Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Persistent memory plugin for OpenClaw agents that stores and retrieves facts across session resets, compaction, and restarts. Uses hybrid search combining SQLite FTS5 keyword lookup with Ollama vector embeddings for semantic similarity. Auto-captures facts from conversations and injects relevant memories as context before each LLM turn.
Combining keyword and semantic search in a single zero-dependency plugin removes the need to configure separate embedding infrastructure for most use cases.
When to use it
- Remembering user preferences set in a previous chat session
- Recalling a project decision made weeks ago without re-explaining context
- Finding past facts about a specific entity using keyword or semantic search
- Preventing an agent from looping on the same topic repeatedly
- Maintaining continuity after an agent gateway restart
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: 'What database did we decide to use for the auth service?'
- 1Extract keywords from the message and run FTS5 search against SQLite
- 2Run vector similarity search via Ollama using nomic-embed-text if available
- 3Merge and deduplicate results, inject top matches as context before the LLM turn
- 4LLM responds using injected memory context
- 5Auto-capture any new facts from the response and store them to SQLite
Agent answers with the previously stored decision, e.g. 'You decided on PostgreSQL for the auth service on March 3rd.'
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.