Git as Shared Memory for Multi-AI Workflows

written on February 16, 2026 · 1 min read

Everyone uses a stack now: Claude Code, Codex, OpenClaw, Lobechat, and more. My bottleneck wasn’t prompting — it was handoff.

I kept wasting time moving context from one AI tool to another:

• what I’m doing today
• what research I already did
• what tasks are still pending

I was inspired by OpenClaw’s source code approach to this problem. So I stopped trying to make tools “integrate perfectly.” I built a tiny shared memory system in a private GitHub repo.

The Setup

Two files, done:

daily/YYYY-MM-DD.md

• what I’m working on today
• today’s focus
• notes for AI agents

inbox.md

• unprocessed thoughts/tasks
• ideas captured during AI chats
• follow-ups and links to revisit

Workflow is simple:

1. Any tool can append to inbox.md
2. At start of day, I pull relevant items into that day’s daily note
3. When something is done, I mark it in daily

That’s it.

Why This Works

Because Git is the common layer.

If each AI tool can read/write via GitHub MCP (or a simple skill/plugin), then they all share one memory backbone — without vendor lock-in.

This is less “AI orchestration” and more human-grade continuity:

• one source of truth
• low friction
• easy to audit
• survives tool churn