Self-Improving AI Memory
AI Memory That Gets Smarter Over Time
The more you use MetaMemory, the better it retrieves. Self-improving retrieval powered by Thompson Sampling, meta-learning, and online adaptation. Works across Claude, ChatGPT, Gemini, and Cursor.
Your agent stores a memory. MetaMemory encodes it with a semantic embedding plus emotion tags, temporal metadata, keywords, and graph entities.
On search, 5 channels run in parallel — semantic, temporal, emotional, keyword, graph — and results are fused via Reciprocal Rank Fusion.
Thompson Sampling + meta-learning + online adaptation pick the best strategy per context. Quality improves with every query you make.
Self-improving retrieval. The more you use it, the smarter it gets.
Why Memory Matters
AI agents need more than context windows
Memory That Degrades
MetaMemory gets smarter as you use it. Thompson Sampling learns which retrieval strategy works best per context. Meta-learning rules accumulate from your queries. Online learning adapts in real time.
RAG Isn't Memory
5-channel fused retrieval (semantic + temporal + emotional + keyword + graph) with adaptive weighting per query type. The system updates its own strategy based on every search you make.
Agents Forget
Episodic storage with 3-level isolation (agent, session, user). Memories persist across sessions, restarts, deployments — and across Claude, ChatGPT, Gemini, Cursor with one account.
Facts Go Stale
A bi-temporal claims ledger extracts facts from memories and tracks when they were true and when the system learned them. Updates supersede instead of contradict — exactly one live current fact per claim, with full point-in-time history.
Features
Self-improving memory, end to end
Three things set MetaMemory apart: retrieval that improves with use, facts that stay current, and shared memory that agents can't corrupt. Everything else is what makes them work.
Adaptive Strategy Selection
Bi-Temporal Claims Ledger
Multi-Agent Arbitration
How It Works
Layered architecture, simple interface
How It Works
Three stages. One seamless pipeline.
Encode
Every interaction is encoded with a semantic embedding plus emotion tags, temporal metadata, keywords, and graph entities. This captures not just what was said, but its meaning, context, and feeling.
- Semantic embeddings via your chosen provider
- Emotional state detection and tagging
- Automatic episode boundary detection
Consolidate
LLM-powered consolidation merges related memories, compresses redundant information, and strengthens important connections — just like sleep does for the human brain.
- Fact extraction into a bi-temporal claims ledger
- Redundancy compression with semantic preservation
- Cross-session memory linking
Retrieve
5 specialized retrieval channels compete and collaborate using Thompson Sampling to surface the most relevant memories for each query, learning and improving over time.
- Adaptive strategy selection via multi-armed bandits
- Gradient-boosted relevance ranking
- Recall that improves with every query
BYOK Providers
Bring your own keys
Use your existing API keys from any supported embedding provider. Keys are validated, encrypted at rest, and rotatable at any time — you control the provider, the model, and the spend.
Cognitive Architecture
Built on research, not heuristics
50
Retrieval Channels
Semantic, temporal, emotional, keyword, and graph — fused via Reciprocal Rank Fusion
20
Time Axes Per Fact
Bi-temporal claims: when it was true, and when we learned it
5000
LongMemEval Questions
Full LongMemEval-S evaluation: 84.4% overall accuracy
60
Emotion Categories
Confident, uncertain, confused, frustrated, insight, breakthrough