Memory Overview

Overview

Memory is what separates a personal AI from a stateless chatbot. ArgentOS uses MemU (Memory Unit) as its persistent memory system -- a SQLite database with full-text search, significance scoring, and rich metadata that gives your agent long-term recall.

Every important fact, preference, event, and relationship your agent learns is stored in MemU and can be recalled across sessions, channels, and time.

How Memory Works

Storing

When the agent encounters important information, it uses the memory_store tool to save it:

{
  "tool": "memory_store",
  "input": {
    "content": "Jason prefers open-source solutions when quality is comparable",
    "type": "preference",
    "significance": 8,
    "entities": ["Jason"]
  }
}

Storage can be triggered by the agent automatically (when it recognizes important information) or by the user asking the agent to remember something.

Recalling

When the agent needs context, it uses memory_recall to search:

{
  "tool": "memory_recall",
  "input": {
    "query": "Jason's software preferences"
  }
}

MemU searches using FTS5 full-text search, ranks results by relevance and significance, and returns the most relevant memories.

Bootstrap Injection

At session start, ArgentOS also injects relevant memories directly into the system prompt. This gives the agent immediate context without needing to explicitly search.

Memory Database

MemU stores memories in a SQLite database at:

~/.argentos/memory.db

The database uses FTS5 (Full-Text Search 5) for fast text search and supports semantic similarity via embeddings.

Key Concepts

• Significance: A 1-10 score indicating how important a memory is (higher = more important)
• Entities: People, places, and things referenced in the memory
• Identity fields: Emotional context, relationships, and personal attributes
• Embeddings: Vector representations for semantic similarity search
• Auto-capture: The agent automatically stores important information without being asked

See the following pages for deep dives:

• MemU Architecture -- Database schema, FTS5, embeddings
• Semantic Search -- How search and ranking work
• Identity Fields -- Rich metadata for nuanced recall