification and analysis. Different tools, different jobs — you can use both.