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OpenMemory is a self-hosted memory engine designed to provide long-term, persistent storage for AI and LLM-powered applications. It enables developers to give otherwise stateless models a structured memory layer that can store, retrieve, and manage contextual information over time. OpenMemory is built around a hierarchical memory architecture that organizes data into semantic sectors and connects them through a graph-based structure for efficient retrieval. It supports multiple embedding strategies, including synthetic and semantic embeddings, allowing developers to balance speed and accuracy depending on their use case. OpenMemory integrates with various AI tools and environments, offering SDKs and APIs that simplify adding memory capabilities to applications. OpenMemory also includes features like memory decay, reinforcement, and temporal filtering to ensure relevant information remains prioritized while outdated data gradually loses importance.
Features
- Persistent long-term memory storage for LLM and AI applications
- Multi-sector embeddings for structured and contextual memory organization
- Graph-based retrieval using waypoint-linked memory relationships
- Configurable performance tiers for speed and accuracy trade-offs
- Temporal filtering and decay mechanisms for relevance management
- SDKs and API support for JavaScript, Python, and MCP integrations
Project Samples
