OpenMemory treats every conversation like a living system—capturing emotion, context, and time with graceful decay and automatic reinforcement.
Five-sector embeddings
Factual, emotional, temporal, relational, and behavioral memory.
Graceful decay curves
Automatic reinforcement keeps relevant context always sharp.
Pulse
Retention
94.7%
Memory health
97%
Factual
23k
Anchored nodes
Emotional
18k
Sentiment cues
Temporal
6h
Average recall
Sync map
OpenMemory delivers superior contextual recall at a fraction of the cost of hosted memory APIs
Query Latency
110ms
vs 350ms (SaaS)
Scalability
∞
Horizontal sharding
Cost / 1M Tokens
$0.35
vs $2.50+ (SaaS)
Throughput
40 ops/s
vs 10 ops/s (SaaS)
Monthly Cost
$6
vs $90+ (SaaS)
Feature | OpenMemory | Supermemory | Mem0 | Vector DBs |
---|---|---|---|---|
Expected Monthly Cost (100k) | $5-8 | $60-120 | $25-40 | $15-40 |
Hosted Embedding Cost | $0.35/1M | $2.50+/1M | $1.20/1M | User-managed |
Storage Cost (1M memories) | ~$3/mo | ~$60+ | ~$20 | ~$10-25 |
Avg Response (100k nodes) | 110ms | 350ms | 250ms | 160ms |
CPU Usage | Moderate | Serverless billed | Moderate | High |
Architecture | HMD v2 | Flat embeddings | JSON memory | Vector index |
Retrieval Depth | Multi-hop | Single embedding | Single embedding | Single embedding |
Ingestion | ✓Multi-format | ✓ | ✗ | ✗ |
Explainable Paths | ✓ | ✗ | ✗ | ✗ |
Open Source | ✓ | ✗ | ✓ | ✓ |
Self-Hosted | ✓ | ✗ | ✓ | ✓ |
Local Embeddings | ✓ | ✗ | ◐ | ✓ |
Data Ownership | 100% | Vendor | 100% | 100% |
Memories fade following curved trajectories, while reinforcement pulses lift critical context back above the retention threshold.
Sector-aware decay
Each memory dimension carries its own slope and minimum floor so emotional cues linger longer than transient facts.
Automatic reinforcement
Signal spikes from conversations or tool outcomes fire a pulse that restores strength without manual resets.
Attribution trails
Every reinforcement links back to its trigger so agents can explain why context remained in play.
Production-ready features for intelligent memory management
Encode conversations into five synchronized dimensional vectors that elegantly preserve nuance across sessions.
Memories decay gracefully along custom curves, while high-signal events trigger reinforcement without manual tuning.
Every memory anchors to a dynamic waypoint graph, powering contextual recall and multi-hop reasoning.
Stream documents, call transcripts, and events through adaptive chunking with automatic context stitching.
SQLite vector search with RAM caching gives you sub-40ms responses without managing infra.
Swap between OpenAI, Gemini, Voyage, or local models without rewriting your pipeline.
Multi-sector embeddings with single-waypoint linking for biologically-inspired memory retrieval
Documents
Conversations
Events
Audio
Web Pages
Parse & Clean
Extract text, remove noise
Chunk
Semantic segmentation
Classify Sector
ML-based routing
Generate Embeddings
5-sector vectors
Episodic
Events & experiences
Semantic
Facts & concepts
Procedural
Skills & patterns
Emotional
Sentiment arcs
Reflective
Meta-cognition
Vector Store (SQLite)
768-dim embeddings per sector with quantization
Waypoint Graph
Single-waypoint associations with weight decay
Sector Fusion
Query against 2-3 likely sectors simultaneously
Activation Spread
1-hop waypoint graph traversal for context
Composite Ranking
Weighted scoring with decay factors
Ranked Results
JSON with attribution paths
Decay Scheduler
Runs every 12h via cron
Reinforcement
Auto-boost on access
Everything is designed so that product teams can deploy rich agent memories without wrangling infrastructure or custom tooling.
Stream documents, conversations, telemetry, and events into OpenMemory via a single REST surface.
Embeddings route into five sectors, re-weighted by the reinforcement engine and waypoint graph.
Query memories with hybrid semantic + graph lookups that surface the right context for every agent decision.
Median latency
36ms
Context depth
5 hops
From chatbots to autonomous agents, power your AI with long-term memory
Equip chat products with persistent preferences, tone awareness, and long-tail recollection so they sound brilliantly personal.
Launch companion agents that accumulate rituals, goals, and collaborative context while staying effortlessly organized.
Convert knowledge bases into living graphs the moment documents land, unlocking razor-sharp semantic discovery.
Give mission-critical agents durable context for planning, tool choice, and retrospective learning.
Simple, intuitive API that gets you from zero to production-ready memory in just a few lines of code
// Add a memory with automatic multi-sector embedding
await fetch('http://localhost:3000/memory/add', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
content: 'User prefers dark mode and minimal UI design',
metadata: { source: 'preferences', category: 'UI' }
})
});
// Query memories with decay-weighted relevance scoring
const response = await fetch('http://localhost:3000/memory/query', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
query: 'What are user interface preferences?',
topK: 5
})
});
const { results } = await response.json();
// Results include memories with decay scores and sector breakdowns
Deploy OpenMemory with a hardened decay engine, multi-sector embeddings, and production-grade retrieval in just a few minutes.
Instant deployment
Spin up the memory graph with one command and start reinforcing context immediately.
Always-on retention
Decay audits, reinforcement pulses, and attribution trails are baked in from day one.
Launch kit
90s setup