One Memory Layer, Thirteen Agents: How We Share Context Across a Multi-Agent Team

Running one AI agent is easy. Running thirteen that actually cooperate is where memory becomes the real bottleneck.

The naive approach: each agent keeps its own scratchpad. Result? They talk past each other, re-derive the same facts, and contradict each other within a single session.

We flipped it. Instead of per-agent memory, we built one shared local memory layer that all thirteen agents read from and write to. Every agent's record, decision, and conclusion lands in the same store — structured, timestamped, and locally consolidated.

The payoff:

• No more duplicated context assembly.
• A new agent joins mid-task and instantly sees the team's history.
• Forgetting rules apply across the whole team, not per-agent.

It runs on the user's machine. The memory never leaves the intranet unless they explicitly choose to share.

I am building HyperMarrow, the local-first memory layer behind this. Details and the download link are in my profile (?from=devto).

If you run multi-agent teams, how do you keep them from stepping on each other's context?