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"
  }
}

4. Configure Parameters Per Agent (Optional)

Beyond isolating memory spaces, you can use agentOverrides to override parameters for each Agent, such as knowledge bases, recall limits, relevance threshold, and whether memory writes are enabled. Fields that are not specified inherit from the global config.

{
  "plugins": {
    "entries": {
      "memos-cloud-openclaw-plugin": {
        "enabled": true,
        "config": {
          "multiAgentMode": true,
          "allowedAgents": ["research-agent", "coding-agent"],
          "knowledgebaseIds": [],
          "memoryLimitNumber": 6,
          "relativity": 0.45,
          "agentOverrides": {
            "research-agent": {
              "knowledgebaseIds": ["kb-research-papers", "kb-academic"],
              "memoryLimitNumber": 12,
              "relativity": 0.3,
              "includeToolMemory": true,
              "captureStrategy": "full_session",
              "queryPrefix": "research context: "
            },
            "coding-agent": {
              "knowledgebaseIds": ["kb-codebase", "kb-api-docs"],
              "memoryLimitNumber": 9,
              "relativity": 0.5,
              "addEnabled": false
            }
          }
        }
      }
    }
  }
}

This configuration means:

research-agent uses research / academic knowledge bases, recalls more memories, and includes tool memory.
coding-agent only reads from codebase and API documentation knowledge bases, and disables memory writes.
Other Agents, if allowed to use memory, inherit global knowledgebaseIds, memoryLimitNumber, and relativity.

You can also configure a JSON string in .env with MEMOS_AGENT_OVERRIDES, but it has lower priority than agentOverrides in openclaw.json:

MEMOS_AGENT_OVERRIDES='{"research-agent":{"memoryLimitNumber":12,"relativity":0.3},"coding-agent":{"memoryLimitNumber":9,"addEnabled":false}}'

Common overridable fields:

Field	Description
`knowledgebaseIds`	Knowledge base IDs retrieved by the current Agent.
`memoryLimitNumber`	Maximum number of memory items recalled by the current Agent.
`preferenceLimitNumber`	Maximum number of preference memories recalled by the current Agent.
`includePreference`	Whether to recall preference memories.
`includeToolMemory`	Whether to recall tool memories.
`toolMemoryLimitNumber`	Maximum number of tool memories to recall.
`relativity`	Relevance threshold, usually between `0` and `1`.
`recallEnabled`	Whether memory recall is enabled for the current Agent.
`addEnabled`	Whether memory writes are enabled for the current Agent.
`captureStrategy`	Capture strategy, such as `last_turn` or `full_session`.
`queryPrefix`	Search query prefix for the current Agent.
`recallFilterEnabled`	Whether secondary recall filtering is enabled.
`allowKnowledgebaseIds`	Knowledge base IDs that the current Agent is allowed to write to.
`tags`	Tags attached when writing memories.

Principles

/search/memory: Memory retrieval — returns only the current Agent's memories
/add/message: Record insertion — automatically tags data for the current Agent
Config merging: the plugin reads global config first, then applies agentOverrides.<agentId> 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

Knowledge Base Usage

Secondary Filtering for Memory Recall