memory-lancedb-pro is a long-term memory plugin for OpenClaw that replaces the built-in memory-lancedb plugin with a fuller retrieval pipeline. It stores memories in LanceDB and combines vector search with BM25 full-text search, then reranks and rescores the fused results before injecting them into the agent's prompt.
Retrieval runs as a pipeline: the query is embedded and matched against LanceDB's ANN index while a BM25 index handles exact keywords, the two result sets are fused, an optional cross-encoder reranker (Jina, SiliconFlow, Pinecone, or any compatible endpoint) rescores them, and recency, importance, length, and time-decay weights, a minimum score, and MMR diversity shape the final list. The plugin hooks before_agent_start, before_prompt_build, and agent_end and exposes memory_recall, memory_store, and memory_forget tools to the agent.
Memories are partitioned into scopes (global, agent: , custom: , project: , user: ) with per-agent access rules; by default each agent reads global plus its own scope. A CLI covers list, search, stats, delete, export, import, re-embed, and migration from the built-in plugin.
Features
- Hybrid retrieval: vector similarity plus BM25 keyword matching with configurable vectorWeight, bm25Weight, and minScore
- Cross-encoder rerank: 60% reranker score and 40% fused score, falling back to cosine reranking when the API fails
- Scoring pipeline: recency boost (14-day half-life), importance weighting, length normalization, time decay (60-day half-life), a hard minimum score, and MMR de-duplication above 0.85 similarity
- Adaptive retrieval: skips greetings, slash commands, confirmations, and emoji, and forces recall on memory keywords, with CJK-aware thresholds
- Noise filtering: drops agent refusals, meta-questions, and greetings at capture and store time
- Session strategies: OpenClaw session-memory, plugin reflection with inherited rules, or none
- Self-improvement log: structured learnings and errors written to .learnings/ with review and skill-extraction tools
- Markdown mirror: dual-writes memories to dated Markdown files alongside LanceDB
- Long-context chunking: splits at sentence boundaries with overlap and averages chunk embeddings when a model's context limit is exceeded
- Embedding providers: any OpenAI-compatible endpoint, including OpenAI, Gemini, Jina, and Ollama, with task-aware query and passage embeddings
