
Memory Lancedb Pro
Production memory system that gives Claude persistent recall across conversations using LanceDB with hybrid vector and BM25 search. Ships with four deployment paths: full Jina embedding plus reranker for best quality, a budget mix with free SiliconFlow reranking, OpenAI-only for simplicity, or fully local with Ollama if you want zero API calls. Smart Extraction uses an LLM to pull structured facts from conversation history, and there's Weibull decay so old memories fade naturally. The workflow is thorough, maybe overly so. It actually checks your API keys with curl before touching config, which saves you from the usual "why isn't this working" debug loop. If you need Claude to remember things between sessions and want control over the retrieval stack, this handles it.
- skills.sh installs
- 269
- GitHub stars
- 229
- last updated
- Aug 21, 2026
Install
$ npx -y skills add cortexreach/memory-lancedb-pro-skill --skill memory-lancedb-pro --agent claude-codeInstalls into .claude/skills of the current project.
Files
memory-lancedb-pro
Production-grade long-term memory system (v1.1.0-beta.8) for OpenClaw AI agents. Provides persistent, intelligent memory storage using LanceDB with hybrid vector + BM25 retrieval, LLM-powered Smart Extraction, Weibull decay lifecycle, and multi-scope isolation.
For full technical details (thresholds, formulas, database schema, source file map), see references/full-reference.md.
Applying the Optimal Config (Step-by-Step Workflow)
When the user says "help me enable the best config", "apply optimal configuration", or similar, follow this exact procedure:
Step 1 — Present configuration plans and let user choose
Present these three plans in a clear comparison, then ask the user to pick one:
Sponsors
Details
- Repository
- github.com/cortexreach/memory-lancedb-pro-skill
- Categories
- Git & Pull Requests, Code Review & Quality, AI & Agent Building
- First seen
- Jun 3, 2026