Workflow
A Multi-Agent Memory Workflow That Stays Local
Coordinate multiple AI clients through one local memory layer without turning memory into a cloud black box.
Article packet
Workflows
Builders using several AI tools on the same body of work
5 min read
01
Shared memory needs the same daemon, data directory, and active space.
02
Use agent profiles and spaces to keep source and context visible.
03
Distill repeated work into pages instead of growing a pile of raw memories.
01
Quick answer
A practical multi-agent memory workflow only works when every client is intentionally pointed at the same daemon, data directory, and active space, with source-agent attribution visible.
Origin makes the daemon the shared local store. Agents can write captures, recall context, and read distilled pages while source attribution and review keep trust visible.
02
When this problem appears
Multi-agent workflows become fragile when each agent writes its own private scratchpad. The user then has to reconcile conflicting state by hand.
03
Keep coordination simple
Do not invent a complex protocol before the memory loop works.
- Connect each MCP client to the same daemon and data directory for shared work.
- Use the same active space for collaborating agents; use separate spaces only when you intentionally want isolation.
- Capture decisions and lessons, not every intermediate thought.
- Use handoffs when one agent's output becomes another agent's starting point.
- Distill repeated topics into pages.
04
What to check next
Shared memory is not automatic truth. Review contradictions and stale context before letting old records steer important work.
Try the local memory loop
Install Origin, connect your AI client, and verify that capture, recall, and handoff work on your machine.
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