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memory-setup

sundial-org/awesome-openclaw-skills

Enable persistent memory for Moltbot/Clawdbot agents to recall past conversations, preferences, and project context.

What is memory-setup?

This skill configures vector-based memory search for Moltbot/Clawdbot, allowing agents to retain and retrieve context across sessions. Use it when setting up an agent for the first time, fixing memory gaps, or enabling semantic search over past conversations and curated knowledge.

  • Configure memorySearch in agent config with embedding provider (Voyage, OpenAI, or local)
  • Create and structure MEMORY.md for long-term curated facts, projects, decisions, and preferences
  • Set up daily logs in memory/logs/ to capture events, decisions, and follow-ups
  • Index multiple sources (MEMORY.md, daily logs, past sessions) for semantic retrieval
  • Tune relevance thresholds (minScore) and result limits (maxResults) for search quality

How to install memory-setup

npx skills add https://github.com/sundial-org/awesome-openclaw-skills --skill memory-setup
Prerequisites
  • Moltbot or Clawdbot installed and configured
  • Access to ~/.clawdbot/clawdbot.json or moltbot.json config file
  • API key for embedding provider (Voyage, OpenAI) or use local provider
  • Workspace directory where MEMORY.md and memory/ folder will be created
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How to use memory-setup

  1. 1.Add memorySearch config block to ~/.clawdbot/clawdbot.json with enabled: true and your chosen provider
  2. 2.Create MEMORY.md in workspace root with sections for user facts, active projects, decisions, and preferences
  3. 3.Create memory/ directory structure with logs/, projects/, groups/, and system/ subdirectories
  4. 4.Create daily logs as memory/logs/YYYY-MM-DD.md to capture events and decisions
  5. 5.Restart gateway with 'clawdbot gateway restart' to apply config changes
  6. 6.Test memory by asking the agent about past topics and verify it searches and returns relevant context

Use cases

Good for
  • Setting up a new agent to remember user preferences, past projects, and key decisions across conversations
  • Fixing 'goldfish brain' where an agent forgets context between sessions
  • Enabling semantic search over project history and group chat context
  • Configuring vector embeddings with Voyage AI, OpenAI, or local providers
  • Troubleshooting memory search relevance by adjusting minScore and maxResults
Who it's for
  • Moltbot/Clawdbot users setting up persistent agent memory
  • Teams managing long-running projects that need continuity across conversations
  • Users wanting semantic search over past decisions and lessons learned
  • Developers configuring embedding providers for memory indexing

memory-setup FAQ

What embedding provider should I use?

Voyage AI is recommended (set VOYAGE_API_KEY). OpenAI embeddings also work (set OPENAI_API_KEY). Use 'local' provider if you have no API keys available.

Why isn't memory search working after I configured it?

Verify memorySearch.enabled is true, MEMORY.md exists in workspace root, and restart the gateway with 'clawdbot gateway restart'. Check that your API key is set in environment if using Voyage or OpenAI.

How do I get more or fewer memory results?

Lower minScore (e.g., 0.2) to get more results, or raise it to 0.4+ for stricter relevance. Increase maxResults to 30 for more snippets, or lower it for fewer.

What should go in MEMORY.md vs. daily logs?

MEMORY.md holds curated, long-term facts (user preferences, key decisions, active projects). Daily logs (memory/logs/YYYY-MM-DD.md) capture timestamped events, tasks, and follow-ups from each day.

Can I index past conversation transcripts?

Yes, set sources to ['memory', 'sessions'] to index both MEMORY.md/memory files and past conversation transcripts for full context retrieval.

Full instructions (SKILL.md)

Source of truth, from sundial-org/awesome-openclaw-skills.


name: memory-setup description: Enable and configure Moltbot/Clawdbot memory search for persistent context. Use when setting up memory, fixing "goldfish brain," or helping users configure memorySearch in their config. Covers MEMORY.md, daily logs, and vector search setup.

Memory Setup Skill

Transform your agent from goldfish to elephant. This skill helps configure persistent memory for Moltbot/Clawdbot.

Quick Setup

1. Enable Memory Search in Config

Add to ~/.clawdbot/clawdbot.json (or moltbot.json):

{
  "memorySearch": {
    "enabled": true,
    "provider": "voyage",
    "sources": ["memory", "sessions"],
    "indexMode": "hot",
    "minScore": 0.3,
    "maxResults": 20
  }
}

2. Create Memory Structure

In your workspace, create:

workspace/
├── MEMORY.md              # Long-term curated memory
└── memory/
    ├── logs/              # Daily logs (YYYY-MM-DD.md)
    ├── projects/          # Project-specific context
    ├── groups/            # Group chat context
    └── system/            # Preferences, setup notes

3. Initialize MEMORY.md

Create MEMORY.md in workspace root:

# MEMORY.md — Long-Term Memory

## About [User Name]
- Key facts, preferences, context

## Active Projects
- Project summaries and status

## Decisions & Lessons
- Important choices made
- Lessons learned

## Preferences
- Communication style
- Tools and workflows

Config Options Explained

SettingPurposeRecommended
enabledTurn on memory searchtrue
providerEmbedding provider"voyage"
sourcesWhat to index["memory", "sessions"]
indexModeWhen to index"hot" (real-time)
minScoreRelevance threshold0.3 (lower = more results)
maxResultsMax snippets returned20

Provider Options

  • voyage — Voyage AI embeddings (recommended)
  • openai — OpenAI embeddings
  • local — Local embeddings (no API needed)

Source Options

  • memory — MEMORY.md + memory/*.md files
  • sessions — Past conversation transcripts
  • both — Full context (recommended)

Daily Log Format

Create memory/logs/YYYY-MM-DD.md daily:

# YYYY-MM-DD — Daily Log

## [Time] — [Event/Task]
- What happened
- Decisions made
- Follow-ups needed

## [Time] — [Another Event]
- Details

Agent Instructions (AGENTS.md)

Add to your AGENTS.md for agent behavior:

## Memory Recall
Before answering questions about prior work, decisions, dates, people, preferences, or todos:
1. Run memory_search with relevant query
2. Use memory_get to pull specific lines if needed
3. If low confidence after search, say you checked

Troubleshooting

Memory search not working?

  1. Check memorySearch.enabled: true in config
  2. Verify MEMORY.md exists in workspace root
  3. Restart gateway: clawdbot gateway restart

Results not relevant?

  • Lower minScore to 0.2 for more results
  • Increase maxResults to 30
  • Check that memory files have meaningful content

Provider errors?

  • Voyage: Set VOYAGE_API_KEY in environment
  • OpenAI: Set OPENAI_API_KEY in environment
  • Use local provider if no API keys available

Verification

Test memory is working:

User: "What do you remember about [past topic]?"
Agent: [Should search memory and return relevant context]

If agent has no memory, config isn't applied. Restart gateway.

Full Config Example

{
  "memorySearch": {
    "enabled": true,
    "provider": "voyage",
    "sources": ["memory", "sessions"],
    "indexMode": "hot",
    "minScore": 0.3,
    "maxResults": 20
  },
  "workspace": "/path/to/your/workspace"
}

Why This Matters

Without memory:

  • Agent forgets everything between sessions
  • Repeats questions, loses context
  • No continuity on projects

With memory:

  • Recalls past conversations
  • Knows your preferences
  • Tracks project history
  • Builds relationship over time

Goldfish → Elephant. 🐘