OpenClaw Agent Memory Plugin: Persistent Context Across Sessions

A developer has created a memory plugin for OpenClaw that gives agents persistent memory across sessions, addressing the frustration of agents forgetting everything between interactions and requiring the same explanations repeatedly.

How it works

The plugin implements a memory layer that hooks into OpenClaw's plugin system. Before every turn, the agent automatically receives relevant context from past conversations. After every turn, new facts and events are extracted and stored.

The system searches three memory types before each turn:

• Facts — Example: "Uses PostgreSQL on Supabase, deploys via Railway."
• Events — Example: "Had OOM issue on Mar 15, fixed with Redis cache."
• Workflows — Example: "Deploy: build → test → push to main → Railway auto-deploys."

Configuration

Setup requires adding a plugin entry to openclaw.json:

{
  "plugins": {
    "entries": {
      "openclaw-mengram": {
        "enabled": true,
        "config": {
          "apiKey": "om-..."
        }
      }
    },
    "slots": {
      "memory": "openclaw-mengram"
    }
  }
}

The plugin includes an auto-recall hook that searches memory before each agent turn. When workflows fail, the agent logs feedback and the procedure automatically evolves to a better version for next time.

The tool is open source under Apache 2.0 license and available at github.com/alibaizhanov/mengram.