Feature
Multi-Channel Retrieval
Traditional memory systems rely on a single similarity search, missing memories that matter for reasons other than semantic closeness. MetaMemory runs five specialized retrieval channels in parallel — semantic, temporal, emotional, keyword, and graph — and fuses their rankings with Reciprocal Rank Fusion, so each memory can surface through whichever signal makes it relevant.
Semantic Channel
Embedding-based similarity search over the factual meaning of interactions. What was discussed, what decisions were made, what information was shared — this channel powers fact-based recall and knowledge queries.
Temporal Channel
Retrieves by time: recency, date references, and the order of events. Questions like "what did we decide last week" resolve through temporal metadata rather than semantic similarity.
Emotional Channel
Every memory carries emotion tags detected at encoding time. Frustration, confidence, confusion, and insight become retrieval signals, letting agents surface the memories that match the moment.
Keyword & Graph Channels
Full-text keyword search catches exact names, identifiers, and terms that embeddings blur, while the graph channel follows entity relationships across memories. Together they recover what pure vector search misses.
5
Retrieval Channels
Reciprocal Rank Fusion
Fusion Method
0.97
Session Recall@10
84.4%
LongMemEval-S Overall
Related Features
Adaptive Strategy Selection
Emotional Intelligence
Related Articles
Graph Memory in AI Agents: How Relationships Change Retrieval
How graph memory works in AI agents: entity extraction, relationship typing, spreading activation, and why cosine similarity misses the connections that matter.
Memory Consolidation in AI Agents: Why Storing Everything Fails
How memory consolidation works in AI agents: similarity triggers, merge strategies, conflict resolution, and why storing everything is worse than forgetting.
What Is Semantic Memory in AI Agents? A Technical Deep Dive
How semantic memory works in AI agents: embeddings, cosine similarity thresholds, multi-vector retrieval, and where pure semantic search structurally fails.