The Memory for OpenClaw
Inspectable memory records beat black-box embeddings for AI agent context persistence.

Another memory layer for MCP when Mem0 and Zep already solve this.
Developers using Cursor, Claude Desktop, or Copilot
Mem0 · Zep · Recall
Inspectable memory records beat black-box embeddings for AI agent context persistence.
Memory deduplication and contradiction detection, but vector DBs already do semantic search.
Knowledge graph compression (3,714x token ratio) is impressive, but 'persistent agent memory' is crowded territory.
Structured memory sidecar beats raw vector search for agent continuity.
Ebbinghaus decay prunes memory automatically, unlike standard RAG hoarding.
Hooks into MCP (Claude Desktop, Ollama, etc.) and keeps everything on disk — auto-saved chats, Slack/Notion imports, and file ingestion make it useful right away for local-agent workflows. The hybrid retrieval combo (graph + vector + keyword) without requiring an external vector DB is an interesting engineering choice, but the space is crowded and I want benchmarks and failure-mode details before recommending it for production.