Feature
Episodic Memory
Human memory organizes experiences into episodes — coherent sequences of events with a beginning, middle, and end. MetaMemory brings this same capability to AI agents, automatically detecting episode boundaries and grouping related interactions.
Automatic Episode Detection
Topic shifts, temporal gaps, and contextual transitions are analyzed to automatically segment the interaction stream into meaningful episodes. No manual tagging required.
Temporal Context
Each memory carries temporal metadata: when it happened, what came before, what followed. This lets agents reason about sequences, durations, and the order of events.
Episode-Level Retrieval
When a memory from an episode is relevant, the system can surface the entire episode for full context. This prevents the "isolated fact" problem that plagues traditional retrieval systems.
Cross-Session Continuity
Episodes span sessions. A debugging conversation that stretches across three days is treated as one coherent episode, giving agents the full arc of context when it matters.
92%
Boundary Accuracy
12 memories
Avg. Episode Size
45%
Context Window Saved
Automatic
Cross-Session Links
Related Features
Emotional Intelligence
Memory Consolidation
Related Articles
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Graph Memory in AI Agents: How Relationships Change Retrieval
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Memory Consolidation in AI Agents: Why Storing Everything Fails
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