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memory-lancedb-pro

AI community intermediate

Description

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 Ex

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

README


name: memory-lancedb-pro description: This skill should be used when working with memory-lancedb-pro, a production-grade long-term memory MCP plugin for OpenClaw AI agents. Use when installing, configuring, or using any feature of memory-lancedb-pro including Smart Extraction, hybrid retrieval, memory lifecycle management, multi-scope isolation, self-improvement governance, or any MCP memory tools (memory_recall, memory_store, memory_forget, memory_update, memory_stats, memory_list, self_improvement_log, self_improvement_extract_skill, self_improvement_review).

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 β€” Present configuration plans and let user choose

Present these three plans in a clear comparison, then ask the user to pick one:


**Plan A β€” πŸ† Full Power (Best Quality)**

  • Embedding: Jina jina-embeddings-v5-text-small (task-aware, 1024-dim)
  • Reranker: Jina jina-reranker-v3 (cross-encoder, same key)
  • LLM: OpenAI gpt-4o-mini (Smart Extraction)
  • Keys needed: JINA_API_KEY + OPENAI_API_KEY
  • Get keys: Jina β†’ https://jina.ai/api-key Β· OpenAI β†’ https://platform.openai.com/api-keys
  • Cost: Both paid (Jina has free tier with limited quota)
  • Best for: Production deployments, highest retrieval quality

**Plan B β€” πŸ’° Budget (Free Reranker)**

  • Embedding: Jina jina-embeddings-v5-text-small
  • Reranker: SiliconFlow BAAI/bge-reranker-v2-m3 (free tier available)
  • LLM: OpenAI gpt-4o-mini
  • Keys needed: JINA_API_KEY + SILICONFLOW_API_KEY + OPENAI_API_KEY
  • Get keys: Jina β†’ https://jina.ai/api-key Β· SiliconFlow β†’ https://cloud.siliconflow.cn/account/ak Β· OpenAI β†’ https://platform.openai.com/api-keys
  • Cost: Jina embedding paid, SiliconFlow reranker free tier, OpenAI paid
  • Best for: Cost-sensitive deployments that still want reranking

**Plan C β€” 🟒 Simple (OpenAI Only)**

  • Embedding: OpenAI text-embedding-3-small
  • Reranker: None (vector+BM25 fusion only, no cross-encoder)
  • LLM: OpenAI gpt-4o-mini
  • Keys needed: OPENAI_API_KEY only
  • Get key: https://platform.openai.com/api-keys
  • Cost: OpenAI paid only
  • Best for: Users who already have OpenAI and want minimal setup

**Plan D β€” πŸ–₯️ Fully Local (Ollama, No API Keys)**

  • Embedding: Ollama mxbai-embed-large (1024-dim, recommended) or nomic-embed-text:v1.5 (768-dim, lighter)
  • Reranker: None β€” Ollama has no cross-encoder reranker; retrieval uses vector+BM25 fusion only
  • LLM: Ollama via OpenAI-compatible endp