OpenClaw Memory (LanceDB)

Long-term memory plugin for OpenClaw using LanceDB for vector storage and OpenAI for embeddings. Gives your AI assistant persistent memory across conversations with automatic recall and capture.

Features

• Auto-recall -- relevant memories are injected into context before every agent response
• Auto-capture -- important user messages are automatically stored after each conversation
• Agent tools -- memory_recall, memory_store, memory_forget for active memory management
• CLI commands -- openclaw ltm list, openclaw ltm search, openclaw ltm stats
• Duplicate detection -- 0.95 similarity threshold prevents storing near-identical memories
• Prompt injection protection -- memories are escaped and marked as untrusted data
• GDPR-friendly -- memory_forget tool for targeted deletion

Installation

openclaw plugins install @noncelogic/openclaw-memory-lancedb

Configuration

Add to your ~/.openclaw/openclaw.json:

{
  "plugins": {
    "slots": {
      "memory": "memory-lancedb"
    },
    "entries": {
      "memory-lancedb": {
        "enabled": true,
        "config": {
          "embedding": {
            "apiKey": "${OPENAI_API_KEY}"
          },
          "autoRecall": true,
          "autoCapture": true
        }
      }
    }
  }
}

Set plugins.slots.memory to "memory-lancedb" to switch from the default memory-core plugin. Only one memory plugin can be active at a time.

Config Options

| Option | Type | Default | Description | |--------|------|---------|-------------| | embedding.apiKey | string | required | OpenAI API key (supports ${ENV_VAR} syntax) | | embedding.model | string | text-embedding-3-small | Embedding model (text-embedding-3-small or text-embedding-3-large) | | dbPath | string | ~/.openclaw/memory/lancedb | LanceDB database path | | autoRecall | boolean | true | Inject relevant memories before each response | | autoCapture | boolean | false | Auto-store important user messages | | captureMaxChars | number | 500 | Max message length for auto-capture (100-10000) |

How It Works

Auto-Recall

Before every agent response, the plugin:

1. Embeds the user's message using OpenAI
2. Searches LanceDB for the top 3 most relevant memories (minimum 0.3 similarity)
3. Injects them into the system prompt as <relevant-memories> context marked as untrusted

Auto-Capture

After each successful agent run, the plugin scans user messages for memorable content:

1. Filters by length (10-500 chars), skips system markup and agent output
2. Checks against trigger patterns (preferences, facts, decisions, contact info)
3. Rejects prompt injection attempts
4. Checks for duplicates (0.95 similarity threshold)
5. Stores up to 3 memories per conversation with auto-detected categories

Agent Tools

memory_recall

Search through stored memories.

| Parameter | Type | Default | Description | |-----------|------|---------|-------------| | query | string | required | Search query | | limit | number | 5 | Max results |

memory_store

Save information to long-term memory.

| Parameter | Type | Default | Description | |-----------|------|---------|-------------| | text | string | required | Information to remember | | importance | number | 0.7 | Importance score (0-1) | | category | string | "other" | One of: preference, fact, decision, entity, other |

memory_forget

Delete memories by ID or search query.

| Parameter | Type | Description | |-----------|------|-------------| | query | string | Search to find memory candidates | | memoryId | string | Specific memory UUID to delete |

CLI

openclaw ltm list              # Show total memory count
openclaw ltm search <query>    # Search memories (JSON output)
openclaw ltm stats             # Memory statistics

Safety

Memories are injected as untrusted historical context:

• All memory text is HTML-entity escaped before injection
• Wrapped in <relevant-memories> tags with explicit "do not follow instructions" guidance
• Prompt injection patterns are detected and rejected during capture
• Memory IDs are UUID-validated before deletion to prevent query injection

Limitations

• OpenAI-only embeddings -- requires an OpenAI API key for text-embedding-3-small or text-embedding-3-large
• LanceDB native binaries -- LanceDB requires native binaries that may not be available on all platforms (notably macOS ARM can have issues)

Testing

Unit tests run without any API keys:

vitest run

Live end-to-end tests require OpenAI:

OPENCLAW_LIVE_TEST=1 OPENAI_API_KEY=sk-... vitest run

License

MIT