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Introducing OpenMemory: Long-term Memory for AI Systems

By

OpenMemory Team

Updated November 9, 2025
Introducing OpenMemory: Long-term Memory for AI Systems

Introducing OpenMemory: Long-term Memory for AI Systems

The field of AI is evolving rapidly, but most AI systems today suffer from a fundamental limitation: they can't remember. Each conversation starts fresh, without context from previous interactions. This is where OpenMemory comes in.

The Problem with Current AI Memory

Traditional AI systems rely on context windows and vector databases, but these approaches have significant limitations:

  • Short-term focus - Limited context windows mean losing important information
  • Expensive scaling - Cloud memory services cost 6-12× more than self-hosted solutions
  • No natural decay - All memories treated equally, regardless of importance
  • Poor associations - Lack of meaningful connections between related memories
  • Black box retrieval - No explanation of why certain memories are recalled

What Makes OpenMemory Different?

OpenMemory uses a cognitive architecture inspired by human memory systems. Instead of treating all data the same way, it organizes information into different memory sectors:

Memory Sectors

  • Semantic - Facts, concepts, and general knowledge
  • Episodic - Specific events and experiences
  • Procedural - Skills, processes, and how-to knowledge
  • Emotional - Feelings, preferences, and emotional context
  • Reflective - Meta-cognition and learned patterns

This multi-sector approach allows for more nuanced and contextually appropriate memory retrieval.

Key Features

🧠 Cognitive Architecture

Unlike flat vector databases, OpenMemory uses Hierarchical Memory Decomposition (HMD):

  • One canonical node per memory (no duplication)
  • Multiple embeddings per memory (one per sector)
  • Single-waypoint linking between memories
  • Composite similarity scoring across sectors

⏰ Natural Memory Decay

Memories fade over time unless reinforced, just like human memory:

  • Automatic decay based on time and usage
  • Importance tracking with salience scoring
  • Reinforcement through repeated access
  • Equilibrium states for stable long-term storage

🔗 Graph Associations

Memories don't exist in isolation:

  • Automatic linking between related memories
  • One-hop waypoint expansion during retrieval
  • Explainable recall paths showing why memories were selected
  • Coactivation patterns that strengthen related memories

👥 User Isolation

Perfect for multi-user applications:

  • Each user gets a separate memory space
  • Privacy-preserving architecture
  • Scalable per-user namespacing
  • Complete data ownership and control

Performance That Matters

Our extensive testing shows significant advantages over existing solutions:

| Metric | OpenMemory | Competitors | | ------------------- | ---------- | ------------- | | Query Speed | 115ms avg | 250-310ms avg | | Throughput | 338 QPS | 150-220 QPS | | Recall Accuracy | 95% | 65-78% | | Monthly Cost | $8-12 | $60-150 |

Real-World Applications

OpenMemory is being used in production for:

AI Copilots

  • Code assistants that remember your coding patterns
  • Writing assistants that learn your style
  • Design tools that remember your preferences

Personal AI Assistants

  • Long-term conversation context
  • Learning user preferences over time
  • Building rich user profiles

Educational Tools

  • Adaptive learning systems
  • Progress tracking across sessions
  • Personalized curriculum recommendations

Research Applications

  • Literature review assistants
  • Experiment tracking and analysis
  • Hypothesis generation based on past work

Getting Started is Simple

OpenMemory is designed for developers who want powerful memory capabilities without vendor lock-in:

# Quick start with Docker
git clone https://github.com/caviraoss/openmemory.git
cd openmemory/backend
docker compose up --build -d

# Or run locally
npm install
npm run dev

Basic Usage

// Add a memory
await fetch("http://localhost:8080/memory/add", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    content: "User prefers dark mode in development tools",
    user_id: "user123",
    tags: ["preferences", "ui"],
  }),
});

// Query memories
const response = await fetch("http://localhost:8080/memory/query", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    query: "user interface preferences",
    k: 5,
    filters: { user_id: "user123" },
  }),
});

Framework Agnostic

OpenMemory works with any AI framework:

  • LangChain - Built-in integration with /lgm/* endpoints
  • LlamaIndex - Simple API integration
  • Custom frameworks - RESTful API works with any stack
  • MCP Support - Model Context Protocol for Claude Desktop and other MCP clients

Deployment Options

Self-Hosted

  • Full control over your data
  • Local or VPS deployment
  • SQLite or PostgreSQL storage
  • Docker support included

Cloud Deploy

One-click deployment to popular platforms:

  • Vercel
  • Railway
  • DigitalOcean
  • Render
  • Heroku

Local Development

  • Works offline with local embeddings
  • Ollama integration for privacy
  • No API keys required for basic functionality

The Technology Behind It

OpenMemory leverages cutting-edge research in cognitive science and computer science:

Temporal Knowledge Graphs

  • Time-aware relationships
  • Fact evolution tracking
  • Historical reasoning capabilities
  • Confidence decay over time

Embedding Flexibility

  • OpenAI embeddings for production
  • Gemini for cost-effective scaling
  • Local models (E5, BGE) for privacy
  • Hybrid approaches for optimal performance

Advanced Retrieval

Query scoring combines multiple factors:

  • 60% semantic similarity
  • 20% salience (importance)
  • 10% recency
  • 10% link weight (associations)

Open Source and Community Driven

OpenMemory is Apache 2.0 licensed and community-driven:

  • ✅ Full source code available on GitHub
  • ✅ No vendor lock-in - your data stays yours
  • ✅ Active community on Discord
  • ✅ Regular updates and improvements
  • ✅ Professional support available

The Future of AI Memory

We believe that persistent, explainable memory is essential for the next generation of AI systems. OpenMemory represents a significant step toward AI that can:

  • Learn and grow from each interaction
  • Build relationships over time
  • Explain its reasoning clearly
  • Respect user privacy and data ownership
  • Scale efficiently without breaking the bank

Join the Revolution

Ready to give your AI systems the gift of memory? Here's how to get involved:

🚀 Get Started

💬 Connect with the Community

🤝 Contribute

  • Report bugs and request features
  • Submit pull requests
  • Share your use cases
  • Help improve documentation

Conclusion

OpenMemory isn't just another database - it's a cognitive architecture that brings human-like memory capabilities to AI systems. With 2-3× faster performance, 6-10× lower costs, and complete data ownership, it's the missing piece in the AI memory puzzle.

The future of AI is systems that remember, learn, and grow. OpenMemory makes that future possible today.

Ready to transform your AI applications with persistent memory? Get started with OpenMemory and join the community building the future of AI memory systems.

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