Technical concepts behind Nowledge Mem. For users who want to understand the system more deeply.

This section explains what happens under the hood when you use Nowledge Mem. It is written for users who want to understand why the system behaves the way it does, not just how to use it.

You do not need to read these pages to use Mem effectively. Everything here runs automatically. But if you have ever wondered why certain memories rank higher in search, how the system detects contradictions, or what happens while you sleep, this is where to look.

The Context branch in Nowledge Mem Tree

What is covered

LLM Wiki frames why the Library reads as a wiki, what the system does on its own, and what stays in your hands. This is the entry point if you want the model behind v0.8.
Open Knowledge Format (OKF) covers how you export your whole graph as an open, vendor-neutral markdown bundle any tool or agent can read.
Knowledge evolution explains how memories transform over time. When you refine a decision or learn something that contradicts earlier thinking, the system tracks the relationship instead of overwriting history.
Memory Links explain how to connect two memories inside a space when they should be read together, even when the relationship is not version history or an entity link.
AI Profiles explain how Mem keeps long-running agents distinct from the tool that happens to run them.
Rules explain always-on behavior rules, how they differ from Skills and Memories, and how they reach connected agents.
Search architecture describes how search combines multiple perspectives (semantic meaning, keywords, entity linking, community clusters, labels, and graph traversal) to find what you need.
Memory decay covers how the system decides what to surface first. Recent and frequently used knowledge ranks higher. Important knowledge never disappears.
Background intelligence walks through the daily pipeline: what runs, when, and what safeguards keep it from wasting resources or generating noise.
Skills explains how the system turns a repeated way of working into a procedure your AI can follow, and how a Skill earns trust before it is treated as proven.
Crystals explains how the system synthesizes stable reference knowledge when multiple independent sources converge on the same insight.

Where to start

If you are trying to understand why a specific search result appeared (or did not appear), start with search architecture and memory decay.

If you want to understand the background features (briefings, contradictions, entity extraction), start with background intelligence.

If you are setting up several agents, start with AI Profiles, Rules, and Context.

This section explains what happens under the hood when you use Nowledge Mem. It is written for users who want to understand why the system behaves the way it does, not just how to use it.

The Context branch in Nowledge Mem Tree

What is covered

LLM Wiki frames why the Library reads as a wiki, what the system does on its own, and what stays in your hands. This is the entry point if you want the model behind v0.8.
Open Knowledge Format (OKF) covers how you export your whole graph as an open, vendor-neutral markdown bundle any tool or agent can read.
Knowledge evolution explains how memories transform over time. When you refine a decision or learn something that contradicts earlier thinking, the system tracks the relationship instead of overwriting history.
Memory Links explain how to connect two memories inside a space when they should be read together, even when the relationship is not version history or an entity link.
AI Profiles explain how Mem keeps long-running agents distinct from the tool that happens to run them.
Rules explain always-on behavior rules, how they differ from Skills and Memories, and how they reach connected agents.
Search architecture describes how search combines multiple perspectives (semantic meaning, keywords, entity linking, community clusters, labels, and graph traversal) to find what you need.
Memory decay covers how the system decides what to surface first. Recent and frequently used knowledge ranks higher. Important knowledge never disappears.
Background intelligence walks through the daily pipeline: what runs, when, and what safeguards keep it from wasting resources or generating noise.
Skills explains how the system turns a repeated way of working into a procedure your AI can follow, and how a Skill earns trust before it is treated as proven.
Crystals explains how the system synthesizes stable reference knowledge when multiple independent sources converge on the same insight.

Where to start

If you are trying to understand why a specific search result appeared (or did not appear), start with search architecture and memory decay.

If you want to understand the background features (briefings, contradictions, entity extraction), start with background intelligence.

If you are setting up several agents, start with AI Profiles, Rules, and Context.

How it works

What is covered

Where to start

On this page

How it works

What is covered

Where to start

On this page