Product
Give your AI agent deep, structured memory that grows smarter over time.
Core Operations
Three APIs that handle everything — store, search, and understand.
Memory Architecture
Four memory types inspired by cognitive psychology, each serving a distinct purpose.
Fact
Discrete, objective, verifiable information extracted from conversations. Stored with vector embeddings and knowledge graph links.
Episode
Time-bound events and experiences with emotion metadata (valence, arousal, emotion labels) and temporal expressions.
Trait
Behavioral patterns discovered through multi-session reflection. Evolves through a 6-stage lifecycle from trend to core.
Document
Coming SoonStatic reference materials uploaded for RAG-style retrieval. Specialized search for uploaded PDFs and files.
Trait Lifecycle
Traits progress through 6 stages as evidence accumulates. Confidence scores decay when contradicted and strengthen when reinforced.
Three-Layer Trait Classification
Traits are classified into three layers based on confidence: Behavior (observable patterns) → Preference (dispositional tendencies) → Core (stable personality traits).
Behavior
0.3 – 0.6
Preference
0.6 – 0.85
Core
> 0.85
Benchmark
Evaluated on LoCoMo, a public long-conversation memory benchmark testing single-hop, multi-hop, temporal, and open-domain reasoning.
81.7%
LoCoMo Overall Score
82.9%
Single-Hop
84.3%
Multi-Hop
81.1%
Open-Domain
76.6%
Temporal
Compared with mainstream AI memory frameworks on public benchmarks
See It in Action
A preview of neuromem's memory management dashboard — available immediately after sign-up.