About MetaMemory

What is MetaMemory

MetaMemory is a persistent memory engine for AI agents. It encodes every interaction with rich retrieval signals, retrieves the right memories at the right time through adaptive channels, and understands not just what was said, but how it felt.

Think of it as long-term memory that actually works: each memory carries a semantic embedding plus emotional, temporal, keyword, and entity signals from every conversation. Adaptive retrieval learns which memories matter most for each new context. The result is an agent that builds genuine continuity across sessions instead of starting from scratch every time.

Why We Built This

Every AI agent today has the same problem: it forgets everything the moment a session ends. Buffer memory fills up and gets discarded. RAG pipelines return fragments with no context. There is no system that treats memory as a first-class capability, something that encodes, consolidates, and retrieves across time the way human memory does.

Existing solutions bolt on a vector database and call it memory. That gives you keyword matching at best. It doesn't adapt to what the user actually needs. It doesn't distinguish between facts, procedures, emotions, and episodes. It doesn't get better the more you use it.

We built MetaMemory to close that gap: a memory system designed from the ground up for agents that need to remember, learn, and evolve.

Research Foundation

MetaMemory is grounded in cognitive science, not just engineering heuristics. Tulving's theory of episodic memory informs how we separate "what happened" from "what I know," giving agents distinct memory systems for facts, events, skills, and feelings.

The retrieval layer draws on multi-armed bandit algorithms to learn which memory channels work best for each type of query. Gradient boosting ranks candidate memories by relevance, recency, and emotional weight. Bayesian optimization tunes the entire pipeline over time so the system gets sharper the more it's used.

The goal isn't academic novelty. It's building memory that behaves the way you'd expect a thoughtful assistant's memory to behave.

How It Works

At a high level, MetaMemory operates across three stages: encode, consolidate, retrieve.

• Rich encoding — every interaction is stored with a semantic embedding plus emotion tags, temporal metadata, keywords, and graph entities.
• 5 retrieval channels, each specialized for a different access pattern, compete and collaborate to surface the best memories for each query.
• Adaptive pipeline from raw encoding through consolidation to final ranking ensures memories are not just stored but meaningfully organized and continuously refined.

For a deeper look at the architecture, see the documentation.