Tri-Node Memory: Open Source Persistent Agent Memory with Human/Agent Vault Separation

Tri-Node Memory is an open source memory architecture for AI coding agents that separates the agent's persistent memory from the human's own notes. Instead of dumping everything into one bucket like OpenClaw's default approach, it uses three distinct nodes: a human vault, an agent journal, and an inference layer that brings them together.

How It Works

The system uses two Obsidian vaults with separate git repositories:

• Human Vault: Curated notes and project files. The agent reads from this vault but never writes without permission.
• Agent Journal: A mirror of OpenClaw's internal memory plus a place for the agent to store relevant information. The agent reads your stuff and writes to its own vault—never crossing the boundary unless you explicitly ask.

The inference layer orchestrates both vaults during agent response generation.

Configuration

Tri-Node is harness-agnostic and model-agnostic. Drop a config file in, point it at your vaults, and your agent materializes with identity, memory, and boundaries intact. It works with OpenClaw, Claude Code, and Codex.

Why This Matters

Existing systems from Google, Karpathy, and others don't separate the agent's identity and memory into a system-agnostic framework. Tri-Node gives developers fine-grained control over what the agent remembers and what remains private.

Getting Started

The full guide is available on Substack, and the code is on GitHub. Clone the repo, set up two Obsidian vaults, configure the paths, and your agent will start maintaining separate memory.