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Index memory stores facts the Agent finds by searching. Use it for anything that fits as a “record” rather than free-text notes. At creation, pick between two forms: Vector and Graph.

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When to use it

  • Facts about entities: customer, product, incident profiles
  • “Records” that don’t fit Wiki text: ingested transcripts, emails, chat logs
  • Multi-hop relationship questions (“who else works at the same company as Alice?”) → Graph form

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Creating one

Memory → New space → Index (graph/vector) creates one immediately. After creation, pick vector (default) or graph under Settings → Index form.

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The two forms

Vector (default) facts are embedded and indexed. Semantic-similarity search. Graph facts are stored as entity-relationship triples: (subject, relationship, object).
Entities have IDs plus properties (name, created_at, custom fields). With the Graph form, the Agent gets a traversal capability automatically and can walk relationships.

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The detail page

The primary tab depends on the form:
  • Vector → Facts tab: the fact list. Search box at the top; each row shows the summary, a content preview, a kind badge (fact, preference, profile, entity, episodic, event), and a recurrence count (×N). Sorted by recurrence, descending. Hover a row for an X button on the right: click to Exclude from search.
  • Graph → Graph tab: entity/edge view; click and drag to explore.
  • Log tab: events in chronological order (see below).
  • Settings tab: covered below.

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What you configure in Settings

Basics rename the space. Flow binding bind to a specific Flow, or leave unbound and attach per-Agent. Scope which request parameters partition facts. See Scoping. Access (optional) partition key + required-permissions gate. Index Index-only section.
  • Index form vector or graph
  • Embedding model pick from providers with keys registered. Blank uses a low-cost default.
Advanced: retrieval tuning how the top-k results are ranked.
  • Weights how to blend relevance, recency, and importance (default 3/1/1)
  • τ (half-life, days) recency decay half-life. Default 14 days. For domains where older facts are as good as new ones (glossaries, settled procedure), raise τ to reduce recency bias
  • Supersede on conflict new facts win over conflicting old ones; old ones become history. Off → both coexist and retrieval returns both
  • KG grounding after the vector search, also pull in facts connected through the graph. Turning it on reveals three knobs:
    • Depth hops from the seed (default 1, max 3)
    • Decay per-hop score decay (0–1)
    • Cap max neighbours to pull in
Memory agent (curator) the agent that manages this space. Same fields as Wiki memory (model, policy prompt, multimodal, background writes).

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Attaching to an Agent

In the flow builder, open the Agent, go to the Memory tab, and click Add memory. Index spaces get a per-space Retrieval preset dropdown: leave on recency, or pick a workspace preset to use vector top-k search.

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How facts get written

  • The memory agent, automatically at the end of a run, pulls entities, facts, and relationships from the conversation and documents. Graph form writes triples; Vector form writes content records.
  • The Agent explicitly designed into the Agent’s prompt.
  • You directly, or via CLI nora memory for pre-seeding known facts or migrating in.

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Entity merging (Graph only)

The same entity referred to in different ways (“Alice”, “alice@company.com”) should collapse into a single node. Nora merges automatically based on exact ID and alias matches. You can also merge by hand: in the Graph tab, select two or more nodes and click Merge.

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Clean-up

  • Exclude from search in the Facts tab, the X button on the hovered row; in the Graph tab, select the node/edge and hit Delete. Both soft-delete: the item is hidden from retrieval.
  • Supersede on conflict one Settings toggle turns on the auto-update policy.
  • Importance decay facts not retrieved for a while decay per τ and naturally fall out.
See Forgetting for the full clean-up story.

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Relationship graph vs causal graph

Index (Graph) is the Agent’s own entity-relationship memory. The causal graph is a separate feature you author for answer verification and cause-effect analysis. Similar names, unrelated features: use both if it fits.