Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
MCP server that gives agents a 4-tier memory system: episodic, semantic, procedural, and self-model. Each tier decays at its own daily rate, and memories are retrieved using hybrid vector and keyword search. Memories are committed on-chain to Solana as SHA-256 hashed memo transactions.
Unlike flat key-value memory stores, it applies type-specific decay and hybrid retrieval combining vector similarity, keyword matching, and tag scoring, so the most relevant memories surface even across long time spans.
When to use it
- Persisting user preferences across separate agent conversations
- Building an agent that recalls past interactions with specific users
- Tracking learned behaviors and procedural knowledge over time
- Searching prior conversation history by topic or tag
- Giving an agent a stable self-model that evolves with experience
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: 'What did we discuss last week about the deployment pipeline?'
- 1Call recall_memories with query 'deployment pipeline' and memory_types ['episodic', 'semantic']
- 2Score results by relevance, recency, and importance using the hybrid retrieval system
- 3Filter by min_importance threshold to drop low-signal entries
- 4Return top-ranked memory entries to the agent context
Agent surfaces relevant memory entries from the prior conversation and uses them to answer the user's question about the deployment pipeline
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.