How should FinanceOS represent a family — its members, their individual vs. shared financial goals, and their conflicting preferences — so that a recommendation is 'for the family' rather than for one person?
Why now. Your notes give you agent orchestration and a memory layer, but every one of them assumes a single user or an engineering team. Before you can build a household CFO, you need a data model where the family (not a user) is the unit — this is the prerequisite that all FinanceOS features sit on, and it is currently absent from your saved knowledge.
Capability it may unlock. A concrete household data schema that lets every agent reason about shared finances and multi-member goals.
~60 min · confidence 78%
· model inference (not direct note evidence)
How to start
Start from your Cognee note: sketch the knowledge-graph entities a family needs (members, accounts, goals, obligations, preferences) and the relationships between them. Then map how the tools/skills/subagents spectrum attaches to that graph. Explicitly design for conflict: what happens when two members' goals disagree? Use first-principles thinking (馬斯克 note) to avoid copying single-user app patterns.
Supporting notes (3)
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.
AI agents can be decomposed into tools, skills, or subagents depending on task complexity and autonomy needs
AI agents exist on a spectrum from simple tool-use (single API calls) to complex multi-agent systems. Understanding this decomposition helps builders choose the right architecture—whether a basic prompt with tool access, a stateful skill with branching logic, or a full subagent that can pursue goals autonomously—for each use case.
