Cognee-style memory architecture for user capability state
The question to explore
How should Marrow represent not just what notes a user saved, but their evolving capability relationships—like 'understood concept X last month, applied it in project Y, now questioning assumption Z'?
Why now. You've saved detailed notes on Cognee's knowledge graph approach and multi-agent memory patterns. Your current challenge is moving beyond note exposure to capability state tracking. Cognee's four operations (remember, recall, improve, forget) map directly to tracking user progress over time.
Capability it may unlock. Build a persistent capability graph that evolves as users explore, apply, and revise concepts—enabling truly longitudinal recommendation improvement
~60 min · confidence 75%
· grounded in your notes
How to start
Map Cognee's entity-relationship extraction to Marrow's domain: entities are concepts, relationships are capability dependencies or contradictions. Design how 'improve' updates confidence scores when users mark unlocks as useful. Sketch how 'forget' handles outdated assumptions or changed goals. Test with your own Marrow usage history.
Supporting notes (2)
Cognee: A Memory Layer for LLMs That Builds Queryable Knowledge Graphs from Documents
Cognee is an open-source system that transforms documents into persistent AI memory by automatically building knowledge graphs of entities, concepts, and relationships. It provides four core operations—remember, recall, improve, and forget—to manage a full memory lifecycle for stateless LLMs.
Claude can be configured to function as a coordinated team of AI agents, each with specialized roles like architect, developer, and reviewer, rather than as a single monolithic assistant. This approach leverages role-specific prompting and structured workflows to simulate collaborative software engineering.