Overview
OpenMemory is a self-hosted long-term memory layer for LLM-powered applications. It implements a Hierarchical Memory Decomposition (HMD) with multi-sector embeddings and a single-waypoint associative graph, enabling explainable recall paths and efficient storage without duplication. The project supports multiple embedding backends and vector stores and includes an MCP-compatible HTTP endpoint for easy Agent integration.
Key Features
- Sectorized memory model for differentiated handling of episodic, semantic, procedural and other memory types.
- Single-waypoint graph and sparse linking for transparent retrieval paths.
- Pluggable embedding backends (local models, OpenAI, Gemini, Ollama) and vector stores (SQLite, pgvector, Weaviate).
- Built-in MCP (Model Context Protocol) HTTP server to simplify tool and Agent integration.
Use Cases
- Assistants and copilots that require cross-session user preferences and context retention.
- Long-term note and journal retrieval with evidence-backed recall.
- Agent orchestration where a persistent memory layer improves coordination and decision-making.
- Self-hosted enterprise deployments that require data ownership and compliance.
Technical Highlights
- Cost-aware local operation to minimize embedding and storage expenses for large memory footprints.
- Hybrid retrieval combining sector fusion and activation spreading to boost relevance for multi-step workflows.
- Observability and governance features including auditability, erasure, and tenant isolation for production readiness.