Every time your AI starts a new session,
it forgets everything.
AGENT_OS fixes this with a structured memory architecture that cuts cold-start tokens by 97% — from 10,400 to 700 tokens. Set up in 5 minutes.
The Cold Start Problem
Three Key Features
Structured Memory System
7 purpose-built databases — Memory Bank, Mission Control, Wealth Engine, Skills Registry, Key Contacts, Session Log, and Action Audit Log. Each database has importance tiers, confidence scores, and auto-decay so your context window stays clean.
Two-Stage Loading Protocol
Stage 1 scans one-liner metadata summaries (~100 tokens). Stage 2 loads full detail only for relevant entries. This reduces token consumption by 60-70% compared to loading everything at once.
Session Continuity
Every session builds on the last. Cross-session pattern detection, persistent identity, and structured handoff protocols mean your AI never starts from zero again.
7 Databases. Every Problem Solved.
Memory Bank
Importance tiers, confidence scores, auto-decay. Stale memories archive themselves.
Mission Control
Task state machine with AI/human ownership. Never lose track of what needs doing.
Wealth Engine
Revenue streams tracked by AI effort level. Know what your AI is actually building.
Skills Registry
Catalog of capabilities and integrations. Load only what's needed for the task.
Key Contacts
Relationship CRM with follow-up automation. Your AI remembers who matters.
Session Log
Cross-session continuity and pattern detection. Every session builds on the last.
Action Audit Log
Every action logged. Every suppressed error surfaced. No more silent failures.
Stop rebuilding context. Start building.
Open-source schemas on GitHub. Full Notion implementation for $49.