Feature

Multi-Vector Embeddings

Traditional memory systems encode information into a single vector space, losing nuance in the process. MetaMemory encodes every interaction across four distinct vector spaces simultaneously — semantic, emotional, process, and context — capturing the full dimensionality of each memory.

Semantic Embeddings

Capture the factual meaning of interactions. What was discussed, what decisions were made, what information was shared. These vectors power fact-based recall and knowledge queries.

Emotional Embeddings

Capture the affective dimension of interactions. Frustration, confidence, confusion, and insight are all detectable and encoded, allowing agents to respond with appropriate empathy.

Process Embeddings

Represent how-to knowledge and skills. Step-by-step processes, workflows, and procedures are encoded in a way that supports task-oriented retrieval.

Context Embeddings

Encode the temporal and contextual structure of events. When something happened, what preceded it, what followed. These give agents a sense of narrative and sequence.

Vector Spaces

Provider-optimized

Embedding Dimensions

<50ms

Encoding Latency

92%

Multi-Hop F1

Related Features

Adaptive Strategy Selection

Thompson Sampling and UCB algorithms automatically select the best retrieval strategy based on historical performance.

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Emotional Intelligence

6 computational emotional states (confident, uncertain, confused, frustrated, insight, breakthrough) with real-time detection.

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Multi-Vector Embeddings Explained: Beyond Single-Vector Search

Why encoding memories across 4 embedding types — semantic, emotional, process, and context — dramatically outperforms single-vector approaches for AI agent memory retrieval.