Intelligent Memory System

The Memory Engine for AI Agents

Persistent context, emotional awareness, and retrieval strategies that learn. Your agents never start from zero.

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92%

Multi-hop F1

Near-human

100%

Episodic Recall

Chronological

83%

Conflict Resolution

vs 6% baseline

vs Mem0

LoCoMo F1

Benchmark ResultsF1 Score %

LoCoMoLong-context memory

68.0%

MetaMemory

SimpleMem

43.2

Mem0

34.2

GPT-4

32.1

HotpotQAMulti-hop reasoning

92.0%

MetaMemory

Human

95.6

EpBenchEpisodic recall

100.0%

MetaMemory

GPT-4

65.0

MemAgentBenchAgent competencies

72.7%

MetaMemory

GPT-4o

60.0

5 standardized benchmarks. Evaluated Feb 2026.

Why Memory Matters

AI agents need more than context windows

Agents Forget

Traditional agents lose context between sessions. Every conversation starts from zero.

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

RAG retrieves documents but lacks emotional context, temporal awareness, or learning from past retrievals.

4 embedding types (semantic, emotional, process, context) with adaptive strategy selection. Scores 92% F1 on multi-hop reasoning, near human performance.

Static Context Windows

Fixed token limits force tradeoffs between breadth and depth of context.

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

4 embedding types (semantic, emotional, process, context) for richer memory representation and more precise retrieval.

Adaptive Strategy Selection

Thompson Sampling and UCB algorithms automatically select the best retrieval strategy based on historical performance.

Emotional Intelligence

6 computational emotional states (confident, uncertain, confused, frustrated, insight, breakthrough) with real-time detection.

Memory Consolidation

LLM-powered memory merging achieves 70% compression while preserving semantic relevance and emotional context.

Online Learning

Real-time model adaptation with drift detection and automatic rollback. The system continuously improves from usage patterns.

Episodic Memory

Group related interactions into episodes with automatic memory creation. Temporal context gives agents a sense of narrative and sequence.

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.

LangChainFramework

CrewAIFramework

OpenAILLM

GeminiLLM

PineconeVector DB

PostgreSQLDatabase

RedisCache

Neo4jGraph

The Difference

Your agent, before and after

Without MetaMemory

I prefer short, direct answers.

Got it! I’ll keep things concise.

next session

What’s our project status?

I’d be happy to help! Could you provide more context about which project you’re referring to? I can give you a detailed breakdown of timelines, milestones, dependencies, and…

With MetaMemory

I prefer short, direct answers.

Got it! I’ll keep things concise.

next session

What’s our project status?

Dashboard redesign: 80% done, on track for Friday. API migration: blocked on auth review.

Proven Performance

Benchmarked across 5 standardized evaluations

Human Ceiling Reached

LoCoMo long-context memory benchmark

Accurate Retrieval

Perfect factual recall on MemAgentBench

Conflict Resolution

vs 6% baseline on contradictory updates

Avg Query Latency

Embedding + vector search + LLM generation

The Memory Engine for AI Agents

AI agents need more than context windows

Everything an AI agent needs to remember

Layered architecture, simple interface

Works with your stack

Your agent, before and after

Benchmarked across 5 standardized evaluations

Production-Ready Memory for AI