Glossary

Short-term memory

Conversations and messages, with sequential FIRST_MESSAGE / NEXT_MESSAGE linking and embedded message content for similarity search. Accessed via client.short_term.

Long-term memory

Entities (POLE+O or custom typed), preferences, and facts. The declarative knowledge layer. Accessed via client.long_term.

Reasoning memory

Reasoning traces, reasoning steps, and tool calls — captured per agent task. The "how did we get there" layer that complements the "what we know" of long-term memory. Accessed via client.reasoning.

POLE+O

The default entity ontology — Person, Object, Location, Event, Organization. See POLE+O Model.

:Entity super-label

The label every long-term memory entity carries in addition to its type/subtype labels (e.g. :Entity:Person:Individual). The library MERGEs on (:Entity {name, type}) for deduplication.

Adoption

The process of attaching the :Entity super-label and library-managed properties (id, type, name) to nodes from a pre-existing domain graph, so the library’s writes link to the existing nodes instead of duplicating them. See Adopt an Existing Domain Graph.

Context graph

The full three-layer graph this library builds (short-term + long-term + reasoning).

Zep uses "context graph" to describe a persisted graph attached to a session. neo4j-agent-memory’s closest analogue is the unified graph spanning the three memory layers, with cross-layer edges like `(ReasoningTrace)-[:INITIATED_BY]→(Message).

Memify / memification

Long-term memory ingestion (add_entity, add_preference, add_fact) plus the entity-extraction pipeline.

Cognee’s "memify" pipeline corresponds roughly to entity extraction
deduplication + embedding here. The library does not bundle a single-call "memify(text)" entry point — extraction and storage are separate by design.

Core memory

Long-term memory (entities, preferences, facts) — the persistent, queryable layer.

Letta’s "core memory" maps to long-term memory, especially preferences and entity-attached attributes that persist across sessions.

Episodic memory

Short-term memory (messages and conversations).

Several agent frameworks distinguish "semantic" (long-term) from "episodic" (per-session). neo4j-agent-memory uses short-term / long-term, with reasoning memory as a third axis that doesn’t fit either label cleanly.

Working memory

Not a first-class concept here.

What other systems call "working memory" (current-turn context) is typically assembled per-call via client.get_context(…​) rather than persisted as its own layer.