OpenHuman memory tree: structure personal AI memory before it spreads

What a Memory Tree should answer

A Memory Tree is useful when it makes AI memory visible. The user should be able to see which people, projects, decisions, meetings, notes, and connector sources influenced an answer.

The goal is not to archive everything. The goal is to keep durable context organized enough that a person can inspect, correct, and reuse it.

Useful scenarios

A Memory Tree becomes valuable when context repeats. It helps more with ongoing relationships and projects than with one-off prompts.

• Weekly team meetings where decisions and owners carry over.
• Customer accounts where history changes how follow-up should sound.
• Repository work where issues, pull requests, and release notes need context boundaries.
• Research projects where web findings should become compressed notes instead of scattered tabs.

How to build the first tree

Start with a small tree that a human can audit in minutes.

• Create top-level nodes for people, projects, decisions, and source status.
• Add short Markdown notes rather than raw transcripts.
• Mark facts as confirmed, inferred, stale, or needs review.
• Connect each note to the source type that produced it.
• Run the planner again after the first real meeting or follow-up.

Common risks

Bad memory trees fail quietly. They look organized but contain stale assumptions, copied noise, and missing provenance.

• Raw source dumps make search feel powerful while lowering trust.
• Unlabeled uncertainty can turn a guess into repeated context.
• Overly broad nodes make future review slow.
• No deletion or correction habit means the assistant gets worse over time.

Product connection

The OpenHuman Online planner previews a Memory Tree map, connector status, and Obsidian-style note output before checkout so buyers can understand what the paid workspace will organize.

Quick answers