Intelligent Memory System
The Cognitive Memory Engine for AI Agents
Persistent context, emotional awareness, and retrieval strategies that learn. Your agents never start from zero.
Your agent stores a memory. MetaMemory encodes it across 4 specialized embedding spaces using your OpenAI or Gemini key.
On search, 5 channels run in parallel — semantic, temporal, emotional, keyword, graph — and results are fused into a single ranked list.
The adaptive stack learns which strategies work for your queries. Retrieval quality improves automatically with every search.
Bring your own keys. Your data, your AI provider, our cognitive architecture.
Why Memory Matters
AI agents need more than context windows
Agents Forget
MetaMemory provides episodic storage with 3-level isolation (agent, session, user). Memories persist across sessions, restarts, and deployments.
RAG Isn't Memory
4 embedding types (semantic, emotional, process, context) with adaptive strategy selection. Multi-channel retrieval that learns which strategies work best for your queries.
Static Context Windows
LLM-powered consolidation shrinks context automatically while keeping what matters. No manual summarization or token counting.
Features
Everything an AI agent needs to remember
Multi-Vector Embeddings
Adaptive Strategy Selection
Emotional Intelligence
Memory Consolidation
Online Learning
Episodic Memory
How It Works
Layered architecture, simple interface
How It Works
Three stages. One seamless pipeline.
Encode
Every interaction is encoded across 4 vector spaces: semantic, emotional, process, and context. This captures not just what was said, but its meaning, context, and feeling.
- Multi-vector embeddings in parallel
- 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.
- 70% compression with semantic preservation
- Cross-session memory linking
- Importance-weighted decay curves
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
- Sub-100ms retrieval at scale
BYOK Providers
Bring your own keys
Use your existing API keys from any supported embedding provider. We validate, encrypt, and manage them — you keep full control.
Cognitive Architecture
Built on research, not heuristics
40
Embedding Spaces
Semantic, emotional, process, and context
50
Retrieval Channels
Fused via Reciprocal Rank Fusion
50
Benchmarks Evaluated
LoCoMo, HotpotQA, EpBench, LongMemEval, MemAgentBench
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Emotion Categories
Confident, uncertain, confused, frustrated, insight, breakthrough