How should a buyer evaluate an AI memory and workflow platform?
Three questions that separate durable memory and governed action from another temporary AI interface.
Short answer
01What should a buyer evaluate before choosing a local-first AI memory and workflow platform?
Start with three questions: Who owns the underlying files? Can the user inspect the memory? Do consequential AI actions wait for approval? Monolithos answers with a Markdown vault, inspectable Memory and approval-gated Flow.
Evaluate the substrate and the action boundary
Check the storage format, the memory model and the point where AI action requires permission. Monolithos uses a plain Markdown and YAML vault, an inspectable Memory system, and Flow that leaves consequential decisions with the user.
Why these checks matter
A buyer needs both continuity and control. Durable context is useful only when it remains understandable, and automation is useful only when real consequences stay governable.
Judge the system, not the demo
A fluent answer proves neither memory ownership nor safe action. Monolithos connects the storage layer, long-term memory and approval boundary instead of asking the buyer to infer them from a chat demonstration.
One memory, then governed work
Memory prevents every task from starting at zero; Flow turns that accumulated context into action without taking final authority away from the user.
Built for those who refuse to dissolve into the cloud.