Intelligent Memory System
The Memory Engine for AI Agents
Persistent context, emotional awareness, and retrieval strategies that learn. Your agents never start from zero.
4 standardized benchmarks. Evaluated Feb 2026.
Why Memory Matters
AI agents need more than context windows
Agents Forget
MetaMemory provides episodic storage with 3-level isolation (agent, session, user). On LoCoMo, it reaches 77% of human ceiling for long-term conversational recall.
RAG Isn't Memory
4 embedding types (semantic, emotional, process, context) with adaptive strategy selection. Scores 92% F1 on multi-hop reasoning, near human performance.
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
Integrations
Works with your stack
Drop-in adapters for popular AI frameworks. Native support for industry-standard databases and services.
The Difference
Your agent, before and after
Proven Performance
Benchmarked across 4 standardized evaluations
0%
Human Ceiling Reached
LoCoMo long-context memory benchmark
0%
Agent Competencies
Overall score on MemAgentBench
0%
Conflict Resolution
vs 6% baseline on contradictory updates
0s
Avg Query Latency
Embedding + vector search + LLM generation