SeekDB Memory — Cloud-Native Agent Memory#

Persistent long-term memory for OpenClaw agents. Once the m0 plugin is installed, your agent automatically remembers facts across conversations and recalls relevant context before each reply.

What It Does#

CapabilityHow It WorksAuto-CaptureAfter each conversation, extracts key facts via LLM and stores them in the cloudAuto-RecallBefore each reply, searches for relevant memories and injects them as contextHybrid SearchVector similarity (HNSW) + fulltext (BM25) + RRF fusion + rerankQuery RewriteResolves pronouns, splits compound questions, expands keywordsExperience LearningDistills successful tool-use patterns into reusable recipesCross-DeviceCloud-native — memories are available from any device, no sync needed

Memory Tools#

Use these tools to manage the agent's long-term memory:

memory_recall#

Search memories by semantic similarity. Returns the most relevant memories.

text

memory_recall query="user's preferred programming language" limit=5

Use when:

• The user asks about something discussed in a previous conversation
• You need context about user preferences, past decisions, or facts
• The user references something with "last time", "before", "remember when"

memory_store#

Save an important fact, preference, or decision to long-term memory.

text

memory_store text="User prefers TypeScript over JavaScript for all new projects" metadata={"category": "preference", "importance": 0.9}

Use when:

• The user states a preference ("I always use...", "I prefer...")
• A decision is made ("Let's go with...", "We decided...")
• The user corrects you ("Actually, it's...", "No, I meant...")
• Important facts are shared (deadlines, names, project details)

memory_forget#

Delete a specific memory by ID.

text

memory_forget id=42

Use when:

• The user asks to forget something
• A memory is outdated or incorrect
• Cleaning up duplicate memories

Automatic Behavior#

With default configuration, the agent does NOT need to call tools manually for basic memory operations:

1. Before each conversation: The m0 plugin automatically searches for relevant memories based on the user's message and injects them as context. The agent sees them as <relevant-memories> in the prompt.

2. After each conversation: The m0 plugin automatically extracts key facts from the conversation and stores them. No manual memory_store needed for routine facts.

3. Manual tools are for explicit user requests ("remember this", "forget that", "what did I say about X?").

Experience System#

The m0 plugin tracks successful tool-use patterns and distills them into reusable experiences.

experience_detail#

View the full description of a distilled experience.

text

experience_detail id=7

Experiences are automatically injected alongside memories when relevant. They help the agent avoid repeating mistakes and reuse proven approaches.

Best Practices#

1. Don't over-store: Auto-capture handles routine facts. Only use memory_store for high-importance items the user explicitly wants remembered.
2. Use metadata: Add category (preference, decision, fact, deadline) and importance (0.0-1.0) to help with retrieval.
3. Trust auto-recall: Don't call memory_recall at the start of every conversation — the plugin already injects relevant context automatically.
4. Respect forget requests: When a user says "forget X", use memory_forget immediately.

Setup#

If the m0 plugin is not yet installed, run the setup skill:

text

/openclaw-m0-setup

Or install manually:

bash

openclaw add m0

Then configure in ~/.openclaw/openclaw.json:

json

{
  "plugins": {
    "entries": {
      "m0": {
        "enabled": true,
        "config": {
          "apiKey": "ak_your_key_here",
          "baseUrl": "https://your-endpoint",
          "autoCapture": true,
          "autoRecall": true,
          "recallLimit": 10
        }
      }
    }
  }
}

Requirements#

• OpenClaw >= 2026.2.2
• m0 plugin installed and configured with a valid API key