Agent Memory

Persistent memory layer for AI agents. Store context, conversation history, and preferences across sessions with vector and graph-based retrieval.

3 MEMORIES
MEMORY_V1
Knowledge Graph12 nodes · 8 edges
user
context
pref
userPrefers dark mode0.95
factWorks at Acme Corp0.91
prefUses Python primarily0.88
semantic search across all memories...
Vector + Graph
<50ms recall
ArangoDB
PERSISTENT CONTEXT

Give your agents long-term memory.

Vector search + knowledge graph. Store and recall memories with sub-50ms latency. Automatic summarization and entity extraction.

Vector Storage

Semantic search across all stored memories and context.

Knowledge Graph

Relationship-based memory with entity extraction and linking.

User Profiles

Persistent profiles that evolve with every interaction.

Auto-Summarize

Automatic summarization of long conversation histories.

WHY MEMORY

Build agents that remember.

Persistent, personalized context for every agent.

Sub-50ms Recall

Lightning-fast memory retrieval that won't slow your agents.

01

Privacy First

Full data isolation per user with encryption at rest.

02

Simple SDK

Store and recall memories with just a few lines of code.

03

YOUR AGENTS DESERVE
REAL INFRASTRUCTURE.

START BUILDING AGENTS THAT DO REAL WORK.

Deploy Your First Agent