OpenClaw Agent Persistent Memory

Configure OpenClaw to use sovseal as its local memory backend. This gives your long-running, autonomous agents the ability to persist context, learned solutions, and execution logs without incurring network round-trips.

Installation

Install the @sovseal/mcp-server package globally or execute it via npx:

npm install -g @sovseal/mcp-server

Configuration

To register the sovseal-memory plugin in OpenClaw, modify your agent's configuration file:

• File Path: ~/.openclaw/config.json

Add the following slots configuration:

{
  "plugins": {
    "slots": {
      "memory": "openclaw-sovseal"
    },
    "entries": {
      "openclaw-sovseal": {
        "enabled": true,
        "config": {
          "command": "npx",
          "args": ["-y", "@sovseal/mcp-server"],
          "userId": "alice",
          "configDir": "~/.sovseal"
        }
      }
    }
  }
}

How it Affects the Planning Loop

Integrating local memory alters OpenClaw's core execution loop in three key ways:

1. Context-Aware Initialization: Before commencing a task, the agent queries recall_memory to check for past execution attempts, known file structures, and deprecated functions in the workspace.
2. Dynamic Skill Loading: Based on retrieved memories, OpenClaw dynamically selects which helper tools to load, reducing LLM token budget waste.
3. Automatic Fact Extraction: Upon task completion, the agent calls store_memory to write the lessons learned to the local LanceDB store, ensuring state continuity for subsequent runs.

Command Line Interface (CLI) Usage

OpenClaw exposes a command-line interface to inspect the memory node directly:

1. Query memories

openclaw memory search "database schema modifications"

2. List recent memories

openclaw memory list --limit 10

3. Remove a corrupted entry

openclaw memory delete --id <memory-uuid>