Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
HDF5-backed persistent memory store for AI agents. Saves conversation chunks with embedding vectors and retrieves them via hybrid vector + BM25 search. Supports WAL writes, Hebbian learning, temporal decay, and AGENTS.md generation.
Stores embeddings directly in a standard HDF5 file, so memory is portable and readable by any HDF5 library without a separate database server.
When to use it
- Persisting agent conversation history across sessions
- Retrieving relevant past exchanges by semantic similarity
- Backing up and snapshotting agent memory files
- Generating AGENTS.md summaries from stored memory
- Exporting all memory entries for inspection or migration
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: 'What did we discuss about the weather last week?'
- 1Embed the query text using an external embedding model
- 2Run edgehdf5 search with the embedding vector and query text against the .h5 file
- 3Retrieve top-k matching chunks ranked by hybrid vector + BM25 score
- 4Inject returned conversation chunks into the prompt as context
- 5Generate a response grounded in the recalled memory
Agent responds with relevant details from the stored conversation history
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.