OpenMemory

Add long-term, semantic, and contextual memory to any AI system.
Open source. Self-hosted. Explainable. Framework-agnostic.

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1. Overview

OpenMemory is a self-hosted, modular AI memory engine designed to provide persistent, structured, and semantic memory for large language model (LLM) applications.
It enables AI agents, assistants, and copilots to remember user data, preferences, and prior interactions — securely and efficiently.

Unlike traditional vector databases or SaaS “memory layers”, OpenMemory implements a Hierarchical Memory Decomposition (HMD) architecture:

• One canonical node per memory (no data duplication)
• Multi-sector embeddings (episodic, semantic, procedural, emotional, reflective)
• Single-waypoint linking (sparse, biologically-inspired graph)
• Composite similarity retrieval (sector fusion + activation spreading)

This design offers better recall, lower latency, and explainable reasoning at a fraction of the cost.

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2. Competitor Comparison

Feature / Metric	OpenMemory	Supermemory (SaaS)	Mem0	OpenAI Memory	LangChain Memory	Vector DBs (Chroma / Weaviate / Pinecone)
Open-source	✅ MIT	❌ Closed	✅ Apache	❌ Closed	✅ Apache	✅ Varies
Self-hosted	✅	❌	✅	❌	✅	✅
Architecture	HMD v2 (multi-sector + single-waypoint graph)	Flat embeddings	Flat JSON memory	Proprietary long-term cache	Context cache	Vector index
Avg response time (100k nodes)	110-130 ms	350-400 ms	250 ms	300 ms	200 ms	160 ms
Retrieval depth	Multi-sector fusion + 1-hop waypoint	Single embedding	Single embedding	Unspecified	1 session only	Single embedding
Explainable recall paths	✅	❌	❌	❌	❌	❌
Cost per 1M tokens (with hosted embeddings)	~$0.30-0.40	~$2.50+	~$1.20	~$3.00	User-managed	User-managed
Local embeddings support	✅ (Ollama / E5 / BGE)	❌	✅	❌	Partial	✅
Ingestion	✅ (pdf, docx, txt, audio, website)	✅	❌	❌	❌	❌
Scalability model	Horizontally sharded by sector	Vendor scale only	Single node	Vendor scale	In-memory	Horizontally scalable
Deployment	Local / Docker / Cloud	Web only	Node app	Cloud	Python SDK	Docker / Cloud
Data ownership	100% yours	Vendor	100% yours	Vendor	Yours	Yours
Use-case fit	Long-term agent memory, assistants, journaling, enterprise copilots	SaaS AI assistants	Basic agent memory	ChatGPT-only	LLM framework	Generic vector search

Summary:
OpenMemory delivers 2-3× faster contextual recall and 6-10× lower cost than hosted “memory APIs”, while being fully explainable and privacy-controlled.

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3. Setup

Manual Setup (Recommended for development)

Prerequisites

• Node.js 20+
• SQLite 3.40+ (bundled)
• Optional: Ollama / OpenAI / Gemini embeddings

git clone https://github.com/nullure/openmemory.git
cp .env.example .env
cd openmemory/backend
npm install
npm run dev

Example .env configuration:

OM_PORT=8080
OM_DB_PATH=./data/openmemory.sqlite
OM_EMBEDDINGS=openai
OPENAI_API_KEY=
GEMINI_API_KEY=
OLLAMA_URL=http://localhost:11434
OM_VEC_DIM=768
OM_MIN_SCORE=0.3
OM_DECAY_LAMBDA=0.02

Start server:

npx tsx src/server.ts

OpenMemory runs on http://localhost:8080.

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Docker Setup (Production)

docker compose up --build -d

Default ports:

• 8080 → OpenMemory API
• Data persisted in /data/openmemory.sqlite

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4. Architecture and Technology Stack

Core Components

Layer	Technology	Description
Backend	Typescript	REST API and orchestration
Storage	SQLite (WAL)	Memory metadata, vectors, waypoints
Embeddings	E5 / BGE / OpenAI / Gemini / Ollama	Sector-specific embeddings
Graph Logic	In-process	Single-waypoint associative graph
Scheduler	node-cron	Decay, pruning, log repair

Retrieval Flow

1. User request → Text sectorized into 2–3 likely memory types
2. Query embeddings generated for those sectors
3. Search over sector vectors + optional mean cache
4. Top-K matches → one-hop waypoint expansion
5. Ranked by composite score:
   0.6 × similarity + 0.2 × salience + 0.1 × recency + 0.1 × link weight

Architecture Diagram (simplified)

[User / Agent]
      │
      ▼
 [OpenMemory API]
      │
 ┌───────────────┬───────────────┐
 │ SQLite (meta) │  Vector Store │
 │  memories.db  │  sector blobs │
 └───────────────┴───────────────┘
      │
      ▼
  [Waypoint Graph]

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5. API Overview

Method	Endpoint	Description
POST	/memory/add	Add a memory item
POST	/memory/query	Retrieve similar memories
GET	/memory/all	List all stored memories
DELETE	/memory/:id	Delete a memory
GET	/health	Health check

Example

curl -X POST http://localhost:8080/memory/add   -H "Content-Type: application/json"   -d '{"content": "User prefers dark mode"}'

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6. Performance and Cost Analysis

Metric	OpenMemory (self-hosted)	Supermemory (SaaS)	Mem0	Vector DB (avg)
Query latency (100k nodes)	110-130 ms	350-400 ms	250 ms	160 ms
Memory addition throughput	~40 ops/s	~10 ops/s	~25 ops/s	~35 ops/s
CPU usage	Moderate (vector math only)	Serverless billed	Moderate	High
Storage cost (per 1M memories)	15 GB ($3/month VPS)	~$60+	~$20	~$10-25
Hosted embedding cost	~$0.30-0.40 / 1M tokens	~$2.50+	~$1.20	User-managed
Local embedding cost	$0 (Ollama/E5/BGE)	Not supported	Partial	Supported
Expected monthly cost (100k memories)	~$5-8 (self-hosted)	~$60-120	~$25-40	~$15-40

Result: 5-10× cheaper than SaaS memory solutions, with comparable or better recall fidelity.

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7. Security and Privacy

• Bearer authentication required for write APIs
• Optional AES-GCM content encryption
• PII scrubbing and anonymization hooks
• Tenant isolation for multi-user deployments
• Full erasure via DELETE /memory/:id or /memory/delete_all?tenant=X
• No vendor data exposure; 100% local control

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8. Roadmap

Phase	Focus	Status
v1.0	Core HMD backend (multi-sector memory)	✅ Complete
v1.1	Python SDK + Node SDK	✅ Complete
v1.2	Dashboard (React) + metrics	⏳ In progress
v1.3	Learned sector classifier (Tiny Transformer)	🔜 Planned
v1.4	Federated multi-node mode	🔜 Planned
v1.5	Pluggable vector backends (pgvector, Weaviate)	🔜 Planned

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9. Contributing

Contributions are welcome.
See CONTRIBUTING.md, GOVERNANCE.md, and CODE_OF_CONDUCT.md for guidelines.

make build
make test

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10. License

MIT License.
Copyright (c) 2025 OpenMemory.

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Positioning Statement

OpenMemory aims to become the standard open-source memory layer for AI agents and assistants — combining persistent semantic storage, graph-based recall, and explainability in a system that runs anywhere.

It bridges the gap between vector databases and cognitive memory systems, delivering high-recall reasoning at low cost — a foundation for the next generation of intelligent, memory-aware AI.