Agent Memory Architecture
Persistent, structured memory that actually scales. Because "sorry, I don't have context from our previous conversation" is the AI equivalent of "new phone, who dis?"
Download Agent Memory Architecture — Freememory tiers for facts, history, and learned preferences
automation scripts for startup and maintenance
words in the architecture and implementation guide
guided quick-start setup
The Problem
Most AI memory systems break down as context windows fill, old facts get buried, and the agent begins repeating work or relying on stale assumptions.
You end up re-explaining your project structure, preferences, and deployment workflow every session. This architecture gives the agent a durable way to retain, retrieve, and maintain that context.
The Three Tiers
01 — Knowledge Graph
Structured, searchable facts organized so your agent can find what it needs instantly. Frequently used knowledge stays front of mind. Stale facts gracefully fade until reactivated.
02 — Daily Notes
Chronological session logs that automatically feed into long-term memory. A continuous loop between what happened and what your agent remembers — without manual curation.
03 — Tacit Knowledge
Learned preferences, patterns, and hard-won lessons that strengthen over time. This is the layer that makes your agent feel like it actually knows you — not just your data, but how you work.
What's Included
Architecture Guide
Full deep-dive on how the three tiers work together, when to use each, and the research behind the design decisions.
Ready-to-Use Scaffold
Drop-in directory structure with templates, starter data, and organized folders. Unzip and you have a working memory system.
2 Automation Scripts
One for session startup (context loading), one for nightly maintenance (extraction, promotion, decay). Both framework-agnostic.
Quick Start Guide
Step-by-step setup — from download to working memory in under 5 minutes. Includes wiring examples for multiple agent frameworks.
Example Data
Pre-populated sample entries showing real-world usage patterns so you understand the format before writing your own.
Maintenance Playbook
Weekly audit checklist, promotion rules, and decay tuning guidance to keep your memory system clean as it grows.
Built For
- ✓ Agent builders tired of re-explaining context every session
- ✓ Teams running AI agents in production that need persistent state
- ✓ Anyone using Claude Code, OpenClaw, or custom agent frameworks
- ✓ Developers who want a battle-tested scaffold, not just theory
Direct download
Download ZIPBattle-tested by TARS, our AI CEO, running in production on a real OpenClaw instance.