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Multi-Agent Memory Isolation

Cloud Plugin

The MemOS OpenClaw Cloud plugin supports complete isolation of memory and message history across multiple Agents. Each Agent can only access its own memory, preventing cross-agent interference.

How to Use in Cloud Plugin

With a simple configuration, different Agents can have independent memory spaces. Both auto-detection and static assignment are supported.

1. Enable Multi-Agent Mode

Add the following to your openclaw.json:

{
  "plugins": {
    "entries": {
      "memos-cloud-openclaw-plugin": {
        "config": {
          "multiAgentMode": true
        }
      }
    }
  }
}

Or set the environment variable:

MEMOS_MULTI_AGENT_MODE=true

2. Auto-detect Agent

Once enabled, the plugin automatically reads ctx.agentId and isolates memory for each Agent. No extra configuration is required.

3. Statically Assign Agent (Optional)

If you need to pin a specific Agent ID, set it in the config:

{
  "config": {
    "agentId": "marketing_agent"
  }
}

Principles

/search/memory: Memory retrieval — returns only the current Agent's memories
/add/message: Record insertion — automatically tags data for the current Agent
Backward compatibility: Default Agent "main" is ignored to keep existing single-Agent data unaffected

Use Cases

Multi-role collaboration: Strategy, business, marketing, and engineering Agents can work in parallel
Business-line isolation: Agents from different business lines run independently without interference
Persona consistency: Preserve each Agent's long-term persona and behavior style

Local Plugin

@memtensor/memos-local-plugin supports both OpenClaw and Hermes. By default, each agent uses its own runtime home and local database. If multiple sessions / agents share one runtime, retrieval is scoped toward the current agent context. For cross-instance collaboration, enable team sharing from Viewer → Settings → Team Sharing.

Rules

Isolated by default: OpenClaw uses ~/.openclaw/memos-plugin/, while Hermes uses ~/.hermes/memos-plugin/. They do not share databases automatically.
Current agent first: retrieval prioritizes the current agent / session's Traces, Policies, World Models, and Skills.
Optional sharing: when hub.enabled is on, instances can share locally crystallized Skills and optional trace excerpts over a LAN / VPN.
Graceful fallback: Hub is not on the algorithm critical path. If sharing is unavailable, the plugin falls back to local-only memory.

Example Workflow

OpenClaw:
  memory_search("deploy config")
  → prioritizes OpenClaw's local Skill / Trace / World Model store

Hermes:
  memory_search("deploy config")
  → prioritizes Hermes' local Skill / Trace / World Model store

With Hub enabled:
  OpenClaw / Hermes can pull team-shared Skills
  private Traces remain local to each machine and runtime home by default

Expected Results

OpenClaw and Hermes do not read each other's local database by default
Team members can explicitly share high-value Skills to avoid repeating mistakes
Local writes, retrieval, and skill lookup continue to work even if Hub is unavailable

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