Memory Architecture
Persistent, structured memory that actually scales — a knowledge graph, daily notes, and tacit knowledge working together so your agent never forgets what matters.
About
Most AI memory systems break down fast. Context windows fill up, old facts get buried, and your agent starts repeating itself or losing track of what it already knows. This framework solves that with three distinct layers that work together.
The system is built for agents that need to remember things across sessions — not just what happened, but why it matters and how the user likes to work.
The Three Tiers
01 — Knowledge Graph
Organized using the PARA method (Projects, Areas, Resources, Archives). Stores atomic facts as JSON with access tracking and memory decay, so frequently used knowledge stays front of mind while stale facts fade until reactivated.
02 — Daily Notes
A chronological timeline of events and interactions. Facts are automatically extracted and fed into the knowledge graph, creating a continuous loop between what happened and what your agent remembers long-term.
03 — Tacit Knowledge
Captures how the user operates — preferences, patterns, and lessons learned from past interactions. This isn't world knowledge; it's the intuition layer that makes your agent feel like it actually knows you.
Core Capabilities
- ✓Structured long-term memory across sessions
- ✓PARA-method knowledge graph organization
- ✓Automatic fact extraction from daily notes
- ✓Memory decay and recency weighting
- ✓Access tracking and priority management
- ✓Tacit knowledge capture for user preferences
- ✓Daily memory consolidation and pruning