Memory Setup
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 co…
jrbobbyhansen-pixel
@jrbobbyhansen-pixel
Install
$ openclaw skills install @jrbobbyhansen-pixel/memory-setupMemory 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
| Setting | Purpose | Recommended |
|---|---|---|
enabled | Turn on memory search | true |
provider | Embedding provider | "voyage" |
sources | What to index | ["memory", "sessions"] |
indexMode | When to index | "hot" (real-time) |
minScore | Relevance threshold | 0.3 (lower = more results) |
maxResults | Max snippets returned | 20 |
Provider Options
voyage— Voyage AI embeddings (recommended)openai— OpenAI embeddingslocal— Local embeddings (no API needed)
Source Options
memory— MEMORY.md + memory/*.md filessessions— Past conversation transcriptsboth— 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?
- Check
memorySearch.enabled: truein config - Verify MEMORY.md exists in workspace root
- Restart gateway:
clawdbot gateway restart
Results not relevant?
- Lower
minScoreto0.2for more results - Increase
maxResultsto30 - Check that memory files have meaningful content
Provider errors?
- Voyage: Set
VOYAGE_API_KEYin environment - OpenAI: Set
OPENAI_API_KEYin environment - Use
localprovider 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. 🐘
Related skills
Triple Memory
@ktpriyathamComplete memory system combining LanceDB auto-recall, Git-Notes structured memory, and file-based workspace search. Use when setting up comprehensive agent memory, when you need persistent context across sessions, or when managing decisions/preferences/tasks with multiple memory backends working together.
Vector Memory Hack
@mig6671Fast semantic search for AI agent memory files using TF-IDF and SQLite. Enables instant context retrieval from MEMORY.md or any markdown documentation. Use when the agent needs to (1) Find relevant context before starting a task, (2) Search through large memory files efficiently, (3) Retrieve specific rules or decisions without reading entire files, (4) Enable semantic similarity search instead of keyword matching. Lightweight alternative to heavy embedding models - zero external dependencies, <10ms search time.
MongoDB
@ivangdavilaDesigns MongoDB schemas, indexes, and aggregation pipelines, and debugs slow queries, connection errors, and replica set failures. Use when modeling documents, deciding embed vs reference, reading an explain plan, or fixing a COLLSCAN, and when a query times out, a pipeline aborts at the memory limit, a cursor dies mid-loop, the pool exhausts and server selection times out, writes fail with duplicate key or "not writable primary", a secondary lags, the oplog window closes, a shard key hotspots, or WiredTiger cache stalls the cluster. Covers mongosh and Compass, Atlas, Mongoose and driver connection strings, transactions and retry loops, change streams, time-series collections, Atlas Search and vector search, sharding, backups, restores, and upgrades. Not for SQL or relational modeling — normalization instincts actively mislead here.
Triple Memory
@ktpriyathamComplete memory system combining LanceDB auto-recall, Git-Notes structured memory, and file-based workspace search. Use when setting up comprehensive agent memory, when you need persistent context across sessions, or when managing decisions/preferences/tasks with multiple memory backends working together.
Memory Hygiene
@dylanbaker24Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.
Elite Longterm Memory
@nextfrontierbuildsUltimate AI agent memory system for Cursor, Claude, ChatGPT & Copilot. WAL protocol + vector search + git-notes + cloud backup. Never lose context again. Vibe-coding ready.