MemoryOS
Reference Implementation for Context Compilation Theory
Context & Problem
AI systems lack persistent, governed memory. Every interaction starts from zero because there is no portable, inspectable context layer that preserves continuity of cognition across changing data, models, and experiences.
Solution & Architecture
MemoryOS implements the source and substrate layers of the Context Compilation reference architecture. It captures multi-modal context from real-world work, structures it as portable Markdown artifacts, and makes it queryable through AI — demonstrating that context can be compiled, not just retrieved.
Key Components
- Multi-layer architecture with clear separation of concerns
- Integration with enterprise systems and data sources
- Scalable infrastructure designed for high availability
- Security and governance built into the core design
Impact
Companion repository for the published paper 'Toward a Theory of Context Compilation for Human-AI Systems.' Demonstrates that a portable, inspectable context layer is feasible and practical for real-world AI workflows.
What's Next
- Full Context IR generation from captured context
- CompileBench integration for compilation quality measurement
- Multi-agent context sharing through portable IR exchange
- Governance and auditability layers for compiled context