Tutorial
Memory Architecture: OpenClaw's memory system enables persistent, long-term context storage across conversations—essential for a truly personalized AI assistant.
Understanding OpenClaw Memory
Unlike chat-based AI that forgets everything between sessions, OpenClaw maintains a persistent memory of your interactions. This is enabled through:
- Session storage: Conversation history per channel/user
- Long-term memory: Persistent facts and preferences
- Vector search: Semantic memory retrieval
- Memory tiers: Active, archived, and compressed storage
Memory Tiers Explained
Active Memory
The most recent conversations, kept in fast-access storage:
- Typically the last 50-100 messages per session
- Stored in memory for instant access
- Included in every API call for context
Archived Memory
Older conversations moved to persistent storage:
- Stored as Markdown files in the data directory
- Searchable by keyword and semantic similarity
- Retrieved when relevant to the current context
Compressed Memory
Summarized versions of very old conversations:
- AI-generated summaries preserving key information
- Drastically reduced token count
- Used for long-term pattern recognition
Configuring Memory Settings
Set Memory Limits
openclaw config set memory.active_messages=100
openclaw config set memory.archive_after_days=30
openclaw config set memory.compress_after_days=90
Enable Semantic Search
openclaw config set memory.semantic_search=true
openclaw config set memory.vector_db="chroma"
Memory Commands
View Memory Usage
openclaw memory status
Search Memory
openclaw memory search "project deadline"
Manually Archive
openclaw memory archive --session-id=abc123
Best Practices
- Regular pruning: Archive old conversations to manage costs
- Tag important info: Use /remember to store critical facts
- Monitor token usage: Large memory increases API costs
- Back up memory: Your memory is valuable—back it up regularly
Advanced: Custom Memory Backends
For enterprise deployments, OpenClaw supports custom memory backends:
- PostgreSQL for structured memory storage
- Pinecone for vector search at scale
- S3/S3-compatible for cloud archival