Understanding Aspects

Aspects are the foundation of how Yanai organizes your context. They represent distinct areas of your life and work — containers that give structure to everything you know, owned and controlled by you.

What Is an Aspect?

An aspect is a named category for a slice of your life. Each aspect holds its own memories, facts, and consolidated summary. When an AI tool connects to Yanai, it sees your aspects and can read, search, and write to them.

Think of aspects like labeled filing cabinets. Instead of dumping everything into one giant pile, you organize knowledge into meaningful groups that make retrieval fast and accurate. And unlike platform-specific memory, these cabinets travel with you to every AI tool you use.

Examples of Aspects

Personal vs. Organization Aspects

There are two types of aspects:

Personal Aspects

These belong to you and are only accessible through a personal-scoped MCP session. Your AI tools see these when connected with personal scope.

Examples: Family Life, Health, Hobbies, Personal Projects

Organization Aspects

These belong to your organization and are accessible through an org-scoped MCP session. They represent your work context within a specific company.

Examples: Work — Jaxon (within Acme Corp), Engineering Team Knowledge, Product Roadmap

The scoping is enforced at the token level — personal tools can never see org data, and org tools can never see personal data. You control the boundaries.

How to Organize Your Aspects

Start simple

Don't over-organize from day one. Start with 3-5 aspects that match the major areas of your life:

• One for work (or one per job/role)
• One for your primary hobby or interest
• One for family or home life
• One for health (if relevant)

Split when it gets noisy

If one aspect is accumulating too many unrelated memories, split it. For example, if your "Work" aspect has both deep technical architecture context and management/hiring context, consider splitting into "Engineering" and "Team Lead" aspects.

Use descriptive names

Name your aspects clearly. "Work — Jaxon at Yanai" is better than "Work." "Mountain Biking" is better than "Hobby 1." AI tools use aspect names as context signals.

Don't worry about perfect categorization

Yanai's store_memory tool can auto-classify memories to the best-matching aspect. If you store a memory without specifying an aspect, the system picks the best one for you. Aspects are organizational aids, not rigid constraints.

What Lives Inside an Aspect

Each aspect contains:

Context Entries (Memories)

Individual pieces of knowledge stored with a key, content, tags, and metadata. Each memory is classified into a cognitive sector and has a salience score that determines its priority.

Aspect Summary

A high-level AI-generated summary of everything known about this aspect. This summary is automatically updated as memories are added and reinforced. AI tools often read aspect summaries first to get the big picture before diving into specific memories.

How AI Tools Use Aspects

When a connected AI tool starts a conversation:

1. It calls search_context to find relevant memories across your aspects — this also returns your profile, aspect list, and team info in a single call
2. It uses aspect summaries and memory results to understand your current context
3. As the conversation progresses, it stores new knowledge back to the most relevant aspect using store_memory

The tool handles routing automatically — just pass the content and Yanai classifies it to the right aspect (or even multiple aspects if the memory is cross-cutting).

Tips

• Quality over quantity — A few well-organized aspects with rich context beat many sparse ones
• Let AI do the sorting — Use store_memory without specifying an aspect and let the AI classify
• Review periodically — Check your aspects in the web app occasionally to see what's accumulating and reorganize if needed
• Separate personal and work — This is especially important if you use org-scoped sessions, since the scope boundary is hard